27 aout

diff --git a/DOCS/mean_shift_exemple_yket1999.pdf b/DOCS/mean_shift_exemple_yket1999.pdf
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diff --git a/THESE/Chapters/chapter2/chapter2.tex b/THESE/Chapters/chapter2/chapter2.tex
index 53201d62e4a9e94e2b60dc84b98ec5468bb22b31..e4dcf5ecbc6ac8422cd96ca5ba84d7818b281494 100644 (file)
--- a/THESE/Chapters/chapter2/chapter2.tex
+++ b/THESE/Chapters/chapter2/chapter2.tex
@@ -89,13 +89,13 @@ La figure \ref{fig-histo-cochon} illustre le traitement typique de l'histogramme
 
 \begin{figure}
   \centering
 
 \begin{figure}
   \centering

-  \subfigure[Image initiale comportant deux zones : le fond et le cochon (la cible)]{\label{fig-histo-cochon-a} \includegraphics[height=3cm]{/home/zulu/Documents/THESE/codes/cochon256.png}}\quad
-  \subfigure[Histogramme des niveaux de gris]{\label{fig-histo-cochon-b} \includegraphics[height=3cm]{/home/zulu/Documents/THESE/codes/seg_histogramme/histo-cochon256.png}}\quad
-  \subfigure[Image binaire représentant la segmentation. Seuil estimé à 101 après 4 itérations.]{\label{fig-histo-cochon-c} \includegraphics[width=3cm]{/home/zulu/Documents/THESE/codes/seg_histogramme/cochon256-seghisto-101-255.png}}\\
-\subfigure[Image initiale bruitée]{\label{fig-histo-cochon-d} \includegraphics[height=3cm]{/home/zulu/Documents/THESE/codes/cochon256-sig25.png}}\quad
-  \subfigure[Histogramme des niveaux de gris]{\label{fig-histo-cochon-e} \includegraphics[height=3cm]{/home/zulu/Documents/THESE/codes/seg_histogramme/histo-cochon256-sig25.png}}\quad
-  \subfigure[Image binaire représentant la segmentation. Seuil estimé à 99 après 5 itérations.]{\label{fig-histo-cochon-f} \includegraphics[height=3cm]{/home/zulu/Documents/THESE/codes/seg_histogramme/cochon256-sig25-seghisto-99-255.png}}
-  \caption{Segmentation par analyse simple d'histogramme. Colonne de gauche : image d'entrée. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : résultat de la segmentation.}
+  \subfigure[Image initiale comportant deux zones : le fond et le cochon (la cible)]{\label{fig-histo-cochon-a} \includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/cochon256.png}}\quad
+  \subfigure[Histogramme des niveaux de gris]{\label{fig-histo-cochon-b} \includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/seg_histogramme/histo-cochon256.png}}\quad
+  \subfigure[Image binaire représentant la segmentation. Seuil estimé à 101 après 4 itérations.]{\label{fig-histo-cochon-c} \includegraphics[width=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/seg_histogramme/cochon256-seghisto-101-255.png}}\\
+\subfigure[Image initiale bruitée]{\label{fig-histo-cochon-d} \includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/cochon256-sig25.png}}\quad
+  \subfigure[Histogramme des niveaux de gris]{\label{fig-histo-cochon-e} \includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/seg_histogramme/histo-cochon256-sig25.png}}\quad
+  \subfigure[Image binaire représentant la segmentation. Seuil estimé à 99 après 5 itérations.]{\label{fig-histo-cochon-f} \includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/seg_histogramme/cochon256-sig25-seghisto-99-255.png}}
+  \caption{Segmentation d'une image en niveaux de gris de 128 $\times$ 128 pixels par analyse simple d'histogramme. Colonne de gauche : image d'entrée. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : résultat de la segmentation.}

 \label{fig-histo-cochon}
 \end{figure}
  
 \label{fig-histo-cochon}
 \end{figure}
  


@@ -122,11 +122,21 @@ L'essentiel de la problématique réside donc dans la métrique retenue pour év
 Nous pouvons retenir que les premières d'entre elles, qui n'étaient pas spécifiquement dédiées à la segmentation d'images numériques mais au regroupement d'éléments répartis sur un domaine (1D ou 2D), ont été élaborées autour d'une mesure locale des liens basée sur la distance entre les éléments. La réduction du graphe est ensuite effectuée en utilisant un algorithme spécifique, comme le \textit{minimum spanning tree}, dont l'application a été décrite dès 1970 dans \ref{slac-pub-0672} et où il s'agit simplement de supprimer les liens \textit{inconsistants}, c'est à dire ceux dont le poids est significativement plus élevé que la moyenne des voisins se trouvant de chaque coté du lien en question.
 L'extension a rapidement été faite aux images numériques en ajoutant l'intensité des pixels au vecteur des paramètres pris en compte dans l'évaluation du poids des liens.
 D'autres critères de simplification ont aussi été élaborés, avec pour ambition de toujours mieux prendre en compte les caractéristiques structurelles globales des images pour prétendre à une segmentation qui conduise à une meilleure perception conceptuelle.
 Nous pouvons retenir que les premières d'entre elles, qui n'étaient pas spécifiquement dédiées à la segmentation d'images numériques mais au regroupement d'éléments répartis sur un domaine (1D ou 2D), ont été élaborées autour d'une mesure locale des liens basée sur la distance entre les éléments. La réduction du graphe est ensuite effectuée en utilisant un algorithme spécifique, comme le \textit{minimum spanning tree}, dont l'application a été décrite dès 1970 dans \ref{slac-pub-0672} et où il s'agit simplement de supprimer les liens \textit{inconsistants}, c'est à dire ceux dont le poids est significativement plus élevé que la moyenne des voisins se trouvant de chaque coté du lien en question.
 L'extension a rapidement été faite aux images numériques en ajoutant l'intensité des pixels au vecteur des paramètres pris en compte dans l'évaluation du poids des liens.
 D'autres critères de simplification ont aussi été élaborés, avec pour ambition de toujours mieux prendre en compte les caractéristiques structurelles globales des images pour prétendre à une segmentation qui conduise à une meilleure perception conceptuelle.

-Le principe général des solutions actuelles est proche de l'analyse en composantes principales appliquée à une matrice d'affinités qui traduit les liens entre les segments.
+Le principe général des solutions actuelles est proche de l'analyse en composantes principales appliquée à une matrice de similarité qui traduit les liens entre les segments.

 On peut citer, par ordre chronologique, les méthodes reposant sur le \textit{graphe optimal} de Wu et Leahy \ref{wulealy_1993} et plus récemment \ref{cf_notes x5}. Le principal point faible de ces techniques réside essentiellement dans la difficulté  à trouver un compromis acceptable entre identification de structures globales et préservation des éléments de détails. Cela se traduit dans la pratique par un ensemble de paramètres à régler pour chaque type de segmentation à effectuer.
 Cependant, elles sont employées dans les algorithmes de haut niveau les plus récents, comme nous le verrons plus loin.
 On peut citer, par ordre chronologique, les méthodes reposant sur le \textit{graphe optimal} de Wu et Leahy \ref{wulealy_1993} et plus récemment \ref{cf_notes x5}. Le principal point faible de ces techniques réside essentiellement dans la difficulté  à trouver un compromis acceptable entre identification de structures globales et préservation des éléments de détails. Cela se traduit dans la pratique par un ensemble de paramètres à régler pour chaque type de segmentation à effectuer.
 Cependant, elles sont employées dans les algorithmes de haut niveau les plus récents, comme nous le verrons plus loin.

-La figure \ref{fig-graph-cochon} montre un exemple de l'application de l'algorithme \textit{normalized cuts} décrit dans \ref{sm-ncuts}. Quatre paramètres sont à fixer pour réaliser la segmentation, ils correspondent respectivement aux facteurs de forme de la matrice d'affinités en termes de distance et de niveau de gris (2 paramètres), à la surface minimale admise pour un segment ainsi qu'au seuil de ségmentation des graphes. Le choix des valeurs pour les paramètres n'est pas immédiat et la figure illustre bien la difficulté de trouver un compromis acceptable. Enfin, les temps d'exécutions peuvent devenir très rapidement prohibitifs dès lors que l'on utilise des facteurs de forme étendus.
-  
+La figure \ref{fig-graph-cochon} montre un exemple de l'application de l'algorithme \textit{normalized cuts} décrit dans \ref{sm-ncuts pami2000} et implémenté par Cour, Yu et Shi en 2004. Cette implémentation utilise des valeurs pré-établies des paramètres de calcul de la matrice de similarité produisant de bonnes segmentations d'objets et/ou personnes dans les images naturelles, mais requiert de prédéterminer le nombre de segments à obtenir. Les images de la figure représentent les résultats obtenus avec un nombre de segments variant de 2 à 5 et montrent qu'il difficile de trouver un compromis acceptable. Enfin, les temps d'exécutions peuvent devenir très rapidement prohibitifs, même avec des implémentations plus optimisées. Pour information, les résultats de la figure \ref{fig-graph-cochon} ont été obtenus en 1.5~s environ (Matlab R2010 sur CPU intel core i5-2520M @ 2.50GHz - linux 3.2.0) 
+\begin{figure}
+  \centering
+  \subfigure[$s = 2$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/graphe/cochon128_ncuts_2seg.png}}\quad
+  \subfigure[$s = 3$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/graphe/cochon128_ncuts_3seg.png}}\\
+  \subfigure[$s = 4$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/graphe/cochon128_ncuts_4seg.png}}\quad
+  \subfigure[$s = 5$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/graphe/cochon128_ncuts_5seg.png}}
+  \caption{Segmentation d'une image en niveaux de gris de 128 $\times$ 128 pixels par simplification de graphe de type \textit{Normalized cut} pour un nombre $s$ de segments variant de 2 à 5.}
+\label{fig-graph-cochon}
+\end{figure}
+%TODO 
+%donner l'expression générale de la matrice de similarité ?

     
 \subsection{kernel-means, mean-shift et dérivés}
 Parallèlement à la réduction de graphes, d'autres approches ont donné naissance à une multitude de variantes tournées vers la recherche des moindres carrés. 
     
 \subsection{kernel-means, mean-shift et dérivés}
 Parallèlement à la réduction de graphes, d'autres approches ont donné naissance à une multitude de variantes tournées vers la recherche des moindres carrés. 


@@ -143,13 +153,33 @@ Un autre est d'être très dépendant du choix des $k$ éléments initiaux, en n
 Toutefois, vraisemblablement du fait de sa simplicité d'implémentation et de temps d'exécution rapides, la communauté scientifique s'est beaucoup penchée sur cette méthode pour en compenser les défauts, jusqu'à en faire une des plus employées, en particulier par les statisticiens.
 On compte aussi beaucoup de variantes telles les \textit{k-centers} \ref{k_centers} et les \textit{k-médians} \ref{k_medians} qui n'employent pas la moyenne arithmétique comme expression du ``centre'' d'un segment. 
 Des solutions ont aussi été apportées pour l'estimation de $k$ en employant, par exemple, un critère de vraisemblance pour choisir la meilleure valeur de $k$ dans un intervalle donné \ref{x-means}.
 Toutefois, vraisemblablement du fait de sa simplicité d'implémentation et de temps d'exécution rapides, la communauté scientifique s'est beaucoup penchée sur cette méthode pour en compenser les défauts, jusqu'à en faire une des plus employées, en particulier par les statisticiens.
 On compte aussi beaucoup de variantes telles les \textit{k-centers} \ref{k_centers} et les \textit{k-médians} \ref{k_medians} qui n'employent pas la moyenne arithmétique comme expression du ``centre'' d'un segment. 
 Des solutions ont aussi été apportées pour l'estimation de $k$ en employant, par exemple, un critère de vraisemblance pour choisir la meilleure valeur de $k$ dans un intervalle donné \ref{x-means}.

+À titre d'illustration et de comparaison, l'image du cochon a été traitée par une implémentation naïve de l'algorithme original des \textit{k-means} en donnant successivement au nombre de segments les valeurs $s=2$ à $s=5$. Les résultats sont reproduits à la figure \ref{fig-kmeans-cochon} et montrent encore une fois l'influence de $s$ sur la segmentation.
+\begin{figure}
+  \centering
+  \subfigure[$s = 2$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/kmeans/cochon128_kmeans_2seg.png}}\quad
+  \subfigure[$s = 3$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/kmeans/cochon128_kmeans_3seg.png}}\\
+  \subfigure[$s = 4$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/kmeans/cochon128_kmeans_4seg.png}}\quad
+  \subfigure[$s = 5$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/kmeans/cochon128_kmeans_5seg.png}}
+  \caption{Segmentation d'une image en niveaux de gris de 128 $\times$ 128 pixels par algorithme \textit{k-means} pour un nombre $s$ de segments variant de 2 à 5. Chaque couleur est associée à un segment. Les couleurs sont choisies pour une meilleure visualisation des différents segments.}
+\label{fig-kmeans-cochon}
+\end{figure}

 
 Un algorithme initiallement proposé en 1975 par Fukunaga et Hostetler \ref{Lestimation_html} permet de manière plus générique de déterminer le nombre de segments, ou modes, ainsi que les points, ou pixels, qui les composent. Il cherche pour ce faire à localiser les $k$ positions ou le gradient de densité s'annule. 
 Il utilisé un voisinage pondére (ou \textit{kernel}) et détermine le centre de masse des segments en suivant itérativement le gradient de densité dans le voisinage autour de chaque élément du domaine. Lorsque l'algorithme à convergé, les $k$ segments sont identifiés et continennent chacun l'ensemble des points qui ont conduit à leur centre de masse respectif.
 Étonnement, malgré ses qualités intrinsèques, cet algorithme du \textit{mean-shift} est resté longtemps sans susciter de grand intérêt, jusqu'à l'étude de Cheng \ref{meanshift_1995} qui en a demontré les propriétés et établi les lien avec d'autres techniques d'optimisation commme la descente/montée de gradient ou de filtrage commme le floutage.
 Comaniciu et Peer ont alors étendu l'étude et proposé une application à la segmentation en utilisant l'espace colorimétrique CIELUV \ref{Computer Graphics by Foley, van Dam, Feiner, and Hughes, published by Addison-Wesley, 1990} et montré qu'elle permettait une meilleure identification des modes de l'image \ref{mean_shift 1999 2002}.
 
 Un algorithme initiallement proposé en 1975 par Fukunaga et Hostetler \ref{Lestimation_html} permet de manière plus générique de déterminer le nombre de segments, ou modes, ainsi que les points, ou pixels, qui les composent. Il cherche pour ce faire à localiser les $k$ positions ou le gradient de densité s'annule. 
 Il utilisé un voisinage pondére (ou \textit{kernel}) et détermine le centre de masse des segments en suivant itérativement le gradient de densité dans le voisinage autour de chaque élément du domaine. Lorsque l'algorithme à convergé, les $k$ segments sont identifiés et continennent chacun l'ensemble des points qui ont conduit à leur centre de masse respectif.
 Étonnement, malgré ses qualités intrinsèques, cet algorithme du \textit{mean-shift} est resté longtemps sans susciter de grand intérêt, jusqu'à l'étude de Cheng \ref{meanshift_1995} qui en a demontré les propriétés et établi les lien avec d'autres techniques d'optimisation commme la descente/montée de gradient ou de filtrage commme le floutage.
 Comaniciu et Peer ont alors étendu l'étude et proposé une application à la segmentation en utilisant l'espace colorimétrique CIELUV \ref{Computer Graphics by Foley, van Dam, Feiner, and Hughes, published by Addison-Wesley, 1990} et montré qu'elle permettait une meilleure identification des modes de l'image \ref{mean_shift 1999 2002}.

+Une implémentation de la variante proposée par Keselman et Micheli-Tzanakou dans \ref{yket1999} appliquée à notre image de test fournit les résultats reproduits à la figure  \ref{fig-meanshift-cochon}. Pour se rapprocher des traitements précédents, nous avons identifié, par essais successifs, les tailles de voisinage conduisant à des nombre de segments identiques à ceux des figures précedentes (de 2 à 5). Le volume minimal admis pour un segment à été arbitrairement fixé à 100 pixels. 
+\begin{figure}
+  \centering
+  \subfigure[$r=100 \Rightarrow s = 2$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/meanshift/cochon128_meanshift_r100m100.png}}\quad
+  \subfigure[$r=50 \Rightarrow s = 3$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/meanshift/cochon128_meanshift_r50m100.png}}\\
+\subfigure[$r=35 \Rightarrow s = 4$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/meanshift/cochon128_meanshift_r35m100.png}}\quad
+  \subfigure[$r=25 \Rightarrow s = 5$]{\includegraphics[height=3cm]{/home/zulu/Documents/these_gilles/THESE/codes/meanshift/cochon128_meanshift_r25m100.png}}  
+  \caption{Segmentation d'une image en niveaux de gris de 128 $\times$ 128 pixels par algorithme \textit{mean-shift} pour un rayon de voisinage $r$ de 100, 50, 35 et 25 pixels permettant d'obtenir un nombre $s$ de segments variant respectivement de 2 à 5. Le volume minimal admis pour un segment est fixé à 100 pixels. Chaque couleur est associée à un segment. Les couleurs sont choisies pour une meilleure visualisation des différents segments.}
+\label{fig-meanshift-cochon}
+\end{figure}

 
 

-Il est à noter que les segmentations basées sur des algorithmes de \textit{clustering} nécessitent une phase supplémentaire de génération des frontières inter-segments et d'affectation de la valeur de chaque segment aux éléments qui le composent. 
+Il est à noter que les segmentations basées sur des algorithmes de \textit{clustering} comme ceux que l'on vient de présenter nécessitent le plus souvent une phase supplémentaire de génération des frontières inter-segments et d'affectation de la valeur de chaque segment aux éléments qui le composent. 

 Par ailleurs, dans les deux cas du \textit{k-means} et du \textit{mean-shift}, chaque itération génère une réduction de la variance (due au moyennage) et on peut donc rapprocher ces techniques de celles de réduction de bruit par minimisation de variance.
 
 \subsection{Les contours actifs, ou \textit{snakes}}
 Par ailleurs, dans les deux cas du \textit{k-means} et du \textit{mean-shift}, chaque itération génère une réduction de la variance (due au moyennage) et on peut donc rapprocher ces techniques de celles de réduction de bruit par minimisation de variance.
 
 \subsection{Les contours actifs, ou \textit{snakes}}


@@ -177,12 +207,13 @@ Après la formulation initiale de Osher et Sethian en 1988 \ref{level_sets_osher
 \item les \textit{snake} orientés régions, qui visent essentiellement à mieux caractériser les zones à segmenter et améliorer la robustesse vis à vis du bruit en employant une formulation de l'énergie calculée sur le domaine complet de l'image \ref{cohenSMIE93, ronfard}. Les premiers résultats confirment la qualité de cette méthode, mais la nécessité d'effectuer les calculs sur l'image entière générait des temps de traitement prohibitifs jusqu'à ce que Bertaux \textitat{et al.} proposent une amélioration algorithmique exacte permettant à nouveau un calcul en 1D, le long de la courbe, moyennant une simple étape initiale générant un certain nombre d'images intégrales \ref{snake_bertaux}. La section \ref{sec_contrib_snake} qui introduit notre contribution à cette technique en donnera une description détaillée. 
 \end{itemize}
  
 \item les \textit{snake} orientés régions, qui visent essentiellement à mieux caractériser les zones à segmenter et améliorer la robustesse vis à vis du bruit en employant une formulation de l'énergie calculée sur le domaine complet de l'image \ref{cohenSMIE93, ronfard}. Les premiers résultats confirment la qualité de cette méthode, mais la nécessité d'effectuer les calculs sur l'image entière générait des temps de traitement prohibitifs jusqu'à ce que Bertaux \textitat{et al.} proposent une amélioration algorithmique exacte permettant à nouveau un calcul en 1D, le long de la courbe, moyennant une simple étape initiale générant un certain nombre d'images intégrales \ref{snake_bertaux}. La section \ref{sec_contrib_snake} qui introduit notre contribution à cette technique en donnera une description détaillée. 
 \end{itemize}
  

-
+% ne faut-il pas mieux éluder le paragraphe ci-dessous

 \subsection{Méthodes hybrides}
 \subsection{Méthodes hybrides}

-Aujourd'hui, les algorithmes de segmentation les plus performants en terme de qualité emploient des techniques qui tentent de tirer le meilleur parti de plusieurs  des méthodes ``historiques'' décrites précédemment.
-Le meilleur exemple, et le seul que nous citerons, est le détecteur de contour et l'algorithme de segmentation associé proposé par Arbelaez \textit{et al.} en 2010 \ref{amfm_2010}. Il compose avec la constructions d'histogrammes locaux pour générer une matrice de similitude (affinity matrix) et appliquer les techniques liées à la théorie des graphes pour réduire la dimension de l'espace de représentation (calcul des valeurs et vecteurs propres). Il utilise ensuite une technique adaptée de \textit{ligne de partage des eaux} (que l'on aurait rangée avec les mean-shift) pour regrouper les segments. 
+Aujourd'hui, les algorithmes de segmentation les plus performants en terme de qualité emploient des techniques qui tentent de tirer le meilleur parti de plusieurs des méthodes ``historiques'' décrites précédemment.
+Le meilleur exemple, et le seul que nous citerons, est le détecteur de contour et l'algorithme de segmentation associé proposé par Arbelaez \textit{et al.} en 2010 \ref{amfm_2010}. Il compose avec la constructions d'histogrammes locaux pour générer une matrice de similitude (affinity matrix) et appliquer les techniques liées à la théorie des graphes pour réduire la dimension de l'espace de représentation (calcul des valeurs et vecteurs propres). Il utilise ensuite une technique adaptée de \textit{ligne de partage des eaux} \ref{watershed} (que l'on aurait rangée avec les mean-shift) pour regrouper les segments. 

 Les résultats sont très bons et des implémentations efficaces ont dores et déjà été écrites (voir section \ref{sec_ea_gpu}. 
 Les résultats sont très bons et des implémentations efficaces ont dores et déjà été écrites (voir section \ref{sec_ea_gpu}. 

-  
+%TODO 
+%peut-être dire deux mots sur le partage des eaux (avec kmeans et meanshift) puisqu'il est employé dans gPb

 
 
 
 
 
 

diff --git a/THESE/codes/cochon128.pgm b/THESE/codes/cochon128.pgm
new file mode 100644 (file)
index 0000000..48f0c9e
--- /dev/null
+++ b/THESE/codes/cochon128.pgm
@@ -0,0 +1,13 @@
+P5
+# CREATOR: GIMP PNM Filter Version 1.1
+128 128
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diff --git a/THESE/codes/graphe/MIA_final_code/CS 578 Medical Image Analysis Project.docx b/THESE/codes/graphe/MIA_final_code/CS 578 Medical Image Analysis Project.docx
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diff --git a/THESE/codes/graphe/MIA_final_code/CS 578 Medical Image Analysis Project.pdf b/THESE/codes/graphe/MIA_final_code/CS 578 Medical Image Analysis Project.pdf
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diff --git a/THESE/codes/graphe/MIA_final_code/CS578 FinalProject.pptx b/THESE/codes/graphe/MIA_final_code/CS578 FinalProject.pptx
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diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/brain_color.jpg b/THESE/codes/graphe/MIA_final_code/Grow_cut/brain_color.jpg
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diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut.m b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut.m
new file mode 100644 (file)
index 0000000..d68e955
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut.m
@@ -0,0 +1,42 @@
+%  [labels strengths] = growcut(img, labels)
+%
+%  GrowCut algorithm
+%  from "GrowCut" - Interactive Multi-Label N-D Image Segmentation
+%       By Cellular Autonoma
+%  by Vladimir Vezhnevets and Vadim Konouchine
+%
+%  usage: [labels, strengths] = growcutmex(image, labels)
+%         image can be RGB or grayscale
+%         labels has values: -1 (bg), 1 (fg), or 0 (undef.)
+% 
+%         resulting labels will be either 0 (bg) or 1 (fg)
+%         resulting strengths will be between 0 and 1
+%
+%  coded by: Shawn Lankton (www.shawnlankton.com)
+
+
+function [l s] = growcut(img, labels)
+  
+  %-- make sure image is in the double format
+  img = double(img);
+
+  %-- enforce that foreground and background labels exist
+  if(numel(unique(labels))~=3)
+    error('labels must be comprised of -1, 0, 1');
+  end
+
+  %-- enforce that image and labels are the same size
+  si = size(img);
+  sl = size(labels);
+  if(~all(sl(1:2)==si(1:2)))
+       error('labels and image must be the same size');
+  end
+  
+  %-- automatically compile mex file if not already done
+  if(exist('growcutmex','file')~=3)
+    mex growcutmex.cpp;
+  end
+  
+  %-- run the c++ growcut algorithm
+  [l s] = growcutmex(img,labels);
+  

diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.asv b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.asv
new file mode 100644 (file)
index 0000000..99753a7
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.asv
@@ -0,0 +1,25 @@
+% growcut.m
+clear all;close all;clc;
+%-- Load image and seeds
+img = imread('brain_color.jpg');
+imshow(img);
+[x,y)=ginput(10);
+
+labels = double(imread('labels.png'))-1;
+
+subplot(2,2,1), imshow(img);
+subplot(2,2,2), imshow(labels,[]);
+subplot(2,2,3), imshow(img);
+
+%-- For segmentation
+[labels_out, strengths] = growcut(img,labels);
+
+%-- For Smoothing
+labels_out = medfilt2(labels_out,[3,3]);
+
+hold on;
+contour(labels_out,[0 0],'g','linewidth',4);
+contour(labels_out,[0 0],'k','linewidth',2);
+hold off;
+
+subplot(2,2,4), imshow(labels_out);

diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.m b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.m
new file mode 100644 (file)
index 0000000..ad2ad41
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcut_main.m
@@ -0,0 +1,37 @@
+% growcut.m
+clear all;close all;clc;
+%-- Load image and seeds
+img = imread('brain_color.jpg');
+labels=zeros(size(img,1), size(img,2));
+imshow(img);
+disp('Select foreground');
+[x,y]=ginput(10);
+
+for i = 1: 10
+    labels(y(i),x(i))=1;
+end
+disp('Select background');
+[x1,y1]=ginput(10);
+
+for i = 1: 10
+    labels(y1(i),x1(i))=-1;
+end
+
+
+    
+subplot(2,2,1), imshow(img);
+subplot(2,2,2), imshow(labels,[]);
+subplot(2,2,3), imshow(img);
+
+%-- For segmentation
+[labels_out, strengths] = growcut(img,labels);
+
+%-- For Smoothing
+labels_out = medfilt2(labels_out,[3,3]);
+
+hold on;
+contour(labels_out,[0 0],'g','linewidth',4);
+contour(labels_out,[0 0],'k','linewidth',2);
+hold off;
+
+subplot(2,2,4), imshow(labels_out);

diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/growcutmex.cpp b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcutmex.cpp
new file mode 100644 (file)
index 0000000..ece500f
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcutmex.cpp
@@ -0,0 +1,154 @@
+/*******************************************************************
+ * GrowCut algorithm
+ * from "GrowCut" - Interactive Multi-Label N-D Image Segmentation
+ *      By Cellular Autonoma
+ * by Vladimir Vezhnevets and Vadim Konouchine
+ *
+ * coded by: Shawn Lankton (www.shawnlankton.com)
+ *
+ * usage: [labels, strengths] = growcutmex(image, labels)
+ *        image must be a double matrix (RGB or grayscale)
+ *        labels must be a double matrix with values:
+ *         -1 (background), 1 (foreground), or 0 (undefined)
+ * 
+ *        resulting labels will be either 0 (bg) or 1 (fg)
+ *        resulting strengths will be between 0 and 1
+ ******************************************************************/
+
+#include <math.h>
+#include <matrix.h>
+#include <mex.h>   
+
+/* Definitions to keep compatibility with earlier versions of ML */
+#ifndef MWSIZE_MAX
+typedef int mwSize;
+typedef int mwIndex;
+typedef int mwSignedIndex;
+
+#if (defined(_LP64) || defined(_WIN64)) && !defined(MX_COMPAT_32)
+/* Currently 2^48 based on hardware limitations */
+# define MWSIZE_MAX    281474976710655UL
+# define MWINDEX_MAX   281474976710655UL
+# define MWSINDEX_MAX  281474976710655L
+# define MWSINDEX_MIN -281474976710655L
+#else
+# define MWSIZE_MAX    2147483647UL
+# define MWINDEX_MAX   2147483647UL
+# define MWSINDEX_MAX  2147483647L
+# define MWSINDEX_MIN -2147483647L
+#endif
+#define MWSIZE_MIN    0UL
+#define MWINDEX_MIN   0UL
+#endif
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
+{
+    
+    //declare variables
+    mxArray *strens_m, *labels_m, *labelsn_m, *I_m;
+    mwSize numdims;
+    const mwSize *dims;
+    double  *strens, *strensn, *labels, *labelsn, *I;
+    int i,j,k,l,m;
+    int Nx[] = {-1, 1, 0, 0, -1, -1, 1,  1}; //8-neighbors
+    int Ny[] = {0, 0, -1, 1,  1, -1, 1, -1};
+    int dimx,dimy,dimxy,colors;
+    double C,g;
+    int idxq, idxp, idxp1, idxp2, idxq1, idxq2;
+    double maxC = 441.673;
+    int converged;
+    int MAX_ITS = 100; //uncomment below if you want to use this
+    int its = 0;
+    
+    //gets image dimensions
+    numdims = mxGetNumberOfDimensions(prhs[0]);
+    dims = mxGetDimensions(prhs[0]);
+
+    dimy = (int)dims[0];
+    dimx = (int)dims[1];
+    dimxy = dimx*dimy;
+    if(numdims>2) colors = (int)dims[2];
+    else colors = 1;
+    
+    //sets up pointers
+    I_m = mxDuplicateArray(prhs[0]);
+    labels_m = mxDuplicateArray(prhs[1]);
+    labelsn_m = plhs[0] = mxDuplicateArray(prhs[1]);
+    strens_m = plhs[1] = mxCreateDoubleMatrix(dimy,dimx,mxREAL);
+
+    I = mxGetPr(I_m);
+    labels = mxGetPr(labels_m);
+    labelsn = mxGetPr(labelsn_m);
+    strens = mxGetPr(strens_m);
+    strensn = (double*)malloc(dimx*dimy*sizeof(double));
+
+    //initilize seeds
+    for(i=0;i<dimxy;i++) if(labels[i]!=0) strens[i] = 1;; 
+
+    //start main loop
+    converged = 0;
+    while(!converged){
+        its++;
+        converged = 1; //unless we make a change
+
+        //copy prev result
+        for(i=0;i<dimxy;i++)
+        {
+            strensn[i] = strens[i];
+            labelsn[i] = labels[i];
+        }
+        
+        //for every pixel p
+        for(i=1;i<dimx-1;i++)
+        {
+            for(j=1;j<dimy-1;j++)
+            {
+                idxp = j+i*dimy;
+                idxp1 = idxp+dimxy; idxp2 = idxp+dimxy*2;
+
+                //for every neighbor q
+                for(m=0;m<8;m++)
+                {
+                    idxq = idxp + Nx[m]*dimy + Ny[m];
+                    idxq1 = idxq+dimxy; idxq2 = idxq+dimxy*2;
+
+                    if(labels[idxq] == 0) continue; //wimps don't attack
+                    
+                    if(colors>1)
+                        C = sqrt( (I[idxp ] - I[idxq ])*(I[idxp ] - I[idxq ]) +
+                                  (I[idxp1] - I[idxq1])*(I[idxp1] - I[idxq1]) +
+                                  (I[idxp2] - I[idxq2])*(I[idxp2] - I[idxq2]) );
+                    else
+                        C = sqrt((I[idxp ] - I[idxq ])*(I[idxp ] - I[idxq ])); 
+                    
+                    g = 1-(C/maxC); //attack force
+                    
+                    if(g*strens[idxq]>strensn[idxp]) //attack succeeds
+                    {
+                        strens[idxp] = g*strensn[idxq];
+                        labelsn[idxp] = labels[idxq];
+                        converged = 0; // keep iterating
+                    }
+
+                }
+                
+            }
+        }
+
+        //copy prev result
+        for(i=0;i<dimxy;i++) labels[i] = labelsn[i];;
+
+        //lets not go crazy...
+        //if(its == MAX_ITS) break;
+    }
+
+    //condition final result
+    for(i=0;i<dimxy;i++) if(labelsn[i]==-1) labelsn[i]=0;;
+        
+    //mexPrintf("total iterations=%d\n",its);
+    free(strensn);
+    mxDestroyArray(I_m);
+    mxDestroyArray(labels_m);
+
+    return;
+}

diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/growcutmex.mexa64 b/THESE/codes/graphe/MIA_final_code/Grow_cut/growcutmex.mexa64
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diff --git a/THESE/codes/graphe/MIA_final_code/Grow_cut/labels.png b/THESE/codes/graphe/MIA_final_code/Grow_cut/labels.png
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/CODE_MAP.pdf b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/CODE_MAP.pdf
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_COMPILE.txt b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_COMPILE.txt
new file mode 100644 (file)
index 0000000..3ade1db
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_COMPILE.txt
@@ -0,0 +1,46 @@
+THIS FILE INCLUDES COMPILATION INSTRUCTIONS FOR \r
+SEGMENTATION TOOLBOX\r
+\r
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r
+Authors - Mohit Gupta, Krishnan Ramnath\r
+Affiliation - Robotics Institute, CMU, Pittsburgh\r
+2006-05-15\r
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r
+\r
+We provide a freely available Segmentation Toolbox that \r
+can readily be used for image segmentation tasks. \r
+\r
+Please acknowledge the authors if you are using this\r
+ToolBox.\r
+\r
+\r
+Please follow these simple instructions to get the \r
+ToolBox working:\r
+\r
+\r
+1) Download and unzip SegToolBox.zip from the website.\r
+\r
+****** PRECAUTION: Please make sure that the path-name to the directory where files are unzipped **does NOT** contain any spaces. ******\r
+\r
+2) Open Matlab. Cd to the unzipped directory whcih has all the files.\r
+\r
+3) Make sure you have mex C++ compiler installed (Ex: Visual C++.) \r
+   - To choose the compiler type "mex -setup" at the matlab commandline.\r
+   - Follow the instructions therein until you have chosen a C++ \r
+     compiler.\r
+       \r
+4) Build the library files required by the toolbox by executing \r
+   the following at the matlab commandline:\r
+   \r
+>>  mex GraphCutSegment.cpp graph.cpp maxflow.cpp  \r
+AND\r
+>> mex GraphCutSegmentLazy.cpp graph.cpp maxflow.cpp\r
+\r
+This should create the library files (ex: FileName.dll in Windows or\r
+FileName.mexmac in MacOS)\r
+\r
+5) Thats it! You are ready to use the toolbox now.\r
+\r
+6) Please refer to README_GUI for tool usage instructions.\r
+\r
+7) Please refer to CODE_MAP for a list of functions and the call structure.\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_GUI.txt b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_GUI.txt
new file mode 100644 (file)
index 0000000..f6679a3
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/README_GUI.txt
@@ -0,0 +1,205 @@
+THIS FILE INCLUDES USAGE INSTRUCTIONS FOR \r
+SEGMENTATION TOOLBOX\r
+\r
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r
+Authors - Mohit Gupta, Krishnan Ramnath\r
+Affiliation - Robotics Institute, CMU, Pittsburgh\r
+2006-05-15\r
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r
+\r
+We provide a freely available Segmentation Toolbox that \r
+can readily be used for image segmentation tasks. \r
+\r
+Please acknowledge the authors if you are using this\r
+ToolBox.\r
+\r
+\r
+*************************************************************\r
+*************************************************************\r
+                      Important Notes:\r
+\r
+1) The parameters file 'mksopt.m' contains declarations for all\r
+the tunable parametes.\r
+\r
+2) To segment a new image, please close the present working window \r
+and select a tool from the SegTool menu. Then proceed to select a \r
+new image from the file menu and perform operations on that image.\r
+\r
+3) Modifications required to run the code in "Batch Mode" (for \r
+example, on PASCAL database): "Use the core segmentation code \r
+(Segment.m, (LZ) , SegmentGC (GC) ) for running the algorithm on \r
+huge data sets, without using the gui. You just needs to write a \r
+wrapper function that calls these functions repeatedly for each \r
+image in the dataset.\r
+\r
+4) How are the outputs stored?: The outputs of each segmentation is \r
+stored in SegResult.mat file. This has the resulting segmentation \r
+mask (SegMask) and the segmented image (SegImage.)\r
+\r
+***************************************************************\r
+***************************************************************\r
+\r
+\r
+Please follow the instructions below for using the toolbox.\r
+Follow specific instructions for each part of the toolbox.\r
+\r
+--------------\r
+STEP 1:\r
+--------------\r
+\r
+The Segmentation ToolBox is invoked by typing "SegTool" at\r
+the Matlab commandline.\r
+\r
+--------------\r
+STEP 2:\r
+--------------\r
+\r
+The Segmentation ToolBox has three parts:\r
+\r
+1) SmartSelect (Lazy Snapping) Tool : This tool is used to perform\r
+supervised segmentation by marking foreground and background seeds.\r
+\r
+2) AutoCut (Grab CUt) Tool: This tool is used to perform semi-automatic\r
+segmentation by just marking a bounding rectangle (two opposite corners) covering\r
+the object to be segmented.\r
+\r
+3) AutoCutRefine (Grab Cut + Lazy Snapping) Tool: This allows the user to \r
+perform a SmartSelect Refinement on the AutoCut result.\r
+\r
+--------------\r
+STEP 3:\r
+--------------\r
+\r
+There are separate instructions for each tool:\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%\r
+1) SMARTSELECT:\r
+%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+- Choose SegTool->SmartSelect\r
+\r
+- This opens a file menu. From this choose the image you want to segment.\r
+\r
+- The image is displayed, along with two menu options on the right:\r
+       1) SmartRefine\r
+       2) SmartRectangle\r
+These indicate the two ways in which we can mark seeds on the image to\r
+initiate the graph cut segmentation algorithm. We can toggle between the \r
+two ways by choosing the corresponding radio button\r
+\r
+$$$$$$$$$$$$$$$$$$\r
+1) SMARTRECTANGLE: \r
+$$$$$$$$$$$$$$$$$$\r
+Using this we can mark seeds in the form of rectangular \r
+regions on the image.\r
+\r
+Choose SmartRectangle - This gives you three options: \r
+\r
+1) Foreground - Click this to mark foreground seeds\r
+2) Background - Click this to mark background seeds\r
+3) GraphCuts - Click this to run the segmentation.\r
+\r
+Once you click "foreground" or "background" the system waits for input.\r
+The input in this case is two corners of the rectangle (usually top left\r
+and bottom right) that the user selects. \r
+\r
+Clicking on foreground (or background) button and then choosing two points plots a \r
+rectangle which indicates the seeds. (Foreground rectangles are red, background \r
+rectangles are blue)\r
+\r
+To click a new rectangle the user has to click the foreground or the\r
+background button again.\r
+\r
+$$$$$$$$$$$$$$$\r
+2) SMARTREFINE: \r
+$$$$$$$$$$$$$$$\r
+User can use this to specify seeds in the form of strokes.\r
+\r
+Choose SmartRefine radio button - This gives you three options: \r
+\r
+1) Foreground - Click this to mark foreground seeds\r
+2) Background - Click this to mark background seeds\r
+3) GraphCuts - Click this to run the segmentation.\r
+\r
+\r
+Once you click "foreground" or "background" the system waits for input.\r
+The input is specified in the form of strokes. To start marking the stroke\r
+the user clicks on a point on the image and then moves the mouse. (The user\r
+should merely click, ^^^^^^NOT CLICK AND HOLD^^^^^ while moving.) The user marks \r
+the end of the stroke by a second click. \r
+\r
+The user can click as many strokes as \r
+he wants by again clicking once on an image point to initiate the stroke and then click \r
+once again  to stop marking the stroke. \r
+\r
+Sometimes the strokes take time to plot, so if the system is busy the user may not\r
+see the strokes immediately.\r
+\r
+\r
+\r
+Once the user has chosen enough seeds he can start the graph cuts algorithm by \r
+clicking on graphcuts.\r
+\r
+This runs graphcuts and gives the result.\r
+\r
+^^^THE USER CAN REFINE THIS RESULT^^^: by choosing the seeds by doing the same as \r
+described above on the same image. These seeds are added to the previous seeds when \r
+the user calls graphcuts again.\r
+\r
+%%%%%%%%%%%%%%%%%%%%\r
+2) AUTOCUT:\r
+%%%%%%%%%%%%%%%%%%%%\r
+\r
+- Choose SegTool->AutoCut\r
+\r
+- This opens a file menu. From this choose the image you want to segment.\r
+\r
+- Once the image is chosen it is displayed and the system waits for user\r
+input.\r
+\r
+- The input is two points, which indicate the corners of a rectangle ^^^^^COVERING\r
+THE OBJECT TO BE SEGMENTED^^^^^^^.\r
+\r
+- Usually the user chooses the two points as the top left and bottom right \r
+corners of the rectangle over the object to be segmented.\r
+\r
+- The algorithm iterates and gives a final segmented result. \r
+\r
+%%%%%%%%%%%%%%%%%%%%%\r
+3) AUTOCUTREFINE:\r
+%%%%%%%%%%%%%%%%%%%%%\r
+\r
+- Choose SegTool->AutoCutRefine\r
+\r
+- This opens a file menu. From this choose the image you want to segment.\r
+\r
+- Once the image is chosen it is displayed and the system waits for user\r
+input.\r
+\r
+- The input is two points, which indicate the corners of a rectangle ^^^^^COVERING\r
+THE OBJECT TO BE SEGMENTED^^^^^^^.\r
+\r
+- Usually the user chooses the two points as the top left and bottom right \r
+corners of the rectangle over the object to be segmented.\r
+\r
+- The algorithm iterates and gives a segmented result. \r
+\r
+- The segmented result is displayed as a mask image and also as a green \r
+boundary on the input image.\r
+\r
+- The user can now refine this segment by using the SegmentRefine tool (see SmartRefine\r
+above.)\r
+\r
+- The user accesses the refinement tool from the right menu. As before he can\r
+choose Foreground or Background seeds in the form of strokes (A click to start stroke, \r
+a click to stop stroke.)\r
+\r
+- As before he marks foreground and background strokes and then clicks on \r
+SegmentRefine to refine the segmentation.\r
+\r
+Remember: AUTOCUTREFINE = AUTOCUT + SMARTSELECT (SMARTREFINE)\r
+\r
+                               -----END------\r
+\r
+Please refer to README_COMPILE for compilation instructions.\r
+Please refer to CODE_MAP for a list of functions and the call structure.\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCut.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCut.m
new file mode 100644 (file)
index 0000000..b3168cb
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCut.m
@@ -0,0 +1,32 @@
+function [hfig] =  AutoCut
+% AUTOCUT  - Main interface for AutoCut Tool
+% This function creates the user interface (figure window
+% and custom menu)
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+clear all;
+
+% Global Variables
+global sopt;
+
+% Initialize all required parameters
+sopt = mksopt;
+
+% Main Menu 
+hfig = figure('units','pixels','position',[50 100 1100 600],...
+   'tag','AutoCut', 'name','Auto-Cut Segmentation',...
+   'menubar','none','numbertitle','off');
+
+% File menu for figure, with callbacks:
+% Open...    (callback to displayImage)
+% Exit       (closes figure window)
+
+hmenu = uimenu('Label','File');
+
+% AutoSeg function - loads the image file
+uimenu(hmenu,'label','Open...','callback','AutoCutSeg ')
+
+%exit 
+uimenu(hmenu,'label','Exit','callback','closereq','separator','on');
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefSeg.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefSeg.m
new file mode 100644 (file)
index 0000000..1ffbd6c
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefSeg.m
@@ -0,0 +1,50 @@
+function AutoCutSeg
+% AutoSeg  - This function opens a file dialog, loads a image file,
+% displays the image in the figure and calls SegmentGC (Main GC Seg Fn)
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+% Global Variables
+% Req for marking and keeping track of seeds
+global ih_img fgpixels bgpixels;
+
+% Get handle to current figure;
+hfig = gcf;
+
+% Call built-in file dialog to select image file
+[filename,pathname]=uigetfile('images/*.*','Select image file');
+if ~ischar(filename); return; end
+
+% Load  Image file
+longfilename = strcat(pathname,filename);
+
+Im = imread(longfilename);
+
+% Get the position of the image
+data.ui.ah_img = axes('Position',[0.01 0.2 .603 .604]);
+ih_img = image;
+
+% Main GrabCut Segmentation Function
+SegmentGC(longfilename, ih_img, 1);
+
+
+% Refinement 
+strg = 'Refine';
+axis ij;
+
+%%%%%%%%%%%%%%%%Seed Selection Buttons%%%%%%%%%%%%%%%%%%%
+% Calls fginput - gets foreground pixels from the user
+data.ui.push_fg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .6 .1 .05], ...
+    'String','Foreground','Callback', ['fginput ',strg]);
+
+% Calls bginput - gets background pixels from the user
+data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .5 .1 .05], ...
+    'String','Background','Callback', ['bginput ',strg]);
+
+% Calls SegmentRefine - SmartSelect refinement after AutoCut on the image
+data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .4 .1 .05], ...
+    'String','AutoCutRefine','Callback', ['SegmentRefine ',longfilename]);
+
+
+

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefine.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefine.m
new file mode 100644 (file)
index 0000000..f913c67
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutRefine.m
@@ -0,0 +1,35 @@
+function AutoCutRefine
+% AUTOCUTREFINE  - Main interface for AutoCutRefine Tool
+% This function creates the user interface (figure window
+% and custom menu)
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+clear all;
+
+% Global Variables
+global sopt;
+
+% Initialize all required parameters
+sopt = mksopt;
+
+
+% Main Menu 
+hfig=figure('units','pixels','position',[50 100 1100 600],...
+   'tag','AutoCut', 'name','GraB-Cut Segmentation',...
+   'menubar','none','numbertitle','off');
+
+% File menu for figure, with callbacks:
+% Open...    (callback to displayImage)
+% Exit       (closes figure window)
+
+hmenu=uimenu('Label','File');
+
+% AutoSeg function - loads the image file
+uimenu(hmenu,'label','Open...','callback','AutoCutRefSeg ')
+
+% Exit
+uimenu(hmenu,'label','Exit','callback','closereq','separator','on');
+
+

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutSeg.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutSeg.m
new file mode 100644 (file)
index 0000000..72b1345
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/AutoCutSeg.m
@@ -0,0 +1,25 @@
+function AutoSeg
+% AutoSeg  - This function opens a file dialog, loads a image file,
+% displays the image in the figure and calls SegmentGC (Main GC Seg Fn)
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+
+% Get handle to current figure;
+hfig = gcf;
+
+% Call built-in file dialog to select image file
+[filename,pathname]=uigetfile('images/*.*','Select image file');
+if ~ischar(filename); return; end
+
+% Load  Image file
+longfilename = strcat(pathname,filename);
+Im = imread(longfilename);
+
+% Get the position of the image
+data.ui.ah_img = axes('Position',[0.01 0.2 .603 .604]);
+ih_img = image;
+
+% Main GrabCut Segmentation Function
+SegmentGC(longfilename, ih_img, 0);
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.cpp b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.cpp
new file mode 100644 (file)
index 0000000..320beeb
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.cpp
@@ -0,0 +1,339 @@
+/**************************************************************\r
+GRAPHCUTSEGMENT.CPP - Main graph cuts segmentation C++ function\r
+Authors - Mohit Gupta, Krishnan Ramnath\r
+Affiliation - Robotics Institute, CMU, Pittsburgh\r
+2006-05-15\r
+***************************************************************/\r
+ \r
+#include <stdio.h>\r
+#include "mex.h"\r
+#include "graph.h"\r
+#include "helper_functions.h"\r
+\r
+\r
+\r
+ void MakeAdjacencyList(double *L, int numLabels, int numCols, int numRows, set<int, less<int> > BGLabels, vector< set<int, less<int> > > &neighbors)\r
+{\r
+       int i,j;\r
+       neighbors.clear();\r
+\r
+       // Declaring the Adjacency list\r
+       for(i=0;i<numLabels;i++)\r
+       {\r
+               set<int, less<int> > tmp;\r
+               tmp.clear();\r
+               neighbors.push_back(tmp);\r
+       }\r
+       \r
+       //printf("Adjacency code starts... \n");\r
+\r
+       // Filling up the adjacency list\r
+       for(i=0;i<numRows;i++)\r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i]; // Label of the current pixel\r
+                       if(thisLabel==-1)                                                       // If this is A watershed Label --> A boundary Pixel\r
+                       {\r
+                               //printf("%d %d %d \n",i,j,thisLabel);\r
+\r
+                               // 4-neighborhood\r
+                               int UpLabel    = (i>0)            ? (int) L[j*numRows + (i-1)] : thisLabel;\r
+                               int DownLabel  = (i<(numRows-1))  ? (int) L[j*numRows + (i+1)] : thisLabel;\r
+                               int LeftLabel  = (j>0)            ? (int) L[(j-1)*numRows + i] : thisLabel;\r
+                               int RightLabel = (j<(numCols-1))  ? (int) L[(j+1)*numRows + i] : thisLabel;\r
+\r
+                               // 8-neighborhood\r
+                               int NWLabel    = (i>0 && j>0)                                           ? (int) L[(j-1)*numRows + (i-1)] : thisLabel;\r
+                               int NELabel    = (i>0 && j<(numCols-1))                         ? (int) L[(j+1)*numRows + (i-1)] : thisLabel;\r
+                               int SELabel    = (i<(numRows-1) && j<(numCols-1))       ? (int) L[(j+1)*numRows + (i+1)] : thisLabel;\r
+                               int SWLabel    = (i<(numRows-1) && j>0)                         ? (int) L[(j-1)*numRows + (i+1)] : thisLabel;\r
+\r
+                               set<int, less<int> > surround, filt_surround;                                           // Surrounding Labels of this boundary Label\r
+                               \r
+                               surround.insert(UpLabel);\r
+                               surround.insert(DownLabel);\r
+                               surround.insert(LeftLabel);\r
+                               surround.insert(RightLabel);\r
+                               surround.insert(NWLabel);\r
+                               surround.insert(NELabel);\r
+                               surround.insert(SELabel);\r
+                               surround.insert(SWLabel);\r
+\r
+                               surround.erase(-1);                                             // Removing other boundary Labels\r
+\r
+                               // Now set difference -- removing the background labels from reckoning -- we need only U Labels\r
+                               set_difference(surround.begin(), surround.end(), BGLabels.begin(), BGLabels.end(), insert_iterator<set<int, less<int> > >(filt_surround,filt_surround.begin()) );\r
+                               \r
+                               set<int, less<int> >::iterator pIter, qIter;\r
+                               for (pIter = filt_surround.begin(); pIter != filt_surround.end(); pIter++)\r
+                                       for (qIter = filt_surround.begin(); qIter != filt_surround.end(); qIter++)\r
+                                       {\r
+                                               neighbors[*pIter].insert(*qIter);\r
+                                       }\r
+\r
+                       }\r
+               }\r
+\r
+    for(i=0;i<numLabels;i++)\r
+       {\r
+               (neighbors[i]).erase(i);                                // Removing Self Edges from the adjacency list\r
+               \r
+       }\r
+\r
+}\r
+\r
+Graph* MakeGraph( vector< set<int, less<int> > > neighbors, vector< vector<double> > MeanColors, vector<int> UNLabels, vector<double> FDists, vector<double> BDists, int numLabels, Graph::node_id *nodes, vector<int> &labelNodeIndices, double LAMBDA)\r
+{\r
+       Graph *G = new Graph();\r
+       set<int, less<int> >::iterator pIter;\r
+       double beta;\r
+       int i;\r
+\r
+       //printf("graph Making code starts... \n");\r
+\r
+       int numULabels = UNLabels.size();                       // Number of U Labels -- will have a node each for U Labels\r
+       /********* Calculating the value of beta ****************/\r
+       double countCand = 0;\r
+       double SumColors = 0;\r
+       for(i=0;i<numULabels;i++)\r
+       {\r
+               int currLabel = UNLabels[i];\r
+               for (pIter = neighbors[currLabel].begin(); pIter != neighbors[currLabel].end(); pIter++)\r
+               {\r
+                       SumColors += pow(diffVec(MeanColors[currLabel],MeanColors[*pIter]),2);\r
+                       countCand++;\r
+               }\r
+       }\r
+       \r
+       beta = countCand/(2*SumColors);\r
+\r
+\r
+       /**************** Beta Value Made *********************/\r
+\r
+       /******* Start making the graph **************/\r
+       \r
+       //vector<int> labelNodeIndices;                         // for each label, what is the corresponding node index?\r
+       labelNodeIndices.clear();\r
+       for(i=0;i<numLabels;i++)\r
+               labelNodeIndices.push_back(-1);\r
+\r
+       // Add Nodes\r
+       \r
+       for(i=0;i<numULabels;i++)\r
+       {\r
+               nodes[i] = G -> add_node();\r
+               labelNodeIndices[UNLabels[i]] = i;              // index of label no. UNLabels[i] is i\r
+       }\r
+\r
+\r
+       // Setting Terminal Edge Weights\r
+       for(i=0;i<numULabels;i++)\r
+               G -> set_tweights(nodes[i], BDists[i], FDists[i]);\r
+\r
+\r
+       // Setting Neighboring Edge Weights\r
+    float MeanNbEdgeW = 0;\r
+    float MNECount = 0;\r
+    \r
+       for(i=0;i<numULabels;i++)\r
+               for (pIter = neighbors[UNLabels[i]].begin(); pIter != neighbors[UNLabels[i]].end(); pIter++)\r
+               {\r
+                       int tmpN = *pIter;\r
+                       //printf("%d %d \n",i,tmpN);\r
+                       double Energ = exp(-beta*pow(diffVec(MeanColors[UNLabels[i]],MeanColors[tmpN]),2));\r
+                       G -> add_edge(nodes[i], nodes[labelNodeIndices[tmpN]], LAMBDA*Energ, LAMBDA*Energ);\r
+            MeanNbEdgeW += LAMBDA*Energ;\r
+            MNECount++;\r
+            //G -> add_edge(nodes[i], nodes[labelNodeIndices[tmpN]], 0, 0);\r
+               }\r
+    \r
+       return G;\r
+}\r
+\r
+void SegmentImage(double *L, Graph *G, Graph::node_id *nodes, vector<int> labelNodeIndices, int numRows, int numCols, int numULabels, double *SegImage, double *LLabels)\r
+{\r
+       int i,j;\r
+       Graph::flowtype flow = G -> maxflow();\r
+    printf("Energy = %f\n", flow);\r
+    \r
+       /**** Giving Binary (obj/background) labels to all U Labels *****/\r
+       for(i=0;i<numULabels;i++)\r
+       {\r
+               if(G->what_segment(nodes[i]) == Graph::SOURCE)\r
+                       LLabels[i] = 1.0;\r
+               else\r
+                       LLabels[i] = 0.0;\r
+       }\r
+\r
+       for(i=0;i<numRows;i++)\r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i];\r
+                       if(thisLabel>=0)                                                                // If not Boundary pixel\r
+                       {\r
+                               int nodInd = labelNodeIndices[thisLabel];       // Node Index -- different from Label No. \r
+                               \r
+                               if(nodInd>=0)                                                           // if thisLabel is not a fixed back-ground label, then do the classification \r
+                               {\r
+                    if (G->what_segment(nodes[nodInd]) == Graph::SOURCE)               // Do the classification...\r
+                                               SegImage[j*numRows + i] = 1.0;\r
+                                       else\r
+                                               SegImage[j*numRows + i] = 0.0;\r
+                               }\r
+                               else                                                                            // ... ELSE let it be background\r
+                                       SegImage[j*numRows + i] = 0.0;                                                          \r
+                       }\r
+                       else\r
+                               SegImage[j*numRows + i] = 0.0;                                                          // Label the boundary pixels as background for now...\r
+               }\r
+\r
+       // Correctly label the boundary pixels ... Label as foreground if any of the neighbors is foreground\r
+       for(i=0;i<numRows;i++)                                                                                                  \r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i];\r
+                       if (thisLabel==-1)\r
+                       {\r
+                               int UpLabel    = (i>0)            ? (int) L[j*numRows + (i-1)] : thisLabel;\r
+                               int DownLabel  = (i<(numRows-1))  ? (int) L[j*numRows + (i+1)] : thisLabel;\r
+                               int LeftLabel  = (j>0)            ? (int) L[(j-1)*numRows + i] : thisLabel;\r
+                               int RightLabel = (j<(numCols-1))  ? (int) L[(j+1)*numRows + i] : thisLabel;\r
+\r
+                               // 8-neighborhood\r
+                               int NWLabel    = (i>0 && j>0)                                           ? (int) L[(j-1)*numRows + (i-1)] : thisLabel;\r
+                               int NELabel    = (i>0 && j<(numCols-1))                         ? (int) L[(j+1)*numRows + (i-1)] : thisLabel;\r
+                               int SELabel    = (i<(numRows-1) && j<(numCols-1))       ? (int) L[(j+1)*numRows + (i+1)] : thisLabel;\r
+                               int SWLabel    = (i<(numRows-1) && j>0)                         ? (int) L[(j-1)*numRows + (i+1)] : thisLabel;\r
+\r
+                               set<int> surround;                                              // Surrounding Labels of this boundary Label\r
+                               \r
+                               surround.insert(UpLabel);\r
+                               surround.insert(DownLabel);\r
+                               surround.insert(LeftLabel);\r
+                               surround.insert(RightLabel);\r
+                               surround.insert(NWLabel);\r
+                               surround.insert(NELabel);\r
+                               surround.insert(SELabel);\r
+                               surround.insert(SWLabel);\r
+\r
+                               surround.erase(-1);                                             // Removing other boundary Labels\r
+                               set<int>::iterator pIter;\r
+                float total = 0;\r
+                float lab = 0;\r
+                for (pIter = surround.begin(); pIter != surround.end(); pIter++)\r
+                {\r
+                    total = total+1;\r
+                                       int nodInd = labelNodeIndices[*pIter];  // Node Index\r
+                    if(nodInd>=0)                              // Non - Fixed-BackGround Label\r
+                                               if(G->what_segment(nodes[nodInd]) == Graph::SOURCE)\r
+                                                       lab = lab+1;\r
+                }\r
+                SegImage[j*numRows + i] = (lab/total)>0.5? 1.0 : 0.0;\r
+                       }\r
+               }\r
+}\r
+\r
+\r
+\r
+void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] )\r
+{\r
+       double *L, *MeanColors, *ULabels, *BLabels, *FDist, *BDist, *RelateParam;                                               // Input Arguments\r
+       double *SegImage, *LLabels;                                                                                                             // Output Arguments\r
+       int numRows, numCols, numLabels, numULabels, numBLabels;        \r
+       int i;\r
+         \r
+       if(nrhs!=7) {\r
+               mexErrMsgTxt("Incorrect No. of inputs");\r
+       } else if(nlhs!=2) {\r
+               mexErrMsgTxt("Incorrect No. of outputs");\r
+       }\r
+         \r
+       L          = mxGetPr(prhs[0]);\r
+       MeanColors = mxGetPr(prhs[1]);\r
+       ULabels    = mxGetPr(prhs[2]);\r
+       BLabels    = mxGetPr(prhs[3]);  \r
+       FDist      = mxGetPr(prhs[4]);\r
+       BDist      = mxGetPr(prhs[5]);  \r
+       RelateParam = mxGetPr(prhs[6]);  \r
+    \r
+    double LAMBDA = (*RelateParam);\r
+    \r
+       numCols = mxGetN(prhs[0]);              // Image Size\r
+       numRows = mxGetM(prhs[0]);\r
+       numLabels = mxGetM(prhs[1]);    // Number of labels\r
+       numULabels = mxGetM(prhs[2]);   // Number of ForeGround Labels\r
+       numBLabels = mxGetM(prhs[3]);   // Number of BackGround Labels\r
+         \r
+       plhs[0] = mxCreateDoubleMatrix(numRows,numCols, mxREAL);        // Memory Allocated for output SegImage\r
+       plhs[1] = mxCreateDoubleMatrix(numULabels,1, mxREAL);           // Memory Allocated for output LLabels\r
+\r
+       SegImage = mxGetPr(plhs[0]);                                                            // variable assigned to the output SegImage\r
+       LLabels = mxGetPr(plhs[1]);                                                                     // variable assigned to the output SegImage\r
+        \r
+       //printf("Starting MEX Code \n");\r
+\r
+       /**** Cast these data structures into correct types \r
+       and convert into STL Data Structures ****/\r
+       vector< vector<double> > MeanColorsVec;\r
+       MeanColorsVec.clear();\r
+    for(i=0;i<numLabels;i++)\r
+       {\r
+               vector<double> tmp;\r
+               tmp.clear();\r
+               tmp.push_back(MeanColors[0*numLabels + i]);\r
+               tmp.push_back(MeanColors[1*numLabels + i]);\r
+               tmp.push_back(MeanColors[2*numLabels + i]);\r
+\r
+               MeanColorsVec.push_back(tmp);\r
+       }\r
+       \r
+       //printf("Vectorization 1 \n");\r
+\r
+       vector<double> FDistVec;\r
+       FDistVec.clear();\r
+    for(i=0;i<numULabels;i++)\r
+               FDistVec.push_back(FDist[i]);\r
+               \r
+       //printf("Vectorization 2 \n");\r
+\r
+       vector<double> BDistVec;\r
+       BDistVec.clear();\r
+    for(i=0;i<numULabels;i++)\r
+               BDistVec.push_back(BDist[i]);\r
+       \r
+       //printf("Vectorization 3 \n");\r
+\r
+       vector<int> UNLabelsVec;\r
+       UNLabelsVec.clear();\r
+       for(i=0;i<numULabels;i++)\r
+               UNLabelsVec.push_back((int) ULabels[i]);\r
+\r
+       \r
+       //printf("Vectorization 4 \n");\r
+\r
+       set<int, less<int> > BGLabelsVec;\r
+       BGLabelsVec.clear();\r
+       for(i=0;i<numBLabels;i++)\r
+               BGLabelsVec.insert((int) BLabels[i]);\r
+\r
+       \r
+       //printf("Vectorization 5 \n");\r
+\r
+       /********** Data Structures Converted into Vector Form**/\r
+       /*******************************************************/\r
+\r
+       /**** Actual Image Segmentation Code *********/\r
+       vector< set<int, less<int> > > neighbors;                                       // Adjacency list declaration\r
+       Graph::node_id *nodes = new Graph::node_id[numULabels];\r
+       vector<int> labelNodeIndices;\r
+       labelNodeIndices.clear();\r
+\r
+       MakeAdjacencyList(L, numLabels, numCols, numRows, BGLabelsVec, neighbors);\r
+       //printf("Adjacency lists made \n");\r
+       \r
+       Graph* G = MakeGraph(neighbors, MeanColorsVec, UNLabelsVec, FDistVec, BDistVec, numLabels, nodes, labelNodeIndices, LAMBDA);\r
+       //printf("Graph constructed \n");\r
+       \r
+       SegmentImage(L, G, nodes, labelNodeIndices, numRows, numCols, numULabels, SegImage, LLabels);\r
+       //printf("Image Segmented \n");\r
+}
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.dll.old b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.dll.old
new file mode 100644 (file)
index 0000000..957754d
Binary files /dev/null and b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.dll.old differ

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.mexw32 b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.mexw32
new file mode 100644 (file)
index 0000000..2fd711b
Binary files /dev/null and b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegment.mexw32 differ

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.cpp b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.cpp
new file mode 100644 (file)
index 0000000..e8560ed
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.cpp
@@ -0,0 +1,332 @@
+/**************************************************************\r
+GRAPHCUTSEGMENT.CPP - Main graph cuts segmentation C++ function\r
+Authors - Mohit Gupta, Krishnan Ramnath\r
+Affiliation - Robotics Institute, CMU, Pittsburgh\r
+2006-05-15\r
+***************************************************************/\r
+\r
+#include <stdio.h>\r
+#include "mex.h"\r
+#include "graph.h"\r
+#include "helper_functions.h"\r
+\r
+#define LAMBDA 66 // Relative importance term in definition of energy\r
+\r
+ void MakeAdjacencyList(double *L, int numLabels, int numCols, int numRows, vector< set<int> > &neighbors)\r
+{\r
+       int i,j;\r
+       \r
+       neighbors.clear();\r
+\r
+       // Declaring the Adjacency list\r
+       for(i=0;i<numLabels;i++)\r
+       {\r
+               set<int> tmp;\r
+               tmp.clear();\r
+               neighbors.push_back(tmp);\r
+       }\r
+       \r
+       \r
+       printf("Adjacency code starts... \n");\r
+\r
+       // Filling up the adjacency list\r
+       for(i=0;i<numRows;i++)\r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i]; // Label of the current pixel\r
+               \r
+                       if(thisLabel==-1)                                                       // If this is A watershed Label --> A boundary Pixel\r
+                       {\r
+                               //printf("%d %d %d \n",i,j,thisLabel);\r
+\r
+                               // 4-neighborhood\r
+                               int UpLabel    = (i>0)            ? (int) L[j*numRows + (i-1)] : thisLabel;\r
+                               int DownLabel  = (i<(numRows-1))  ? (int) L[j*numRows + (i+1)] : thisLabel;\r
+                               int LeftLabel  = (j>0)            ? (int) L[(j-1)*numRows + i] : thisLabel;\r
+                               int RightLabel = (j<(numCols-1))  ? (int) L[(j+1)*numRows + i] : thisLabel;\r
+\r
+                               // 8-neighborhood\r
+                               int NWLabel    = (i>0 && j>0)                                           ? (int) L[(j-1)*numRows + (i-1)] : thisLabel;\r
+                               int NELabel    = (i>0 && j<(numCols-1))                         ? (int) L[(j+1)*numRows + (i-1)] : thisLabel;\r
+                               int SELabel    = (i<(numRows-1) && j<(numCols-1))       ? (int) L[(j+1)*numRows + (i+1)] : thisLabel;\r
+                               int SWLabel    = (i<(numRows-1) && j>0)                         ? (int) L[(j-1)*numRows + (i+1)] : thisLabel;\r
+\r
+                               set<int> surround;                                              // Surrounding Labels of this boundary Label\r
+                               \r
+                               surround.insert(UpLabel);\r
+                               surround.insert(DownLabel);\r
+                               surround.insert(LeftLabel);\r
+                               surround.insert(RightLabel);\r
+                               surround.insert(NWLabel);\r
+                               surround.insert(NELabel);\r
+                               surround.insert(SELabel);\r
+                               surround.insert(SWLabel);\r
+\r
+                               surround.erase(-1);                                             // Removing other boundary Labels\r
+                               set<int>::iterator pIter, qIter;\r
+                               for (pIter = surround.begin(); pIter != surround.end(); pIter++)\r
+                                       for (qIter = surround.begin(); qIter != surround.end(); qIter++)\r
+                                       {\r
+                                               neighbors[*pIter].insert(*qIter);\r
+                                       }\r
+\r
+                       }\r
+               }\r
+\r
+    for(i=0;i<numLabels;i++)\r
+       {\r
+               (neighbors[i]).erase(i);                                // Removing Self Edges from the adjacency list\r
+               \r
+       }\r
+}\r
+\r
+Graph* MakeGraph( vector< set<int> > neighbors, vector< vector<double> > MeanColors, vector<int> FLabels, vector<int> BLabels, vector< vector<double> >FCClusters, vector< vector<double> > BCClusters, int numLabels, Graph::node_id *nodes)\r
+{\r
+       //Graph::node_id *nodes = new Graph::node_id[numLabels];\r
+       Graph *G = new Graph();\r
+       int i;\r
+       set<int>::iterator pIter;\r
+       const int K = 10000;                                    // Edge weight for infinity -- All other edge weights less than 1 -- so it suffices to have this weight as more than sum of all other edges, i.e. >8\r
+       \r
+       vector<double> ForeEdges, BackEdges;\r
+       ForeEdges.clear();\r
+       BackEdges.clear();\r
+       \r
+       printf("graph Making code starts... \n");\r
+\r
+       /****** Making Terminal Edge Weights ******/\r
+       for(i=0;i<numLabels;i++)\r
+       {\r
+               if(findValVec(FLabels,i))\r
+               {\r
+                       ForeEdges.push_back(K);\r
+                       BackEdges.push_back(0);\r
+               }\r
+\r
+               else if(findValVec(BLabels,i))\r
+               {\r
+                       ForeEdges.push_back(0);\r
+                       BackEdges.push_back(K);\r
+               }\r
+                       \r
+               else\r
+               {\r
+                       double minFD = minVecD(FCClusters, MeanColors[i]);\r
+                       double minBD = minVecD(BCClusters, MeanColors[i]);\r
+\r
+                       ForeEdges.push_back((minBD/(minFD+minBD)));\r
+                       BackEdges.push_back((minFD/(minFD+minBD)));\r
+               }\r
+       }\r
+\r
+       printf("Terminal Edge Weights Made... \n");\r
+\r
+       /******* Start making the graph **************/\r
+       \r
+       // Add Nodes\r
+       for(i=0;i<numLabels;i++)\r
+               nodes[i] = G -> add_node();\r
+\r
+       printf("Nodes Added to Graph... \n");\r
+\r
+       // Setting Terminal Edge Weights\r
+       for(i=0;i<numLabels;i++)\r
+               G -> set_tweights(nodes[i], ForeEdges[i], BackEdges[i]);\r
+\r
+       printf("Terminal Edge Weights Set... \n");\r
+\r
+       // Setting Neighboring Edge Weights\r
+       for(i=0;i<numLabels;i++)\r
+               for (pIter = neighbors[i].begin(); pIter != neighbors[i].end(); pIter++)\r
+               {\r
+                       int tmpN = *pIter;\r
+                       //printf("%d %d \n",i,tmpN);\r
+                       double Energ = 1/(1 + diffVec(MeanColors[i],MeanColors[tmpN]));\r
+                       G -> add_edge(nodes[i], nodes[tmpN], LAMBDA*Energ, LAMBDA*Energ);\r
+               }\r
+\r
+       printf("Graph Made... \n");\r
+       return G;\r
+}\r
+\r
+void SegmentImage(double *L, Graph *G, int numRows, int numCols, double *SegImage, Graph::node_id *nodes)\r
+{\r
+       int i,j;\r
+       Graph::flowtype flow = G -> maxflow();\r
+\r
+       for(i=0;i<numRows;i++)\r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i];\r
+                       if(thisLabel>=0)                                                                // If not Boundary pixel\r
+                       {\r
+                               if (G->what_segment(nodes[thisLabel]) == Graph::SOURCE)         // Do the classification...\r
+                                       SegImage[j*numRows + i] = 1.0;\r
+                               else\r
+                                       SegImage[j*numRows + i] = 0.0;\r
+                       }\r
+                       else\r
+                               SegImage[j*numRows + i] = 0.0;                                                          // Label the boundary pixels as background for now...\r
+               }\r
+\r
+       // Correctly label the boundary pixels ... Label as foreground if any of the neighbors is foreground\r
+       for(i=0;i<numRows;i++)                                                                                                  \r
+               for(j=0;j<numCols;j++)\r
+               {\r
+                       int thisLabel = (int) L[j*numRows + i];\r
+                       if (thisLabel==-1)\r
+                       {\r
+                               int UpLabel    = (i>0)            ? (int) L[j*numRows + (i-1)] : thisLabel;\r
+                               int DownLabel  = (i<(numRows-1))  ? (int) L[j*numRows + (i+1)] : thisLabel;\r
+                               int LeftLabel  = (j>0)            ? (int) L[(j-1)*numRows + i] : thisLabel;\r
+                               int RightLabel = (j<(numCols-1))  ? (int) L[(j+1)*numRows + i] : thisLabel;\r
+\r
+                               // 8-neighborhood\r
+                               int NWLabel    = (i>0 && j>0)                                           ? (int) L[(j-1)*numRows + (i-1)] : thisLabel;\r
+                               int NELabel    = (i>0 && j<(numCols-1))                         ? (int) L[(j+1)*numRows + (i-1)] : thisLabel;\r
+                               int SELabel    = (i<(numRows-1) && j<(numCols-1))       ? (int) L[(j+1)*numRows + (i+1)] : thisLabel;\r
+                               int SWLabel    = (i<(numRows-1) && j>0)                         ? (int) L[(j-1)*numRows + (i+1)] : thisLabel;\r
+\r
+                               set<int> surround;                                              // Surrounding Labels of this boundary Label\r
+                               \r
+                               surround.insert(UpLabel);\r
+                               surround.insert(DownLabel);\r
+                               surround.insert(LeftLabel);\r
+                               surround.insert(RightLabel);\r
+                               surround.insert(NWLabel);\r
+                               surround.insert(NELabel);\r
+                               surround.insert(SELabel);\r
+                               surround.insert(SWLabel);\r
+\r
+                               surround.erase(-1);                                             // Removing other boundary Labels\r
+                               set<int>::iterator pIter;\r
+                float total = 0;\r
+                float lab = 0;\r
+                for (pIter = surround.begin(); pIter != surround.end(); pIter++)\r
+                {\r
+                    total = total+1;;\r
+                    if(G->what_segment(nodes[*pIter]) == Graph::SOURCE)\r
+                        lab = lab+1;\r
+                }\r
+                SegImage[j*numRows + i] = (lab/total)>0.5? 1.0 : 0.0;\r
+                       }\r
+               }\r
+}\r
+\r
+\r
+\r
+void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] )\r
+{\r
+       double *L, *MeanColors, *FLabels, *BLabels, *FCClusters, *BCClusters; // Input Arguments\r
+       double *SegImage;                                                                                                               // Output Argument\r
+       int numRows, numCols, numLabels, numFLabels, numBLabels, numFCClusters, numBCClusters;  \r
+       int i;\r
+         \r
+       if(nrhs!=6) {\r
+               mexErrMsgTxt("Incorrect No. of inputs");\r
+       } else if(nlhs>1) {\r
+               mexErrMsgTxt("Too many output arguments");\r
+       }\r
+         \r
+       L          = mxGetPr(prhs[0]);\r
+       MeanColors = mxGetPr(prhs[1]);\r
+       FLabels    = mxGetPr(prhs[2]);\r
+       BLabels    = mxGetPr(prhs[3]);  \r
+       FCClusters = mxGetPr(prhs[4]);\r
+       BCClusters = mxGetPr(prhs[5]);  \r
+         \r
+       numCols = mxGetN(prhs[0]);              // Image Size\r
+       numRows = mxGetM(prhs[0]);\r
+       numLabels = mxGetM(prhs[1]);    // Number of labels\r
+       numFLabels = mxGetM(prhs[2]);   // Number of ForeGround Labels\r
+       numBLabels = mxGetM(prhs[3]);   // Number of BackGround Labels\r
+       numFCClusters = mxGetM(prhs[4]);// Number of ForeGround Color Clusters\r
+       numBCClusters = mxGetM(prhs[5]);// Number of BackGround Color Clusters\r
+         \r
+       plhs[0] = mxCreateDoubleMatrix(numRows,numCols, mxREAL);        // Memory Allocated for output array\r
+\r
+       SegImage = mxGetPr(plhs[0]);                                                            // variable assigned to the output memory location\r
+        \r
+       printf("Starting MEX Code \n");\r
+\r
+       /**** Cast these data structures into correct types \r
+       and convert into STL Data Structures ****/\r
+       vector< vector<double> > MeanColorsVec;\r
+       MeanColorsVec.clear();\r
+    for(i=0;i<numLabels;i++)\r
+       {\r
+               vector<double> tmp;\r
+               tmp.clear();\r
+               tmp.push_back(MeanColors[0*numLabels + i]);\r
+               tmp.push_back(MeanColors[1*numLabels + i]);\r
+               tmp.push_back(MeanColors[2*numLabels + i]);\r
+\r
+               MeanColorsVec.push_back(tmp);\r
+       }\r
+       \r
+       printf("Vectorization 1 \n");\r
+\r
+       vector< vector<double> > FCClustersVec;\r
+       FCClustersVec.clear();\r
+    for(i=0;i<numFCClusters;i++)\r
+       {\r
+               vector<double> tmp;\r
+               tmp.clear();\r
+               tmp.push_back(FCClusters[0*numFCClusters + i]);\r
+               tmp.push_back(FCClusters[1*numFCClusters + i]);\r
+               tmp.push_back(FCClusters[2*numFCClusters + i]);\r
+\r
+               FCClustersVec.push_back(tmp);\r
+       }\r
+\r
+       \r
+       printf("Vectorization 2 \n");\r
+\r
+       vector< vector<double> > BCClustersVec;\r
+       BCClustersVec.clear();\r
+    for(i=0;i<numBCClusters;i++)\r
+       {\r
+               vector<double> tmp;\r
+               tmp.clear();\r
+               tmp.push_back(FCClusters[0*numBCClusters + i]);\r
+               tmp.push_back(FCClusters[1*numBCClusters + i]);\r
+               tmp.push_back(FCClusters[2*numBCClusters + i]);\r
+\r
+               BCClustersVec.push_back(tmp);\r
+       }\r
+\r
+       \r
+       printf("Vectorization 3 \n");\r
+\r
+       vector<int> FLabelsVec;\r
+       FLabelsVec.clear();\r
+       for(i=0;i<numFLabels;i++)\r
+               FLabelsVec.push_back((int) FLabels[i]);\r
+\r
+       \r
+       printf("Vectorization 4 \n");\r
+\r
+       vector<int> BLabelsVec;\r
+       BLabelsVec.clear();\r
+       for(i=0;i<numBLabels;i++)\r
+               BLabelsVec.push_back((int) BLabels[i]);\r
+\r
+       \r
+       printf("Vectorization 5 \n");\r
+\r
+       /********** Data Structures Converted into Vector Form**/\r
+       /*******************************************************/\r
+\r
+       /**** Actual Image Segmentation Code *********/\r
+       vector< set<int> > neighbors;                                   // Adjacency list declaration\r
+       Graph::node_id *nodes = new Graph::node_id[numLabels];\r
+\r
+       MakeAdjacencyList(L, numLabels, numCols, numRows, neighbors);   // Adjacency list made\r
+       printf("Adjacency lists made \n");\r
+       \r
+       Graph* G = MakeGraph(neighbors, MeanColorsVec, FLabelsVec, BLabelsVec, FCClustersVec, BCClustersVec, numLabels, nodes);\r
+       printf("Graph constructed \n");\r
+       \r
+       SegmentImage(L, G, numRows, numCols, SegImage, nodes);\r
+       printf("Image Segmented \n");\r
+}
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.dll.old b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.dll.old
new file mode 100644 (file)
index 0000000..d66bc2a
Binary files /dev/null and b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.dll.old differ

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.mexw32 b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/GraphCutSegmentLazy.mexw32
new file mode 100644 (file)
index 0000000..48aff1c
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegResult.mat b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegResult.mat
new file mode 100644 (file)
index 0000000..37369c3
Binary files /dev/null and b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegResult.mat differ

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegTool.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegTool.m
new file mode 100644 (file)
index 0000000..082f9df
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegTool.m
@@ -0,0 +1,26 @@
+function SegTool
+% SEGTOOL  - Main interface for Segmentation Tool
+% Tools - SmartSelect, AutoCut, AutoRefine
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+
+% Main Menu
+hfig = figure('units','pixels','position',[100 200 300 400],...
+   'tag','SegTool', 'name','Super Segmentation Tool',...
+   'menubar','none','numbertitle','off');
+
+
+% SmartSelect (Lazy Snapping) Tool Button
+data.ui.smart = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.25 .8 .5 .1], ...
+                    'String','SmartSelect','Callback', 'SmartSelect');
+
+% AutoCut (Grab Cut) Tool Button                
+data.ui.smart = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.25 .7 .5 .1], ...
+                    'String','AutoCut','Callback', 'AutoCut');
+                
+                
+% AutoCutRefine (GrabCut + Lazy Snapping) Tool Button                                
+data.ui.smart = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.25 .6 .5 .1], ...
+                    'String','AutoCutRefine','Callback', 'AutoCutRefine');
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/Segment.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/Segment.m
new file mode 100644 (file)
index 0000000..0427c62
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/Segment.m
@@ -0,0 +1,105 @@
+function  Segment(ImName)\r
+% SEGMENT  - Main graph cuts segmentation function\r
+% SEGMENT(IMNAME) - ImName - Image to segment\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+\r
+% Global variables referenced in this function\r
+global sopt SegImage fgpixels bgpixels;\r
+\r
+% Segmented output placeholder\r
+SegImage = zeros(size(ImName));\r
+\r
+% GUI specific flag\r
+fgflag = 2;\r
+\r
+% Read the input image\r
+I = imread(ImName);\r
+I1 = I(:,:,1);\r
+I2 = I(:,:,2);\r
+I3 = I(:,:,3);\r
+\r
+% Get Foreground and Background Pixels\r
+FY = fgpixels(:,1);\r
+FX = fgpixels(:,2);\r
+\r
+BY = bgpixels(:,1);\r
+BX = bgpixels(:,2);\r
+\r
+% Get Watershed pixels\r
+L = watershed(I(:,:,1));\r
+\r
+%%%%%%%%% Finding Mean colors of the regions  %%%%%%%%%%%%%%%%%%\r
+numLabels = max(L(:));\r
+PI = regionprops(L,'PixelIdxList');\r
+\r
+MeanColors = zeros(numLabels,3);\r
+for i=1:numLabels\r
+    MeanColors(i,1) = mean(I1(PI(i).PixelIdxList));\r
+    MeanColors(i,2) = mean(I2(PI(i).PixelIdxList));\r
+    MeanColors(i,3) = mean(I3(PI(i).PixelIdxList));\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%% Finding Foreground and Background Labels from pixel seeds%%\r
+try\r
+    Findices = sub2ind(size(I1),FX,FY);\r
+    FLabels = L(int32(Findices));\r
+    FLabels = union(FLabels,[]);         %% Set-ifying the set of labels (Sorting)\r
+    if(FLabels(1)==0)                   %% Removing Boundary Labels\r
+        FLabels = FLabels(2:end);\r
+    end\r
+\r
+    Bindices = sub2ind(size(I1),BX,BY);\r
+    BLabels = L(int32(Bindices));\r
+    BLabels = union(BLabels,[]);         %% Set-ifying the set of labels\r
+    if(BLabels(1)==0)                   %% Removing Boundary Labels\r
+        BLabels = BLabels(2:end);\r
+    end\r
+catch\r
+    beep\r
+    disp('Error: Seeds are outside Image limits ..Please restart');\r
+    return;\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%% Finding foregound and background color clusters %%%%%%%%%%%%\r
+\r
+FColors = MeanColors(FLabels,:);\r
+BColors = MeanColors(BLabels,:);\r
+\r
+NumFClusters = sopt.NumFClusters_SS;\r
+NumBClusters = sopt.NumBClusters_SS;\r
+\r
+try\r
+    [IDX, FCClusters] = kmeans(FColors, NumFClusters);  %% FCClusters = Foreground color clusters\r
+    [IDX, BCClusters] = kmeans(BColors, NumBClusters);  %% BCClusters = Background color clusters\r
+catch\r
+    beep\r
+    disp('Error!! Number of seeds should be greater than number of clusters ....');\r
+    disp('Add more seeds or reduce number of clusters ...');\r
+    return;\r
+end\r
+\r
+% Clustering is columnwise, clusters each set of fcolors into n clusters\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%  The Mex Function for GraphCutSegment %%%%%%%%%%%%\r
+L = L-1;        %% Segments labeled from 0 now -- -1 is for boundary pixels\r
+FLabels = FLabels-1;\r
+BLabels = BLabels-1;\r
+SegImage = GraphCutSegmentLazy(L, MeanColors, FLabels, BLabels, FCClusters, BCClusters);\r
+\r
+%%%%%%%%%%%%%%%    Display the segmented image   %%%%%%%%%%%%%%%\r
+SegImage = repmat(SegImage,[1,1,3]);\r
+SegNewImage = uint8(SegImage) .* uint8(I);\r
+figure;imshow(uint8(SegNewImage));\r
+\r
+SegMask = SegImage;\r
+SegResult = SegNewImage;\r
+\r
+% Save Segmentation Result\r
+save('SegResult', 'SegMask', 'SegResult');\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentBoth.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentBoth.m
new file mode 100644 (file)
index 0000000..377aaef
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentBoth.m
@@ -0,0 +1,283 @@
+function [] = SegmentGC(ImName);\r
+%%%Main Grab Cuts segmentation function\r
+global ih_img fgpixels bgpixels;\r
+\r
+load segimage;\r
+BiImage = SegImage;\r
+figure;\r
+imagesc(BiImage);\r
+SegImage = [];\r
+\r
+I = imread(ImName);\r
+I1 = I(:,:,1);\r
+I2 = I(:,:,2);\r
+I3 = I(:,:,3);\r
+\r
+%%%% Final Segmented Image\r
+SegImage = zeros(size(I));\r
+\r
+Findices = find(BiImage);\r
+Bindices = find(BiImage == 0);\r
+\r
+\r
+% Get Foreground and Background Pixels for Smart Refine\r
+FY = fgpixels(:,1);\r
+FX = fgpixels(:,2);\r
+\r
+BY = bgpixels(:,1);\r
+BX = bgpixels(:,2);\r
+\r
+% LZ\r
+FindicesStr = sub2ind(size(I1),FX,FY);\r
+BindicesStr = sub2ind(size(I1),BX,BY);\r
+size(FindicesStr)\r
+size(BindicesStr)\r
+'indices'\r
+\r
+Findices = union(Findices,FindicesStr);\r
+[WrongB WrIndB] = intersect(Bindices,FindicesStr);\r
+size(WrIndB)\r
+'Wrindb'\r
+Bindices(WrIndB) = [];\r
+\r
+\r
+Bindices = union(Bindices,BindicesStr);\r
+[WrongF WrIndF] = intersect(Findices,BindicesStr);\r
+size(WrIndF)\r
+'Wrinf'\r
+Findices(WrIndF) = [];\r
+\r
+\r
+L = watershed(I(:,:,1));        %%%% Doing watershed on the red channel -- SEE if you can do it on the color image\r
+\r
+%%%%%%%%% Finding Mean colors of the regions  %%%%%%%%%%%%%%%%%%\r
+%%%%% SEE if this can be vectorised  %%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+numLabels = max(L(:));\r
+PI = regionprops(L,'PixelIdxList');\r
+\r
+MeanColors = zeros(numLabels,3);\r
+for i=1:numLabels\r
+    MeanColors(i,1) = mean(I1(PI(i).PixelIdxList));\r
+    MeanColors(i,2) = mean(I2(PI(i).PixelIdxList));\r
+    MeanColors(i,3) = mean(I3(PI(i).PixelIdxList));\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%% Finding Foreground and Background Labels from pixels%%\r
+\r
+FLabels = L(int32(Findices));\r
+FLabels = union(FLabels,[]);         %% Set-ifying the set of labels (Sorting?)\r
+if(FLabels(1)==0)                   %% Removing Boundary Labels\r
+    FLabels = FLabels(2:end);\r
+end\r
+\r
+BLabels = L(int32(Bindices));\r
+BLabels = union(BLabels,[]);         %% Set-ifying the set of labels\r
+if(BLabels(1)==0)                   %% Removing Boundary Labels\r
+    BLabels = BLabels(2:end);\r
+end\r
+\r
+\r
+% Common labels and indices among GC and LZ\r
+[FIndCom] = intersect(Findices, FindicesStr);\r
+[BIndCom] = intersect(Bindices, BindicesStr);\r
+\r
+\r
+size(FIndCom)\r
+size(BIndCom)\r
+\r
+%%%% BLabels remain fixed -- no change\r
+%%%% ULabels -- Uncertain labels -- on which optimization done...\r
+ULabels = FLabels;\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%% Finding Initial Estimate of foregound and background color clusters %%%%%%%%%%%%\r
+\r
+NumFClusters = 5;\r
+NumBClusters = 5;\r
+\r
+FColors = MeanColors(FLabels,:);\r
+BColors = MeanColors(BLabels,:);\r
+\r
+%%%%%%%%%%% Initializing the GMM's %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%% Using EM_GM %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+% [FWeights,FCClusters,FCovs,FLikelihood] = EM_GM(FColors,NumFClusters);\r
+% [BWeights,BCClusters,BCovs,BLikelihood] = EM_GM(BColors,NumBClusters);\r
+\r
+%%%%%%%%%%% Using Just kmeans %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+[FId FCClusters] = vgg_kmeans(FColors, NumFClusters);\r
+Fdim = size(FColors,2);\r
+\r
+FCClusters = zeros(Fdim, NumFClusters);\r
+FWeights = zeros(1,NumFClusters);\r
+FCovs = zeros(Fdim, Fdim, NumFClusters);\r
+for k=1:NumFClusters\r
+    relColors = FColors(find(FId==k),:);        %% Colors belonging to cluster k\r
+    FCClusters(:,k) = mean(relColors,1)';\r
+    FCovs(:,:,k) = cov(relColors);\r
+    FWeights(1,k) = length(find(FId==k)) / length(FId);\r
+end\r
+\r
+[BId BCClusters] = vgg_kmeans(BColors, NumBClusters);\r
+Bdim = size(BColors,2);\r
+\r
+BCClusters = zeros(Bdim, NumBClusters);\r
+BWeights = zeros(1,NumBClusters);\r
+BCovs = zeros(Bdim, Bdim, NumBClusters);\r
+for k=1:NumBClusters\r
+    relColors = BColors(find(BId==k),:);        %% Colors belonging to cluster k\r
+    BCClusters(:,k) = mean(relColors,1)';\r
+    BCovs(:,:,k) = cov(relColors);\r
+    BWeights(1,k) = length(find(BId==k)) / length(BId);\r
+end\r
+\r
+%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+% % Initialize FDist and BDist with LZ values\r
+% FDist  = zeros(length(FLabels),1);\r
+% BDist  = zeros(length(BLabels),1);\r
+\r
+\r
+\r
+numIter = 6;\r
+for i=1:numIter\r
+    i\r
+    [FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);\r
+    [BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);\r
+\r
+    % Lets hope somebody doesnt choose the same thing as FG and BG, BG will\r
+    % prevail\r
+    FDist(FIndCom) = 0;\r
+    BDist(FIndCom) = 10000;\r
+\r
+    FDist(BIndCom) = 10000;\r
+    BDist(BIndCom) = 0;\r
+\r
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+    %%%%%%%%%%%%%  The Mex Function for GraphCutSegment %%%%%%%%%%%%\r
+    L = L-1;        %% Segments labeled from 0 now -- -1 is for boundary pixels\r
+    FLabels = FLabels-1;\r
+    BLabels = BLabels-1;\r
+    ULabels = ULabels-1;\r
+    [SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels, FDist, BDist);    %%% SegImage is the segmented image, %%% LLabels is the binary label for each watershed label\r
+\r
+    L = L+1;        %% Again Labeled from 1...\r
+    FLabels = FLabels+1;\r
+    BLabels = BLabels+1;\r
+    ULabels = ULabels+1;\r
+\r
+    %%%%%%%%%% Showing the intermediate segmentation %%%%%%%%%%%%%%%%%%%%%\r
+    %     edge_img = edge(SegImage,'canny');\r
+    %\r
+    %\r
+    %     figure;\r
+    %     imshow(I);\r
+    %     [IInd,JInd] = ind2sub(size(I1),find(edge_img));\r
+    %     hold on;plot(JInd,IInd,'b.');\r
+\r
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+    if(i<numIter)       %%%%%% Do NOT do this if final iteration -- just display the segmented image\r
+        %%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%\r
+        newFLabels = ULabels(find(LLabels==1.0));\r
+        newBLabels = ULabels(find(LLabels==0.0));\r
+\r
+        %         newBLabels = union(newBLabels,BLabels);                 %%%%%% Whether new background labels will contain the old ones?\r
+\r
+        FColors = MeanColors(newFLabels,:);\r
+        BColors = MeanColors(newBLabels,:);\r
+\r
+        %%%%%%%% Making new GMM's based on new segmentation %%%%%%%%%%%%%%%%%%\r
+        %%%%%%%% Used for defining distances in the next iterative step %%%%%%\r
+        %         [FWeights,FCClusters,FCovs,FLikelihood] = EM_GM(FColors,NumFClusters);\r
+        %         [BWeights,BCClusters,BCovs,BLikelihood] = EM_GM(BColors,NumBClusters);\r
+\r
+        [newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);\r
+        [newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);\r
+\r
+        for k=1:NumFClusters\r
+            relColors = FColors(find(newFInd==k),:);        %% Colors belonging to cluster k\r
+            FCClusters(:,k) = mean(relColors,1)';\r
+            FCovs(:,:,k) = cov(relColors);\r
+            FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);\r
+        end\r
+\r
+        for k=1:NumBClusters\r
+            relColors = BColors(find(newBInd==k),:);        %% Colors belonging to cluster k\r
+            BCClusters(:,k) = mean(relColors,1)';\r
+            BCovs(:,:,k) = cov(relColors);\r
+            BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);\r
+        end\r
+    end\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%    Display the segmented image   %%%%%%%%%%%%%%%%%%%\r
+\r
+%set position\r
+% data.ui.ah_img = axes('Position',[0.5 0.2 .603 .604]);%,'drawmode','fast');\r
+% data.ui.ih_img = imagesc;\r
+% %set image data\r
+% set(data.ui.ih_img, 'Cdata', SegImage);\r
+% axis('image');axis('ij');axis('off');\r
+% drawnow;\r
+\r
+edge_img = edge(SegImage,'canny');\r
+\r
+\r
+\r
+% Put image on black background\r
+SegNewImage1 = zeros(size(SegImage));\r
+SegNewImage2 = zeros(size(SegImage));\r
+SegNewImage3 = zeros(size(SegImage));\r
+idx = find(SegImage);\r
+length(idx)\r
+\r
+for(i = 1:length(idx))\r
+    SegNewImage1(int32(idx(i))) = I1(int32(idx(i)));\r
+    SegNewImage2(int32(idx(i))) = I2(int32(idx(i)));\r
+    SegNewImage3(int32(idx(i))) = I3(int32(idx(i)));\r
+end\r
+\r
+SegNewImage(:,:,1) = SegNewImage1;\r
+SegNewImage(:,:,2) = SegNewImage2;\r
+SegNewImage(:,:,3) = SegNewImage3;\r
+\r
+set(ih_img, 'Cdata', uint8(SegNewImage));\r
+axis('image');axis('ij');axis('off');\r
+drawnow;\r
+figure;\r
+imshow(uint8(SegNewImage));\r
+\r
+save segnewimage SegNewImage;\r
+\r
+\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)\r
+\r
+NumFClusters = size(FCClusters,2);\r
+numULabels = size(MeanColors,1);\r
+\r
+FDist = zeros(numULabels,1);\r
+FInd = zeros(numULabels,1);\r
+\r
+Ftmp = zeros(numULabels, NumFClusters);\r
+\r
+for k=1:NumFClusters\r
+    M = FCClusters(:,k);\r
+    CovM = FCovs(:,:,k);\r
+    W = FWeights(1,k);\r
+\r
+    V = MeanColors - repmat(M',numULabels,1);\r
+\r
+    Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));\r
+    %     keyboard\r
+end\r
+\r
+[FDist, FInd] = min(Ftmp,[],2);\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentGC.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentGC.m
new file mode 100644 (file)
index 0000000..b69295c
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentGC.m
@@ -0,0 +1,252 @@
+function [] = SegmentGC(ImName, ih, alg)\r
+% SEGMENTGC  - Main GrabCut segmentation function\r
+% SEGMENTGC(IMNAME, IH, ALG) - ImName - Image to segment\r
+% IH - Image Handle\r
+% ALG - AutoCut or AutoRefine (0 or 1)\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global Variables\r
+global sopt segImageHandle;\r
+\r
+% Read input image\r
+I = imread(ImName);\r
+I1 = I(:,:,1);\r
+I2 = I(:,:,2);\r
+I3 = I(:,:,3);\r
+\r
+% Final Segmented Image\r
+SegImage = zeros(size(I));\r
+\r
+% Stop unnecessary warnings\r
+warning('off','all');\r
+\r
+%%%%%%% Marking the Inner Rectangle around the foreground object\r
+%%%%%%% Get two points from the user\r
+% Set the image\r
+set(ih, 'Cdata', I);\r
+axis('image');axis('ij');axis('off');\r
+drawnow;\r
+\r
+fp = ginput(2);\r
+fp = round(fp);\r
+\r
+% Save the points for future use\r
+save rectpts fp;\r
+\r
+% Width of Strip\r
+StripWidth = sopt.StripWidth_AC_ACR;       \r
+\r
+% Get inner indices\r
+[fxi,fyi] = meshgrid(min(fp(:,1)):max(fp(:,1)),min(fp(:,2)):max(fp(:,2)));\r
+InsideFindices = sub2ind(size(I1),fyi, fxi);\r
+InsideFindices = InsideFindices(:);\r
+\r
+% Form the Outside rectangular bounding box -- add StripWidth on all 4 sides\r
+[fxo,fyo] = meshgrid( max((min(fp(:,1)) - StripWidth),1) : min((max(fp(:,1)) + StripWidth), size(I1,2)) , max((min(fp(:,2)) - StripWidth),1) : min((max(fp(:,2)) + StripWidth), size(I1,1)) );\r
+OutsideFindices = sub2ind(size(I1),fyo, fxo);\r
+OutsideFindices = OutsideFindices(:);\r
+\r
+Bindices = setdiff(OutsideFindices, InsideFindices);\r
+Allindices = [1:size(I1,1)*size(I1,2)]';\r
+Findices = InsideFindices;\r
+\r
+% Water shed pixels\r
+L = watershed(I(:,:,1));\r
+\r
+%%%%%%%%% Finding Mean colors of the regions  %%%%%%%%%%%%%%%%%%\r
+numLabels = max(L(:));\r
+PI = regionprops(L,'PixelIdxList');\r
+\r
+MeanColors = zeros(numLabels,3);\r
+for i=1:numLabels\r
+    MeanColors(i,1) = mean(I1(PI(i).PixelIdxList));\r
+    MeanColors(i,2) = mean(I2(PI(i).PixelIdxList));\r
+    MeanColors(i,3) = mean(I3(PI(i).PixelIdxList));\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%% Finding Foreground and Background Labels from pixels%%\r
+\r
+FLabels = L(int32(Findices));\r
+FLabels = union(FLabels,[]);         %% Set-ifying the set of labels (Sorting)\r
+if(FLabels(1)==0)                   %% Removing Boundary Labels\r
+    FLabels = FLabels(2:end);\r
+end\r
+\r
+BLabels = L(int32(Bindices));\r
+BLabels = union(BLabels,[]);         %% Set-ifying the set of labels\r
+if(BLabels(1)==0)                   %% Removing Boundary Labels\r
+    BLabels = BLabels(2:end);\r
+end\r
+\r
+%%%% BLabels remain fixed -- no change\r
+%%%% ULabels -- Uncertain labels -- on which optimization done...\r
+ULabels = FLabels;\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%% Finding Initial Estimate of foregound and background color clusters %%%%%%%%%%%%\r
+\r
+NumFClusters = sopt.NumFClusters_AC;\r
+NumBClusters = sopt.NumBClusters_AC;\r
+\r
+FColors = MeanColors(FLabels,:);\r
+BColors = MeanColors(BLabels,:);\r
+\r
+%%%%%%%%%%% Initializing the GMM's %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%% Using Just kmeans %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+[FId FCClusters] = kmeans(FColors, NumFClusters);\r
+Fdim = size(FColors,2);\r
+\r
+FCClusters = zeros(Fdim, NumFClusters);\r
+FWeights = zeros(1,NumFClusters);\r
+FCovs = zeros(Fdim, Fdim, NumFClusters);\r
+for k=1:NumFClusters\r
+    relColors = FColors(find(FId==k),:);        %% Colors belonging to cluster k\r
+    FCClusters(:,k) = mean(relColors,1)';\r
+    FCovs(:,:,k) = cov(relColors);\r
+    FWeights(1,k) = length(find(FId==k)) / length(FId);\r
+end\r
+\r
+[BId BCClusters] = kmeans(BColors, NumBClusters);\r
+Bdim = size(BColors,2);\r
+\r
+BCClusters = zeros(Bdim, NumBClusters);\r
+BWeights = zeros(1,NumBClusters);\r
+BCovs = zeros(Bdim, Bdim, NumBClusters);\r
+for k=1:NumBClusters\r
+    relColors = BColors(find(BId==k),:);        %% Colors belonging to cluster k\r
+    BCClusters(:,k) = mean(relColors,1)';\r
+    BCovs(:,:,k) = cov(relColors);\r
+    BWeights(1,k) = length(find(BId==k)) / length(BId);\r
+end\r
+\r
+%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+lambda = sopt.lambda_AC;\r
+numIter = sopt.numIter_AC;\r
+for i = 1:numIter\r
+    disp(['Iteration - ', num2str(i)]);\r
+\r
+    % Foreground and Background edge weights\r
+    [FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);\r
+    [BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);\r
+\r
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+    %% Segments labeled from 0 now -- -1 is for boundary pixels\r
+    L = L-1;\r
+    FLabels = FLabels-1;\r
+    BLabels = BLabels-1;\r
+    ULabels = ULabels-1;\r
+    %%%%%%%%%%%%%  The Mex Function for GraphCutSegment %%%%%%%%%%%%\r
+    %%% SegImage is the segmented image, %%% LLabels is the binary label\r
+    %%% for each watershed label\r
+    [SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels,...\r
+        FDist, BDist, lambda);\r
+\r
+    %% Again Labeled from 1...\r
+    L = L+1;\r
+    FLabels = FLabels+1;\r
+    BLabels = BLabels+1;\r
+    ULabels = ULabels+1;\r
+\r
+    if(i < numIter)\r
+        %%%%%% Do NOT do this if final iteration -- just display the segmented image\r
+        %%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%\r
+        newFLabels = ULabels(find(LLabels==1.0));\r
+        newBLabels = ULabels(find(LLabels==0.0));\r
+\r
+        %%%%%% Whether new background labels will contain the old ones?\r
+        % newBLabels = union(newBLabels,BLabels);\r
+\r
+        FColors = MeanColors(newFLabels,:);\r
+        BColors = MeanColors(newBLabels,:);\r
+\r
+        %%%%%%%% Calculating FG and BG distances based on new segmentation %%%%%%%%%%%%%%%%%%\r
+        [newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);\r
+        [newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);\r
+\r
+        for k = 1:NumFClusters\r
+            relColors = FColors(find(newFInd==k),:);        %% Colors belonging to cluster k\r
+            FCClusters(:,k) = mean(relColors,1)';\r
+            FCovs(:,:,k) = cov(relColors);\r
+            FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);\r
+        end\r
+\r
+        for k=1:NumBClusters\r
+            relColors = BColors(find(newBInd==k),:);        %% Colors belonging to cluster k\r
+            BCClusters(:,k) = mean(relColors,1)';\r
+            BCovs(:,:,k) = cov(relColors);\r
+            BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);\r
+        end\r
+    end\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%    Display the segmented image   %%%%%%%%%%%%%%%%%%%\r
+edge_img = edge(SegImage,'canny');\r
+\r
+% Put image on black background\r
+SegImage = repmat(SegImage,[1,1,3]);\r
+SegNewImage = uint8(SegImage) .* uint8(I);\r
+\r
+% If AutoCut, just show the image\r
+if(~alg)    \r
+    figure;\r
+    imshow(uint8(SegNewImage));\r
+else\r
+    % If AutoRefine mark a segmentation boundary on original image\r
+    [IInd,JInd] = ind2sub(size(I1),find(edge_img));\r
+    boundImage1 = I(:,:,2);\r
+    boundImage1(find(edge_img)) = 255;\r
+    boundImage = I;\r
+    boundImage(:,:,2) = boundImage1;\r
+    \r
+    % Set the image\r
+    set(ih, 'Cdata', uint8(boundImage));\r
+    axis('image');axis('ij');axis('off');\r
+    drawnow;\r
+    segImageHandle = figure;\r
+    imshow(uint8(SegNewImage));\r
+end\r
+SegMask = SegImage;\r
+SegResult = SegNewImage;\r
+\r
+% Save Segmentation Result\r
+save('SegResult', 'SegMask', 'SegResult');\r
+\r
+% Required for AutoCutRefine\r
+save('iter_data','L', 'MeanColors', 'FLabels', 'BLabels', 'FCClusters',...\r
+    'FCovs', 'FWeights', 'BCClusters', 'BCovs', 'BWeights');\r
+\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)\r
+% CLUSTDISTMEMBERSHIP - Calcuates FG and BG Distances\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+NumFClusters = size(FCClusters,2);\r
+numULabels = size(MeanColors,1);\r
+\r
+FDist = zeros(numULabels,1);\r
+FInd = zeros(numULabels,1);\r
+\r
+Ftmp = zeros(numULabels, NumFClusters);\r
+\r
+for k = 1:NumFClusters\r
+    M = FCClusters(:,k);\r
+    CovM = FCovs(:,:,k);\r
+    W = FWeights(1,k);\r
+\r
+    V = MeanColors - repmat(M',numULabels,1);\r
+    Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));\r
+\r
+end\r
+\r
+[FDist, FInd] = min(Ftmp,[],2);\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentRefine.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentRefine.m
new file mode 100644 (file)
index 0000000..0c879e6
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SegmentRefine.m
@@ -0,0 +1,235 @@
+function [] = SegmentRefine(ImName)\r
+% SEGMENTREFINE  - Main graph cuts segmentation function\r
+% SEGMENTREFINE(IMNAME) - ImName - Image to segment\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global Variables\r
+global sopt ih_img fgpixels bgpixels fgflag segImageHandle;\r
+\r
+% GUI specific flag\r
+fgflag = 2;\r
+\r
+% Data from GC(AutoCut) or prev GC+LZ(AutoCutRefine)\r
+load('iter_data');\r
+\r
+% Stop unnecessary warnings\r
+warning('off','all');\r
+\r
+% Read input image\r
+I = imread(ImName);\r
+I1 = I(:,:,1);\r
+I2 = I(:,:,2);\r
+I3 = I(:,:,3);\r
+\r
+%%%% Final Segmented Image\r
+SegImage = zeros(size(I));\r
+\r
+% LZ\r
+% Get Foreground and Background Pixels for Smart Refine\r
+if(~isempty(fgpixels))\r
+    FY = fgpixels(:,1);\r
+    FX = fgpixels(:,2);\r
+end\r
+if(~isempty(bgpixels))\r
+    BY = bgpixels(:,1);\r
+    BX = bgpixels(:,2);\r
+end\r
+\r
+% GC - fp bounding rectangle\r
+load rectpts;\r
+\r
+%% Width of Strip\r
+StripWidth = sopt.StripWidth_AC_ACR;\r
+\r
+[fxi,fyi] = meshgrid(min(fp(:,1)):max(fp(:,1)),min(fp(:,2)):max(fp(:,2)));\r
+InsideFindices = sub2ind(size(I1),fyi, fxi);\r
+InsideFindices = InsideFindices(:);\r
+\r
+%Form the Outside rectangular bounding box -- add StripWidth on all 4 sides\r
+\r
+[fxo,fyo] = meshgrid( max((min(fp(:,1)) - StripWidth),1) : min((max(fp(:,1)) + StripWidth), size(I1,2)) , max((min(fp(:,2)) - StripWidth),1) : min((max(fp(:,2)) + StripWidth), size(I1,1)) );\r
+OutsideFindices = sub2ind(size(I1),fyo, fxo);\r
+OutsideFindices = OutsideFindices(:);\r
+\r
+Bindices = setdiff(OutsideFindices, InsideFindices);\r
+Allindices = [1:size(I1,1)*size(I1,2)]';\r
+Findices = InsideFindices;\r
+\r
+numLabels = max(L(:));\r
+PI = regionprops(L,'PixelIdxList');\r
+\r
+\r
+%%%% BLabels remain fixed -- no change\r
+%%%% ULabels -- Uncertain labels -- on which optimization done...\r
+ULabels = FLabels;\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%% Finding Foreground and Background Labels from pixel seeds%%\r
+try\r
+    if(~isempty(fgpixels))\r
+        FindicesStr = sub2ind(size(I1),FX,FY);\r
+        FLabelsStr = L(int32(FindicesStr));\r
+        FLabelsStr = union(FLabelsStr,[]);         %% Set-ifying the set of labels (Sorting?)\r
+        if(FLabelsStr(1)==0)                   %% Removing Boundary Labels\r
+            FLabelsStr = FLabelsStr(2:end);\r
+        end\r
+        % Get Common label indices to index into fdist and bdist\r
+        [FLabelsCom FIndCom] = intersect(ULabels, FLabelsStr);\r
+    end\r
+\r
+    if(~isempty(bgpixels))\r
+        BindicesStr = sub2ind(size(I1),BX,BY);\r
+        BLabelsStr = L(int32(BindicesStr));\r
+        BLabelsStr = union(BLabelsStr,[]);         %% Set-ifying the set of labels\r
+        if(BLabelsStr(1)==0)                   %% Removing Boundary Labels\r
+            BLabelsStr = BLabelsStr(2:end);\r
+        end\r
+\r
+        [BLabelsCom BIndCom] = intersect(ULabels, BLabelsStr);\r
+    end\r
+catch\r
+    beep\r
+    disp('Error: Seeds are outside Image limits ...Please restart');\r
+    return;\r
+end\r
+\r
+FColors = MeanColors(FLabels,:);\r
+BColors = MeanColors(BLabels,:);\r
+%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+lambda = sopt.lambda_ACR;\r
+numIter = sopt.numIter_ACR;\r
+for i = 1:numIter\r
+    disp(['Iteration - ', num2str(i)]);\r
+\r
+    % Foreground and Background edge weights\r
+    [FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);\r
+    [BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);\r
+\r
+    % Hard Seeds\r
+    if(~isempty(fgpixels))\r
+        FDist(FIndCom) = 0;\r
+        BDist(FIndCom) = 1000;\r
+    end\r
+\r
+    if(~isempty(bgpixels))\r
+        FDist(BIndCom) = 1000;\r
+        BDist(BIndCom) = 0;\r
+    end\r
+\r
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+    %% Segments labeled from 0 now -- -1 is for boundary pixels\r
+    L = L-1;\r
+    FLabels = FLabels-1;\r
+    BLabels = BLabels-1;\r
+    ULabels = ULabels-1;\r
+    %%%%%%%%%%%%%  The Mex Function for GraphCutSegment %%%%%%%%%%%%\r
+    %%% SegImage is the segmented image, %%% LLabels is the binary label\r
+    %%% for each watershed label\r
+    [SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels, FDist, BDist, lambda);    %%% SegImage is the segmented image, %%% LLabels is the binary label for each watershed label\r
+\r
+    %% Again Labeled from 1...\r
+    L = L+1;\r
+    FLabels = FLabels+1;\r
+    BLabels = BLabels+1;\r
+    ULabels = ULabels+1;\r
+\r
+    if(i < numIter)\r
+        %%%%%% Do NOT do this if final iteration -- just display the segmented image\r
+        %%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%\r
+        newFLabels = ULabels(find(LLabels==1.0));\r
+        newBLabels = ULabels(find(LLabels==0.0));\r
+\r
+        %%%%%% Whether new background labels will contain the old ones?\r
+        % newBLabels = union(newBLabels,BLabels);\r
+\r
+        FColors = MeanColors(newFLabels,:);\r
+        BColors = MeanColors(newBLabels,:);\r
+\r
+        %%%%%%%% Calculating FG and BG distances based on new segmentation\r
+        %%%%%%%% %%%%%%%%%%%%%%%%%%\r
+\r
+        [newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);\r
+        [newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);\r
+\r
+        for k=1:NumFClusters\r
+            relColors = FColors(find(newFInd==k),:);        %% Colors belonging to cluster k\r
+            FCClusters(:,k) = mean(relColors,1)';\r
+            FCovs(:,:,k) = cov(relColors);\r
+            FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);\r
+        end\r
+\r
+        for k=1:NumBClusters\r
+            relColors = BColors(find(newBInd==k),:);        %% Colors belonging to cluster k\r
+            BCClusters(:,k) = mean(relColors,1)';\r
+            BCovs(:,:,k) = cov(relColors);\r
+            BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);\r
+        end\r
+    end\r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%    Display the segmented image   %%%%%%%%%%%%%%%%%%%\r
+\r
+edge_img = edge(SegImage,'canny');\r
+\r
+% Put image on black background\r
+SegImage = repmat(SegImage,[1,1,3]);\r
+SegNewImage = uint8(SegImage) .* uint8(I);\r
+\r
+\r
+% Mark a segmentation boundary on original image\r
+% Set the image\r
+[IInd,JInd] = ind2sub(size(I1),find(edge_img));\r
+boundImage1 = I(:,:,2);\r
+boundImage1(find(edge_img)) = 255;\r
+boundImage = I;\r
+boundImage(:,:,2) = boundImage1;\r
+\r
+set(ih_img, 'Cdata', uint8(boundImage));\r
+axis('image');axis('ij');axis('off');\r
+drawnow;\r
+figure(segImageHandle);\r
+imshow(uint8(SegNewImage));\r
+\r
+SegMask = SegImage;\r
+SegResult = SegNewImage;\r
+\r
+% Save Segmentation Result\r
+save('SegResult', 'SegMask', 'SegResult');\r
+\r
+% Required for AutoRefine\r
+save('iter_data','L', 'MeanColors', 'FLabels', 'BLabels', 'FCClusters',...\r
+    'FCovs', 'FWeights', 'BCClusters', 'BCovs', 'BWeights');\r
+\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)\r
+% CLUSTDISTMEMBERSHIP - Calcuates FG and BG Distances\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+NumFClusters = size(FCClusters,2);\r
+numULabels = size(MeanColors,1);\r
+\r
+FDist = zeros(numULabels,1);\r
+FInd = zeros(numULabels,1);\r
+\r
+Ftmp = zeros(numULabels, NumFClusters);\r
+\r
+for k=1:NumFClusters\r
+    M = FCClusters(:,k);\r
+    CovM = FCovs(:,:,k);\r
+    W = FWeights(1,k);\r
+\r
+    V = MeanColors - repmat(M',numULabels,1);\r
+    Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));\r
+end\r
+\r
+[FDist, FInd] = min(Ftmp,[],2);\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelect.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelect.m
new file mode 100644 (file)
index 0000000..4df3c96
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelect.m
@@ -0,0 +1,37 @@
+function SmartSelect\r
+% SMARTSELECT  - Main interface for Smart Select Tool\r
+% This function creates the user interface (figure window\r
+% and custom menu)\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+clear all;\r
+\r
+% Global Variables\r
+global sopt;\r
+\r
+% Initialize all required parameters\r
+sopt = mksopt;\r
+\r
+% Main Menu \r
+hfig = figure('units','pixels','position', [50 100 1100 600],...\r
+   'tag','GUI', 'name','Graph-Cuts Segmentation',...\r
+   'menubar','none','numbertitle','off');\r
+\r
+\r
+% File menu for figure, with callbacks:\r
+% Open...    (callback to displayImage)\r
+% Exit       (closes figure window)\r
+\r
+hmenu = uimenu('Label','File');\r
+\r
+% DisplayImage function - loads the image file\r
+uimenu(hmenu,'label','Open...','callback','SmartSelectSeg')\r
+\r
+% Exit \r
+uimenu(hmenu,'label','Exit','callback','closereq','separator','on');\r
+\r
+\r
+\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelectSeg.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelectSeg.m
new file mode 100644 (file)
index 0000000..f3a70bf
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/SmartSelectSeg.m
@@ -0,0 +1,72 @@
+function SmartSelectSeg\r
+% SMARTSELECTSEG  - This function displays chosen image and creates buttons\r
+% for marking seeds and calling Segment function\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global Variables\r
+% Req by radio button RadioButtonFn\r
+global hfig longfilename;\r
+\r
+% Get handle to current figure;\r
+hfig = gcf;\r
+\r
+\r
+% Call built-in file dialog to select image file\r
+[filename,pathname] = uigetfile('images/*.*','Select image file');\r
+if ~ischar(filename); return; end\r
+\r
+%%%%%%%%%%%%%%%%%%%Radio Buttons%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+h = uibuttongroup('visible','off','Position',[0.7 0.7 0.15 0.18]);\r
+u0 = uicontrol('Style','Radio','String','SmartRectangle',...\r
+    'pos',[10 20 120 30],'parent',h,'HandleVisibility','off');\r
+u1 = uicontrol('Style','Radio','String','SmartRefine',...\r
+    'pos',[10 60 120 30],'parent',h,'HandleVisibility','off');\r
+set(h,'SelectionChangeFcn',@RadioButtonFn);\r
+set(h,'SelectedObject',[]);\r
+set(h,'Visible','on');\r
+\r
+%%%%%%%%%%%%%%%%%%%Draw Image%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+% Load  Image file\r
+longfilename = strcat(pathname,filename);\r
+Im = imread(longfilename);\r
+\r
+% Get the position of the image\r
+data.ui.ah_img = axes('Position',[0.01 0.2 .603 .604]);\r
+data.ui.ih_img = image;\r
+\r
+% Set the image\r
+set(data.ui.ih_img, 'Cdata', Im);\r
+axis('image');axis('ij');axis('off');\r
+drawnow;\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+function RadioButtonFn(source, eventdata)\r
+% RADIOBUTTONFUNCTION - This function is called whenever there is change in\r
+% choice of radio button\r
+\r
+% Global Variables\r
+global hfig longfilename;\r
+\r
+% Pass string value to seed selection function\r
+strg = get(eventdata.NewValue,'String');\r
+\r
+%%%%%%%%%%%%%%%%Seed Selection Buttons%%%%%%%%%%%%%%%%%%%\r
+% Calls fginput - gets foreground pixels from the user\r
+data.ui.push_fg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .6 .1 .05], ...\r
+    'String','Foreground','Callback', ['fginput ',strg]);\r
+\r
+% Calls bginput - gets background pixels from the user\r
+data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .5 .1 .05], ...\r
+    'String','Background','Callback', ['bginput ',strg]);\r
+\r
+% Calls Segment - graph-cuts on the image\r
+data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .4 .1 .05], ...\r
+    'String','Graph Cuts','Callback', ['Segment ',longfilename]);\r
+drawnow;\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/Thumbs.db b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/Thumbs.db
new file mode 100644 (file)
index 0000000..8ddc15b
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/abdomen.jpg b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/abdomen.jpg
new file mode 100644 (file)
index 0000000..b1cdd68
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/bginput.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/bginput.m
new file mode 100644 (file)
index 0000000..6a4fc45
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/bginput.m
@@ -0,0 +1,52 @@
+%--------------------------------------------------------------------------\r
+function  bginput(strg)\r
+% BGINPUT  - This function gets the background pixels from user input\r
+% BGINPUT(STRG) - strg  - Smart Refine or Smart Rectangle\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global variables referenced in this funciton\r
+global fgflag fgbc bgbc fgpixels bgpixels;\r
+\r
+\r
+\r
+if(strcmp(strg,'SmartRectangle'))\r
+\r
+    % Gui related flag\r
+    fgflag = 2;\r
+\r
+    % Get two points from the user\r
+    bp = ginput(2);\r
+    bp = round(bp);\r
+\r
+    % Form the rectangular bounding box from the two points\r
+    [bx,by] = meshgrid(min(bp(:,1)):max(bp(:,1)),min(bp(:,2)):max(bp(:,2)));\r
+    bpixelsx =[];\r
+    bpixelsy =[];\r
+    for(i = 1:size(bx,2))\r
+        bpixelsx = [bpixelsx bx(:,i)'];\r
+        bpixelsy = [bpixelsy by(:,i)'];\r
+    end\r
+\r
+    bpixels = [bpixelsx' bpixelsy'];\r
+\r
+    % Concatenate\r
+    bgpixels = vertcat(bgpixels,bpixels);\r
+\r
+    % Plot the Rectangle\r
+    hfig = gcf;\r
+    axis('image');axis('ij');axis('off');\r
+    hold on;\r
+    plot(bgpixels(:,1),bgpixels(:,2),'b.');\r
+\r
+else\r
+    hfig = gcf;\r
+    hold on;\r
+\r
+    % Gui related flag\r
+    fgflag = 0;\r
+\r
+    % Call track function on button press\r
+    set(hfig,'windowbuttondownfcn',{@track});\r
+end\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/block.h b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/block.h
new file mode 100644 (file)
index 0000000..2d4dee8
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/block.h
@@ -0,0 +1,285 @@
+/* block.h */\r
+/*\r
+    Copyright 2001 Vladimir Kolmogorov (vnk@cs.cornell.edu), Yuri Boykov (yuri@csd.uwo.ca).\r
+\r
+    This program is free software; you can redistribute it and/or modify\r
+    it under the terms of the GNU General Public License as published by\r
+    the Free Software Foundation; either version 2 of the License, or\r
+    (at your option) any later version.\r
+\r
+    This program is distributed in the hope that it will be useful,\r
+    but WITHOUT ANY WARRANTY; without even the implied warranty of\r
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r
+    GNU General Public License for more details.\r
+\r
+    You should have received a copy of the GNU General Public License\r
+    along with this program; if not, write to the Free Software\r
+    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\r
+*/\r
+\r
+\r
+/*\r
+       Template classes Block and DBlock\r
+       Implement adding and deleting items of the same type in blocks.\r
+\r
+       If there there are many items then using Block or DBlock\r
+       is more efficient than using 'new' and 'delete' both in terms\r
+       of memory and time since\r
+       (1) On some systems there is some minimum amount of memory\r
+           that 'new' can allocate (e.g., 64), so if items are\r
+           small that a lot of memory is wasted.\r
+       (2) 'new' and 'delete' are designed for items of varying size.\r
+           If all items has the same size, then an algorithm for\r
+           adding and deleting can be made more efficient.\r
+       (3) All Block and DBlock functions are inline, so there are\r
+           no extra function calls.\r
+\r
+       Differences between Block and DBlock:\r
+       (1) DBlock allows both adding and deleting items,\r
+           whereas Block allows only adding items.\r
+       (2) Block has an additional operation of scanning\r
+           items added so far (in the order in which they were added).\r
+       (3) Block allows to allocate several consecutive\r
+           items at a time, whereas DBlock can add only a single item.\r
+\r
+       Note that no constructors or destructors are called for items.\r
+\r
+       Example usage for items of type 'MyType':\r
+\r
+       ///////////////////////////////////////////////////\r
+       #include "block.h"\r
+       #define BLOCK_SIZE 1024\r
+       typedef struct { int a, b; } MyType;\r
+       MyType *ptr, *array[10000];\r
+\r
+       ...\r
+\r
+       Block<MyType> *block = new Block<MyType>(BLOCK_SIZE);\r
+\r
+       // adding items\r
+       for (int i=0; i<sizeof(array); i++)\r
+       {\r
+               ptr = block -> New();\r
+               ptr -> a = ptr -> b = rand();\r
+       }\r
+\r
+       // reading items\r
+       for (ptr=block->ScanFirst(); ptr; ptr=block->ScanNext())\r
+       {\r
+               printf("%d %d\n", ptr->a, ptr->b);\r
+       }\r
+\r
+       delete block;\r
+\r
+       ...\r
+\r
+       DBlock<MyType> *dblock = new DBlock<MyType>(BLOCK_SIZE);\r
+       \r
+       // adding items\r
+       for (int i=0; i<sizeof(array); i++)\r
+       {\r
+               array[i] = dblock -> New();\r
+       }\r
+\r
+       // deleting items\r
+       for (int i=0; i<sizeof(array); i+=2)\r
+       {\r
+               dblock -> Delete(array[i]);\r
+       }\r
+\r
+       // adding items\r
+       for (int i=0; i<sizeof(array); i++)\r
+       {\r
+               array[i] = dblock -> New();\r
+       }\r
+\r
+       delete dblock;\r
+\r
+       ///////////////////////////////////////////////////\r
+\r
+       Note that DBlock deletes items by marking them as\r
+       empty (i.e., by adding them to the list of free items),\r
+       so that this memory could be used for subsequently\r
+       added items. Thus, at each moment the memory allocated\r
+       is determined by the maximum number of items allocated\r
+       simultaneously at earlier moments. All memory is\r
+       deallocated only when the destructor is called.\r
+*/\r
+\r
+#ifndef __BLOCK_H__\r
+#define __BLOCK_H__\r
+\r
+#include <stdlib.h>\r
+\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+\r
+template <class Type> class Block\r
+{\r
+public:\r
+       /* Constructor. Arguments are the block size and\r
+          (optionally) the pointer to the function which\r
+          will be called if allocation failed; the message\r
+          passed to this function is "Not enough memory!" */\r
+       Block(int size, void (*err_function)(char *) = NULL) { first = last = NULL; block_size = size; error_function = err_function; }\r
+\r
+       /* Destructor. Deallocates all items added so far */\r
+       ~Block() { while (first) { block *next = first -> next; delete first; first = next; } }\r
+\r
+       /* Allocates 'num' consecutive items; returns pointer\r
+          to the first item. 'num' cannot be greater than the\r
+          block size since items must fit in one block */\r
+       Type *New(int num = 1)\r
+       {\r
+               Type *t;\r
+\r
+               if (!last || last->current + num > last->last)\r
+               {\r
+                       if (last && last->next) last = last -> next;\r
+                       else\r
+                       {\r
+                               block *next = (block *) new char [sizeof(block) + (block_size-1)*sizeof(Type)];\r
+                               if (!next) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }\r
+                               if (last) last -> next = next;\r
+                               else first = next;\r
+                               last = next;\r
+                               last -> current = & ( last -> data[0] );\r
+                               last -> last = last -> current + block_size;\r
+                               last -> next = NULL;\r
+                       }\r
+               }\r
+\r
+               t = last -> current;\r
+               last -> current += num;\r
+               return t;\r
+       }\r
+\r
+       /* Returns the first item (or NULL, if no items were added) */\r
+       Type *ScanFirst()\r
+       {\r
+               scan_current_block = first;\r
+               if (!scan_current_block) return NULL;\r
+               scan_current_data = & ( scan_current_block -> data[0] );\r
+               return scan_current_data ++;\r
+       }\r
+\r
+       /* Returns the next item (or NULL, if all items have been read)\r
+          Can be called only if previous ScanFirst() or ScanNext()\r
+          call returned not NULL. */\r
+       Type *ScanNext()\r
+       {\r
+               if (scan_current_data >= scan_current_block -> current)\r
+               {\r
+                       scan_current_block = scan_current_block -> next;\r
+                       if (!scan_current_block) return NULL;\r
+                       scan_current_data = & ( scan_current_block -> data[0] );\r
+               }\r
+               return scan_current_data ++;\r
+       }\r
+\r
+       /* Marks all elements as empty */\r
+       void Reset()\r
+       {\r
+               block *b;\r
+               if (!first) return;\r
+               for (b=first; ; b=b->next)\r
+               {\r
+                       b -> current = & ( b -> data[0] );\r
+                       if (b == last) break;\r
+               }\r
+               last = first;\r
+       }\r
+\r
+/***********************************************************************/\r
+\r
+private:\r
+\r
+       typedef struct block_st\r
+       {\r
+               Type                                    *current, *last;\r
+               struct block_st                 *next;\r
+               Type                                    data[1];\r
+       } block;\r
+\r
+       int             block_size;\r
+       block   *first;\r
+       block   *last;\r
+\r
+       block   *scan_current_block;\r
+       Type    *scan_current_data;\r
+\r
+       void    (*error_function)(char *);\r
+};\r
+\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+\r
+template <class Type> class DBlock\r
+{\r
+public:\r
+       /* Constructor. Arguments are the block size and\r
+          (optionally) the pointer to the function which\r
+          will be called if allocation failed; the message\r
+          passed to this function is "Not enough memory!" */\r
+       DBlock(int size, void (*err_function)(char *) = NULL) { first = NULL; first_free = NULL; block_size = size; error_function = err_function; }\r
+\r
+       /* Destructor. Deallocates all items added so far */\r
+       ~DBlock() { while (first) { block *next = first -> next; delete first; first = next; } }\r
+\r
+       /* Allocates one item */\r
+       Type *New()\r
+       {\r
+               block_item *item;\r
+\r
+               if (!first_free)\r
+               {\r
+                       block *next = first;\r
+                       first = (block *) new char [sizeof(block) + (block_size-1)*sizeof(block_item)];\r
+                       if (!first) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }\r
+                       first_free = & (first -> data[0] );\r
+                       for (item=first_free; item<first_free+block_size-1; item++)\r
+                               item -> next_free = item + 1;\r
+                       item -> next_free = NULL;\r
+                       first -> next = next;\r
+               }\r
+\r
+               item = first_free;\r
+               first_free = item -> next_free;\r
+               return (Type *) item;\r
+       }\r
+\r
+       /* Deletes an item allocated previously */\r
+       void Delete(Type *t)\r
+       {\r
+               ((block_item *) t) -> next_free = first_free;\r
+               first_free = (block_item *) t;\r
+       }\r
+\r
+/***********************************************************************/\r
+\r
+private:\r
+\r
+       typedef union block_item_st\r
+       {\r
+               Type                    t;\r
+               block_item_st   *next_free;\r
+       } block_item;\r
+\r
+       typedef struct block_st\r
+       {\r
+               struct block_st                 *next;\r
+               block_item                              data[1];\r
+       } block;\r
+\r
+       int                     block_size;\r
+       block           *first;\r
+       block_item      *first_free;\r
+\r
+       void    (*error_function)(char *);\r
+};\r
+\r
+\r
+#endif\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/brain.jpg b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/brain.jpg
new file mode 100644 (file)
index 0000000..f7850b0
Binary files /dev/null and b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/brain.jpg differ

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/datain.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/datain.m
new file mode 100644 (file)
index 0000000..2d15e09
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/datain.m
@@ -0,0 +1,28 @@
+function datain(imagefig, varargins)
+% DATAIN - This function is used to track and plot the mouse pointer
+% location
+% Authors - Mohit Gupta, Krishnan Ramnath
+% Affiliation - Robotics Institute, CMU, Pittsburgh
+% 2006-05-15
+
+% Global variables referenced in this funciton
+global fgflag;
+
+hold on;
+% Sample current mouse position, in axes units
+temp = get(gca,'currentpoint'); 
+
+% Keep current position in figure property 'USERDATA'
+set(gcf,'userdata',[get(gcf,'userdata'); temp(1,1:2), toc  ]);
+
+% Get data for processing
+X = get(gcf,'userdata'); 
+Len = size(X,1);
+
+% FG or BG seed?
+if(fgflag == 1)
+    plot(X(Len,1),X(Len,2),'.r'); %%% Plot the last sampled position
+elseif(fgflag == 0)
+    plot(X(Len,1),X(Len,2),'.b'); %%% Plot the last sampled position
+end
+

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/dataout.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/dataout.m
new file mode 100644 (file)
index 0000000..75baecb
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/dataout.m
@@ -0,0 +1,29 @@
+function dataout(imagefig,varargins)\r
+% DATAOUT  - This function adds all the stroke points to previous ones\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global Variables\r
+global sopt fgflag fgpixels bgpixels;\r
+\r
+% Ignore motion\r
+set(gcf,'WindowButtonMotionFcn',[]);\r
+\r
+% If Button down start tracking\r
+set(gcf,'windowbuttondownfcn',{@track});\r
+\r
+\r
+% Get current data\r
+temp=get(gcf,'userdata');\r
+coords = floor(temp(:,1:2));\r
+coords = union(coords,coords,'rows');\r
+\r
+\r
+if(fgflag == 1)\r
+    % Concatenate\r
+    fgpixels = vertcat(fgpixels,coords);\r
+elseif(fgflag == 0)\r
+    % Concatenate\r
+    bgpixels = vertcat(bgpixels,coords);\r
+end\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/fginput.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/fginput.m
new file mode 100644 (file)
index 0000000..f2acaa3
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/fginput.m
@@ -0,0 +1,50 @@
+function  fginput(strg)\r
+% FGINPUT  - This function gets the foreground pixels from user inputn\r
+% FGINPUT(STRG) - strg  - Smart Refine or Smart Rectangle\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+\r
+% Global variables referenced in this funciton\r
+global sopt fgflag fgpixels bgpixels;\r
+\r
+% If using the SmartRectangle Function\r
+if(strcmp(strg,'SmartRectangle'))\r
+\r
+    % GUI related flag\r
+    fgflag = 2;\r
+\r
+    % Get two points from the user\r
+    fp = ginput(2);\r
+    fp = round(fp);\r
+\r
+    % Form the rectangular bounding box from the two points\r
+    [fx,fy] = meshgrid(min(fp(:,1)):max(fp(:,1)),min(fp(:,2)):max(fp(:,2)));\r
+    fpixelsx =[];\r
+    fpixelsy =[];\r
+    for(i = 1:size(fx,2))\r
+        fpixelsx = [fpixelsx fx(:,i)'];\r
+        fpixelsy = [fpixelsy fy(:,i)'];\r
+    end\r
+    fpixels = [fpixelsx' fpixelsy'];\r
+\r
+    % Add to previous strokes\r
+    fgpixels = vertcat(fgpixels,fpixels);\r
+\r
+    % Plot the Rectangle\r
+    hfig = gcf;\r
+    axis('image');axis('ij');axis('off');\r
+    hold on;\r
+    plot(fgpixels(:,1),fgpixels(:,2),'r.');\r
+\r
+else\r
+    hfig = gcf;\r
+    hold on;\r
+\r
+    % Gui related flag\r
+    fgflag = 1;\r
+\r
+    % Call track function on button press\r
+    set(hfig,'windowbuttondownfcn',{@track});\r
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.cpp b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.cpp
new file mode 100644 (file)
index 0000000..77b1e72
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.cpp
@@ -0,0 +1,80 @@
+/* graph.cpp */\r
+/*\r
+    Copyright 2001 Vladimir Kolmogorov (vnk@cs.cornell.edu), Yuri Boykov (yuri@csd.uwo.ca).\r
+\r
+    This program is free software; you can redistribute it and/or modify\r
+    it under the terms of the GNU General Public License as published by\r
+    the Free Software Foundation; either version 2 of the License, or\r
+    (at your option) any later version.\r
+\r
+    This program is distributed in the hope that it will be useful,\r
+    but WITHOUT ANY WARRANTY; without even the implied warranty of\r
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r
+    GNU General Public License for more details.\r
+\r
+    You should have received a copy of the GNU General Public License\r
+    along with this program; if not, write to the Free Software\r
+    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\r
+*/\r
+\r
+\r
+#include <stdio.h>\r
+#include "graph.h"\r
+\r
+Graph::Graph(void (*err_function)(char *))\r
+{\r
+       error_function = err_function;\r
+       node_block = new Block<node>(NODE_BLOCK_SIZE, error_function);\r
+       arc_block  = new Block<arc>(NODE_BLOCK_SIZE, error_function);\r
+       flow = 0;\r
+}\r
+\r
+Graph::~Graph()\r
+{\r
+       delete node_block;\r
+       delete arc_block;\r
+}\r
+\r
+Graph::node_id Graph::add_node()\r
+{\r
+       node *i = node_block -> New();\r
+\r
+       i -> first = NULL;\r
+       i -> tr_cap = 0;\r
+\r
+       return (node_id) i;\r
+}\r
+\r
+void Graph::add_edge(node_id from, node_id to, captype cap, captype rev_cap)\r
+{\r
+       arc *a, *a_rev;\r
+\r
+       a = arc_block -> New(2);\r
+       a_rev = a + 1;\r
+\r
+       a -> sister = a_rev;\r
+       a_rev -> sister = a;\r
+       a -> next = ((node*)from) -> first;\r
+       ((node*)from) -> first = a;\r
+       a_rev -> next = ((node*)to) -> first;\r
+       ((node*)to) -> first = a_rev;\r
+       a -> head = (node*)to;\r
+       a_rev -> head = (node*)from;\r
+       a -> r_cap = cap;\r
+       a_rev -> r_cap = rev_cap;\r
+}\r
+\r
+void Graph::set_tweights(node_id i, captype cap_source, captype cap_sink)\r
+{\r
+       flow += (cap_source < cap_sink) ? cap_source : cap_sink;\r
+       ((node*)i) -> tr_cap = cap_source - cap_sink;\r
+}\r
+\r
+void Graph::add_tweights(node_id i, captype cap_source, captype cap_sink)\r
+{\r
+       register captype delta = ((node*)i) -> tr_cap;\r
+       if (delta > 0) cap_source += delta;\r
+       else           cap_sink   -= delta;\r
+       flow += (cap_source < cap_sink) ? cap_source : cap_sink;\r
+       ((node*)i) -> tr_cap = cap_source - cap_sink;\r
+}\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.h b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.h
new file mode 100644 (file)
index 0000000..187a86c
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/graph.h
@@ -0,0 +1,180 @@
+/* graph.h */\r
+/*\r
+       This software library implements the maxflow algorithm\r
+       described in\r
+\r
+               An Experimental Comparison of Min-Cut/Max-Flow Algorithms\r
+               for Energy Minimization in Vision.\r
+               Yuri Boykov and Vladimir Kolmogorov.\r
+               In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), \r
+               September 2004\r
+\r
+       This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov\r
+       at Siemens Corporate Research. To make it available for public use,\r
+       it was later reimplemented by Vladimir Kolmogorov based on open publications.\r
+\r
+       If you use this software for research purposes, you should cite\r
+       the aforementioned paper in any resulting publication.\r
+*/\r
+       \r
+/*     \r
+       Copyright 2001 Vladimir Kolmogorov (vnk@cs.cornell.edu), Yuri Boykov (yuri@csd.uwo.ca).\r
+\r
+    This program is free software; you can redistribute it and/or modify\r
+    it under the terms of the GNU General Public License as published by\r
+    the Free Software Foundation; either version 2 of the License, or\r
+    (at your option) any later version.\r
+\r
+    This program is distributed in the hope that it will be useful,\r
+    but WITHOUT ANY WARRANTY; without even the implied warranty of\r
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r
+    GNU General Public License for more details.\r
+\r
+    You should have received a copy of the GNU General Public License\r
+    along with this program; if not, write to the Free Software\r
+    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\r
+*/\r
+\r
+\r
+/*\r
+       For description, example usage, discussion of graph representation\r
+       and memory usage see README.TXT.\r
+*/\r
+\r
+#ifndef __GRAPH_H__\r
+#define __GRAPH_H__\r
+\r
+#include "block.h"\r
+\r
+/*\r
+       Nodes, arcs and pointers to nodes are\r
+       added in blocks for memory and time efficiency.\r
+       Below are numbers of items in blocks\r
+*/\r
+#define NODE_BLOCK_SIZE 512\r
+#define ARC_BLOCK_SIZE 1024\r
+#define NODEPTR_BLOCK_SIZE 128\r
+\r
+class Graph\r
+{\r
+public:\r
+       typedef enum\r
+       {\r
+               SOURCE  = 0,\r
+               SINK    = 1\r
+       } termtype; /* terminals */\r
+\r
+       /* Type of edge weights.\r
+          Can be changed to char, int, float, double, ... */\r
+       typedef double captype;\r
+       /* Type of total flow */\r
+       typedef double flowtype;\r
+\r
+       typedef void * node_id;\r
+\r
+       /* interface functions */\r
+\r
+       /* Constructor. Optional argument is the pointer to the\r
+          function which will be called if an error occurs;\r
+          an error message is passed to this function. If this\r
+          argument is omitted, exit(1) will be called. */\r
+       Graph(void (*err_function)(char *) = NULL);\r
+\r
+       /* Destructor */\r
+       ~Graph();\r
+\r
+       /* Adds a node to the graph */\r
+       node_id add_node();\r
+\r
+       /* Adds a bidirectional edge between 'from' and 'to'\r
+          with the weights 'cap' and 'rev_cap' */\r
+       void add_edge(node_id from, node_id to, captype cap, captype rev_cap);\r
+\r
+       /* Sets the weights of the edges 'SOURCE->i' and 'i->SINK'\r
+          Can be called at most once for each node before any call to 'add_tweights'.\r
+          Weights can be negative */\r
+       void set_tweights(node_id i, captype cap_source, captype cap_sink);\r
+\r
+       /* Adds new edges 'SOURCE->i' and 'i->SINK' with corresponding weights\r
+          Can be called multiple times for each node.\r
+          Weights can be negative */\r
+       void add_tweights(node_id i, captype cap_source, captype cap_sink);\r
+\r
+       /* After the maxflow is computed, this function returns to which\r
+          segment the node 'i' belongs (Graph::SOURCE or Graph::SINK) */\r
+       termtype what_segment(node_id i);\r
+\r
+       /* Computes the maxflow. Can be called only once. */\r
+       flowtype maxflow();\r
+\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+/***********************************************************************/\r
+       \r
+private:\r
+       /* internal variables and functions */\r
+\r
+       struct arc_st;\r
+\r
+       /* node structure */\r
+       typedef struct node_st\r
+       {\r
+               arc_st                  *first;         /* first outcoming arc */\r
+\r
+               arc_st                  *parent;        /* node's parent */\r
+               node_st                 *next;          /* pointer to the next active node\r
+                                                                          (or to itself if it is the last node in the list) */\r
+               int                             TS;                     /* timestamp showing when DIST was computed */\r
+               int                             DIST;           /* distance to the terminal */\r
+               short                   is_sink;        /* flag showing whether the node is in the source or in the sink tree */\r
+\r
+               captype                 tr_cap;         /* if tr_cap > 0 then tr_cap is residual capacity of the arc SOURCE->node\r
+                                                                          otherwise         -tr_cap is residual capacity of the arc node->SINK */\r
+       } node;\r
+\r
+       /* arc structure */\r
+       typedef struct arc_st\r
+       {\r
+               node_st                 *head;          /* node the arc points to */\r
+               arc_st                  *next;          /* next arc with the same originating node */\r
+               arc_st                  *sister;        /* reverse arc */\r
+\r
+               captype                 r_cap;          /* residual capacity */\r
+       } arc;\r
+\r
+       /* 'pointer to node' structure */\r
+       typedef struct nodeptr_st\r
+       {\r
+               node_st                 *ptr;\r
+               nodeptr_st              *next;\r
+       } nodeptr;\r
+\r
+       Block<node>                     *node_block;\r
+       Block<arc>                      *arc_block;\r
+       DBlock<nodeptr>         *nodeptr_block;\r
+\r
+       void    (*error_function)(char *);      /* this function is called if a error occurs,\r
+                                                                                  with a corresponding error message\r
+                                                                                  (or exit(1) is called if it's NULL) */\r
+\r
+       flowtype                        flow;           /* total flow */\r
+\r
+/***********************************************************************/\r
+\r
+       node                            *queue_first[2], *queue_last[2];        /* list of active nodes */\r
+       nodeptr                         *orphan_first, *orphan_last;            /* list of pointers to orphans */\r
+       int                                     TIME;                                                           /* monotonically increasing global counter */\r
+\r
+/***********************************************************************/\r
+\r
+       /* functions for processing active list */\r
+       void set_active(node *i);\r
+       node *next_active();\r
+\r
+       void maxflow_init();\r
+       void augment(arc *middle_arc);\r
+       void process_source_orphan(node *i);\r
+       void process_sink_orphan(node *i);\r
+};\r
+\r
+#endif\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/helper_functions.h b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/helper_functions.h
new file mode 100644 (file)
index 0000000..f016f4f
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/helper_functions.h
@@ -0,0 +1,42 @@
+#ifndef __HELPER_FUNCTIONS_H__\r
+#define __HELPER_FUNCTIONS_H__\r
+\r
+#include <math.h>\r
+#include <vector>\r
+#include <algorithm>\r
+#include <set>\r
+\r
+using namespace std;\r
+\r
+bool findValVec(vector<int> Labels, int i)                             // Whether a given integer is present in a vector of integers\r
+{\r
+       unsigned int n;\r
+       bool found = false;\r
+       for(n=0;n < Labels.size();n++)\r
+               if(Labels[n] == i)\r
+                       found = true;\r
+\r
+       return found;\r
+}\r
+\r
+double diffVec(vector<double> V1, vector<double> V2)   // returns L2-distance between two 3-vectors\r
+{\r
+       return pow((V1[1]-V2[1])*(V1[1]-V2[1]) + (V1[2]-V2[2])*(V1[2]-V2[2]) + (V1[3]-V2[3])*(V1[3]-V2[3]), 0.5);       \r
+\r
+}\r
+\r
+double minVecD(vector< vector<double> > CClusters, vector<double> MeanColors)  // Given a 3-vector, this function finds its closest 3-vector from among a vector of 3-vectors in the L2-distance norm\r
+{\r
+       double minVal = diffVec(CClusters[0],MeanColors);\r
+       unsigned int i;\r
+       double tmpVal;\r
+       for(i=0;i<CClusters.size();i++)\r
+       {\r
+               tmpVal = diffVec(CClusters[i],MeanColors);\r
+               minVal = (minVal<tmpVal)? minVal : tmpVal;\r
+       }\r
+       return minVal;\r
+}\r
+\r
+\r
+#endif
\ No newline at end of file

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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/maxflow.cpp b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/maxflow.cpp
new file mode 100644 (file)
index 0000000..8dd13ce
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/maxflow.cpp
@@ -0,0 +1,517 @@
+/* maxflow.cpp */\r
+/*\r
+    Copyright 2001 Vladimir Kolmogorov (vnk@cs.cornell.edu), Yuri Boykov (yuri@csd.uwo.ca).\r
+\r
+    This program is free software; you can redistribute it and/or modify\r
+    it under the terms of the GNU General Public License as published by\r
+    the Free Software Foundation; either version 2 of the License, or\r
+    (at your option) any later version.\r
+\r
+    This program is distributed in the hope that it will be useful,\r
+    but WITHOUT ANY WARRANTY; without even the implied warranty of\r
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r
+    GNU General Public License for more details.\r
+\r
+    You should have received a copy of the GNU General Public License\r
+    along with this program; if not, write to the Free Software\r
+    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\r
+*/\r
+\r
+\r
+#include <stdio.h>\r
+#include "graph.h"\r
+\r
+/*\r
+       special constants for node->parent\r
+*/\r
+#define TERMINAL ( (arc *) 1 )         /* to terminal */\r
+#define ORPHAN   ( (arc *) 2 )         /* orphan */\r
+\r
+#define INFINITE_D 1000000000          /* infinite distance to the terminal */\r
+\r
+/***********************************************************************/\r
+\r
+/*\r
+       Functions for processing active list.\r
+       i->next points to the next node in the list\r
+       (or to i, if i is the last node in the list).\r
+       If i->next is NULL iff i is not in the list.\r
+\r
+       There are two queues. Active nodes are added\r
+       to the end of the second queue and read from\r
+       the front of the first queue. If the first queue\r
+       is empty, it is replaced by the second queue\r
+       (and the second queue becomes empty).\r
+*/\r
+\r
+inline void Graph::set_active(node *i)\r
+{\r
+       if (!i->next)\r
+       {\r
+               /* it's not in the list yet */\r
+               if (queue_last[1]) queue_last[1] -> next = i;\r
+               else               queue_first[1]        = i;\r
+               queue_last[1] = i;\r
+               i -> next = i;\r
+       }\r
+}\r
+\r
+/*\r
+       Returns the next active node.\r
+       If it is connected to the sink, it stays in the list,\r
+       otherwise it is removed from the list\r
+*/\r
+inline Graph::node * Graph::next_active()\r
+{\r
+       node *i;\r
+\r
+       while ( 1 )\r
+       {\r
+               if (!(i=queue_first[0]))\r
+               {\r
+                       queue_first[0] = i = queue_first[1];\r
+                       queue_last[0]  = queue_last[1];\r
+                       queue_first[1] = NULL;\r
+                       queue_last[1]  = NULL;\r
+                       if (!i) return NULL;\r
+               }\r
+\r
+               /* remove it from the active list */\r
+               if (i->next == i) queue_first[0] = queue_last[0] = NULL;\r
+               else              queue_first[0] = i -> next;\r
+               i -> next = NULL;\r
+\r
+               /* a node in the list is active iff it has a parent */\r
+               if (i->parent) return i;\r
+       }\r
+}\r
+\r
+/***********************************************************************/\r
+\r
+void Graph::maxflow_init()\r
+{\r
+       node *i;\r
+\r
+       queue_first[0] = queue_last[0] = NULL;\r
+       queue_first[1] = queue_last[1] = NULL;\r
+       orphan_first = NULL;\r
+\r
+       for (i=node_block->ScanFirst(); i; i=node_block->ScanNext())\r
+       {\r
+               i -> next = NULL;\r
+               i -> TS = 0;\r
+               if (i->tr_cap > 0)\r
+               {\r
+                       /* i is connected to the source */\r
+                       i -> is_sink = 0;\r
+                       i -> parent = TERMINAL;\r
+                       set_active(i);\r
+                       i -> TS = 0;\r
+                       i -> DIST = 1;\r
+               }\r
+               else if (i->tr_cap < 0)\r
+               {\r
+                       /* i is connected to the sink */\r
+                       i -> is_sink = 1;\r
+                       i -> parent = TERMINAL;\r
+                       set_active(i);\r
+                       i -> TS = 0;\r
+                       i -> DIST = 1;\r
+               }\r
+               else\r
+               {\r
+                       i -> parent = NULL;\r
+               }\r
+       }\r
+       TIME = 0;\r
+}\r
+\r
+/***********************************************************************/\r
+\r
+void Graph::augment(arc *middle_arc)\r
+{\r
+       node *i;\r
+       arc *a;\r
+       captype bottleneck;\r
+       nodeptr *np;\r
+\r
+\r
+       /* 1. Finding bottleneck capacity */\r
+       /* 1a - the source tree */\r
+       bottleneck = middle_arc -> r_cap;\r
+       for (i=middle_arc->sister->head; ; i=a->head)\r
+       {\r
+               a = i -> parent;\r
+               if (a == TERMINAL) break;\r
+               if (bottleneck > a->sister->r_cap) bottleneck = a -> sister -> r_cap;\r
+       }\r
+       if (bottleneck > i->tr_cap) bottleneck = i -> tr_cap;\r
+       /* 1b - the sink tree */\r
+       for (i=middle_arc->head; ; i=a->head)\r
+       {\r
+               a = i -> parent;\r
+               if (a == TERMINAL) break;\r
+               if (bottleneck > a->r_cap) bottleneck = a -> r_cap;\r
+       }\r
+       if (bottleneck > - i->tr_cap) bottleneck = - i -> tr_cap;\r
+\r
+\r
+       /* 2. Augmenting */\r
+       /* 2a - the source tree */\r
+       middle_arc -> sister -> r_cap += bottleneck;\r
+       middle_arc -> r_cap -= bottleneck;\r
+       for (i=middle_arc->sister->head; ; i=a->head)\r
+       {\r
+               a = i -> parent;\r
+               if (a == TERMINAL) break;\r
+               a -> r_cap += bottleneck;\r
+               a -> sister -> r_cap -= bottleneck;\r
+               if (!a->sister->r_cap)\r
+               {\r
+                       /* add i to the adoption list */\r
+                       i -> parent = ORPHAN;\r
+                       np = nodeptr_block -> New();\r
+                       np -> ptr = i;\r
+                       np -> next = orphan_first;\r
+                       orphan_first = np;\r
+               }\r
+       }\r
+       i -> tr_cap -= bottleneck;\r
+       if (!i->tr_cap)\r
+       {\r
+               /* add i to the adoption list */\r
+               i -> parent = ORPHAN;\r
+               np = nodeptr_block -> New();\r
+               np -> ptr = i;\r
+               np -> next = orphan_first;\r
+               orphan_first = np;\r
+       }\r
+       /* 2b - the sink tree */\r
+       for (i=middle_arc->head; ; i=a->head)\r
+       {\r
+               a = i -> parent;\r
+               if (a == TERMINAL) break;\r
+               a -> sister -> r_cap += bottleneck;\r
+               a -> r_cap -= bottleneck;\r
+               if (!a->r_cap)\r
+               {\r
+                       /* add i to the adoption list */\r
+                       i -> parent = ORPHAN;\r
+                       np = nodeptr_block -> New();\r
+                       np -> ptr = i;\r
+                       np -> next = orphan_first;\r
+                       orphan_first = np;\r
+               }\r
+       }\r
+       i -> tr_cap += bottleneck;\r
+       if (!i->tr_cap)\r
+       {\r
+               /* add i to the adoption list */\r
+               i -> parent = ORPHAN;\r
+               np = nodeptr_block -> New();\r
+               np -> ptr = i;\r
+               np -> next = orphan_first;\r
+               orphan_first = np;\r
+       }\r
+\r
+\r
+       flow += bottleneck;\r
+}\r
+\r
+/***********************************************************************/\r
+\r
+void Graph::process_source_orphan(node *i)\r
+{\r
+       node *j;\r
+       arc *a0, *a0_min = NULL, *a;\r
+       nodeptr *np;\r
+       int d, d_min = INFINITE_D;\r
+\r
+       /* trying to find a new parent */\r
+       for (a0=i->first; a0; a0=a0->next)\r
+       if (a0->sister->r_cap)\r
+       {\r
+               j = a0 -> head;\r
+               if (!j->is_sink && (a=j->parent))\r
+               {\r
+                       /* checking the origin of j */\r
+                       d = 0;\r
+                       while ( 1 )\r
+                       {\r
+                               if (j->TS == TIME)\r
+                               {\r
+                                       d += j -> DIST;\r
+                                       break;\r
+                               }\r
+                               a = j -> parent;\r
+                               d ++;\r
+                               if (a==TERMINAL)\r
+                               {\r
+                                       j -> TS = TIME;\r
+                                       j -> DIST = 1;\r
+                                       break;\r
+                               }\r
+                               if (a==ORPHAN) { d = INFINITE_D; break; }\r
+                               j = a -> head;\r
+                       }\r
+                       if (d<INFINITE_D) /* j originates from the source - done */\r
+                       {\r
+                               if (d<d_min)\r
+                               {\r
+                                       a0_min = a0;\r
+                                       d_min = d;\r
+                               }\r
+                               /* set marks along the path */\r
+                               for (j=a0->head; j->TS!=TIME; j=j->parent->head)\r
+                               {\r
+                                       j -> TS = TIME;\r
+                                       j -> DIST = d --;\r
+                               }\r
+                       }\r
+               }\r
+       }\r
+\r
+       if (i->parent = a0_min)\r
+       {\r
+               i -> TS = TIME;\r
+               i -> DIST = d_min + 1;\r
+       }\r
+       else\r
+       {\r
+               /* no parent is found */\r
+               i -> TS = 0;\r
+\r
+               /* process neighbors */\r
+               for (a0=i->first; a0; a0=a0->next)\r
+               {\r
+                       j = a0 -> head;\r
+                       if (!j->is_sink && (a=j->parent))\r
+                       {\r
+                               if (a0->sister->r_cap) set_active(j);\r
+                               if (a!=TERMINAL && a!=ORPHAN && a->head==i)\r
+                               {\r
+                                       /* add j to the adoption list */\r
+                                       j -> parent = ORPHAN;\r
+                                       np = nodeptr_block -> New();\r
+                                       np -> ptr = j;\r
+                                       if (orphan_last) orphan_last -> next = np;\r
+                                       else             orphan_first        = np;\r
+                                       orphan_last = np;\r
+                                       np -> next = NULL;\r
+                               }\r
+                       }\r
+               }\r
+       }\r
+}\r
+\r
+void Graph::process_sink_orphan(node *i)\r
+{\r
+       node *j;\r
+       arc *a0, *a0_min = NULL, *a;\r
+       nodeptr *np;\r
+       int d, d_min = INFINITE_D;\r
+\r
+       /* trying to find a new parent */\r
+       for (a0=i->first; a0; a0=a0->next)\r
+       if (a0->r_cap)\r
+       {\r
+               j = a0 -> head;\r
+               if (j->is_sink && (a=j->parent))\r
+               {\r
+                       /* checking the origin of j */\r
+                       d = 0;\r
+                       while ( 1 )\r
+                       {\r
+                               if (j->TS == TIME)\r
+                               {\r
+                                       d += j -> DIST;\r
+                                       break;\r
+                               }\r
+                               a = j -> parent;\r
+                               d ++;\r
+                               if (a==TERMINAL)\r
+                               {\r
+                                       j -> TS = TIME;\r
+                                       j -> DIST = 1;\r
+                                       break;\r
+                               }\r
+                               if (a==ORPHAN) { d = INFINITE_D; break; }\r
+                               j = a -> head;\r
+                       }\r
+                       if (d<INFINITE_D) /* j originates from the sink - done */\r
+                       {\r
+                               if (d<d_min)\r
+                               {\r
+                                       a0_min = a0;\r
+                                       d_min = d;\r
+                               }\r
+                               /* set marks along the path */\r
+                               for (j=a0->head; j->TS!=TIME; j=j->parent->head)\r
+                               {\r
+                                       j -> TS = TIME;\r
+                                       j -> DIST = d --;\r
+                               }\r
+                       }\r
+               }\r
+       }\r
+\r
+       if (i->parent = a0_min)\r
+       {\r
+               i -> TS = TIME;\r
+               i -> DIST = d_min + 1;\r
+       }\r
+       else\r
+       {\r
+               /* no parent is found */\r
+               i -> TS = 0;\r
+\r
+               /* process neighbors */\r
+               for (a0=i->first; a0; a0=a0->next)\r
+               {\r
+                       j = a0 -> head;\r
+                       if (j->is_sink && (a=j->parent))\r
+                       {\r
+                               if (a0->r_cap) set_active(j);\r
+                               if (a!=TERMINAL && a!=ORPHAN && a->head==i)\r
+                               {\r
+                                       /* add j to the adoption list */\r
+                                       j -> parent = ORPHAN;\r
+                                       np = nodeptr_block -> New();\r
+                                       np -> ptr = j;\r
+                                       if (orphan_last) orphan_last -> next = np;\r
+                                       else             orphan_first        = np;\r
+                                       orphan_last = np;\r
+                                       np -> next = NULL;\r
+                               }\r
+                       }\r
+               }\r
+       }\r
+}\r
+\r
+/***********************************************************************/\r
+\r
+Graph::flowtype Graph::maxflow()\r
+{\r
+       node *i, *j, *current_node = NULL;\r
+       arc *a;\r
+       nodeptr *np, *np_next;\r
+\r
+       maxflow_init();\r
+       nodeptr_block = new DBlock<nodeptr>(NODEPTR_BLOCK_SIZE, error_function);\r
+\r
+       while ( 1 )\r
+       {\r
+               if (i=current_node)\r
+               {\r
+                       i -> next = NULL; /* remove active flag */\r
+                       if (!i->parent) i = NULL;\r
+               }\r
+               if (!i)\r
+               {\r
+                       if (!(i = next_active())) break;\r
+               }\r
+\r
+               /* growth */\r
+               if (!i->is_sink)\r
+               {\r
+                       /* grow source tree */\r
+                       for (a=i->first; a; a=a->next)\r
+                       if (a->r_cap)\r
+                       {\r
+                               j = a -> head;\r
+                               if (!j->parent)\r
+                               {\r
+                                       j -> is_sink = 0;\r
+                                       j -> parent = a -> sister;\r
+                                       j -> TS = i -> TS;\r
+                                       j -> DIST = i -> DIST + 1;\r
+                                       set_active(j);\r
+                               }\r
+                               else if (j->is_sink) break;\r
+                               else if (j->TS <= i->TS &&\r
+                                        j->DIST > i->DIST)\r
+                               {\r
+                                       /* heuristic - trying to make the distance from j to the source shorter */\r
+                                       j -> parent = a -> sister;\r
+                                       j -> TS = i -> TS;\r
+                                       j -> DIST = i -> DIST + 1;\r
+                               }\r
+                       }\r
+               }\r
+               else\r
+               {\r
+                       /* grow sink tree */\r
+                       for (a=i->first; a; a=a->next)\r
+                       if (a->sister->r_cap)\r
+                       {\r
+                               j = a -> head;\r
+                               if (!j->parent)\r
+                               {\r
+                                       j -> is_sink = 1;\r
+                                       j -> parent = a -> sister;\r
+                                       j -> TS = i -> TS;\r
+                                       j -> DIST = i -> DIST + 1;\r
+                                       set_active(j);\r
+                               }\r
+                               else if (!j->is_sink) { a = a -> sister; break; }\r
+                               else if (j->TS <= i->TS &&\r
+                                        j->DIST > i->DIST)\r
+                               {\r
+                                       /* heuristic - trying to make the distance from j to the sink shorter */\r
+                                       j -> parent = a -> sister;\r
+                                       j -> TS = i -> TS;\r
+                                       j -> DIST = i -> DIST + 1;\r
+                               }\r
+                       }\r
+               }\r
+\r
+               TIME ++;\r
+\r
+               if (a)\r
+               {\r
+                       i -> next = i; /* set active flag */\r
+                       current_node = i;\r
+\r
+                       /* augmentation */\r
+                       augment(a);\r
+                       /* augmentation end */\r
+\r
+                       /* adoption */\r
+                       while (np=orphan_first)\r
+                       {\r
+                               np_next = np -> next;\r
+                               np -> next = NULL;\r
+\r
+                               while (np=orphan_first)\r
+                               {\r
+                                       orphan_first = np -> next;\r
+                                       i = np -> ptr;\r
+                                       nodeptr_block -> Delete(np);\r
+                                       if (!orphan_first) orphan_last = NULL;\r
+                                       if (i->is_sink) process_sink_orphan(i);\r
+                                       else            process_source_orphan(i);\r
+                               }\r
+\r
+                               orphan_first = np_next;\r
+                       }\r
+                       /* adoption end */\r
+               }\r
+               else current_node = NULL;\r
+       }\r
+\r
+       delete nodeptr_block;\r
+    printf("so far so good!\n");\r
+       return flow;\r
+}\r
+\r
+/***********************************************************************/\r
+\r
+Graph::termtype Graph::what_segment(node_id i)\r
+{\r
+               if (((node*)i)->parent && !((node*)i)->is_sink) \r
+                       return SOURCE;\r
+               else\r
+                       return SINK;\r
+               \r
+}\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/mksopt.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/mksopt.m
new file mode 100644 (file)
index 0000000..68894f6
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/mksopt.m
@@ -0,0 +1,50 @@
+function sopt = mksopt\r
+% MKSOPT - Initializes all constants associated with the Segmentation task\r
+% Anything and everything that needs to be tweaked is here.\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+\r
+% Lambda value and number of iterations for AutoCut (GrabCut) Tool\r
+% (SegmentGC.m)\r
+% Lamdba refers to the relative weightage between the region and the\r
+% boundary term in the graph cut minimization function. Higher values of\r
+% lambda favors more region ie more grouping.\r
+% Lambda is probably the most important parameter and can be tweaked to get\r
+% a good segmentation\r
+sopt.lambda_AC = 0.9;\r
+\r
+% Numer of iterations the grab cut algorithm should run. Higher values are\r
+% usually good, but only upto a certain limit, no point in iterating when\r
+% the algorithm has converged.\r
+sopt.numIter_AC = 12;\r
+\r
+% Lambda value and number of iterations for \r
+% AutoCutRefine (GrabCut + Lazy Snapping) Tool \r
+% (SegmentRefine.m)\r
+% This parameter is the lambda value for the refine ment step. We usually\r
+% want higher lambdas for refinement (more coherent regions.)\r
+sopt.lambda_ACR = 5;\r
+\r
+% Number of iterations for the refinement step. This should usually be just\r
+% one\r
+sopt.numIter_ACR = 1;\r
+\r
+\r
+% Number of clusters used by kmeans for SmartSelect (Lazy Snapping) Tool\r
+% (Segment.m)\r
+sopt.NumFClusters_SS = 60;\r
+sopt.NumBClusters_SS = 60;\r
+\r
+% Strip width specifying background pixels for AutoCut (GrabCut) \r
+% and AutoCutRefine (GrabCut + Lazy Snapping) Tool\r
+% (SegmentGC.m and SegmentRefine.m)\r
+sopt.StripWidth_AC_ACR = 30;   \r
+\r
+% Number of clusters used by kmeans for AutoCut (GrabCut) Tool\r
+% (SegmentGC.m)\r
+sopt.NumFClusters_AC = 5;\r
+sopt.NumBClusters_AC = 5;\r
+\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/rectpts.mat b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/rectpts.mat
new file mode 100644 (file)
index 0000000..f68f6cb
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diff --git a/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/track.m b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/track.m
new file mode 100644 (file)
index 0000000..9f0712e
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/Lazy_Grab_cut/SegTool/SegToolBox/SegToolBox/track.m
@@ -0,0 +1,21 @@
+function track(imagefig, varargins)\r
+% TRACK - This function is used to track the mouse pointer position\r
+% Authors - Mohit Gupta, Krishnan Ramnath\r
+% Affiliation - Robotics Institute, CMU, Pittsburgh\r
+% 2006-05-15\r
+\r
+% Global Variables\r
+global fgflag;\r
+\r
+% Setting figure properties to start tracking\r
+hold on;\r
+if(fgflag == 0 || fgflag == 1)\r
+    set(gcf,'windowbuttondownfcn',@dataout);\r
+    set(gcf,'WindowButtonMotionFcn',@datain);\r
+    set(gcf,'userdata',[]);\r
+\r
+    % Begin time measuring (if needed)\r
+    tic;\r
+end\r
+\r
+\r

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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/README.TXT b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/README.TXT
new file mode 100644 (file)
index 0000000..367afef
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/README.TXT
@@ -0,0 +1,123 @@
+###################################################################
+#                                                                 #
+#    MAXFLOW - software for computing mincut/maxflow in a graph   #
+#                        Version 3.01                             #
+#    http://www.cs.ucl.ac.uk/staff/V.Kolmogorov/software.html     #
+#                                                                 #
+#    Yuri Boykov (yuri@csd.uwo.ca)                                #
+#    Vladimir Kolmogorov (v.kolmogorov@cs.ucl.ac.uk)              #
+#    2001-2006                                                    #
+#                                                                 #
+###################################################################
+
+1. Introduction.
+
+This software library implements the maxflow algorithm described in
+
+       "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision."
+       Yuri Boykov and Vladimir Kolmogorov.
+       In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 
+       September 2004
+
+This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov
+at Siemens Corporate Research. To make it available for public use,
+it was later reimplemented by Vladimir Kolmogorov based on open publications.
+
+If you use this software for research purposes, you should cite
+the aforementioned paper in any resulting publication.
+
+----------------------------------------------------------------------
+
+REUSING TREES:
+
+Starting with version 3.0, there is a also an option of reusing search
+trees from one maxflow computation to the next, as described in
+
+       "Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts."
+       Pushmeet Kohli and Philip H.S. Torr
+       International Conference on Computer Vision (ICCV), 2005
+
+If you use this option, you should cite
+the aforementioned paper in any resulting publication.
+
+Tested under windows, Visual C++ 6.0 compiler and unix (SunOS 5.8
+and RedHat Linux 7.0, GNU c++ compiler).
+
+##################################################################
+
+2. License & disclaimer.
+
+    Copyright 2001-2006 Vladimir Kolmogorov (v.kolmogorov@cs.ucl.ac.uk), Yuri Boykov (yuri@csd.uwo.ca).
+
+    This software can be used for research purposes only.
+    If you require another license, you may consider using version 2.21
+    (which implements exactly the same algorithm, but does not have the option of reusing search trees).
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+##################################################################
+
+3. Example usage.
+
+This section shows how to use the library to compute
+a minimum cut on the following graph:
+
+                       SOURCE
+                      /       \
+                    1/         \2
+                    /      3    \
+                  node0 -----> node1
+                    |   <-----   |
+                    |      4     |
+                    \            /
+                    5\          /6
+                      \        /
+                         SINK
+
+///////////////////////////////////////////////////
+
+#include <stdio.h>
+#include "graph.h"
+
+int main()
+{
+       typedef Graph<int,int,int> GraphType;
+       GraphType *g = new GraphType(/*estimated # of nodes*/ 2, /*estimated # of edges*/ 1); 
+
+       g -> add_node(); 
+       g -> add_node(); 
+
+       g -> add_tweights( 0,   /* capacities */  1, 5 );
+       g -> add_tweights( 1,   /* capacities */  2, 6 );
+       g -> add_edge( 0, 1,    /* capacities */  3, 4 );
+
+       int flow = g -> maxflow();
+
+       printf("Flow = %d\n", flow);
+       printf("Minimum cut:\n");
+       if (g->what_segment(0) == GraphType::SOURCE)
+               printf("node0 is in the SOURCE set\n");
+       else
+               printf("node0 is in the SINK set\n");
+       if (g->what_segment(1) == GraphType::SOURCE)
+               printf("node1 is in the SOURCE set\n");
+       else
+               printf("node1 is in the SINK set\n");
+
+       delete g;
+
+       return 0;
+}
+
+
+///////////////////////////////////////////////////

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/abdomen.jpg b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/abdomen.jpg
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index 0000000..b1cdd68
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/brain.jpg b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/brain.jpg
new file mode 100644 (file)
index 0000000..f7850b0
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/calc_weights.m b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/calc_weights.m
new file mode 100644 (file)
index 0000000..d83feac
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/calc_weights.m
@@ -0,0 +1,63 @@
+function T=calc_weights(im,fg,bg,K,lambda)\r
+% im-> input image\r
+% fg-> foreground pixel locations in row matrixes Mx2\r
+% bg-> background pixel locations in row matrixes Nx2\r
+[height,width] = size(im);\r
+N=height*width;\r
+T=zeros(N,2);\r
+fgx=fg(:,1);% Foreground or object pixels\r
+fgy=fg(:,2);\r
+bgx=bg(:,1);% Background pixels\r
+bgy=bg(:,2);\r
+for i = 1:size(fgx,1)\r
+    obj_pixels(i)=im(fgx(i),fgy(i));% Extract values of foreground pixels\r
+end\r
+\r
+for i = 1:size(bgx,1)\r
+    bkg_pixels(i)=im(bgx(i),bgy(i));% Extract values of Background pixels\r
+end\r
+\r
+n_obj=hist(obj_pixels,0:255);% Form Histogram\r
+n_obj=n_obj./max(n_obj);% Calculate probability of object pixels\r
+\r
+n_bkg=hist(bkg_pixels,0:255);\r
+n_bkg=n_bkg./max(n_bkg);% Calculate probability of background pixels\r
+count=1;\r
+% S terminal(Object)\r
+for i = 1 : width\r
+    for j = 1 : height\r
+        if(size(find(fgx==j),1)&&size(find(fgy==i),1))% Check for foreground pixels\r
+            T(count,1)=K;\r
+        else if(size(find(bgx==j),1)&&size(find(bgy==i),1))% Check for background pixels\r
+                T(count,1)=0;\r
+            else\r
+                 T(count,1)=-lambda*log(n_bkg(im(j,i)+1)); % Else\r
+            end\r
+        end\r
+\r
+        count=count+1;\r
+    end\r
+end\r
+\r
+\r
+count=1;\r
+% T terminal(Background)\r
+for i = 1 : width\r
+    for j = 1 : height\r
+        if(size(find(fgx==j),1)&&size(find(fgy==i),1))\r
+            T(count,2)=0;\r
+        else if(size(find(bgx==j),1)&&size(find(bgy==i),1))\r
+                T(count,2)=K;\r
+            else\r
+                 T(count,2)=-lambda*log(n_obj(im(j,i)+1));\r
+            end\r
+        end\r
+\r
+        count=count+1;\r
+    end\r
+end\r
+\r
+\r
+\r
+T=sparse(T);\r
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/edges4connected.m b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/edges4connected.m
new file mode 100644 (file)
index 0000000..7fe3e4f
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/edges4connected.m
@@ -0,0 +1,33 @@
+function E = edges4connected(height,width)\r
+\r
+% EDGE4CONNECTED Creates edges where each node\r
+%   is connected to its four adjacent neighbors on a \r
+%   height x width grid.\r
+%   E - a vector in which each row i represents an edge\r
+%   E(i,1) --> E(i,2). The edges are listed is in the following \r
+%   neighbor order: down,up,right,left, where nodes \r
+%   indices are taken column-major.\r
+%\r
+%   (c) 2008 Michael Rubinstein, WDI R&D and IDC\r
+%   $Revision$\r
+%   $Date$\r
+%\r
+\r
+N = height*width;\r
+I = []; J = [];\r
+\r
+% connect vertically (down, then up)\r
+\r
+is = [1:N]'; is([height:height:N])=[];\r
+js = is+1;\r
+I = [I;is;js];\r
+J = [J;js;is];\r
+% connect horizontally (right, then left)\r
+is = [1:N-height]';\r
+js = is+height;\r
+I = [I;is;js];\r
+J = [J;js;is];\r
+\r
+E = [I,J];\r
+\r
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground.mat b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground.mat
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index 0000000..4afb4f6
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_abdomen.mat b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_abdomen.mat
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_brain.mat b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_brain.mat
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_lotus.mat b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/ground_lotus.mat
new file mode 100644 (file)
index 0000000..00db037
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diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/interactive_graph_cuts.m b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/interactive_graph_cuts.m
new file mode 100644 (file)
index 0000000..fb9a01c
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/interactive_graph_cuts.m
@@ -0,0 +1,68 @@
+% Interactive Graph cuts Segmentation Algorithm\r
+% Towards Medical Image Analysis course\r
+% By Ravindra Gadde, Raghu Yalamanchili\r
+\r
+% Interactive Graph cut segmentation using max flow algorithm as described\r
+% in Y. Boykov, M. Jolly, "Interactive Graph Cuts for Optimal Boundary and\r
+% Region Segmentation of Objects in N-D Images", ICCV, 2001.\r
+clear all;\r
+close all;\r
+clc;\r
+K=10; % Large constant\r
+sigma=1;% Similarity variance\r
+lambda=10^12;% Terminal Constant\r
+c=10^8;% Similarity Constant\r
+mex maxflowmex.cpp maxflow-v3.0/graph.cpp maxflow-v3.0/maxflow.cpp % Mex\r
+\r
+im = imread('abdomen.jpg'); % Read image\r
+im = imnoise(im,'gaussian');\r
+\r
+m = double(rgb2gray(im));\r
+[height,width] = size(m);\r
+\r
+disp('building graph');\r
+\r
+N = height*width;\r
+\r
+% construct graph\r
+% Calculate weighted graph\r
+E = edges4connected(height,width);\r
+V = c*exp(-abs(m(E(:,1))-m(E(:,2))))./(2*sigma^2);\r
+A = sparse(E(:,1),E(:,2),V,N,N,4*N);\r
+  load 'ground_abdomen.mat' % Comment this part to manually select seeds\r
+% Uncomment this part to manually select seeds\r
+% imshow(m,[]);\r
+% disp('select foreground pixels');\r
+% [x,y] = ginput(50);\r
+% fg(:,1)=ceil(y);\r
+% fg(:,2)=ceil(x);\r
+% % calcualate Terminal weights\r
+% close all;\r
+% imshow(m,[]);\r
+% disp('select background pixels');\r
+% [x1,y1] = ginput(50);\r
+% bg(:,1)=ceil(y1);\r
+% bg(:,2)=ceil(x1);\r
+T = calc_weights(m,fg,bg,K,lambda); \r
+\r
+\r
+%Max flow Algorithm\r
+disp('calculating maximum flow');\r
+\r
+[flow,labels] = maxflow(A,T);\r
+labels = reshape(labels,[height width]);\r
+for i = 1: height\r
+    for j = 1: width\r
+        if(labels(i,j)==0)\r
+            im_segment(i,j)=im(i,j);\r
+            \r
+        else\r
+            im_segment(i,j)=0;\r
+            \r
+            \r
+        end\r
+    end\r
+end\r
+imagesc(labels); title('labels');\r
+imshow(uint8(im_segment),[]);\r
+\r

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/CHANGES.TXT b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/CHANGES.TXT
new file mode 100644 (file)
index 0000000..edd859d
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/CHANGES.TXT
@@ -0,0 +1,29 @@
+List of changes from version 3.0:
+- Moved line
+       #include "instances.inc"
+to the end of cpp files to make it compile under GNU c++ compilers 4.2(?) and above
+
+List of changes from version 2.2:
+
+- Added functions for accessing graph structure, residual capacities, etc.
+  (They are needed for implementing maxflow-based algorithms such as primal-dual algorithm for convex MRFs.)
+- Added option of reusing trees.
+- node_id's are now integers starting from 0. Thus, it is not necessary to store node_id's in a separate array.
+- Capacity types are now templated.
+- Fixed bug in block.h. (After Block<type>::Reset, ScanFirst() and ScanNext() did not work properly).
+- Implementation with a forward star representation of the graph is no longer supported. (It needs less memory, but slightly slower than adjacency list representation.) If you still wish to use it, download version 2.2.
+- Note: version 3.0 is released under a different license than version 2.2.
+
+List of changes from version 2.1:
+
+- Put the code under GPL license
+
+List of changes from version 2.02:
+
+- Fixed a bug in the implementation that uses forward star representation
+
+List of changes from version 2.01:
+
+- Added new interface function - Graph::add_tweights(Node_id, captype, captype)
+  (necessary for the "ENERGY" software package)
+

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/README.TXT b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/README.TXT
new file mode 100644 (file)
index 0000000..367afef
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/README.TXT
@@ -0,0 +1,123 @@
+###################################################################
+#                                                                 #
+#    MAXFLOW - software for computing mincut/maxflow in a graph   #
+#                        Version 3.01                             #
+#    http://www.cs.ucl.ac.uk/staff/V.Kolmogorov/software.html     #
+#                                                                 #
+#    Yuri Boykov (yuri@csd.uwo.ca)                                #
+#    Vladimir Kolmogorov (v.kolmogorov@cs.ucl.ac.uk)              #
+#    2001-2006                                                    #
+#                                                                 #
+###################################################################
+
+1. Introduction.
+
+This software library implements the maxflow algorithm described in
+
+       "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision."
+       Yuri Boykov and Vladimir Kolmogorov.
+       In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 
+       September 2004
+
+This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov
+at Siemens Corporate Research. To make it available for public use,
+it was later reimplemented by Vladimir Kolmogorov based on open publications.
+
+If you use this software for research purposes, you should cite
+the aforementioned paper in any resulting publication.
+
+----------------------------------------------------------------------
+
+REUSING TREES:
+
+Starting with version 3.0, there is a also an option of reusing search
+trees from one maxflow computation to the next, as described in
+
+       "Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts."
+       Pushmeet Kohli and Philip H.S. Torr
+       International Conference on Computer Vision (ICCV), 2005
+
+If you use this option, you should cite
+the aforementioned paper in any resulting publication.
+
+Tested under windows, Visual C++ 6.0 compiler and unix (SunOS 5.8
+and RedHat Linux 7.0, GNU c++ compiler).
+
+##################################################################
+
+2. License & disclaimer.
+
+    Copyright 2001-2006 Vladimir Kolmogorov (v.kolmogorov@cs.ucl.ac.uk), Yuri Boykov (yuri@csd.uwo.ca).
+
+    This software can be used for research purposes only.
+    If you require another license, you may consider using version 2.21
+    (which implements exactly the same algorithm, but does not have the option of reusing search trees).
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+##################################################################
+
+3. Example usage.
+
+This section shows how to use the library to compute
+a minimum cut on the following graph:
+
+                       SOURCE
+                      /       \
+                    1/         \2
+                    /      3    \
+                  node0 -----> node1
+                    |   <-----   |
+                    |      4     |
+                    \            /
+                    5\          /6
+                      \        /
+                         SINK
+
+///////////////////////////////////////////////////
+
+#include <stdio.h>
+#include "graph.h"
+
+int main()
+{
+       typedef Graph<int,int,int> GraphType;
+       GraphType *g = new GraphType(/*estimated # of nodes*/ 2, /*estimated # of edges*/ 1); 
+
+       g -> add_node(); 
+       g -> add_node(); 
+
+       g -> add_tweights( 0,   /* capacities */  1, 5 );
+       g -> add_tweights( 1,   /* capacities */  2, 6 );
+       g -> add_edge( 0, 1,    /* capacities */  3, 4 );
+
+       int flow = g -> maxflow();
+
+       printf("Flow = %d\n", flow);
+       printf("Minimum cut:\n");
+       if (g->what_segment(0) == GraphType::SOURCE)
+               printf("node0 is in the SOURCE set\n");
+       else
+               printf("node0 is in the SINK set\n");
+       if (g->what_segment(1) == GraphType::SOURCE)
+               printf("node1 is in the SOURCE set\n");
+       else
+               printf("node1 is in the SINK set\n");
+
+       delete g;
+
+       return 0;
+}
+
+
+///////////////////////////////////////////////////

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/block.h b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/block.h
new file mode 100644 (file)
index 0000000..c4dfa46
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/block.h
@@ -0,0 +1,268 @@
+/* block.h */
+/*
+       Template classes Block and DBlock
+       Implement adding and deleting items of the same type in blocks.
+
+       If there there are many items then using Block or DBlock
+       is more efficient than using 'new' and 'delete' both in terms
+       of memory and time since
+       (1) On some systems there is some minimum amount of memory
+           that 'new' can allocate (e.g., 64), so if items are
+           small that a lot of memory is wasted.
+       (2) 'new' and 'delete' are designed for items of varying size.
+           If all items has the same size, then an algorithm for
+           adding and deleting can be made more efficient.
+       (3) All Block and DBlock functions are inline, so there are
+           no extra function calls.
+
+       Differences between Block and DBlock:
+       (1) DBlock allows both adding and deleting items,
+           whereas Block allows only adding items.
+       (2) Block has an additional operation of scanning
+           items added so far (in the order in which they were added).
+       (3) Block allows to allocate several consecutive
+           items at a time, whereas DBlock can add only a single item.
+
+       Note that no constructors or destructors are called for items.
+
+       Example usage for items of type 'MyType':
+
+       ///////////////////////////////////////////////////
+       #include "block.h"
+       #define BLOCK_SIZE 1024
+       typedef struct { int a, b; } MyType;
+       MyType *ptr, *array[10000];
+
+       ...
+
+       Block<MyType> *block = new Block<MyType>(BLOCK_SIZE);
+
+       // adding items
+       for (int i=0; i<sizeof(array); i++)
+       {
+               ptr = block -> New();
+               ptr -> a = ptr -> b = rand();
+       }
+
+       // reading items
+       for (ptr=block->ScanFirst(); ptr; ptr=block->ScanNext())
+       {
+               printf("%d %d\n", ptr->a, ptr->b);
+       }
+
+       delete block;
+
+       ...
+
+       DBlock<MyType> *dblock = new DBlock<MyType>(BLOCK_SIZE);
+       
+       // adding items
+       for (int i=0; i<sizeof(array); i++)
+       {
+               array[i] = dblock -> New();
+       }
+
+       // deleting items
+       for (int i=0; i<sizeof(array); i+=2)
+       {
+               dblock -> Delete(array[i]);
+       }
+
+       // adding items
+       for (int i=0; i<sizeof(array); i++)
+       {
+               array[i] = dblock -> New();
+       }
+
+       delete dblock;
+
+       ///////////////////////////////////////////////////
+
+       Note that DBlock deletes items by marking them as
+       empty (i.e., by adding them to the list of free items),
+       so that this memory could be used for subsequently
+       added items. Thus, at each moment the memory allocated
+       is determined by the maximum number of items allocated
+       simultaneously at earlier moments. All memory is
+       deallocated only when the destructor is called.
+*/
+
+#ifndef __BLOCK_H__
+#define __BLOCK_H__
+
+#include <stdlib.h>
+
+/***********************************************************************/
+/***********************************************************************/
+/***********************************************************************/
+
+template <class Type> class Block
+{
+public:
+       /* Constructor. Arguments are the block size and
+          (optionally) the pointer to the function which
+          will be called if allocation failed; the message
+          passed to this function is "Not enough memory!" */
+       Block(int size, void (*err_function)(char *) = NULL) { first = last = NULL; block_size = size; error_function = err_function; }
+
+       /* Destructor. Deallocates all items added so far */
+       ~Block() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } }
+
+       /* Allocates 'num' consecutive items; returns pointer
+          to the first item. 'num' cannot be greater than the
+          block size since items must fit in one block */
+       Type *New(int num = 1)
+       {
+               Type *t;
+
+               if (!last || last->current + num > last->last)
+               {
+                       if (last && last->next) last = last -> next;
+                       else
+                       {
+                               block *next = (block *) new char [sizeof(block) + (block_size-1)*sizeof(Type)];
+                               if (!next) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }
+                               if (last) last -> next = next;
+                               else first = next;
+                               last = next;
+                               last -> current = & ( last -> data[0] );
+                               last -> last = last -> current + block_size;
+                               last -> next = NULL;
+                       }
+               }
+
+               t = last -> current;
+               last -> current += num;
+               return t;
+       }
+
+       /* Returns the first item (or NULL, if no items were added) */
+       Type *ScanFirst()
+       {
+               for (scan_current_block=first; scan_current_block; scan_current_block = scan_current_block->next)
+               {
+                       scan_current_data = & ( scan_current_block -> data[0] );
+                       if (scan_current_data < scan_current_block -> current) return scan_current_data ++;
+               }
+               return NULL;
+       }
+
+       /* Returns the next item (or NULL, if all items have been read)
+          Can be called only if previous ScanFirst() or ScanNext()
+          call returned not NULL. */
+       Type *ScanNext()
+       {
+               while (scan_current_data >= scan_current_block -> current)
+               {
+                       scan_current_block = scan_current_block -> next;
+                       if (!scan_current_block) return NULL;
+                       scan_current_data = & ( scan_current_block -> data[0] );
+               }
+               return scan_current_data ++;
+       }
+
+       /* Marks all elements as empty */
+       void Reset()
+       {
+               block *b;
+               if (!first) return;
+               for (b=first; ; b=b->next)
+               {
+                       b -> current = & ( b -> data[0] );
+                       if (b == last) break;
+               }
+               last = first;
+       }
+
+/***********************************************************************/
+
+private:
+
+       typedef struct block_st
+       {
+               Type                                    *current, *last;
+               struct block_st                 *next;
+               Type                                    data[1];
+       } block;
+
+       int             block_size;
+       block   *first;
+       block   *last;
+
+       block   *scan_current_block;
+       Type    *scan_current_data;
+
+       void    (*error_function)(char *);
+};
+
+/***********************************************************************/
+/***********************************************************************/
+/***********************************************************************/
+
+template <class Type> class DBlock
+{
+public:
+       /* Constructor. Arguments are the block size and
+          (optionally) the pointer to the function which
+          will be called if allocation failed; the message
+          passed to this function is "Not enough memory!" */
+       DBlock(int size, void (*err_function)(char *) = NULL) { first = NULL; first_free = NULL; block_size = size; error_function = err_function; }
+
+       /* Destructor. Deallocates all items added so far */
+       ~DBlock() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } }
+
+       /* Allocates one item */
+       Type *New()
+       {
+               block_item *item;
+
+               if (!first_free)
+               {
+                       block *next = first;
+                       first = (block *) new char [sizeof(block) + (block_size-1)*sizeof(block_item)];
+                       if (!first) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }
+                       first_free = & (first -> data[0] );
+                       for (item=first_free; item<first_free+block_size-1; item++)
+                               item -> next_free = item + 1;
+                       item -> next_free = NULL;
+                       first -> next = next;
+               }
+
+               item = first_free;
+               first_free = item -> next_free;
+               return (Type *) item;
+       }
+
+       /* Deletes an item allocated previously */
+       void Delete(Type *t)
+       {
+               ((block_item *) t) -> next_free = first_free;
+               first_free = (block_item *) t;
+       }
+
+/***********************************************************************/
+
+private:
+
+       typedef union block_item_st
+       {
+               Type                    t;
+               block_item_st   *next_free;
+       } block_item;
+
+       typedef struct block_st
+       {
+               struct block_st                 *next;
+               block_item                              data[1];
+       } block;
+
+       int                     block_size;
+       block           *first;
+       block_item      *first_free;
+
+       void    (*error_function)(char *);
+};
+
+
+#endif
+

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.cpp b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.cpp
new file mode 100644 (file)
index 0000000..fad3854
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.cpp
@@ -0,0 +1,115 @@
+/* graph.cpp */
+
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include "graph.h"
+
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       Graph<captype, tcaptype, flowtype>::Graph(int node_num_max, int edge_num_max, void (*err_function)(char *))
+       : node_num(0),
+         nodeptr_block(NULL),
+         error_function(err_function)
+{
+       if (node_num_max < 16) node_num_max = 16;
+       if (edge_num_max < 16) edge_num_max = 16;
+
+       nodes = (node*) malloc(node_num_max*sizeof(node));
+       arcs = (arc*) malloc(2*edge_num_max*sizeof(arc));
+       if (!nodes || !arcs) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }
+
+       node_last = nodes;
+       node_max = nodes + node_num_max;
+       arc_last = arcs;
+       arc_max = arcs + 2*edge_num_max;
+
+       maxflow_iteration = 0;
+       flow = 0;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       Graph<captype,tcaptype,flowtype>::~Graph()
+{
+       if (nodeptr_block) 
+       { 
+               delete nodeptr_block; 
+               nodeptr_block = NULL; 
+       }
+       free(nodes);
+       free(arcs);
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::reset()
+{
+       node_last = nodes;
+       arc_last = arcs;
+       node_num = 0;
+
+       if (nodeptr_block) 
+       { 
+               delete nodeptr_block; 
+               nodeptr_block = NULL; 
+       }
+
+       maxflow_iteration = 0;
+       flow = 0;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::reallocate_nodes(int num)
+{
+       int node_num_max = (int)(node_max - nodes);
+       node* nodes_old = nodes;
+
+       node_num_max += node_num_max / 2;
+       if (node_num_max < node_num + num) node_num_max = node_num + num;
+       nodes = (node*) realloc(nodes_old, node_num_max*sizeof(node));
+       if (!nodes) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }
+
+       node_last = nodes + node_num;
+       node_max = nodes + node_num_max;
+
+       if (nodes != nodes_old)
+       {
+               arc* a;
+               for (a=arcs; a<arc_last; a++)
+               {
+                       a->head = (node*) ((char*)a->head + (((char*) nodes) - ((char*) nodes_old)));
+               }
+       }
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::reallocate_arcs()
+{
+       int arc_num_max = (int)(arc_max - arcs);
+       int arc_num = (int)(arc_last - arcs);
+       arc* arcs_old = arcs;
+
+       arc_num_max += arc_num_max / 2; if (arc_num_max & 1) arc_num_max ++;
+       arcs = (arc*) realloc(arcs_old, arc_num_max*sizeof(arc));
+       if (!arcs) { if (error_function) (*error_function)("Not enough memory!"); exit(1); }
+
+       arc_last = arcs + arc_num;
+       arc_max = arcs + arc_num_max;
+
+       if (arcs != arcs_old)
+       {
+               node* i;
+               arc* a;
+               for (i=nodes; i<node_last; i++)
+               {
+                       if (i->first) i->first = (arc*) ((char*)i->first + (((char*) arcs) - ((char*) arcs_old)));
+               }
+               for (a=arcs; a<arc_last; a++)
+               {
+                       if (a->next) a->next = (arc*) ((char*)a->next + (((char*) arcs) - ((char*) arcs_old)));
+                       a->sister = (arc*) ((char*)a->sister + (((char*) arcs) - ((char*) arcs_old)));
+               }
+       }
+}
+
+#include "instances.inc"

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.h b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.h
new file mode 100644 (file)
index 0000000..87efe7e
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/graph.h
@@ -0,0 +1,506 @@
+/* graph.h */
+/*
+       This software library implements the maxflow algorithm
+       described in
+
+               "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision."
+               Yuri Boykov and Vladimir Kolmogorov.
+               In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 
+               September 2004
+
+       This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov
+       at Siemens Corporate Research. To make it available for public use,
+       it was later reimplemented by Vladimir Kolmogorov based on open publications.
+
+       If you use this software for research purposes, you should cite
+       the aforementioned paper in any resulting publication.
+
+       ----------------------------------------------------------------------
+
+       REUSING TREES:
+
+       Starting with version 3.0, there is a also an option of reusing search
+       trees from one maxflow computation to the next, as described in
+
+               "Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts."
+               Pushmeet Kohli and Philip H.S. Torr
+               International Conference on Computer Vision (ICCV), 2005
+
+       If you use this option, you should cite
+       the aforementioned paper in any resulting publication.
+*/
+       
+
+
+/*
+       For description, license, example usage see README.TXT.
+*/
+
+#ifndef __GRAPH_H__
+#define __GRAPH_H__
+
+#include <string.h>
+#include "block.h"
+
+#include <assert.h>
+// NOTE: in UNIX you need to use -DNDEBUG preprocessor option to supress assert's!!!
+
+
+
+// captype: type of edge capacities (excluding t-links)
+// tcaptype: type of t-links (edges between nodes and terminals)
+// flowtype: type of total flow
+//
+// Current instantiations are in instances.inc
+template <typename captype, typename tcaptype, typename flowtype> class Graph
+{
+public:
+       typedef enum
+       {
+               SOURCE  = 0,
+               SINK    = 1
+       } termtype; // terminals 
+       typedef int node_id;
+
+       /////////////////////////////////////////////////////////////////////////
+       //                     BASIC INTERFACE FUNCTIONS                       //
+    //              (should be enough for most applications)               //
+       /////////////////////////////////////////////////////////////////////////
+
+       // Constructor. 
+       // The first argument gives an estimate of the maximum number of nodes that can be added
+       // to the graph, and the second argument is an estimate of the maximum number of edges.
+       // The last (optional) argument is the pointer to the function which will be called 
+       // if an error occurs; an error message is passed to this function. 
+       // If this argument is omitted, exit(1) will be called.
+       //
+       // IMPORTANT: It is possible to add more nodes to the graph than node_num_max 
+       // (and node_num_max can be zero). However, if the count is exceeded, then 
+       // the internal memory is reallocated (increased by 50%) which is expensive. 
+       // Also, temporarily the amount of allocated memory would be more than twice than needed.
+       // Similarly for edges.
+       // If you wish to avoid this overhead, you can download version 2.2, where nodes and edges are stored in blocks.
+       Graph(int node_num_max, int edge_num_max, void (*err_function)(char *) = NULL);
+
+       // Destructor
+       ~Graph();
+
+       // Adds node(s) to the graph. By default, one node is added (num=1); then first call returns 0, second call returns 1, and so on. 
+       // If num>1, then several nodes are added, and node_id of the first one is returned.
+       // IMPORTANT: see note about the constructor 
+       node_id add_node(int num = 1);
+
+       // Adds a bidirectional edge between 'i' and 'j' with the weights 'cap' and 'rev_cap'.
+       // IMPORTANT: see note about the constructor 
+       void add_edge(node_id i, node_id j, captype cap, captype rev_cap);
+
+       // Adds new edges 'SOURCE->i' and 'i->SINK' with corresponding weights.
+       // Can be called multiple times for each node.
+       // Weights can be negative.
+       // NOTE: the number of such edges is not counted in edge_num_max.
+       //       No internal memory is allocated by this call.
+       void add_tweights(node_id i, tcaptype cap_source, tcaptype cap_sink);
+
+
+       // Computes the maxflow. Can be called several times.
+       // FOR DESCRIPTION OF reuse_trees, SEE mark_node().
+       // FOR DESCRIPTION OF changed_list, SEE remove_from_changed_list().
+       flowtype maxflow(bool reuse_trees = false, Block<node_id>* changed_list = NULL);
+
+       // After the maxflow is computed, this function returns to which
+       // segment the node 'i' belongs (Graph<captype,tcaptype,flowtype>::SOURCE or Graph<captype,tcaptype,flowtype>::SINK).
+       //
+       // Occasionally there may be several minimum cuts. If a node can be assigned
+       // to both the source and the sink, then default_segm is returned.
+       termtype what_segment(node_id i, termtype default_segm = SOURCE);
+
+
+
+       //////////////////////////////////////////////
+       //       ADVANCED INTERFACE FUNCTIONS       //
+       //      (provide access to the graph)       //
+       //////////////////////////////////////////////
+
+private:
+       struct node;
+       struct arc;
+
+public:
+
+       ////////////////////////////
+       // 1. Reallocating graph. //
+       ////////////////////////////
+
+       // Removes all nodes and edges. 
+       // After that functions add_node() and add_edge() must be called again. 
+       //
+       // Advantage compared to deleting Graph and allocating it again:
+       // no calls to delete/new (which could be quite slow).
+       //
+       // If the graph structure stays the same, then an alternative
+       // is to go through all nodes/edges and set new residual capacities
+       // (see functions below).
+       void reset();
+
+       ////////////////////////////////////////////////////////////////////////////////
+       // 2. Functions for getting pointers to arcs and for reading graph structure. //
+       //    NOTE: adding new arcs may invalidate these pointers (if reallocation    //
+       //    happens). So it's best not to add arcs while reading graph structure.   //
+       ////////////////////////////////////////////////////////////////////////////////
+
+       // The following two functions return arcs in the same order that they
+       // were added to the graph. NOTE: for each call add_edge(i,j,cap,cap_rev)
+       // the first arc returned will be i->j, and the second j->i.
+       // If there are no more arcs, then the function can still be called, but
+       // the returned arc_id is undetermined.
+       typedef arc* arc_id;
+       arc_id get_first_arc();
+       arc_id get_next_arc(arc_id a);
+
+       // other functions for reading graph structure
+       int get_node_num() { return node_num; }
+       int get_arc_num() { return (int)(arc_last - arcs); }
+       void get_arc_ends(arc_id a, node_id& i, node_id& j); // returns i,j to that a = i->j
+
+       ///////////////////////////////////////////////////
+       // 3. Functions for reading residual capacities. //
+       ///////////////////////////////////////////////////
+
+       // returns residual capacity of SOURCE->i minus residual capacity of i->SINK
+       tcaptype get_trcap(node_id i); 
+       // returns residual capacity of arc a
+       captype get_rcap(arc* a);
+
+       /////////////////////////////////////////////////////////////////
+       // 4. Functions for setting residual capacities.               //
+       //    NOTE: If these functions are used, the value of the flow //
+       //    returned by maxflow() will not be valid!                 //
+       /////////////////////////////////////////////////////////////////
+
+       void set_trcap(node_id i, tcaptype trcap); 
+       void set_rcap(arc* a, captype rcap);
+
+       ////////////////////////////////////////////////////////////////////
+       // 5. Functions related to reusing trees & list of changed nodes. //
+       ////////////////////////////////////////////////////////////////////
+
+       // If flag reuse_trees is true while calling maxflow(), then search trees
+       // are reused from previous maxflow computation. 
+       // In this case before calling maxflow() the user must
+       // specify which parts of the graph have changed by calling mark_node():
+       //   add_tweights(i),set_trcap(i)    => call mark_node(i)
+       //   add_edge(i,j),set_rcap(a)       => call mark_node(i); mark_node(j)
+       //
+       // This option makes sense only if a small part of the graph is changed.
+       // The initialization procedure goes only through marked nodes then.
+       // 
+       // mark_node(i) can either be called before or after graph modification.
+       // Can be called more than once per node, but calls after the first one
+       // do not have any effect.
+       // 
+       // NOTE: 
+       //   - This option cannot be used in the first call to maxflow().
+       //   - It is not necessary to call mark_node() if the change is ``not essential'',
+       //     i.e. sign(trcap) is preserved for a node and zero/nonzero status is preserved for an arc.
+       //   - To check that you marked all necessary nodes, you can call maxflow(false) after calling maxflow(true).
+       //     If everything is correct, the two calls must return the same value of flow. (Useful for debugging).
+       void mark_node(node_id i);
+
+       // If changed_list is not NULL while calling maxflow(), then the algorithm
+       // keeps a list of nodes which could potentially have changed their segmentation label.
+       // Nodes which are not in the list are guaranteed to keep their old segmentation label (SOURCE or SINK).
+       // Example usage:
+       //
+       //              typedef Graph<int,int,int> G;
+       //              G* g = new Graph(nodeNum, edgeNum);
+       //              Block<G::node_id>* changed_list = new Block<G::node_id>(128);
+       //
+       //              ... // add nodes and edges
+       //
+       //              g->maxflow(); // first call should be without arguments
+       //              for (int iter=0; iter<10; iter++)
+       //              {
+       //                      ... // change graph, call mark_node() accordingly
+       //
+       //                      g->maxflow(true, changed_list);
+       //                      G::node_id* ptr;
+       //                      for (ptr=changed_list->ScanFirst(); ptr; ptr=changed_list->ScanNext())
+       //                      {
+       //                              G::node_id i = *ptr; assert(i>=0 && i<nodeNum);
+       //                              g->remove_from_changed_list(i);
+       //                              // do something with node i...
+       //                              if (g->what_segment(i) == G::SOURCE) { ... }
+       //                      }
+       //                      changed_list->Reset();
+       //              }
+       //              delete changed_list;
+       //              
+       // NOTE:
+       //  - If changed_list option is used, then reuse_trees must be used as well.
+       //  - In the example above, the user may omit calls g->remove_from_changed_list(i) and changed_list->Reset() in a given iteration.
+       //    Then during the next call to maxflow(true, &changed_list) new nodes will be added to changed_list.
+       //  - If the next call to maxflow() does not use option reuse_trees, then calling remove_from_changed_list()
+       //    is not necessary. ("changed_list->Reset()" or "delete changed_list" should still be called, though).
+       void remove_from_changed_list(node_id i) 
+       { 
+               assert(i>=0 && i<node_num && nodes[i].is_in_changed_list); 
+               nodes[i].is_in_changed_list = 0;
+       }
+
+
+
+
+
+
+/////////////////////////////////////////////////////////////////////////
+/////////////////////////////////////////////////////////////////////////
+/////////////////////////////////////////////////////////////////////////
+       
+private:
+       // internal variables and functions
+
+       struct node
+       {
+               arc                     *first;         // first outcoming arc
+
+               arc                     *parent;        // node's parent
+               node            *next;          // pointer to the next active node
+                                                               //   (or to itself if it is the last node in the list)
+               int                     TS;                     // timestamp showing when DIST was computed
+               int                     DIST;           // distance to the terminal
+               int                     is_sink : 1;    // flag showing whether the node is in the source or in the sink tree (if parent!=NULL)
+               int                     is_marked : 1;  // set by mark_node()
+               int                     is_in_changed_list : 1; // set by maxflow if 
+
+               tcaptype        tr_cap;         // if tr_cap > 0 then tr_cap is residual capacity of the arc SOURCE->node
+                                                               // otherwise         -tr_cap is residual capacity of the arc node->SINK 
+
+       };
+
+       struct arc
+       {
+               node            *head;          // node the arc points to
+               arc                     *next;          // next arc with the same originating node
+               arc                     *sister;        // reverse arc
+
+               captype         r_cap;          // residual capacity
+       };
+
+       struct nodeptr
+       {
+               node            *ptr;
+               nodeptr         *next;
+       };
+       static const int NODEPTR_BLOCK_SIZE = 128;
+
+       node                            *nodes, *node_last, *node_max; // node_last = nodes+node_num, node_max = nodes+node_num_max;
+       arc                                     *arcs, *arc_last, *arc_max; // arc_last = arcs+2*edge_num, arc_max = arcs+2*edge_num_max;
+
+       int                                     node_num;
+
+       DBlock<nodeptr>         *nodeptr_block;
+
+       void    (*error_function)(char *);      // this function is called if a error occurs,
+                                                                               // with a corresponding error message
+                                                                               // (or exit(1) is called if it's NULL)
+
+       flowtype                        flow;           // total flow
+
+       // reusing trees & list of changed pixels
+       int                                     maxflow_iteration; // counter
+       Block<node_id>          *changed_list;
+
+       /////////////////////////////////////////////////////////////////////////
+
+       node                            *queue_first[2], *queue_last[2];        // list of active nodes
+       nodeptr                         *orphan_first, *orphan_last;            // list of pointers to orphans
+       int                                     TIME;                                                           // monotonically increasing global counter
+
+       /////////////////////////////////////////////////////////////////////////
+
+       void reallocate_nodes(int num); // num is the number of new nodes
+       void reallocate_arcs();
+
+       // functions for processing active list
+       void set_active(node *i);
+       node *next_active();
+
+       // functions for processing orphans list
+       void set_orphan_front(node* i); // add to the beginning of the list
+       void set_orphan_rear(node* i);  // add to the end of the list
+
+       void add_to_changed_list(node* i);
+
+       void maxflow_init();             // called if reuse_trees == false
+       void maxflow_reuse_trees_init(); // called if reuse_trees == true
+       void augment(arc *middle_arc);
+       void process_source_orphan(node *i);
+       void process_sink_orphan(node *i);
+
+       void test_consistency(node* current_node=NULL); // debug function
+};
+
+
+
+
+
+
+
+
+
+
+
+///////////////////////////////////////
+// Implementation - inline functions //
+///////////////////////////////////////
+
+
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline typename Graph<captype,tcaptype,flowtype>::node_id Graph<captype,tcaptype,flowtype>::add_node(int num)
+{
+       assert(num > 0);
+
+       if (node_last + num > node_max) reallocate_nodes(num);
+
+       if (num == 1)
+       {
+               node_last -> first = NULL;
+               node_last -> tr_cap = 0;
+               node_last -> is_marked = 0;
+               node_last -> is_in_changed_list = 0;
+
+               node_last ++;
+               return node_num ++;
+       }
+       else
+       {
+               memset(node_last, 0, num*sizeof(node));
+
+               node_id i = node_num;
+               node_num += num;
+               node_last += num;
+               return i;
+       }
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::add_tweights(node_id i, tcaptype cap_source, tcaptype cap_sink)
+{
+       assert(i >= 0 && i < node_num);
+
+       tcaptype delta = nodes[i].tr_cap;
+       if (delta > 0) cap_source += delta;
+       else           cap_sink   -= delta;
+       flow += (cap_source < cap_sink) ? cap_source : cap_sink;
+       nodes[i].tr_cap = cap_source - cap_sink;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::add_edge(node_id _i, node_id _j, captype cap, captype rev_cap)
+{
+       assert(_i >= 0 && _i < node_num);
+       assert(_j >= 0 && _j < node_num);
+       assert(_i != _j);
+       assert(cap >= 0);
+       assert(rev_cap >= 0);
+
+       if (arc_last == arc_max) reallocate_arcs();
+
+       arc *a = arc_last ++;
+       arc *a_rev = arc_last ++;
+
+       node* i = nodes + _i;
+       node* j = nodes + _j;
+
+       a -> sister = a_rev;
+       a_rev -> sister = a;
+       a -> next = i -> first;
+       i -> first = a;
+       a_rev -> next = j -> first;
+       j -> first = a_rev;
+       a -> head = j;
+       a_rev -> head = i;
+       a -> r_cap = cap;
+       a_rev -> r_cap = rev_cap;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline typename Graph<captype,tcaptype,flowtype>::arc* Graph<captype,tcaptype,flowtype>::get_first_arc()
+{
+       return arcs;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline typename Graph<captype,tcaptype,flowtype>::arc* Graph<captype,tcaptype,flowtype>::get_next_arc(arc* a) 
+{
+       return a + 1; 
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::get_arc_ends(arc* a, node_id& i, node_id& j)
+{
+       assert(a >= arcs && a < arc_last);
+       i = (node_id) (a->sister->head - nodes);
+       j = (node_id) (a->head - nodes);
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline tcaptype Graph<captype,tcaptype,flowtype>::get_trcap(node_id i)
+{
+       assert(i>=0 && i<node_num);
+       return nodes[i].tr_cap;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline captype Graph<captype,tcaptype,flowtype>::get_rcap(arc* a)
+{
+       assert(a >= arcs && a < arc_last);
+       return a->r_cap;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::set_trcap(node_id i, tcaptype trcap)
+{
+       assert(i>=0 && i<node_num); 
+       nodes[i].tr_cap = trcap;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::set_rcap(arc* a, captype rcap)
+{
+       assert(a >= arcs && a < arc_last);
+       a->r_cap = rcap;
+}
+
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline typename Graph<captype,tcaptype,flowtype>::termtype Graph<captype,tcaptype,flowtype>::what_segment(node_id i, termtype default_segm)
+{
+       if (nodes[i].parent)
+       {
+               return (nodes[i].is_sink) ? SINK : SOURCE;
+       }
+       else
+       {
+               return default_segm;
+       }
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::mark_node(node_id _i)
+{
+       node* i = nodes + _i;
+       if (!i->next)
+       {
+               /* it's not in the list yet */
+               if (queue_last[1]) queue_last[1] -> next = i;
+               else               queue_first[1]        = i;
+               queue_last[1] = i;
+               i -> next = i;
+       }
+       i->is_marked = 1;
+}
+
+
+#endif

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/instances.inc b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/instances.inc
new file mode 100644 (file)
index 0000000..9c9ee37
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/instances.inc
@@ -0,0 +1,16 @@
+#include "graph.h"
+
+#ifdef _MSC_VER
+#pragma warning(disable: 4661)
+#endif
+
+// Instantiations: <captype, tcaptype, flowtype>
+// IMPORTANT: 
+//    flowtype should be 'larger' than tcaptype 
+//    tcaptype should be 'larger' than captype
+
+template class Graph<int,int,int>;
+template class Graph<short,int,int>;
+template class Graph<float,float,float>;
+template class Graph<double,double,double>;
+

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/maxflow.cpp b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/maxflow.cpp
new file mode 100644 (file)
index 0000000..a2fc042
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow-v3.0/maxflow.cpp
@@ -0,0 +1,684 @@
+/* maxflow.cpp */
+
+
+#include <stdio.h>
+#include "graph.h"
+
+
+/*
+       special constants for node->parent
+*/
+#define TERMINAL ( (arc *) 1 )         /* to terminal */
+#define ORPHAN   ( (arc *) 2 )         /* orphan */
+
+
+#define INFINITE_D ((int)(((unsigned)-1)/2))           /* infinite distance to the terminal */
+
+/***********************************************************************/
+
+/*
+       Functions for processing active list.
+       i->next points to the next node in the list
+       (or to i, if i is the last node in the list).
+       If i->next is NULL iff i is not in the list.
+
+       There are two queues. Active nodes are added
+       to the end of the second queue and read from
+       the front of the first queue. If the first queue
+       is empty, it is replaced by the second queue
+       (and the second queue becomes empty).
+*/
+
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::set_active(node *i)
+{
+       if (!i->next)
+       {
+               /* it's not in the list yet */
+               if (queue_last[1]) queue_last[1] -> next = i;
+               else               queue_first[1]        = i;
+               queue_last[1] = i;
+               i -> next = i;
+       }
+}
+
+/*
+       Returns the next active node.
+       If it is connected to the sink, it stays in the list,
+       otherwise it is removed from the list
+*/
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline typename Graph<captype,tcaptype,flowtype>::node* Graph<captype,tcaptype,flowtype>::next_active()
+{
+       node *i;
+
+       while ( 1 )
+       {
+               if (!(i=queue_first[0]))
+               {
+                       queue_first[0] = i = queue_first[1];
+                       queue_last[0]  = queue_last[1];
+                       queue_first[1] = NULL;
+                       queue_last[1]  = NULL;
+                       if (!i) return NULL;
+               }
+
+               /* remove it from the active list */
+               if (i->next == i) queue_first[0] = queue_last[0] = NULL;
+               else              queue_first[0] = i -> next;
+               i -> next = NULL;
+
+               /* a node in the list is active iff it has a parent */
+               if (i->parent) return i;
+       }
+}
+
+/***********************************************************************/
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::set_orphan_front(node *i)
+{
+       nodeptr *np;
+       i -> parent = ORPHAN;
+       np = nodeptr_block -> New();
+       np -> ptr = i;
+       np -> next = orphan_first;
+       orphan_first = np;
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::set_orphan_rear(node *i)
+{
+       nodeptr *np;
+       i -> parent = ORPHAN;
+       np = nodeptr_block -> New();
+       np -> ptr = i;
+       if (orphan_last) orphan_last -> next = np;
+       else             orphan_first        = np;
+       orphan_last = np;
+       np -> next = NULL;
+}
+
+/***********************************************************************/
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       inline void Graph<captype,tcaptype,flowtype>::add_to_changed_list(node *i)
+{
+       if (changed_list && !i->is_in_changed_list)
+       {
+               node_id* ptr = changed_list->New();
+               *ptr = (node_id)(i - nodes);
+               i->is_in_changed_list = true;
+       }
+}
+
+/***********************************************************************/
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::maxflow_init()
+{
+       node *i;
+
+       queue_first[0] = queue_last[0] = NULL;
+       queue_first[1] = queue_last[1] = NULL;
+       orphan_first = NULL;
+
+       TIME = 0;
+
+       for (i=nodes; i<node_last; i++)
+       {
+               i -> next = NULL;
+               i -> is_marked = 0;
+               i -> is_in_changed_list = 0;
+               i -> TS = TIME;
+               if (i->tr_cap > 0)
+               {
+                       /* i is connected to the source */
+                       i -> is_sink = 0;
+                       i -> parent = TERMINAL;
+                       set_active(i);
+                       i -> DIST = 1;
+               }
+               else if (i->tr_cap < 0)
+               {
+                       /* i is connected to the sink */
+                       i -> is_sink = 1;
+                       i -> parent = TERMINAL;
+                       set_active(i);
+                       i -> DIST = 1;
+               }
+               else
+               {
+                       i -> parent = NULL;
+               }
+       }
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::maxflow_reuse_trees_init()
+{
+       node* i;
+       node* j;
+       node* queue = queue_first[1];
+       arc* a;
+       nodeptr* np;
+
+       queue_first[0] = queue_last[0] = NULL;
+       queue_first[1] = queue_last[1] = NULL;
+       orphan_first = orphan_last = NULL;
+
+       TIME ++;
+
+       while ((i=queue))
+       {
+               queue = i->next;
+               if (queue == i) queue = NULL;
+               i->next = NULL;
+               i->is_marked = 0;
+               set_active(i);
+
+               if (i->tr_cap == 0)
+               {
+                       if (i->parent) set_orphan_rear(i);
+                       continue;
+               }
+
+               if (i->tr_cap > 0)
+               {
+                       if (!i->parent || i->is_sink)
+                       {
+                               i->is_sink = 0;
+                               for (a=i->first; a; a=a->next)
+                               {
+                                       j = a->head;
+                                       if (!j->is_marked)
+                                       {
+                                               if (j->parent == a->sister) set_orphan_rear(j);
+                                               if (j->parent && j->is_sink && a->r_cap > 0) set_active(j);
+                                       }
+                               }
+                               add_to_changed_list(i);
+                       }
+               }
+               else
+               {
+                       if (!i->parent || !i->is_sink)
+                       {
+                               i->is_sink = 1;
+                               for (a=i->first; a; a=a->next)
+                               {
+                                       j = a->head;
+                                       if (!j->is_marked)
+                                       {
+                                               if (j->parent == a->sister) set_orphan_rear(j);
+                                               if (j->parent && !j->is_sink && a->sister->r_cap > 0) set_active(j);
+                                       }
+                               }
+                               add_to_changed_list(i);
+                       }
+               }
+               i->parent = TERMINAL;
+               i -> TS = TIME;
+               i -> DIST = 1;
+       }
+
+       //test_consistency();
+
+       /* adoption */
+       while ((np=orphan_first))
+       {
+               orphan_first = np -> next;
+               i = np -> ptr;
+               nodeptr_block -> Delete(np);
+               if (!orphan_first) orphan_last = NULL;
+               if (i->is_sink) process_sink_orphan(i);
+               else            process_source_orphan(i);
+       }
+       /* adoption end */
+
+       //test_consistency();
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::augment(arc *middle_arc)
+{
+       node *i;
+       arc *a;
+       tcaptype bottleneck;
+
+
+       /* 1. Finding bottleneck capacity */
+       /* 1a - the source tree */
+       bottleneck = middle_arc -> r_cap;
+       for (i=middle_arc->sister->head; ; i=a->head)
+       {
+               a = i -> parent;
+               if (a == TERMINAL) break;
+               if (bottleneck > a->sister->r_cap) bottleneck = a -> sister -> r_cap;
+       }
+       if (bottleneck > i->tr_cap) bottleneck = i -> tr_cap;
+       /* 1b - the sink tree */
+       for (i=middle_arc->head; ; i=a->head)
+       {
+               a = i -> parent;
+               if (a == TERMINAL) break;
+               if (bottleneck > a->r_cap) bottleneck = a -> r_cap;
+       }
+       if (bottleneck > - i->tr_cap) bottleneck = - i -> tr_cap;
+
+
+       /* 2. Augmenting */
+       /* 2a - the source tree */
+       middle_arc -> sister -> r_cap += bottleneck;
+       middle_arc -> r_cap -= bottleneck;
+       for (i=middle_arc->sister->head; ; i=a->head)
+       {
+               a = i -> parent;
+               if (a == TERMINAL) break;
+               a -> r_cap += bottleneck;
+               a -> sister -> r_cap -= bottleneck;
+               if (!a->sister->r_cap)
+               {
+                       set_orphan_front(i); // add i to the beginning of the adoption list
+               }
+       }
+       i -> tr_cap -= bottleneck;
+       if (!i->tr_cap)
+       {
+               set_orphan_front(i); // add i to the beginning of the adoption list
+       }
+       /* 2b - the sink tree */
+       for (i=middle_arc->head; ; i=a->head)
+       {
+               a = i -> parent;
+               if (a == TERMINAL) break;
+               a -> sister -> r_cap += bottleneck;
+               a -> r_cap -= bottleneck;
+               if (!a->r_cap)
+               {
+                       set_orphan_front(i); // add i to the beginning of the adoption list
+               }
+       }
+       i -> tr_cap += bottleneck;
+       if (!i->tr_cap)
+       {
+               set_orphan_front(i); // add i to the beginning of the adoption list
+       }
+
+
+       flow += bottleneck;
+}
+
+/***********************************************************************/
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::process_source_orphan(node *i)
+{
+       node *j;
+       arc *a0, *a0_min = NULL, *a;
+       int d, d_min = INFINITE_D;
+
+       /* trying to find a new parent */
+       for (a0=i->first; a0; a0=a0->next)
+       if (a0->sister->r_cap)
+       {
+               j = a0 -> head;
+               if (!j->is_sink && (a=j->parent))
+               {
+                       /* checking the origin of j */
+                       d = 0;
+                       while ( 1 )
+                       {
+                               if (j->TS == TIME)
+                               {
+                                       d += j -> DIST;
+                                       break;
+                               }
+                               a = j -> parent;
+                               d ++;
+                               if (a==TERMINAL)
+                               {
+                                       j -> TS = TIME;
+                                       j -> DIST = 1;
+                                       break;
+                               }
+                               if (a==ORPHAN) { d = INFINITE_D; break; }
+                               j = a -> head;
+                       }
+                       if (d<INFINITE_D) /* j originates from the source - done */
+                       {
+                               if (d<d_min)
+                               {
+                                       a0_min = a0;
+                                       d_min = d;
+                               }
+                               /* set marks along the path */
+                               for (j=a0->head; j->TS!=TIME; j=j->parent->head)
+                               {
+                                       j -> TS = TIME;
+                                       j -> DIST = d --;
+                               }
+                       }
+               }
+       }
+
+       if (i->parent = a0_min)
+       {
+               i -> TS = TIME;
+               i -> DIST = d_min + 1;
+       }
+       else
+       {
+               /* no parent is found */
+               add_to_changed_list(i);
+
+               /* process neighbors */
+               for (a0=i->first; a0; a0=a0->next)
+               {
+                       j = a0 -> head;
+                       if (!j->is_sink && (a=j->parent))
+                       {
+                               if (a0->sister->r_cap) set_active(j);
+                               if (a!=TERMINAL && a!=ORPHAN && a->head==i)
+                               {
+                                       set_orphan_rear(j); // add j to the end of the adoption list
+                               }
+                       }
+               }
+       }
+}
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::process_sink_orphan(node *i)
+{
+       node *j;
+       arc *a0, *a0_min = NULL, *a;
+       int d, d_min = INFINITE_D;
+
+       /* trying to find a new parent */
+       for (a0=i->first; a0; a0=a0->next)
+       if (a0->r_cap)
+       {
+               j = a0 -> head;
+               if (j->is_sink && (a=j->parent))
+               {
+                       /* checking the origin of j */
+                       d = 0;
+                       while ( 1 )
+                       {
+                               if (j->TS == TIME)
+                               {
+                                       d += j -> DIST;
+                                       break;
+                               }
+                               a = j -> parent;
+                               d ++;
+                               if (a==TERMINAL)
+                               {
+                                       j -> TS = TIME;
+                                       j -> DIST = 1;
+                                       break;
+                               }
+                               if (a==ORPHAN) { d = INFINITE_D; break; }
+                               j = a -> head;
+                       }
+                       if (d<INFINITE_D) /* j originates from the sink - done */
+                       {
+                               if (d<d_min)
+                               {
+                                       a0_min = a0;
+                                       d_min = d;
+                               }
+                               /* set marks along the path */
+                               for (j=a0->head; j->TS!=TIME; j=j->parent->head)
+                               {
+                                       j -> TS = TIME;
+                                       j -> DIST = d --;
+                               }
+                       }
+               }
+       }
+
+       if (i->parent = a0_min)
+       {
+               i -> TS = TIME;
+               i -> DIST = d_min + 1;
+       }
+       else
+       {
+               /* no parent is found */
+               add_to_changed_list(i);
+
+               /* process neighbors */
+               for (a0=i->first; a0; a0=a0->next)
+               {
+                       j = a0 -> head;
+                       if (j->is_sink && (a=j->parent))
+                       {
+                               if (a0->r_cap) set_active(j);
+                               if (a!=TERMINAL && a!=ORPHAN && a->head==i)
+                               {
+                                       set_orphan_rear(j); // add j to the end of the adoption list
+                               }
+                       }
+               }
+       }
+}
+
+/***********************************************************************/
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       flowtype Graph<captype,tcaptype,flowtype>::maxflow(bool reuse_trees, Block<node_id>* _changed_list)
+{
+       node *i, *j, *current_node = NULL;
+       arc *a;
+       nodeptr *np, *np_next;
+
+       if (!nodeptr_block)
+       {
+               nodeptr_block = new DBlock<nodeptr>(NODEPTR_BLOCK_SIZE, error_function);
+       }
+
+       changed_list = _changed_list;
+       if (maxflow_iteration == 0 && reuse_trees) { if (error_function) (*error_function)("reuse_trees cannot be used in the first call to maxflow()!"); exit(1); }
+       if (changed_list && !reuse_trees) { if (error_function) (*error_function)("changed_list cannot be used without reuse_trees!"); exit(1); }
+
+       if (reuse_trees) maxflow_reuse_trees_init();
+       else             maxflow_init();
+
+       // main loop
+       while ( 1 )
+       {
+               // test_consistency(current_node);
+
+               if ((i=current_node))
+               {
+                       i -> next = NULL; /* remove active flag */
+                       if (!i->parent) i = NULL;
+               }
+               if (!i)
+               {
+                       if (!(i = next_active())) break;
+               }
+
+               /* growth */
+               if (!i->is_sink)
+               {
+                       /* grow source tree */
+                       for (a=i->first; a; a=a->next)
+                       if (a->r_cap)
+                       {
+                               j = a -> head;
+                               if (!j->parent)
+                               {
+                                       j -> is_sink = 0;
+                                       j -> parent = a -> sister;
+                                       j -> TS = i -> TS;
+                                       j -> DIST = i -> DIST + 1;
+                                       set_active(j);
+                                       add_to_changed_list(j);
+                               }
+                               else if (j->is_sink) break;
+                               else if (j->TS <= i->TS &&
+                                        j->DIST > i->DIST)
+                               {
+                                       /* heuristic - trying to make the distance from j to the source shorter */
+                                       j -> parent = a -> sister;
+                                       j -> TS = i -> TS;
+                                       j -> DIST = i -> DIST + 1;
+                               }
+                       }
+               }
+               else
+               {
+                       /* grow sink tree */
+                       for (a=i->first; a; a=a->next)
+                       if (a->sister->r_cap)
+                       {
+                               j = a -> head;
+                               if (!j->parent)
+                               {
+                                       j -> is_sink = 1;
+                                       j -> parent = a -> sister;
+                                       j -> TS = i -> TS;
+                                       j -> DIST = i -> DIST + 1;
+                                       set_active(j);
+                                       add_to_changed_list(j);
+                               }
+                               else if (!j->is_sink) { a = a -> sister; break; }
+                               else if (j->TS <= i->TS &&
+                                        j->DIST > i->DIST)
+                               {
+                                       /* heuristic - trying to make the distance from j to the sink shorter */
+                                       j -> parent = a -> sister;
+                                       j -> TS = i -> TS;
+                                       j -> DIST = i -> DIST + 1;
+                               }
+                       }
+               }
+
+               TIME ++;
+
+               if (a)
+               {
+                       i -> next = i; /* set active flag */
+                       current_node = i;
+
+                       /* augmentation */
+                       augment(a);
+                       /* augmentation end */
+
+                       /* adoption */
+                       while ((np=orphan_first))
+                       {
+                               np_next = np -> next;
+                               np -> next = NULL;
+
+                               while ((np=orphan_first))
+                               {
+                                       orphan_first = np -> next;
+                                       i = np -> ptr;
+                                       nodeptr_block -> Delete(np);
+                                       if (!orphan_first) orphan_last = NULL;
+                                       if (i->is_sink) process_sink_orphan(i);
+                                       else            process_source_orphan(i);
+                               }
+
+                               orphan_first = np_next;
+                       }
+                       /* adoption end */
+               }
+               else current_node = NULL;
+       }
+       // test_consistency();
+
+       if (!reuse_trees || (maxflow_iteration % 64) == 0)
+       {
+               delete nodeptr_block; 
+               nodeptr_block = NULL; 
+       }
+
+       maxflow_iteration ++;
+       return flow;
+}
+
+/***********************************************************************/
+
+
+template <typename captype, typename tcaptype, typename flowtype> 
+       void Graph<captype,tcaptype,flowtype>::test_consistency(node* current_node)
+{
+       node *i;
+       arc *a;
+       int r;
+       int num1 = 0, num2 = 0;
+
+       // test whether all nodes i with i->next!=NULL are indeed in the queue
+       for (i=nodes; i<node_last; i++)
+       {
+               if (i->next || i==current_node) num1 ++;
+       }
+       for (r=0; r<3; r++)
+       {
+               i = (r == 2) ? current_node : queue_first[r];
+               if (i)
+               for ( ; ; i=i->next)
+               {
+                       num2 ++;
+                       if (i->next == i)
+                       {
+                               if (r<2) assert(i == queue_last[r]);
+                               else     assert(i == current_node);
+                               break;
+                       }
+               }
+       }
+       assert(num1 == num2);
+
+       for (i=nodes; i<node_last; i++)
+       {
+               // test whether all edges in seach trees are non-saturated
+               if (i->parent == NULL) {}
+               else if (i->parent == ORPHAN) {}
+               else if (i->parent == TERMINAL)
+               {
+                       if (!i->is_sink) assert(i->tr_cap > 0);
+                       else             assert(i->tr_cap < 0);
+               }
+               else
+               {
+                       if (!i->is_sink) assert (i->parent->sister->r_cap > 0);
+                       else             assert (i->parent->r_cap > 0);
+               }
+               // test whether passive nodes in search trees have neighbors in
+               // a different tree through non-saturated edges
+               if (i->parent && !i->next)
+               {
+                       if (!i->is_sink)
+                       {
+                               assert(i->tr_cap >= 0);
+                               for (a=i->first; a; a=a->next)
+                               {
+                                       if (a->r_cap > 0) assert(a->head->parent && !a->head->is_sink);
+                               }
+                       }
+                       else
+                       {
+                               assert(i->tr_cap <= 0);
+                               for (a=i->first; a; a=a->next)
+                               {
+                                       if (a->sister->r_cap > 0) assert(a->head->parent && a->head->is_sink);
+                               }
+                       }
+               }
+               // test marking invariants
+               if (i->parent && i->parent!=ORPHAN && i->parent!=TERMINAL)
+               {
+                       assert(i->TS <= i->parent->head->TS);
+                       if (i->TS == i->parent->head->TS) assert(i->DIST > i->parent->head->DIST);
+               }
+       }
+}
+
+#include "instances.inc"

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow.m b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow.m
new file mode 100644 (file)
index 0000000..c19fcff
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflow.m
@@ -0,0 +1,40 @@
+function [flow,labels] = maxflow(A,T)\r
+\r
+%MAXFLOW    Max-flow/min-cut calculation using the\r
+%   Boykov-Kolmogorov's algorithm. Let G=(V,E) be\r
+%   a directed graph with |V| vertices and |E| edges.\r
+%   w:(i,j)-->R, (i,j) in E, a weight function on the graph's\r
+%   edges.\r
+%\r
+%   A (smoothness term) - a sparse matrix of size |V|x|V| containing, where\r
+%   A(i,j) = w(i,j). Typically, for a grid graph, the node \r
+%   indices would be taken in column-major order, but it is not assumed.\r
+%   T (data term) - a sparse matrix of size |V|x|L|, where |L| is the number\r
+%   of labels (The library currently supports |L|=2 only).\r
+%   T(i,j) = the cost of assigning label j to node i.\r
+%   \r
+%   flow - the calculated maximum flow value\r
+%   labels - a vector of size |V|, where labels(i) is 0 or 1 if\r
+%   node i belongs to S (source) or T (sink) respectively.\r
+%\r
+%   Also refer to the following link for tips on creating large\r
+%   sparse matrices efficiently.\r
+%   In case of a dense adjacency matrix B, simply wrap it in a \r
+%   sparse structure before passing it to this function:\r
+%   A = sparse(B);\r
+%   [flow,labels] = maxflow(A,T);\r
+%\r
+%   This library can be used for reseatch purposes only, and is\r
+%   supplied with no guarantees.\r
+%\r
+%   (c) 2008 Michael Rubinstein, WDI R&D and IDC\r
+%   $Revision: 140 $\r
+%   $Date: 2008-09-15 15:35:01 -0700 (Mon, 15 Sep 2008) $\r
+%\r
+\r
+[flow,labels] = maxflowmex(A,T);\r
+\r
+% release the dll\r
+clear maxflowmex\r
+\r
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflowmex.cpp b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflowmex.cpp
new file mode 100644 (file)
index 0000000..c40dab4
--- /dev/null
+++ b/THESE/codes/graphe/MIA_final_code/yuri_interactive_maxflow/maxflowmex.cpp
@@ -0,0 +1,144 @@
+//------------------------------------------------------------------------\r
+// MAXFLOWMEX  A wrapper library for maximum flow calculation.\r
+//     Input:\r
+//     A - an NxN sparse matrix of real entries.\r
+//     T - as Nx2 sparse matrix of real entries.\r
+//  Output:\r
+//  flow - maximum flow value\r
+//  labels - a vector of size Nx1 containing the label of each node \r
+//           respectively\r
+//\r
+//  Note that it is not guaranteed that A will be checked for correct\r
+//  construction (e.g. self loops, etc). That is, garbage in - garbage out.\r
+// \r
+//     (c) 2008 Michael Rubinstein, WDI R&D and IDC\r
+//     $Revision: 140 $\r
+//     $Date: 2008-09-15 15:35:01 -0700 (Mon, 15 Sep 2008) $\r
+//------------------------------------------------------------------------\r
+\r
+#include "mex.h"\r
+#include "maxflow-v3.0\graph.h"\r
+#include "maxflow-v3.0\maxflow.cpp"\r
+#include "maxflow-v3.0\graph.cpp" \r
+\r
+void mexFunction(int                   nlhs,           /* number of expected outputs */\r
+                                mxArray                *plhs[],        /* mxArray output pointer array */\r
+                                int                    nrhs,           /* number of inputs */\r
+                                const mxArray  *prhs[]         /* mxArray input pointer array */)\r
+{\r
+       // input checks\r
+       if (nrhs != 2 || !mxIsSparse(prhs[0]) || !mxIsSparse(prhs[1]))\r
+       {\r
+               mexErrMsgTxt ("USAGE: [flow,labels] = maxflowmex(A,T)");\r
+       }\r
+       const mxArray *A = prhs[0];\r
+       const mxArray *T = prhs[1];\r
+       if (mxIsComplex(A) || mxIsComplex(T))\r
+       {\r
+               mexErrMsgTxt ("Complex entries are not supported!");\r
+       }\r
+       // fetch its dimensions\r
+       // actually, we must have m=n\r
+       mwSize m = mxGetM(A);\r
+       mwSize n = mxGetN(A);\r
+       mwSize nzmax = mxGetNzmax(A);\r
+       if (m != n)\r
+       {\r
+               mexErrMsgTxt ("Matrix A should be square!");\r
+       }\r
+       if (n != mxGetM(T) || mxGetN(T) != 2)\r
+       {\r
+               mexErrMsgTxt ("T should be of size Nx2");\r
+       }\r
+\r
+       // sparse matrices have a different storage convention from that of full matrices in MATLAB. \r
+       // The parameters pr and pi are still arrays of double-precision numbers, but these arrays \r
+       // contain only nonzero data elements. There are three additional parameters: nzmax, ir, and jc.\r
+       // nzmax - is an integer that contains the length of ir, pr, and, if it exists, pi. It is the maximum \r
+       // possible number of nonzero elements in the sparse matrix.\r
+       // ir - points to an integer array of length nzmax containing the row indices of the corresponding \r
+       // elements in pr and pi.\r
+       // jc - points to an integer array of length n+1, where n is the number of columns in the sparse matrix. \r
+       // The jc array contains column index information. If the jth column of the sparse matrix has any nonzero\r
+       // elements, jc[j] is the index in ir and pr (and pi if it exists) of the first nonzero element in the jth\r
+       // column, and jc[j+1] - 1 is the index of the last nonzero element in that column. For the jth column of \r
+       // the sparse matrix, jc[j] is the total number of nonzero elements in all preceding columns. The last \r
+       // element of the jc array, jc[n], is equal to nnz, the number of nonzero elements in the entire sparse matrix. \r
+       // If nnz is less than nzmax, more nonzero entries can be inserted into the array without allocating additional \r
+       // storage.\r
+\r
+       double *pr = mxGetPr(A);\r
+       mwIndex *ir = mxGetIr(A);\r
+       mwIndex *jc = mxGetJc(A);\r
+\r
+       // create graph\r
+       typedef Graph<float,float,float> GraphType;\r
+       // estimations for number of nodes and edges - we know these exactly!\r
+       GraphType *g = new GraphType(/*estimated # of nodes*/ n, /*estimated # of edges*/ jc[n]); \r
+       \r
+       // add the nodes\r
+       // NOTE: their indices are 0-based\r
+    g->add_node(n);\r
+\r
+       // traverse the adjacency matrix and add n-links\r
+       unsigned int i, j, k;\r
+       float v;\r
+       for (j = 0; j < n; j++)\r
+       {\r
+               // this is a simple check whether there are non zero\r
+               // entries in the j'th column\r
+               if (jc[j] == jc[j+1])\r
+               {\r
+                       continue; // nothing in this column\r
+               }\r
+               for (k = jc[j]; k <= jc[j+1]-1; k++)\r
+               {\r
+                       i = ir[k];\r
+                       v = (float)pr[k];\r
+                       //mexPrintf("non zero entry: (%d,%d) = %.2f\n", i+1, j+1, v);\r
+                       g->add_edge(i, j, v, 0.0f);\r
+               }\r
+       }\r
+\r
+       // traverse the terminal matrix and add t-links\r
+       pr = mxGetPr(T);\r
+       ir = mxGetIr(T);\r
+       jc = mxGetJc(T);\r
+       for (j = 0; j <= 1; j++)\r
+       {\r
+               if (jc[j] == jc[j+1])\r
+               {\r
+                       continue;\r
+               }\r
+               for (k = jc[j]; k <= jc[j+1]-1; k++)\r
+               {\r
+                       i = ir[k];\r
+                       v = (float)pr[k];\r
+\r
+                       if (j == 0) // source weight\r
+                       {\r
+                               g->add_tweights(i, v, 0.0f);\r
+                       }\r
+                       else if (j == 1) // sink weight\r
+                       {\r
+                               g->add_tweights(i, 0.0f, v);\r
+                       }\r
+               }\r
+       }\r
+\r
+       plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL);\r
+       double* flow = mxGetPr(plhs[0]);\r
+       *flow = g->maxflow();\r
+\r
+       // figure out segmentation\r
+       plhs[1] = mxCreateNumericMatrix(n, 1, mxINT32_CLASS, mxREAL);\r
+       int* labels = (int*)mxGetData(plhs[1]);\r
+       for (i = 0; i < n; i++)\r
+       {\r
+               labels[i] = g->what_segment(i);\r
+       }\r
+\r
+       // cleanup\r
+       delete g;\r
+}\r
+\r

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diff --git a/THESE/codes/graphe/Ncut_9/._spmtimesd.cpp b/THESE/codes/graphe/Ncut_9/._spmtimesd.cpp
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diff --git a/THESE/codes/graphe/Ncut_9/ICgraph.m b/THESE/codes/graphe/Ncut_9/ICgraph.m
new file mode 100755 (executable)
index 0000000..31c4730
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/ICgraph.m
@@ -0,0 +1,37 @@
+function [W,imageEdges] = ICgraph(I,dataW,dataEdgemap);\r
+% [W,imageEdges] = ICgraph(I,dataW,dataEdgemap);\r
+% Input:\r
+% I = gray-level image\r
+% optional parameters: \r
+% dataW.sampleRadius=10;\r
+% dataW.sample_rate=0.3;\r
+% dataW.edgeVariance = 0.1;\r
+% \r
+% dataEdgemap.parametres=[4,3, 21,3];%[number of filter orientations, number of scales, filter size, elongation]\r
+% dataEdgemap.threshold=0.02;\r
+% \r
+% Output: \r
+% W: npixels x npixels similarity matrix based on Intervening Contours\r
+% imageEdges: image showing edges extracted in the image\r
+%\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+\r
+\r
+\r
+[p,q] = size(I);\r
+\r
+if (nargin< 2) | isempty(dataW),\r
+    dataW.sampleRadius=10;\r
+    dataW.sample_rate=0.3;\r
+    dataW.edgeVariance = 0.1;\r
+end\r
+\r
+if (nargin<3) | isempty(dataEdgemap),\r
+    dataEdgemap.parametres=[4,3, 21,3];%[number of filter orientations, number of scales, filter size, elongation]\r
+    dataEdgemap.threshold=0.02;\r
+end\r
+\r
+\r
+edgemap = computeEdges(I,dataEdgemap.parametres,dataEdgemap.threshold);\r
+imageEdges = edgemap.imageEdges;\r
+W = computeW(I,dataW,edgemap.emag,edgemap.ephase);\r

diff --git a/THESE/codes/graphe/Ncut_9/NcutImage.m b/THESE/codes/graphe/Ncut_9/NcutImage.m
new file mode 100755 (executable)
index 0000000..6362d01
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/NcutImage.m
@@ -0,0 +1,26 @@
+function [SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W,imageEdges]= NcutImage(I,nbSegments);
+%  [SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W,imageEdges]= NcutImage(I);
+%  Input: I = brightness image
+%         nbSegments = number of segmentation desired
+%  Output: SegLable = label map of the segmented image
+%          NcutDiscrete = Discretized Ncut vectors
+%  
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.
+
+
+ 
+if nargin <2,
+   nbSegments = 10;
+end
+
+[W,imageEdges] = ICgraph(I);
+
+[NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbSegments);
+
+%% generate segmentation label map
+[nr,nc,nb] = size(I);
+
+SegLabel = zeros(nr,nc);
+for j=1:size(NcutDiscrete,2),
+    SegLabel = SegLabel + j*reshape(NcutDiscrete(:,j),nr,nc);
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/README.txt b/THESE/codes/graphe/Ncut_9/README.txt
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index 0000000..605c9f6
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@@ -0,0 +1,52 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%     Normalized Cut Segmentation Code                              %
+%                                                                                          %   
+%  Timothee Cour (INRIA), Stella Yu (Berkeley), Jianbo Shi (UPENN)  %
+%                                                                                      %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+License
+This software is made publicly for research use only. It may be modified and redistributed under the terms of the GNU General Public License. \r
+
+Citation
+Please cite the following if you plan to use the code in your own work: 
+* Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2000
+* Normalized Cut Segmentation Code, Timothee Cour, Stella Yu, Jianbo Shi. Copyright 2004 University of Pennsylvania, Computer and Information Science Department.
+
+Tested on matlab R2009b.
+\r
+Installation Notes :\r
+\r
+1) After you unzipped the files to mydir, \r
+   put the Current Directory in Matlab to mydir\r
+
+2) In the matlab command prompt,\r
+   type compileDir_simple to compile the mex files (ignore the error on the C++ non-mex file; needs to be done once)\r
+
+3) You can now try any of the functions
+
+type demoNcutImage to see a demo of image segmentation
+type demoNcutClustering to see a demo of point cloud clustering\r
+\r
+
+Other top level functions:
+
+NcutImage.m: given image "I", segment it into "nbSegments" segments
+    [SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W]= NcutImage(I,nbSegments);
+    
+ICgraph.m: compute Intervening Contour based pixel similarity matrix W
+    W = ICgraph(I);
+    
+ncutW.m: Given a similarity graph "W", computes Ncut clustering on the graph into "nbSegments" groups;
+    [NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbSegments);
+
+
+Release notes:
+
+2010, January 22: release of all c++ source mex files compatible with matlab R2009b
+2006, May 04: release version 8: fixed incompatibility issues with new matlab
+2004, June 18: release version 7: initial release
+
+Maintained by Timothee Cour, timothee dot cour at gmail dot com
+
+January 22, 2010.
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/X2distances.m b/THESE/codes/graphe/Ncut_9/X2distances.m
new file mode 100755 (executable)
index 0000000..0a53776
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/X2distances.m
@@ -0,0 +1,9 @@
+function distances = X2distances(X,Sigma);\r
+%Timothee Cour, 2004\r
+[n,k] = size(X);\r
+if nargin >= 2\r
+    X = X*inv(sqrtm(Sigma));\r
+end\r
+temp = sum(X.*X,2);\r
+temp = repmat(temp,1,n);\r
+distances = -2*X*X' + temp + temp';
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/a_times_b_cmplx.cpp b/THESE/codes/graphe/Ncut_9/a_times_b_cmplx.cpp
new file mode 100755 (executable)
index 0000000..2f11321
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/a_times_b_cmplx.cpp
@@ -0,0 +1,422 @@
+/*================================================================
+a_times_b_cmplx.c = used by a couple of mex functions 
+provide Matrix vector multiplications,
+and solve triangular systems
+(sparse matrix and full vector)
+
+CSC_CmplxVecMult_CAB_double, CSR_CmplxVecMult_CAB_double, 
+CSCsymm_CmplxVecMult_CAB_double added by Mirko Visontai (10/24/2003)
+
+*=================================================================*/
+# include "math.h"
+
+///*c<-a'*b */
+//void scalar_product(
+//                 const int m,  const int k, /*nb_rows, nb_columns*/
+//                 const double *a,  
+//                 const double *b,  
+//                 double *c                 
+//                 )
+//{
+//  int i;
+//  double d;
+//  d = 0;
+//    for (i=0;i!=m;i++) 
+//     d+=a[i]*b[i];                 
+//  c[0] = d;
+//
+//}
+
+
+/*C<-a*A*B+C*/
+void CSC_VecMult_CaABC_double(
+                 const int m,  const int k, const double alpha,
+                 const double *val, const int *indx, 
+                 const int *pntrb,
+                 const double *b,  
+                 double *c)
+{
+  int i,j,jb,je;
+
+    for (i=0;i!=k;i++){
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++)
+        c[indx[j]] += alpha * b[i] * val[j];
+    }
+}
+
+/*C<-a*A'*B+C*/
+void CSR_VecMult_CaABC_double(
+                 const int k,  const int m, const double alpha,
+                 const double *val, const mwIndex *indx, 
+                 const mwIndex *pntrb,
+                 const double *b,  
+                 double *c)
+{
+  double t;
+  const double *pval;
+  int i,j,jb,je;
+
+    pval = val;
+    for (i=0;i!=m;i++) {
+      t = 0;
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++)
+        t += alpha * b[indx[j]] * (*pval++);
+      c[i] += t;
+    }
+}
+
+
+/*C<-A*b */
+void CSC_VecMult_CAB_double(
+                 const int m,  const int k, /*nb_rows, nb_columns*/
+                 const double *val, const int *indx, 
+                 const int *pntrb,                  
+                 const double *b,  
+                 double *c                 
+                 )
+{
+  int i,j,jb,je;
+  double *pc=c;                                       
+    for (i=0;i!=m;i++) *pc++ = 0;                 
+
+    for (i=0;i!=k;i++){
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++)
+        c[indx[j]] +=  b[i] * val[j];
+    }
+}
+
+/*C<-A*b (complex)*/
+void CSC_CmplxVecMult_CAB_double(
+                 const int m,  const int k, 
+                 const double *valr, const double *vali,
+                                const int *indx, 
+                 const int *pntrb,                  
+                 const double *br, const double *bi,  
+                 double *cr, double *ci                 
+                 )
+{
+  int i,j,jb,je;
+  double *pcr=cr;                                       
+  double *pci=ci;                                       
+    for (i=0;i!=m;i++){
+               *pcr++ = 0.0;
+               *pci++ = 0.0;
+       }
+
+    for (i=0;i!=k;i++){
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++){
+        cr[indx[j]] +=  (br[i] * valr[j]) - (bi[i] * vali[j]);
+        ci[indx[j]] +=  (br[i] * vali[j]) + (bi[i] * valr[j]);
+         }
+    }
+}
+
+/*C<-A'*b 
+ plus rapide que CSC_VecMult_CAB_double */
+void CSR_VecMult_CAB_double(
+                 const int k,  const int m,
+                 const double *val, const int *indx, 
+                 const int *pntrb,
+                 const double *b,  
+                 double *c
+                 )
+{
+  double t;
+  const double *pval;
+  double *pc=c;                                       
+  int i,j,jb,je;
+ 
+    for (i=0;i!=m;i++) *pc++ = 0;                 
+
+    pval = val;
+    for (i=0;i!=m;i++) {
+      t = 0;
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++)
+        t +=  b[indx[j]] * (*pval++);
+      c[i] += t;
+    }
+}
+
+/*C<-A'*b (complex) 
+ plus rapide que CSC_VecMult_CAB_double */
+void CSR_CmplxVecMult_CAB_double(
+                 const int k,  const int m, 
+                 const double *valr, const double *vali,
+                                const int *indx, 
+                 const int *pntrb,
+                 const double *br, const double *bi,  
+                 double *cr, double *ci
+                 )
+{
+  double tr, ti;
+  const double *pvalr;
+  const double *pvali;
+  double *pcr=cr;                                       
+  double *pci=ci;                                       
+  int i,j,jb,je;
+ 
+    for (i=0;i!=m;i++){
+               *pcr++ = 0.0;                 
+               *pci++ = 0.0;
+       }               
+
+    pvalr = valr;
+    pvali = vali;
+    for (i=0;i!=m;i++) {
+      tr = 0.0;
+         ti = 0.0;
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++){
+        tr +=  (br[indx[j]] * (*pvalr)) - (bi[indx[j]] * (*pvali));
+        ti +=  (br[indx[j]] * (*pvali++)) + (bi[indx[j]] * (*pvalr++));
+         }
+      cr[i] += tr;
+         ci[i] += ti;
+    }
+}
+
+
+
+/* C<-A*b (A is symmetric) */
+void CSRsymm_VecMult_CAB_double(
+                 const int k,  const int m, 
+                 const double *val, const mwIndex *indx, 
+                 const mwIndex *pntrb,  
+                 const double *b,  
+                 double *c 
+                 )
+{
+  const double *pval;
+  double *pc=c;                                       
+  int i,j;
+  int jj;
+  int rpntrb, rpntre;
+  int index, nvals;
+  
+      
+    for (i=0;i!=m;i++) *pc++ = 0;                 
+    pval = val;
+    for (j=0;j!=k;j++){
+      rpntrb = pntrb[j];
+      rpntre = pntrb[j+1];
+      for (jj=rpntrb;jj!=rpntre;jj++) {
+        index = indx[jj];
+        if ( index == j ) {
+          c[j] +=  b[j] * (*pval++);
+          continue;
+        }
+               if ( index > j ) {
+                       c[index] +=  b[j] * (*pval);
+
+                       c[j] +=  b[index] * (*pval++);
+               }
+               else {
+                       pval++;
+               }
+      }
+    }
+}
+
+
+/* C<-A*b (A is symmetric and complex) */
+void CSRsymm_CmplxVecMult_CAB_double(
+                 const int k,  const int m, 
+                 const double *valr, const double *vali,
+                                const int *indx, 
+                 const int *pntrb,  
+                 const double *br, const double *bi,  
+                 double *cr, double *ci
+                 )
+{
+  const double *pvalr, *pvali;
+  double *pcr=cr;                                       
+  double *pci=ci;                                       
+  int i,j;
+  int jj;
+  int rpntrb, rpntre;
+  int index, nvals;
+  
+      
+    for (i=0;i!=m;i++){
+               *pcr++ = 0.0;                 
+               *pci++ = 0.0;                 
+       }
+
+    pvalr = valr;
+    pvali = vali;
+    for (j=0;j!=k;j++){
+      rpntrb = pntrb[j];
+      rpntre = pntrb[j+1];
+      for (jj=rpntrb;jj!=rpntre;jj++) {
+        index = indx[jj];
+        if ( index == j ) {
+          cr[j] +=  (br[j] * (*pvalr)) - (bi[j] * (*pvali));
+          ci[j] +=  (br[j] * (*pvali++)) + (bi[j] * (*pvalr++));
+          continue;
+        }
+               if ( index > j ) {
+               cr[index] +=  (br[j] * (*pvalr)) - (bi[j] * (*pvali));
+               ci[index] +=  (br[j] * (*pvali)) + (bi[j] * (*pvalr));
+        
+               cr[j] +=  (br[index] * (*pvalr)) - (bi[index] * (*pvali));
+               ci[j] +=  (br[index] * (*pvali++)) + (bi[index] * (*pvalr++));
+               }
+               else {
+                       pvalr++;
+                       pvali++;
+               }
+        
+      }
+    }
+}
+
+
+/*C<-A\B; with Lower triangular A*/
+void CSC_VecTriangSlvLD_CAB_double( 
+                 const int m,  
+                 const double *val,
+                 const int *indx, const int *pntrb, 
+                 const double *b,  
+                 double *c)
+{
+  int i, j, jb, je;
+  double *pc=c;
+  double z; 
+
+    for (i=0;i!=m;i++){
+      *pc = b[i];
+      pc++;
+    }                                     
+
+    pc=c;
+    for (i=0;i!=m;i++) {
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      z =  pc[i] / val[jb];
+      pc[i] = z;
+      for (j=jb+1;j<je;j++) {
+        c[indx[j]] -= z*val[j];
+      }
+    }
+}
+
+/*C<-A\B; with Upper triangular A*/
+void CSC_VecTriangSlvUD_CAB_double(
+                 const int m,  
+                   const double *val,
+                 const int *indx, const int *pntrb,
+                 const double *b,  
+                 double *c)
+{
+  int i, j, jb, je, index;
+  double *pc=c;
+  double z; 
+
+    for (i=0;i!=m;i++){
+      *pc = b[i];
+      pc++;
+    }                                     
+
+    pc=c;
+    for (i=m-1;i!=-1;i--) {
+      jb = pntrb[i];
+      je = pntrb[i+1]-1;
+      z = pc[i] /val[je];
+      pc[i] = z;
+      for (j=jb;j<je;j++) {
+          c[indx[j]] -= z * val[j];
+      }
+    }
+}
+/*C<-A'\B; where A is upper (little slower than CSC)*/
+void CSR_VecTriangSlvLD_CAB_double(
+                 const int m,   
+                 const double *val, 
+                 const int *indx, const int *pntrb,
+                 const double *b,   
+                 double *c)
+{
+  int i, j, jb, je, index;
+  double *pc=c;
+  double z; 
+  double valtmp;
+
+    pc=c;
+    for (i=0;i!=m;i++) {
+      z = 0;
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j<je;j++) {
+        index = indx[j];
+        if ( index == i ) {
+          valtmp = val[j];
+        } else {
+          z += c[index] * val[j];
+        }
+      }
+      pc[i] = (b[i] - z) / valtmp;
+    }
+}
+
+/*C<-A'\B; where A is lower  (little slower than CSC)*/
+void CSR_VecTriangSlvUD_CAB_double(
+                 const int m,   
+                   const double *val, 
+                 const int *indx, const int *pntrb, 
+                 const double *b,   
+                 double *c)
+{
+  int i, j, jb, je, index;
+  double *pc=c;
+  double valtmp;
+  double z; 
+
+    pc=c;
+    for (i=m-1;i!=-1; i--) {
+      z = 0;
+      jb = pntrb[i];
+      je =  pntrb[i+1];
+      for (j=jb+1; j<je; j++) {
+        z += c[indx[j]] * val[j];
+      }
+      pc[i] = (b[i] - z) / val[jb];
+    }
+}
+
+/*C<-A*B, where A is (m,k) and B is (k,n)*/
+void CSC_MatMult_CAB_double(
+                 const int m, const int n, const int k, 
+                 const double *val, const int *indx, 
+                 const int *pntrb,
+                 const double *b, const int ldb, 
+                 double *c, const int ldc)
+{
+  int i,j,jb,je;
+  double *pc=c;                                       
+  int l;                                               
+
+  for (l=0;l!=n;l++)                           
+    for (i=0;i!=m;i++) *pc++ = 0;                 
+
+  for (l=0;l!=n;l++) {                                 
+    for (i=0;i!=k;i++){
+      jb = pntrb[i];
+      je = pntrb[i+1];
+      for (j=jb;j!=je;j++)
+        c[indx[j]] +=  b[i] * val[j];
+    }
+    /*c += ldc; b += ldb;  */                              
+    c += m; b += m;                                
+  }                                                    
+}

diff --git a/THESE/codes/graphe/Ncut_9/affinityic.cpp b/THESE/codes/graphe/Ncut_9/affinityic.cpp
new file mode 100755 (executable)
index 0000000..0be2fc1
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/affinityic.cpp
@@ -0,0 +1,187 @@
+/*================================================================\r
+* function w = affinityic(emag,ephase,pi,pj,sigma)\r
+* Input:\r
+*   emag = edge strength at each pixel\r
+*   ephase = edge phase at each pixel\r
+*   [pi,pj] = index pair representation for MALTAB sparse matrices\r
+*   sigma = sigma for IC energy\r
+* Output:\r
+*   w = affinity with IC at [pi,pj]\r
+*\r
+\r
+% test sequence\r
+f = synimg(10);\r
+[i,j] = cimgnbmap(size(f),2);\r
+[ex,ey,egx,egy] = quadedgep(f);\r
+a = affinityic(ex,ey,egx,egy,i,j)\r
+show_dist_w(f,a);\r
+\r
+* Stella X. Yu, Nov 19, 2001.\r
+*=================================================================*/\r
+\r
+# include "mex.h"\r
+# include "math.h"\r
+\r
+void mexFunction(\r
+    int nargout,\r
+    mxArray *out[],\r
+    int nargin,\r
+    const mxArray *in[]\r
+)\r
+{\r
+    /* declare variables */\r
+    int nr, nc, np, total;\r
+    int i, j, k, ix, iy, jx, jy, ii, jj, iip1, jjp1, iip2, jjp2, step;\r
+    double sigma, di, dj, a, z, maxori, phase1, phase2, slope;\r
+//     int *ir, *jc;\r
+    mwIndex*ir,*jc;\r
+       unsigned int *pi, *pj;\r
+       double *emag, *ephase, *w;\r
+    \r
+    /* check argument */\r
+    if (nargin<4) {\r
+        mexErrMsgTxt("Four input arguments required");\r
+    }\r
+    if (nargout>1) {\r
+        mexErrMsgTxt("Too many output arguments");\r
+    }\r
+\r
+    /* get edgel information */\r
+       nr = mxGetM(in[0]);\r
+       nc = mxGetN(in[0]);\r
+       if ( nr*nc ==0 || nr != mxGetM(in[1]) || nc != mxGetN(in[1]) ) {\r
+           mexErrMsgTxt("Edge magnitude and phase shall be of the same image size");\r
+       }\r
+    emag = mxGetPr(in[0]);\r
+    ephase = mxGetPr(in[1]);\r
+    np = nr * nc;\r
+    \r
+    /* get new index pair */\r
+    if (!mxIsUint32(in[2]) | !mxIsUint32(in[3])) {\r
+        mexErrMsgTxt("Index pair shall be of type UINT32");\r
+    }\r
+    if (mxGetM(in[3]) * mxGetN(in[3]) != np + 1) {\r
+        mexErrMsgTxt("Wrong index representation");\r
+    }\r
+    pi = (unsigned int*)mxGetData(in[2]);\r
+    pj = (unsigned int*)mxGetData(in[3]);    \r
+\r
+    /* create output */\r
+    out[0] = mxCreateSparse(np,np,pj[np],mxREAL);\r
+    if (out[0]==NULL) {\r
+           mexErrMsgTxt("Not enough memory for the output matrix");\r
+       }\r
+       w = mxGetPr(out[0]);\r
+       ir = mxGetIr(out[0]);\r
+       jc = mxGetJc(out[0]);\r
+       \r
+    /* find my sigma */\r
+       if (nargin<5) {\r
+           sigma = 0;\r
+       for (k=0; k<np; k++) { \r
+           if (emag[k]>sigma) { sigma = emag[k]; }\r
+       }\r
+       sigma = sigma / 6;\r
+       printf("sigma = %6.5f",sigma);\r
+       } else {\r
+           sigma = mxGetScalar(in[4]);\r
+       }\r
+       a = 0.5 / (sigma * sigma);\r
+       \r
+    /* computation */ \r
+    total = 0;\r
+    for (j=0; j<np; j++) {            \r
+\r
+        jc[j] = total;\r
+        jx = j / nr; /* col */\r
+        jy = j % nr; /* row */\r
+        \r
+        for (k=pj[j]; k<pj[j+1]; k++) {\r
+        \r
+            i = pi[k];\r
+          \r
+            if (i==j) {\r
+                maxori = 1;\r
+            \r
+            } else {\r
+        \r
+                ix = i / nr; \r
+                iy = i % nr;\r
+\r
+                /* scan */            \r
+                di = (double) (iy - jy);\r
+                dj = (double) (ix - jx);\r
+            \r
+                maxori = 0.;\r
+                   phase1 = ephase[j];\r
+\r
+                      \r
+                /* sample in i direction */\r
+                if (abs(di) >= abs(dj)) {  \r
+                   slope = dj / di;\r
+                   step = (iy>=jy) ? 1 : -1;\r
+               \r
+                   iip1 = jy;\r
+                   jjp1 = jx;\r
+       \r
+                      \r
+                       for (ii=0;ii<abs(di);ii++){\r
+                           iip2 = iip1 + step;\r
+                           jjp2 = (int)(0.5 + slope*(iip2-jy) + jx);\r
+                 \r
+                           phase2 = ephase[iip2+jjp2*nr];\r
+               \r
+                           if (phase1 != phase2) {\r
+                               z = (emag[iip1+jjp1*nr] + emag[iip2+jjp2*nr]);\r
+                               if (z > maxori){\r
+                                   maxori = z;\r
+                               }\r
+                           } \r
+                    \r
+                           iip1 = iip2;\r
+                           jjp1 = jjp2;\r
+                           phase1 = phase2;\r
+                       }\r
+                   \r
+                   /* sample in j direction */    \r
+                } else { \r
+                       slope = di / dj;\r
+                       step =  (ix>=jx) ? 1: -1;\r
+\r
+                   jjp1 = jx;\r
+                       iip1 = jy;                 \r
+           \r
+        \r
+                       for (jj=0;jj<abs(dj);jj++){\r
+                           jjp2 = jjp1 + step;\r
+                           iip2 = (int)(0.5+ slope*(jjp2-jx) + jy);\r
+                 \r
+                           phase2 = ephase[iip2+jjp2*nr];\r
+            \r
+                           if (phase1 != phase2){\r
+                               z = (emag[iip1+jjp1*nr] + emag[iip2+jjp2*nr]);\r
+                               if (z > maxori){ \r
+                                   maxori = z; \r
+                               }\r
+                               \r
+                           }\r
+         \r
+                           iip1 = iip2;\r
+                           jjp1 = jjp2;\r
+                           phase1 = phase2;\r
+                       }\r
+                }            \r
+            \r
+                maxori = 0.5 * maxori;\r
+                maxori = exp(-maxori * maxori * a);\r
+            }       \r
+                   ir[total] = i;\r
+\r
+                   w[total] = maxori;\r
+                   total = total + 1;\r
+                       \r
+               } /* i */\r
+    } /* j */\r
+        \r
+    jc[np] = total;\r
+}  \r

diff --git a/THESE/codes/graphe/Ncut_9/affinityic.mexa64 b/THESE/codes/graphe/Ncut_9/affinityic.mexa64
new file mode 100755 (executable)
index 0000000..abf54e0
Binary files /dev/null and b/THESE/codes/graphe/Ncut_9/affinityic.mexa64 differ

diff --git a/THESE/codes/graphe/Ncut_9/build_scene.m b/THESE/codes/graphe/Ncut_9/build_scene.m
new file mode 100755 (executable)
index 0000000..8de45e2
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/build_scene.m
@@ -0,0 +1,116 @@
+function [data,size_cluster] = build_scene(caseid)
+% [data,size_cluster] = build_scene(case)
+%
+% case 1: random gaussian shaped blobs.
+% case 2: one circular plus some gaussian blob, one inside, one outside 
+% case 3: one circular plus some gaussian blob, one inside, two outside 
+% Jianbo Shi, 1997
+
+
+if caseid ==1,
+  sigma_h = 2;
+  sigma_v = 10;
+
+  s_v = 10;
+  s_h = 30;
+
+  a = [sigma_h*randn(1,40);sigma_v*randn(1,40)];
+  b = [s_h;s_v]*ones(1,50) + [sigma_h*randn(1,50);...
+        sigma_v*randn(1,50)];
+
+  data = [a,b];
+  size_cluster = [40,50];
+
+elseif caseid == 2,
+   num_cluster = 3;
+   radius = 15;
+   size_cluster = [80,20,20];
+
+   raw_data = randn(2,sum(size_cluster));
+   tmp = rand(2,size_cluster(1))-0.5;
+
+   [t,idt] = sort(tmp(2,:));
+   r_noise = 4;
+   raw_data2 = [tmp(1,idt)*r_noise;...
+             tmp(2,idt)*2];
+
+   data = [(radius-raw_data2(1,1:size_cluster(1))).*...
+        cos(pi*raw_data2(2,1:size_cluster(1)));...
+        (radius-raw_data2(1,1:size_cluster(1))).*...
+        sin(pi*raw_data2(2,1:size_cluster(1)))];
+
+   
+   center = [0,0];sig = [1,2];
+   % size_cluster_base
+   scb = size_cluster(1)+1;
+   scb_next = scb+size_cluster(2)-1;
+   data = [data,[center(1)+sig(1)*raw_data(1,scb:scb_next);...
+                center(2)+sig(2)*raw_data(2,scb:scb_next)]];
+
+
+   center = [radius+10,0]; sig = [1,1];
+   scb = scb_next+1;
+   scb_next = scb+size_cluster(3)-1;
+   data = [data,[center(1)+sig(1)*raw_data(1,scb:scb_next);...
+                center(2)+sig(2)*raw_data(2,scb:scb_next)]];
+elseif caseid==3,
+   num_cluster = 4;
+   radius = 15;
+   size_cluster = [80,10,20,20];
+
+   raw_data = randn(2,sum(size_cluster));
+   tmp = rand(2,size_cluster(1))-0.5;
+
+   [t,idt] = sort(tmp(2,:));
+   r_noise = 4;
+   raw_data2 = [tmp(1,idt)*r_noise;...
+             tmp(2,idt)*2];
+
+   data = [(radius-raw_data2(1,1:size_cluster(1))).*...
+        cos(pi*raw_data2(2,1:size_cluster(1)));...
+        (radius-raw_data2(1,1:size_cluster(1))).*...
+        sin(pi*raw_data2(2,1:size_cluster(1)))];
+
+   
+   center = [0,0];sig = [1,2];
+   % size_cluster_base
+   scb = size_cluster(1)+1;
+   scb_next = scb+size_cluster(2)-1;
+   data = [data,[center(1)+sig(1)*raw_data(1,scb:scb_next);...
+                center(2)+sig(2)*raw_data(2,scb:scb_next)]];
+
+
+   center = [radius+25,8]; sig = [1,2.3];
+   scb = scb_next+1;
+   scb_next = scb+size_cluster(3)-1;
+   data = [data,[center(1)+sig(1)*raw_data(1,scb:scb_next);...
+                center(2)+sig(2)*raw_data(2,scb:scb_next)]];
+
+   center = [radius+25,-6]; sig = [1.5,2.4];
+   scb = scb_next+1;
+   scb_next = scb+size_cluster(4)-1;
+   data = [data,[center(1)+sig(1)*raw_data(1,scb:scb_next);...
+                center(2)+sig(2)*raw_data(2,scb:scb_next)]];
+elseif caseid == 4,
+   size_cluster = [100,10,10];
+   radius = 10;
+   tmp = rand(2,size_cluster(1))-0.5;
+
+   [t,idt] = sort(tmp(2,:));
+   r_noise = 4;
+   raw_data2 = [tmp(1,idt)*r_noise;...
+             tmp(2,idt)*2];
+
+   data = [(radius-raw_data2(1,1:size_cluster(1))).*...
+        cos(pi*raw_data2(2,1:size_cluster(1)));...
+        (radius-raw_data2(1,1:size_cluster(1))).*...
+        sin(pi*raw_data2(2,1:size_cluster(1)))];
+
+   
+   result = zeros(1,size_cluster(1));
+
+  % for j =1:size_cluster(1),
+%      result(j) = sum(sum(A(1:j,1:j)))/j;
+%   end
+
+end

diff --git a/THESE/codes/graphe/Ncut_9/cimgnbmap.cpp b/THESE/codes/graphe/Ncut_9/cimgnbmap.cpp
new file mode 100755 (executable)
index 0000000..016fc36
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/cimgnbmap.cpp
@@ -0,0 +1,191 @@
+/*================================================================\r
+* function [i,j] = cimgnbmap([nr,nc], nb_r, sample_rate)\r
+*   computes the neighbourhood index matrix of an image,\r
+*   with each neighbourhood sampled.\r
+* Input:\r
+*   [nr,nc] = image size\r
+*   nb_r = neighbourhood radius, could be [r_i,r_j] for i,j\r
+*   sample_rate = sampling rate, default = 1\r
+* Output:\r
+*   [i,j] = each is a column vector, give indices of neighbour pairs\r
+*     UINT32 type\r
+*       i is of total length of valid elements, 0 for first row\r
+*       j is of length nr * nc + 1\r
+*\r
+* See also: imgnbmap.c, id2cind.m\r
+*\r
+* Examples: \r
+*   [i,j] = imgnbmap(10, 20); % [10,10] are assumed\r
+*\r
+* Stella X. Yu, Nov 12, 2001.\r
+\r
+% test sequence:\r
+nr = 15;\r
+nc = 15;\r
+nbr = 1;\r
+[i,j] = cimgnbmap([nr,nc], nbr);\r
+mask = csparse(i,j,ones(length(i),1),nr*nc);\r
+show_dist_w(rand(nr,nc),mask)\r
+\r
+*=================================================================*/\r
+\r
+# include "mex.h"\r
+# include "math.h"\r
+# include <time.h>\r
+\r
+void mexFunction(\r
+    int nargout,\r
+    mxArray *out[],\r
+    int nargin,\r
+    const mxArray *in[]\r
+)\r
+{\r
+    /* declare variables */\r
+    int nr, nc, np, nb, total;\r
+       double *dim, sample_rate;\r
+    int r_i, r_j, a1, a2, b1, b2, self, neighbor;\r
+    int i, j, k, s, t, nsamp, th_rand, no_sample;\r
+    unsigned long *p;\r
+    \r
+    \r
+    /* check argument */\r
+    if (nargin < 2) {\r
+        mexErrMsgTxt("Two input arguments required");\r
+    }\r
+    if (nargout> 2) {\r
+        mexErrMsgTxt("Too many output arguments.");\r
+    }\r
+\r
+    /* get image size */\r
+    i = mxGetM(in[0]);\r
+    j = mxGetN(in[0]);\r
+    dim = (double *)mxGetData(in[0]);\r
+    nr = (int)dim[0];\r
+    if (j>1 || i>1) {\r
+        nc = (int)dim[1];\r
+    } else {\r
+        nc = nr;\r
+    }\r
+    np = nr * nc;\r
+    \r
+    /* get neighbourhood size */\r
+    i = mxGetM(in[1]);\r
+    j = mxGetN(in[1]);\r
+    dim = (double*)mxGetData(in[1]);\r
+    r_i = (int)dim[0];\r
+    if (j>1 || i>1) {\r
+        r_j = (int)dim[1];             \r
+    } else {\r
+        r_j = r_i;\r
+    }\r
+    if (r_i<0) { r_i = 0; }\r
+       if (r_j<0) { r_j = 0; }\r
+\r
+       /* get sample rate */\r
+       if (nargin==3) {                \r
+               sample_rate = (mxGetM(in[2])==0) ? 1: mxGetScalar(in[2]);\r
+    } else {\r
+               sample_rate = 1;\r
+    }\r
+       /* prepare for random number generator */\r
+       if (sample_rate<1) {\r
+        srand( (unsigned)time( NULL ) );\r
+        th_rand = (int)ceil((double)RAND_MAX * sample_rate);\r
+        no_sample = 0;\r
+    } else {\r
+               sample_rate = 1;\r
+        th_rand = RAND_MAX;\r
+        no_sample = 1;\r
+    }\r
+    \r
+       /* figure out neighbourhood size */\r
+\r
+    nb = (r_i + r_i + 1) * (r_j + r_j + 1); \r
+    if (nb>np) {\r
+        nb = np;\r
+    }\r
+    nb = (int)ceil((double)nb * sample_rate);    \r
+\r
+       /* intermediate data structure */\r
+       p = (unsigned long *)mxCalloc(np * (nb+1), sizeof(unsigned long));\r
+       if (p==NULL) {\r
+        mexErrMsgTxt("Not enough space for my computation.");\r
+       }\r
+       \r
+    /* computation */    \r
+       total = 0;\r
+    for (j=0; j<nc; j++) {\r
+    for (i=0; i<nr; i++) {\r
+\r
+               self = i + j * nr;\r
+\r
+               /* put self in, otherwise the index is not ordered */\r
+               p[self] = p[self] + 1;\r
+               p[self+p[self]*np] = self;\r
+\r
+        /* j range */\r
+               b1 = j;\r
+        b2 = j + r_j;\r
+        if (b2>=nc) { b2 = nc-1; }                \r
+    \r
+               /* i range */\r
+        a1 = i - r_i;\r
+               if (a1<0) { a1 = 0; }\r
+        a2 = i + r_i;\r
+        if (a2>=nr) { a2 = nr-1; }\r
+       \r
+               /* number of more samples needed */\r
+               nsamp = nb - p[self];\r
+\r
+               k = 0;          \r
+               t = b1;\r
+               s = i + 1;\r
+               if (s>a2) {\r
+                       s = a1;\r
+                       t = t + 1;\r
+               }\r
+               while (k<nsamp && t<=b2) {\r
+                       if (no_sample || (rand()<th_rand)) {\r
+                               k = k + 1;\r
+                               neighbor = s + t * nr;\r
+                               \r
+                               p[self] = p[self] + 1;                                  \r
+                               p[self+p[self]*np] = neighbor;\r
+                       \r
+                               p[neighbor] = p[neighbor] + 1;\r
+                               p[neighbor+p[neighbor]*np] = self;\r
+                       }\r
+                       s = s + 1;\r
+                       if (s>a2) {\r
+                s = a1;\r
+                               t = t + 1;\r
+                       }\r
+               } /* k */\r
+\r
+               total = total + p[self];\r
+       } /* i */\r
+    } /* j */\r
+    \r
+    /* i, j */\r
+    out[0] = mxCreateNumericMatrix(total, 1, mxUINT32_CLASS, mxREAL);\r
+       out[1] = mxCreateNumericMatrix(np+1,  1, mxUINT32_CLASS, mxREAL);\r
+    unsigned int *qi = (unsigned int *)mxGetData(out[0]);\r
+       unsigned int *qj = (unsigned int *)mxGetData(out[1]);\r
+       if (out[0]==NULL || out[1]==NULL) {\r
+           mexErrMsgTxt("Not enough space for the output matrix.");\r
+       }\r
+\r
+       total = 0;\r
+    for (j=0; j<np; j++) {\r
+               qj[j] = total;\r
+               s = j + np;\r
+               for (t=0; t<p[j]; t++) {\r
+                   qi[total] = p[s];\r
+                       total = total + 1;\r
+                       s = s + np;\r
+               }\r
+    }\r
+       qj[np] = total;\r
+\r
+       mxFree(p);\r
+}  \r

diff --git a/THESE/codes/graphe/Ncut_9/cimgnbmap.mexa64 b/THESE/codes/graphe/Ncut_9/cimgnbmap.mexa64
new file mode 100755 (executable)
index 0000000..13c2c74
Binary files /dev/null and b/THESE/codes/graphe/Ncut_9/cimgnbmap.mexa64 differ

diff --git a/THESE/codes/graphe/Ncut_9/compileDir_simple.m b/THESE/codes/graphe/Ncut_9/compileDir_simple.m
new file mode 100755 (executable)
index 0000000..767eaf4
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/compileDir_simple.m
@@ -0,0 +1,22 @@
+function compileDir_simple(Cdir);
+if nargin<1
+    Cdir=pwd;
+end
+
+files = dir(fullfile(Cdir,'*.cpp'));
+
+oldDir=pwd;
+cd(Cdir);
+for j=1:length(files)
+    try
+%         cm = sprintf('mex %s',files(j).name);
+        cm = sprintf('mex -largeArrayDims %s',files(j).name);
+        disp(cm);
+        eval(cm);
+    catch
+        disp(lasterr);
+        disp('IGNORE if the file is a C++ file which is not a mex file (ie without a mexFunction inside)');
+    end
+end
+
+cd(oldDir);
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/computeEdges.m b/THESE/codes/graphe/Ncut_9/computeEdges.m
new file mode 100755 (executable)
index 0000000..404d6b0
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/computeEdges.m
@@ -0,0 +1,38 @@
+function edgemap = computeEdges(imageX,parametres,threshold)\r
+% edgemap = computeEdges(imageX,parametres,threshold)\r
+%\r
+% computes the edge in imageX with parameters parametres and threshold\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+\r
+[ex,ey,egx,egy,eg_par,eg_th,emag,ephase , g ] = quadedgep(imageX,parametres,threshold);\r
+% example : [ex,ey,egx,egy,eg_par,eg_th,emag,ephase] = quadedgep(imageX,[4,3,30,3],0.05);\r
+\r
+% [emagTrie,eindex] = sort(emag);\r
+\r
+%edges3 = sparse(floor(ex),floor(ey),(egx.^2+egy.^2).^(1/2),size(imageX,2),size(imageX,1))';\r
+\r
+try\r
+    edges2 = emag .* edge(imageX,'canny') ;\r
+    %edges2 = emag .* edge(imageX,'sobel') ;\r
+catch\r
+    edges2 = 0 * emag;\r
+end\r
+\r
+edges2 = edges2 .* (edges2 > threshold);\r
+egx1 = g(:,:,1);\r
+egy1 = g(:,:,2);\r
+eindex = find(edges2);\r
+[ey,ex,values] = find(edges2);\r
+\r
+egx = egx1(eindex);\r
+egy = egy1(eindex);\r
+\r
+edgemap.eindex = eindex;\r
+edgemap.values = values;\r
+edgemap.x = ex;\r
+edgemap.y = ey;\r
+edgemap.gx = egx;\r
+edgemap.gy = egy;\r
+edgemap.emag = emag;\r
+edgemap.ephase = ephase;\r
+edgemap.imageEdges = edges2;\r

diff --git a/THESE/codes/graphe/Ncut_9/computeW.m b/THESE/codes/graphe/Ncut_9/computeW.m
new file mode 100755 (executable)
index 0000000..37ae9a5
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/computeW.m
@@ -0,0 +1,9 @@
+function W = computeW(imageX,dataW,emag,ephase)\r
+% W = computeW(imageX,dataW,emag,ephase)\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+[p,q] = size(imageX);\r
+\r
+[w_i,w_j] = cimgnbmap([p,q],dataW.sampleRadius,dataW.sample_rate);\r
+\r
+W = affinityic(emag,ephase,w_i,w_j,max(emag(:)) * dataW.edgeVariance);\r
+W = W/max(W(:));\r

diff --git a/THESE/codes/graphe/Ncut_9/compute_relation.m b/THESE/codes/graphe/Ncut_9/compute_relation.m
new file mode 100755 (executable)
index 0000000..4d46cd5
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/compute_relation.m
@@ -0,0 +1,31 @@
+function [W,distances] = compute_relation(data,scale_sig,order)
+%
+%      [W,distances] = compute_relation(data,scale_sig) 
+%       Input: data= Feature_dimension x Num_data
+%       ouput: W = pair-wise data similarity matrix
+%              Dist = pair-wise Euclidean distance
+%
+%
+% Jianbo Shi, 1997 
+
+
+distances = zeros(length(data),length(data));
+for j = 1:length(data),
+  distances(j,:) = (sqrt((data(1,:)-data(1,j)).^2 +...
+                (data(2,:)-data(2,j)).^2));
+end
+
+% distances = X2distances(data');
+
+if (~exist('scale_sig')),
+    scale_sig = 0.05*max(distances(:));
+end
+
+if (~exist('order')),
+  order = 2;
+end
+
+tmp = (distances/scale_sig).^order;
+
+W = exp(-tmp);
+

diff --git a/THESE/codes/graphe/Ncut_9/compute_relation2.m b/THESE/codes/graphe/Ncut_9/compute_relation2.m
new file mode 100755 (executable)
index 0000000..5bb545d
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/compute_relation2.m
@@ -0,0 +1,43 @@
+function [A,B] = compute_relation2(data,scale_sig,order)\r
+%\r
+%      [W,Dist] = compute_relation(data,scale_sig) \r
+%       Input: data= Feature_dimension x Num_data\r
+%       ouput: W = pair-wise data similarity matrix\r
+%              Dist = pair-wise Euclidean distance\r
+%\r
+%\r
+% Jianbo Shi, 1997 \r
+\r
+if (~exist('order')),\r
+  order = 2;\r
+%   order = 1;\r
+end\r
+\r
+n = size(data,2);\r
+\r
+B = zeros(n);\r
+for j = 1:n\r
+  B(:,j) = (sqrt((data(1,:)-data(1,j)).^2 +...\r
+                (data(2,:)-data(2,j)).^2))';\r
+end\r
+\r
+if (~exist('scale_sig')),\r
+    scale_sig = 0.05*max(B(:));\r
+%     scale_sig = 0.04*max(B(:));\r
+end\r
+\r
+% kNN = 5;\r
+% B2 = B;\r
+% for j=1:n\r
+%     [ignore,ind] = sort(B(:,j));\r
+%     B2(ind(kNN+1:end),j) = Inf;\r
+%     B2(ind(1:kNN),j) = B(ind(1:kNN),j) / max(B(ind(1:kNN),j)) * 0.5;\r
+% end\r
+% scale_sig = 1;\r
+% B = min(B2,B2');%(B+B')/2;\r
+\r
+tmp = (B/scale_sig).^order;\r
+\r
+A = exp(-tmp);\r
+\r
+\r

diff --git a/THESE/codes/graphe/Ncut_9/demoNcutClustering.m b/THESE/codes/graphe/Ncut_9/demoNcutClustering.m
new file mode 100755 (executable)
index 0000000..32a57f8
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/demoNcutClustering.m
@@ -0,0 +1,41 @@
+function demoNcutClustering;\r
+% demoNcutClustering\r
+% \r
+% demo for NcutClustering\r
+% also initialize matlab paths to subfolders\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+\r
+disp('Ncut Clustering demo');\r
+\r
+%% make up a point data set\r
+caseid = 2;%3\r
+[data,size_cluster] = build_scene(caseid);\r
+figure(1);clf;\r
+plot(data(1,:),data(2,:),'ks', 'MarkerFaceColor','k','MarkerSize',5); axis image; hold on; \r
+\r
+disp('This is the input data points to be clustered, press Enter to continue...');\r
+pause;\r
+\r
+disp('Compute clustering...');\r
+\r
+% compute similarity matrix\r
+[W,Dist] = compute_relation(data);\r
+\r
+% clustering graph in\r
+nbCluster = 4;\r
+tic;\r
+[NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbCluster);\r
+disp(['The computation took ' num2str(toc) ' seconds']);\r
+figure(3);\r
+plot(NcutEigenvectors);\r
+\r
+% display clustering result\r
+cluster_color = ['rgbmyc'];\r
+figure(2);clf;\r
+for j=1:nbCluster,\r
+    id = find(NcutDiscrete(:,j));\r
+    plot(data(1,id),data(2,id),[cluster_color(j),'s'], 'MarkerFaceColor',cluster_color(j),'MarkerSize',5); hold on; \r
+end\r
+hold off; axis image;\r
+disp('This is the clustering result');\r
+disp('The demo is finished.');\r

diff --git a/THESE/codes/graphe/Ncut_9/demoNcutImage.m b/THESE/codes/graphe/Ncut_9/demoNcutImage.m
new file mode 100755 (executable)
index 0000000..4dfa3fb
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/demoNcutImage.m
@@ -0,0 +1,48 @@
+function demoNcutImage;\r
+% demoNcutImage\r
+% \r
+% demo for NcutImage\r
+% also initialize matlab paths to subfolders\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+\r
+disp('Ncut Image Segmentation demo');\r
+\r
+%% read image, change color image to brightness image, resize to 160x160\r
+I = imread_ncut('jpg_images/3.jpg',160,160);\r
+\r
+%% display the image\r
+figure(1);clf; imagesc(I);colormap(gray);axis off;\r
+disp('This is the input image to segment, press Enter to continue...');\r
+pause;\r
+\r
+%% compute the edges imageEdges, the similarity matrix W based on\r
+%% Intervening Contours, the Ncut eigenvectors and discrete segmentation\r
+nbSegments = 5;\r
+disp('computing Ncut eigenvectors ...');\r
+tic;\r
+[SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W,imageEdges]= NcutImage(I,nbSegments);\r
+disp(['The computation took ' num2str(toc) ' seconds on the ' num2str(size(I,1)) 'x' num2str(size(I,2)) ' image']);\r
+\r
+\r
+%% display the edges\r
+figure(2);clf; imagesc(imageEdges); axis off\r
+disp('This is the edges computed, press Enter to continue...');\r
+pause;\r
+\r
+%% display the segmentation\r
+figure(3);clf\r
+bw = edge(SegLabel,0.01);\r
+J1=showmask(I,imdilate(bw,ones(2,2))); imagesc(J1);axis off\r
+disp('This is the segmentation, press Enter to continue...');\r
+pause;\r
+\r
+%% display Ncut eigenvectors\r
+figure(4);clf;set(gcf,'Position',[100,500,200*(nbSegments+1),200]);\r
+[nr,nc,nb] = size(I);\r
+for i=1:nbSegments\r
+    subplot(1,nbSegments,i);\r
+    imagesc(reshape(NcutEigenvectors(:,i) , nr,nc));axis('image');axis off;\r
+end\r
+disp('This is the Ncut eigenvectors...');\r
+disp('The demo is finished.');\r
+\r

diff --git a/THESE/codes/graphe/Ncut_9/discretisation.m b/THESE/codes/graphe/Ncut_9/discretisation.m
new file mode 100755 (executable)
index 0000000..c251e59
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/discretisation.m
@@ -0,0 +1,40 @@
+function [EigenvectorsDiscrete,EigenVectors]=discretisation(EigenVectors)\r
+% \r
+% EigenvectorsDiscrete=discretisation(EigenVectors)\r
+% \r
+% Input: EigenVectors = continuous Ncut vector, size = ndata x nbEigenvectors \r
+% Output EigenvectorsDiscrete = discrete Ncut vector, size = ndata x nbEigenvectors\r
+%\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004\r
+\r
+[n,k]=size(EigenVectors);\r
+\r
+vm = sqrt(sum(EigenVectors.*EigenVectors,2));\r
+EigenVectors = EigenVectors./repmat(vm,1,k);\r
+\r
+R=zeros(k);\r
+R(:,1)=EigenVectors(1+round(rand(1)*(n-1)),:)';\r
+c=zeros(n,1);\r
+for j=2:k\r
+    c=c+abs(EigenVectors*R(:,j-1));\r
+    [minimum,i]=min(c);\r
+    R(:,j)=EigenVectors(i,:)';\r
+end\r
+\r
+lastObjectiveValue=0;\r
+exitLoop=0;\r
+nbIterationsDiscretisation = 0;\r
+nbIterationsDiscretisationMax = 20;%voir\r
+while exitLoop== 0 \r
+    nbIterationsDiscretisation = nbIterationsDiscretisation + 1 ;   \r
+    EigenvectorsDiscrete = discretisationEigenVectorData(EigenVectors*R);\r
+    [U,S,V] = svd(EigenvectorsDiscrete'*EigenVectors,0);    \r
+    NcutValue=2*(n-trace(S));\r
+    \r
+    if abs(NcutValue-lastObjectiveValue) < eps | nbIterationsDiscretisation > nbIterationsDiscretisationMax\r
+        exitLoop=1;\r
+    else\r
+        lastObjectiveValue = NcutValue;\r
+        R=V*U';\r
+    end\r
+end
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/discretisationEigenVectorData.m b/THESE/codes/graphe/Ncut_9/discretisationEigenVectorData.m
new file mode 100755 (executable)
index 0000000..4626e3d
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/discretisationEigenVectorData.m
@@ -0,0 +1,12 @@
+function Y = discretisationEigenVectorData(EigenVector)\r
+% Y = discretisationEigenVectorData(EigenVector)\r
+%\r
+% discretizes previously rotated eigenvectors in discretisation\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004\r
+\r
+[n,k]=size(EigenVector);\r
+\r
+\r
+[Maximum,J]=max(EigenVector');\r
+ \r
+Y=sparse(1:n,J',1,n,k);    \r

diff --git a/THESE/codes/graphe/Ncut_9/doog1.m b/THESE/codes/graphe/Ncut_9/doog1.m
new file mode 100755 (executable)
index 0000000..e66028e
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/doog1.m
@@ -0,0 +1,30 @@
+function H=doog1(sig,r,th,N);
+% H=doog1(sig,r,th,N);
+% Serge Belongie
+
+no_pts=N;  % no. of points in x,y grid
+
+[x,y]=meshgrid(-(N/2)+1/2:(N/2)-1/2,-(N/2)+1/2:(N/2)-1/2);
+
+phi=pi*th/180;
+sigy=sig;
+sigx=r*sig;
+R=[cos(phi) -sin(phi); sin(phi) cos(phi)];
+C=R*diag([sigx,sigy])*R';
+
+X=[x(:) y(:)];
+
+Gb=gaussian(X,[0 0]',C);
+Gb=reshape(Gb,N,N);
+
+m=R*[0 sig]';
+
+a=1;
+b=-1;
+
+% make odd-symmetric filter
+Ga=gaussian(X,m/2,C);
+Ga=reshape(Ga,N,N);
+Gb=rot90(Ga,2);
+H=a*Ga+b*Gb; 
+

diff --git a/THESE/codes/graphe/Ncut_9/doog2.m b/THESE/codes/graphe/Ncut_9/doog2.m
new file mode 100755 (executable)
index 0000000..c471e8f
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/doog2.m
@@ -0,0 +1,39 @@
+function G=doog2(sig,r,th,N);
+% G=doog2(sig,r,th,N);
+% Make difference of offset gaussians kernel
+% theta is in degrees
+% (see Malik & Perona, J. Opt. Soc. Amer., 1990)
+%
+% Example:
+% >> imagesc(doog2(1,12,0,64,1))
+% >> colormap(gray)
+%
+% Serge Belongie
+
+
+no_pts=N;  % no. of points in x,y grid
+
+[x,y]=meshgrid(-(N/2)+1/2:(N/2)-1/2,-(N/2)+1/2:(N/2)-1/2);
+
+phi=pi*th/180;
+sigy=sig;
+sigx=r*sig;
+R=[cos(phi) -sin(phi); sin(phi) cos(phi)];
+C=R*diag([sigx,sigy])*R';
+
+X=[x(:) y(:)];
+
+Gb=gaussian(X,[0 0]',C);
+Gb=reshape(Gb,N,N);
+
+m=R*[0 sig]';
+Ga=gaussian(X,m,C);
+Ga=reshape(Ga,N,N);
+Gc=rot90(Ga,2);
+
+a=-1;
+b=2;
+c=-1;
+
+G = a*Ga + b*Gb + c*Gc;
+

diff --git a/THESE/codes/graphe/Ncut_9/eigs_new.m b/THESE/codes/graphe/Ncut_9/eigs_new.m
new file mode 100644 (file)
index 0000000..abee4cd
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/eigs_new.m
@@ -0,0 +1,1563 @@
+function  varargout = eigs(varargin)
+%EIGS  Find a few eigenvalues and eigenvectors of a matrix using ARPACK
+%   D = EIGS(A) returns a vector of A's 6 largest magnitude eigenvalues.
+%   A must be square and should be large and sparse.
+%
+%   [V,D] = EIGS(A) returns a diagonal matrix D of A's 6 largest magnitude
+%   eigenvalues and a matrix V whose columns are the corresponding
+%   eigenvectors.
+%
+%   [V,D,FLAG] = EIGS(A) also returns a convergence flag. If FLAG is 0 then
+%   all the eigenvalues converged; otherwise not all converged.
+%
+%   EIGS(A,B) solves the generalized eigenvalue problem A*V == B*V*D. B
+%   must be symmetric (or Hermitian) positive definite and the same size as
+%   A. EIGS(A,[],...) indicates the standard eigenvalue problem A*V == V*D.
+%
+%   EIGS(A,K) and EIGS(A,B,K) return the K largest magnitude eigenvalues.
+%
+%   EIGS(A,K,SIGMA) and EIGS(A,B,K,SIGMA) return K eigenvalues. If SIGMA is:
+%      'LM' or 'SM' - Largest or Smallest Magnitude
+%   For real symmetric problems, SIGMA may also be:
+%      'LA' or 'SA' - Largest or Smallest Algebraic
+%      'BE' - Both Ends, one more from high end if K is odd
+%   For nonsymmetric and complex problems, SIGMA may also be:
+%      'LR' or 'SR' - Largest or Smallest Real part
+%      'LI' or 'SI' - Largest or Smallest Imaginary part
+%   If SIGMA is a real or complex scalar including 0, EIGS finds the
+%   eigenvalues closest to SIGMA. For scalar SIGMA, and when SIGMA = 'SM',
+%   B need only be symmetric (or Hermitian) positive semi-definite since it
+%   is not Cholesky factored as in the other cases.
+%
+%   EIGS(A,K,SIGMA,OPTS) and EIGS(A,B,K,SIGMA,OPTS) specify options:
+%   OPTS.issym: symmetry of A or A-SIGMA*B represented by AFUN [{false} |
+%   true]
+%   OPTS.isreal: complexity of A or A-SIGMA*B represented by AFUN [false | {true}]
+%   OPTS.tol: convergence: Ritz estimate residual <= tol*NORM(A) [scalar | {eps}]
+%   OPTS.maxit: maximum number of iterations [integer | {300}]
+%   OPTS.p: number of Lanczos vectors: K+1<p<=N [integer | {2K}]
+%   OPTS.v0: starting vector [N-by-1 vector | {randomly generated}]
+%   OPTS.disp: diagnostic information display level [0 | {1} | 2]
+%   OPTS.cholB: B is actually its Cholesky factor CHOL(B) [{false} | true]
+%   OPTS.permB: sparse B is actually CHOL(B(permB,permB)) [permB | {1:N}]
+%   Use CHOL(B) instead of B when SIGMA is a string other than 'SM'.
+%
+%   EIGS(AFUN,N) accepts the function AFUN instead of the matrix A. AFUN is
+%   a function handle and Y = AFUN(X) should return
+%      A*X            if SIGMA is unspecified, or a string other than 'SM'
+%      A\X            if SIGMA is 0 or 'SM'
+%      (A-SIGMA*I)\X  if SIGMA is a nonzero scalar (standard problem)
+%      (A-SIGMA*B)\X  if SIGMA is a nonzero scalar (generalized problem)
+%   N is the size of A. The matrix A, A-SIGMA*I or A-SIGMA*B represented by
+%   AFUN is assumed to be real and nonsymmetric unless specified otherwise
+%   by OPTS.isreal and OPTS.issym. In all these EIGS syntaxes, EIGS(A,...)
+%   may be replaced by EIGS(AFUN,N,...).
+%
+%   Example:
+%      A = delsq(numgrid('C',15));  d1 = eigs(A,5,'SM');
+%
+%   Equivalently, if dnRk is the following one-line function:
+%      %----------------------------%
+%      function y = dnRk(x,R,k)
+%      y = (delsq(numgrid(R,k))) \ x;
+%      %----------------------------%
+%
+%      n = size(A,1);  opts.issym = 1;
+%      d2 = eigs(@(x)dnRk(x,'C',15),n,5,'SM',opts);
+%
+%   See also EIG, SVDS, ARPACKC, FUNCTION_HANDLE.
+
+%   Copyright 1984-2008 The MathWorks, Inc.
+%   $Revision: 1.45.4.11 $  $Date: 2008/12/01 07:19:19 $
+
+%   EIGS provides the reverse communication interface to ARPACK library
+%   routines. EIGS attempts to provide an interface for as many different
+%   algorithms as possible. The reverse communication interfaces are
+%   documented in the ARPACK Users' Guide, ISBN 0-89871-407-9.
+
+cputms = zeros(5,1);
+t0 = cputime; % start timing pre-processing
+
+% Process inputs and do error-checking
+if (nargout > 3)
+    error('MATLAB:eigs:TooManyOutputs', 'Too many output arguments.')
+end
+
+% Platform dependent integer type
+if strfind(computer, '64')
+    intconvert = @(arraytoconvert) int64(arraytoconvert);
+    inttype = 'int64';
+else
+    intconvert = @(arraytoconvert) int32(arraytoconvert);
+    inttype = 'int32';
+end
+
+% Error check inputs and derive some information from them
+[A,Amatrix,isrealprob,issymA,n,B,classAB,k,eigs_sigma,whch, ...
+    sigma,tol,maxit,p,info,eigs_display,cholB,permB,resid,useeig, ...
+    afunNargs,style,mode,Afactors,Bfactors] = ...
+    checkInputs(varargin{:});
+
+% Now have enough information to do early return on cases EIGS does not
+% handle. For these cases, use the full EIG code.
+if useeig
+    fullEig(nargout);
+    return
+end
+
+if ~isempty(Afactors)
+    L = Afactors.L;     Afactors.L = [];
+    U = Afactors.U;     Afactors.U = [];
+    pp = Afactors.pp;   Afactors.pp = [];
+    qq = Afactors.qq;   Afactors.qq = [];
+    dgAsB = Afactors.dgAsB;    Afactors.dgAsB = [];
+    clear Afactors;
+end
+
+if ~isempty(Bfactors)
+    BisHpd = Bfactors.BisHpd;
+    if BisHpd
+        RB = Bfactors.RB;       Bfactors.RB = [];
+        RBT = Bfactors.RBT;     Bfactors.RBT = [];
+        permB = Bfactors.permB; Bfactors.permB = [];
+    else
+        LB = Bfactors.LB;       Bfactors.LB = [];
+        UB = Bfactors.UB;       Bfactors.UB = [];
+        ppB = Bfactors.ppB;     Bfactors.ppB = [];
+        qqB = Bfactors.qqB;     Bfactors.qqB = [];
+        dgB = Bfactors.dgB;     Bfactors.dgB = [];
+    end
+    clear Bfactors;
+end
+
+if isempty(B)
+    if mode ~= 3
+        % OP = A
+        applyOP = @(v)Amtimes(v);
+    else
+        % OP = (A-\sigma*I)^{-1}
+        applyOP = @(v)AminusSigmaBsolve(v);
+    end
+else
+    if mode ~= 3 && BisHpd == true
+        % OP = L^{-1}AL^{-T} (B = LL^T)
+        applyOP = @(v)RBTsolve(Amtimes(RBsolve(v)));
+    elseif mode ~= 3 && BisHpd == false
+        % OP = U^{-1}L^{-1}A (B = LU)
+        applyOP = @(v)Bsolve(Amtimes(v));
+    else
+        % OP = (A-\sigma*B)^{-1}B
+        applyOP = @(v)AminusSigmaBsolve(Bmtimes(v));
+    end
+end
+if strcmp(style,'G')
+    if ~isempty(B) && BisHpd == true && mode == 3
+        applyM = @(v) Bmtimes(v);
+    else
+        applyM = @(v) v;
+    end
+end
+
+% Allocate outputs and ARPACK work variables
+if isrealprob
+    if issymA % real and symmetric
+        if strcmp(classAB,'single')
+            aupdfun = 'ssaupd';
+            eupdfun = 'sseupd';
+        else
+            aupdfun = 'dsaupd';
+            eupdfun = 'dseupd';
+        end
+      lworkl = intconvert(p*(p+8));
+        d = zeros(k,1,classAB);
+    else % real but not symmetric
+        if strcmp(classAB,'single')
+            aupdfun = 'snaupd';
+            eupdfun = 'sneupd';
+        else
+            aupdfun = 'dnaupd';
+            eupdfun = 'dneupd';
+        end
+      lworkl = intconvert(3*p*(p+2));
+        workev = zeros(3*p,1,classAB);
+        d = zeros(k+1,1,classAB);
+        di = zeros(k+1,1,classAB);
+    end
+    v = zeros(n,p,classAB);
+    workd = zeros(n,3,classAB);
+    workl = zeros(lworkl,1,classAB);
+else % complex
+    if strcmp(classAB,'single')
+        aupdfun = 'cnaupd';
+        eupdfun = 'cneupd';
+    else
+        aupdfun = 'znaupd';
+        eupdfun = 'zneupd';
+    end
+    zv = zeros(2*n*p,1,classAB);
+    workd = complex(zeros(n,3,classAB));
+    zworkd = zeros(2*numel(workd),1,classAB);
+   lworkl = intconvert(2*(3*p^2+5*p));
+   workl = zeros(lworkl,1,classAB);
+    workev = zeros(2*2*p,1,classAB);
+    zd = zeros(2*(k+1),1,classAB);
+    rwork = zeros(p,1,classAB);
+end
+ldv = intconvert(n);
+ipntr = zeros(15,1,inttype);
+ido = intconvert(0); % reverse communication parameter, initial value
+if strcmp(style,'S')
+    bmat = 'I'; % standard eigenvalue problem
+else
+    bmat = 'G'; % generalized eigenvalue problem
+end
+nev = intconvert(k); % number of eigenvalues requested
+ncv = intconvert(p); % number of Lanczos vectors
+iparam = zeros(11,1,inttype);
+% iparam(1) = ishift = 1 ensures we are never asked to handle ido=3
+iparam([1 3 7]) = [1 maxit mode];
+select = zeros(p,1,inttype);
+
+% To Do: Remove this error when ARPACKC supports singles
+if strcmp(classAB,'single')
+    error('MATLAB:eigs:single', ...
+        'EIGS does not support single precision inputs.')
+end
+
+% The ARPACK routines return to EIGS many times per each iteration but we
+% only want to display the Ritz values once per iteration (if opts.disp>0).
+% Keep track of whether we've displayed this iteration yet in eigs_iter.
+eigs_iter = 0;
+
+cputms(1) = cputime - t0; % end timing pre-processing
+
+% Iterate until ARPACK's reverse communication parameter ido says to stop
+while (ido ~= 99)
+    
+    t0 = cputime; % start timing ARPACK calls **aupd
+    
+    if isrealprob
+        arpackc( aupdfun, ido, ...
+         bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
+            v, ldv, iparam, ipntr, workd, workl, lworkl, info );
+    else
+        % The FORTRAN ARPACK routine expects the complex input zworkd to have
+        % real and imaginary parts interleaved, but the OP about to be
+        % applied to workd expects it in MATLAB's complex representation with
+        % separate real and imaginary parts. Thus we need both.
+        zworkd(1:2:end-1) = real(workd);
+        zworkd(2:2:end) = imag(workd);
+        arpackc( aupdfun, ido, ...
+         bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
+            zv, ldv, iparam, ipntr, zworkd, workl, lworkl, rwork, info );
+        workd = reshape(complex(zworkd(1:2:end-1),zworkd(2:2:end)),[n,3]);
+    end
+    
+    if (info < 0)
+        error('MATLAB:eigs:ARPACKroutineError', ...
+            'Error with ARPACK routine %s: info = %d', ...
+            aupdfun,full(double(info)))
+    end
+    
+    cputms(2) = cputms(2) + (cputime-t0); % end timing ARPACK calls **aupd
+    t0 = cputime; % start timing MATLAB OP(X)
+    
+    % Compute which columns of workd ipntr references
+    cols = checkIpntr;
+    
+    % The ARPACK reverse communication parameter ido tells EIGS what to do
+    
+    switch ido
+        case -1
+            workd(:,cols(2)) = applyOP(workd(:,cols(1)));
+            if strcmp(style,'G') && mode ~= 3
+                workd(:,cols(1)) = workd(:,cols(2));
+            end
+        case 1
+            if strcmp(style,'G') && mode == 3 && (isempty(B) || BisHpd)
+                % use with B-inner product for mode 3; see applyM
+                workd(:,cols(2)) = AminusSigmaBsolve(workd(:,cols(3)));
+            else
+                % same work as case -1
+                workd(:,cols(2)) = applyOP(workd(:,cols(1)));
+                if strcmp(style,'G') && mode ~= 3
+                    % use with std inner product; see applyM
+                    workd(:,cols(1)) = workd(:,cols(2));
+                end
+            end
+        case 2
+            workd(:,cols(2)) = applyM(workd(:,cols(1)));
+        case 99
+            % ARPACK has converged
+        otherwise
+            error('MATLAB:eigs:UnknownIdo','Unknown ido.');
+    end
+    
+    cputms(3) = cputms(3) + (cputime-t0); % end timing MATLAB OP(X)
+    
+    if eigs_display
+        displayRitzValues;
+    end
+    
+end % while (ido ~= 99)
+
+t0 = cputime; % start timing post-processing
+
+if (info < 0)
+    error('MATLAB:eigs:ARPACKroutineError', ...
+        'Error with ARPACK routine %s: info = %d',aupdfun,full(info));
+end % if (info < 0)
+
+if (nargout >= 2)
+   rvec = intconvert(true); % compute eigenvectors
+else
+   rvec = intconvert(false); % do not compute eigenvectors
+end
+
+if isrealprob
+    if issymA
+        arpackc( eupdfun, rvec, 'A', select, ...
+            d, v, ldv, sigma, ...
+         bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
+            v, ldv, iparam, ipntr, workd, workl, lworkl, info );
+        if strcmp(whch,'LM') || strcmp(whch,'LA')
+            d = flipud(d);
+            if (rvec == 1)
+                v(:,1:k) = v(:,k:-1:1);
+            end
+        end
+        if ((strcmp(whch,'SM') || strcmp(whch,'SA')) && (rvec == 0))
+            d = flipud(d);
+        end
+    else
+        % If sigma is complex, isrealprob=true and we use [c,z]neupd.
+        % So use sigmar=sigma and sigmai=0 here in dneupd.
+        arpackc( eupdfun, rvec, 'A', select, ...
+            d, di, v, ldv, sigma, 0, workev, ...
+         bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
+            v, ldv, iparam, ipntr, workd, workl, lworkl, info );
+        d = complex(d,di);
+        if rvec
+            d(k+1) = [];
+        else
+            zind = find(d == 0);
+            if isempty(zind)
+                d = d(k+1:-1:2);
+            else
+                d(max(zind)) = [];
+                d = flipud(d);
+            end
+        end
+    end
+else
+    zsigma = [real(sigma); imag(sigma)];
+    arpackc( eupdfun, rvec, 'A', select, ...
+        zd, zv, ldv, zsigma, workev, ...
+      bmat, intconvert(n), whch, nev, tol, resid, ncv, zv, ...
+        ldv, iparam, ipntr, zworkd, workl, lworkl, ...
+        rwork, info );
+    if issymA
+        d = zd(1:2:end-1);
+    else
+        d = complex(zd(1:2:end-1),zd(2:2:end));
+    end
+    v = reshape(complex(zv(1:2:end-1),zv(2:2:end)),[n p]);
+end
+
+flag = processEUPDinfo(nargin<3);
+
+if (issymA) || (~isrealprob)
+    if (nargout <= 1)
+        if isrealprob
+            varargout{1} = d;
+        else
+            varargout{1} = d(k:-1:1,1);
+        end
+    else
+        varargout{1} = v(:,1:k);
+        varargout{2} = diag(d(1:k,1));
+        if (nargout >= 3)
+            varargout{3} = flag;
+        end
+    end
+else
+    if (nargout <= 1)
+        varargout{1} = d;
+    else
+        cplxd = find(di ~= 0);
+        % complex conjugate pairs of eigenvalues occur together
+        cplxd = cplxd(1:2:end);
+        v(:,[cplxd cplxd+1]) = [complex(v(:,cplxd),v(:,cplxd+1)) ...
+            complex(v(:,cplxd),-v(:,cplxd+1))];
+        varargout{1} = v(:,1:k);
+        varargout{2} = diag(d);
+        if (nargout >= 3)
+            varargout{3} = flag;
+        end
+    end
+end
+
+if (nargout >= 2) && (mode ~= 3) && ~isempty(B) && BisHpd == true
+    varargout{1} = RBsolve(varargout{1});
+end
+
+if ~isempty(B)
+    if nargout >= 2
+        varargout{2} = varargout{2}/scaleB;
+    else
+        varargout{1} = varargout{1}/scaleB;
+    end
+end
+
+if BisHpd == true && (nargout >= 2)
+    vnorms = zeros(k,1);
+    for ii = 1 : k
+        vnorms(ii) = scaleB*(varargout{1}(:,ii)'*(Bmtimes(varargout{1}(:,ii))));
+        varargout{1}(:,ii) = varargout{1}(:,ii)/sqrt(vnorms(ii));
+    end
+end
+
+cputms(4) = cputime-t0; % end timing post-processing
+
+cputms(5) = sum(cputms(1:4)); % total time
+
+if (eigs_display == 2)
+    printTimings;
+end
+
+%-------------------------------------------------------------------------%
+% Nested functions
+%-------------------------------------------------------------------------%
+
+% checkInputs error checks the inputs to EIGS and also derives some
+%   variables from them:
+% A may be a matrix or a function applying OP.
+% Amatrix is true if A is a matrix, false if A is a function.
+% isrealprob is true if all of A, B and sigma are real, false otherwise.
+% issymA is true if A is symmetric, false otherwise.
+% n is the size of (square) A and B.
+% B is [] for the standard problem. Otherwise it may be one of B, CHOL(B)
+%   or CHOL(B(permB,permB)).
+% classAB is single if either A or B is single, otherwise double.
+% k is the number of eigenvalues to be computed.
+% eigs_sigma is the value for sigma passed in by the user, 'LM' if it was
+%   unspecified. eigs_sigma may be either a string or a scalar value.
+% whch is the ARPACK string corresponding to eigs_sigma and mode.
+% sigma is the ARPACK scalar corresponding to eigs_sigma and mode.
+% tol is the convergence tolerance.
+% maxit is the maximum number of iterations.
+% p is the number of Lanczos vectors.
+% info is the start value, initialized to 1 or 0 to indicate whether to use
+% resid as the start vector or not.
+% eigs_display is true if Ritz values should be displayed, false otherwise.
+% cholB is true if CHOL(B) was passed in instead of B, false otherwise.
+% permB may be [], otherwise it is the permutation in CHOL(B(permB,permB)).
+% resid is the start vector if specified and info=1, otherwise all zero.
+% useeig is true if we need to use EIG instead of ARPACK, otherwise false.
+% afunNargs is the range of EIGS' varargin that are to be passed as
+%   trailing parameters to the function as in afun(X,P1,P2,...).
+    function [A,Amatrix,isrealprob,issymA,n,B,classAB,k, ...
+            eigs_sigma,whch,sigma,tol,maxit,p,info,eigs_display,cholB,...
+            permB,resid,useeig,afunNargs,style,mode,Afactors,Bfactors] = checkInputs(varargin)
+        % Process inputs and do error-checking
+        
+        % Process the input A or the inputs AFUN and N
+        % Start to derive some qualities (real, symmetric) about the problem
+        if isfloat(varargin{1})
+            A = varargin{1};
+            Amatrix = true;
+        else
+            % By checking the function A with fcnchk, we can now use direct
+            % function evaluation on the result, without resorting to feval
+            A = fcnchk(varargin{1});
+            Amatrix = false;
+        end
+        isrealprob = true;
+        issymA = false;
+        if Amatrix
+            isrealprob = isreal(A);
+            issymA = ishermitian(A);
+            [m,n] = size(A);
+            if (m ~= n)
+                error('MATLAB:eigs:NonSquareMatrixOrFunction',...
+                    'A must be a square matrix or a function.')
+            end
+        else
+            n = varargin{2};
+            nstr = 'Size of problem, ''n'', must be a positive integer.';
+            if ~isscalar(n) || ~isreal(n)
+                error('MATLAB:eigs:NonPosIntSize', nstr)
+            end
+            if issparse(n)
+                n = full(n);
+            end
+            if (round(n) ~= n)
+                warning('MATLAB:eigs:NonPosIntSize',['%s\n         ' ...
+                    'Rounding input size.'],nstr)
+                n = round(n);
+            end
+        end
+        
+        % Process the input B and derive the class of the problem.
+        % Is B present in the eigs call or not?
+        Bpresent = true;
+        Bstr = 'Generalized matrix B must be the same size as A.';
+        if (nargin < (3-Amatrix))
+            B = [];
+            Bpresent = false;
+        else
+            % Is the next input B or K?
+            B = varargin{3-Amatrix};
+            if ~isempty(B) % allow eigs(A,[],k,sigma,opts);
+                if isscalar(B)
+                    if n ~= 1
+                        % this input is really K and B is not specified
+                        B = [];
+                        Bpresent = false;
+                    else
+                        % This input could be B or K.
+                        % If A is scalar, then the only valid value for k is 1.
+                        % So if this input is scalar, let it be B, namely
+                        % eigs(4,2,...) assumes A=4, B=2, NOT A=4, k=2
+                        if ~isnumeric(B)
+                            error('MATLAB:eigs:BsizeMismatchA', Bstr);
+                        end
+                        % Unless, of course, the scalar is 1, in which case
+                        % assume the that it is meant to be K.
+                        if (B == 1) && ((Amatrix && nargin <= 3) || ...
+                                (~Amatrix && nargin <= 4))
+                            B = [];
+                            Bpresent = false;
+                        elseif ~isfloat(B)
+                            error('MATLAB:eigs:BsizeMismatchA', Bstr);
+                        end
+                    end
+                else
+                    % B is a not a scalar.
+                    if ~isfloat(B) || ~isequal(size(B),[n,n])
+                        error('MATLAB:eigs:BsizeMismatchA', Bstr);
+                    end
+                    isrealprob = isrealprob && isreal(B);
+                end
+            end
+        end
+        % ARPACK can only handle homogeneous inputs
+        if Amatrix
+            classAB = superiorfloat(A,B);
+            A = cast(A,classAB);
+            B = cast(B,classAB);
+        else
+            if ~isempty(B)
+                classAB = class(B);
+            else
+                classAB = 'double';
+            end
+        end
+        
+        % argOffset tells us where to get the eigs inputs K, SIGMA and OPTS.
+        % If A is really the function afun, then it also helps us find the
+        % trailing parameters in eigs(afun,n,[B],k,sigma,opts,P1,P2,...)
+        % Values of argOffset:
+        %  0: Amatrix is false and Bpresent is true:
+        %     eigs(afun,n,B,k,sigma,opts,P1,P2,...)
+        %  1: Amatrix and Bpresent are both true, or both false
+        %     eigs(A,B,k,sigma,opts)
+        %     eigs(afun,n,k,sigma,opts,P1,P2,...)
+        %  2: Amatrix is true and Bpresent is false:
+        %     eigs(A,k,sigma,opts)
+        argOffset = Amatrix + ~Bpresent;
+        
+        if Amatrix && ((nargin - Bpresent)>4)
+            error('MATLAB:eigs:TooManyInputs', 'Too many inputs.')
+        end
+        
+        % Process the input K.
+        if (nargin < (4-argOffset))
+            k = min(n,6);
+        else
+            k = varargin{4-argOffset};
+            kstr = ['Number of eigenvalues requested, k, must be a' ...
+                ' positive integer <= n.'];
+            if ~isnumeric(k) || ~isscalar(k) || ~isreal(k) || (k>n)
+                error('MATLAB:eigs:NonIntegerEigQty', kstr)
+            end
+            if issparse(k)
+                k = full(k);
+            end
+            if (round(k) ~= k)
+                warning('MATLAB:eigs:NonIntegerEigQty',['%s\n         ' ...
+                    'Rounding number of eigenvalues.'],kstr)
+                k = round(k);
+            end
+        end
+        
+        % Process the input SIGMA and derive ARPACK values whch and sigma.
+        % eigs_sigma is the value documented in the help as "SIGMA" that is
+        % passed in to EIGS. eigs_sigma may be either a scalar, including 0,
+        % or a string, including 'SM'.
+        % In ARPACK, eigs_sigma corresponds to two variables:
+        % 1.  which, called "whch" to avoid conflict with MATLAB's function
+        % 2.  sigma
+        % whch is always a string. sigma is always a scalar.
+        % Valid combinations are shown below. Note eigs_sigma = 0/'SM' has
+        % the same sigma/whch values as eigs_sigma='LM' (default) so these
+        % must be distinguished by the mode.
+        % eigs_sigma = 'SM' or 0 => sigma = 0, whch = 'LM' (mode=3)
+        % eigs_sigma is a string not 'SM' => sigma = 0, whch = eigs_sigma (mode=1)
+        % eigs_sigma is any scalar => sigma = eigs_sigma, whch = 'LM'
+        % (mode=1)
+        whchstr = 'Eigenvalue range sigma must be a valid 2-element string.';
+        if (nargin < (5-argOffset))
+            % default: eigs 'LM' => ARPACK which='LM', sigma=0
+            eigs_sigma = 'LM';
+            whch = 'LM';
+            sigma = 0;
+        else
+            eigs_sigma = varargin{5-argOffset};
+            if ischar(eigs_sigma)
+                % eigs(string) => ARPACK which=string, sigma=0
+                if ~isequal(size(eigs_sigma),[1,2])
+                    error('MATLAB:eigs:EigenvalueRangeNotValid', ...
+                        [whchstr '\nFor real symmetric A, the' ...
+                        ' choices are ''%s'', ''%s'', ''%s'', ''%s'' or ''%s''.' ...
+                        '\nFor non-symmetric or complex' ...
+                        ' A, the choices are ''%s'', ''%s'', ''%s'', ''%s'',' ...
+                        ' ''%s'' or ''%s''.\n'], ...
+                        'LM','SM','LA','SA','BE','LM','SM','LR','SR','LI','SI')
+                end
+                eigs_sigma = upper(eigs_sigma);
+                if strcmp(eigs_sigma,'SM')
+                    % eigs('SM') => ARPACK which='LM', sigma=0
+                    whch = 'LM';
+                else
+                    % eigs(string), where string~='SM' => ARPACK which=string, sigma=0
+                    whch = eigs_sigma;
+                end
+                sigma = zeros(classAB);
+            else
+                % eigs(scalar) => ARPACK which='LM', sigma=scalar
+                if ~isfloat(eigs_sigma) || ~isscalar(eigs_sigma)
+                    error('MATLAB:eigs:EigenvalueShiftNonScalar',...
+                        'Eigenvalue shift sigma must be a scalar.')
+                end
+                sigma = eigs_sigma;
+                if issparse(sigma)
+                    sigma = full(sigma);
+                end
+                sigma = cast(sigma,classAB);
+                isrealprob = isrealprob && isreal(sigma);
+                whch = 'LM';
+            end
+        end
+        
+        % Process the input OPTS and derive some ARPACK values.
+        % ARPACK's minimum tolerance is eps/2 ([S/D]LAMCH's EPS)
+        tol = eps(classAB);
+        maxit = [];
+        p = [];
+        style = [];
+        % Always use resid as the start vector, whether it is OPTS.v0 or
+        % randomly generated within eigs.  We default resid to empty here.
+        % If the user does not initialize it, we provide a random residual
+        % below.
+      info = intconvert(1);
+        resid = [];
+        eigs_display = 1;
+        cholB = false; % do we have B or its Cholesky factor?
+        permB = []; % if cholB, is it chol(B), or chol(B(permB,permB))?
+        if (nargin >= (6-argOffset))
+            opts = varargin{6-argOffset};
+            if ~isa(opts,'struct')
+                error('MATLAB:eigs:OptionsNotStructure',...
+                    'Options argument must be a structure.')
+            end
+            if isfield(opts,'issym') && ~Amatrix
+                issymA = opts.issym;
+                if (issymA ~= false) && (issymA ~= true)
+                    error('MATLAB:eigs:InvalidOptsIssym', ...
+                        'opts.issym must be true or false.')
+                end
+            end
+            if isfield(opts,'isreal') && ~Amatrix
+                if (opts.isreal ~= false) && (opts.isreal ~= true)
+                    error('MATLAB:eigs:InvalidOptsIsreal', ...
+                        'opts.isreal must be true or false.')
+                end
+                isrealprob = isrealprob && opts.isreal;
+            end
+            if ~isempty(B) && (isfield(opts,'cholB') || isfield(opts,'permB'))
+                if isfield(opts,'cholB')
+                    cholB = opts.cholB;
+                    if (cholB ~= false) && (cholB ~= true)
+                        error('MATLAB:eigs:InvalidOptsCholB', ...
+                            'opts.cholB must be true or false.')
+                    end
+                    if isfield(opts,'permB')
+                        if issparse(B) && cholB
+                            permB = opts.permB;
+                            if ~isvector(permB) || ~isequal(sort(permB(:)),(1:n)')
+                                error('MATLAB:eigs:InvalidOptsPermB',...
+                                    'opts.permB must be a permutation of 1:n.')
+                            end
+                        else
+                            warning('MATLAB:eigs:IgnoredOptionPermB', ...
+                                ['Ignoring opts.permB since B is not its sparse' ...
+                                ' Cholesky factor.'])
+                        end
+                    end
+                end
+            end
+            if isfield(opts,'tol')
+                if ~isfloat(tol) || ~isscalar(opts.tol) || ~isreal(opts.tol) || (opts.tol<=0)
+                    error('MATLAB:eigs:InvalidOptsTol',...
+                        ['Convergence tolerance opts.tol must be a strictly' ...
+                        ' positive real scalar.'])
+                end
+                tol = cast(full(opts.tol),classAB);
+            end
+            if isfield(opts,'p')
+                p = opts.p;
+                pstr = ['Number of basis vectors opts.p must be a positive' ...
+                    ' integer <= n.'];
+                if ~isnumeric(p) || ~isscalar(p) || ~isreal(p) || (p<=0) || (p>n)
+                    error('MATLAB:eigs:InvalidOptsP', pstr)
+                end
+                if issparse(p)
+                    p = full(p);
+                end
+                if (round(p) ~= p)
+                    warning('MATLAB:eigs:NonIntegerVecQty',['%s\n         ' ...
+                        'Rounding number of basis vectors.'],pstr)
+                    p = round(p);
+                end
+            end
+            if isfield(opts,'maxit')
+                maxit = opts.maxit;
+                str = ['Maximum number of iterations opts.maxit must be' ...
+                    ' a positive integer.'];
+                if ~isnumeric(maxit) || ~isscalar(maxit) || ~isreal(maxit) || (maxit<=0)
+                    error('MATLAB:eigs:OptsMaxitNotPosInt', str)
+                end
+                if issparse(maxit)
+                    maxit = full(maxit);
+                end
+                if (round(maxit) ~= maxit)
+                    warning('MATLAB:eigs:NonIntegerIterationQty',['%s\n         ' ...
+                        'Rounding number of iterations.'],str)
+                    maxit = round(maxit);
+                end
+            end
+            if isfield(opts,'v0')
+                if ~isfloat(opts.v0) || ~isequal(size(opts.v0),[n,1])
+                    error('MATLAB:eigs:WrongSizeOptsV0',...
+                        'Start vector opts.v0 must be n-by-1.')
+                end
+                if isrealprob
+                    if ~isreal(opts.v0)
+                        error('MATLAB:eigs:NotRealOptsV0',...
+                            'Start vector opts.v0 must be real for real problems.')
+                    end
+                    resid(1:n,1) = full(opts.v0);
+                else
+                    resid(2:2:2*n,1) = full(imag(opts.v0));
+                    resid(1:2:(2*n-1),1) = full(real(opts.v0));
+                end
+            end
+            if isfield(opts,'disp')
+                eigs_display = opts.disp;
+                dispstr = 'Diagnostic level opts.disp must be an integer.';
+                if ~isnumeric(eigs_display) || ~isscalar(eigs_display) || ...
+                        ~isreal(eigs_display) || (eigs_display<0)
+                    error('MATLAB:eigs:NonIntegerDiagnosticLevel', dispstr)
+                end
+                if (round(eigs_display) ~= eigs_display)
+                    warning('MATLAB:eigs:NonIntegerDiagnosticLevel', ...
+                        '%s\n         Rounding diagnostic level.',dispstr)
+                    eigs_display = round(eigs_display);
+                end
+            end
+            if isfield(opts,'cheb')
+                error('MATLAB:eigs:ObsoleteOptionCheb', ...
+                    'Polynomial acceleration opts.cheb is an obsolete option.');
+            end
+            if isfield(opts,'stagtol')
+                error('MATLAB:eigs:ObsoleteOptionStagtol', ...
+                    'Stagnation tolerance opts.stagtol is an obsolete option.');
+            end
+            if isfield(opts,'style')
+                style = opts.style;
+                str = 'style must be ''S'' or ''G''.';
+                if ~ischar(style)
+                    error('MATLAB:eigs:InvalidStyle',str);
+                elseif ~(strcmp(style,'S') || strcmp(style,'G'))
+                    error('MATLAB:eigs:InvalidStyle',str);
+                end
+            end
+        end
+        if (isempty(resid))
+            if isrealprob
+                resid = cast(rand(n,1),classAB);
+            else
+                resid = cast(rand(2*n,1),classAB);
+            end
+        end
+        
+        afunNargs = zeros(1,0);
+        if ~Amatrix
+            % The trailing parameters for afun start at varargin{7-argOffset}
+            % in eigs(afun,n,[B],k,sigma,opts,P1,P2,...). If there are no
+            % trailing parameters in eigs, then afunNargs is a 1-by-0 empty
+            % and no trailing parameters are passed to afun(x)
+            afunNargs = 7-argOffset:nargin;
+        end
+        
+        % Now that OPTS has been processed, do final error checking and
+        % assign ARPACK variables
+        
+        % Extra check on input B
+        if ~isempty(B)
+            % B must be symmetric (Hermitian) positive (semi-)definite
+            if cholB
+                if ~isequal(triu(B),B)
+                    error('MATLAB:eigs:BNotChol', ...
+                        'opts.CHOLB specified, but B is not upper triangular.');
+                end
+            end
+        end
+        
+        if isempty(style)
+            if strcmp(eigs_sigma,'SM') || isscalar(eigs_sigma) || ~isempty(B)
+                style = 'G';
+            else
+                style = 'S';
+            end
+        end
+        
+        if ~isempty(B)
+            scaleB = norm(B,'fro')/sqrt(n);
+            scaleB = 2^(floor(log2(scaleB+1)));
+            B = B/scaleB;
+            if cholB
+                scaleB = scaleB^2;
+            end
+            if isscalar(eigs_sigma)
+                sigma = scaleB*eigs_sigma;
+            end
+        end
+        
+        
+        if strcmp(eigs_sigma,'SM') || ~ischar(eigs_sigma)
+            % eigs(A,B,k,scalarSigma) or eigs(A,B,k,'SM'), B may be []
+            % Note: sigma must be real for [s,d]saupd and [s,d]naupd
+            % If sigma is complex, even if A and B are both real, we use
+            % [c,z]naupd.
+            % This means that mode=3 in [s,d]naupd, which has
+            % OP = real(inv(A - sigma*M)*M) and B = M
+            % reduces to the same OP as [s,d]saupd and [c,z]naupd.
+            % A*x = lambda*M*x, M symmetric (positive) semi-definite
+            % => OP = inv(A - sigma*M)*M and B = M
+            % => shift-and-invert mode
+            mode = 3;
+        elseif strcmp(style,'S')
+            % eigs(A,k,stringSigma) or eigs(A,[],k,stringSigma),
+            % stringSigma~='SM'
+            % A*x = lambda*x
+            % => OP = A and B = I
+            mode = 1;
+        else
+            % eigs(A,B,k,stringSigma), stringSigma~='SM'
+            % A*x = lambda*B*x
+            % => OP = inv(B)*A and use standard inner product.
+            mode = 2;
+        end
+        
+        BisHpd = false;
+        if cholB || (~isempty(B) && ishermitian(B))
+            % The reordering permutation permB is [] unless B is sparse
+            [RB,RBT,permB,BisHpd] = CHOLfactorB;
+            if mode == 3 && ~cholB
+                RB = [];    RBT = [];   permB = [];
+            end
+        end
+        
+        qqB = [];
+        if BisHpd == false && (mode == 1 || mode == 2)
+            [LB,UB,ppB,qqB,dgB] = LUfactorB;
+        end
+        
+        Bfactors = [];
+        if ~isempty(B)
+            if BisHpd == true
+                Bfactors = struct('RB',RB,'RBT',RBT,'permB',permB,'BisHpd',BisHpd);
+            elseif (mode == 1 || mode == 2)
+                Bfactors = struct('LB',LB,'UB',UB,'ppB',ppB,'qqB',qqB,'dgB',dgB,'BisHpd',BisHpd);
+            end
+        end
+        
+        Afactors = [];
+        qq = [];
+        if (mode == 3) && Amatrix % need lu(A-sigma*B)
+            % The reordering permutation permAsB is [] unless A-sigma*B is sparse
+            [L,U,pp,qq,dgAsB] = LUfactorAminusSigmaB;
+            Afactors = struct('L',L,'U',U,'pp',pp,'qq',qq,'dgAsB',dgAsB);
+        end % if (mode == 3) && Amatrix        
+        
+        % under these conditions, OP must be unsymmetric
+        % note that OP = inv(A-\sigma*B)*B IS symmetric if A is symmetric
+        % and B-inner product is used!
+        if ~isempty(B) && (BisHpd == false || (strcmp(style,'S') && mode == 3))
+            issymA = false;
+        end
+        % Extra check on input K
+        % We fall back on using the full EIG code if K is too large.
+        useeig = false;
+        if isrealprob && issymA
+            knstr = sprintf(['For real symmetric problems, must have' ...
+                ' number of eigenvalues k < n.\n']);
+        else
+            knstr = sprintf(['For nonsymmetric and complex problems,' ...
+                ' must have number of eigenvalues k < n-1.\n']);
+        end
+        if isempty(B)
+            knstr = [knstr 'Using eig(full(A)) instead.'];
+        else
+            knstr = [knstr 'Using eig(full(A),full(B)) instead.'];
+        end
+        if (k == 0)
+            useeig = true;
+        end
+        if isrealprob && issymA
+            if (k > n-1)
+                if (n >= 6)
+                    warning('MATLAB:eigs:TooManyRequestedEigsForRealSym', ...
+                        '%s',knstr)
+                end
+                useeig = true;
+            end
+        else
+            if (k > n-2)
+                if (n >= 7)
+                    warning('MATLAB:eigs:TooManyRequestedEigsForComplexNonsym', ...
+                        '%s',knstr)
+                end
+                useeig = true;
+            end
+        end
+        
+        % Extra check on input SIGMA
+        if isrealprob && issymA
+            if ~isreal(sigma)
+                error('MATLAB:eigs:ComplexShiftForRealSymProblem',...
+                    ['For real symmetric problems, eigenvalue shift sigma must' ...
+                    ' be real.'])
+            end
+        else
+            if ~isrealprob && issymA && ~isreal(sigma)
+                warning('MATLAB:eigs:ComplexShiftForHermitianProblem', ...
+                    ['Complex eigenvalue shift sigma on a Hermitian problem' ...
+                    ' (all real eigenvalues).'])
+            end
+        end
+        if isrealprob && issymA
+            if strcmp(whch,'LR')
+                whch = 'LA';
+                warning('MATLAB:eigs:SigmaChangedToLA', ...
+                    ['For real symmetric problems, sigma value ''LR''' ...
+                    ' (Largest Real) is now ''LA'' (Largest Algebraic).'])
+            end
+            if strcmp(whch,'SR')
+                whch = 'SA';
+                warning('MATLAB:eigs:SigmaChangedToSA', ...
+                    ['For real symmetric problems, sigma value ''SR''' ...
+                    ' (Smallest Real) is now ''SA'' (Smallest Algebraic).'])
+            end
+            if ~ismember(whch,{'LM', 'SM', 'LA', 'SA', 'BE'})
+                error('MATLAB:eigs:EigenvalueRangeNotValid', ...
+                    [whchstr '\nFor real symmetric A, the' ...
+                    ' choices are ''%s'', ''%s'', ''%s'', ''%s'' or ''%s''.'], ...
+                    'LM','SM','LA','SA','BE');
+            end
+        else
+            if strcmp(whch,'BE')
+                warning('MATLAB:eigs:SigmaChangedToLM', ...
+                    ['Sigma value ''BE'' is now only available for real' ...
+                    ' symmetric problems.  Computing ''LM'' eigenvalues instead.'])
+                whch = 'LM';
+            end
+            if ~ismember(whch,{'LM', 'SM', 'LR', 'SR', 'LI', 'SI'})
+                error('MATLAB:eigs:EigenvalueRangeNotValid', ...
+                    [whchstr '\nFor non-symmetric or complex' ...
+                    ' A, the choices are ''%s'', ''%s'', ''%s'', ''%s'',' ...
+                    ' ''%s'' or ''%s''.\n'],'LM','SM','LR','SR','LI','SI');
+            end
+        end
+        
+        % The remainder of the error checking does not apply for the large
+        % values of K that force us to use full EIG instead of ARPACK.
+        if useeig
+            return
+        end
+        
+        % Extra check on input OPTS.p
+        if isempty(p)
+            if isrealprob && ~issymA
+                p = min(max(2*k+1,20),n);
+            else
+                p = min(max(2*k,20),n);
+            end
+        else
+            if isrealprob && issymA
+                if (p <= k)
+                    error('MATLAB:eigs:InvalidOptsPforRealSymProb',...
+                        ['For real symmetric problems, must have number of' ...
+                        ' basis vectors opts.p > k.'])
+                end
+            else
+                if (p <= k+1)
+                    error('MATLAB:eigs:InvalidOptsPforComplexOrNonSymProb',...
+                        ['For nonsymmetric and complex problems, must have number of' ...
+                        ' basis vectors opts.p > k+1.'])
+                end
+            end
+        end
+        
+        % Extra check on input OPTS.maxit
+        if isempty(maxit)
+            maxit = max(300,ceil(2*n/max(p,1)));
+        end
+        
+        
+        % Nested functions in checkInputs
+        %-------------------------------------------------------------------------%
+        function [RB,RBT,ppB,BisHpd] = CHOLfactorB
+            % permB may be [] (from checkInputs) if the problem is not sparse
+            % or if it was not passed in as opts.permB
+            ppB = permB;
+            if cholB
+                % CHOL(B) was passed in as B
+                RB = B;
+                RBT = B';
+                BisHpd = true;
+            else
+                % CHOL(B) was not passed into EIGS
+                if ~isempty(B)
+                    % Algorithm requires CHOL(B) to be computed
+                    if issparse(B)
+                        ppB = symamd(B);
+                        [RB,idxB] = chol(B(ppB,ppB));
+                    else
+                        [RB,idxB] = chol(B);
+                    end
+                    if mode == 3
+                        RB = [];    ppB = [];
+                    end
+                    RBT = RB';
+                    if (idxB == 0)
+                        BisHpd = true;
+                    elseif mode == 3 && isreal(B)
+                        % LDL decomposition is only for 'SM' eigenvalues of the
+                        % pair (A,B) where B is Hermitian positive
+                        % semi-definite; in this case, as ARPACK users' guide
+                        % suggests, one should still use B-(semi)inner product
+                        [LB,DB,pvB] = ldl(B,'vector'); %#ok
+                        BisHpd = checkTridiagForHSD(diag(DB), diag(DB,1));
+                    else
+                        BisHpd = false;
+                    end
+                end
+            end
+            if ~isempty(B) && issparse(B)
+               tmp = speye(length(B));               
+               ppB = tmp(ppB,1:length(B));
+            end
+        end % CHOLfactorB
+        %-------------------------------------------------------------------------%
+        function [LB,UB,ppB,qqB,dgB] = LUfactorB
+            % LU factor B, including a reordering perm if it is sparse
+            if issparse(B)
+                [LB,UB,ppB,qqB,dgB] = lu(B);
+            else
+                [LB,UB,ppB] = lu(B,'vector');
+                qqB = []; dgB = [];
+            end            
+            % Warn if lu(B) is ill-conditioned
+            dUB = diag(UB);
+            if any(dUB == 0) || any(diag(LB) == 0)
+                error('MATLAB:eigs:SingularB', ...
+                    ['B is singular.\nUnable to compute the specified eigenvalues'...
+                     ' because infinite eigenvalue(s) exist']);
+            end
+            rcondestUB = full(min(abs(dUB)) / max(abs(dUB)));
+            if (rcondestUB < eps)
+                warning('MATLAB:eigs:IllConditionedB',...
+                    ['B has small reciprocal condition' ...
+                    ' estimate: %f\n' ...
+                    '         indicating that B^{-1}A might not be applied accurately.\n' ...
+                    '         and there may be infinite eigenvalue(s).\n'],...
+                    rcondestUB);
+            end
+        end % LUfactorB
+        
+        %-------------------------------------------------------------------------%
+        function [L,U,pp,qq,dgAsB] = LUfactorAminusSigmaB
+            % LU factor A-sigma*B, including a reordering perm if it is sparse
+            if sigma == 0
+                AsB = A;
+            elseif isempty(B)
+                if issparse(A)
+                    AsB = A - sigma * speye(n);
+                else
+                    AsB = A - sigma * eye(n);
+                end
+            else
+                if cholB
+                    if issparse(B)
+                        AsB = A - sigma * checkInputBmtimes(speye(n));
+                    else
+                        AsB = A - sigma * checkInputBmtimes(eye(n));
+                    end
+                else
+                    AsB = A - sigma * B;
+                end
+            end
+            if issparse(AsB)
+                [L,U,pp,qq,dgAsB] = lu(AsB);
+            else
+                [L,U,pp] = lu(AsB,'vector');
+                qq = []; dgAsB = [];
+            end
+            % Warn if lu(A-sigma*B) is ill-conditioned
+            % => sigma is close to an exact eigenvalue of (A,B)
+            if isempty(B)
+                ds = '(A-sigma*I)';
+            else
+                ds = '(A-sigma*B)';
+            end
+            dU = diag(U);
+            if any(dU == 0) || any(diag(L) == 0)
+                error('MATLAB:eigs:AminusBSingular', ...
+                    [ds 'is singular. The shift is an eigenvalue.\n' ...
+                    'Try to use some other shift please.\n']);
+            end
+            rcondestU = full(min(abs(dU)) / max(abs(dU)));
+            if (rcondestU < eps)                
+                warning('MATLAB:eigs:SigmaNearExactEig',...
+                    [ds ' has small reciprocal condition' ...
+                    ' estimate: %f\n' ...
+                    '         indicating that sigma is near an exact' ...
+                    ' eigenvalue.\n         The algorithm may not converge unless' ...
+                    ' you try a new value for sigma.\n'], ...
+                    rcondestU);
+            end
+        end % LUfactorAminusSigmaB
+        
+%-------------------------------------------------------------------------%        
+        function v = checkInputBmtimes(u)
+            % Matrix-vector multiply v = B*u
+            if cholB % use B's cholesky factor and its transpose
+                if ~isempty(permB)
+                    v = permB'*(RBT * (RB * (permB*u)));
+                else
+                    v = RBT * (RB * u);
+                end
+            else
+                v = B * u;
+            end
+        end
+        
+    end % checkInputs
+
+%-------------------------------------------------------------------------%
+    function fullEig(nOutputs)
+        % Use EIG(FULL(A)) or EIG(FULL(A),FULL(B)) instead of ARPACK
+        if ~isempty(B)
+            B = Bmtimes(eye(n));
+            B = B*scaleB;
+        end
+        if isfloat(A)
+            if issparse(A);
+                A = full(A);
+            end
+        else
+            % A is specified by a function.
+            % Form the matrix A by applying the function.
+            if ischar(eigs_sigma) && ~strcmp(eigs_sigma,'SM')
+                % A is a function multiplying A*x
+                AA = eye(n);
+                for i = 1:n
+                    AA(:,i) = A(AA(:,i),varargin{afunNargs});
+                end
+                A = AA;
+            else
+                if (isfloat(eigs_sigma) && eigs_sigma == 0) || strcmp(eigs_sigma,'SM')
+                    % A is a function solving A\x
+                    invA = eye(n);
+                    for i = 1:n
+                        invA(:,i) = A(invA(:,i),varargin{afunNargs});
+                    end
+                    A = eye(n) / invA;
+                else
+                    % A is a function solving (A-sigma*B)\x
+                    % B may be [], indicating the identity matrix
+                    % U = (A-sigma*B)\sigma*B
+                    % => (A-sigma*B)*U = sigma*B
+                    % => A*U = sigma*B(U + eye(n))
+                    % => A = sigma*B(U + eye(n)) / U
+                    if isempty(B)
+                        sB = eigs_sigma*eye(n);
+                    else
+                        sB = eigs_sigma*B;
+                    end
+                    U = zeros(n,n);
+                    for i = 1:n
+                        U(:,i) = A(sB(:,i),varargin{afunNargs});
+                    end
+                    A = sB*(U+eye(n)) / U;
+                end
+            end
+        end
+        
+        if isempty(B)
+            eigInputs = {A};
+        else
+            eigInputs = {A,B};
+        end
+        % Now with full floating point matrices A and B, use EIG:
+        if (nOutputs <= 1)
+            d = eig(eigInputs{:});
+        else
+            [V,D] = eig(eigInputs{:});
+            d = diag(D);
+        end
+        
+        % Grab the eigenvalues we want, based on sigma
+        firstKindices = 1:k;
+        lastKindices = n:-1:n-k+1;
+        if ischar(eigs_sigma)
+            switch eigs_sigma
+                case 'LM'
+                    [ignore,ind] = sort(abs(d));
+                    range = lastKindices;
+                case 'SM'
+                    [ignore,ind] = sort(abs(d));
+                    range = firstKindices;
+                case 'LA'
+                    [ignore,ind] = sort(d);
+                    range = lastKindices;
+                case 'SA'
+                    [ignore,ind] = sort(d);
+                    range = firstKindices;
+                case 'LR'
+                    [ignore,ind] = sort(abs(real(d)));
+                    range = lastKindices;
+                case 'SR'
+                    [ignore,ind] = sort(abs(real(d)));
+                    range = firstKindices;
+                case 'LI'
+                    [ignore,ind] = sort(abs(imag(d)));
+                    range = lastKindices;
+                case 'SI'
+                    [ignore,ind] = sort(abs(imag(d)));
+                    range = firstKindices;
+                case 'BE'
+                    [ignore,ind] = sort(abs(d));
+                    range = [1:floor(k/2), n-ceil(k/2)+1:n];
+                otherwise
+                    error('MATLAB:eigs:fullEigSigma','Unknown value of sigma');
+            end
+        else
+            % sigma is a scalar
+            [ignore,ind] = sort(abs(d-eigs_sigma));
+            range = 1:k;
+        end
+        
+        if (nOutputs <= 1)
+            varargout{1} = d(ind(range));
+        else
+            varargout{1} = V(:,ind(range));
+            varargout{2} = D(ind(range),ind(range));
+            if (nOutputs == 3)
+                % flag indicates "convergence"
+                varargout{3} = 0;
+            end
+        end
+        
+    end % FULLEIG
+
+
+%-------------------------------------------------------------------------%
+    function cols = checkIpntr
+        % Check that ipntr returned from ARPACK refers to the start of a
+        % column of workd.
+        if (ido == 1) && (mode == 3) && strcmp(style,'G') 
+            inds = double(ipntr(1:3));
+        else
+            inds = double(ipntr(1:2));
+        end
+        rows = mod(inds-1,n)+1;
+        cols = (inds-rows)/n+1;
+        if ~all(rows==1)
+            error('MATLAB:eigs:ipntrMismatchWorkdColumn', ...
+                ['One of ipntr(1:3) does not refer to the start' ...
+                ' of a column of the %d-by-3 array workd.'],n)
+        end
+    end % checkIpntr
+
+%-------------------------------------------------------------------------%
+    function v = Amtimes(u)
+        % Matrix-vector multiply v = A*u
+        if Amatrix
+            v = A * u;
+        else % A is a function
+            v = A(u,varargin{afunNargs});
+            if isrealprob && ~isreal(v)
+                error('MATLAB:eigs:complexFunction', ...
+                    'AFUN is complex; set opts.isreal = false.');
+            end
+        end
+    end
+
+%-------------------------------------------------------------------------%
+    function v = Bmtimes(u)
+        % Matrix-vector multiply v = B*u
+        if cholB % use B's cholesky factor and its transpose
+            if ~isempty(permB)
+                v = permB'*(RBT * (RB * (permB*u)));
+            else
+                v = RBT * (RB * u);
+            end
+        else
+            v = B * u;
+        end
+    end
+
+%-------------------------------------------------------------------------%
+    function v = RBsolve(u)
+        % Solve v = RB\u for v
+        if issparse(B)
+            if ~isempty(permB)
+                v = permB'*(RB \ u);
+            else
+                v = RB \ u;
+            end
+        else
+            RBopts.UT = true;
+            v = linsolve(RB,u,RBopts);
+        end
+    end
+
+%-------------------------------------------------------------------------%
+    function v = RBTsolve(u)
+        % Solve v = RB'\u for v
+        if issparse(B)
+            if ~isempty(permB)
+                v = RBT \ (permB*u);
+            else
+                v = RBT \ u;
+            end
+        else
+            RBTopts.LT = true;
+            v = linsolve(RBT,u,RBTopts);
+        end
+    end
+
+%-------------------------------------------------------------------------%
+    function v = AminusSigmaBsolve(u)
+        % Solve v = (A-sigma*B)\u for v
+        if Amatrix
+            if ~isempty(dgAsB)
+                % use LU reordering permAsB
+                v = qq*(U \ (L \ (pp*(dgAsB\u))));
+            else
+                v = U \ (L \ u(pp));
+            end
+        else % A is a function
+            v = A(u,varargin{afunNargs});
+            if isrealprob && ~isreal(v)
+                error('MATLAB:eigs:complexFunction', ...
+                    'AFUN is complex; set opts.isreal = false.');
+            end
+        end
+    end % AminusSigmaBsolve
+%-------------------------------------------------------------------------%
+    function v = Bsolve(u)
+        % Solve v = (A-sigma*B)\u for v
+        if ~isempty(dgB)
+            % use LU reordering permAsB
+            v = qqB*(UB \ (LB \ (ppB*(dgB\u))));
+        else
+            v = UB \ (LB \ u(ppB));
+        end
+    end % AminusSigmaBsolve
+
+%-------------------------------------------------------------------------%
+    function displayRitzValues
+        % Display a few Ritz values at the current iteration
+        iter = double(ipntr(15));
+        if (iter > eigs_iter) && (ido ~= 99)
+            eigs_iter = iter;
+            ds = sprintf(['Iteration %d: a few Ritz values of the' ...
+                ' %d-by-%d matrix:'],iter,p,p);
+            disp(ds)
+            if isrealprob
+                if issymA
+                    dispvec = workl(double(ipntr(6))+(0:p-1));
+                    if strcmp(whch,'BE')
+                        % roughly k Large eigenvalues and k Small eigenvalues
+                        disp(dispvec(max(end-2*k+1,1):end))
+                    else
+                        % k eigenvalues
+                        disp(dispvec(max(end-k+1,1):end))
+                    end
+                else
+                    dispvec = complex(workl(double(ipntr(6))+(0:p-1)), ...
+                        workl(double(ipntr(7))+(0:p-1)));
+                    % k+1 eigenvalues (keep complex conjugate pairs together)
+                    disp(dispvec(max(end-k,1):end))
+                end
+            else
+                dispvec = complex(workl(2*double(ipntr(6))-1+(0:2:2*(p-1))), ...
+                    workl(2*double(ipntr(6))+(0:2:2*(p-1))));
+                disp(dispvec(max(end-k+1,1):end))
+            end
+        end
+    end
+
+%-------------------------------------------------------------------------%
+    function flag = processEUPDinfo(warnNonConvergence)
+        % Process the info flag returned by the ARPACK routine **eupd
+        flag = 0;
+        if (info ~= 0)
+            es = ['Error with ARPACK routine ' eupdfun ':\n'];
+            switch double(info)
+                case 2
+                    ss = sum(select);
+                    if (ss < k)
+                        error('MATLAB:eigs:ARPACKroutineError02ssLTk', ...
+                            [es 'The logical variable select was only set' ...
+                            ' with %d 1''s instead of nconv=%d (k=%d).\n' ...
+                            'Please report this to the ARPACK authors at' ...
+                            ' arpack@caam.rice.edu.'], ...
+                            ss,double(iparam(5)),k)
+                    else
+                        error('MATLAB:eigs:ARPACKroutineError02', ...
+                            [es 'The LAPACK reordering routine %strsen' ...
+                            ' did not return all %d eigenvalues.'], ...
+                            aupdfun(1),k);
+                    end
+                case 1
+                    error('MATLAB:eigs:ARPACKroutineError01', ...
+                        [es 'The Schur form could not be reordered by the' ...
+                        ' LAPACK routine %strsen.\nPlease report this to the' ...
+                        ' ARPACK authors at arpack@caam.rice.edu.'], ...
+                        aupdfun(1))
+                case -14
+                    error('MATLAB:eigs:ARPACKroutineErrorMinus14', ...
+                        [es aupdfun ...
+                        ' did not find any eigenvalues to sufficient accuracy.']);
+                otherwise
+                    error('MATLAB:eigs:ARPACKroutineError', ...
+                        [es 'info = %d. Please consult the ARPACK Users''' ...
+                        ' Guide for more information.'],full(info));
+            end
+        else
+            nconv = double(iparam(5));
+            if (nconv == 0)
+                if (warnNonConvergence)
+                    warning('MATLAB:eigs:NoEigsConverged', ...
+                        'None of the %d requested eigenvalues converged.',k)
+                else
+                    flag = 1;
+                end
+            elseif (nconv < k)
+                if (warnNonConvergence)
+                    warning('MATLAB:eigs:NotAllEigsConverged', ...
+                        'Only %d of the %d requested eigenvalues converged.', ...
+                        nconv,k)
+                else
+                    flag = 1;
+                end
+            end
+        end
+    end % processEUPDinfo
+
+%-------------------------------------------------------------------------%
+    function printTimings
+        % Print the time taken for each major stage of the EIGS algorithm
+        if (mode == 1)
+            innerstr = sprintf(['Compute A*X:' ...
+                '                               %f\n'],cputms(3));
+        elseif (mode == 3)
+            if isempty(B)
+                innerstr = sprintf(['Solve (A-SIGMA*I)*X=Y for X:' ...
+                    '               %f\n'],cputms(3));
+            else
+                innerstr = sprintf(['Solve (A-SIGMA*B)*X=B*Y for X:' ...
+                    '             %f\n'],cputms(3));
+            end
+        end
+        if ((mode == 3) && (Amatrix))
+            if isempty(B)
+                prepstr = sprintf(['Pre-processing, including lu(A-sigma*I):' ...
+                    '   %f\n'],cputms(1));
+            else
+                prepstr = sprintf(['Pre-processing, including lu(A-sigma*B):' ...
+                    '   %f\n'],cputms(1));
+            end
+        else
+            prepstr = sprintf(['Pre-processing:' ...
+                '                            %f\n'],cputms(1));
+        end
+        sstr = sprintf('***********CPU Timing Results in seconds***********');
+        ds = sprintf(['\n' sstr '\n' ...
+            prepstr ...
+            'ARPACK''s %s:                           %f\n' ...
+            innerstr ...
+            'Post-processing with ARPACK''s %s:      %f\n' ...
+            '***************************************************\n' ...
+            'Total:                                     %f\n' ...
+            sstr '\n'], ...
+            aupdfun,cputms(2),eupdfun,cputms(4),cputms(5));
+        disp(ds)
+    end % printTimings
+
+%-------------------------------------------------------------------------%
+% End of nested functions
+%-------------------------------------------------------------------------%
+
+end % EIGS
+
+%-------------------------------------------------------------------------%
+% Subfunctions
+%-------------------------------------------------------------------------%
+function tf = ishermitian(A)
+%ISHERMITIAN
+tf = isequal(A,A');
+end % ishermititan
+
+function BisHpd = checkTridiagForHSD(alpha, beta)
+% CHECKTRIDIAGFORHSD
+%   Uses Sturm sequence on alpha (diagonal) and beta (superdiagonal) to
+%   determine if the matrix diag(alpha,0) + diag(beta,1) + diag(beta,-1) is
+%   Positive Semi-definite.
+n = length(alpha);
+BisHpd = true;
+d = alpha(1);
+if d < 0
+    BisHpd = false;
+    return;
+end
+for k = 1:(n-1)
+    if d == 0
+        d = eps*(abs(beta(k))+eps);
+    end
+    d = alpha(k+1) - beta(k)*(beta(k)/d);
+    if d < 0
+        BisHpd = false;
+        return;
+    end
+end
+end % checkTridiagForHSD
+%-------------------------------------------------------------------------%
+% End of subfunctions
+%-------------------------------------------------------------------------%

diff --git a/THESE/codes/graphe/Ncut_9/fft_filt_2.m b/THESE/codes/graphe/Ncut_9/fft_filt_2.m
new file mode 100755 (executable)
index 0000000..5930bbc
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/fft_filt_2.m
@@ -0,0 +1,30 @@
+function FI=fft_filt_2(V,FB,sf);
+% FI=fft_filt_2(V,FB,sf);
+% fft-based filtering
+% requires image to be called "V"
+% and filters to be in FB
+% sf is the subsampling factor
+%
+% FI is the result
+% Jianbo Shi, 1997
+
+[M1,M2,N3]=size(FB);
+% prepare FFT of image for filtering
+[N1,N2]=size(V);
+I=zeros(size(V)+[M1-1 M2-1]);
+I(1:N1,1:N2)=V;
+N1s=length(1:sf:N1);
+N2s=length(1:sf:N2);
+IF=fft2(I);
+FI=zeros(N1s,N2s,N3);
+
+% apply filters
+for n=1:N3;
+   f=rot90(FB(:,:,n),2);
+   fF=fft2(f,N1+M1-1,N2+M2-1);
+   IfF=IF.*fF;
+   If=real(ifft2(IfF));
+   If=If(ceil((M1+1)/2):ceil((M1+1)/2)+N1-1,ceil((M2+1)/2):ceil((M2+1)/2)+N2-1);
+   FI(:,:,n)=If(1:sf:N1,1:sf:N2);
+end
+

diff --git a/THESE/codes/graphe/Ncut_9/gaussian.m b/THESE/codes/graphe/Ncut_9/gaussian.m
new file mode 100755 (executable)
index 0000000..54d7ef0
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/gaussian.m
@@ -0,0 +1,31 @@
+function p=gaussian(x,m,C);
+% p=gaussian(x,m,C);
+%
+% Evaluate the multi-variate density with mean vector m and covariance
+% matrix C for the input vector x.
+% 
+% p=gaussian(X,m,C);
+% 
+% Vectorized version: Here X is a matrix of column vectors, and p is 
+% a vector of probabilities for each vector.
+% Jianbo Shi, 1997
+d=length(m);
+
+if size(x,1)~=d
+   x=x';
+end
+N=size(x,2);
+
+detC = det(C);
+if rcond(C)<eps
+%   fprintf(1,'Covariance matrix close to singular. (gaussian.m)\n');
+   p = zeros(N,1);
+else
+   m=m(:);
+   M=m*ones(1,N);
+   denom=(2*pi)^(d/2)*sqrt(abs(detC));
+   mahal=sum(((x-M)'*inv(C)).*(x-M)',2);   % Chris Bregler's trick
+   numer=exp(-0.5*mahal);
+   p=numer/denom;
+end
+

diff --git a/THESE/codes/graphe/Ncut_9/imread_ncut.m b/THESE/codes/graphe/Ncut_9/imread_ncut.m
new file mode 100755 (executable)
index 0000000..6d1b1af
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/imread_ncut.m
@@ -0,0 +1,18 @@
+function I = imread_ncut(Image_file_name,nr,nc);
+%  I = imread_ncut(Image_file_name);
+%
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.
+
+
+%% read image 
+
+I = imread(Image_file_name);
+[Inr,Inc,nb] = size(I);
+
+if (nb>1),
+    I =double(rgb2gray(I));
+else
+    I = double(I);
+end
+
+I = imresize(I,[nr, nc],'bicubic');

diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/._1.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/._1.jpg
new file mode 100755 (executable)
index 0000000..8eebf82
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/._2.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/._2.jpg
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/._3.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/._3.jpg
new file mode 100755 (executable)
index 0000000..8eebf82
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/._Thumbs.db b/THESE/codes/graphe/Ncut_9/jpg_images/._Thumbs.db
new file mode 100755 (executable)
index 0000000..3098fd2
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/1.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/1.jpg
new file mode 100755 (executable)
index 0000000..9e7cd26
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/2.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/2.jpg
new file mode 100755 (executable)
index 0000000..afcfcb3
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/3.jpg b/THESE/codes/graphe/Ncut_9/jpg_images/3.jpg
new file mode 100755 (executable)
index 0000000..4cfc19e
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diff --git a/THESE/codes/graphe/Ncut_9/jpg_images/Thumbs.db b/THESE/codes/graphe/Ncut_9/jpg_images/Thumbs.db
new file mode 100755 (executable)
index 0000000..1c3bd04
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diff --git a/THESE/codes/graphe/Ncut_9/make_filterbank_even2.m b/THESE/codes/graphe/Ncut_9/make_filterbank_even2.m
new file mode 100755 (executable)
index 0000000..34e1e7f
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/make_filterbank_even2.m
@@ -0,0 +1,46 @@
+function FB = make_filterbank(num_ori,filter_scales,wsz,enlong)
+%
+%  F = make_filterbank(num_ori,num_scale,wsz)
+% Jianbo Shi, 1997
+
+
+if nargin<4,
+    enlong = 3;
+end
+
+enlong = 2*enlong;
+
+% definine filterbank
+%num_ori=6;
+%num_scale=3;
+
+num_scale = length(filter_scales);
+
+M1=wsz; % size in pixels
+M2=M1;
+
+ori_incr=180/num_ori;
+ori_offset=ori_incr/2; % helps with equalizing quantiz. error across filter set
+
+FB=zeros(M1,M2,num_ori,num_scale);
+
+% elongated filter set
+counter = 1;
+
+for m=1:num_scale  
+   for n=1:num_ori
+      % r=12 here is equivalent to Malik's r=3;
+      f=doog2(filter_scales(m),enlong,ori_offset+(n-1)*ori_incr,M1);
+      FB(:,:,n,m)=f;
+   end
+end
+
+FB=reshape(FB,M1,M2,num_scale*num_ori);
+total_num_filt=size(FB,3);
+
+for j=1:total_num_filt,
+  F = FB(:,:,j);
+  a = sum(sum(abs(F)));
+  FB(:,:,j) = FB(:,:,j)/a;
+end
+

diff --git a/THESE/codes/graphe/Ncut_9/make_filterbank_odd2.m b/THESE/codes/graphe/Ncut_9/make_filterbank_odd2.m
new file mode 100755 (executable)
index 0000000..3cb59ef
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/make_filterbank_odd2.m
@@ -0,0 +1,46 @@
+function FB = make_filterbank(num_ori,filter_scales,wsz,enlong)
+%
+%  F = make_filterbank(num_ori,num_scale,wsz)
+% Jianbo Shi, 1997
+
+if nargin<4,
+    enlong = 3;
+end
+
+enlong = enlong*2;
+
+% definine filterbank
+%num_ori=6;
+%num_scale=3;
+
+num_scale = length(filter_scales);
+
+M1=wsz; % size in pixels
+M2=M1;
+
+ori_incr=180/num_ori;
+ori_offset=ori_incr/2; % helps with equalizing quantiz. error across filter set
+
+FB=zeros(M1,M2,num_ori,num_scale);
+
+
+% elongated filter set
+counter = 1;
+
+for m=1:num_scale  
+   for n=1:num_ori
+      % r=12 here is equivalent to Malik's r=3;
+      f=doog1(filter_scales(m),enlong,ori_offset+(n-1)*ori_incr,M1);
+      FB(:,:,n,m)=f;
+   end
+end
+
+FB=reshape(FB,M1,M2,num_scale*num_ori);
+total_num_filt=size(FB,3);
+
+for j=1:total_num_filt,
+  F = FB(:,:,j);
+  a = sum(sum(abs(F)));
+  FB(:,:,j) = FB(:,:,j)/a;
+end
+

diff --git a/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.cpp b/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.cpp
new file mode 100755 (executable)
index 0000000..2c45bcc
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.cpp
@@ -0,0 +1,57 @@
+/*================================================================\r
+* mex_w_times_x_symmetric.c = used by ncuthard2.m in eigensolver.\r
+*\r
+* Examples: \r
+*     mex_w_times_x_c_symmetric(x,triu(A)) = A*x;     \r
+*     A is sparse and symmetric, but x is full\r
+*\r
+* Timothee Cour, Oct 12, 2003.\r
+\r
+% test sequence:\r
+    m=100;\r
+    n=50;    \r
+    x=rand(n,1);\r
+    A=sprand(m,n,0.01);    \r
+\r
+    y2 = mex_w_times_x_c_symmetric(x,triu(A));\r
+    y1=A*x;\r
+    max(abs(y1-y2))\r
+*=================================================================*/\r
+\r
+# include "math.h"\r
+# include "mex.h"\r
+# include "a_times_b_cmplx.cpp"\r
+/*# include "a_times_b.c"*/\r
+\r
+\r
+void mexFunction(\r
+    int nargout,\r
+    mxArray *out[],\r
+    int nargin,\r
+    const mxArray *in[]\r
+)\r
+{\r
+    int np, nc;\r
+    mwIndex*ir, *jc;\r
+    double *x, *y, *pr;\r
+    \r
+    if (nargin < 2) {//voir\r
+        mexErrMsgTxt("Four input arguments required !");\r
+    }\r
+    if (nargout>1) {\r
+        mexErrMsgTxt("Too many output arguments.");\r
+    }\r
+        \r
+       x = mxGetPr(in[0]);\r
+        pr = mxGetPr(in[1]);\r
+        ir = mxGetIr(in[1]);\r
+        jc = mxGetJc(in[1]);\r
+   \r
+       np = mxGetM(in[1]);\r
+       nc = mxGetN(in[1]);\r
+           \r
+       out[0] = mxCreateDoubleMatrix(np,1,mxREAL);\r
+       y = mxGetPr(out[0]);\r
+       \r
+       CSRsymm_VecMult_CAB_double(np,nc,pr,ir,jc,x,y); \r
+} \r

diff --git a/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.mexa64 b/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.mexa64
new file mode 100755 (executable)
index 0000000..b3f8d11
Binary files /dev/null and b/THESE/codes/graphe/Ncut_9/mex_w_times_x_symmetric.mexa64 differ

diff --git a/THESE/codes/graphe/Ncut_9/ncut.m b/THESE/codes/graphe/Ncut_9/ncut.m
new file mode 100755 (executable)
index 0000000..e8cd7c3
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/ncut.m
@@ -0,0 +1,95 @@
+function [Eigenvectors,Eigenvalues] = ncut(W,nbEigenValues,dataNcut);\r
+% function [Eigenvectors,Eigenvalues] = ncut(W,nbEigenValues,dataNcut);\r
+% \r
+% Input:\r
+%     W= symmetric similarity matrix\r
+%     nbEigenValues=  number of Ncut eigenvectors computed\r
+%     dataNcut= optional parameters\r
+%\r
+%     default parameters for dataNcut:\r
+%     dataNcut.offset = 5e-1; offset in the diagonal of W\r
+%     dataNcut.verbose = 0; 0 for verbose off mode, 1,2,3 for verbose on modes\r
+%     dataNcut.maxiterations = 100; max number of iterations in eigensolver\r
+%     dataNcut.eigsErrorTolerance = 1e-6; error tolerance in eigensolver\r
+%     dataNcut.valeurMin=1e-6; % truncates any values in W less than valeurMin\r
+% \r
+% Output: \r
+%    Eigenvectors= continuouse Ncut eigenvectos, size = length(W) x nbEigenValues\r
+%    Eigenvalues= Ncut eigenvalues, size = 1x nbEigenValues\r
+%\r
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004.\r
+\r
+if nargin < 2\r
+    nbEigenValues = 8;\r
+end\r
+if nargin < 3\r
+    dataNcut.offset = 5e-1;\r
+    dataNcut.verbose = 0;\r
+    dataNcut.maxiterations = 300;\r
+    dataNcut.eigsErrorTolerance = 1e-8;\r
+    dataNcut.valeurMin=1e-6;\r
+end\r
+% if nargin < 3\r
+%     dataNcut.offset = 5e-1;\r
+%     dataNcut.verbose = 0;\r
+%     dataNcut.maxiterations = 100;\r
+%     dataNcut.eigsErrorTolerance = 1e-6;\r
+%     dataNcut.valeurMin=1e-6;\r
+% end\r
+\r
+% make W matrix sparse\r
+W = sparsifyc(W,dataNcut.valeurMin);\r
+\r
+% check for matrix symmetry\r
+if max(max(abs(W-W'))) > 1e-10 %voir (-12) \r
+    %disp(max(max(abs(W-W'))));\r
+    error('W not symmetric');\r
+end\r
+\r
+n = size(W,1);\r
+nbEigenValues = min(nbEigenValues,n);\r
+offset = dataNcut.offset;\r
+\r
+\r
+% degrees and regularization\r
+d = sum(abs(W),2);\r
+dr = 0.5 * (d - sum(W,2));\r
+d = d + offset * 2;\r
+dr = dr + offset;\r
+W = W + spdiags(dr,0,n,n);\r
+\r
+Dinvsqrt = 1./sqrt(d+eps);\r
+P = spmtimesd(W,Dinvsqrt,Dinvsqrt);\r
+clear W;\r
+\r
+options.issym = 1;\r
+     \r
+if dataNcut.verbose\r
+    options.disp = 3; \r
+else\r
+    options.disp = 0; \r
+end\r
+options.maxit = dataNcut.maxiterations;\r
+options.tol = dataNcut.eigsErrorTolerance;\r
+\r
+options.v0 = ones(size(P,1),1);\r
+options.p = max(35,2*nbEigenValues); %voir\r
+options.p = min(options.p,n);\r
+\r
+%warning off\r
+% [vbar,s,convergence] = eigs2(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); \r
+[vbar,s,convergence] = eigs_new(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); \r
+%warning on\r
+\r
+s = real(diag(s));\r
+[x,y] = sort(-s); \r
+Eigenvalues = -x;\r
+vbar = vbar(:,y);\r
+Eigenvectors = spdiags(Dinvsqrt,0,n,n) * vbar;\r
+\r
+for  i=1:size(Eigenvectors,2)\r
+    Eigenvectors(:,i) = (Eigenvectors(:,i) / norm(Eigenvectors(:,i))  )*norm(ones(n,1));\r
+    if Eigenvectors(1,i)~=0\r
+        Eigenvectors(:,i) = - Eigenvectors(:,i) * sign(Eigenvectors(1,i));\r
+    end\r
+end\r

diff --git a/THESE/codes/graphe/Ncut_9/ncutW.m b/THESE/codes/graphe/Ncut_9/ncutW.m
new file mode 100755 (executable)
index 0000000..f904148
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/ncutW.m
@@ -0,0 +1,15 @@
+function [NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbcluster);
+% [NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbcluster);
+% 
+% Calls ncut to compute NcutEigenvectors and NcutEigenvalues of W with nbcluster clusters
+% Then calls discretisation to discretize the NcutEigenvectors into NcutDiscrete
+% Timothee Cour, Stella Yu, Jianbo Shi, 2004
+
+% compute continuous Ncut eigenvectors
+[NcutEigenvectors,NcutEigenvalues] = ncut(W,nbcluster);
+
+% compute discretize Ncut vectors
+[NcutDiscrete,NcutEigenvectors] =discretisation(NcutEigenvectors);
+
+
+NcutDiscrete = full(NcutDiscrete);
\ No newline at end of file

diff --git a/THESE/codes/graphe/Ncut_9/quadedgep.m b/THESE/codes/graphe/Ncut_9/quadedgep.m
new file mode 100755 (executable)
index 0000000..ae2d8b0
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/quadedgep.m
@@ -0,0 +1,103 @@
+% function [x,y,gx,gy,par,threshold,mag,mage,g,FIe,FIo,mago] = quadedgep(I,par,threshold);\r
+% Input:\r
+%    I = image\r
+%    par = vector for 4 parameters\r
+%      [number of filter orientations, number of scales, filter size, elongation]\r
+%      To use default values, put 0.\r
+%    threshold = threshold on edge strength\r
+% Output:\r
+%    [x,y,gx,gy] = locations and gradients of an ordered list of edgels\r
+%       x,y could be horizontal or vertical or 45 between pixel sites\r
+%       but it is guaranteed that there [floor(y) + (floor(x)-1)*nr] \r
+%       is ordered and unique.  In other words, each edgel has a unique pixel id.\r
+%    par = actual par used\r
+%    threshold = actual threshold used\r
+%    mag = edge magnitude\r
+%    mage = phase map\r
+%    g = gradient map at each pixel\r
+%    [FIe,FIo] = odd and even filter outputs\r
+%    mago = odd filter output of optimum orientation\r
+%\r
+% Stella X. Yu, 2001\r
+\r
+\r
+\r
+function [x,y,gx,gy,par,threshold,mag,mage,g,FIe,FIo,mago] = quadedgep(I,par,threshold);\r
+\r
+if nargin<3 | isempty(threshold),\r
+    threshold = 0.2;\r
+end\r
+\r
+[r,c] = size(I);\r
+def_par = [8,1,20,3];\r
+\r
+% take care of parameters, any missing value is substituted by a default value\r
+if nargin<2 | isempty(par),\r
+   par = def_par;\r
+end\r
+par(end+1:4)=0;\r
+par = par(:);\r
+j = (par>0);\r
+have_value = [ j, 1-j ];\r
+j = 1; n_filter = have_value(j,:) * [par(j); def_par(j)];\r
+j = 2; n_scale  = have_value(j,:) * [par(j); def_par(j)];\r
+j = 3; winsz    = have_value(j,:) * [par(j); def_par(j)];\r
+j = 4; enlong   = have_value(j,:) * [par(j); def_par(j)];\r
+\r
+% always make filter size an odd number so that the results will not be skewed\r
+j = winsz/2;\r
+if not(j > fix(j) + 0.1),\r
+    winsz = winsz + 1;\r
+end\r
+\r
+% filter the image with quadrature filters\r
+FBo = make_filterbank_odd2(n_filter,n_scale,winsz,enlong);\r
+FBe = make_filterbank_even2(n_filter,n_scale,winsz,enlong);\r
+n = ceil(winsz/2);\r
+f = [fliplr(I(:,2:n+1)), I, fliplr(I(:,c-n:c-1))];\r
+f = [flipud(f(2:n+1,:)); f; flipud(f(r-n:r-1,:))];\r
+FIo = fft_filt_2(f,FBo,1); \r
+FIo = FIo(n+[1:r],n+[1:c],:);\r
+FIe = fft_filt_2(f,FBe,1);\r
+FIe = FIe(n+[1:r],n+[1:c],:);\r
+\r
+% compute the orientation energy and recover a smooth edge map\r
+% pick up the maximum energy across scale and orientation\r
+% even filter's output: as it is the second derivative, zero cross localize the edge\r
+% odd filter's output: orientation\r
+mag = sqrt(sum(FIo.^2,3)+sum(FIe.^2,3));\r
+mag_a = sqrt(FIo.^2+FIe.^2);\r
+[tmp,max_id] = max(mag_a,[],3);\r
+base_size = r * c;\r
+id = [1:base_size]';\r
+mage = reshape(FIe(id+(max_id(:)-1)*base_size),[r,c]);\r
+mage = (mage>0) - (mage<0);\r
+\r
+ori_incr=pi/n_filter; % to convert jshi's coords to conventional image xy\r
+ori_offset=ori_incr/2;\r
+theta = ori_offset+([1:n_filter]-1)*ori_incr; % orientation detectors\r
+% [gx,gy] are image gradient in image xy coords, winner take all\r
+mago = reshape(FIo(id+(max_id(:)-1)*base_size),[r,c]);\r
+ori = theta(max_id);\r
+ori = ori .* (mago>0) + (ori + pi).*(mago<0);\r
+gy = mag .* cos(ori);\r
+gx = -mag .* sin(ori);\r
+g = cat(3,gx,gy);\r
+\r
+% phase map: edges are where the phase changes\r
+mag_th = max(mag(:)) * threshold;\r
+eg = (mag>mag_th);\r
+h = eg & [(mage(2:r,:) ~= mage(1:r-1,:)); zeros(1,c)];\r
+v = eg & [(mage(:,2:c) ~= mage(:,1:c-1)), zeros(r,1)];\r
+[y,x] = find(h | v);\r
+k = y + (x-1) * r;\r
+h = h(k);\r
+v = v(k);\r
+y = y + h * 0.5; % i\r
+x = x + v * 0.5; % j\r
+t = h + v * r;\r
+gx = g(k) + g(k+t);\r
+k = k + (r * c);\r
+gy = g(k) + g(k+t);\r
+\r
+\r

diff --git a/THESE/codes/graphe/Ncut_9/showmask.m b/THESE/codes/graphe/Ncut_9/showmask.m
new file mode 100755 (executable)
index 0000000..0b56038
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/showmask.m
@@ -0,0 +1,21 @@
+function RGB=showmask(V,M,display_flag);
+% showmask(V,M);
+%
+% M is a nonneg. mask
+% Jianbo Shi, 1997
+
+V=V-min(V(:));
+V=V/max(V(:));
+V=.25+0.75*V; %brighten things up a bit
+
+M=M-min(M(:));
+M=M/max(M(:));
+
+H=0.0+zeros(size(V));
+S=M;
+RGB=hsv2rgb(H,S,V);
+
+%if nargin>2
+   image(RGB)
+   axis('image')
+%end

diff --git a/THESE/codes/graphe/Ncut_9/sparsifyc.cpp b/THESE/codes/graphe/Ncut_9/sparsifyc.cpp
new file mode 100755 (executable)
index 0000000..5bb32f8
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/sparsifyc.cpp
@@ -0,0 +1,232 @@
+/*=================================================================
+* syntax: SPMX = SPARSIFY(MX, THRES)
+*
+* SPARSIFY  - sparsify the input matrix, i.e. ignore the values 
+*                      of the matrix which     are below a threshold
+*                      
+*                      Input:  - MX: m-by-n matrix (sparse or full)
+*                                      - THRES: threshold value (double)
+*
+*                      Output: - SPMX: m-by-n sparse matrix only with values 
+*                                      whose absolut value is above the given threshold
+*
+*                      Written by Mirko Visontai (10/24/2003)
+*=================================================================*/
+
+
+#include <math.h>
+#include "mex.h"
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
+{
+    /* Declare variable */
+    int i,m,n,nzmax,newnnz,col,processed,passed;
+       int starting_row_index, current_row_index, stopping_row_index;
+    double *in_pr,*in_pi,*out_pr,*out_pi;
+    mwIndex *in_ir,*in_jc,*out_ir,*out_jc;
+       double thres;
+
+    /* Check for proper number of input and output arguments */    
+    if ((nlhs != 1) || (nrhs != 2)){
+               mexErrMsgTxt("usage: SPMX = SPARSIFY(MX, THRES).");
+    } 
+       /* if matrix is complex threshold the norm of the numbers */
+       if (mxIsComplex(prhs[0])){
+               /* Check data type of input argument  */
+               if (mxIsSparse(prhs[0])){
+
+                       /* read input */
+                       in_pr   = mxGetPr(prhs[0]);
+                       in_pi   = mxGetPi(prhs[0]);
+                       in_ir   = mxGetIr(prhs[0]);
+                       in_jc   = mxGetJc(prhs[0]);
+                       nzmax   = mxGetNzmax(prhs[0]);
+                       m               = mxGetM(prhs[0]);
+                       n               = mxGetN(prhs[0]);
+                       thres   = mxGetScalar(prhs[1]);
+
+                       /* Count new nonzeros */
+                       newnnz=0;
+                       for(i=0; i<nzmax; i++){
+                               if (sqrt(in_pr[i]*in_pr[i] + in_pi[i]*in_pi[i])>thres) {newnnz++;}
+                       }
+
+                       if (newnnz>0){
+                               /* create output */
+                               plhs[0]         = mxCreateSparse(m,n,newnnz,mxCOMPLEX);
+                               if (plhs[0]==NULL)
+                                       mexErrMsgTxt("Could not allocate enough memory!\n");
+                               out_pr          = mxGetPr(plhs[0]);
+                               out_pi          = mxGetPr(plhs[0]);
+                               out_ir          = mxGetIr(plhs[0]);
+                               out_jc          = mxGetJc(plhs[0]);
+                               passed          = 0;
+                               out_jc[0]       = 0;
+                               for (col=0; col<n; col++){
+                                       starting_row_index = in_jc[col];
+                                       stopping_row_index = in_jc[col+1];
+                                       out_jc[col+1] = out_jc[col];
+                                       if (starting_row_index == stopping_row_index)
+                                               continue;
+                                       else {
+                                               for (current_row_index = starting_row_index;
+                                                       current_row_index < stopping_row_index;
+                                                       current_row_index++)  {
+                                                               if (sqrt(in_pr[current_row_index]*in_pr[current_row_index] + 
+                                                                            in_pi[current_row_index]*in_pi[current_row_index] ) > thres){
+
+                                                                       out_pr[passed]=in_pr[current_row_index];        
+                                                                       out_pi[passed]=in_pi[current_row_index];        
+                                                                       out_ir[passed]=in_ir[current_row_index];        
+                                                                       out_jc[col+1] = out_jc[col+1]+1;
+                                                                       passed++;
+                                                               }
+                                               }
+                                       }
+                               }
+                       }
+                       else{
+                               plhs[0] = mxCreateSparse(m,n,0,mxCOMPLEX);
+                       }
+               }
+               else{ /* for full matrices */
+                       /* read input */
+                       in_pr   = mxGetPr(prhs[0]);
+                       in_pi   = mxGetPr(prhs[0]);
+                       m               = mxGetM(prhs[0]);
+                       n               = mxGetN(prhs[0]);
+                       thres   = mxGetScalar(prhs[1]);
+
+                       /* Count new nonzeros */
+                       newnnz=0;
+                       for(i=0; i<m*n; i++){
+                               if (sqrt(in_pr[i]*in_pr[i] + in_pi[i]*in_pi[i])>thres) {newnnz++;}
+                       }
+
+                       if (newnnz>0){
+                               /* create output */
+                               plhs[0]         = mxCreateSparse(m,n,newnnz,mxCOMPLEX);
+                               if (plhs[0]==NULL)
+                                       mexErrMsgTxt("Could not allocate enough memory!\n");
+                               out_pr          = mxGetPr(plhs[0]);
+                               out_pi          = mxGetPi(plhs[0]);
+                               out_ir          = mxGetIr(plhs[0]);
+                               out_jc          = mxGetJc(plhs[0]);
+                               passed          = 0;
+                               out_jc[0]       = 0;
+
+                               for (col=0; col<n; col++){
+                                       out_jc[col+1] = out_jc[col];
+                                       for (current_row_index=0; current_row_index<m; current_row_index++){
+                                                       if (sqrt(in_pr[current_row_index+m*col]*in_pr[current_row_index+m*col] +
+                                                                        in_pi[current_row_index+m*col]*in_pi[current_row_index+m*col]) > thres){
+                                                               
+                                                               out_pr[passed]=in_pr[current_row_index+m*col];  
+                                                               out_ir[passed]=current_row_index;       
+                                                               out_jc[col+1] = out_jc[col+1]+1;
+                                                               passed++;
+                                                       }
+                                       }
+                               }
+                       }
+                       else{
+                               plhs[0] = mxCreateSparse(m,n,0,mxCOMPLEX);
+                       }
+               }
+       }
+       else { 
+       /* Check data type of input argument  */
+       if (mxIsSparse(prhs[0])){
+
+                       /* read input */
+                       in_pr   = mxGetPr(prhs[0]);
+                       in_ir   = mxGetIr(prhs[0]);
+               in_jc   = mxGetJc(prhs[0]);
+                       nzmax   = mxGetNzmax(prhs[0]);
+                       n               = mxGetN(prhs[0]);
+                       m               = mxGetM(prhs[0]);
+                       thres   = mxGetScalar(prhs[1]);
+
+                       /* Count new nonzeros */
+                       newnnz=0;
+                       for(i=0; i<nzmax; i++){
+                               if ((fabs(in_pr[i]))>thres) {newnnz++;}
+                       }
+
+                       if (newnnz>0){
+                               /* create output */
+                               plhs[0]         = mxCreateSparse(m,n,newnnz,mxREAL);
+                               if (plhs[0]==NULL)
+                                       mexErrMsgTxt("Could not allocate enough memory!\n");
+                               out_pr          = mxGetPr(plhs[0]);
+                       out_ir          = mxGetIr(plhs[0]);
+                       out_jc          = mxGetJc(plhs[0]);
+                               passed          = 0;
+                               out_jc[0]       = 0;
+                               for (col=0; col<n; col++){
+                                       starting_row_index = in_jc[col];
+                                       stopping_row_index = in_jc[col+1];
+                                       out_jc[col+1] = out_jc[col];
+                                       if (starting_row_index == stopping_row_index)
+                                               continue;
+                                       else {
+                                               for (current_row_index = starting_row_index;
+                                                       current_row_index < stopping_row_index;
+                                                       current_row_index++)  {
+                                                               if (fabs(in_pr[current_row_index])>thres){
+                                                                       out_pr[passed]=in_pr[current_row_index];        
+                                                                       out_ir[passed]=in_ir[current_row_index];        
+                                                                       out_jc[col+1] = out_jc[col+1]+1;
+                                                                       passed++;
+                                                               }
+                                               }
+                                       }
+                               }
+                       }
+                       else{
+                       plhs[0] = mxCreateSparse(m,n,0,mxREAL);
+                       }
+               }
+               else{ /* for full matrices */
+                       /* read input */
+                       in_pr   = mxGetPr(prhs[0]);
+                       n               = mxGetN(prhs[0]);
+                       m               = mxGetM(prhs[0]);
+                       thres   = mxGetScalar(prhs[1]);
+
+                       /* Count new nonzeros */
+                       newnnz=0;
+                       for(i=0; i<m*n; i++){
+                               if ((fabs(in_pr[i]))>thres) {newnnz++;}
+                       }
+
+                       if (newnnz>0){
+                               /* create output */
+                               plhs[0]         = mxCreateSparse(m,n,newnnz,mxREAL);
+                               if (plhs[0]==NULL)
+                                       mexErrMsgTxt("Could not allocate enough memory!\n");
+                               out_pr          = mxGetPr(plhs[0]);
+                       out_ir          = mxGetIr(plhs[0]);
+                       out_jc          = mxGetJc(plhs[0]);
+                               passed          = 0;
+                       out_jc[0]       = 0;
+
+                               for (col=0; col<n; col++){
+                                       out_jc[col+1] = out_jc[col];
+                                       for (current_row_index=0; current_row_index<m; current_row_index++){
+                                                       if (fabs(in_pr[current_row_index+m*col])>thres){
+                                                               out_pr[passed]=in_pr[current_row_index+m*col];  
+                                                               out_ir[passed]=current_row_index;       
+                                                               out_jc[col+1] = out_jc[col+1]+1;
+                                                               passed++;
+                                                       }
+                                       }
+                               }
+                       }
+                       else{
+                       plhs[0] = mxCreateSparse(m,n,0,mxREAL);
+                       }
+               }
+       }
+}
+ 

diff --git a/THESE/codes/graphe/Ncut_9/sparsifyc.mexa64 b/THESE/codes/graphe/Ncut_9/sparsifyc.mexa64
new file mode 100755 (executable)
index 0000000..a787605
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diff --git a/THESE/codes/graphe/Ncut_9/spmtimesd.cpp b/THESE/codes/graphe/Ncut_9/spmtimesd.cpp
new file mode 100755 (executable)
index 0000000..5c976cd
--- /dev/null
+++ b/THESE/codes/graphe/Ncut_9/spmtimesd.cpp
@@ -0,0 +1,141 @@
+/*================================================================
+* spmtimesd.c
+* This routine computes a sparse matrix times a diagonal matrix
+* whose diagonal entries are stored in a full vector.
+*
+* Examples: 
+*     spmtimesd(m,d,[]) = diag(d) * m,
+*     spmtimesd(m,[],d) = m * diag(d)
+*     spmtimesd(m,d1,d2) = diag(d1) * m * diag(d2)
+*     m could be complex, but d is assumed real
+*
+* Stella X. Yu's first MEX function, Nov 9, 2001.
+
+% test sequence:
+
+m = 1000;
+n = 2000;
+a=sparse(rand(m,n));
+d1 = rand(m,1);
+d2 = rand(n,1);
+tic; b=spmtimesd(a,d1,d2); toc
+tic; bb = spdiags(d1,0,m,m) * a * spdiags(d2,0,n,n); toc
+e = (bb-b);
+max(abs(e(:)))
+
+*=================================================================*/
+
+# include "mex.h"
+
+void mexFunction(
+    int nargout,
+    mxArray *out[],
+    int nargin,
+    const mxArray *in[]
+)
+{
+    /* declare variables */
+    int i, j, k, m, n, nzmax, xm, yn;
+    mwIndex *pir, *pjc, *qir, *qjc;
+    double *x, *y, *pr, *pi, *qr, *qi;
+    
+    /* check argument */
+    if (nargin != 3) {
+        mexErrMsgTxt("Three input arguments required");
+    }
+    if (nargout>1) {
+        mexErrMsgTxt("Too many output arguments.");
+    }
+    if (!(mxIsSparse(in[0]))) {
+        mexErrMsgTxt("Input argument #1 must be of type sparse");
+    }
+    if ( mxIsSparse(in[1]) || mxIsSparse(in[2]) ) {
+        mexErrMsgTxt("Input argument #2 & #3 must be of type full");
+    }
+  
+    /* computation starts */
+    m = mxGetM(in[0]);
+    n = mxGetN(in[0]);
+    pr = mxGetPr(in[0]);
+    pi = mxGetPi(in[0]);
+    pir = mxGetIr(in[0]);
+    pjc = mxGetJc(in[0]);
+ 
+    i = mxGetM(in[1]); 
+    j = mxGetN(in[1]);
+    xm = ((i>j)? i: j);
+
+    i = mxGetM(in[2]); 
+    j = mxGetN(in[2]);
+    yn = ((i>j)? i: j);
+   
+    if ( xm>0 && xm != m) {
+        mexErrMsgTxt("Row multiplication dimension mismatch.");
+    }
+    if ( yn>0 && yn != n) {
+        mexErrMsgTxt("Column multiplication dimension mismatch.");
+    }
+    
+ 
+    nzmax = mxGetNzmax(in[0]);
+    mxComplexity cmplx = (pi==NULL ? mxREAL : mxCOMPLEX);    
+    out[0] = mxCreateSparse(m,n,nzmax,cmplx);
+    if (out[0]==NULL) {
+        mexErrMsgTxt("Not enough space for the output matrix.");
+    }    
+   
+    qr = mxGetPr(out[0]);
+    qi = mxGetPi(out[0]);
+    qir = mxGetIr(out[0]);
+    qjc = mxGetJc(out[0]);
+    
+    /* left multiplication */
+    x = mxGetPr(in[1]);
+    if (yn==0) {
+        for (j=0; j<n; j++) {
+            qjc[j] = pjc[j];
+            for (k=pjc[j]; k<pjc[j+1]; k++) {
+                i = pir[k];   
+                qir[k] = i;
+                 qr[k] = x[i] * pr[k];
+                 if (cmplx) {
+                     qi[k] = x[i] * pi[k];
+                 }
+            }
+        }
+        qjc[n] = k;
+        return;
+    }
+    
+    /* right multiplication */
+    y = mxGetPr(in[2]);
+    if (xm==0) {
+        for (j=0; j<n; j++) {
+            qjc[j] = pjc[j];
+            for (k=pjc[j]; k<pjc[j+1]; k++) {
+                qir[k] = pir[k];
+                qr[k]  = pr[k] * y[j];
+                if (cmplx) {
+                    qi[k] = qi[k] * y[j];
+                }
+            }
+       }
+       qjc[n] = k;
+       return;
+   }
+    
+   /* both sides */
+   for (j=0; j<n; j++) {
+       qjc[j] = pjc[j];
+       for (k=pjc[j]; k<pjc[j+1]; k++) {
+           i = pir[k];
+           qir[k]= i;
+           qr[k] = x[i] * pr[k] * y[j];
+           if (cmplx) {
+               qi[k] = x[i] * qi[k] * y[j];      
+           }
+       }
+       qjc[n] = k;
+   }
+   
+}   

diff --git a/THESE/codes/graphe/Ncut_9/spmtimesd.mexa64 b/THESE/codes/graphe/Ncut_9/spmtimesd.mexa64
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index 0000000..a991416
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diff --git a/THESE/codes/graphe/cochon128_ncuts_2seg.png b/THESE/codes/graphe/cochon128_ncuts_2seg.png
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diff --git a/THESE/codes/graphe/cochon128_ncuts_3seg.png b/THESE/codes/graphe/cochon128_ncuts_3seg.png
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diff --git a/THESE/codes/graphe/cochon128_ncuts_4seg.png b/THESE/codes/graphe/cochon128_ncuts_4seg.png
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diff --git a/THESE/codes/graphe/cochon128_ncuts_5seg.png b/THESE/codes/graphe/cochon128_ncuts_5seg.png
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diff --git a/THESE/codes/graphe/graphcuts.m b/THESE/codes/graphe/graphcuts.m
new file mode 100644 (file)
index 0000000..4c08037
--- /dev/null
+++ b/THESE/codes/graphe/graphcuts.m
@@ -0,0 +1,55 @@
+function [Ncut] = graphcuts(I,pad,MAXVAL)\r
+% function [Ncut] = graphcuts(I)\r
+% Input: I image\r
+% pad: spatial connectivity; eg. 3\r
+% MAXVAL: maximum image value\r
+% Output: Ncut: Binary map 0 or 1 corresponding to image segmentation\r
+I = double(I); [H,W] = size(I);\r
+% Find weights between nodes I1 and I2, w = exp(a*abs(I1-I2));\r
+% Set a to have a weight of 0.01 for diff = MAXVAL\r
+a = log(0.01)/MAXVAL; x = [0:MAXVAL/100:MAXVAL]'; y = exp(a*x);\r
+figure;plot(x,y);xlabel('intensity diff');ylabel('weights'); title('weights')\r
+ws = 2*pad + 1;\r
+if(ws <= 3)\r
+    ws = 3;\r
+end\r
+%Build the weight matrix\r
+disp('Building Weight Matrix'); close all; tic\r
+WM = zeros(H*W,H*W); countWM = 0;\r
+for kk = 1:W\r
+for jj = 1:H\r
+mask = logical(zeros(H,W));\r
+cs = kk-pad; ce = kk+pad; rs = jj-pad; re = jj+pad;\r
+if(cs<1) \r
+    cs = 1; \r
+end;\r
+if(ce>W)\r
+    ce = W; \r
+end;\r
+if(rs<1) \r
+    rs = 1;\r
+end;\r
+if(re>H) \r
+    re = H; \r
+end;\r
+mask(rs:re,cs:ce) = 1;\r
+idx = find(mask==1);\r
+p = abs(I(idx) - I(jj,kk)); p = exp(a*p);\r
+countWM = countWM + 1; WM(countWM,idx) = p(:)';\r
+end\r
+end\r
+ttime = toc; disp(sprintf('Time for generating weight matrix = %f',ttime)); clear countWM\r
+% Weight between a node and iteself is 0\r
+for jj = 1:H*W \r
+    WM(jj,jj) = 0; \r
+end; \r
+WM = sparse(WM);\r
+% Shi and Malik Algorithm: second smallest eigen vector\r
+disp('Finding Eigen Vector');\r
+d = sum(WM,2); D = diag(d); tic\r
+B = (D-WM); B = (B+B')/2; OPTS.disp = 0;\r
+[v,d,flag] = eigs(B,D,2,'SA',OPTS); ttime = toc;\r
+disp(sprintf('Time for finding eigen vector = %f',ttime)); clear OPTS\r
+y = v(:,2);\r
+Ncut = reshape(y,H,W);\r
+Ncut = Ncut > 0;
\ No newline at end of file

diff --git a/THESE/codes/kmeans/cochon128_kmeans_2seg.png b/THESE/codes/kmeans/cochon128_kmeans_2seg.png
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diff --git a/THESE/codes/kmeans/cochon128_kmeans_3seg.png b/THESE/codes/kmeans/cochon128_kmeans_3seg.png
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diff --git a/THESE/codes/kmeans/cochon128_kmeans_4seg.png b/THESE/codes/kmeans/cochon128_kmeans_4seg.png
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diff --git a/THESE/codes/kmeans/cochon128_kmeans_5seg.png b/THESE/codes/kmeans/cochon128_kmeans_5seg.png
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diff --git a/THESE/codes/kmeans/kmeans.m b/THESE/codes/kmeans/kmeans.m
new file mode 100644 (file)
index 0000000..82b9eda
--- /dev/null
+++ b/THESE/codes/kmeans/kmeans.m
@@ -0,0 +1,80 @@
+function [mu,mask]=kmeans(ima,k)\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+%\r
+%   kmeans image segmentation\r
+%\r
+%   Input:\r
+%          ima: grey color image\r
+%          k: Number of classes\r
+%   Output:\r
+%          mu: vector of class means \r
+%          mask: clasification image mask\r
+%\r
+%   Author: Jose Vicente Manjon Herrera\r
+%    Email: jmanjon@fis.upv.es\r
+%     Date: 27-08-2005\r
+%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+% check image\r
+ima=double(ima);\r
+copy=ima;         % make a copy\r
+ima=ima(:);       % vectorize ima\r
+mi=min(ima);      % deal with negative \r
+ima=ima-mi+1;     % and zero values\r
+\r
+s=length(ima);\r
+\r
+% create image histogram\r
+\r
+m=max(ima)+1;\r
+h=zeros(1,m);\r
+hc=zeros(1,m);\r
+\r
+for i=1:s\r
+  if(ima(i)>0) h(ima(i))=h(ima(i))+1;end;\r
+end\r
+ind=find(h);\r
+hl=length(ind);\r
+\r
+% initiate centroids\r
+\r
+mu=(1:k)*m/(k+1);\r
+\r
+% start process\r
+\r
+while(true)\r
+  \r
+  oldmu=mu;\r
+  % current classification  \r
+ \r
+  for i=1:hl\r
+      c=abs(ind(i)-mu);\r
+      cc=find(c==min(c));\r
+      hc(ind(i))=cc(1);\r
+  end\r
+  \r
+  %recalculation of means  \r
+  \r
+  for i=1:k, \r
+      a=find(hc==i);\r
+      mu(i)=sum(a.*h(a))/sum(h(a));\r
+  end\r
+  \r
+  if(mu==oldmu) break;end;\r
+  \r
+end\r
+\r
+% calculate mask\r
+s=size(copy);\r
+mask=zeros(s);\r
+for i=1:s(1),\r
+for j=1:s(2),\r
+  c=abs(copy(i,j)-mu);\r
+  a=find(c==min(c));  \r
+  mask(i,j)=a(1);\r
+end\r
+end\r
+\r
+mu=mu+mi-1;   % recover real range\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Biomed_demo.m b/THESE/codes/meanshift/Ms_segmenter/Biomed_demo.m
new file mode 100644 (file)
index 0000000..5547b24
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Biomed_demo.m
@@ -0,0 +1,4 @@
+global Biomed_on;\r
+Biomed_on = 1;\r
+Segment_demo;\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd1.m b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd1.m
new file mode 100644 (file)
index 0000000..c60b398
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd1.m
@@ -0,0 +1,67 @@
+function [New_image, Sorted_modes] = ...\r
+   Color_segment_svd (Luv_image, Window_radius, Min_group_size)\r
+% Now first tries to find "the most valuable components" of the image by\r
+% using svd decomposition, which gives 3 matrices - U, having the same number\r
+% of rows as the original vector, and 2 matrices - diagonal which indicates\r
+% weights of the columns, and V which is disregarded at the moment.  Then the\r
+% original meanshift algorithm is applied to either first or two first columns\r
+% of U, both done sufficiently quickly and more or less reliably\r
+\r
+% Min_group_size : need at least that many pixels to qualify for a color\r
+Rows = size(Luv_image, 1);\r
+Cols = size(Luv_image, 2);\r
+Min_image_size = 0.05*Rows*Cols;               % to stop when image is small\r
+\r
+Luv_vect = reshape(shiftdim(Luv_image, 2), [3, Rows*Cols]);\r
+[U, S, V] = svd(double(Luv_vect'), 0);\r
+\r
+% now segmenting based on the first column of U only\r
+U_col = U(:, 1); \r
+Max = max(U_col);\r
+Min = min(U_col);\r
+U_new = round((U_col-Min*ones(size(U_col)))/(Max-Min)*255);\r
+Gray_image = reshape(U_new, [Rows, Cols]);\r
+\r
+% creating a new array - color based\r
+Cumulative = sparse(1, max(max(Gray_image))+1);\r
+for i=1:Rows\r
+   for j=1:Cols\r
+      Cumulative(Gray_image(i, j)+1) = Cumulative(Gray_image(i, j)+1)+1;\r
+   end\r
+end\r
+\r
+Cumulative(1) = 0;             % corresponds to only manually zeroed out regions\r
+k = 0; L = length(Cumulative);\r
+while sum(Cumulative) > Min_image_size\r
+   Non_zero_pos = find(Cumulative > 0);\r
+   if length(Non_zero_pos) == 0\r
+      break;\r
+   end\r
+   \r
+   Initial = Non_zero_pos(1);\r
+   [Mode, Number_values] = M_shift1(Cumulative, Window_radius, Initial);\r
+   Mode = full(round(Mode));\r
+   \r
+   if Number_values > Min_group_size   % good group\r
+               k = k+1;\r
+      Modes(k) = Mode;\r
+      Cumulative(max(1, Mode-Window_radius):min(L, Mode+Window_radius)) = ...\r
+      zeros(size(Cumulative(max(1, Mode-Window_radius): ...\r
+      min(L, Mode+Window_radius))));\r
+   else\r
+       Cumulative(max(1, Initial-Window_radius):min(L, Initial+Window_radius))...\r
+                       = zeros(size(Cumulative(max(1, Initial-Window_radius): ...\r
+       min(L, Initial+Window_radius))));\r
+   end\r
+end\r
+\r
+Sorted_modes = sort(Modes);\r
+New_image = zeros(size(Gray_image));\r
+for i=1:k\r
+       To_group = find((Gray_image >= Sorted_modes(i)-Window_radius)...\r
+               & (Gray_image < Sorted_modes(i)+Window_radius));\r
+       New_image(To_group) = i;\r
+end\r
+% Gray_image == 0 added to provide for background detection\r
+To_zero = find(Gray_image == 0);\r
+New_image(To_zero) = 0;\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd2.m b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd2.m
new file mode 100644 (file)
index 0000000..1486443
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd2.m
@@ -0,0 +1,81 @@
+function [New_image, Modes] = ...\r
+   Color_segment_svd2 (Luv_image, Wind, Min_group_size)\r
+% Now first tries to find "the most valuable components" of the image by\r
+% using svd decomposition, which gives 3 matrices - U, having the same number\r
+% of rows as the original vector, and 2 matrices - diagonal which indicates\r
+% weights of the columns, and V which is disregarded at the moment.  Then the\r
+% original meanshift algorithm is applied to either first or two first columns\r
+% of U, both done sufficiently quickly and more or less reliably\r
+\r
+% Min_group_size : need at least that many pixels to qualify for a color\r
+Rows = size(Luv_image, 1);\r
+Cols = size(Luv_image, 2);\r
+Min_image_size = 0.005*Rows*Cols;              % to stop when image is small\r
+\r
+Luv_vect = reshape(shiftdim(Luv_image, 2), [3, Rows*Cols]);\r
+[U, S, V] = svd(double(Luv_vect'), 0);\r
+\r
+% now segmenting based on the first and second columns of U only\r
+U_col = U(:, 1:2);\r
+U_col(:, 2) = U_col(:, 2)*S(2, 2)/S(1, 1);             % scaling it\r
+Max = max(max(U_col));\r
+Min = min(min(U_col));\r
+Max_axis = 255;\r
+U_new = round((U_col-Min*ones(size(U_col)))/(Max-Min)*Max_axis)';\r
+\r
+% creating a new matrix - color based\r
+Cumulative = sparse(Max_axis+1, Max_axis+1);\r
+for i=1:size(U_new, 2)\r
+       Cumulative(U_new(1, i)+1, U_new(2, i)+1) = ...\r
+               Cumulative(U_new(1, i)+1, U_new(2, i)+1) + 1;\r
+end\r
+\r
+% for background detection only\r
+Cumulative(1, 1) = 0;\r
+\r
+k = 0; L = Max_axis+1;  Max_iterats = 1000;\r
+while sum(sum(Cumulative)) > Min_image_size\r
+   Non_zero_pos = find(Cumulative > 0);\r
+   if length(Non_zero_pos) == 0\r
+      break;\r
+   end\r
+   \r
+       [Init(1), Init(2)] = ind2sub(size(Cumulative), Non_zero_pos(1));\r
+   \r
+   [Mode, Number_values] = M_shift2(Cumulative, Wind, Init, Min_group_size/5);\r
+   Mode = full(round(Mode));\r
+   if Number_values > Min_group_size   % good group\r
+               k = k+1;\r
+      Modes(:, k) = Mode';\r
+               Cumulative(...\r
+         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ...\r
+         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)) = ...\r
+         zeros(size(Cumulative(...\r
+         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ...\r
+         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind))));\r
+   else\r
+               Cumulative(...\r
+         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ...\r
+         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)) = ...\r
+         zeros(size(Cumulative(...\r
+         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ...\r
+         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind))));\r
+   end\r
+       Max_iterats = Max_iterats-1;\r
+       if Max_iterats < 0\r
+               break;\r
+       end\r
+end\r
+\r
+Ones = ones(1, size(Luv_vect, 2));\r
+New_image = zeros(1, size(Luv_vect, 2));\r
+for i=1:size(Modes, 2)\r
+   Center = Modes(:, i) * Ones;\r
+   Norms = max(abs(Center - U_new));\r
+   To_take = find(Norms <= Wind);\r
+   New_image(To_take) = i;\r
+end\r
+% detecting background\r
+To_zero = find(Luv_vect(1, :) == 0);\r
+New_image(To_zero) = 0;\r
+New_image = reshape(New_image, [size(Luv_image, 1), size(Luv_image, 2)]);\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd3.m b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd3.m
new file mode 100644 (file)
index 0000000..268a8e9
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Color_segment_svd3.m
@@ -0,0 +1,103 @@
+function [New_image, Modes] = Color_segm_sps (Luv_img, Wind, Min_group_size)\r
+% Color_segment - segment an Luv image according to Meer's algorithm\r
+% gets image from the caller, finds significant maxima by mean shift algo \r
+% groups all the pixels \r
+% whose color falls within the given interval (with peak being the \r
+% center, and Window_radius being the radius), \r
+% Min_group_size : need at least that many pixels to qualify for a color\r
+\r
+Step = 4;\r
+% Step = 1;\r
+Max_axis = 255;\r
+Rows = size(Luv_img, 1);\r
+Cols = size(Luv_img, 2);\r
+Luv_image = Luv_img(1:Step:Rows, 1:Step:Cols, :);% taking part of it only\r
+Min_image_size = 0.005*Rows/Step*Cols/Step;% to stop when image is small\r
+\r
+Luv_vect = reshape(shiftdim(Luv_image, 2), ...\r
+       [3, size(Luv_image, 1)*size(Luv_image, 2)]);\r
+Max_luv = max(Luv_vect, [], 2);% scaling it\r
+for i=1:3\r
+   Luv_vect(i, :) = floor(Luv_vect(i, :)/(Max_luv(i)+1)*Max_axis)+1;\r
+end\r
+\r
+Wind = round(Wind/255*Max_axis);% as if the max was 255\r
+\r
+% creating a new array - color based\r
+for i=1:Max_axis+1\r
+   Cumulative{i} = sparse(Max_axis+1, Max_axis+1);\r
+end\r
+\r
+Nz = sparse(1, Max_axis+1);\r
+for i=1:size(Luv_vect, 2)\r
+   Cumulative{Luv_vect(1, i)}(Luv_vect(2, i), Luv_vect(3, i)) = ...\r
+      Cumulative{Luv_vect(1, i)}(Luv_vect(2, i), Luv_vect(3, i))+1;\r
+   Nz(Luv_vect(1, i)) = Nz(Luv_vect(1, i))+1;\r
+end\r
+\r
+% for background detection only\r
+Cumulative{1} = sparse(Max_axis+1, Max_axis+1);\r
+Nz(1) = 0;\r
+\r
+k = 0; L = Max_axis+1;\r
+while sum(Nz) > Min_image_size\r
+   Non_zero_pos = find(Nz > 0);\r
+   if length(Non_zero_pos) == 0\r
+      break;\r
+   end\r
+   Init(1) = Non_zero_pos(1);\r
+   Non_zero_pos_slice = find(Cumulative{Init(1)} > 0);\r
+   if length(Non_zero_pos_slice) == 0\r
+      fprintf('Something is wrong\n');\r
+               break;\r
+   end\r
+   \r
+   [Init(2), Init(3)] = ind2sub(size(Cumulative{Init(1)}), ...\r
+               Non_zero_pos_slice(1));\r
+   \r
+   [Mode, Number_values, Flag] = M_shift3_sps(Cumulative, Wind, ...\r
+               Init, Min_image_size/5);\r
+   \r
+   if not(Flag)% good group\r
+               k = k+1;\r
+      Modes(:, k) = Mode';\r
+      Nz = Nz-Number_values;\r
+      for i=max(1, Mode(1)-Wind):min(L, Mode(1)+Wind)\r
+         Cumulative{i}(...\r
+            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ...\r
+            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)) = ...\r
+          zeros(size(Cumulative{i}(...\r
+            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ...\r
+            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind))));\r
+      end\r
+   else\r
+      Nz = Nz-Number_values;\r
+      for i=max(1, Mode(1)-Wind):min(L, Mode(1)+Wind)\r
+         Cumulative{i}(...\r
+            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ...\r
+            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)) = ...\r
+          zeros(size(Cumulative{i}(...\r
+            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ...\r
+            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind))));\r
+      end\r
+   end\r
+end\r
+\r
+Luv_vect = reshape(shiftdim(Luv_img, 2), [3, Rows*Cols]);\r
+for i=1:3\r
+   Luv_vect(i, :) = floor(Luv_vect(i, :)/(Max_luv(i)+1)*Max_axis)+1;\r
+end\r
+\r
+Ones = ones(1, size(Luv_vect, 2));\r
+New_image = zeros(1, size(Luv_vect, 2));\r
+for i=1:size(Modes, 2)\r
+   Center = Modes(:, i) * Ones;\r
+   Norms = max(abs(Center - Luv_vect));\r
+   To_take = find(Norms <= Wind);\r
+   New_image(To_take) = i;\r
+end\r
+\r
+% detecting background\r
+To_zero = find(Luv_vect(1, :) == 0);\r
+New_image(To_zero) = 0;\r
+New_image = reshape(New_image, [Rows, Cols]);   \r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Cut_boundary_reg.m b/THESE/codes/meanshift/Ms_segmenter/Cut_boundary_reg.m
new file mode 100644 (file)
index 0000000..3c2375b
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Cut_boundary_reg.m
@@ -0,0 +1,32 @@
+function [Boundary1, Inner1, Boundary2, Inner2] = ...\r
+   Cut_boundary(Region, Dense_pts)\r
+\r
+Min_x = min([Region.Shifts(1, 1), Dense_pts(1, :)]);\r
+Min_y = min([Region.Shifts(2, 1), Dense_pts(2, :)]);\r
+Max_x = max([Region.Shifts(1, 2), Dense_pts(1, :)]);\r
+Max_y = max([Region.Shifts(2, 2), Dense_pts(2, :)]);\r
+Image = zeros(Max_x-Min_x+1, Max_y-Min_y+1);\r
+\r
+Image(sub2ind(size(Image), ...\r
+       Region.Boundary(1, :)-Min_x+1, Region.Boundary(2, :)-Min_y+1)) = 1;\r
+Image(sub2ind(size(Image), Dense_pts(1,:)-Min_x+1, Dense_pts(2,:)-Min_y+1)) = 1;\r
+\r
+% take the inner points to be close to midpoint of the line, on both sides\r
+Ldp = size(Dense_pts, 2);\r
+Midpt = Dense_pts(:, floor(Ldp/2)) + [1-Min_x; 1-Min_y];\r
+Normal = [Dense_pts(2, Ldp)-Dense_pts(2, 1); Dense_pts(1, 1)-Dense_pts(1, Ldp)];\r
+Normal = Normal/norm(Normal);\r
+Inner1 = Midpt + floor(5*Normal);\r
+Inner2 = Midpt - floor(5*Normal);\r
+\r
+Image1 = double(bwfill(Image, Inner1(2), Inner1(1)));\r
+Image2 = bwfill(Image, Inner2(2), Inner2(1));\r
+% not Image has zeros at the position of boundary and cutting line.\r
+% thickening nearly restores the original image boundary\r
+Boundary1 = Extract_region_reg(bwmorph(Image1&not(Image),'thicken'), 1);\r
+Boundary2 = Extract_region_reg(bwmorph(Image2&not(Image),'thicken'), 1);\r
+\r
+Boundary1 = Boundary1 + [Min_x-1; Min_y-1]*ones(1, size(Boundary1, 2));\r
+Boundary2 = Boundary2 + [Min_x-1; Min_y-1]*ones(1, size(Boundary2, 2));\r
+Inner1 = Inner1 + [Min_x-1; Min_y-1];\r
+Inner2 = Inner2 + [Min_x-1; Min_y-1];\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Display_Boundary.m b/THESE/codes/meanshift/Ms_segmenter/Display_Boundary.m
new file mode 100644 (file)
index 0000000..92f26e9
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Display_Boundary.m
@@ -0,0 +1,11 @@
+function Display_Boundary (Boundary)\r
+\r
+Max_x = max(Boundary(1, :));\r
+Min_x = min(Boundary(1, :));\r
+Max_y = max(Boundary(2, :));\r
+Min_y = min(Boundary(2, :));\r
+\r
+Mb = zeros(Max_x-Min_x+1, Max_y-Min_y+1);\r
+Mb(sub2ind(size(Mb), Boundary(1, :)+1-Min_x, Boundary(2, :)+1-Min_y)) = 1;\r
+\r
+figure; image(Mb*255); colormap(gray(256));\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Display_Place_at.m b/THESE/codes/meanshift/Ms_segmenter/Display_Place_at.m
new file mode 100644 (file)
index 0000000..07bc9d6
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Display_Place_at.m
@@ -0,0 +1,4 @@
+function Display_Place_at (Point)\r
+% to display the pairs of points\r
+fprintf ('%d %d\n', Point(1), Point(2));\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Draw_rect.m b/THESE/codes/meanshift/Ms_segmenter/Draw_rect.m
new file mode 100644 (file)
index 0000000..ef80099
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Draw_rect.m
@@ -0,0 +1,12 @@
+function Lines = Draw_rect (Hsize, Vsize, Center, Color)\r
+\r
+Upper_left = Center + [-Hsize/2, Vsize/2];  \r
+Upper_right = Center +[Hsize/2, Vsize/2];\r
+Lower_left = Center - [Hsize/2, Vsize/2];\r
+Lower_right = Center +[Hsize/2, -Vsize/2];\r
+\r
+Lines = line('XData', [Upper_left(1), Upper_right(1), Lower_right(1), ...\r
+       Lower_left(1), Upper_left(1)], ...\r
+                                'YData', [Upper_left(2), Upper_right(2), Lower_right(2), ...\r
+       Lower_left(2), Upper_left(2)], 'Color', Color, 'LineStyle', '-', ...\r
+       'LineWidth', 2);\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Extract_region_reg.m b/THESE/codes/meanshift/Ms_segmenter/Extract_region_reg.m
new file mode 100644 (file)
index 0000000..b81f4f3
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Extract_region_reg.m
@@ -0,0 +1,19 @@
+function [Boundary, Area] = Extract_region (Image_given, Min_area)\r
+% Extract_region - given Initial_value (a pixel close to the actual\r
+% boundary of a segment), and Image containing the segment,\r
+% outputs the supposed boundary of the piece (not necessarily connected)\r
+% by finding the connected component containing the given point first,\r
+% and finding the perimeter of the piece second\r
+\r
+[Rows_i, Cols_i] = size(Image_given);\r
+Enclosing = zeros(Rows_i+2, Cols_i+2);\r
+Enclosing(2:Rows_i+1, 2:Cols_i+1) = Image_given;\r
+Area = bwarea(Enclosing);\r
+if Area < Min_area\r
+       Boundary = 0;\r
+       return;\r
+end\r
+Boundary = bwperim(Enclosing);\r
+[Rows, Cols] = find(Boundary);\r
+Boundary = [Rows'; Cols'];\r
+Boundary = Boundary-ones(size(Boundary));\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Fit_spline.m b/THESE/codes/meanshift/Ms_segmenter/Fit_spline.m
new file mode 100644 (file)
index 0000000..058c518
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Fit_spline.m
@@ -0,0 +1,37 @@
+function Dense_pts = Fit_spline (Pts)\r
+\r
+Xdata = Pts(1, :);\r
+Ydata = Pts(2, :);\r
+\r
+% making splines separately for X and Y, but with common parameter\r
+Length = 0;\r
+Param(1) = Length;\r
+for i=1:length(Xdata)-1\r
+       Length = Length + norm(Pts(:, i+1) - Pts(:, i));\r
+       Param(i+1) = Length;\r
+end\r
+\r
+% sampling at a dense set of points\r
+Gridpts = [min(Param):0.1:max(Param)];\r
+Xspline = round(spline(Param, Xdata, Gridpts));\r
+Yspline = round(spline(Param, Ydata, Gridpts));\r
+\r
+% if not sufficiently dense, adjusting those\r
+k = 0;  Dense_pts = zeros(2, 0);\r
+for i=1:length(Xspline)-1\r
+       if max(abs(Xspline(i+1)-Xspline(i)), abs(Yspline(i+1)-Yspline(i))) > 1\r
+               for j=0:abs(Xspline(i+1)-Xspline(i))\r
+                       k = k+1;\r
+                       Dense_pts(:, k) = [Xspline(i) + j*sign(Xspline(i+1)-Xspline(i)); ...\r
+                               Yspline(i)];\r
+               end\r
+               for j=0:abs(Yspline(i+1)-Yspline(i))\r
+                       k = k+1;\r
+                       Dense_pts(:, k) = [Xspline(i+1); ...\r
+                               Yspline(i) + j*sign(Yspline(i+1)-Yspline(i))];\r
+               end\r
+        else\r
+               k = k+1;\r
+               Dense_pts(:, k) = [Xspline(i); Yspline(i)];\r
+       end\r
+end\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Full_graph.m b/THESE/codes/meanshift/Ms_segmenter/Full_graph.m
new file mode 100644 (file)
index 0000000..e789173
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Full_graph.m
@@ -0,0 +1,3 @@
+function One = Full(Region1, Region2)\r
+% a test function\r
+One = 1;\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Gravitat.m b/THESE/codes/meanshift/Ms_segmenter/Gravitat.m
new file mode 100644 (file)
index 0000000..1cd753a
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Gravitat.m
@@ -0,0 +1,22 @@
+function Gravit = Gravitat(Gray_image)\r
+% finds out forces acting on pixels from other pixels\r
+[Rows, Cols] = size(Gray_image);\r
+Gray_image = double(Gray_image);\r
+\r
+% creating the filter\r
+Wind = 3;\r
+for i=1:2*Wind-1\r
+   for j=1:2*Wind-1\r
+      Filter(i, j) = exp(-abs(Wind-i) - abs(Wind-j));\r
+   end\r
+end\r
+Filter(Wind, Wind) = 0;\r
+\r
+Gravit = zeros(size(Gray_image(Wind:Rows-Wind+1, Wind:Cols-Wind+1)));\r
+for i=1:2*Wind-1\r
+   for j=1:2*Wind-1\r
+      Gravit = Gravit + Filter(i, j) * ...\r
+         exp(-abs(Gray_image(Wind:Rows-Wind+1, Wind:Cols-Wind+1) - ...\r
+         Gray_image(i:Rows-Wind+1+i-Wind, j:Cols-Wind+1+j-Wind)));\r
+   end\r
+end
\ No newline at end of file

diff --git a/THESE/codes/meanshift/Ms_segmenter/Gray_segment.m b/THESE/codes/meanshift/Ms_segmenter/Gray_segment.m
new file mode 100644 (file)
index 0000000..d62548c
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Gray_segment.m
@@ -0,0 +1,58 @@
+function [New_image, Sorted_modes] = ...\r
+   Gray_segm (Gray_image, Window_radius, Min_group_size)\r
+% Gray_segment - segment a gray-level image according to Meer's algorithm\r
+% reads image from the file, finds significant maxima by mean shift algo \r
+% (to generalize to the case of 3-d vectors), groups all the pixels \r
+% whose color falls within the given interval (with peak being the \r
+% center, and Window_radius being the radius), \r
+\r
+% Min_group_size : need at least that many pixels to qualify for a color\r
+\r
+Rows = size(Gray_image, 1);\r
+Cols = size(Gray_image, 2);\r
+Min_image_size = 0.005*Rows*Cols;              % to stop when image is small\r
+Gray_image = round(Gray_image);\r
+\r
+% creating a new array - color based\r
+Cumulative = sparse(1, max(max(Gray_image))+1);\r
+for i=1:Rows\r
+   for j=1:Cols\r
+      Cumulative(Gray_image(i, j)+1) = Cumulative(Gray_image(i, j)+1)+1;\r
+   end\r
+end\r
+\r
+Cumulative(1) = 0;             % corresponds to only manually zeroed out regions\r
+k = 0; L = length(Cumulative);\r
+while sum(Cumulative) > Min_image_size\r
+   Non_zero_pos = find(Cumulative > 0);\r
+   if length(Non_zero_pos) == 0\r
+      break;\r
+   end\r
+   \r
+   Initial = Non_zero_pos(1);\r
+   [Mode, Number_values] = M_shift1(Cumulative, Window_radius, Initial);\r
+   Mode = full(round(Mode));\r
+   \r
+   if Number_values > Min_group_size% good group\r
+               k = k+1;\r
+      Modes(k) = Mode;\r
+      Cumulative(max(1, Mode-Window_radius):min(L, Mode+Window_radius)) = ...\r
+      zeros(size(Cumulative(max(1, Mode-Window_radius): ...\r
+      min(L, Mode+Window_radius))));\r
+   else\r
+      Cumulative(max(1,Initial-Window_radius):min(L,Initial+Window_radius)) =...\r
+      zeros(size(Cumulative(max(1, Initial-Window_radius): ...\r
+      min(L, Initial+Window_radius))));\r
+   end\r
+end\r
+\r
+Sorted_modes = sort(Modes);\r
+New_image = zeros(size(Gray_image));\r
+for i=1:k\r
+       To_group = find((Gray_image >= Sorted_modes(i)-Window_radius)...\r
+               & (Gray_image < Sorted_modes(i)+Window_radius));\r
+       New_image(To_group) = i;\r
+end\r
+% Gray_image == 0 added to provide for background detection\r
+To_zero = find(Gray_image == 0);\r
+New_image(To_zero) = 0;\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/M_shift1.m b/THESE/codes/meanshift/Ms_segmenter/M_shift1.m
new file mode 100644 (file)
index 0000000..107dd69
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/M_shift1.m
@@ -0,0 +1,44 @@
+function [Mode, Number_values] = M_shift1 (Values, Window_radius, Initial)\r
+% assumes that Values contains cumulative information, i.e.\r
+% the number of elements of the particular value in each Values cell\r
+\r
+Current_val = round(Initial+2);\r
+Next_val = round(Initial);\r
+L = size(Values, 2);\r
+\r
+% global plot_number plot_xmin plot_xmax plot_ymin plot_ymax\r
+% if plot_number == 1\r
+%      figure;\r
+% end\r
+% II = find(Values > 0);\r
+% if plot_number == 1\r
+%      plot_xmin = min(II)-Window_radius/2;\r
+%      plot_xmax = max(II)+Window_radius/2;\r
+%      plot_ymin = min(Values)-Window_radius/2;\r
+%      plot_ymax = max(Values)+Window_radius/2;\r
+% end\r
+% subplot(2, 2, plot_number);\r
+% plot(Values, '.');\r
+% xlabel('feature space (intensity)');\r
+% ylabel('density');\r
+% axis([plot_xmin plot_xmax plot_ymin plot_ymax])\r
+% text(Next_val+Window_radius, Values(Next_val)-Window_radius/4, 'Initial');\r
+\r
+while abs(Next_val-Current_val) > 1\r
+\r
+%      Draw_rect(1.8*Window_radius, Window_radius, [Next_val, Values(Next_val)], 'magenta');\r
+\r
+       Current_val = Next_val;\r
+   Range = [max(1, Current_val-Window_radius): ...\r
+      min(L, Current_val+Window_radius)];\r
+   Window = Values(Range);\r
+   Sum_values = full(Window * Range');\r
+   Number_values = full(sum(Window));\r
+   if Number_values > 0\r
+      Next_val = round(Sum_values/Number_values);\r
+   end\r
+end\r
+Mode = Next_val;\r
+\r
+% text(Next_val+Window_radius, Values(Next_val)+Window_radius/4, 'Final');\r
+% plot_number = plot_number + 1;\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/M_shift2.m b/THESE/codes/meanshift/Ms_segmenter/M_shift2.m
new file mode 100644 (file)
index 0000000..f08a8b2
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/M_shift2.m
@@ -0,0 +1,62 @@
+function [Mode, Number_values] = M_shift2 (Values, Window_radius, Initial, Mv)\r
+% assumes that Values contains cumulative information, i.e.\r
+% the number of elements of the particular value in each Values cell\r
+\r
+Current_val = round(Initial+2);\r
+Next_val = round(Initial);\r
+L1 = size(Values, 1);  L2 = size(Values, 2);\r
+\r
+% global plot_number plot_xmin plot_xmax plot_ymin plot_ymax\r
+% if plot_number == 1\r
+%      figure;\r
+% end\r
+% [II, JJ] = find(Values > 0);\r
+% if plot_number == 1\r
+%      plot_xmin = min(II)-Window_radius/2;\r
+%      plot_xmax = max(II)+Window_radius/2;\r
+%      plot_ymin = min(JJ)-Window_radius/2;\r
+%      plot_ymax = max(JJ)+Window_radius/2;\r
+% end\r
+% \r
+% subplot(2, 2, plot_number);\r
+% plot(II, JJ, '.');\r
+% xlabel('first principal component');\r
+% ylabel('second prrincipal component');\r
+% axis([plot_xmin plot_xmax plot_ymin plot_ymax])\r
+% text(Next_val(1)+Window_radius/1.8, Next_val(2)-Window_radius/4, 'Initial');\r
+\r
+while norm(Next_val-Current_val) > 2\r
+\r
+%      Draw_rect(Window_radius, Window_radius, Next_val, 'magenta');\r
+\r
+       Current_val = Next_val;\r
+       clear x;\r
+   Window = Values(...\r
+               max(1, Current_val(1)-Window_radius): ...\r
+      min(L1, Current_val(1)+Window_radius), ...\r
+               max(1, Current_val(2)-Window_radius): ...\r
+               min(L2, Current_val(2)+Window_radius));\r
+       [x(1, :, :), x(2, :, :)] = ...\r
+      ndgrid(...\r
+      [max(1, Current_val(1)-Window_radius): ...\r
+      min(L1, Current_val(1)+Window_radius)], ...\r
+      [max(1, Current_val(2)-Window_radius): ...\r
+      min(L2, Current_val(2)+Window_radius)]);\r
+\r
+       for i=1:2\r
+               Sum_values(1, i) = sum(sum(squeeze(x(i, :, :)) .* Window));\r
+       end\r
+   Number_values = sum(sum(Window));\r
+       \r
+       if Number_values < Mv\r
+               Mode = Initial;\r
+               Number_values = 0;\r
+               return;\r
+       end\r
+\r
+   Next_val = round(Sum_values/Number_values);\r
+end\r
+Mode = Next_val;\r
+\r
+% text(Next_val(1)+Window_radius/1.8, Next_val(2)+Window_radius/4, 'Final');\r
+% plot_number = plot_number+1;\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/M_shift3.m b/THESE/codes/meanshift/Ms_segmenter/M_shift3.m
new file mode 100644 (file)
index 0000000..1ed6848
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/M_shift3.m
@@ -0,0 +1,44 @@
+function [Mode, Number_values] = M_shift3 (Values, Window_radius, Initial)\r
+% assumes that Values contains cumulative information, i.e.\r
+% the number of elements of the particular value in each Values cell\r
+\r
+Current_val = round(Initial+2);Next_val = round(Initial);\r
+L1 = size(Values, 1);L2 = size(Values, 2);L3 = size(Values, 3);\r
+Min_number_values = 1; % Window_radius^4\r
+\r
+while norm(Next_val-Current_val) > 1\r
+   Current_val = Next_val;\r
+   clear x;\r
+   [x(1, :, :, :), x(2, :, :, :), x(3, :, :, :)] = ...\r
+      ndgrid(...\r
+      [max(1, Current_val(1)-Window_radius): ...\r
+      min(L1, Current_val(1)+Window_radius)], ...\r
+      [max(1, Current_val(2)-Window_radius): ...\r
+      min(L2, Current_val(2)+Window_radius)], ...\r
+      [max(1, Current_val(3)-Window_radius): ...\r
+      min(L3, Current_val(3)+Window_radius)]);\r
+   \r
+   Window = Values(...\r
+      max(1, Current_val(1)-Window_radius): ...\r
+      min(L1, Current_val(1)+Window_radius), ...\r
+      max(1, Current_val(2)-Window_radius): ...\r
+      min(L2, Current_val(2)+Window_radius), ...\r
+      max(1, Current_val(3)-Window_radius): ...\r
+      min(L3, Current_val(3)+Window_radius));\r
+   \r
+   for i=1:3\r
+      Sum_values(1, i) = sum(sum(sum(squeeze(x(i, :, :, :)) .* Window)));\r
+   end\r
+   \r
+   Number_values = sum(sum(sum(Window)));\r
+   \r
+   if Number_values < Min_number_values\r
+      Mode = Initial;\r
+      Number_values = 0;\r
+      return;\r
+   end\r
+   \r
+   Next_val = round(Sum_values/Number_values);\r
+end\r
+Mode = Next_val;\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/M_shift3_sps.m b/THESE/codes/meanshift/Ms_segmenter/M_shift3_sps.m
new file mode 100644 (file)
index 0000000..999621d
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/M_shift3_sps.m
@@ -0,0 +1,48 @@
+function [Mode, Number_values, Flag] = M_shift3 (Values, Window_radius, Initial, Min_number)\r
+% assumes that Values contains cumulative information, i.e.\r
+% the number of elements of the particular value in each Values cell\r
+\r
+Current_val = round(Initial+2);Next_val = round(Initial);\r
+L1 = length(Values);\r
+L2 = size(Values{1}, 1);\r
+L3 = size(Values{1}, 2);\r
+\r
+while norm(Next_val-Current_val) > 1\r
+   Current_val = Next_val;\r
+   clear x; Sum_values(1, 1:3) = zeros(1, 3);Number_values = sparse(1, L1);\r
+   for i=max(1, Current_val(1)-Window_radius):min(L1, Current_val(1)+Window_radius)\r
+      [x(1, :, :), x(2, :, :)] = ...\r
+         ndgrid(...\r
+         [max(1, Current_val(2)-Window_radius): ...\r
+            min(L2, Current_val(2)+Window_radius)], ...\r
+         [max(1, Current_val(3)-Window_radius): ...\r
+            min(L3, Current_val(3)+Window_radius)]);\r
+      \r
+      Window = Values{i}(...\r
+         max(1, Current_val(2)-Window_radius): ...\r
+         min(L2, Current_val(2)+Window_radius), ...\r
+         max(1, Current_val(3)-Window_radius): ...\r
+         min(L3, Current_val(3)+Window_radius));\r
+      \r
+      Number_values(i) = sum(sum(Window));\r
+      Sum_values(1, 1) = Sum_values(1, 1) + Number_values(i)*i;\r
+      \r
+      for j=1:2\r
+         Sum_values(1, j+1) = Sum_values(1, j+1)+sum(sum(squeeze(x(j, :, :)) .* Window));\r
+      end\r
+   end\r
+   \r
+   Number_values_sum = sum(Number_values);\r
+   \r
+   if Number_values_sum < Min_number\r
+      Mode = Initial;\r
+      Flag = 1;\r
+return;\r
+   end\r
+   \r
+   Next_val = round(Sum_values/Number_values_sum);\r
+end\r
+% Mode = Next_val;\r
+Mode = Current_val;\r
+Flag = 0;\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Merge_regions.m b/THESE/codes/meanshift/Ms_segmenter/Merge_regions.m
new file mode 100644 (file)
index 0000000..59508ae
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Merge_regions.m
@@ -0,0 +1,28 @@
+function [New_inner_pts, New_boundary, New_shifts] = ...\r
+       Merge_regions (Region1, Region2);\r
+\r
+Min_x = min(Region1.Shifts(1, 1), Region2.Shifts(1, 1));\r
+Max_x = max(Region1.Shifts(1, 2), Region2.Shifts(1, 2));\r
+Min_y = min(Region1.Shifts(2, 1), Region2.Shifts(2, 1));\r
+Max_y = max(Region1.Shifts(2, 2), Region2.Shifts(2, 2));\r
+\r
+Image1 = zeros(Max_x-Min_x+1, Max_y-Min_y+1);\r
+Image1(sub2ind(size(Image1), Region1.Boundary(1, :)-Min_x+1, ...\r
+       Region1.Boundary(2, :)-Min_y+1)) = 1;\r
+To_start_fill = [Region1.Inner_pts(2, :)-Min_y+1; ...\r
+       Region1.Inner_pts(1, :)-Min_x+1];\r
+Image1 = bwfill(Image1, To_start_fill(1, :), To_start_fill(2, :));\r
+\r
+Image2 = zeros(Max_x-Min_x+1, Max_y-Min_y+1);\r
+Image2(sub2ind(size(Image2), Region2.Boundary(1, :)-Min_x+1, ...\r
+       Region2.Boundary(2, :)-Min_y+1)) = 1;\r
+To_start_fill = [Region2.Inner_pts(2, :)-Min_y+1; ...\r
+       Region2.Inner_pts(1, :)-Min_x+1];\r
+Image2 = bwfill(Image2, To_start_fill(1, :), To_start_fill(2, :));\r
+\r
+Image1 = or(Image1, Image2);\r
+\r
+New_boundary = Extract_region_reg(Image1, 1);\r
+New_boundary = New_boundary + [Min_x-1; Min_y-1]*ones(1, size(New_boundary, 2));\r
+New_inner_pts = [Region1.Inner_pts, Region2.Inner_pts];\r
+New_shifts = [Min_x, Max_x; Min_y, Max_y];\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/My_inputdlg.m b/THESE/codes/meanshift/Ms_segmenter/My_inputdlg.m
new file mode 100644 (file)
index 0000000..47e8324
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/My_inputdlg.m
@@ -0,0 +1,255 @@
+function Answer=My_inputdlg(Prompt, Title, NumLines, DefAns)\r
+%INPUTDLG Input dialog box.\r
+%  Answer = inputdlg(Prompt) creates a modal dialog box that returns\r
+%  user input for multiple prompts in the cell array Answer.  Prompt\r
+%  is a cell array containing the Prompt strings.\r
+%\r
+%  Answer = inputdlg(Prompt,Title) specifies the Title for the dialog.\r
+%\r
+%  Answer = inputdlg(Prompt,Title,LineNo) specifies the number of lines\r
+%  for each answer in LineNo.  LineNo may be a constant value or a \r
+%  vector having one element per Prompt.\r
+%\r
+%  Answer = inputdlg(Prompt,Title,LineNo,DefAns) specifies the default\r
+%  answer to display for each Prompt.  DefAns must contain the same\r
+%  number of elements as Prompt and must be a cell array.\r
+%  Example:\r
+%  prompt={'Enter the matrix size:','Enter the colormap name:'};\r
+%  def={'20','hsv'};\r
+%  title='Input for Peaks function';\r
+%  lineNo=1;\r
+%  answer=inputdlg(prompt,title,lineNo,def);\r
+%\r
+%  See also TEXTWRAP.\r
+\r
+%  Loren Dean   May 24, 1995.\r
+%  Copyright (c) 1984-97 by The MathWorks, Inc.\r
+%  $Revision: 1.36 $\r
+\r
+%%%%%%%%%%%%%%%%%%%%%\r
+%%% General Info. %%%\r
+%%%%%%%%%%%%%%%%%%%%%\r
+Black      =[0       0        0      ]/255;\r
+LightGray  =[192     192      192    ]/255;\r
+LightGray2 =[160     160      164    ]/255;\r
+MediumGray =[128     128      128    ]/255;\r
+White      =[255     255      255    ]/255;\r
+\r
+%%%%%%%%%%%%%%%%%%%%\r
+%%% Nargin Check %%%\r
+%%%%%%%%%%%%%%%%%%%%\r
+if nargout~=1,error('Wrong number of output arguents for INPUTDLG');end\r
+if nargin<1,error('Too few arguments for INPUTDLG');end\r
+\r
+if nargin==1,Title=' ';end\r
+if nargin<=2, NumLines=1;end\r
+\r
+if ~iscell(Prompt),\r
+  Prompt={Prompt};\r
+end\r
+\r
+NumQuest=prod(size(Prompt));    \r
+\r
+if nargin<=3, \r
+  DefAns=cell(NumQuest,1);\r
+  for lp=1:NumQuest, DefAns{lp}=''; end\r
+end\r
+if nargin>4,error('Too many input arguments');end\r
+\r
+% Backwards Compatibility\r
+if isstr(NumLines),\r
+  warning(['Please see the INPUTDLG help for correct input syntax.' 10 ...\r
+           '         OKCallback no longer supported.' ]);\r
+  NumLines=1;\r
+end\r
+\r
+if length(NumLines)~=NumQuest,NumLines=ones(NumQuest,1)*NumLines(1);end\r
+\r
+if ~iscell(DefAns),\r
+  error('Default Answer must be a cell array in INPUTDLG.');  \r
+end\r
+\r
+%%%%%%%%%%%%%%%%%%%%%%%\r
+%%% Create InputFig %%%\r
+%%%%%%%%%%%%%%%%%%%%%%%\r
+FigWidth=300;FigHeight=100;\r
+FigPos(3:4)=[FigWidth FigHeight];\r
+FigColor=get(0,'Defaultuicontrolbackgroundcolor');\r
+InputFig=dialog(                            ...\r
+               'Visible'         ,'off'   , ...\r
+               'Name'            ,Title   , ...\r
+               'Pointer'         ,'arrow' , ...\r
+               'Units'           ,'points', ...\r
+               'UserData'        ,''      , ...\r
+               'Tag'             ,Title   , ...\r
+               'HandleVisibility','on'    , ...               \r
+               'Color'           ,FigColor, ...               \r
+               'NextPlot'        ,'add'     ...\r
+               );\r
+  \r
+\r
+%%%%%%%%%%%%%%%%%%%%%\r
+%%% Set Positions %%%\r
+%%%%%%%%%%%%%%%%%%%%%\r
+DefOffset=5;\r
+SmallOffset=2;\r
+\r
+DefBtnWidth=50;\r
+BtnHeight=20;\r
+BtnYOffset=DefOffset;\r
+BtnFontSize=get(0,'FactoryUIControlFontSize');\r
+BtnWidth=DefBtnWidth;\r
+TxtBackClr=FigColor;\r
+TxtForeClr=Black;\r
+\r
+StInfo.Style              ='text'     ;\r
+StInfo.Units              ='points'   ;   \r
+StInfo.FontSize           =BtnFontSize;\r
+StInfo.HorizontalAlignment='left'     ;\r
+StInfo.BackgroundColor    =TxtBackClr ;\r
+StInfo.ForegroundColor    =TxtForeClr ;\r
+StInfo.HandleVisibility   ='callback' ;\r
+\r
+\r
+EdInfo=StInfo;\r
+EdInfo.Style='edit';\r
+EdInfo.BackgroundColor=White;\r
+\r
+BtnInfo=StInfo;\r
+BtnInfo.Style='pushbutton';\r
+BtnInfo.HorizontalAlignment='center';\r
+\r
+% Determine # of lines for all Prompts\r
+ExtControl=uicontrol(StInfo, ...\r
+                     'String'   ,''         , ...    \r
+                     'Position' ,[DefOffset                  DefOffset  ...\r
+                                 0.95*(FigWidth-2*DefOffset) BtnHeight  ...\r
+                                ]            , ...\r
+                     'Visible'  ,'off'         ...\r
+                     );\r
+                     \r
+WrapQuest=cell(NumQuest,1);\r
+QuestPos=zeros(NumQuest,4);\r
+\r
+for ExtLp=1:NumQuest,\r
+  [WrapQuest{ExtLp},QuestPos(ExtLp,1:4)]= ...\r
+            textwrap(ExtControl,Prompt(ExtLp),75);\r
+end % for ExtLp\r
+delete(ExtControl);\r
+\r
+QuestHeight=QuestPos(:,4);\r
+\r
+TxtHeight=QuestHeight(1)/size(WrapQuest{1,1},1);\r
+EditHeight=TxtHeight*NumLines;\r
+EditHeight(NumLines==1)=EditHeight(NumLines==1)+4;\r
+\r
+FigHeight=(NumQuest+2)*DefOffset    + ...\r
+          BtnHeight+sum(EditHeight) + ...\r
+          sum(QuestHeight);%          + ...\r
+                           %NumQuest*SmallOffset;\r
+\r
+FigWidth=max(175,max(QuestPos(:,3)))+2*DefOffset;\r
+FigPos=get(InputFig,'Position');\r
+\r
+Temp=get(0,'Units');\r
+set(0,'Units','points');\r
+ScreenSize=get(0,'ScreenSize');\r
+set(0,'Units',Temp);\r
+\r
+FigPos(1)=(ScreenSize(3)-FigWidth)/2;\r
+FigPos(2)=(ScreenSize(4)-FigHeight)/2;\r
+FigPos(3)=FigWidth;\r
+FigPos(4)=FigHeight;\r
+set(InputFig,'Position',FigPos);\r
+\r
+TxtXOffset=DefOffset;\r
+TxtWidth=FigWidth-2*DefOffset;\r
+TxtForeClr=Black;\r
+TxtBackClr=get(InputFig,'Color');\r
+\r
+QuestYOffset=zeros(NumQuest,1);\r
+EditYOffset=zeros(NumQuest,1);\r
+QuestYOffset(1)=FigHeight-DefOffset-QuestHeight(1);\r
+EditYOffset(1)=QuestYOffset(1)-EditHeight(1);% -SmallOffset;\r
+\r
+for YOffLp=2:NumQuest,\r
+  QuestYOffset(YOffLp)=EditYOffset(YOffLp-1)-QuestHeight(YOffLp)-DefOffset;\r
+  EditYOffset(YOffLp)=QuestYOffset(YOffLp)-EditHeight(YOffLp); %-SmallOffset;\r
+end % for YOffLp\r
+\r
+QuestHandle=[];\r
+EditHandle=[];\r
+for lp=1:NumQuest,\r
+  QuestTag=['Prompt' num2str(lp)];\r
+  EditTag=['Edit' num2str(lp)];\r
+  QuestHandle(lp)=uicontrol(InputFig  ,                         ...\r
+                           StInfo     , ...\r
+                           'Max'      ,size(Prompt{lp},1), ...\r
+                           'Position' ,[ TxtXOffset QuestYOffset(lp) ...\r
+                                         TxtWidth   QuestHeight(lp)  ...\r
+                                       ]                      , ...\r
+                           'String'   ,WrapQuest{lp}       , ...\r
+                           'Tag'      ,QuestTag                 ...\r
+                           );\r
+\r
+  EditHandle(lp)=uicontrol(InputFig   ,                       ...\r
+                           EdInfo     , ...\r
+                          'Max'       ,NumLines(lp)         , ...\r
+                          'Position'  ,[ TxtXOffset EditYOffset(lp) ...\r
+                                         TxtWidth   EditHeight(lp)  ...\r
+                                       ]                    , ...\r
+                          'String'    ,DefAns{lp}           , ...\r
+                          'Tag'       ,QuestTag               ...\r
+                          );\r
+                                   \r
+end % for lp\r
+\r
+CBString='set(gcf,''UserData'',''Cancel'');uiresume';\r
+\r
+CancelHandle=uicontrol(InputFig   ,              ...\r
+                      BtnInfo     , ...\r
+                      'Position'  ,[ DefOffset DefOffset ...\r
+                                    BtnWidth  BtnHeight  ...\r
+                                   ]           , ...\r
+                      'String'    ,'Cancel'    , ...\r
+                      'Callback'  ,CBString    , ...\r
+                      'Tag'       ,'Cancel'      ...\r
+                      );\r
+                                   \r
+                                   \r
+CBString='set(gcf,''UserData'',''OK'');uiresume';\r
+\r
+OKHandle=uicontrol(InputFig    ,              ...\r
+                   BtnInfo     , ...\r
+                   'Position'  ,[ FigWidth-BtnWidth-DefOffset DefOffset ...\r
+                                  BtnWidth                    BtnHeight ...\r
+                                ]           , ...\r
+                  'Callback'   ,CBString    , ...\r
+                  'String'     ,'OK'        , ...\r
+                  'Tag'        ,'OK'          ...\r
+                  );\r
+    \r
+set(InputFig, 'KeyPressFcn', CBString);\r
+set(InputFig ,'Visible','on');\r
+set(findobj(InputFig),'Units','normalized','HandleVisibility','callback');\r
+set(InputFig,'Units','points');\r
+\r
+uiwait(InputFig);\r
+\r
+TempHide=get(0,'ShowHiddenHandles');\r
+set(0,'ShowHiddenHandles','on');\r
+\r
+if any(get(0,'Children')==InputFig),\r
+  Answer={};\r
+  if strcmp(get(InputFig,'UserData'),'OK'),\r
+    Answer=cell(NumQuest,1);\r
+    for lp=1:NumQuest,\r
+      Answer(lp)=get(EditHandle(lp),{'String'});\r
+    end % for\r
+  end % if strcmp\r
+  delete(InputFig);\r
+else,\r
+  Answer={};\r
+end % if any\r
+\r
+set(0,'ShowHiddenHandles',TempHide);\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Neighbor.m b/THESE/codes/meanshift/Ms_segmenter/Neighbor.m
new file mode 100644 (file)
index 0000000..66131dc
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Neighbor.m
@@ -0,0 +1,23 @@
+function Answer = Neighbor(Region1, Region2)\r
+\r
+Min_x = min(Region1.Shifts(1, 1), Region2.Shifts(1, 1));\r
+Max_x = max(Region1.Shifts(1, 2), Region2.Shifts(1, 2));\r
+Min_y = min(Region1.Shifts(2, 1), Region2.Shifts(2, 1));\r
+Max_y = max(Region1.Shifts(2, 2), Region2.Shifts(2, 2));\r
+\r
+Image1 = zeros(Max_x-Min_x+3, Max_y-Min_y+3);\r
+Image1(sub2ind(size(Image1), Region1.Boundary(1, :)-Min_x+2, ...\r
+               Region1.Boundary(2, :)-Min_y+2)) = 1;\r
+Image1 = double(bwmorph(Image1, 'thicken'));\r
+Image2 = zeros(Max_x-Min_x+3, Max_y-Min_y+3);\r
+Image2(sub2ind(size(Image2), Region2.Boundary(1, :)-Min_x+2, ...\r
+               Region2.Boundary(2, :)-Min_y+2)) = 1;\r
+Image2 = double(bwmorph(Image2, 'thicken'));\r
+\r
+Image1 = Image1+Image2;\r
+\r
+if length(find(Image1>1))>0            % there are some points with value of 2\r
+       Answer = 1;\r
+else\r
+       Answer = 0;\r
+end\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Operation_prompt.m b/THESE/codes/meanshift/Ms_segmenter/Operation_prompt.m
new file mode 100644 (file)
index 0000000..f103beb
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Operation_prompt.m
@@ -0,0 +1,40 @@
+if Region_left{I} == 0 % region was not chosen via "Select"\r
+       % prompt for an operation - one region only\r
+       Scr_sz = get(0, 'ScreenSize');\r
+       New_wind = dialog('Name', 'Choose region(s)', 'Position', ...\r
+          [Scr_sz(3)*0.3 Scr_sz(4)*0.4 Scr_sz(3)*0.3 Scr_sz(4)*0.1]);\r
+       \r
+       k = 0;  Ok_button = 0;  Interactive = 1;\r
+       if Show_all_regions{I}          % not just active\r
+               k = Bndred_regions{I};\r
+               for i=1:Bndred_regions{I}\r
+                         Regions_disp{i} = sprintf('%d', i);\r
+               end\r
+       else\r
+         for i=1:Bndred_regions{I}\r
+                 if Regions{I}{i}.Private.Active\r
+                         k = k+1;\r
+                         Regions_disp{k} = sprintf('%d', i);\r
+                 end\r
+         end\r
+       end\r
+       \r
+       if k > 0\r
+               Region_left{I} = base2dec(Regions_disp{1}, 10);         % to correspond\r
+               Choice_left = uicontrol('Parent', New_wind);\r
+               Call_back_left=sprintf('Segment_demo(''Left'',%18.16f,%d)', Choice_left, I);\r
+               set(Choice_left, 'Style', 'Popup', 'String', Regions_disp, ...\r
+                       'Units', 'Normalized', 'Position', [0 0.5 0.45 0.5], ...\r
+                       'Callback', Call_back_left);\r
+               Ok_button = uicontrol('Parent', New_wind, ...\r
+             'String', 'OK', 'Style', 'Pushbutton', ...\r
+             'Units', 'Normalized', 'Position', [0.1 0 0.3 0.4]);\r
+       end\r
+       \r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Cancel operation', 0, I);\r
+       Cancel_button = uicontrol('Parent', New_wind, ...\r
+          'String', 'Cancel', 'Style', 'Pushbutton', 'Callback', Callback, ...\r
+          'Units', 'Normalized', 'Position', [0.6 0 0.3 0.4]);\r
+else\r
+       Interactive = 0;\r
+end\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Operation_prompt_right.m b/THESE/codes/meanshift/Ms_segmenter/Operation_prompt_right.m
new file mode 100644 (file)
index 0000000..225825a
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Operation_prompt_right.m
@@ -0,0 +1,41 @@
+if Region_right{I} == 0\r
+       % prompt for an operation - one region only\r
+       Scr_sz = get(0, 'ScreenSize');\r
+       New_wind = dialog('Name', 'Choose region(s)', 'Position', ...\r
+          [Scr_sz(3)*0.3 Scr_sz(4)*0.4 Scr_sz(3)*0.3 Scr_sz(4)*0.1]);\r
+       \r
+       k = 0;  Ok_button = 0;  Interactive = 1;\r
+       if Show_all_regions{I}          % not just active\r
+               k = Bndred_regions{I};\r
+               for i=1:Bndred_regions{I}\r
+                         Regions_disp{i} = sprintf('%d', i);\r
+               end\r
+       else\r
+         for i=1:Bndred_regions{I}\r
+                 if Regions{I}{i}.Private.Active\r
+                         k = k+1;\r
+                         Regions_disp{k} = sprintf('%d', i);\r
+                 end\r
+         end\r
+       end\r
+       \r
+       if k > 0\r
+               Region_right{I} = base2dec(Regions_disp{1}, 10);                % to correspond\r
+               Choice_right = uicontrol('Parent', New_wind);\r
+               Call_back_right = ...\r
+               sprintf('Segment_demo(''Right'', %18.16f, %d)', Choice_right, I);\r
+               set(Choice_right, 'Style', 'Popup', 'String', Regions_disp, ...\r
+               'Units', 'Normalized', 'Position', [0.55 0.5 0.45 0.5], ...\r
+               'Callback', Call_back_right);\r
+               Ok_button = uicontrol('Parent', New_wind, ...\r
+             'String', 'OK', 'Style', 'Pushbutton', ...\r
+             'Units', 'Normalized', 'Position', [0.1 0 0.3 0.4]);\r
+       end\r
+       \r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Cancel operation', 0, I);\r
+       Cancel_button = uicontrol('Parent', New_wind, ...\r
+          'String', 'Cancel', 'Style', 'Pushbutton', 'Callback', Callback, ...\r
+          'Units', 'Normalized', 'Position', [0.6 0 0.3 0.4]);\r
+else\r
+       Interactive = 0;\r
+end\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Place_at.m b/THESE/codes/meanshift/Ms_segmenter/Place_at.m
new file mode 100644 (file)
index 0000000..6d858ab
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Place_at.m
@@ -0,0 +1,7 @@
+function Vertex_coords = Vertex_at (Region)\r
+% tells where on the plane to place the corresponding to the region vertex\r
+% will assume for the time being that regions are represented as \r
+% objects with various characteristics - Boundary, Matrix, Inner pt, etc.\r
+% this function could be overwritten, but should be based on the same props.\r
+\r
+Vertex_coords = round(mean(Region.Boundary, 2));  % take the gravicenter\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Place_images.m b/THESE/codes/meanshift/Ms_segmenter/Place_images.m
new file mode 100644 (file)
index 0000000..48ce230
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Place_images.m
@@ -0,0 +1,10 @@
+function Place_images (Dimension, Images)\r
+% creates a new window that contains all the specified existing windows/images\r
+% along with possible lines and other graphics, but without menus\r
+% Dimension is in the form [x, y] and Images is an array of their numbers\r
+\r
+figure;\r
+for i=1:size(Images, 1)\r
+       subplot(Dimension(1), Dimension(2), i);\r
+       image_children = get(Images[i], 'Children');\r
+       \r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Region_matrix.m b/THESE/codes/meanshift/Ms_segmenter/Region_matrix.m
new file mode 100644 (file)
index 0000000..0f171ce
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Region_matrix.m
@@ -0,0 +1,16 @@
+function Image = Region_matrix(Region)\r
+\r
+Min_x = Region.Shifts(1, 1);\r
+Min_y = Region.Shifts(2, 1);\r
+Max_x = Region.Shifts(1, 2);\r
+Max_y = Region.Shifts(2, 2);\r
+\r
+Image = zeros(Max_x-Min_x+1, Max_y-Min_y+1);\r
+\r
+Image(sub2ind(size(Image), Region.Boundary(1, :)-Min_x+1, ...\r
+       Region.Boundary(2, :)-Min_y+1)) = 1;\r
+\r
+To_start_fill = [Region.Inner_pts(2, :)-Min_y+1; ...\r
+       Region.Inner_pts(1, :)-Min_x+1];\r
+Image = bwfill(Image, To_start_fill(1, :), To_start_fill(2, :));\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Rgb_to_luv.m b/THESE/codes/meanshift/Ms_segmenter/Rgb_to_luv.m
new file mode 100644 (file)
index 0000000..9bb8589
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Rgb_to_luv.m
@@ -0,0 +1,41 @@
+function Luv_back = Rgb_to_luv (Im_rgb, Type)\r
+% takes an image in Rgb coords and returns either a vector or an image\r
+% in Luv coords, depending on Type (Image or vector).\r
+\r
+Vector = shiftdim(Im_rgb, 2);\r
+Vector = double(reshape(Vector, [3, size(Im_rgb, 1)*size(Im_rgb, 2)]));\r
+Vector_nz = 4096*(sum(Vector, 1) > 0);\r
+Vector_z = not(Vector_nz);\r
+Vector = max(Vector, [Vector_z; Vector_z; Vector_z]);  % to avoid all zero pixels\r
+\r
+Matrix = [...\r
+      0.490 0.310 0.200; ...\r
+      0.177 0.812 0.011; ...\r
+      0.000 0.010 0.990];\r
+\r
+XYZ = Matrix*Vector;           % now get a vector in XYZ coords\r
+\r
+Y0 = 1;                % is it true?\r
+X0 = 1;\r
+Z0 = 1;\r
+\r
+u0 = 4*X0/(X0+15*Y0+3*Z0);\r
+v0 = 9*Y0/(X0+15*Y0+3*Z0);\r
+\r
+Luv_back = zeros(size(Vector));\r
+Luv_back(1, :) = 25*(100*XYZ(2, :)/Y0).^(0.333)-16;\r
+Zeros = zeros(size(Luv_back(1, :)));\r
+Luv_back(1, :) = max(Luv_back(1, :), Zeros);\r
+u = 4*XYZ(1, :) ./ (XYZ(1, :)+15*XYZ(2, :)+3*XYZ(3, :));\r
+v = 9*XYZ(2, :) ./ (XYZ(1, :)+15*XYZ(2, :)+3*XYZ(3, :));\r
+Luv_back(2, :) = 13*Luv_back(1, :).*(u-u0);\r
+Luv_back(2, :) = max(Luv_back(2, :), Zeros);\r
+Luv_back(3, :) = 13*Luv_back(1, :).*(v-v0);\r
+Luv_back(3, :) = max(Luv_back(3, :), Zeros);\r
+Luv_back = min(Luv_back, [Vector_nz; Vector_nz; Vector_nz]);   % zeroing out\r
+Luv_back = Luv_back/(718.3176/256);\r
+if strcmp(Type, 'Image')\r
+   Luv_back = reshape(Luv_back, [3, size(Im_rgb, 1), size(Im_rgb, 2)]);\r
+   Luv_back = shiftdim(Luv_back, 1);\r
+end\r
+\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Right_button.m b/THESE/codes/meanshift/Ms_segmenter/Right_button.m
new file mode 100644 (file)
index 0000000..887cb98
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Right_button.m
@@ -0,0 +1,14 @@
+if Interactive\r
+       Region_right{I} = Region_left{I};\r
+       Choice_right = uicontrol('Parent', New_wind);\r
+       Call_back_right = ...\r
+       sprintf('Segment_demo(''Right'', %18.16f, %d)', Choice_right, I);\r
+       set(Choice_right, 'Style', 'Popup', 'String', Regions_disp, ...\r
+       'Units', 'Normalized', 'Position', [0.55 0.5 0.45 0.5], ...\r
+       'Callback', Call_back_right);\r
+else\r
+       if Region_right{I} == 0\r
+               msgbox('Please select 2-nd region', '2-nd region needed');\r
+       end\r
+       return;\r
+end\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Segment_demo.m b/THESE/codes/meanshift/Ms_segmenter/Segment_demo.m
new file mode 100644 (file)
index 0000000..40afdb3
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Segment_demo.m
@@ -0,0 +1,1382 @@
+function Segment_demo (Action, Arguments, varargin)\r
+\r
+global All_vars Biomed_on;\r
+if size(All_vars, 2) > 0\r
+   eval (['global ', All_vars]);\r
+end\r
+% regions are represented as structures of:\r
+% private chars:\r
+       % Number - region number\r
+       % Color - the original color to draw the region\r
+       % Line - the actual points as a line object\r
+       % Active - whether it was merged into another region\r
+       % From - which regions it was obtained from (by merging or cutting)\r
+% public chars:\r
+       % Boundary (Boundary) given as set of points\r
+       % Area - total area of the regions, since computed anyway\r
+       % Inner point - telling where the interior of region is (Inner_pts)\r
+       % Shifts - a matrix of size 2 by 2 giving min, max x and y vals for boundary\r
+       % Region_matr - the filled with pixels boundary for various operations\r
+% user defined chars:\r
+\r
+if nargin < 1\r
+       warning off;\r
+       Common_vars =[' Gray_image Image Luv_image Image_is_gray ', ...\r
+      ' Region_colors Bndred_regions Curr_pt Curr_pt_line ',...\r
+      ' Slices Centers Store_mouse_pts ', ...\r
+      ' Regions Region_menu_added Min_region_size ', ...\r
+      ' Region_menu Main_figure Main_axis Marker_size ', ...\r
+               ' Region_left Region_right Show_all_regions ', ...\r
+               ' Instance Active_instances Biomed_on '];\r
+\r
+       Biomed_vars = [' Amnt_rot Window_regs_chosen Window1 Window2 '];\r
+\r
+       All_vars = [Common_vars, Biomed_vars];\r
+       eval (['global ', All_vars]);\r
+       Action = 'Initialize';\r
+end\r
+\r
+if strcmp(Action, 'Initialize')\r
+   if nargin < 2                                                       % the very first call to it\r
+      Msg_box_hndl = msgbox('Welcome to the system ...', 'Welcome');\r
+      Child = get(Msg_box_hndl, 'Children');                   % the OK button\r
+      set(Child(length(Child)), 'Style', 'Text',    'String', '');\r
+      Image_menu = uimenu(Msg_box_hndl, 'Label','&Image');\r
+      uimenu(Image_menu, 'Label', '&Open...', ...\r
+         'Callback', 'close; Segment_demo(''Read image'')', 'Accelerator', 'O');\r
+      uimenu(Image_menu, 'Label', '&Quit', ...\r
+         'Callback', 'close', 'Separator','on', 'Accelerator', 'Q');\r
+               Screen_size = get(0, 'ScreenSize');\r
+               Marker_size = round(max(Screen_size)/200);\r
+   else\r
+               I = varargin{length(varargin)};         % last argument always\r
+      if length(size(Arguments)) == 3          % color image\r
+         Image_is_gray{I} = 0;\r
+         Image{I} = Arguments;\r
+      else\r
+         Image_is_gray{I} = 1;\r
+         Image{I} = Arguments;\r
+         Gray_image{I} = double(Arguments);\r
+      end\r
+\r
+               if not(varargin{1})             % does not need new window\r
+                       Kids = get(Main_axis{I}, 'Children');\r
+                       for i=1:length(Kids)\r
+                               if strcmp(get(Kids(i), 'Type'), 'line')\r
+                                       set(Kids(i), 'XData', 0, 'YData', 0, 'Visible', 'off');\r
+                               end\r
+                       end\r
+                       Segment_demo('Refresh image', 0, I);\r
+               end\r
+\r
+      Regions{I} = {};\r
+               Bndred_regions{I} = 0;\r
+      Region_left{I} = 0;  Region_right{I} = 0;\r
+      Store_mouse_pts{I} = 0; Curr_pt_line{I} = 0;\r
+               Show_all_regions{I} = 0;        % will show only active regions by default\r
+\r
+               % Biomed vars go here\r
+               if Instance == 1\r
+                       Window_regs_chosen = 0;\r
+               end\r
+       Amnt_rot{I} = 8;\r
+\r
+      % the list of colors\r
+      Region_colors{1, 1} = 'magenta';\r
+      Region_colors{1, 2} = 'yellow';\r
+      Region_colors{1, 3} = 'green';\r
+      Region_colors{1, 4} = 'red';\r
+      Region_colors{1, 5} = 'cyan';\r
+      Region_colors{1, 6} = 'blue';\r
+      Region_colors{1, 7} = 'black';\r
+\r
+               if varargin{1}          % needs new window\r
+       Region_menu_added{I} = 0;\r
+                       ButtonDown = sprintf('Segment_demo(''%s'',%d,%d)', 'Down', 0, I);\r
+                       CloseReq = sprintf('Segment_demo(''%s'',%d,%d)', 'Exit', 0, I);\r
+             set(gcf, 'WindowButtonDownFcn', ButtonDown, 'MenuBar', 'none', ...\r
+                               'CloseRequestFcn', CloseReq);\r
+             Main_figure{I} = gcf;\r
+                       Main_axis{I} = gca;\r
+             Image_menu = uimenu('Label','&Image');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Read image', 0, I);\r
+             uimenu(Image_menu, 'Label', '&Open...', ...\r
+                'Callback', Callback, 'Accelerator', 'O');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Smooth prompt', 0, I);\r
+             uimenu(Image_menu, 'Label', 's&Mooth...', ...\r
+                'Callback', Callback, 'Accelerator', 'M');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Segment prompt', 0, I);\r
+             uimenu(Image_menu, 'Label', 'se&Gment...', ...\r
+                'Callback', Callback, 'Accelerator', 'G');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Crop', 0, I);\r
+             uimenu(Image_menu, 'Label', '&Crop...', ...\r
+                'Callback', Callback, 'Accelerator', 'C');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Refresh image', 0, I);\r
+             uimenu(Image_menu, 'Label', 'r&Efresh!', ...\r
+                'Callback', Callback, 'Accelerator', 'E');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Label prompt', 0, I);\r
+             uimenu(Image_menu, 'Label', 'l&abel', ...\r
+                'Callback', Callback, 'Accelerator', 'A');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Save image', 0, I);\r
+             uimenu(Image_menu, 'Label', '&Save as...', ...\r
+                'Callback', Callback, 'Accelerator', 'S');\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Exit', 0, I);\r
+             uimenu(Image_menu, 'Label', '&Quit', ...\r
+                'Callback', Callback, 'Separator','on', 'Accelerator', 'Q');\r
+                       Segment_demo('Show image', 1, I);               % needs window size setting\r
+               else\r
+                       Segment_demo('Show image', 0, I);               % no window size setting\r
+               end\r
+   end\r
+end\r
+\r
+\r
+%-----------------------------------------\r
+% exiting if desired, after warning\r
+if strcmp(Action, 'Exit')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   set(Main_figure{I}, 'CloseRequestFcn', 'closereq');\r
+   close;\r
+       Active_instances = Active_instances-1;\r
+       if Active_instances == 0\r
+               eval (['clear global ', All_vars]);\r
+       end\r
+\r
+%-----------------------------------------\r
+% keeping track of the pixel clicked onto by mouse\r
+elseif strcmp(Action, 'Down')  % request to pick region follows\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Curr_pt_get{I} = get(gca, 'CurrentPoint');\r
+   Curr_pt_get{I} = round(Curr_pt_get{I}(1, 1:2));\r
+   Curr_pt_get{I} = [Curr_pt_get{I}(2); Curr_pt_get{I}(1)]; % switching x and y\r
+   if Store_mouse_pts{I}\r
+      Curr_pt{I} = [Curr_pt{I}, Curr_pt_get{I}];\r
+   else\r
+      Curr_pt{I} = Curr_pt_get{I};\r
+   end\r
+   if Curr_pt_line{I}\r
+      set(Curr_pt_line{I}, 'XData',Curr_pt{I}(2, :), 'YData',Curr_pt{I}(1, :));\r
+   else\r
+      Curr_pt_line{I} = ...\r
+                       line('XData', Curr_pt{I}(2, :), 'YData', Curr_pt{I}(1, :), ...\r
+         'Marker', '.', 'Color', 'yellow', 'EraseMode', 'none');\r
+   end\r
+ \r
+ \r
+%-----------------------------------------\r
+% segmenting the image into a number of subregions by the subroutine\r
+elseif strcmp(Action, 'Segment prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Prompt  = {...\r
+               'Window size (small window => small variation)', ...\r
+               'Color components to use (fewer => quicker)', ...\r
+      'Color group size (small size => fewer colors left)', ...\r
+      'Min region size (small size => more small regions)', ...\r
+      'Would you like to see separate layers?'};\r
+       if Image_is_gray{I}\r
+               Default = {20, 1, 200, 20, 'No'};\r
+        else\r
+       Default = {30, 2, 200, 20, 'No'};\r
+       end\r
+   Title = 'Segmentation parameters';\r
+   Line_number  = 1;\r
+   Segment_input  = My_inputdlg(Prompt, Title, Line_number, Default);\r
+   if size(Segment_input, 2) > 0\r
+      Segment_demo ('Initialize', Image{I}, 0, I);     % does not need new wind\r
+      Segment_demo ('Segment', Segment_input, I);\r
+   end\r
+\r
+\r
+%-----------------------------------------\r
+% segmenting the image into a number of subregions by the subroutine\r
+elseif strcmp(Action, 'Segment')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Segment_input = Arguments;\r
+   Window_radius = base2dec(Segment_input{1}, 10);\r
+       Components = base2dec(Segment_input{2}, 10);\r
+       % Components = round(max(Components, 2));               % for a while\r
+   Color_group = base2dec(Segment_input{3}, 10);\r
+   Min_region_size{I} = base2dec(Segment_input{4}, 10);\r
+       Plot_slices = length(findstr(lower(Segment_input{5}), 'y'));\r
+   if Image_is_gray{I}\r
+      [Segmented, Centers] = ...\r
+         Gray_segment (Gray_image{I}, Window_radius, Color_group);\r
+   else\r
+      Luv_image{I} = Rgb_to_luv(Image{I}, 'Image');\r
+      Gray_image{I} = squeeze(Luv_image{I}(:, :, 1));\r
+               Lhs = '[Segmented, Centers] = ';\r
+               Rhs = [sprintf('Color_segment_svd%d', Components), ...\r
+                       '(Luv_image{I}, Window_radius, Color_group);'];\r
+               eval([Lhs, Rhs]);\r
+   end\r
+\r
+   Segments = size(Centers, 2);\r
+   Slices{I} = zeros(size(Gray_image{I}));\r
+\r
+   if Plot_slices\r
+      figure;  % plotting the resulting regions\r
+      Square_side = ceil(sqrt(Segments));\r
+   end\r
+\r
+   for i=1:Segments\r
+      Slice = (Segmented == i);\r
+      % some image processing - taken from morphology file\r
+      Slice = bwmorph(Slice, 'majority');\r
+      Slice = (Slice == 0);    % negation\r
+      Slice = bwmorph(Slice, 'majority');\r
+      Slice = (Slice == 0);    % negation\r
+      % Slice = bwmorph(Slice, 'thicken', 2);    % no thickening for a while\r
+      Slices{I}(:, :, i) = bwlabel(Slice);\r
+      if Plot_slices\r
+         subplot(Square_side, Square_side, i);\r
+         image((Slices{I}(:, :, i)>0)*255); axis off;\r
+         colormap(gray(255));\r
+      end\r
+   end\r
+       if Plot_slices\r
+       figure(Main_figure{I});\r
+       end\r
+\r
+       % appending the new submenu\r
+       if not(Region_menu_added{I})\r
+               Region_menu{I} = uimenu('Label', '&Region');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Find boundary', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', '&Find boundary', ...\r
+                       'Callback', Callback, 'Accelerator', 'F');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Select left region', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', '(un)select &1-st region', 'Enable', 'off', ...\r
+            'Callback', Callback, 'Accelerator', '1');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Select right region', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', '(un)select &2-nd region', 'Enable', 'off', ...\r
+            'Callback', Callback, 'Accelerator', '2');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)','Save boundary prompt',0,I);\r
+               uimenu(Region_menu{I}, 'Label', 'sa&Ve as...', 'Enable', 'off', ...\r
+                       'Callback', Callback, 'Accelerator', 'V');\r
+               Compare_menu = uimenu(Region_menu{I}, 'Label', '&Compare to...', ...\r
+                       'Enable', 'off');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', ...\r
+                       'Compare disk regions prompt', 0, I);\r
+               uimenu(Compare_menu, 'Label', '&Disk region...', ...\r
+                       'Callback', Callback, 'Accelerator', 'D');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', ...\r
+                       'Compare image regions prompt', 0, I);\r
+               uimenu(Compare_menu, 'Label', '&Image region...', ...\r
+                       'Callback', Callback, 'Accelerator', 'I');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', ...\r
+                       'Compare window regions prompt', 0, I);\r
+               uimenu(Compare_menu, 'Label', '&Window region...', ...\r
+                       'Callback', Callback, 'Accelerator', 'W');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Merge prompt', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', 'me&Rge...', 'Enable', 'off', ...\r
+                       'Callback', Callback, 'Accelerator', 'R');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Cut prompt', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', 'c&Ut...', 'Enable', 'off', ...\r
+                       'Callback', Callback, 'Accelerator', 'U');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', ...\r
+                       'Intensity 3-d prompt', 0, I);\r
+               uimenu(Region_menu{I},'Label', 'intensity &3-d...', 'Enable','off', ...\r
+                       'Callback', Callback, 'Accelerator', '3');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', ...\r
+                       'Background region prompt',0, I);\r
+               uimenu(Region_menu{I}, 'Label', 'bac&Kground...', 'Enable', 'off', ...\r
+            'Callback', Callback, 'Accelerator', 'K');\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Color prompt', 0, I);\r
+               uimenu(Region_menu{I}, 'Label', 'co&Lor...', 'Enable', 'off', ...\r
+            'Callback', Callback, 'Accelerator', 'L');\r
+               Callback=sprintf('global Marker_size; Marker_size = Marker_size + 2;');\r
+               uimenu(Region_menu{I}, 'Label', 'Thicken next line', 'Enable', 'off', ...\r
+                       'Callback', Callback);\r
+               Callback=sprintf('global Marker_size; Marker_size = Marker_size - 2;');\r
+               uimenu(Region_menu{I}, 'Label', 'Thin next line', 'Enable', 'off', ...\r
+                       'Callback', Callback);\r
+               Region_menu_added{I} = 1;\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Crop')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       msgbox('Please crop a subregion', 'Cropping');\r
+   waitforbuttonpress;\r
+   Point1 = get(gca,'CurrentPoint');    % button down detected\r
+   Final_rect = rbbox;                  % return Figure units\r
+   Point2 = get(gca,'CurrentPoint');    % button up detected\r
+   Point1 = round(Point1(1,1:2));       % extract x and y\r
+   Point2 = round(Point2(1,1:2));\r
+   P1 = min(Point1,Point2);             % calculate locations\r
+   Offset = abs(Point1-Point2);         % and dimensions\r
+   Rect_x = [P1(1) P1(1)+Offset(1) P1(1)+Offset(1) P1(1) P1(1)];\r
+   Rect_y = [P1(2) P1(2) P1(2)+Offset(2) P1(2)+Offset(2) P1(2)];\r
+   hold on;\r
+   axis manual;\r
+   plot(Rect_x, Rect_y);\r
+\r
+   Button = questdlg('Is it suitable?', 'Crop/Undo cropping','Yes','No','Yes');\r
+   if strcmp(Button,'Yes')\r
+      Left_x = min(Rect_y);  Right_x = max(Rect_y);    % again shift\r
+      Low_y = min(Rect_x);  High_y = max(Rect_x);              % again shift\r
+      if Image_is_gray{I}\r
+         Image_cropped = Gray_image{I}(Left_x:Right_x, Low_y:High_y);\r
+      else\r
+         Image_cropped = Image{I}(Left_x:Right_x, Low_y:High_y, :);\r
+      end\r
+\r
+               % creating a window for the newly cropped image\r
+               figure;                                                 % creating one more window\r
+               set (gcf, 'Name', [get(Main_figure{I}, 'Name'), ' cropped']);\r
+               if length(Instance) > 0 % was defined\r
+                       Instance = Instance+1;\r
+                       Active_instances = Active_instances+1;\r
+                else\r
+                       Instance = 1;\r
+                       Active_instances = 1;\r
+               end\r
+       Segment_demo ('Initialize', Image_cropped, 1, Instance);        %needs new window\r
+       else\r
+               Segment_demo('Refresh image', 0, I);\r
+   end   \r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Smooth prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Scr_sz = get(0, 'ScreenSize');\r
+   New_wind = dialog('Name', 'Which filter to use?', 'Position', ...\r
+      [Scr_sz(3)*0.3 Scr_sz(4)*0.4 Scr_sz(3)*0.3 Scr_sz(4)*0.1]);\r
+   Choice = uicontrol('Parent', New_wind);\r
+   Callback = sprintf('Segment_demo(''Smooth'', %18.16f, %d)', Choice, I);\r
+   set(Choice, 'Style', 'Popup', ...\r
+      'String', {'Circle-5', 'Symlet', 'Avg-3'}, ...\r
+      'Units', 'Normalized', 'Position', [0 0.5 1 0.5]);\r
+   Cancel_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'Cancel', 'Style', 'Pushbutton', 'Callback', 'close', ...\r
+      'Units', 'Normalized', 'Position', [0.6 0.3 0.3 0.4]);\r
+   Ok_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'OK', 'Style', 'Pushbutton', 'Callback', Callback, ...\r
+      'Units', 'Normalized', 'Position', [0.1 0.3 0.3 0.4]);\r
+\r
+\r
+%----------------------------------------\r
+elseif strcmp(Action, 'Smooth')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Choice = get(Arguments, 'Value');\r
+       close;                  % closing the prompt window\r
+       switch Choice\r
+       case 2\r
+               Filter = 'Symlets';\r
+       case 3\r
+               Filter = ones(3);\r
+               Filter = Filter/sum(sum(Filter));\r
+       case 1\r
+               Filter = ones(5);\r
+               Filter(1:2, 1) = zeros(2, 1); Filter(4:5, 1) = zeros(2, 1);\r
+               Filter(1:2, 5) = zeros(2, 1); Filter(4:5, 5) = zeros(2, 1);\r
+               Filter(1, 2) = 0; Filter(5, 2) = 0;\r
+               Filter(2, 5) = 0; Filter(5, 4) = 0;\r
+               Filter = Filter/sum(sum(Filter));\r
+       otherwise\r
+               Filter = 1;\r
+       end\r
+       if size(Filter, 2) > 1\r
+               if strcmp(Filter, 'Symlets')\r
+                       if Image_is_gray{I}\r
+                               Image_filtered = Smooth_wv(Gray_image{I});\r
+                       else\r
+                               for i=1:3\r
+                                       Image_filtered(:, :, i) = Smooth_wv(Image{I}(:, :, i));\r
+                               end\r
+                       end\r
+               else\r
+                       if Image_is_gray{I}\r
+                               Image_filtered = filter2(Filter, Gray_image{I});\r
+                       else\r
+                               for i=1:3\r
+                                       Image_filtered(:, :, i) = filter2(Filter, Image{I}(:, :, i));\r
+                               end\r
+                       end\r
+               end\r
+               Segment_demo ('Initialize', Image_filtered, 0, I);      % no new window\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+% now saves both in low-quality and high quality formats in jpeg/tif/bmp and\r
+% in eps format with a standard size\r
+elseif strcmp(Action, 'Save image')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   % Filter = '*.jpeg; *.jpg; *.tif; *.tiff; *.bmp; *.eps; *.ps';\r
+   Filter = '*.jpeg; *.jpg; *.tif; *.bmp; *.eps';\r
+   [File_name, Path_name] = uiputfile(Filter, 'Save to file ...');\r
+   [Screen, Screen_map] = capture(Main_figure{I});\r
+   % splitting file name into extension and file itself\r
+   for i=length(File_name):-1:1\r
+      if File_name(i) == '.'\r
+         File_type = File_name(i+1:length(File_name));\r
+                       File_name = File_name(1:i-1);\r
+         break;\r
+      end\r
+   end\r
+   \r
+   if File_name(1) > 0\r
+               if (strcmp(File_type, 'jpeg') | strcmp(File_type, 'jpg'))\r
+                       % low-level file first\r
+       imwrite(Screen, Screen_map, [Path_name, File_name, '_low.', File_type], ...\r
+                               File_type, 'Quality', 80);\r
+                       print_string = sprintf('%s %s', 'print -djpeg ', ...\r
+                               [Path_name, File_name, '_high.', File_type]);\r
+                       eval(print_string);\r
+                       msgbox(['Image was saved in ',  File_name, '_low.', File_type, ...\r
+                               '  and  ',  File_name, '_high.', File_type], 'Files saved');\r
+               end\r
+               if (strcmp(File_type, 'tiff') | strcmp(File_type, 'tif'))\r
+                       % low-level file first\r
+       imwrite(Screen, Screen_map, [Path_name, File_name, '_low.', File_type], ...\r
+                               File_type);\r
+                       print_string = sprintf('%s %s', 'print -dtiff ', ...\r
+                               [Path_name, File_name, '_high.', File_type]);\r
+                       eval(print_string);\r
+                       msgbox(['Image was saved in ',  File_name, '_low.', File_type, ...\r
+                               '  and  ',  File_name, '_high.', File_type], 'Files saved');\r
+               end\r
+               if strcmp(File_type, 'bmp')\r
+                       % low-level file first\r
+       imwrite(Screen, Screen_map, [Path_name, File_name, '.', File_type], ...\r
+                               File_type);\r
+                       print_string = sprintf('%s %s', 'print -djpeg ', ...\r
+                               [Path_name, File_name, '.jpg']);\r
+                       eval(print_string);\r
+                       msgbox(['Image was saved in ',  File_name, '.', File_type, ...\r
+                               '  and  ',  File_name, '.jpg'], 'Files saved');\r
+               end\r
+               if (strcmp(File_type, 'eps') | strcmp(File_type, 'ps'))\r
+                       print_string = sprintf('%s %s', 'print -depsc -loose ', ...\r
+                               [Path_name, File_name, '.', File_type]);\r
+                       eval(print_string);\r
+                       msgbox(['Image was saved in ',  File_name, '.', File_type], ...\r
+                               'Files saved');\r
+               end\r
+   end\r
+\r
+ \r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Read image')\r
+   Filter = '*.jpeg; *.jpg; *.tif; *.tiff; *.bmp';\r
+   [File_name, Path_name] = uigetfile(Filter, 'Open file');\r
+       Image_name = File_name;\r
+   File_name = [Path_name, File_name];\r
+   if File_name(1) > 0                                         % not zeros there\r
+      [Im_read, Map] = imread(File_name);\r
+               if length(Map) > 0              % not gray or RGB, but could be made such\r
+                       Im_read = ind2rgb(Im_read, Map);\r
+                       Im_read = 256*Im_read;\r
+               end\r
+               Im_read = double(Im_read);\r
+      if length(size(Im_read)) == 3            % color image\r
+                       for i=1:3\r
+                         Img(:,:,i) = min(Im_read(:,:,i)+ones(size(Im_read(:,:,i))), ...\r
+                                       255*ones(size(Im_read(:,:,i))));\r
+                       end\r
+               else\r
+         Img = min(Im_read+ones(size(Im_read)),255*ones(size(Im_read)));\r
+               end\r
+               figure;                                                 % creating one more window\r
+               set (gcf, 'Name', Image_name);\r
+               if length(Instance) > 0 % was defined\r
+                       Instance = Instance+1;\r
+                       Active_instances = Active_instances+1;\r
+                else\r
+                       Instance = 1;\r
+                       Active_instances = 1;\r
+               end\r
+      Segment_demo('Initialize', Img, 1, Instance);            % needs new window\r
+   end\r
+ \r
+ \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Refresh image')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if length(Curr_pt_line) >= I\r
+               if Curr_pt_line{I} > 0\r
+                       set(Curr_pt_line{I}, 'EraseMode', 'normal');\r
+                       set(Curr_pt_line{I}, 'Visible', 'off');\r
+                       set(Curr_pt_line{I}, 'Visible', 'on');\r
+                       set(Curr_pt_line{I}, 'EraseMode', 'none');\r
+               end\r
+       end\r
+\r
+       % deleting rectangles if present - they have few points\r
+       Kids = get(Main_axis{I}, 'Children');\r
+       for i=1:length(Kids)\r
+               if strcmp(get(Kids(i), 'type'), 'line') & ...\r
+                               length(get(Kids(i), 'XData')) == 5      % rectangle\r
+                       set(Kids(i), 'XData', 0, 'YData', 0, 'Visible', 'off');\r
+               end\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+% labeling the image at a chosen place\r
+elseif strcmp(Action, 'Label prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       % determining whether near an existing label\r
+       Curr_pt_local = get(gca, 'CurrentPoint');\r
+       Curr_pt_local = round(Curr_pt_local(1, 1:2));\r
+       kids = get(gca, 'children');\r
+       for i=1:length(kids)\r
+               if strcmp(get(kids(i), 'type'), 'text') & ...\r
+                               strcmp(get(kids(i), 'visible'), 'on') & ...\r
+                               length(get(kids(i), 'string')) > 0\r
+                       text_pos = get(kids(i), 'position');\r
+                       text_pos = text_pos(1:2);\r
+                       if norm(Curr_pt_local-text_pos)<8\r
+                               set(kids(i), 'editing', 'on');\r
+                               return;\r
+                       end\r
+               end\r
+       end\r
+\r
+   Prompt  = {'Label', 'Font size', 'Font type', 'Font color', 'Rotated'};\r
+       Default = {'Region ', 10, 'Courier', 'black', 0};\r
+   Title = 'Labeling image';\r
+   Line_number  = 1;\r
+   Label_input  = My_inputdlg(Prompt, Title, Line_number, Default);\r
+   if size(Label_input, 2) > 0\r
+               new_text = text(Curr_pt_local(1), Curr_pt_local(2), Label_input{1});\r
+               set(new_text, 'fontsize', base2dec(Label_input{2}, 10), ...\r
+                       'fontname', Label_input{3}, 'color', Label_input{4}, ...\r
+                       'rotation',  base2dec(Label_input{5}, 10));\r
+       Button = questdlg('Is it suitable?', 'Labeling', 'Yes', 'No', 'Yes');\r
+       if strcmp(Button, 'No')\r
+                       set(new_text, 'visible', 'off');\r
+               end\r
+   end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Show image')                    % for internal purposes only\r
+       I = varargin{length(varargin)};                 % last argument always\r
+       Rows = size(Image{I}, 1);\r
+       Cols = size(Image{I}, 2);\r
+       if Arguments                                                                    % needs size setting\r
+          Max_rc = max(Rows, Cols);\r
+          Scr_sz = get(0, 'ScreenSize');\r
+               % "tiling" the images on the screen into 3 by 4 grid\r
+               Grid_v = mod(floor((I-1)/4), 3);                % finding row\r
+               Grid_h = mod(I+3, 4);                                   % finding column\r
+          New_position(1:2) = [Scr_sz(3)*(0.04+0.24*Grid_h) ...\r
+                                                                       Scr_sz(4)*(0.08+0.31*Grid_v)];\r
+          New_position(3) = Scr_sz(3)*0.22*Cols/Max_rc;\r
+          New_position(4) = Scr_sz(4)*0.22*Rows/Max_rc;\r
+          set(Main_figure{I}, 'Position', New_position);\r
+       end\r
+       Axis_children = get(Main_axis{I}, 'Children');\r
+       Image_handle = 0;\r
+       for i=length(Axis_children):-1:1\r
+               if strcmp(get(Axis_children(i), 'Type'), 'image')\r
+                       Image_handle = Axis_children(i);\r
+                       break;\r
+               end\r
+       end\r
+   if Image_is_gray{I}\r
+               if not(Image_handle)\r
+                       Image_handle = image(Gray_image{I});\r
+                       set(Image_handle, 'Parent', Main_axis{I});\r
+                       axis([1, Cols, 1, Rows]);\r
+               else\r
+                       set(Image_handle, 'CData', Gray_image{I});\r
+                       axis([1, Cols, 1, Rows]);\r
+               end\r
+               colormap(gray(255));\r
+   else\r
+               if not(Image_handle)\r
+                       Image_handle = image(uint8(floor(double(Image{I}))));   % no maps\r
+                       set(Image_handle, 'Parent', Main_axis{I});\r
+                       axis([1, Cols, 1, Rows]);\r
+               else\r
+                       set(Image_handle, 'CData', uint8(floor(double(Image{I}))));\r
+                       axis([1, Cols, 1, Rows]);\r
+               end\r
+   end\r
+       set(Main_axis{I}, 'Position', [0 0 1 1], 'XTick', [], 'YTick', []);\r
+\r
+ \r
+%----------------------------------------\r
+elseif strcmp(Action, 'Enable region')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % call from interactive input\r
+               close;                          % prompt\r
+               Arguments = Region_left{I};\r
+               Region_left{I} = 0;\r
+       end\r
+       Regions{I}{Arguments}.Private.Active = 1;\r
+       Segment_demo('Draw boundary', Arguments, I);\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Background region prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Operation_prompt;               % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Background region', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Background region', 1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+\r
+%----------------------------------------\r
+elseif strcmp(Action, 'Background region')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % from interactive\r
+               close;  % prompt\r
+       end\r
+       [Rows, Cols] = size(Regions{I}{Region_left{I}}.Public.Region_matr);\r
+       Shift = Regions{I}{Region_left{I}}.Public.Shifts(1:2, 1);\r
+       if Image_is_gray{I}\r
+               for i=1:Rows\r
+             for j=1:Cols\r
+                if Regions{I}{Region_left{I}}.Public.Region_matr(i, j)\r
+                                       Image{I}(i+Shift(1)-1, j+Shift(2)-1) = 0;\r
+                   Gray_image{I}(i+Shift(1)-1, j+Shift(2)-1) = 0;\r
+                               end\r
+                       end\r
+               end\r
+       else\r
+               for i=1:Rows\r
+             for j=1:Cols\r
+                if Regions{I}{Region_left{I}}.Public.Region_matr(i, j)\r
+                                       Image{I}(i+Shift(1)-1, j+Shift(2)-1, :) = [0, 0, 0];\r
+                               end\r
+                       end\r
+               end\r
+       end\r
+   Segment_demo('Draw boundary', Region_left{I},  'none', I);\r
+   Region_left{I} = 0;\r
+       Segment_demo('Show image', 0, I);\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Color prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Operation_prompt;               % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Region color prompt', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Region color prompt', 1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Region color prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % from interactive\r
+               close;  % prompt\r
+       end\r
+   Scr_sz = get(0, 'ScreenSize');\r
+   New_wind = dialog('Name', 'Choose region color', 'Position', ...\r
+      [Scr_sz(3)*0.3 Scr_sz(4)*0.4 Scr_sz(3)*0.3 Scr_sz(4)*0.1]);\r
+   set(New_wind, 'WindowStyle', 'normal');\r
+       Choice_color = uicontrol('Parent', New_wind);\r
+       set(Choice_color, 'Style', 'Popup', 'String', Region_colors, ...\r
+                       'Units', 'Normalized', 'Position', [0 0.5 0.45 0.5]);\r
+   Cancel_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'Cancel', 'Style', 'Pushbutton', 'Callback', 'close', ...\r
+      'Units', 'Normalized', 'Position', [0.6 0.3 0.3 0.4]);\r
+       Callback=sprintf('Segment_demo(''%s'',%18.16f,%d)', 'Region color', Choice_color, I);\r
+   Ok_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'OK', 'Style', 'Pushbutton', 'Callback', Callback, ...\r
+      'Units', 'Normalized', 'Position', [0.1 0.3 0.3 0.4]);\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Region color')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Index_color = get(Arguments, 'Value');\r
+       Index_string = get(Arguments, 'String');\r
+       close;  % input window\r
+       Regions{I}{Region_left{I}}.Private.Color = Index_string{Index_color};\r
+       Segment_demo('Draw boundary', Region_left{I}, I);\r
+       Region_left{I} = 0;\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Select left region')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Curr_pt_local = get(gca, 'CurrentPoint');\r
+       Curr_pt_local = round(Curr_pt_local(1, 1:2));\r
+       Curr_pt_local = Curr_pt_local(2:-1:1);\r
+       for i=Bndred_regions{I}:-1:1\r
+               Min_x = Regions{I}{i}.Public.Shifts(1, 1);\r
+               Min_y = Regions{I}{i}.Public.Shifts(2, 1);\r
+               Max_x = Regions{I}{i}.Public.Shifts(1, 2);\r
+               Max_y = Regions{I}{i}.Public.Shifts(2, 2);\r
+               if Curr_pt_local(1) >= Min_x & Curr_pt_local(1) <= Max_x\r
+               if Curr_pt_local(2) >= Min_y & Curr_pt_local(2) <= Max_y\r
+                       if Regions{I}{i}.Public.Region_matr(...\r
+                               Curr_pt_local(1)-Min_x+1, Curr_pt_local(2)-Min_y+1) > 0\r
+                               % then the region has been found\r
+                       if Region_left{I}                                                                       % was assigned some value\r
+                       if not(Region_left{I} == Region_right{I})\r
+                               Segment_demo('Draw boundary', Region_left{I}, I);\r
+                       end\r
+                       end\r
+                               if Region_left{I} == i          % this was selected, now is deselected\r
+                                       Region_left{I} = 0;\r
+                               else\r
+                               Region_left{I} = i;\r
+                               Segment_demo('Draw boundary', Region_left{I}, 'white', I);\r
+                               end\r
+                               break;\r
+                       end\r
+               end\r
+               end\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Select right region')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Curr_pt_local = get(gca, 'CurrentPoint');\r
+       Curr_pt_local = round(Curr_pt_local(1, 1:2));\r
+       Curr_pt_local = Curr_pt_local(2:-1:1);\r
+       for i=Bndred_regions{I}:-1:1\r
+               Min_x = Regions{I}{i}.Public.Shifts(1, 1);\r
+               Min_y = Regions{I}{i}.Public.Shifts(2, 1);\r
+               Max_x = Regions{I}{i}.Public.Shifts(1, 2);\r
+               Max_y = Regions{I}{i}.Public.Shifts(2, 2);\r
+               if Curr_pt_local(1) >= Min_x & Curr_pt_local(1) <= Max_x\r
+               if Curr_pt_local(2) >= Min_y & Curr_pt_local(2) <= Max_y\r
+                       if Regions{I}{i}.Public.Region_matr(...\r
+                               Curr_pt_local(1)-Min_x+1, Curr_pt_local(2)-Min_y+1) > 0\r
+                               % then the region has been found\r
+                       if Region_right{I}                                                                      % was assigned some value\r
+                       if not(Region_left{I} == Region_right{I})\r
+                               Segment_demo('Draw boundary', Region_right{I}, I);\r
+                       end\r
+                       end\r
+                               if Region_right{I} == i         % this was selected, now is deselected\r
+                                       Region_right{I} = 0;\r
+                               else\r
+                               Region_right{I} = i;\r
+                               Segment_demo('Draw boundary', Region_right{I}, 'white', I);\r
+                               end\r
+                               break;\r
+                       end\r
+               end\r
+               end\r
+       end\r
+\r
+%----------------------------------------\r
+elseif strcmp(Action, 'Disable region')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % call from interactive input\r
+               close;                  % prompt\r
+               Arguments = Region_left{I};\r
+               Region_left{I} = 0;\r
+       end\r
+       Regions{I}{Arguments}.Private.Active = 0;\r
+       Segment_demo('Draw boundary', Arguments,  'none', I);\r
+       Segment_demo('Refresh image', 0, I);\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Find boundary')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Rows = size(Image{I}, 1);\r
+       Cols = size(Image{I}, 2);\r
+       if length(Curr_pt{I}) > 0       % was defined\r
+     if ((Curr_pt{I}(1) > 0) & (Curr_pt{I}(2) > 0) & ...\r
+           (Curr_pt{I}(1) < Rows) & (Curr_pt{I}(2) < Cols))\r
+        for i=1:size(Slices{I}, 3)\r
+           if Slices{I}(Curr_pt{I}(1), Curr_pt{I}(2), i) > 0   % in this slice\r
+              [Boundary, Area] = Extract_region_reg(Slices{I}(:, :, i) == ...\r
+                 Slices{I}(Curr_pt{I}(1),Curr_pt{I}(2), i), Min_region_size{I});\r
+              break;\r
+           end\r
+        end\r
+     end\r
+     \r
+     if exist('Boundary', 'var')\r
+     if size(Boundary, 2) > 1\r
+               Bndred_regions{I} = Bndred_regions{I}+1;\r
+               Regions{I}{Bndred_regions{I}}.Private.Number = Bndred_regions{I};\r
+               Regions{I}{Bndred_regions{I}}.Public.Boundary = Boundary;\r
+               Min_x = min(Boundary(1, :));  Max_x = max(Boundary(1, :));\r
+               Min_y = min(Boundary(2, :));  Max_y = max(Boundary(2, :));\r
+               Regions{I}{Bndred_regions{I}}.Public.Shifts = ...\r
+                               [Min_x, Max_x; Min_y, Max_y];\r
+               Regions{I}{Bndred_regions{I}}.Public.Inner_pts = ...\r
+                               [Curr_pt{I}(1); Curr_pt{I}(2)];\r
+                  Regions{I}{Bndred_regions{I}}.Public.Region_matr = ...\r
+                               Region_matrix(Regions{I}{Bndred_regions{I}}.Public);\r
+               Regions{I}{Bndred_regions{I}}.Private.Color = ...\r
+               Region_colors{1, 1+rem(Bndred_regions{I}-1, size(Region_colors, 2))};\r
+        % Regions{I}{Bndred_regions{I}}.Private.Color = ...\r
+        %   global_color;\r
+               Regions{I}{Bndred_regions{I}}.Private.Line = 0;\r
+                       Regions{I}{Bndred_regions{I}}.Private.From = [];\r
+                 % setting up variables for region info values\r
+               Regions{I}{Bndred_regions{I}}.Public.Area = -99;\r
+               Regions{I}{Bndred_regions{I}}.Public.Volume= -99;\r
+               Regions{I}{Bndred_regions{I}}.Public.Avg_intensity = -99;\r
+               Menu_children = get(Region_menu{I}, 'Children');\r
+               for i=1:length(Menu_children)\r
+               set (Menu_children(i), 'Enable', 'on');\r
+               end\r
+               Segment_demo('Draw boundary first time', Bndred_regions{I}, I);\r
+               Segment_demo('Enable region', Bndred_regions{I}, I);\r
+    end\r
+    end\r
+  end\r
+  \r
+  \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Save boundary prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       Operation_prompt;               % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Save boundary', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Save boundary', 1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+\r
+%----------------------------------------\r
+elseif strcmp(Action, 'Save boundary')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % from interactive\r
+               close;  % prompt\r
+       end\r
+   Filter = '*.rgn';\r
+   [File_name, Path_name] = uiputfile(Filter, 'Save to ...');\r
+   if File_name(1) > 0\r
+      File_name = [Path_name, File_name];\r
+      fid = fopen(File_name, 'w');\r
+      fprintf(fid, '%d %d\n', Regions{I}{Region_left{I}}.Public.Inner_pts(1:2));\r
+               fprintf(fid, '%d %d\n', 0, 0);  % the separation for Inner pts\r
+      fprintf (fid, '%d %d\n', Regions{I}{Region_left{I}}.Public.Boundary);\r
+      fclose(fid);\r
+   end\r
+   Segment_demo('Draw boundary', Region_left{I}, I);\r
+   Region_left{I} = 0;\r
+   \r
+   \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare window regions prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if not(Window_regs_chosen)                              % need to open one more window\r
+       Operation_prompt;                                               % the common piece\r
+               if Interactive \r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare window regions', 0, I);\r
+                       if Ok_button\r
+                               set(Ok_button, 'Callback', Callback);\r
+                       end\r
+               else\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare window regions', 1, I);\r
+                       eval(Callback);\r
+               end\r
+       else\r
+               Operation_prompt_right;\r
+               if Interactive \r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare window regions', 0, I);\r
+                       if Ok_button\r
+                               set(Ok_button, 'Callback', Callback);\r
+                       end\r
+               else\r
+                       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare window regions', 1, I);\r
+                       eval(Callback);\r
+               end\r
+       end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare window regions')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % interactive input\r
+       close;\r
+       end\r
+       if not(Window_regs_chosen)              % need to open one more window\r
+       msgbox('Please choose another region', 'Another');\r
+               Window1 = I;\r
+               Window_regs_chosen = 1;\r
+       else\r
+               Window2 = I;\r
+               % computing chars if needed\r
+               if Regions{Window1}{Region_left{Window1}}.Public.Area < 0\r
+                       Segment_demo('Intensity 3-d', Region_left{Window1}, Window1);\r
+               end\r
+               if Regions{Window2}{Region_right{Window2}}.Public.Area < 0\r
+                       Segment_demo('Intensity 3-d', Region_right{Window2}, Window2);\r
+               end\r
+               [Bdry_dist, Reg_dist] = ...\r
+                       Svd_dist_reg(Regions{Window1}{Region_left{Window1}}.Public, ...\r
+                               Regions{Window2}{Region_right{Window2}}.Public);\r
+       Msg = [sprintf('Boundary difference: %6.2f\n\n', Bdry_dist), ...\r
+               sprintf('Region  difference: %6.2f\n\n', Reg_dist), ...\r
+                       sprintf('Areas ratio: %6.2f\n\n', ...\r
+                               Regions{Window1}{Region_left{Window1}}.Public.Area/...\r
+                               Regions{Window2}{Region_right{Window2}}.Public.Area), ...\r
+                       sprintf('Volumes ratio: %6.2f\n\n', ...\r
+                               Regions{Window1}{Region_left{Window1}}.Public.Volume/...\r
+                               Regions{Window2}{Region_right{Window2}}.Public.Volume), ...\r
+                       sprintf('Avg intensities ratio: %6.2f', ...\r
+                               Regions{Window1}{Region_left{Window1}}.Public.Avg_intensity/...\r
+                               Regions{Window2}{Region_right{Window2}}.Public.Avg_intensity)\r
+                       ];\r
+               Wind_name = sprintf('%d and %d regions differences and ratios', ...\r
+                       Region_left{Window1}, Region_right{Window2});\r
+       msgbox(Msg, Wind_name);\r
+\r
+       Segment_demo('Draw boundary', Region_left{Window1}, Window1);\r
+       Segment_demo('Draw boundary', Region_right{Window2}, Window2);\r
+       Region_left{Window1} = 0; Region_right{Window2} = 0;\r
+               Window_regs_chosen = 0;\r
+       end\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare image regions prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Operation_prompt;                   % the common piece\r
+   Right_button;                               % one more button here  if Interactive \r
+       if Interactive\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare image regions', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare image regions', 1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare image regions')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0                                                       % interactive call\r
+       close;  % prompt\r
+   end\r
+       if Region_right{I} ==  0\r
+               return;\r
+       end\r
+       [Bdry_dist, Reg_dist] = ...\r
+               Svd_dist_reg(Regions{I}{Region_left{I}}.Public, ...\r
+                       Regions{I}{Region_right{I}}.Public);\r
+       % computing chars if needed\r
+       if Regions{I}{Region_left{I}}.Public.Area < 0\r
+               Segment_demo('Intensity 3-d', Region_left{I}, I);\r
+       end\r
+       if Regions{I}{Region_right{I}}.Public.Area < 0\r
+               Segment_demo('Intensity 3-d', Region_right{I}, I);\r
+       end\r
+       [Bdry_dist, Reg_dist] = ...\r
+               Svd_dist_reg(Regions{I}{Region_left{I}}.Public, ...\r
+                       Regions{I}{Region_right{I}}.Public);\r
+       Msg = [sprintf('Boundary difference: %6.2f\n\n', Bdry_dist), ...\r
+               sprintf('Region  difference: %6.2f\n\n', Reg_dist), ...\r
+               sprintf('Areas ratio: %6.2f\n\n', ...\r
+                       Regions{I}{Region_left{I}}.Public.Area/...\r
+                       Regions{I}{Region_right{I}}.Public.Area), ...\r
+               sprintf('Volumes ratio: %6.2f\n\n', ...\r
+                       Regions{I}{Region_left{I}}.Public.Volume/...\r
+                       Regions{I}{Region_right{I}}.Public.Volume), ...\r
+               sprintf('Avg intensities ratio: %6.2f', ...\r
+                       Regions{I}{Region_left{I}}.Public.Avg_intensity/...\r
+                       Regions{I}{Region_right{I}}.Public.Avg_intensity)\r
+               ];\r
+       Wind_name = sprintf('%d and %d regions differences and ratios', ...\r
+               Region_left{I}, Region_right{I});\r
+       msgbox(Msg, Wind_name);\r
+\r
+   Segment_demo('Draw boundary', Region_left{I}, I);\r
+   Segment_demo('Draw boundary', Region_right{I}, I);\r
+   Region_left{I} = 0; Region_right{I} = 0;\r
+\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare disk regions prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Operation_prompt;                   % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare disk regions', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Compare disk regions', 1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Compare disk regions')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % interactive input\r
+       close;  % prompt\r
+       end\r
+   Filter = '*.rgn';\r
+   [File_name, Path_name] = uigetfile(Filter, 'Compare to ...');\r
+   if File_name(1) > 0\r
+      File_name = [Path_name, File_name];\r
+      fid = fopen(File_name, 'r');\r
+               k = 0;\r
+               Pt_scanned(1:2, 1) = fscanf(fid, '%d', [2, 1]);\r
+               while sum(Pt_scanned) > 0\r
+                       k = k+1;\r
+       Region2.Public.Inner_pts(:, k) = Pt_scanned;\r
+                       Pt_scanned(1:2, 1) = fscanf(fid, '%d', [2, 1]);\r
+               end\r
+      Region2.Public.Boundary = fscanf(fid, '%d', [2, inf]);\r
+               Min_x = min(Region2.Public.Boundary(1, :));  \r
+               Max_x = max(Region2.Public.Boundary(1, :));\r
+      Min_y = min(Region2.Public.Boundary(2, :));\r
+               Max_y = max(Region2.Public.Boundary(2, :));\r
+      Region2.Public.Shifts = [Min_x, Max_x; Min_y, Max_y];\r
+      fclose(fid);\r
+      [Bdry_dist, Reg_dist] = ...\r
+                       Svd_dist_reg(Regions{I}{Region_left{I}}.Public, Region2.Public);\r
+      Msg = [sprintf('Boundary difference is  %6.2f\n\n', Bdry_dist), ...\r
+                sprintf('Region  difference  is  %6.2f', Reg_dist)]; \r
+      msgbox(Msg, 'Difference');\r
+   end\r
+   Segment_demo('Draw boundary', Region_left{I}, I);\r
+   Region_left{I} = 0;\r
+   \r
+   \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Intensity 3-d prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Operation_prompt;                                                                   % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Intensity 3-d', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Intensity 3-d', -1, I);\r
+               eval(Callback);\r
+       end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Intensity 3-d')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0                                                       % interactive call\r
+               Region_number = Region_left{I};\r
+       close;                                                                  % prompt\r
+       elseif Arguments == -1                                  % was selected not by menu\r
+               Region_number = Region_left{I};\r
+       else\r
+               Region_number = Arguments;\r
+       end\r
+   Volume = 0;\r
+       Area = 0;\r
+   [Rows, Cols] = size(Regions{I}{Region_number}.Public.Region_matr);\r
+   To_display = zeros(Cols, Rows);\r
+   Shift = Regions{I}{Region_number}.Public.Shifts(1:2, 1);\r
+   for i=1:Rows\r
+      for j=1:Cols\r
+         if Regions{I}{Region_number}.Public.Region_matr(i, j)\r
+            To_display(j, i) = Gray_image{I}(i+Shift(1)-1, j+Shift(2)-1);\r
+            Volume = Volume+To_display(j, i);\r
+                               Area = Area+1;\r
+         end\r
+      end\r
+   end\r
+   Min = min(To_display(find(To_display > 0)));\r
+   To_display = max(To_display, ones(size(To_display))*Min);\r
+       figure; % (2)\r
+   mesh(To_display);\r
+       set(gca, 'XTick', [], 'YTick', [], 'view', [77, 67]);   % a better viewpoint\r
+       zlabel('Intensity');\r
+       Wind_name = sprintf('Region %d Intensity 3-d', Region_number);\r
+   set(gcf, 'MenuBar', 'none', 'Name', Wind_name);\r
+   Rotate_menu = uimenu('Label','&Rotate');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Rotate left', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Left', ...\r
+      'Callback', Callback, 'Accelerator', 'L');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Rotate right', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Right', ...\r
+      'Callback', Callback, 'Accelerator', 'R');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Rotate up', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Up', ...\r
+      'Callback', Callback, 'Accelerator', 'U');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Rotate down', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Down', ...\r
+      'Callback', Callback, 'Accelerator', 'D');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Increase rotation', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Increase', ...\r
+      'Callback', Callback, 'Accelerator', 'A');\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Decrease rotation', 0, I);\r
+   uimenu (Rotate_menu, 'Label', 'Decrease', ...\r
+      'Callback', Callback, 'Accelerator', 'V');\r
+   \r
+   Max = max(max(To_display));\r
+       \r
+       Regions{I}{Region_number}.Public.Area = Area;           % storing area\r
+   Area_string = sprintf('Region area\t%10.2f', Area);\r
+       Regions{I}{Region_number}.Public.Volume = Volume;       % storing volume\r
+   Volume_string = sprintf('Volume\t%10.2f', Volume); % compute avg intens\r
+       Avg_intensity = Volume/Area;\r
+   Regions{I}{Region_number}.Public.Avg_intensity = Avg_intensity;\r
+   Avg_intensity_string = sprintf('Average intensity\t%10.2f', Avg_intensity);\r
+   Intensity_normed_volume_string = ...\r
+      sprintf('Intensity normalized volume\t%10.2f', Volume/Max);\r
+       \r
+   Values_menu = uimenu('Label', '&Values');\r
+   uimenu (Values_menu, 'Label', Area_string);\r
+   uimenu (Values_menu, 'Label', Volume_string);\r
+   uimenu (Values_menu, 'Label', Avg_intensity_string);\r
+   uimenu (Values_menu, 'Label', Intensity_normed_volume_string);\r
+\r
+       if Arguments <= 0               % interactive\r
+       Segment_demo('Draw boundary', Region_left{I}, I);\r
+       Region_left{I} = 0;\r
+       end\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Rotate right')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Az_el = get(gca, 'View');\r
+   set(gca, 'View', [Az_el(1)-Amnt_rot{I}, Az_el(2)]);\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Rotate left')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Az_el = get(gca, 'View');\r
+   set(gca, 'View', [Az_el(1)+Amnt_rot{I}, Az_el(2)]);\r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Rotate up')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Az_el = get(gca, 'View');\r
+   set(gca, 'View', [Az_el(1), Az_el(2)-Amnt_rot{I}]);\r
+\r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Rotate down')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Az_el = get(gca, 'View');\r
+   set(gca, 'View', [Az_el(1), Az_el(2)+Amnt_rot{I}]);\r
+ \r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Increase rotation')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Amnt_rot{I} = Amnt_rot{I}*2;\r
+\r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Decrease rotation')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Amnt_rot{I} = Amnt_rot{I}/2;\r
+\r
+   \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Cut prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Operation_prompt;                                                                   % the common piece\r
+       if Interactive \r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Region cut prompt', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Region cut prompt', 1, I);\r
+               eval(Callback);\r
+       end\r
+ \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Region cut prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0               % interactive input\r
+       close;\r
+       end\r
+   Scr_sz = get(0, 'ScreenSize');\r
+   New_wind = dialog('Name', 'Draw cutting line', 'Position', ...\r
+      [Scr_sz(3)*0.3 Scr_sz(4)*0.4 Scr_sz(3)*0.3 Scr_sz(4)*0.1]);\r
+   set(New_wind, 'WindowStyle', 'normal');\r
+   Cancel_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'Cancel', 'Style', 'Pushbutton', 'Callback', 'close', ...\r
+      'Units', 'Normalized', 'Position', [0.6 0.3 0.3 0.4]);\r
+       Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Cut', 0, I);\r
+   Ok_button = uicontrol('Parent', New_wind, ...\r
+      'String', 'OK', 'Style', 'Pushbutton', 'Callback', Callback, ...\r
+      'Units', 'Normalized', 'Position', [0.1 0.3 0.3 0.4]);\r
+   Store_mouse_pts{I} = 1;\r
+   Curr_pt{I}= zeros(2, 0);\r
+ \r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Cut')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   close;                                                                                      % the dialogue window\r
+   Dense_pts = Fit_spline(Curr_pt{I});\r
+   \r
+   [Bdry{1}, Inner{1}, Bdry{2}, Inner{2}] = ...\r
+      Cut_boundary_reg(Regions{I}{Region_left{I}}.Public, Dense_pts);\r
+   if (size(Bdry{1}, 2) > 1) & (size(Bdry{2}, 2) > 1)          % detected boundaries\r
+      for i=1:2\r
+         Bndred_regions{I} = Bndred_regions{I}+1;\r
+         Regions{I}{Bndred_regions{I}}.Public.Boundary = Bdry{i};\r
+         Regions{I}{Bndred_regions{I}}.Public.Inner_pts = Inner{i};\r
+         Min_x = min(Regions{I}{Bndred_regions{I}}.Public.Boundary(1, :));  \r
+         Max_x = max(Regions{I}{Bndred_regions{I}}.Public.Boundary(1, :));\r
+         Min_y = min(Regions{I}{Bndred_regions{I}}.Public.Boundary(2, :));  \r
+         Max_y = max(Regions{I}{Bndred_regions{I}}.Public.Boundary(2, :));\r
+         Regions{I}{Bndred_regions{I}}.Public.Shifts = ...\r
+                               [Min_x, Max_x; Min_y, Max_y];\r
+         Regions{I}{Bndred_regions{I}}.Private.Number = Bndred_regions{I};\r
+                  Regions{I}{Bndred_regions{I}}.Public.Region_matr = ...\r
+                               Region_matrix(Regions{I}{Bndred_regions{I}}.Public);\r
+         Regions{I}{Bndred_regions{I}}.Private.Color = ...\r
+            Region_colors{1, 1+rem(Bndred_regions{I}-1,size(Region_colors, 2))};\r
+                  Regions{I}{Bndred_regions{I}}.Private.From = [Region_left{I}];\r
+               Regions{I}{Bndred_regions{I}}.Public.Area = -99;\r
+               Regions{I}{Bndred_regions{I}}.Public.Volume= -99;\r
+               Regions{I}{Bndred_regions{I}}.Public.Avg_intensity = -99;\r
+         Segment_demo('Draw boundary first time', Bndred_regions{I}, I);% Line\r
+                       Segment_demo('Enable region', Bndred_regions{I}, I);\r
+      end\r
+               Segment_demo('Disable region', Region_left{I}, I);\r
+   end\r
+\r
+   Region_left{I} = 0;\r
+   Store_mouse_pts{I} = 0;\r
+       set(Curr_pt_line{I}, 'EraseMode', 'normal');\r
+       set(Curr_pt_line{I}, 'Visible', 'off');\r
+       Curr_pt_line{I} = 0;\r
+   Segment_demo('Down', 0, I);\r
+       Segment_demo('Refresh', 0, I);\r
+ \r
+\r
+% ----------------------------------------- "left" region\r
+elseif strcmp(Action, 'Left')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Index_left = get(Arguments, 'Value');\r
+   Labels = get(Arguments, 'String');\r
+   Temp_region_left = base2dec(Labels{Index_left}, 10);\r
+   % restoring the old region first\r
+   if Region_left{I}                                                                   % was assigned some value\r
+      if not(Region_left{I} == Region_right{I})\r
+         Segment_demo('Draw boundary', Region_left{I}, I);\r
+      end\r
+   end\r
+   Region_left{I} = Temp_region_left;\r
+   Segment_demo('Draw boundary', Region_left{I}, 'white', I);\r
+   \r
+ \r
+%-----------------------------------------   \r
+% "right" region\r
+elseif strcmp(Action, 'Right')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Index_right = get(Arguments, 'Value');\r
+   Labels = get(Arguments, 'String');\r
+   Temp_region_right = base2dec(Labels{Index_right}, 10);\r
+   % restoring the old region first\r
+   if Region_right{I}                                                          % was defined\r
+      if not(Region_left{I} == Region_right{I})\r
+         Segment_demo('Draw boundary', Region_right{I}, I);\r
+      end\r
+   end\r
+   Region_right{I} = Temp_region_right;\r
+   Segment_demo('Draw boundary', Region_right{I}, 'white', I);\r
+ \r
+\r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Cancel operation')              % don't perform operation\r
+       I = varargin{length(varargin)};         % last argument always\r
+   close;\r
+   if Region_left{I}\r
+      Segment_demo('Draw boundary', Region_left{I}, I);\r
+      Region_left{I} = 0;\r
+   end\r
+   if Region_right{I}\r
+      Segment_demo('Draw boundary', Region_right{I}, I);\r
+      Region_right{I} = 0;   \r
+   end\r
+   Store_mouse_pts{I} = 0;\r
+ \r
+\r
+%-----------------------------------------\r
+elseif strcmp(Action, 'Merge prompt')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Operation_prompt;                                                           % the common piece\r
+   Right_button;                                                                       % one more button here\r
+       if Interactive\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Merge', 0, I);\r
+               if Ok_button\r
+                       set(Ok_button, 'Callback', Callback);\r
+               end\r
+       else\r
+               Callback=sprintf('Segment_demo(''%s'',%d,%d)', 'Merge', 1, I);\r
+               eval(Callback);\r
+       end\r
+ \r
+%-----------------------------------------   \r
+elseif strcmp(Action, 'Merge')         % the two regions have been defined\r
+       I = varargin{length(varargin)};         % last argument always\r
+       if Arguments == 0                                                       % interactive call\r
+       close;  % prompt\r
+   end\r
+   Bndred_regions{I} = Bndred_regions{I}+1;\r
+   [Regions{I}{Bndred_regions{I}}.Public.Inner_pts, ...\r
+       Regions{I}{Bndred_regions{I}}.Public.Boundary, ...\r
+               Regions{I}{Bndred_regions{I}}.Public.Shifts] = Merge_regions(...\r
+                       Regions{I}{Region_left{I}}.Public, Regions{I}{Region_right{I}}.Public);\r
+   Regions{I}{Bndred_regions{I}}.Public.Region_matr = ...\r
+               Region_matrix(Regions{I}{Bndred_regions{I}}.Public);\r
+   Regions{I}{Bndred_regions{I}}.Private.Color = ...\r
+      Region_colors{1, 1+rem(Bndred_regions{I}-1, size(Region_colors, 2))};\r
+   Regions{I}{Bndred_regions{I}}.Private.Number = Bndred_regions{I};\r
+       Regions{I}{Bndred_regions{I}}.Private.From=[Region_left{I}, Region_right{I}];\r
+   Regions{I}{Bndred_regions{I}}.Public.Area = -99;\r
+   Regions{I}{Bndred_regions{I}}.Public.Volume = -99;\r
+   Regions{I}{Bndred_regions{I}}.Public.Avg_intensity = -99;\r
+       Segment_demo('Draw boundary first time', Bndred_regions{I}, I);\r
+\r
+       Segment_demo('Enable region', Bndred_regions{I}, I);\r
+   \r
+       Segment_demo('Disable region', Region_left{I}, I);\r
+       Segment_demo('Disable region', Region_right{I}, I);\r
+   Region_left{I} = 0; Region_right{I} = 0;\r
+\r
+\r
+% --------------------\r
+elseif strcmp(Action, 'Draw boundary first time')\r
+       I = varargin{length(varargin)};         % last argument always\r
+   Regions{I}{Arguments}.Private.Line = ...\r
+      line('XData', Regions{I}{Arguments}.Public.Boundary(2, :), ...\r
+      'YData', Regions{I}{Arguments}.Public.Boundary(1, :), ...\r
+      'Parent', Main_axis{I}, 'Color', Regions{I}{Arguments}.Private.Color, ...\r
+               'Marker', '.', 'LineStyle', '.', 'EraseMode', 'none', ...\r
+               'Visible', 'off', 'LineWidth', Marker_size, 'MarkerSize', Marker_size);\r
+\r
+% -------------------- since repeates so often\r
+elseif strcmp(Action, 'Draw boundary')   \r
+       I = varargin{length(varargin)};         % last argument always\r
+   if length(varargin) == 1                                    % draw the region in the usual color\r
+               if Regions{I}{Arguments}.Private.Active\r
+       set(Regions{I}{Arguments}.Private.Line, 'Visible', 'off');\r
+       set(Regions{I}{Arguments}.Private.Line, ...\r
+               'Color', Regions{I}{Arguments}.Private.Color, 'Visible', 'on');\r
+               else\r
+       set(Regions{I}{Arguments}.Private.Line, 'Visible', 'off');\r
+               end\r
+   else\r
+      if strcmp(varargin{1}, 'white')          % draw in white\r
+         set(Regions{I}{Arguments}.Private.Line, ...\r
+            'Color', 'white', 'Visible', 'on');\r
+      elseif strcmp(varargin{1}, 'none')       % don't draw\r
+         set(Regions{I}{Arguments}.Private.Line, 'Visible', 'off');\r
+      end\r
+   end\r
+end\r
+\r
+% let's represent regions (whose boundaries we were asked to find) by\r
+% those boundaries and make the merging part as filling those\r
+% boundaries and taking union as it was done before.  In this fashion\r
+% we can handle new regions, not to abuse the original image.  Merging\r
+% will be done on pairs of regions resulting in a new region\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Smooth_wv.m b/THESE/codes/meanshift/Ms_segmenter/Smooth_wv.m
new file mode 100644 (file)
index 0000000..ef418ba
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Smooth_wv.m
@@ -0,0 +1,22 @@
+function Smoothed = Smooth_wv(G_image)\r\r
+[Low, High, Low_i, High_i] = symlets(12);\r\r
+\r\r
+[Rows, Cols] = size(G_image);\r\r
+Log_size = floor(log2(max(Rows, Cols)));\r\r
+New_size = 2^Log_size;\r\r
+Resized = imresize(G_image, [New_size, New_size], 'bilinear');\r\r
+\r\r
+Forward = wt2d(Resized, Low, High, Log_size);\r\r
+Forward_r = -abs(reshape(Forward, 1, New_size*New_size));\r\r
+Forward_s = sort(Forward_r);\r\r
+Cutoff = -Forward_s(floor(New_size*New_size*0.1));             % taking 10%\r\r
+To_take = abs(Forward) > Cutoff;\r\r
+Forward = Forward .* To_take;\r\r
+\r\r
+% Forward(New_size/2+1:New_size, :) = ...\r\r
+%   zeros(size(Forward(New_size/2+1:New_size, :)));\r\r
+% Forward(:, New_size/2+1:New_size) = ...\r\r
+%     zeros(size(Forward(:, New_size/2+1:New_size)));\r\r
+Smoothed = iwt2d(Forward, Low_i, High_i, Log_size, New_size, New_size);\r\r
+Smoothed = imresize(Smoothed, [Rows, Cols], 'bilinear');\r\r
+Smoothed = Smoothed .* (Smoothed >= 0);
\ No newline at end of file

diff --git a/THESE/codes/meanshift/Ms_segmenter/Svd_dist_reg.m b/THESE/codes/meanshift/Ms_segmenter/Svd_dist_reg.m
new file mode 100644 (file)
index 0000000..69875ab
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Svd_dist_reg.m
@@ -0,0 +1,93 @@
+function [Svd_dist_bdry, Svd_dist_reg] = Svd_dist (Region1, Region2)\r
+% first represent the two boundaries as matrices where ones stand for\r
+% boundary pixels and zeros elsewhere, next use SVD decomposition to\r
+% find eigenvalues, and finally adjust those (take shorter array and\r
+% multiply it by the lengths ratio) - observed from resizing the same\r
+% image\r
+\r
+Subints = 8;\r
+\r
+ \r
+if length(Region1.Boundary) < length(Region2.Boundary) % rotate 1 is quicker\r
+       Temp = Region1;\r
+       Region1 = Region2;\r
+       Region2 = Temp;\r
+end\r
+\r
+\r
+% finding the first region boundary and filled\r
+Max_x1 = Region1.Shifts(1, 2); Min_x1 = Region1.Shifts(1, 1);\r
+Max_y1 = Region1.Shifts(2, 2); Min_y1 = Region1.Shifts(2, 1);\r
+Mb1 = zeros(Max_x1-Min_x1+1, Max_y1-Min_y1+1);\r
+Mb1(sub2ind(size(Mb1), Region1.Boundary(1, :)-Min_x1+1, ...\r
+       Region1.Boundary(2, :)-Min_y1+1)) = 1;\r
+       \r
+Bdry_diag1 = svd(Mb1);\r
+To_start_fill = [Region1.Inner_pts(2, :)-Min_y1+1; ...\r
+       Region1.Inner_pts(1, :)-Min_x1+1];\r
+Mb1 = bwfill(Mb1, To_start_fill(1, :), To_start_fill(2, :));\r
+\r
+% global Mb1f;\r
+% Mb1f = Mb1;\r
+% global Mb2f;\r
+\r
+Reg_diag1 = svd(double(Mb1));\r
+Ld1 = length(Reg_diag1);\r
+[Rows1, Cols1] = size(Mb1);\r
+\r
+% finding the second region filled\r
+Max_x2 = Region2.Shifts(1, 2); Min_x2 = Region2.Shifts(1, 1);\r
+Max_y2 = Region2.Shifts(2, 2); Min_y2 = Region2.Shifts(2, 1);\r
+Mb2_orig = zeros(Max_x2-Min_x2+1, Max_y2-Min_y2+1);\r
+Mb2_orig(sub2ind(size(Mb2_orig), Region2.Boundary(1, :)-Min_x2+1, ...\r
+       Region2.Boundary(2, :)-Min_y2+1)) = 1;\r
+To_start_fill = [Region2.Inner_pts(2, :)-Min_y2+1; ...\r
+       Region2.Inner_pts(1, :)-Min_x2+1];\r
+Mb2_orig = bwfill(Mb2_orig, To_start_fill(1, :), To_start_fill(2, :));\r
+\r
+% Mb2f = Mb2_orig;\r
+\r
+[Rows, Cols] = find(Mb2_orig);\r
+Mb2_orig = [Rows'; Cols'];\r
+\r
+for i=1:Subints+1\r
+       % obtaining rotated second filled and extracting boundary next\r
+       Theta = (i-1)*(pi/2)/(Subints+1);\r
+       Matrix = [cos(Theta), sin(Theta); -sin(Theta), cos(Theta)];\r
+       Mb2_ind = Matrix * Mb2_orig;\r
+       Mb2_ind = [round(Mb2_ind), floor(Mb2_ind), ceil(Mb2_ind)];\r
+       Max_x2 = max(Mb2_ind(1, :)); Min_x2 = min(Mb2_ind(1, :));\r
+       Max_y2 = max(Mb2_ind(2, :)); Min_y2 = min(Mb2_ind(2, :));\r
+       Mb2 = zeros(Max_x2-Min_x2+1, Max_y2-Min_y2+1);\r
+       Mb2(sub2ind(size(Mb2), Mb2_ind(1, :)-Min_x2+1, Mb2_ind(2, :)-Min_y2+1)) = 1;\r
+       Reg_diag2 = svd(Mb2);\r
+       Ld2 = length(Reg_diag2);\r
+       Mb2 = double(bwperim(Mb2));\r
+       Bdry_diag2 = svd(Mb2);\r
+       [Rows2, Cols2] = size(Mb2);\r
+\r
+       if Ld1 > Ld2\r
+               Bdry_diag_1 = Bdry_diag1(1:Ld2);\r
+               Reg_diag_1 = Reg_diag1(1:Ld2);\r
+       else\r
+               Bdry_diag_1 = Bdry_diag1;\r
+               Reg_diag_1 = Reg_diag1;\r
+               Bdry_diag2 = Bdry_diag2(1:Ld1);\r
+               Reg_diag2 = Reg_diag2(1:Ld1);\r
+       end\r
+\r
+       Factor = sqrt((Rows1*Cols1)/(Rows2*Cols2));\r
+       if Factor > 1\r
+               Bdry_diag2 = Bdry_diag2*Factor;\r
+               Reg_diag2 = Reg_diag2*Factor;\r
+        else\r
+               Bdry_diag1 = Bdry_diag1/Factor;\r
+               Reg_diag1 = Reg_diag1/Factor;\r
+       end\r
+\r
+       Svd_dist_bdry(i) = norm(Bdry_diag_1-Bdry_diag2);\r
+       Svd_dist_reg(i) = norm(Reg_diag_1-Reg_diag2);\r
+end\r
+\r
+Svd_dist_bdry = min(Svd_dist_bdry);\r
+Svd_dist_reg = min(Svd_dist_reg);\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/Vertex_at.m b/THESE/codes/meanshift/Ms_segmenter/Vertex_at.m
new file mode 100644 (file)
index 0000000..6d858ab
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/Vertex_at.m
@@ -0,0 +1,7 @@
+function Vertex_coords = Vertex_at (Region)\r
+% tells where on the plane to place the corresponding to the region vertex\r
+% will assume for the time being that regions are represented as \r
+% objects with various characteristics - Boundary, Matrix, Inner pt, etc.\r
+% this function could be overwritten, but should be based on the same props.\r
+\r
+Vertex_coords = round(mean(Region.Boundary, 2));  % take the gravicenter\r

diff --git a/THESE/codes/meanshift/Ms_segmenter/_README_ b/THESE/codes/meanshift/Ms_segmenter/_README_
new file mode 100644 (file)
index 0000000..1b98dc1
--- /dev/null
+++ b/THESE/codes/meanshift/Ms_segmenter/_README_
@@ -0,0 +1,219 @@
+Some directions and helpful hints on how to work with the region extraction/\r
+shape classification system.\r
+\r
+NEW FEATURES:\r
+- region comparison now gives additional info, i.e. ratios of areas,\r
+volumes, and average intensities.\r
+- pixels are now removed according to regions, not to colors.\r
+- labeling of the image is now possible. Please read below under "place a label"\r
+- it is possible to change colors of regions ("Region" menu, "Color" submenu).\r
+- it is now possible to pick regions not from their lists but by simply\r
+clicking and using "(un)select 1-st/2-nd region".  This is equivalent to\r
+using the pop-up menus which remain.\r
+- lines with which the region boundaries are drawn could be made thinner\r
+and thicker ("Region" menu, "Thin next line"/"Thicken next line" submenus).\r
+- images are now saved in "low" and "high" quality formats - with different\r
+resolutions and sizes of files.\r
+\r
+\r
+- To start the system:  type in "Biomed_demo" at Matlab prompt.\r
+\r
+- To read in an image:  "Image" menu "Open" submenu ("Alt-I" and "Alt-O", or\r
+"Ctrl-O") either in the start window or in any window containing an image.\r
+In any case the image will appear in a separate window, not overwriting an \r
+existing image.  Currently only "tiff", "jpeg", and "bmp" formats are supported.\r
+It is preferrable to crop the part of the image you will work with first in\r
+some other editor, as Matlab seems to take much memory to process it when\r
+reading, even without any processing.\r
+\r
+- To smooth an opened image:  "Image" menu "Smooth" submenu ("Alt-I" and\r
+"Alt-M", or "Ctrl-M").  Available filters are: "Circle-5" - averaging over a\r
+"circular" window of diameter 5 (a matrix of size 5 by 5 is taken first, 3\r
+corner elements in each of the 4 corners are set to 0, and all others are set\r
+to 1/(sum of elts)); "Symlet" - each color component is smoothed by means of\r
+the particular wavelet family in the usual fashion; "Avg-3" - square 3x3\r
+matrix of 1/(sum of elts) is taken as a filter).  I prefer the first filter as\r
+it provides quick and sufficiently good results.  The wavelet-based one seems\r
+to need some debugging (as of 11/25/98).\r
+\r
+- To crop a part of an image: "Image" menu "Crop" submenu ("Alt-I" and\r
+"Alt-C", or "Ctrl-C").  Then pick a vertex of the future rectangular window\r
+by clicking once on the image with the mouse and select a rectangle that will \r
+become a new image.  When rectangle is completed a prompt whether to crop or \r
+not appears.  If "Yes" is pressed, the cropped part appears in a separate \r
+window, while blue rectangle in the original image indicates where the part\r
+came from.  To get rid of the rectangle use "Image"/"Refresh".\r
+\r
+- To refresh an image (get rid of various extraneous dots and lines): "Image" \r
+menu "Refresh" submenu ("Alt-I" and "Alt-E", or "Ctrl-E").\r
+\r
+- (NEW) To place a label on the image click on the place where the label\r
+should begin first.  Then go to "Image" menu "Label" submenu ("Alt-I" and\r
+"Alt-L", or "Ctrl-L").  A menu offering the title for the label, the font\r
+size, type, and color will pop up.  When the label is entered, it will be\r
+first tentatively placed on the image upon which a confirmation dialogue will\r
+pop up.  If "No" is chosen the label will not be placed.  To edit an existing\r
+label, click with the mouse close to the starting point of the label (leftmost\r
+character) and again select "Label" submenu of "Image" menu.  To erase an\r
+existing label this step can be followed - entering in the editing mode first\r
+and blanking the label second.\r
+\r
+- To save an image:  "Image" menu "Save as" submenu ("Alt-I" and "Alt-S", or\r
+"Ctrl-S").  Currently only "tiff", "jpeg", "bmp", "eps" formats are supported.\r
+The figures are saved along with all the lines they may contain.  The saved\r
+figures are usually stored in two files ("file_low.xxx" and "file_high.xxx")\r
+where the first file is of low quality and the latter file (of usually\r
+much bigger size) is of high quality.  "Eps" is the Encapsulated PostScript\r
+and there is just one version of it.  For "bmp" the low-quality image is\r
+placed in the file "file.bmp" while the high quality one into "file.jpg".\r
+\r
+- To segment an image (MAIN OPERATION): "Image" menu "Segment" submenu\r
+("Alt-I" and "Alt-G", or "Ctrl-G").  Segmentation means splitting the image\r
+pixels into color clusters first (as prescribed by the "Mean Shift Algorithm")\r
+and into connected regions second (as is usually done in case of images).\r
+The segmentation parameters (appear in the parameter setting window):\r
+       - Window size: determines the radius of the search window used by the \r
+       segmentation algorithm.  The smaller the window size the more likely the\r
+       search for the maximum density location will end up in a local maximum, and\r
+       therefore the bigger number of initial color clusters will be.  It also\r
+       affects the value of the "cluster diameter", i.e. the value by which \r
+       two colors of the same color cluster could differ.\r
+       - Color components to use: determines the number of "principal components"\r
+       to use when segmenting the image.  Since a color image consists of 3 color\r
+       components in any color system, there is a possibility to use 1-3 principal\r
+       ones for segmentation purpose.  When 1 component is used, it is equivalent\r
+       to using just intensity info, while 3 components uses full color info.\r
+       The fewer color components are used, the faster the segmentation is.\r
+       The parameter is automatically set to 1 in case of grey-scale images.\r
+       - Color group size: determines the minimum number of pixels in a color\r
+       group for it to be considered a "color cluster".  This parameter usually has\r
+       little effect on the final segmentation due to the fact that the number \r
+       of pixels of nearly the same color is fairly high in a typical image.\r
+       - Min region size: determines the minimum number of 8-connected pixels\r
+       to be considered as a separate connected region.  Thus to find only\r
+       sufficiently big and regular regions (as opposed to small specks) the\r
+       number must be set to a sufficiently high value.\r
+       - Separate layers: determines whether to show ("yes") or not ("no") all \r
+       color clusters at once as "layers" of the original image, i.e. nearly all \r
+       the image pixels are placed into one of the layers.  The white color of a \r
+       particular pixel in the produced black and white figures is interpreted \r
+       as "the particular pixels is placed in the particular layer", and vice \r
+       versa.  Some pixels not belonging to any color clusters (due to\r
+       their color deviation) will not belong to any of the layers.\r
+       NOTE: the regions handling which will now follow is based on the layers. \r
+\r
+Having segmented the image (the operation may be repeated if necessary) we\r
+now can apply various operations to the resulting segments.  Segments are\r
+always connected (unless obtained by explicit merging of two disconnected\r
+ones).\r
+\r
+- To find a region's boundary: click on a point belonging to the desired region,\r
+then "Region" menu, "Find boundary" submenu ("Alt-R" and "Alt-F", or "Ctrl-F").\r
+\r
+NOTE: the following operations will require selecting one or two regions.  The\r
+selection window is basically the same for all the operations.  A list of\r
+regions (with their numbers) is presented.  Upon (tentatively) selecting a\r
+region the region's color changes from a non-white to white.  Upon deselecting\r
+a region the region's color is changed back from white to a non-white.\r
+Selecting is done by clicking on the region's number in the list (which should\r
+hang down when the down arrow in right side is pressed).  Deselecting is done\r
+automatically when another region is selected or "cancel" button is pressed.\r
+\r
+NEW: now region selection can be done by clicking on the region and choosing\r
+"(un)select 1-st/2-nd region" from "Region" menu (or "Ctrl-1"/"Ctrl-2").\r
+The region chosen again becomes white and turns its usual color when another\r
+region is selected.  For most operations (e.g. Region save/Cut/Intensity 3-d)\r
+choose the region to be the 1-st.  The second region needs to be chosen only\r
+for binary operations, e.g. comparison or merge.  Thus to compare two regions\r
+in the same window one would click/select 1-st and 2-nd region and then choose\r
+"Compare...Image regions".  To compare two regions in different window one\r
+however needs to pick 1-st region in one window and select \r
+"Compare...Window regions" in that window.  Then 2-nd region needs to be\r
+chosen in the second window and the same menu item "Compare...Window regions"\r
+needs to be selected, so that the system would know which two 1-st/2-nd\r
+pair of regions to compare.\r
+\r
+- To save a region (in a format that can be later used for shape comparison\r
+with other regions): "Region" menu, "Save as" submenu ("Alt-R" and "Alt-V", \r
+or "Ctrl-V").  The files have extension ".rgn" (for "ReGioN").\r
+\r
+- To compare two regions in terms of their shapes (as described in the\r
+article) and obtain some additional info:\r
+"Compare to" submenu of "Region menu".  A region can be compared with a \r
+region of the same image ("Image region" submenu of "Compare to" submenu \r
+("Alt-R" and "Alt-C" and "Alt-I", or "Ctrl-I"), or a region in a \r
+different window ("Window region" submenu of "Compare to" submenu ("Alt-R" \r
+and "Alt-C" and "Alt-W", or "Ctrl-W"), or a region previously saved on disk\r
+("Disk region" submenu of "Compare to" submenu ("Alt-R" and "Alt-C" and \r
+"Alt-D", or "Ctrl-D").  In the first case the choice of the two regions is\r
+done in the same dialogue.  In the second case the action of choosing the\r
+regions must be done in the two windows (same sequence of keys must be\r
+repeated in both windows, and windows may coincide).  In the third case after\r
+the image region is chosen a prompt to choose a saved region is given.\r
+\r
+- To merge two regions (which may be disconnected): "Merge" submenu of\r
+"Region" menu ("Alt-R" and "Alt-R", or "Ctrl-R").  The region is then treated\r
+as a single entity, while the regions from which it was formed "disappear" \r
+from various menus (e.g. "Compare", "Merge", etc).\r
+\r
+- To cut a region into two pieces: "Cut" submenu of "Region" menu ("Alt-R" \r
+and "Alt-U", or "Ctrl-U").  Upon choosing the region a prompt to draw the\r
+separating line appears.  Please be very careful drawing the line as the\r
+algorithm for separating the region is quite unstable and must be improved.\r
+The line must start and end a little bit outside the region.  Upon successful\r
+splitting 2 new regions will appear while the old one will disappear.\r
+\r
+- To visualize a region and obtain some basic info: "Intensity 3-d"  submenu\r
+of "Region" menu ("Alt-R" and "Alt-3", or "Ctrl-3").  As of today (11/29/98)\r
+the region is shown correctly, not flipped.  In the image the horizontal axes\r
+correspond to image dimensions, while the vertical one corresponds to image\r
+intensity.  The 3-dimensional graph can be rotated left and right, and up and\r
+down by means of "Ctrl-L", "Ctrl-R", "Ctrl-U", and "Ctrl-D" keys while the \r
+window is active.  The amount by which the graph is rotated could be \r
+increased/decreased by means of "Ctrl-A" and "Ctrl-V" keys.  While these\r
+operations don't allow to choose an arbitrary point of view, large flexibility\r
+is achieved.  To obtain some relevant to the region information use "Values"\r
+menu of the graph window.\r
+\r
+- To exclude some regions from further consideration (will be shown usefulness\r
+of later): "Background"  submenu of "Region" menu ("Alt-R" and "Alt-K", \r
+or "Ctrl-K").  Now the pixels are removed according to the found regions,\r
+not underlying colors. Thus the background should preferrably consist of\r
+one or few regions.\r
+\r
+- To change the color of a region's boundary, first select the region, and\r
+second click on "Region"/"Color" submenu (or "Ctrl-L").  A list of possible\r
+colors to choose from will be presented.\r
+\r
+- To thicken/thin the next boundary use "Thicken/Thin next line" submenu of\r
+"Region" menu.  This could be useful if the lines are too thin for printing\r
+purposes.  If the next line is still not thick enough, try to do it several\r
+times.\r
+\r
+A typical use of the system:\r
+- read in an image ("jpeg", "tiff", "bmp" formats only) (Image/Open)\r
+- select a rectangular region to work with (Image/Crop)\r
+- segment with the initially given parameters (Image/Segment)\r
+       - if the resulting regions are too small (oversegmentation), increase the\r
+       window size in Segmentation parameters window and resegment;\r
+       - if the resulting regions are too large (undersegmentation), reduce the\r
+       window size in Segmentation parameters window and resegment;\r
+- if there is large uniformity in "the background": \r
+       - first segment the image with a small window size but large enough \r
+       to capture most of the background into one or few regions;\r
+       - second use "Background" operation from "Region" menu to exclude the \r
+       corresponding regions from further processing (pixel removal);\r
+       - third resegment the image with a larger window size so that to capture\r
+       less uniform foreground;\r
+- if a region is best obtained as a result of merge of two fairly different\r
+(in color respect) regions that are adjacent, use "Region/Merge" operation.\r
+- if a region has some extraneous parts (may be from other similar regions\r
+as in case of cells), use "Region/Cut" operation.\r
+- having obtained a suitable region it can be visualized in terms of its\r
+intensity (Region/Intensity-3d).\r
+- having obtained two regions (in the same window or different ones) they can\r
+be compared in terms of their shapes and some other respects (e.g. areas or\r
+average intensities) (Region/Compare to/[Disk/Window/Region])\r
+- an exemplary region can be saved (shape info only, no intensity info) for\r
+future comparisons.\r
+- an image with or without lines could be saved for future work (Image/Save).\r

diff --git a/THESE/codes/meanshift/cochon128_meanshift_r100m100.png b/THESE/codes/meanshift/cochon128_meanshift_r100m100.png
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diff --git a/THESE/codes/meanshift/cochon128_meanshift_r50m100.png b/THESE/codes/meanshift/cochon128_meanshift_r50m100.png
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diff --git a/THESE/codes/snake/basic_code/DrawSegmentedArea2D.m b/THESE/codes/snake/basic_code/DrawSegmentedArea2D.m
new file mode 100755 (executable)
index 0000000..bf090c6
--- /dev/null
+++ b/THESE/codes/snake/basic_code/DrawSegmentedArea2D.m
@@ -0,0 +1,48 @@
+function J=DrawSegmentedArea2D(P,Isize)\r
+% Draw the contour as one closed line in a logical image, \r
+% and make the inside of the contour true with imfill\r
+%\r
+%  J=DrawSegmentedArea2D(P,Isize)\r
+%\r
+% inputs,\r
+%  P : The list with contour points 2 x N\r
+%  Isize : The size of the output image [x y]\r
+%\r
+% outputs,\r
+%  J : The binary image with the contour filled\r
+%\r
+% example:\r
+%   y=[182 233 251 205 169];\r
+%   x=[163 166 207 248 210];\r
+%   P=[x(:) y(:)];\r
+%\r
+%   J=DrawSegmentedArea(P,[400 400]);\r
+%   figure, imshow(J); \r
+%   hold on; plot([P(:,2);P(1,2)],[P(:,1);P(1,1)]);\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+\r
+J=false(Isize+2);\r
+% Loop through all line coordinates\r
+x=round([P(:,1);P(1,1)]); x=min(max(x,1),Isize(1));\r
+y=round([P(:,2);P(1,2)]); y=min(max(y,1),Isize(2));\r
+for i=1:(length(x)-1)\r
+   % Calculate the pixels needed to construct a line of 1 pixel thickness\r
+   % between two coordinates.\r
+   xp=[x(i) x(i+1)];  yp=[y(i) y(i+1)]; \r
+   dx=abs(xp(2)-xp(1)); dy=abs(yp(2)-yp(1));\r
+   if(dx==dy)\r
+     if(xp(2)>xp(1)), xline=xp(1):xp(2); else xline=xp(1):-1:xp(2); end\r
+     if(yp(2)>yp(1)), yline=yp(1):yp(2); else yline=yp(1):-1:yp(2); end\r
+   elseif(dx>dy)\r
+     if(xp(2)>xp(1)), xline=xp(1):xp(2); else xline=xp(1):-1:xp(2); end\r
+     yline=linspace(yp(1),yp(2),length(xline));\r
+   else\r
+     if(yp(2)>yp(1)), yline=yp(1):yp(2); else yline=yp(1):-1:yp(2); end\r
+     xline=linspace(xp(1),xp(2),length(yline));   \r
+   end\r
+   % Insert all pixels in the fill image\r
+   J(round(xline+1)+(round(yline+1)-1)*size(J,1))=1;\r
+end\r
+J=bwfill(J,1,1); J=~J(2:end-1,2:end-1);
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/ExternalForceImage2D.m b/THESE/codes/snake/basic_code/ExternalForceImage2D.m
new file mode 100755 (executable)
index 0000000..0cb5440
--- /dev/null
+++ b/THESE/codes/snake/basic_code/ExternalForceImage2D.m
@@ -0,0 +1,29 @@
+function Eextern = ExternalForceImage2D(I,Wline, Wedge, Wterm,Sigma)\r
+% Eextern = ExternalForceImage2D(I,Wline, Wedge, Wterm,Sigma)\r
+% \r
+% inputs, \r
+%  I : The image\r
+%  Sigma : Sigma used to calculated image derivatives \r
+%  Wline : Attraction to lines, if negative to black lines otherwise white\r
+%          lines\r
+%  Wedge : Attraction to edges\r
+%  Wterm : Attraction to terminations of lines (end points) and corners\r
+%\r
+% outputs,\r
+%  Eextern : The energy function described by the image\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+Ix=ImageDerivatives2D(I,Sigma,'x');\r
+Iy=ImageDerivatives2D(I,Sigma,'y');\r
+Ixx=ImageDerivatives2D(I,Sigma,'xx');\r
+Ixy=ImageDerivatives2D(I,Sigma,'xy');\r
+Iyy=ImageDerivatives2D(I,Sigma,'yy');\r
+\r
+\r
+Eline = imgaussian(I,Sigma);\r
+Eterm = (Iyy.*Ix.^2 -2*Ixy.*Ix.*Iy + Ixx.*Iy.^2)./((1+Ix.^2 + Iy.^2).^(3/2));\r
+Eedge = sqrt(Ix.^2 + Iy.^2); \r
+\r
+Eextern= (Wline*Eline - Wedge*Eedge -Wterm * Eterm); \r
+\r

diff --git a/THESE/codes/snake/basic_code/ExternalForceImage3D.m b/THESE/codes/snake/basic_code/ExternalForceImage3D.m
new file mode 100755 (executable)
index 0000000..88daf66
--- /dev/null
+++ b/THESE/codes/snake/basic_code/ExternalForceImage3D.m
@@ -0,0 +1,24 @@
+function Eextern = ExternalForceImage3D(I,Wline, Wedge,Sigma)\r
+% Eextern = ExternalForceImage3D(I,Wline, Wedge,Sigma)\r
+% \r
+% inputs, \r
+%  I : The image\r
+%  Sigma : Sigma used to calculated image derivatives \r
+%  Wline : Attraction to lines, if negative to black lines otherwise white\r
+%          lines\r
+%  Wterm : Attraction to terminations of lines (end points) and corners\r
+%\r
+% outputs,\r
+%  Eextern : The energy function described by the image\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+Ix=ImageDerivatives3D(I,Sigma,'x');\r
+Iy=ImageDerivatives3D(I,Sigma,'y');\r
+Iz=ImageDerivatives3D(I,Sigma,'z');\r
+\r
+Eline =  imgaussian(I,Sigma);\r
+Eedge = sqrt(Ix.^2 + Iy.^2 + Iz.^2); \r
+\r
+Eextern= (Wline*Eline - Wedge*Eedge); \r
+\r

diff --git a/THESE/codes/snake/basic_code/GVFOptimizeImageForces2D.m b/THESE/codes/snake/basic_code/GVFOptimizeImageForces2D.m
new file mode 100755 (executable)
index 0000000..8db388e
--- /dev/null
+++ b/THESE/codes/snake/basic_code/GVFOptimizeImageForces2D.m
@@ -0,0 +1,44 @@
+function Fext=GVFOptimizeImageForces2D(Fext, Mu, Iterations, Sigma)\r
+% This function "GVFOptimizeImageForces" does gradient vector flow (GVF)\r
+% on a vector field. GVF gives the edge-forces a larger capature range,\r
+% to make the snake also reach concave regions\r
+%\r
+% Fext = GVFOptimizeImageForces2D(Fext, Mu, Iterations, Sigma) \r
+% \r
+% inputs,\r
+%   Fext : The image force vector field N x M x 2\r
+%   Mu : Is a trade of scalar between noise and real edge forces\r
+%   Iterations : The number of GVF itterations\r
+%   Sigma : Used when calculating the Laplacian\r
+% \r
+% outputs,\r
+%   Fext : The GVF optimized image force vector field\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Squared magnitude of force field\r
+Fx= Fext(:,:,1);\r
+Fy= Fext(:,:,2);\r
+\r
+% Calculate magnitude\r
+sMag = Fx.^2+ Fy.^2;\r
+\r
+% Set new vector-field to initial field\r
+u=Fx;  v=Fy;\r
+  \r
+% Iteratively perform the Gradient Vector Flow (GVF)\r
+for i=1:Iterations,\r
+  % Calculate Laplacian\r
+  Uxx=ImageDerivatives2D(u,Sigma,'xx');\r
+  Uyy=ImageDerivatives2D(u,Sigma,'yy');\r
+  \r
+  Vxx=ImageDerivatives2D(v,Sigma,'xx');\r
+  Vyy=ImageDerivatives2D(v,Sigma,'yy');\r
+\r
+  % Update the vector field\r
+  u = u + Mu*(Uxx+Uyy) - sMag.*(u-Fx);\r
+  v = v + Mu*(Vxx+Vyy) - sMag.*(v-Fy);\r
+end\r
+\r
+Fext(:,:,1) = u;\r
+Fext(:,:,2) = v;\r

diff --git a/THESE/codes/snake/basic_code/GVFOptimizeImageForces3D.m b/THESE/codes/snake/basic_code/GVFOptimizeImageForces3D.m
new file mode 100755 (executable)
index 0000000..decdf56
--- /dev/null
+++ b/THESE/codes/snake/basic_code/GVFOptimizeImageForces3D.m
@@ -0,0 +1,58 @@
+function Fext=GVFOptimizeImageForces3D(Fext, Mu, Iterations, Sigma)\r
+% This function "GVFOptimizeImageForces" does gradient vector flow (GVF)\r
+% on a vector field. GVF gives the edge-forces a larger capature range,\r
+% to make the snake also reach concave regions\r
+%\r
+% Fext = GVFOptimizeImageForces3D(Fext, Mu, Iterations, Sigma) \r
+% \r
+% inputs,\r
+%   Fext : The image force vector field N x M x 3\r
+%   Mu : Is a trade of scalar between noise and real edge forces\r
+%   Iterations : The number of GVF itterations\r
+%   Sigma : Used when calculating the Laplacian\r
+% \r
+% outputs,\r
+%   Fext : The GVF optimized image force vector field\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Squared magnitude of force field\r
+Fx= Fext(:,:,:,1);\r
+Fy= Fext(:,:,:,2);\r
+Fz= Fext(:,:,:,3);\r
+\r
+% Calculate magnitude\r
+sMag = Fx.^2+ Fy.^2 + Fz.^2;\r
+\r
+% Set new vector-field to initial field\r
+u=Fx; v=Fy; w=Fz;\r
+\r
+% Iteratively perform the Gradient Vector Flow (GVF)\r
+for i=1:Iterations,\r
+  % Calculate Laplacian\r
+  Uxx=ImageDerivatives3D(u,Sigma,'xx');\r
+  Uyy=ImageDerivatives3D(u,Sigma,'yy');\r
+  Uzz=ImageDerivatives3D(u,Sigma,'zz');\r
+\r
+  % Update the vector field\r
+  u = u + Mu*(Uxx+Uyy+Uzz) - sMag.*(u-Fx);\r
+  clear('Uxx','Uyy','Uzz');\r
+  \r
+  Vxx=ImageDerivatives3D(v,Sigma,'xx');\r
+  Vyy=ImageDerivatives3D(v,Sigma,'yy');\r
+  Vzz=ImageDerivatives3D(v,Sigma,'zz');\r
+   \r
+  v = v + Mu*(Vxx+Vyy+Vzz) - sMag.*(v-Fy);\r
+  clear('Vxx','Vyy','Vzz');\r
+  \r
+  Wxx=ImageDerivatives3D(w,Sigma,'xx');\r
+  Wyy=ImageDerivatives3D(w,Sigma,'yy');\r
+  Wzz=ImageDerivatives3D(w,Sigma,'zz');\r
+  \r
+  w = w + Mu*(Wxx+Wyy+Wzz) - sMag.*(w-Fz);\r
+  clear('Wxx','Wyy','Wzz');\r
+end\r
+\r
+Fext(:,:,:,1) = u;\r
+Fext(:,:,:,2) = v;\r
+Fext(:,:,:,3) = w;
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/GetContourNormals2D.m b/THESE/codes/snake/basic_code/GetContourNormals2D.m
new file mode 100755 (executable)
index 0000000..05f22ca
--- /dev/null
+++ b/THESE/codes/snake/basic_code/GetContourNormals2D.m
@@ -0,0 +1,33 @@
+function N=GetContourNormals2D(P)\r
+% This function calculates the normals, of the contour points\r
+% using the neighbouring points of each contour point\r
+%\r
+% N=GetContourNormals2D(P)\r
+% \r
+% inputs,\r
+%  P : List with contour coordinates M x 2\r
+%\r
+% outputs,\r
+%  N : List with contour normals M x 2\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Use the n'th neighbour to calculate the normal (more stable)\r
+a=4;\r
+\r
+% From array to separate x,y\r
+xt=P(:,1); yt=P(:,2);\r
+\r
+% Derivatives of contour\r
+n=length(xt);\r
+f=(1:n)+a; f(f>n)=f(f>n)-n;\r
+b=(1:n)-a; b(b<1)=b(b<1)+n;\r
+\r
+dx=xt(f)-xt(b);\r
+dy=yt(f)-yt(b);\r
+\r
+% Normals of contourpoints\r
+l=sqrt(dx.^2+dy.^2);\r
+nx = -dy./l; \r
+ny =  dx./l;\r
+N(:,1)=nx; N(:,2)=ny;\r

diff --git a/THESE/codes/snake/basic_code/ImageDerivatives2D.m b/THESE/codes/snake/basic_code/ImageDerivatives2D.m
new file mode 100755 (executable)
index 0000000..dc49363
--- /dev/null
+++ b/THESE/codes/snake/basic_code/ImageDerivatives2D.m
@@ -0,0 +1,32 @@
+function J=ImageDerivatives2D(I,sigma,type)\r
+% Gaussian based image derivatives\r
+%\r
+%  J=ImageDerivatives2D(I,sigma,type)\r
+%\r
+% inputs,\r
+%   I : The 2D image\r
+%   sigma : Gaussian Sigma\r
+%   type : 'x', 'y', 'xx', 'xy', 'yy'\r
+%\r
+% outputs,\r
+%   J : The image derivative\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Make derivatives kernels\r
+[x,y]=ndgrid(floor(-3*sigma):ceil(3*sigma),floor(-3*sigma):ceil(3*sigma));\r
+\r
+switch(type)\r
+    case 'x'\r
+        DGauss=-(x./(2*pi*sigma^4)).*exp(-(x.^2+y.^2)/(2*sigma^2));\r
+    case 'y'\r
+        DGauss=-(y./(2*pi*sigma^4)).*exp(-(x.^2+y.^2)/(2*sigma^2));\r
+    case 'xx'\r
+        DGauss = 1/(2*pi*sigma^4) * (x.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2)/(2*sigma^2));\r
+    case {'xy','yx'}\r
+        DGauss = 1/(2*pi*sigma^6) * (x .* y)           .* exp(-(x.^2 + y.^2)/(2*sigma^2));\r
+    case 'yy'\r
+        DGauss = 1/(2*pi*sigma^4) * (y.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2)/(2*sigma^2));\r
+end\r
+\r
+J = imfilter(I,DGauss,'conv','symmetric');\r

diff --git a/THESE/codes/snake/basic_code/ImageDerivatives3D.m b/THESE/codes/snake/basic_code/ImageDerivatives3D.m
new file mode 100755 (executable)
index 0000000..20b7e5c
--- /dev/null
+++ b/THESE/codes/snake/basic_code/ImageDerivatives3D.m
@@ -0,0 +1,43 @@
+function J=ImageDerivatives3D(I,sigma,type)\r
+% Gaussian based image derivatives\r
+%\r
+%  J=ImageDerivatives3D(I,sigma,type)\r
+%\r
+% inputs,\r
+%   I : The 3D image\r
+%   sigma : Gaussian Sigma\r
+%   type : 'x', 'y', 'z', 'xx', 'yy', 'zz', 'xy', 'xz', 'yz'\r
+%\r
+% outputs,\r
+%   J : The image derivative\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Make derivatives kernels\r
+[x,y,z]=ndgrid(floor(-3*sigma):ceil(3*sigma),floor(-3*sigma):ceil(3*sigma),floor(-3*sigma):ceil(3*sigma));\r
+\r
+switch(type)\r
+    case 'x'\r
+        DGauss=-(x./((2*pi)^(3/2)*sigma^5)).*exp(-(x.^2+y.^2+z.^2)/(2*sigma^2));\r
+    case 'y'\r
+        DGauss=-(y./((2*pi)^(3/2)*sigma^5)).*exp(-(x.^2+y.^2+z.^2)/(2*sigma^2));\r
+    case 'z'\r
+        DGauss=-(z./((2*pi)^(3/2)*sigma^5)).*exp(-(x.^2+y.^2+z.^2)/(2*sigma^2));\r
+    case 'xx'\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^5) * (x.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+    case 'yy'\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^5) * (y.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+    case 'zz'\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^5) * (z.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+    case {'xy','yx'}\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^7) * (x .* y)           .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+    case {'xz','zx'}\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^7) * (x .* z)           .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+    case {'yz','zy'}\r
+        DGauss = 1/((2*pi)^(3/2)*sigma^7) * (y .* z)           .* exp(-(x.^2 + y.^2 + z.^2)/(2*sigma^2));\r
+end\r
+\r
+K=SeparateKernel(DGauss);\r
+J = imfilter(I,K{1},'conv','symmetric');\r
+J = imfilter(J,K{2},'conv','symmetric');\r
+J = imfilter(J,K{3},'conv','symmetric');
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/InterpolateContourPoints2D.m b/THESE/codes/snake/basic_code/InterpolateContourPoints2D.m
new file mode 100755 (executable)
index 0000000..7de88cc
--- /dev/null
+++ b/THESE/codes/snake/basic_code/InterpolateContourPoints2D.m
@@ -0,0 +1,45 @@
+function K=InterpolateContourPoints2D(P,nPoints)\r
+% This function resamples a few points describing a countour , to a smooth\r
+% contour of uniform sampled points.\r
+%\r
+% K=InterpolateContourPoints(P,nPoints)\r
+%\r
+% input,\r
+%  P : Inpute Contour, size N x 2  (with N>=4)\r
+%  nPoints : Number of Contour points as output\r
+% \r
+% output,\r
+%  K : Uniform sampled Contour points, size nPoints x 2\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+%\r
+% example, \r
+%  % Show an image\r
+%   figure, imshow(imread('moon.tif'));\r
+%  % Select some points with the mouse\r
+%   [y,x] = getpts;\r
+%  % Make an array with the clicked coordinates\r
+%   P=[x(:) y(:)];\r
+%  % Interpolate inbetween the points\r
+%   Pnew=InterpolateContourPoints2D(P,100)\r
+%  % Show the result\r
+%   hold on; plot(P(:,2),P(:,1),'b*');\r
+%   plot(Pnew(:,2),Pnew(:,1),'r.');\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Interpolate points inbetween\r
+O(:,1)=interp([P(end-3:end,1);P(:,1);P(:,1);P(1:4,1)],10);\r
+O(:,2)=interp([P(end-3:end,2);P(:,2);P(:,2);P(1:4,2)],10);\r
+O=O(41:end-39,:); \r
+\r
+% Calculate distance between points\r
+dis=[0;cumsum(sqrt(sum((O(2:end,:)-O(1:end-1,:)).^2,2)))];\r
+\r
+% Resample to make uniform points\r
+K(:,1) = interp1(dis,O(:,1),linspace(0,dis(end),nPoints*2));\r
+K(:,2) = interp1(dis,O(:,2),linspace(0,dis(end),nPoints*2));\r
+K=K(round(end/4):round(end/4)+nPoints-1,:);\r
+\r
+ \r
+ 
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/MakeContourClockwise2D.m b/THESE/codes/snake/basic_code/MakeContourClockwise2D.m
new file mode 100755 (executable)
index 0000000..cd9fd89
--- /dev/null
+++ b/THESE/codes/snake/basic_code/MakeContourClockwise2D.m
@@ -0,0 +1,16 @@
+function P=MakeContourClockwise2D(P)\r
+% This function MakeContourClockwise will make a contour clockwise \r
+% contour clockwise. This is done by calculating the area inside the \r
+% contour, if it is positive we change the contour orientation.\r
+%\r
+%  P=MakeContourClockwise2D(P);\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Area inside contour\r
+O=[P;P(1:2,:)];\r
+area = 0.5*sum((O((1:size(P,1))+1,1) .* (O((1:size(P,1))+2,2) - O((1:size(P,1)),2))));\r
+\r
+% If the area inside  the contour is positive, change from counter-clockwise to \r
+% clockwise\r
+if(area>0), P=P(end:-1:1,:); end\r

diff --git a/THESE/codes/snake/basic_code/MakeContourClockwise3D.m b/THESE/codes/snake/basic_code/MakeContourClockwise3D.m
new file mode 100755 (executable)
index 0000000..ab82e49
--- /dev/null
+++ b/THESE/codes/snake/basic_code/MakeContourClockwise3D.m
@@ -0,0 +1,36 @@
+function FV=MakeContourClockwise3D(FV)\r
+% This function MakeContourClockwise will make a surface clockwise \r
+% contour clockwise. This is done by calculating the volume inside the \r
+% surface, if it is negative we change the surface orientation.\r
+%\r
+%  FV=MakeContourClockwise2D(FV);\r
+%\r
+%  input/output,\r
+%    FV : Triangulated surface description with FV.faces and FV.vertices\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Volume inside contour\r
+volume=0;\r
+for i=1:size(FV.faces,1)\r
+    a=FV.vertices(FV.faces(i,1),:); b=FV.vertices(FV.faces(i,2),:); c=FV.vertices(FV.faces(i,3),:);\r
+\r
+    k=cross(b,c);\r
+    v = (a(1)*k(1)+a(2)*k(2)+a(3)*k(3))/6;\r
+    \r
+    volume=volume+v;\r
+end\r
+volume=-(volume);\r
+ \r
+% If the area inside  the contour is positive, change from counter-clockwise to \r
+% clockwise\r
+if(volume<0), \r
+    FV.faces=[FV.faces(:,3) FV.faces(:,2) FV.faces(:,1)]; \r
+end\r
+\r
+function c=cross(a,b)\r
+a=a(:); b=b(:); \r
+c = [a(2,:).*b(3,:)-a(3,:).*b(2,:)\r
+     a(3,:).*b(1,:)-a(1,:).*b(3,:)\r
+     a(1,:).*b(2,:)-a(2,:).*b(1,:)];\r
+ 
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/PatchNormals3D.m b/THESE/codes/snake/basic_code/PatchNormals3D.m
new file mode 100755 (executable)
index 0000000..a979191
--- /dev/null
+++ b/THESE/codes/snake/basic_code/PatchNormals3D.m
@@ -0,0 +1,65 @@
+function N=PatchNormals3D(FV)\r
+% This function PATCHNORMALS calculates the normals of a triangulated\r
+% mesh. PATCHNORMALS calls the patchnormal_double.c mex function which \r
+% first calculates the normals of all faces, and after that calculates \r
+% the vertice normals from the face normals weighted by the angles \r
+% of the faces.\r
+%\r
+% N=PatchNormals3D(FV);\r
+%\r
+% Inputs,\r
+%   FV : A struct containing FV.faces with a facelist Nx3 and FV.vertices\r
+%        with a Nx3 vertices list. Such a structure is created by Matlab\r
+%        Patch function\r
+% Outputs,\r
+%   N : A Mx3 list with the normals of all vertices\r
+%\r
+% Example,\r
+%   % Compile the c-coded function\r
+%   mex PatchNormalsDouble.c -v\r
+%\r
+%   % Load a triangulated mesh of a sphere\r
+%   load sphere; \r
+%\r
+%   % Calculate the normals\r
+%   N=PatchNormals3D(FV);\r
+%\r
+%   % Show the normals\r
+%   figure, patch(FV,'FaceColor',[1 0 0]); axis square; hold on;\r
+%   for i=1:size(N,1);\r
+%       p1=FV.vertices(i,:); p2=FV.vertices(i,:)+10*N(i,:);       \r
+%       plot3([p1(1) p2(1)],[p1(2) p2(2)],[p1(3) p2(3)],'g-');\r
+%   end       \r
+%\r
+% Function is written by D.Kroon University of Twente (June 2009)\r
+\r
+\r
+sizev=size(FV.vertices);\r
+% Check size of vertice array\r
+if((sizev(2)~=3)||(length(sizev)~=2))\r
+    error('patchnormals:inputs','The vertice list is not a m x 3 array')\r
+end\r
+\r
+sizef=size(FV.faces);\r
+% Check size of vertice array\r
+if((sizef(2)~=3)||(length(sizef)~=2))\r
+    error('patchnormals:inputs','The vertice list is not a m x 3 array')\r
+end\r
+\r
+% Check if vertice indices exist\r
+if(max(FV.faces(:))>size(FV.vertices,1))\r
+    error('patchnormals:inputs','The face list contains an undefined vertex index')\r
+end\r
+\r
+% Check if vertice indices exist\r
+if(min(FV.faces(:))<1)\r
+    error('patchnormals:inputs','The face list contains an vertex index smaller then 1')\r
+end\r
+\r
+[Nx,Ny,Nz]=PatchNormalsDouble(double(FV.faces(:,1)),double(FV.faces(:,2)),double(FV.faces(:,3)),double(FV.vertices(:,1)),double(FV.vertices(:,2)),double(FV.vertices(:,3)));\r
+\r
+N=zeros(length(Nx),3);\r
+N(:,1)=Nx;\r
+N(:,2)=Ny;\r
+N(:,3)=Nz;\r
+\r

diff --git a/THESE/codes/snake/basic_code/PatchNormalsDouble.c b/THESE/codes/snake/basic_code/PatchNormalsDouble.c
new file mode 100755 (executable)
index 0000000..6da7ed2
--- /dev/null
+++ b/THESE/codes/snake/basic_code/PatchNormalsDouble.c
@@ -0,0 +1,168 @@
+#include "mex.h"\r
+#include "math.h"\r
+\r
+/* \r
+ * \r
+ * function [Nx,Ny,Nz]=patchnormals_double(Fa,Fb,Fc,Vx,Vy,Vz)\r
+ *\r
+*/\r
+\r
+/* Coordinates to index */\r
+int mindex3(int x, int y, int z, int sizx, int sizy) { return z*sizx*sizy+y*sizx+x;}\r
+int mindex2(int x, int y, int sizx) { return y*sizx+x;}\r
+\r
+/* The matlab mex function */\r
+void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] )\r
+{\r
+    /* All inputs */\r
+    double *FacesA, *FacesB, *FacesC, *VerticesX, *VerticesY, *VerticesZ;\r
+    \r
+    /* All outputs, Vertex Normals */\r
+    double *NormalsX, *NormalsY, *NormalsZ;\r
+        \r
+    /* Temporary Face Normals */\r
+    double *FaceNormalsX, *FaceNormalsY, *FaceNormalsZ;\r
+    \r
+    /* Temporary Face angles */\r
+    double *AnglesA,  *AnglesB,  *AnglesC;\r
+    \r
+    /* Number of faces */\r
+    const mwSize *FacesDims;\r
+    int FacesN=0;\r
+   \r
+    /* Number of vertices */\r
+    const mwSize *VertexDims;\r
+    int VertexN=0;\r
+    int VertexNA[1]={0};    \r
+    \r
+    /* 1D Index  */\r
+    int index0, index1, index2;\r
+            \r
+    /* Edge coordinates and lenght */\r
+    double e0x, e0y, e0z, e0l;\r
+    double e1x, e1y, e1z, e1l;\r
+    double e2x, e2y, e2z, e2l;\r
+          \r
+    /* Length of normal */\r
+    double nl;\r
+    \r
+    /* Loop variable */\r
+    int i;\r
+            \r
+    /* Check for proper number of arguments. */\r
+   if(nrhs!=6) {\r
+     mexErrMsgTxt("6 inputs are required.");\r
+   } else if(nlhs!=3) {\r
+     mexErrMsgTxt("3 outputs are required");\r
+   }\r
+   \r
+   /* Read all inputs (faces and vertices) */\r
+   FacesA=mxGetPr(prhs[0]);\r
+   FacesB=mxGetPr(prhs[1]);\r
+   FacesC=mxGetPr(prhs[2]);\r
+   VerticesX=mxGetPr(prhs[3]);\r
+   VerticesY=mxGetPr(prhs[4]);\r
+   VerticesZ=mxGetPr(prhs[5]);\r
+\r
+   /* Get number of FacesN */\r
+   FacesDims = mxGetDimensions(prhs[0]);   \r
+   FacesN=FacesDims[0]*FacesDims[1];\r
+   \r
+   /* Get number of VertexN */\r
+   VertexDims = mxGetDimensions(prhs[3]);   \r
+   VertexN=VertexDims[0]*VertexDims[1];\r
+   \r
+   /* Create Output arrays for the Normal coordinates */\r
+   VertexNA[0]=VertexN;\r
+   plhs[0] = mxCreateNumericArray(1, VertexNA, mxDOUBLE_CLASS, mxREAL);\r
+   plhs[1] = mxCreateNumericArray(1, VertexNA, mxDOUBLE_CLASS, mxREAL);\r
+   plhs[2] = mxCreateNumericArray(1, VertexNA, mxDOUBLE_CLASS, mxREAL);\r
+   NormalsX = mxGetPr(plhs[0]);\r
+   NormalsY = mxGetPr(plhs[1]);\r
+   NormalsZ = mxGetPr(plhs[2]);\r
+      \r
+\r
+   FaceNormalsX = (double *)malloc( FacesN* sizeof(double) );\r
+   FaceNormalsY = (double *)malloc( FacesN* sizeof(double) );\r
+   FaceNormalsZ = (double *)malloc( FacesN* sizeof(double) );\r
+   \r
+   AnglesA = (double *)malloc( FacesN* sizeof(double) );\r
+   AnglesB = (double *)malloc( FacesN* sizeof(double) );\r
+   AnglesC = (double *)malloc( FacesN* sizeof(double) );\r
+   \r
+   \r
+   /* Calculate all face normals and angles */\r
+   for (i=0; i<FacesN; i++)\r
+   {\r
+       /* Get indices of face vertices */\r
+       index0=(int)FacesA[i]-1;\r
+       index1=(int)FacesB[i]-1;\r
+       index2=(int)FacesC[i]-1;\r
+      \r
+       /* Make edge vectors */\r
+       e0x=VerticesX[index0]-VerticesX[index1];  \r
+       e0y=VerticesY[index0]-VerticesY[index1];  \r
+       e0z=VerticesZ[index0]-VerticesZ[index1];\r
+       \r
+       e1x=VerticesX[index1]-VerticesX[index2];  \r
+       e1y=VerticesY[index1]-VerticesY[index2];  \r
+       e1z=VerticesZ[index1]-VerticesZ[index2];\r
+       \r
+       e2x=VerticesX[index2]-VerticesX[index0];  \r
+       e2y=VerticesY[index2]-VerticesY[index0];  \r
+       e2z=VerticesZ[index2]-VerticesZ[index0];\r
+\r
+       /* Normalize the edge vectors */\r
+       e0l = sqrt(e0x*e0x+e0y*e0y+e0z*e0z)+1e-14;\r
+       e0x/=e0l; e0y/=e0l; e0z/=e0l;\r
+       e1l = sqrt(e1x*e1x+e1y*e1y+e1z*e1z)+1e-14; \r
+       e1x/=e1l; e1y/=e1l; e1z/=e1l;\r
+       e2l = sqrt(e2x*e2x+e2y*e2y+e2z*e2z)+1e-14; \r
+       e2x/=e2l; e2y/=e2l; e2z/=e2l;\r
+\r
+       /* Calculate angles of face seen from vertices */\r
+       AnglesA[i]= acos(e0x*(-e2x)+e0y*(-e2y)+e0z*(-e2z));\r
+       AnglesB[i]= acos(e1x*(-e0x)+e1y*(-e0y)+e1z*(-e0z));\r
+       AnglesC[i]= acos(e2x*(-e1x)+e2y*(-e1y)+e2z*(-e1z));\r
+              \r
+       /* Normal of the face */\r
+       FaceNormalsX[i]=e0y*(e2z) - e0z * (e2y);\r
+       FaceNormalsY[i]=e0z*(e2x) - e0x * (e2z);\r
+       FaceNormalsZ[i]=e0x*(e2y) - e0y * (e2x);\r
+   }\r
+   \r
+   /* Calculate all vertex normals and angles */\r
+   for (i=0; i<FacesN; i++)\r
+   {\r
+       index0=(int)FacesA[i]-1;\r
+       NormalsX[index0]+= FaceNormalsX[i]*AnglesA[i];\r
+       NormalsY[index0]+= FaceNormalsY[i]*AnglesA[i];\r
+       NormalsZ[index0]+= FaceNormalsZ[i]*AnglesA[i];\r
+       index1=(int)FacesB[i]-1;\r
+       NormalsX[index1]+= FaceNormalsX[i]*AnglesB[i];\r
+       NormalsY[index1]+= FaceNormalsY[i]*AnglesB[i];\r
+       NormalsZ[index1]+= FaceNormalsZ[i]*AnglesB[i];\r
+       index2=(int)FacesC[i]-1;\r
+       NormalsX[index2]+= FaceNormalsX[i]*AnglesC[i];\r
+       NormalsY[index2]+= FaceNormalsY[i]*AnglesC[i];\r
+       NormalsZ[index2]+= FaceNormalsZ[i]*AnglesC[i];\r
+   }\r
+    \r
+   /* Normalize the Normals */\r
+   for (i=0; i<VertexN; i++)\r
+   {\r
+       nl= sqrt(NormalsX[i]*NormalsX[i]+NormalsY[i]*NormalsY[i]+NormalsZ[i]*NormalsZ[i])+1e-14; ;\r
+       NormalsX[i]/=nl; NormalsY[i]/=nl; NormalsZ[i]/=nl;\r
+   }\r
+\r
+   /* Free memory */\r
+   free(FaceNormalsX);\r
+   free(FaceNormalsY);\r
+   free(FaceNormalsZ);\r
+   free(AnglesA);\r
+   free(AnglesB);\r
+   free(AnglesC);\r
+}\r
+ \r
+\r
+\r

diff --git a/THESE/codes/snake/basic_code/PatchNormalsDouble.m b/THESE/codes/snake/basic_code/PatchNormalsDouble.m
new file mode 100755 (executable)
index 0000000..e97e8b4
--- /dev/null
+++ b/THESE/codes/snake/basic_code/PatchNormalsDouble.m
@@ -0,0 +1,39 @@
+function [Nx,Ny,Nz]=PatchNormalsDouble(Fa,Fb,Fc,Vx,Vy,Vz)\r
+%\r
+%  [Nx,Ny,Nz]=PatchNormalsDouble(Fa,Fb,Fc,Vx,Vy,Vz)\r
+%\r
+\r
+FV.vertices=zeros(length(Vx),3);\r
+FV.vertices(:,1)=Vx;\r
+FV.vertices(:,2)=Vy;\r
+FV.vertices(:,3)=Vz;\r
+\r
+% Get all edge vectors\r
+e1=FV.vertices(Fa,:)-FV.vertices(Fb,:);\r
+e2=FV.vertices(Fb,:)-FV.vertices(Fc,:);\r
+e3=FV.vertices(Fc,:)-FV.vertices(Fa,:);\r
+\r
+% Normalize edge vectors\r
+e1_norm=e1./repmat(sqrt(e1(:,1).^2+e1(:,2).^2+e1(:,3).^2),1,3); \r
+e2_norm=e2./repmat(sqrt(e2(:,1).^2+e2(:,2).^2+e2(:,3).^2),1,3); \r
+e3_norm=e3./repmat(sqrt(e3(:,1).^2+e3(:,2).^2+e3(:,3).^2),1,3);\r
+\r
+% Calculate Angle of face seen from vertices\r
+Angle =  [acos(dot(e1_norm',-e3_norm'));acos(dot(e2_norm',-e1_norm'));acos(dot(e3_norm',-e2_norm'))]';\r
+\r
+% Calculate normal of face\r
+ Normal=cross(e1,e3);\r
+\r
+% Calculate Vertice Normals \r
+VerticeNormals=zeros([size(FV.vertices,1) 3]);\r
+for i=1:size(Fa,1),\r
+    VerticeNormals(Fa(i),:)=VerticeNormals(Fa(i),:)+Normal(i,:)*Angle(i,1);\r
+    VerticeNormals(Fb(i),:)=VerticeNormals(Fb(i),:)+Normal(i,:)*Angle(i,2);\r
+    VerticeNormals(Fc(i),:)=VerticeNormals(Fc(i),:)+Normal(i,:)*Angle(i,3);\r
+end\r
+\r
+V_norm=sqrt(VerticeNormals(:,1).^2+VerticeNormals(:,2).^2+VerticeNormals(:,3).^2)+eps;\r
+VerticeNormals=VerticeNormals./repmat(V_norm,1,3);\r
+Nx=VerticeNormals(:,1);\r
+Ny=VerticeNormals(:,2);\r
+Nz=VerticeNormals(:,3);\r

diff --git a/THESE/codes/snake/basic_code/SeparateKernel.m b/THESE/codes/snake/basic_code/SeparateKernel.m
new file mode 100755 (executable)
index 0000000..2c66185
--- /dev/null
+++ b/THESE/codes/snake/basic_code/SeparateKernel.m
@@ -0,0 +1,273 @@
+function [K1 KN ERR]=SeparateKernel(H)\r
+% This function SEPARATEKERNEL will separate ( do decomposition ) any \r
+% 2D, 3D or nD kernel into 1D kernels. Ofcourse only a sub-set of Kernels\r
+% are separable such as a Gaussian Kernel, but it will give least-squares\r
+% sollutions for non-separatable kernels.\r
+% \r
+% Separating a 3D or 4D image filter in to 1D filters will give an large\r
+% speed-up in image filtering with for instance the function imfilter.\r
+%\r
+% [K1 KN ERR]=SeparateKernel(H);\r
+%   \r
+% inputs,\r
+%   H : The 2D, 3D ..., ND kernel\r
+%   \r
+% outputs,\r
+%   K1 : Cell array with the 1D kernels\r
+%   KN : Approximation of the ND input kernel by the 1D kernels\r
+%   ERR : The sum of absolute difference between approximation and input kernel\r
+%\r
+% \r
+% How the algorithm works:\r
+% If we have a separable kernel like\r
+% \r
+%  H = [1 2 1\r
+%       2 4 2\r
+%       3 6 3];\r
+%\r
+% We like to solve unknow 1D kernels,\r
+%  a=[a(1) a(2) a(3)]\r
+%  b=[b(1) b(2) b(3)]\r
+%\r
+% We know that,\r
+%  H = a'*b\r
+%\r
+%       b(1)    b(2)    b(3)\r
+%       --------------------\r
+%  a(1)|h(1,1) h(1,2) h(1,3)\r
+%  a(2)|h(2,1) h(2,2) h(2,3)\r
+%  a(3)|h(3,1) h(3,2) h(3,3)\r
+%\r
+% Thus,\r
+%  h(1,1) == a(1)*b(1)\r
+%  h(2,1) == a(2)*b(1)\r
+%  h(3,1) == a(3)*b(1)\r
+%  h(4,1) == a(1)*b(2)\r
+%  ...\r
+%\r
+% We want to solve this by using fast matrix (least squares) math,\r
+%\r
+%  c = M * d; \r
+%  \r
+%  c a column vector with all kernel values H\r
+%  d a column vector with the unknown 1D kernels \r
+%\r
+% But matrices "add" values and we have something like  h(1,1) == a(1)*b(1);\r
+% We solve this by taking the log at both sides \r
+% (We replace zeros by a small value. Whole lines/planes of zeros are\r
+%  removed at forehand and re-added afterwards)\r
+%\r
+%  log( h(1,1) ) == log(a(1)) + log b(1))\r
+%\r
+% The matrix is something like this,\r
+%\r
+%      a1 a2 a3 b1 b2 b3    \r
+% M = [1  0  0  1  0  0;  h11\r
+%      0  1  0  1  0  0;  h21\r
+%      0  0  1  1  0  0;  h31\r
+%      1  0  0  0  1  0;  h21\r
+%      0  1  0  0  1  0;  h22\r
+%      0  0  1  0  1  0;  h23\r
+%      1  0  0  0  0  1;  h31\r
+%      0  1  0  0  0  1;  h32\r
+%      0  0  1  0  0  1]; h33\r
+%\r
+% Least squares solution\r
+%  d = exp(M\log(c))\r
+%\r
+% with the 1D kernels\r
+%\r
+%  [a(1);a(2);a(3);b(1);b(2);b(3)] = d\r
+%\r
+% The Problem of Negative Values!!!\r
+%\r
+% The log of a negative value is possible it gives a complex value, log(-1) = i*pi\r
+% if we take the expontential it is back to the old value, exp(i*pi) = -1 \r
+%\r
+%  But if we use the solver with on of the 1D vectors we get something like, this :\r
+%\r
+%  input         result        abs(result)    angle(result) \r
+%   -1     -0.0026 + 0.0125i     0.0128         1.7744 \r
+%    2      0.0117 + 0.0228i     0.0256         1.0958 \r
+%   -3     -0.0078 + 0.0376i     0.0384         1.7744  \r
+%    4      0.0234 + 0.0455i     0.0512         1.0958\r
+%    5      0.0293 + 0.0569i     0.0640         1.0958\r
+% \r
+% The absolute value is indeed correct (difference in scale is compensated\r
+% by the order 1D vectors)\r
+%\r
+% As you can see the angle is correlated with the sign of the values. But I\r
+% didn't found the correlation yet. For some matrices it is something like\r
+%\r
+%  sign=mod(angle(solution)*scale,pi) == pi/2;\r
+%\r
+% In the current algorithm, we just flip the 1D kernel values one by one.\r
+% The sign change which gives the smallest error is permanently swapped. \r
+% Until swapping signs no longer decreases the error\r
+%\r
+% Examples,\r
+%   a=permute(rand(5,1),[1 2 3 4])-0.5;\r
+%   b=permute(rand(5,1),[2 1 3 4])-0.5;\r
+%   c=permute(rand(5,1),[3 2 1 4])-0.5;\r
+%   d=permute(rand(5,1),[4 2 3 1])-0.5;\r
+%   H = repmat(a,[1 5 5 5]).*repmat(b,[5 1 5 5]).*repmat(c,[5 5 1 5]).*repmat(d,[5 5 5 1]);\r
+%   [K,KN,err]=SeparateKernel(H);\r
+%   disp(['Summed Absolute Error between Real and approximation by 1D filters : ' num2str(err)]);\r
+%\r
+%   a=permute(rand(3,1),[1 2 3])-0.5;\r
+%   b=permute(rand(3,1),[2 1 3])-0.5;\r
+%   c=permute(rand(3,1),[3 2 1])-0.5;\r
+%   H = repmat(a,[1 3 3]).*repmat(b,[3 1 3 ]).*repmat(c,[3 3 1 ])\r
+%   [K,KN,err]=SeparateKernel(H); err\r
+%\r
+%   a=permute(rand(4,1),[1 2 3])-0.5;\r
+%   b=permute(rand(4,1),[2 1 3])-0.5;\r
+%   H = repmat(a,[1 4]).*repmat(b,[4 1]);\r
+%   [K,KN,err]=SeparateKernel(H); err\r
+%\r
+% Function is written by D.Kroon, uses "log idea" from A. J. Hendrikse, \r
+% University of Twente (July 2010)\r
+\r
+\r
+% We first make some structure which contains information about\r
+% the transformation from kernel to 1D kernel array, number of dimensions\r
+% and other stuff\r
+data=InitializeDataStruct(H);\r
+\r
+% Make the matrix of c = M * d; \r
+M=makeMatrix(data);\r
+\r
+% Solve c = M * d with least squares\r
+warning('off','MATLAB:rankDeficientMatrix');\r
+par=exp(M\log(abs(data.H(:))));\r
+\r
+% Improve the values by solving the remaining difference\r
+KN = Filter1DtoFilterND(par,data);\r
+par2=exp(M\log(abs(KN(:)./data.H(:))));\r
+par=par./par2;\r
+\r
+% Change the sign of a 1D filtering value if it decrease the error\r
+par = FilterCorrSign(par,data);\r
+\r
+% Split the solution d in separate 1D kernels\r
+K1 = ValueList2Filter1D(par,data);\r
+\r
+% Re-add the removed zero rows/planes to the 1D vectors\r
+K1=re_add_zero_rows(data,K1);\r
+\r
+% Calculate the approximation of the ND kernel if using the 1D kernels\r
+KN = Filter1DtoFilterND(par,data,K1);\r
+\r
+% Calculate the absolute error\r
+ERR =sum(abs(H(:)-KN(:)));\r
+\r
+function par = FilterCorrSign(par,data)\r
+Ert=zeros(1,length(par));\r
+ERR=inf; t=0;\r
+par=sign(rand(size(par))-0.5).*par;\r
+while(t<ERR)\r
+    % Calculate the approximation of the ND kernel if using the 1D kernels\r
+    KN = Filter1DtoFilterND(par,data);\r
+    % Calculate the absolute error\r
+    ERR =sum(abs(data.H(:)-KN(:)));\r
+    % Flip the sign of every 1D filter value, and look if the error\r
+    % improves\r
+    for i=1:length(par)\r
+        par2=par; par2(i)=-par2(i);\r
+        KN = Filter1DtoFilterND(par2,data);\r
+        Ert(i) =sum(abs(data.H(:)-KN(:)));\r
+    end\r
+    % Flip the sign of the 1D filter value with the largest improvement\r
+    [t,j]=min(Ert); if(t<ERR), par(j)=-par(j); end\r
+end\r
+\r
+function data=InitializeDataStruct(H)\r
+data.sizeHreal=size(H);\r
+data.nreal=ndims(H);\r
+[H,preserve_zeros]=remove_zero_rows(H);\r
+data.H=H;\r
+data.n=ndims(H);\r
+data.preserve_zeros=preserve_zeros;\r
+data.H(H==0)=eps;\r
+data.sizeH=size(data.H);\r
+data.sep_parb=cumsum([1 data.sizeH(1:data.n-1)]);\r
+data.sep_pare=cumsum(data.sizeH);\r
+data.sep_parl=data.sep_pare-data.sep_parb+1;\r
+data.par=(1:numel(H))+1;\r
+\r
+function [H,preserve_zeros]=remove_zero_rows(H)\r
+% Remove whole columns/rows/planes with zeros,\r
+% because we know at forehand that they will give a kernel 1D value of 0\r
+% and will otherwise increase the error in the end result.\r
+preserve_zeros=zeros(numel(H),2); pz=0;\r
+sizeH=size(H);\r
+for i=1:ndims(H)\r
+    H2D=reshape(H,size(H,1),[]);\r
+    check_zero=~any(H2D,2);\r
+    if(any(check_zero))\r
+        zero_rows=find(check_zero);\r
+        for j=1:length(zero_rows)\r
+            pz=pz+1;\r
+            preserve_zeros(pz,:)=[i zero_rows(j)];\r
+            sizeH(1)=sizeH(1)-1;\r
+        end\r
+        H2D(check_zero,:)=[];\r
+        H=reshape(H2D,sizeH);\r
+    end\r
+    H=shiftdim(H,1);\r
+    sizeH=circshift(sizeH,[0 -1]);\r
+    H=reshape(H,sizeH);\r
+end\r
+preserve_zeros=preserve_zeros(1:pz,:);\r
+\r
+function K1=re_add_zero_rows(data,K1)\r
+% Re-add the 1D kernel values responding to a whole column/row or plane\r
+% of zeros\r
+for i=1:size(data.preserve_zeros,1)\r
+    di=data.preserve_zeros(i,1);\r
+    pos=data.preserve_zeros(i,2);\r
+    if(di>length(K1)), K1{di}=1; end\r
+    val=K1{di};\r
+    val=val(:);\r
+    val=[val(1:pos-1);0;val(pos:end)];\r
+    dim=ones(1,data.nreal); dim(di)=length(val);\r
+    K1{di}=reshape(val,dim);\r
+end\r
+\r
+function M=makeMatrix(data)\r
+ M = zeros(numel(data.H),sum(data.sizeH));\r
+ K1 = (1:numel(data.H))';\r
+ for i=1:data.n;\r
+    p=data.par(data.sep_parb(i):data.sep_pare(i)); p=p(:);\r
+    dim=ones(1,data.n); dim(i)=data.sep_parl(i);\r
+    Ki=reshape(p(:),dim);\r
+    dim=data.sizeH; dim(i)=1;\r
+    K2=repmat(Ki,dim)-1;\r
+    M(sub2ind(size(M),K1(:),K2(:)))=1;\r
+ end\r
+ \r
+function Kt = Filter1DtoFilterND(par,data,K1)\r
+if(nargin==2)\r
+ Kt=ones(data.sizeH);\r
+ for i=1:data.n\r
+     p=par(data.sep_parb(i):data.sep_pare(i)); p=p(:);\r
+     dim=ones(1,data.n); dim(i)=data.sep_parl(i);\r
+     Ki=reshape(p(:),dim);\r
+     dim=data.sizeH; dim(i)=1;\r
+     Kt=Kt.*repmat(Ki,dim);\r
+ end\r
+else\r
+  Kt=ones(data.sizeHreal);\r
+  for i=1:data.n\r
+    dim=data.sizeHreal; dim(i)=1;\r
+    Kt=Kt.*repmat(K1{i},dim);\r
+  end\r
+end\r
+\r
+function K = ValueList2Filter1D(par,data)\r
+ K=cell(1,data.n);\r
+ for i=1:data.n\r
+     p=par(data.sep_parb(i):data.sep_pare(i)); p=p(:);\r
+     dim=ones(1,data.n); dim(i)=data.sep_parl(i);\r
+     K{i}=reshape(p(:),dim);\r
+ end
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/Snake2D.m b/THESE/codes/snake/basic_code/Snake2D.m
new file mode 100755 (executable)
index 0000000..97e5aa3
--- /dev/null
+++ b/THESE/codes/snake/basic_code/Snake2D.m
@@ -0,0 +1,181 @@
+function [P,J]=Snake2D(I,P,Options)\r
+% This function SNAKE implements the basic snake segmentation. A snake is an \r
+% active (moving) contour, in which the points are attracted by edges and\r
+% other boundaries. To keep the contour smooth, an membrame and thin plate\r
+% energy is used as regularization.\r
+%\r
+% [O,J]=Snake2D(I,P,Options)\r
+%  \r
+% inputs,\r
+%   I : An Image of type double preferable ranged [0..1]\r
+%   P : List with coordinates descriping the rough contour N x 2\r
+%   Options : A struct with all snake options\r
+%   \r
+% outputs,\r
+%   O : List with coordinates of the final contour M x 2\r
+%   J : Binary image with the segmented region\r
+%\r
+% options (general),\r
+%  Option.Verbose : If true show important images, default false\r
+%  Options.nPoints : Number of contour points, default 100\r
+%  Options.Gamma : Time step, default 1\r
+%  Options.Iterations : Number of iterations, default 100\r
+%\r
+% options (Image Edge Energy / Image force))\r
+%  Options.Sigma1 : Sigma used to calculate image derivatives, default 10\r
+%  Options.Wline : Attraction to lines, if negative to black lines otherwise white\r
+%                    lines , default 0.04\r
+%  Options.Wedge : Attraction to edges, default 2.0\r
+%  Options.Wterm : Attraction to terminations of lines (end points) and\r
+%                    corners, default 0.01\r
+%  Options.Sigma2 : Sigma used to calculate the gradient of the edge energy\r
+%                    image (which gives the image force), default 20\r
+%\r
+% options (Gradient Vector Flow)\r
+%  Options.Mu : Trade of between real edge vectors, and noise vectors,\r
+%                default 0.2. (Warning setting this to high >0.5 gives\r
+%                an instable Vector Flow)\r
+%  Options.GIterations : Number of GVF iterations, default 0\r
+%  Options.Sigma3 : Sigma used to calculate the laplacian in GVF, default 1.0\r
+%\r
+% options (Snake)\r
+%  Options.Alpha : Membrame energy  (first order), default 0.2\r
+%  Options.Beta : Thin plate energy (second order), default 0.2\r
+%  Options.Delta : Baloon force, default 0.1\r
+%  Options.Kappa : Weight of external image force, default 2\r
+%\r
+%\r
+% Literature:\r
+%   - Michael Kass, Andrew Witkin and Demetri TerzoPoulos "Snakes : Active\r
+%       Contour Models", 1987\r
+%   - Jim Ivins amd John Porrill, "Everything you always wanted to know\r
+%       about snakes (but wer afraid to ask)\r
+%   - Chenyang Xu and Jerry L. Prince, "Gradient Vector Flow: A New\r
+%       external force for Snakes\r
+%\r
+% Example, Basic:\r
+%\r
+%  % Read an image\r
+%   I = imread('testimage.png');\r
+%  % Convert the image to double data type\r
+%   I = im2double(I); \r
+%  % Show the image and select some points with the mouse (at least 4)\r
+%  %figure, imshow(I); [y,x] = getpts;\r
+%   y=[182 233 251 205 169];\r
+%   x=[163 166 207 248 210];\r
+%  % Make an array with the clicked coordinates\r
+%   P=[x(:) y(:)];\r
+%  % Start Snake Process\r
+%   Options=struct;\r
+%   Options.Verbose=true;\r
+%   Options.Iterations=300;\r
+%   [O,J]=Snake2D(I,P,Options);\r
+%  % Show the result\r
+%   Irgb(:,:,1)=I;\r
+%   Irgb(:,:,2)=I;\r
+%   Irgb(:,:,3)=J;\r
+%   figure, imshow(Irgb,[]); \r
+%   hold on; plot([O(:,2);O(1,2)],[O(:,1);O(1,1)]);\r
+%  \r
+% Example, GVF:\r
+%   I=im2double(imread('testimage2.png'));\r
+%   x=[96 51 98 202 272 280 182];\r
+%   y=[63 147 242 262 211 97 59];\r
+%   P=[x(:) y(:)];\r
+%   Options=struct;\r
+%   Options.Verbose=true;\r
+%   Options.Iterations=400;\r
+%   Options.Wedge=2;\r
+%   Options.Wline=0;\r
+%   Options.Wterm=0;\r
+%   Options.Kappa=4;\r
+%   Options.Sigma1=8;\r
+%   Options.Sigma2=8;\r
+%   Options.Alpha=0.1;\r
+%   Options.Beta=0.1;\r
+%   Options.Mu=0.2;\r
+%   Options.Delta=-0.1;\r
+%   Options.GIterations=600;\r
+%   [O,J]=Snake2D(I,P,Options);\r
+%   \r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Process inputs\r
+defaultoptions=struct('Verbose',false,'nPoints',100,'Wline',0.04,'Wedge',2,'Wterm',0.01,'Sigma1',10,'Sigma2',20,'Alpha',0.2,'Beta',0.2,'Delta',0.1,'Gamma',1,'Kappa',2,'Iterations',100,'GIterations',0,'Mu',0.2,'Sigma3',1);\r
+if(~exist('Options','var')), \r
+    Options=defaultoptions; \r
+else\r
+    tags = fieldnames(defaultoptions);\r
+    for i=1:length(tags)\r
+         if(~isfield(Options,tags{i})), Options.(tags{i})=defaultoptions.(tags{i}); end\r
+    end\r
+    if(length(tags)~=length(fieldnames(Options))), \r
+        warning('snake:unknownoption','unknown options found');\r
+    end\r
+end\r
+\r
+% Convert input to double\r
+I = double(I);\r
+\r
+% If color image convert to grayscale\r
+if(size(I,3)==3), I=rgb2gray(I); end\r
+\r
+% The contour must always be clockwise (because of the balloon force)\r
+P=MakeContourClockwise2D(P);\r
+\r
+% Make an uniform sampled contour description\r
+P=InterpolateContourPoints2D(P,Options.nPoints);\r
+\r
+% Transform the Image into an External Energy Image\r
+Eext = ExternalForceImage2D(I,Options.Wline, Options.Wedge, Options.Wterm,Options.Sigma1);\r
+\r
+% Make the external force (flow) field.\r
+Fx=ImageDerivatives2D(Eext,Options.Sigma2,'x');\r
+Fy=ImageDerivatives2D(Eext,Options.Sigma2,'y');\r
+Fext(:,:,1)=-Fx*2*Options.Sigma2^2;\r
+Fext(:,:,2)=-Fy*2*Options.Sigma2^2;\r
+\r
+% Do Gradient vector flow, optimalization\r
+Fext=GVFOptimizeImageForces2D(Fext, Options.Mu, Options.GIterations, Options.Sigma3);\r
+\r
+% Show the image, contour and force field\r
+if(Options.Verbose)\r
+    h=figure; set(h,'render','opengl')\r
+     subplot(2,2,1),\r
+      imshow(I,[]); \r
+      hold on; plot(P(:,2),P(:,1),'b.'); hold on;\r
+      title('The image with initial contour')\r
+     subplot(2,2,2),\r
+      imshow(Eext,[]); \r
+      title('The external energy');\r
+     subplot(2,2,3), \r
+      [x,y]=ndgrid(1:10:size(Fext,1),1:10:size(Fext,2));\r
+      imshow(I), hold on; quiver(y,x,Fext(1:10:end,1:10:end,2),Fext(1:10:end,1:10:end,1));\r
+      title('The external force field ')\r
+     subplot(2,2,4), \r
+      imshow(I), hold on; plot(P(:,2),P(:,1),'b.'); \r
+      title('Snake movement ')\r
+    drawnow\r
+end\r
+\r
+\r
+% Make the interal force matrix, which constrains the moving points to a\r
+% smooth contour\r
+S=SnakeInternalForceMatrix2D(Options.nPoints,Options.Alpha,Options.Beta,Options.Gamma);\r
+h=[];\r
+for i=1:Options.Iterations\r
+    P=SnakeMoveIteration2D(S,P,Fext,Options.Gamma,Options.Kappa,Options.Delta);\r
+\r
+    % Show current contour\r
+    if(Options.Verbose)\r
+        if(ishandle(h)), delete(h), end\r
+        h=plot(P(:,2),P(:,1),'r.');\r
+        c=i/Options.Iterations;\r
+        plot([P(:,2);P(1,2)],[P(:,1);P(1,1)],'-','Color',[c 1-c 0]);  drawnow\r
+    end\r
+end\r
+\r
+if(nargout>1)\r
+     J=DrawSegmentedArea2D(P,size(I));\r
+end\r
+\r

diff --git a/THESE/codes/snake/basic_code/Snake3D.m b/THESE/codes/snake/basic_code/Snake3D.m
new file mode 100755 (executable)
index 0000000..31b04d9
--- /dev/null
+++ b/THESE/codes/snake/basic_code/Snake3D.m
@@ -0,0 +1,151 @@
+function FV=Snake3D(I,FV,Options)\r
+% This function SNAKE implements the basic snake segmentation. A snake is an \r
+% active (moving) contour, in which the points are attracted by edges and\r
+% other boundaries. To keep the contour smooth, an membrame and thin plate\r
+% energy is used as regularization.\r
+%\r
+% OV=Snake3D(I,FV,Options)\r
+%  \r
+% inputs,\r
+%   I : An Image of type double preferable ranged [0..1]\r
+%   FV : Structure with triangulated mesh, with list of faces FV.faces N x 3\r
+%        and list of vertices M x 3\r
+%   Options : A struct with all snake options\r
+%   \r
+% outputs,\r
+%   OV : Structure with triangulated mesh of the final surface\r
+%\r
+% options (general),\r
+%  Option.Verbose : If true show important images, default false\r
+%  Options.Gamma : Time step, default 1\r
+%  Options.Iterations : Number of iterations, default 100\r
+%\r
+% options (Image Edge Energy / Image force))\r
+%  Options.Sigma1 : Sigma used to calculate image derivatives, default 2\r
+%  Options.Wline : Attraction to lines, if negative to black lines otherwise white\r
+%                    lines , default 0.04\r
+%  Options.Wedge : Attraction to edges, default 2.0\r
+%  Options.Sigma2 : Sigma used to calculate the gradient of the edge energy\r
+%                    image (which gives the image force), default 2\r
+%\r
+% options (Gradient Vector Flow)\r
+%  Options.Mu : Trade of between real edge vectors, and noise vectors,\r
+%                default 0.2. (Warning setting this to high >0.5 gives\r
+%                an instable Vector Flow)\r
+%  Options.GIterations : Number of GVF iterations, default 0\r
+%  Options.Sigma3 : Sigma used to calculate the laplacian in GVF, default 1.0\r
+%\r
+% options (Snake)\r
+%  Options.Alpha : Membrame energy  (first order), default 0.2\r
+%  Options.Beta : Thin plate energy (second order), default 0.2\r
+%  Options.Delta : Baloon force, default 0.1\r
+%  Options.Kappa : Weight of external image force, default 2\r
+%  Options.Lambda : Weight which changes the direction of the image \r
+%                   potential force to the direction of the surface\r
+%                   normal, value default 0.8 (range [0..1])\r
+%                   (Keeps the surface from self intersecting)\r
+%\r
+% Literature:\r
+%   - Michael Kass, Andrew Witkin and Demetri TerzoPoulos "Snakes : Active\r
+%       Contour Models", 1987\r
+%   - Christoph Lurig, Leif Kobbelt, Thomas Ertl, "Hierachical solutions\r
+%       for the Deformable Surface Problem in Visualization"\r
+%\r
+% Example,\r
+%\r
+% load testvolume\r
+% load SphereMesh\r
+% Options=struct;\r
+% Options.Verbose=1;\r
+% Options.Wedge=0;\r
+% Options.Wline=-1;\r
+% Options.Alpha=0.2;\r
+% Options.Beta=0.2;\r
+% Options.Kappa=0.5;\r
+% Options.Delta=0.1000;\r
+% Options.Gamma=0.1000;\r
+% Options.Iterations=20;\r
+% Options.Sigma1=2;\r
+% Options.Sigma2=2;\r
+% Options.Lambda=0.8;\r
+% FV.vertices(:,1)=FV.vertices(:,1)+35;\r
+% FV.vertices(:,2)=FV.vertices(:,2)+25;\r
+% FV.vertices(:,3)=FV.vertices(:,3)+20;\r
+% OV=Snake3D(I,FV,Options)\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Process inputs\r
+defaultoptions=struct('Verbose',false,'Wline',0.04,'Wedge',2,'Sigma1',2,'Sigma2',2,'Alpha',0.2,'Beta',0.2,'Delta',0.1,'Gamma',1,'Kappa',2,'Iterations',100,'GIterations',0,'Mu',0.2,'Sigma3',1,'Lambda',0.8);\r
+if(~exist('Options','var')), \r
+    Options=defaultoptions; \r
+else\r
+    tags = fieldnames(defaultoptions);\r
+    for i=1:length(tags)\r
+         if(~isfield(Options,tags{i})), Options.(tags{i})=defaultoptions.(tags{i}); end\r
+    end\r
+    if(length(tags)~=length(fieldnames(Options))), \r
+        warning('snake:unknownoption','unknown options found');\r
+    end\r
+end\r
+\r
+% Convert input to single if xintxx\r
+if(~strcmpi(class(I),'single')&&~strcmpi(class(I),'double'))\r
+    I=single(I);\r
+end\r
+\r
+% The surface faces must always be clockwise (because of the balloon force)\r
+FV=MakeContourClockwise3D(FV);\r
+\r
+% Transform the Image into an External Energy Image\r
+Eext = ExternalForceImage3D(I,Options.Wline, Options.Wedge,Options.Sigma1);\r
+\r
+% Make the external force (flow) field.\r
+Fx=ImageDerivatives3D(Eext,Options.Sigma2,'x');\r
+Fy=ImageDerivatives3D(Eext,Options.Sigma2,'y');\r
+Fz=ImageDerivatives3D(Eext,Options.Sigma2,'z');\r
+\r
+Fext(:,:,:,1)=-Fx*2*Options.Sigma2^2;\r
+Fext(:,:,:,2)=-Fy*2*Options.Sigma2^2;\r
+Fext(:,:,:,3)=-Fz*2*Options.Sigma2^2;\r
+\r
+% Do Gradient vector flow, optimalization\r
+Fext=GVFOptimizeImageForces3D(Fext, Options.Mu, Options.GIterations, Options.Sigma3);\r
+\r
+% Show the image, contour and force field\r
+if(Options.Verbose)\r
+     drawnow; pause(0.1);\r
+     h=figure; set(h,'render','opengl')\r
+     subplot(2,3,1),imshow(squeeze(Eext(:,:,round(end/2))),[]);\r
+     subplot(2,3,2),imshow(squeeze(Eext(:,round(end/2),:)),[]);\r
+     subplot(2,3,3),imshow(squeeze(Eext(round(end/2),:,:)),[]);\r
+     subplot(2,3,4),imshow(squeeze(Fext(:,:,round(end/2),:))+0.5);\r
+     subplot(2,3,5),imshow(squeeze(Fext(:,round(end/2),:,:))+0.5);\r
+     subplot(2,3,6),imshow(squeeze(Fext(round(end/2),:,:,:))+0.5);\r
+     h=figure; set(h,'render','opengl'); hold on;\r
+     %patch(i,'facecolor',[1 0 0],'facealpha',0.1);\r
+     ind=find(I(:)>0);\r
+     [ix,iy,iz]=ind2sub(size(Eext),ind);\r
+     plot3(ix,iy,iz,'b.');\r
+     hold on;\r
+     h=patch(FV,'facecolor',[1 0 0],'facealpha',0.1);\r
+     drawnow; pause(0.1);\r
+end\r
+\r
+% Make the interal force matrix, which constrains the moving points to a\r
+% smooth contour\r
+S=SnakeInternalForceMatrix3D(FV,Options.Alpha,Options.Beta,Options.Gamma);\r
+for i=1:Options.Iterations\r
+    FV=SnakeMoveIteration3D(S,FV,Fext,Options.Gamma,Options.Kappa,Options.Delta,Options.Lambda);\r
+\r
+    % Show current contour\r
+    if(Options.Verbose)\r
+        if(ishandle(h));\r
+            delete(h);\r
+            h=patch(FV,'facecolor',[1 0 0],'facealpha',0.1);\r
+            drawnow; %pause(0.1);\r
+        end\r
+    end\r
+end\r
+\r
+\r

diff --git a/THESE/codes/snake/basic_code/SnakeInternalForceMatrix2D.m b/THESE/codes/snake/basic_code/SnakeInternalForceMatrix2D.m
new file mode 100755 (executable)
index 0000000..f8a2902
--- /dev/null
+++ b/THESE/codes/snake/basic_code/SnakeInternalForceMatrix2D.m
@@ -0,0 +1,33 @@
+function B=SnakeInternalForceMatrix2D(nPoints,alpha,beta,gamma)\r
+%\r
+% B=SnakeInternalForceMatrix2D(nPoints,alpha,beta,gamma)\r
+%\r
+% inputs,\r
+%   nPoints : The number of snake contour points\r
+%   alpha : membrame energy  (first order)\r
+%   beta : thin plate energy (second order)\r
+%   gamma : Step Size (Time)\r
+%\r
+% outputs,\r
+%   B : The Snake Smoothness regulation matrix\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Penta diagonal matrix, one row:\r
+b(1)=beta;\r
+b(2)=-(alpha + 4*beta);\r
+b(3)=(2*alpha + 6 *beta);\r
+b(4)=b(2);\r
+b(5)=b(1);\r
+\r
+% Make the penta matrix (for every contour point)\r
+A=b(1)*circshift(eye(nPoints),2);\r
+A=A+b(2)*circshift(eye(nPoints),1);\r
+A=A+b(3)*circshift(eye(nPoints),0);\r
+A=A+b(4)*circshift(eye(nPoints),-1);\r
+A=A+b(5)*circshift(eye(nPoints),-2);\r
+\r
+% Calculate the inverse\r
+B=inv(A + gamma.* eye(nPoints));\r
+\r
+\r

diff --git a/THESE/codes/snake/basic_code/SnakeInternalForceMatrix3D.m b/THESE/codes/snake/basic_code/SnakeInternalForceMatrix3D.m
new file mode 100755 (executable)
index 0000000..bf76710
--- /dev/null
+++ b/THESE/codes/snake/basic_code/SnakeInternalForceMatrix3D.m
@@ -0,0 +1,52 @@
+function B=SnakeInternalForceMatrix3D(FV,alpha,beta,gamma)\r
+%\r
+% B=SnakeInternalForceMatrix3D(F,alpha,beta,gamma)\r
+%\r
+% inputs,\r
+%   FV : Struct (Patch) with the triangulated surface\r
+%   alpha : membrame energy  (first order)\r
+%   beta : thin plate energy (second order)\r
+%   gamma : Step Size (Time)\r
+%\r
+% outputs,\r
+%   B : The Snake Smoothness regulation matrix\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+Ne=VertexNeighbours(FV.faces,FV.vertices);\r
+nV=size(FV.vertices,1);\r
+\r
+% Matrix for umbrella mesh derivative function in (sparce) matrix form\r
+NeMatrix = spalloc(nV,nV,nV*10);\r
+for i=1:nV\r
+    Nc=Ne{i};\r
+    % Add the neighbours\r
+    NeMatrix(i,Nc)=1/length(Nc);\r
+    % Add the vertex it self \r
+    NeMatrix(i,i)=-1;\r
+end\r
+\r
+% Total internal force matrix\r
+B=inv(gamma*speye(nV,nV)-alpha*NeMatrix+beta*NeMatrix*NeMatrix);\r
+\r
+function Ne=VertexNeighbours(F,V)\r
+% Function which return the neighbouring vertices of every vertex\r
+% in a cell array list. (Basic version, not sorted by rotation)\r
+\r
+% Neighbourh cell array \r
+Ne=cell(1,size(V,1));\r
+\r
+% Loop through all faces\r
+for i=1:length(F)\r
+    % Add the neighbors of each vertice of a face\r
+    % to his neighbors list.\r
+    Ne{F(i,1)}=[Ne{F(i,1)} [F(i,2) F(i,3)]];\r
+    Ne{F(i,2)}=[Ne{F(i,2)} [F(i,3) F(i,1)]];\r
+    Ne{F(i,3)}=[Ne{F(i,3)} [F(i,1) F(i,2)]];\r
+end\r
+\r
+% Remove duplicate vertices\r
+for i=1:size(V,1), Ne{i}=unique(Ne{i}); end\r
+\r
+\r
+\r

diff --git a/THESE/codes/snake/basic_code/SnakeMoveIteration2D.m b/THESE/codes/snake/basic_code/SnakeMoveIteration2D.m
new file mode 100755 (executable)
index 0000000..7b00dd4
--- /dev/null
+++ b/THESE/codes/snake/basic_code/SnakeMoveIteration2D.m
@@ -0,0 +1,43 @@
+function P=SnakeMoveIteration2D(B,P,Fext,gamma,kappa,delta)\r
+% This function will calculate one iteration of contour Snake movement\r
+%\r
+% P=SnakeMoveIteration2D(S,P,Fext,gamma,kappa)\r
+%\r
+% inputs,\r
+%   B : Internal force (smoothness) matrix\r
+%   P : The contour points N x 2;\r
+%   Fext : External vector field (from image)\r
+%   gamma : Time step\r
+%   kappa : External (image) field weight\r
+%   delta : Balloon Force weight\r
+%\r
+% outputs,\r
+%   P : The (moved) contour points N x 2;\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+% Clamp contour to boundary\r
+P(:,1)=min(max(P(:,1),1),size(Fext,1));\r
+P(:,2)=min(max(P(:,2),1),size(Fext,2));\r
+\r
+% Get image force on the contour points\r
+Fext1(:,1)=kappa*interp2(Fext(:,:,1),P(:,2),P(:,1));\r
+Fext1(:,2)=kappa*interp2(Fext(:,:,2),P(:,2),P(:,1));\r
+% Interp2, can give nan's if contour close to border\r
+Fext1(isnan(Fext1))=0;\r
+\r
+% Calculate the baloonforce on the contour points\r
+N=GetContourNormals2D(P);\r
+Fext2=delta*N;\r
+\r
+% Update contour positions\r
+ssx = gamma*P(:,1) + Fext1(:,1) + Fext2(:,1);\r
+ssy = gamma*P(:,2) + Fext1(:,2) + Fext2(:,2);\r
+P(:,1) = B * ssx;\r
+P(:,2) = B * ssy;\r
+\r
+% Clamp contour to boundary\r
+P(:,1)=min(max(P(:,1),1),size(Fext,1));\r
+P(:,2)=min(max(P(:,2),1),size(Fext,2));\r
+\r
+    \r

diff --git a/THESE/codes/snake/basic_code/SnakeMoveIteration3D.m b/THESE/codes/snake/basic_code/SnakeMoveIteration3D.m
new file mode 100755 (executable)
index 0000000..70e7ad7
--- /dev/null
+++ b/THESE/codes/snake/basic_code/SnakeMoveIteration3D.m
@@ -0,0 +1,54 @@
+function FV=SnakeMoveIteration3D(B,FV,Fext,gamma,kappa,delta,lambda)\r
+% This function will calculate one iteration of contour Snake movement\r
+%\r
+% FV=SnakeMoveIteration2D(S,FV,Fext,gamma,kappa)\r
+%\r
+% inputs,\r
+%   B : Internal force (smoothness) matrix\r
+%   FV : The triangulated surface\r
+%   Fext : External vector field (from image)\r
+%   gamma : Time step\r
+%   kappa : External (image) field weight\r
+%   delta : Balloon Force weight\r
+%   lambda: Weight which changes the direction of the image  potential force \r
+%           to the direction of the surface normal, value default 0.8 \r
+%           (range [0..1]) (Keeps the surface from self intersecting)\r
+%\r
+% outputs,\r
+%   P : The (moved) contour points N x 2;\r
+%\r
+% Function is written by D.Kroon University of Twente (July 2010)\r
+\r
+V=FV.vertices;\r
+\r
+% Clamp contour to boundary\r
+V(:,1)=min(max(V(:,1),1),size(Fext,1));\r
+V(:,2)=min(max(V(:,2),1),size(Fext,2));\r
+V(:,3)=min(max(V(:,3),1),size(Fext,3));\r
+\r
+% Get image force on the contour points\r
+Fext1(:,1)=kappa*interp3(Fext(:,:,:,1),V(:,2),V(:,1),V(:,3));\r
+Fext1(:,2)=kappa*interp3(Fext(:,:,:,2),V(:,2),V(:,1),V(:,3));\r
+Fext1(:,3)=kappa*interp3(Fext(:,:,:,3),V(:,2),V(:,1),V(:,3));\r
+\r
+% Interp3, can give nan's if contour close to border\r
+Fext1(isnan(Fext1))=0;\r
+\r
+% Calculate the baloonforce on the contour points\r
+N=PatchNormals3D(FV);\r
+Fext2=delta*N;\r
+\r
+% This is the potential force, but only the component in the  direction of \r
+% the surface normal\r
+Fext3=repmat(dot(Fext1,N,2),1,3).*N;\r
+\r
+V(:,1)=B * (gamma * V(:,1) + (Fext1(:,1)*(1-lambda) + Fext3(:,1)*lambda + Fext2(:,1)));\r
+V(:,2)=B * (gamma * V(:,2) + (Fext1(:,2)*(1-lambda) + Fext3(:,2)*lambda + Fext2(:,2)));\r
+V(:,3)=B * (gamma * V(:,3) + (Fext1(:,3)*(1-lambda) + Fext3(:,3)*lambda + Fext2(:,3)));\r
+\r
+% Clamp contour to boundary\r
+V(:,1)=min(max(V(:,1),1),size(Fext,1));\r
+V(:,2)=min(max(V(:,2),1),size(Fext,2));\r
+V(:,3)=min(max(V(:,3),1),size(Fext,3));\r
+    \r
+FV.vertices=V;
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/SphereMesh.mat b/THESE/codes/snake/basic_code/SphereMesh.mat
new file mode 100755 (executable)
index 0000000..a115848
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diff --git a/THESE/codes/snake/basic_code/imgaussian.m b/THESE/codes/snake/basic_code/imgaussian.m
new file mode 100755 (executable)
index 0000000..d95483a
--- /dev/null
+++ b/THESE/codes/snake/basic_code/imgaussian.m
@@ -0,0 +1,58 @@
+function I=imgaussian(I,sigma,siz)\r
+% IMGAUSSIAN filters an 1D, 2D color/greyscale or 3D image with an \r
+% Gaussian filter. This function uses for filtering IMFILTER or if \r
+% compiled the fast  mex code imgaussian.c . Instead of using a \r
+% multidimensional gaussian kernel, it uses the fact that a Gaussian \r
+% filter can be separated in 1D gaussian kernels.\r
+%\r
+% J=IMGAUSSIAN(I,SIGMA,SIZE)\r
+%\r
+% inputs,\r
+%   I: The 1D, 2D greyscale/color, or 3D input image with \r
+%           data type Single or Double\r
+%   SIGMA: The sigma used for the Gaussian kernel\r
+%   SIZE: Kernel size (single value) (default: sigma*6)\r
+% \r
+% outputs,\r
+%   J: The gaussian filtered image\r
+%\r
+% note, compile the code with: mex imgaussian.c -v\r
+%\r
+% example,\r
+%   I = im2double(imread('peppers.png'));\r
+%   figure, imshow(imgaussian(I,10));\r
+% \r
+% Function is written by D.Kroon University of Twente (September 2009)\r
+\r
+if(~exist('siz','var')), siz=sigma*6; end\r
+\r
+if(sigma>0)\r
+    % Make 1D Gaussian kernel\r
+    x=-ceil(siz/2):ceil(siz/2);\r
+    H = exp(-(x.^2/(2*sigma^2)));\r
+    H = H/sum(H(:));\r
+\r
+    % Filter each dimension with the 1D Gaussian kernels\\r
+    if(ndims(I)==1)\r
+        I=imfilter(I,H, 'same' ,'replicate');\r
+    elseif(ndims(I)==2)\r
+        Hx=reshape(H,[length(H) 1]);\r
+        Hy=reshape(H,[1 length(H)]);\r
+        I=imfilter(imfilter(I,Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate');\r
+    elseif(ndims(I)==3)\r
+        if(size(I,3)<4) % Detect if 3D or color image\r
+            Hx=reshape(H,[length(H) 1]);\r
+            Hy=reshape(H,[1 length(H)]);\r
+            for k=1:size(I,3)\r
+                I(:,:,k)=imfilter(imfilter(I(:,:,k),Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate');\r
+            end\r
+        else\r
+            Hx=reshape(H,[length(H) 1 1]);\r
+            Hy=reshape(H,[1 length(H) 1]);\r
+            Hz=reshape(H,[1 1 length(H)]);\r
+            I=imfilter(imfilter(imfilter(I,Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate'),Hz, 'same' ,'replicate');\r
+        end\r
+    else\r
+        error('imgaussian:input','unsupported input dimension');\r
+    end\r
+end
\ No newline at end of file

diff --git a/THESE/codes/snake/basic_code/license.txt b/THESE/codes/snake/basic_code/license.txt
new file mode 100644 (file)
index 0000000..39e1380
--- /dev/null
+++ b/THESE/codes/snake/basic_code/license.txt
@@ -0,0 +1,24 @@
+Copyright (c) 2010, Dirk-Jan Kroon
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without 
+modification, are permitted provided that the following conditions are 
+met:
+
+    * Redistributions of source code must retain the above copyright 
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright 
+      notice, this list of conditions and the following disclaimer in 
+      the documentation and/or other materials provided with the distribution
+      
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 
+POSSIBILITY OF SUCH DAMAGE.

diff --git a/THESE/codes/snake/basic_code/testimage.png b/THESE/codes/snake/basic_code/testimage.png
new file mode 100755 (executable)
index 0000000..9963a1a
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diff --git a/THESE/codes/snake/basic_code/testimage2.png b/THESE/codes/snake/basic_code/testimage2.png
new file mode 100755 (executable)
index 0000000..b10f747
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diff --git a/THESE/codes/snake/basic_code/testvolume.mat b/THESE/codes/snake/basic_code/testvolume.mat
new file mode 100755 (executable)
index 0000000..8bce904
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diff --git a/THESE/codes/snake/cochon128_tradi_snake_deform.png b/THESE/codes/snake/cochon128_tradi_snake_deform.png
new file mode 100644 (file)
index 0000000..44aa8e4
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diff --git a/THESE/codes/snake/cochon128_tradi_snake_result.png b/THESE/codes/snake/cochon128_tradi_snake_result.png
new file mode 100644 (file)
index 0000000..45d9077
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diff --git a/THESE/codes/snake/gvf_dist_v4.2c/README b/THESE/codes/snake/gvf_dist_v4.2c/README
new file mode 100644 (file)
index 0000000..0718b0a
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/README
@@ -0,0 +1,140 @@
+GRADIENT VECTOR FLOW DEMONSTRATION USING MATLAB
+Chenyang Xu and Jerry Prince
+Image Analysis and Communications Laboratory
+Johns Hopkins University
+June 17, 1997, updated on September 9, 1999.
+
+Please see contact and copyright information below.
+
+1. Introduction and Installation Instructions
+----------------------------------------------
+
+Welcome to the Gradient Vector Flow (GVF) Active Contour Toolbox.  The
+Matlab *.m files in this software distribution implement our GVF snake,
+the original (Kass, Witkin, and Terzopoulos) snake, the balloon snake,
+and the distance snake. These programs were designed, written and tested
+by Chenyang Xu and Jerry Prince. 
+
+To install this software, simply unzip the file into an empty directory,
+making sure to ask your unzipper program to restore the directory
+structure of the zipped files. In this documentation, we will assume
+that you have unzipped the files into C:\GVF.
+
+
+2. Documentation
+----------------
+
+We have included two research papers that describe GVF. You can find
+these in C:\GVF\papers.
+
+Brief descriptions of the software modules can be found in this file
+below.  For more detail, you should run Matlab, add the path
+C:\GVF\snake, and type "help function-name".  For even more help, you can
+read the *.m files themselves. 
+
+Also, see our GVF demonstration website: http://iacl.ece.jhu.edu/projects/gvf
+
+
+3. MATLAB functions
+-------------------
+
+The MATLAB functions included in the distribution are found in
+C:\GVF\snake (assuming the distribution was unzipped into C:\GVF).
+They are classified and listed as follows:
+
+ Image input/output
+   rawread       - Read a Portable Bitmap file, or a raw file
+   rawwrite      - Write a Portable Bitmap file, or a raw file
+
+ Image Display
+   imdisp        - Display an image
+
+ Active Contour
+   snakeinit     - Initialize the snake manually
+   snakedeform   - Deform snake in the given external force field
+   snakedeform2  - Deform snake in the given external force field with
+                   pressure force
+   snakedisp     - Display a snake
+
+   snakeinterp   - Interpolate the snake adaptively
+   snakeinterp1  - Interpolate the snake at a fixed resolution
+                   (better implemented than snakeinterp)
+
+ Gradient Vector Flow
+   GVF           - Compute the gradient vector flow field
+
+ Other
+   dt            - Simple distance transform
+   gaussianBlur  - Blurring an image using gaussian kernel   
+   gaussianMask  - Generate a discrete gaussian mask
+
+To use these functions, add the path C:\GVF\snake to the Matlab path
+using the "path" command. Once the path is set, you can type "help
+snake" to list more details about the above functions.
+
+
+4. Examples
+
+To see how these functions work, it is best to start by running
+the following example macros. To do this, in Matlab cd to the directory
+C:\GVF\examples.  Then type one of the following commands and follow
+the instructions of the demo. 
+
+   balloon_ex.m
+   distance_ex.m
+   gvf_ex.m
+   tradition_ex.m
+
+5. Test Images
+--------------
+
+./images/U64.pgm   - a U-shaped object 
+./images/room.pgm  - a room-shaped object
+
+
+6. Contact Information
+----------------------
+
+Image Analysis and Communications Lab
+Department of Electrical and Computer Engineering
+The Johns Hopkins University
+Baltimore, MD 21218
+
+E-mail: chenyang@jhu.edu, prince@jhu.edu
+Phone: 410-516-7567 (lab)
+FAX:   410-516-5566
+
+
+7. Copyright and Disclaimer
+---------------------------
+
+Copyright (c) 1996-1997 Chenyang Xu and Jerry Prince.
+
+This software is copyrighted by Chenyang Xu and Jerry Prince.  The
+following terms apply to all files associated with the software unless
+explicitly disclaimed in individual files. 
+
+The authors hereby grant permission to use, copy, and distribute this
+software and its documentation for any purpose, provided that existing
+copyright notices are retained in all copies and that this notice is
+included verbatim in any distributions. Additionally, the authors grant
+permission to modify this software and its documentation for any
+purpose, provided that such modifications are not distributed without
+the explicit consent of the authors and that existing copyright notices
+are retained in all copies. 
+
+IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY FOR
+DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING
+OUT OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY DERIVATIVES
+THEREOF, EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE. 
+
+THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.
+THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, AND THE AUTHORS AND
+DISTRIBUTORS HAVE NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,
+UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 
+
+=========================================================================
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/balloon_ex.m b/THESE/codes/snake/gvf_dist_v4.2c/examples/balloon_ex.m
new file mode 100644 (file)
index 0000000..f2ea29a
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/balloon_ex.m
@@ -0,0 +1,62 @@
+% EXAMPLE     an example of balloon model on the room object       
+%
+
+%   Chenyang Xu and Jerry Prince 6/17/97
+%   Copyright (c) 1996-97 by Chenyang Xu and Jerry Prince
+
+   cd ..;   s = cd;   s = [s, '/snake']; path(s, path); cd examples;
+   
+   help balloon_ex;
+
+   % Read in the 64x64 room image
+     [I,map] = rawread('../images/room.pgm');  
+     
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = 1 - I/255; 
+     f0 = gaussianBlur(f,1);
+     % note: snake potential is the negative of edge map
+     disp(' Comute the traditional external force ...');
+     [px,py] = gradient(f0);
+
+   % display the results
+     figure(1); 
+     subplot(121); imdisp(-f); title('snake potential');
+     subplot(122); quiver(px,py); 
+     axis('square', 'equal', 'off', 'ij');     % fix the axis 
+     title('traditional force');
+       
+   % balloon deformation
+     disp(' ');
+     disp(' Press any key to start the deformation');
+     pause;
+     figure(1); subplot(121); cla; 
+     colormap(gray(64)); image(((1-f)+1)*40); axis('square', 'equal', 'off');
+     t = 0:0.5:6.28;
+     x = 32 + 3*cos(t);
+     y = 32 + 3*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r') 
+     pause(1);
+
+     for i=1:15,
+       [x,y] = snakedeform2(x,y,0.05,0,1, 2, 0.15,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' ');
+     disp(' Press any key to display the final result');
+     pause;
+     figure(1); subplot(121); cla; 
+     colormap(gray(64)); image(((1-f)+1)*40); axis('square', 'equal', 'off');
+     snakedisp(x,y,'r'); 
+     title(['Final result,  iter = ' num2str(i*5)]);
+
+     
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/circle.m b/THESE/codes/snake/gvf_dist_v4.2c/examples/circle.m
new file mode 100644 (file)
index 0000000..b88498b
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/circle.m
@@ -0,0 +1,17 @@
+function [x,y] = circle(delta)
+
+hold on
+
+% Loop, picking up the points
+disp('Left mouse button picks the center.')
+[s, t, but] = ginput(2);
+
+cx = s(1); cy = t(1);
+tx = s(2); ty = t(2);
+R = sqrt((tx-cx).^2+(ty-cy).^2);
+
+t = (0:delta:2*pi)';   
+x = cx + R*cos(t);   
+y = cy + R*sin(t); 
+
+plot([x;x(1,1)],[y;y(1,1)],'r-');

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/distance_ex.m b/THESE/codes/snake/gvf_dist_v4.2c/examples/distance_ex.m
new file mode 100644 (file)
index 0000000..0c92cb1
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/distance_ex.m
@@ -0,0 +1,60 @@
+% EXAMPLE     an example of distance snake on U shape boundary          
+%
+
+%   Chenyang Xu and Jerry Prince 6/17/97
+%   Copyright (c) 1996-97 by Chenyang Xu and Jerry Prince
+
+   cd ..;   s = cd;   s = [s, '/snake']; path(s, path); cd examples;
+   
+   help distance_ex;
+
+   % Read in the 64x64 U-shape image
+     [I,map] = rawread('../images/U64.pgm');  
+     
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = 1 - I/255; 
+
+   % Compute the distance transform
+     disp(' Compute distance transform ...');
+     D = dt(f>0.5);   % (f>0.5) to gurantee the binary input for dt
+     disp(' Compute the distance external force ...');
+     [px,py] = gradient(-D);
+
+   % display the results
+     figure(1); 
+     subplot(121); imdisp(D); title('distance transform');
+     subplot(122); quiver(px,py); 
+     axis('square', 'equal', 'off', 'ij');     % fix the axis 
+     title('distance force');
+
+   % snake deformation
+     disp(' ');
+     disp(' Press any key to start the deformation');
+     pause;
+     figure(1); subplot(121); cla; 
+     colormap(gray(64)); image(((1-f)+1)*40); 
+     axis('square', 'equal', 'off');
+     t = 0:0.05:6.28;
+     x = 32 + 30*cos(t);
+     y = 32 + 30*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r') 
+     pause(1);
+
+     for i=1:25,
+       [x,y] = snakedeform(x,y,0.05,0,1,0.5,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' ');
+     disp(' Press any key to display the final result');
+     pause;
+     figure(1); subplot(121); cla; 
+     colormap(gray(64)); image(((1-f)+1)*40); 
+     axis('square', 'equal', 'off');
+     snakedisp(x,y,'r') 
+     title(['Final result,  iter = ' num2str(i*5)]);

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m b/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m
new file mode 100644 (file)
index 0000000..57d53e2
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m
@@ -0,0 +1,160 @@
+% EXAMPLE     GVF snake examples on two simulated object boundaries.
+%
+% NOTE: 
+% 
+% traditional snake and distance snake differed from GVF snake only 
+%   by using different external force field. In order to produce the
+%   corresponding external force field, use the following (all
+%   assuming edge map f is in memory).
+%
+% traditional snake:
+%   f0 = gaussianBlur(f,1);
+%   [px,py] = gradient(f0);
+%
+% distance snake:
+%   D = dt(f>0.5);  % distance transform (dt) require binary edge map
+%   [px,py] = gradient(-D);
+%
+% [px,py] is the external force field in both cases
+%
+% balloon model using a different matlab function "snakedeform2"
+% instead of "snakedeform". The external force could be any force
+% field generated above.
+%
+% an example of using it is as follows
+%       [x,y] = snakedeform2(x,y, alpha, beta, gamma, kappa, kappap, px,py,2);
+% do a "help snakedeform2" for more details
+% 
+
+%   Chenyang Xu and Jerry Prince 6/17/97
+%   Copyright (c) 1996-97 by Chenyang Xu and Jerry Prince
+
+   cd ..;   s = cd;   s = [s, '/snake']; path(s, path); cd examples;
+   
+   help gvf_ex;
+
+   % ==== Example 1: U-shape object ====
+
+   % Read in the 64x64 U-shape image
+     %[I,map] = rawread('../images/cochon64.pgm');  
+     I = imread('../images/cochon64.pgm');
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = gaussianBlur(1 - I/255,1); 
+
+     
+   % Compute the GVF of the edge map f
+     disp(' Compute GVF ...');
+     [u,v] = GVF(f, 0.2, 80); 
+     disp(' Nomalizing the GVF external force ...');
+     mag = sqrt(u.*u+v.*v);
+     px = u./(mag+1e-10); py = v./(mag+1e-10); 
+
+   % display the results
+     figure(1); 
+     subplot(221); imdisp(I); title('test image');
+     subplot(222); imdisp(f); title('edge map');
+
+     % display the gradient of the edge map
+     [fx,fy] = gradient(f)
+     subplot(223); quiver(fx,fy); 
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('edge map gradient');
+
+     % display the GVF 
+     subplot(224); quiver(px,py);
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('normalized GVF field');
+
+   % snake deformation
+     disp(' Press any key to start GVF snake deformation');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     t = 0:0.05:6.28;
+     x = 32 + 30*cos(t);
+     y = 32 + 30*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r') 
+     pause(1);
+
+     for i=1:50,
+       [x,y] = snakedeform(x,y,2,0,1,0.1,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' Press any key to display the final result');
+     pause;
+     cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     snakedisp(x,y,'r') 
+     title(['Final result,  iter = ' num2str(i*5)]);
+     disp(' ');
+     disp(' Press any key to run the next example');
+     pause;
+
+     
+   % ==== Example 2: Room like object ====     
+
+   % Read in the 64x64 room image
+     [I,map] = rawread('../images/room.pgm');  
+     
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = 1 - I/255; 
+
+   % Compute the GVF of the edge map f
+     disp(' Compute GVF ...');
+     [px,py] = GVF(f, 0.2, 80); 
+
+   % display the results
+     figure(1); 
+     subplot(221); imdisp(I); title('test image');
+     subplot(222); imdisp(f); title('edge map');
+
+     % display the gradient of the edge map
+     [fx,fy] = gradient(f); 
+     subplot(223); quiver(fx,fy); 
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('edge map gradient');
+
+     % display the GVF 
+     subplot(224); quiver(px,py);
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('GVF field');
+       
+   % snake deformation
+     disp(' Press any key to start GVF snake deformation');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     t = 0:0.5:6.28;
+     x = 32 + 3*cos(t);
+     y = 32 + 3*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r'); 
+     pause(1);
+
+     for i=1:15,
+       [x,y] = snakedeform(x,y,0.05,0,1,2,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' Press any key to display the final result');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     snakedisp(x,y,'r'); 
+     title(['Final result,  iter = ' num2str(i*5)]);
+
+     
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m~ b/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m~
new file mode 100644 (file)
index 0000000..851a4a1
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/gvf_ex.m~
@@ -0,0 +1,160 @@
+% EXAMPLE     GVF snake examples on two simulated object boundaries.
+%
+% NOTE: 
+% 
+% traditional snake and distance snake differed from GVF snake only 
+%   by using different external force field. In order to produce the
+%   corresponding external force field, use the following (all
+%   assuming edge map f is in memory).
+%
+% traditional snake:
+%   f0 = gaussianBlur(f,1);
+%   [px,py] = gradient(f0);
+%
+% distance snake:
+%   D = dt(f>0.5);  % distance transform (dt) require binary edge map
+%   [px,py] = gradient(-D);
+%
+% [px,py] is the external force field in both cases
+%
+% balloon model using a different matlab function "snakedeform2"
+% instead of "snakedeform". The external force could be any force
+% field generated above.
+%
+% an example of using it is as follows
+%       [x,y] = snakedeform2(x,y, alpha, beta, gamma, kappa, kappap, px,py,2);
+% do a "help snakedeform2" for more details
+% 
+
+%   Chenyang Xu and Jerry Prince 6/17/97
+%   Copyright (c) 1996-97 by Chenyang Xu and Jerry Prince
+
+   cd ..;   s = cd;   s = [s, '/snake']; path(s, path); cd examples;
+   
+   help gvf_ex;
+
+   % ==== Example 1: U-shape object ====
+
+   % Read in the 64x64 U-shape image
+     %[I,map] = rawread('../images/cochon64.pgm');  
+     I = imread('../images/cochon128.pgm');
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = gaussianBlur(1 - I/255,1); 
+
+     
+   % Compute the GVF of the edge map f
+     disp(' Compute GVF ...');
+     [u,v] = GVF(f, 0.2, 80); 
+     disp(' Nomalizing the GVF external force ...');
+     mag = sqrt(u.*u+v.*v);
+     px = u./(mag+1e-10); py = v./(mag+1e-10); 
+
+   % display the results
+     figure(1); 
+     subplot(221); imdisp(I); title('test image');
+     subplot(222); imdisp(f); title('edge map');
+
+     % display the gradient of the edge map
+     [fx,fy] = gradient(f)
+     subplot(223); quiver(fx,fy); 
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('edge map gradient');
+
+     % display the GVF 
+     subplot(224); quiver(px,py);
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('normalized GVF field');
+
+   % snake deformation
+     disp(' Press any key to start GVF snake deformation');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     t = 0:0.05:6.28;
+     x = 64 + 30*cos(t);
+     y = 64 + 30*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r') 
+     pause(1);
+
+     for i=1:50,
+       [x,y] = snakedeform(x,y,5,0,1,1,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' Press any key to display the final result');
+     pause;
+     cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     snakedisp(x,y,'r') 
+     title(['Final result,  iter = ' num2str(i*5)]);
+     disp(' ');
+     disp(' Press any key to run the next example');
+     pause;
+
+     
+   % ==== Example 2: Room like object ====     
+
+   % Read in the 64x64 room image
+     [I,map] = rawread('../images/room.pgm');  
+     
+   % Compute its edge map
+     disp(' Compute edge map ...');
+     f = 1 - I/255; 
+
+   % Compute the GVF of the edge map f
+     disp(' Compute GVF ...');
+     [px,py] = GVF(f, 0.2, 80); 
+
+   % display the results
+     figure(1); 
+     subplot(221); imdisp(I); title('test image');
+     subplot(222); imdisp(f); title('edge map');
+
+     % display the gradient of the edge map
+     [fx,fy] = gradient(f); 
+     subplot(223); quiver(fx,fy); 
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('edge map gradient');
+
+     % display the GVF 
+     subplot(224); quiver(px,py);
+     axis off; axis equal; axis 'ij';     % fix the axis 
+     title('GVF field');
+       
+   % snake deformation
+     disp(' Press any key to start GVF snake deformation');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     t = 0:0.5:6.28;
+     x = 32 + 3*cos(t);
+     y = 32 + 3*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r'); 
+     pause(1);
+
+     for i=1:15,
+       [x,y] = snakedeform(x,y,0.05,0,1,2,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' Press any key to display the final result');
+     pause;
+     figure(1); subplot(221); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); axis('equal', 'off');
+     snakedisp(x,y,'r'); 
+     title(['Final result,  iter = ' num2str(i*5)]);
+
+     
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/examples/tradition_ex.m b/THESE/codes/snake/gvf_dist_v4.2c/examples/tradition_ex.m
new file mode 100644 (file)
index 0000000..37e6cc5
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/examples/tradition_ex.m
@@ -0,0 +1,61 @@
+% EXAMPLE     an example of traditional snake on U-shape image
+%
+
+%   Chenyang Xu and Jerry Prince 6/17/97
+%   Copyright (c) 1996-97 by Chenyang Xu and Jerry Prince
+
+   cd ..;   s = cd;   s = [s, '/snake']; path(s, path); cd examples;
+   
+   help tradition_ex;
+   % ==== Example 1: U-shape object ====
+
+   % Read in the 64x64 U-shape image
+     %[I,map] = rawread('../images/cochon64.pgm');  
+     I = imread('../images/cochon128.pgm');
+   % Compute its edge map, 
+     disp(' Compute edge map ...');
+     f = 1 - I/255; 
+     f0 = gaussianBlur(f,1);
+     % note: snake potential is the negative of edge map
+     disp(' Comute the traditional external force ...');
+     [px,py] = gradient(f0);
+
+   % display the results
+     figure(1); 
+     subplot(121); imdisp(-f); title('snake potential');
+     subplot(122); quiver(px,py); 
+     axis('square', 'equal', 'off', 'ij');     % fix the axis 
+     title('traditional force');
+
+   % snake deformation
+     disp(' ');
+     disp(' Press any key to start the deformation');
+     pause;
+     figure(1); subplot(121); cla;
+     colormap(gray(64)); image(((1-f)+1)*40); 
+     axis('square', 'equal', 'off');
+     disp('');
+     disp('... Now capture range is small, use closer initialization.')
+     t = 0:0.05:6.28;
+     x = 64 + 60*cos(t);  
+     y = 64 + 60*sin(t);
+     [x,y] = snakeinterp(x,y,2,0.5);
+     snakedisp(x,y,'r') 
+     pause(1);
+
+     disp('... Press <CTRL>-C to stop the program at any time.');
+     for i=1:100,
+       [x,y] = snakedeform(x,y,5,0,0.1,5,px,py,5);
+       [x,y] = snakeinterp(x,y,2,0.5);
+       snakedisp(x,y,'r') 
+       title(['Deformation in progress,  iter = ' num2str(i*5)])
+       pause(0.5);
+     end
+
+     disp(' ');
+     disp(' Press any key to display the final result');
+     pause;
+     figure(2); clf; 
+     colormap(gray(64)); image(((1-f)+1)*40); axis equal
+     snakedisp(x,y,'r') 
+     title(['Final result,  iter = ' num2str(i*5)]);

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/images/U64.pgm b/THESE/codes/snake/gvf_dist_v4.2c/images/U64.pgm
new file mode 100644 (file)
index 0000000..c32b62b
Binary files /dev/null and b/THESE/codes/snake/gvf_dist_v4.2c/images/U64.pgm differ

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/images/cochon128.pgm b/THESE/codes/snake/gvf_dist_v4.2c/images/cochon128.pgm
new file mode 100644 (file)
index 0000000..48f0c9e
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/images/cochon128.pgm
@@ -0,0 +1,13 @@
+P5
+# CREATOR: GIMP PNM Filter Version 1.1
+128 128
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diff --git a/THESE/codes/snake/gvf_dist_v4.2c/images/cochon64.pgm b/THESE/codes/snake/gvf_dist_v4.2c/images/cochon64.pgm
new file mode 100644 (file)
index 0000000..a5b4493
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/images/cochon64.pgm
@@ -0,0 +1,8 @@
+P5
+# CREATOR: GIMP PNM Filter Version 1.1
+64 64
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diff --git a/THESE/codes/snake/gvf_dist_v4.2c/images/room.pgm b/THESE/codes/snake/gvf_dist_v4.2c/images/room.pgm
new file mode 100644 (file)
index 0000000..7539959
Binary files /dev/null and b/THESE/codes/snake/gvf_dist_v4.2c/images/room.pgm differ

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/interp4.m b/THESE/codes/snake/gvf_dist_v4.2c/interp4.m
new file mode 100644 (file)
index 0000000..5f2f62d
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/interp4.m
@@ -0,0 +1,102 @@
+function F = interp4(arg1,arg2,arg3,arg4,arg5)
+%INTERP4 2-D bilinear data interpolation.
+%      ZI = INTERP4(X,Y,Z,XI,YI) uses bilinear interpolation to
+%      find ZI, the values of the underlying 2-D function in Z at the points
+%      in matrices XI and YI.  Matrices X and Y specify the points at which 
+%      the data Z is given.  X and Y can also be vectors specifying the 
+%      abscissae for the matrix Z as for MESHGRID. In both cases, X
+%      and Y must be equally spaced and monotonic.
+%
+%      Values of NaN are returned in ZI for values of XI and YI that are 
+%      outside of the range of X and Y.
+%
+%      If XI and YI are vectors, INTERP4 returns vector ZI containing
+%      the interpolated values at the corresponding points (XI,YI).
+%
+%      ZI = INTERP4(Z,XI,YI) assumes X = 1:N and Y = 1:M, where
+%      [M,N] = SIZE(Z).
+%
+%      ZI = INTERP4(Z,NTIMES) returns the matrix Z expanded by interleaving
+%      bilinear interpolates between every element, working recursively
+%      for NTIMES.  INTERP4(Z) is the same as INTERP4(Z,1).
+%
+%      This function needs about 4 times SIZE(XI) memory to be available.
+%
+%      See also INTERP2, INTERP5.
+
+%      Copyright (c) 1984-94 by The MathWorks, Inc.
+%      Clay M. Thompson 4-26-91, revised 7-3-91, 3-22-93 by CMT.
+
+if nargin==1, % interp4(z), Expand Z
+  [nrows,ncols] = size(arg1);
+  s = 1:.5:ncols; sizs = size(s);
+  t = (1:.5:nrows)'; sizt = size(t);
+  s = s(ones(sizt),:);
+  t = t(:,ones(sizs));
+
+elseif nargin==2, % interp4(z,n), Expand Z n times
+  [nrows,ncols] = size(arg1);
+  ntimes = floor(arg2);
+  s = 1:1/(2*ntimes):ncols; sizs = size(s);
+  t = (1:1/(2*ntimes):nrows)'; sizt = size(t);
+  s = s(ones(sizt),:);
+  t = t(:,ones(sizs));
+
+elseif nargin==3, % interp4(z,s,t), No X or Y specified.
+  [nrows,ncols] = size(arg1);
+  s = arg2; t = arg3;
+
+elseif nargin==4,
+  error('Wrong number of input arguments.');
+
+elseif nargin==5, % interp4(x,y,z,s,t), X and Y specified.
+  [nrows,ncols] = size(arg3);
+  mx = prod(size(arg1)); my = prod(size(arg2));
+  if any([mx my] ~= [ncols nrows]) & any(size(arg1)~=size(arg3) | ...
+     size(arg2)~=size(arg3)), 
+    error('The lengths of the X and Y vectors must match Z.');
+  end
+  s = 1 + (arg4-arg1(1))*((ncols-1)/(arg1(mx)-arg1(1)));
+  t = 1 + (arg5-arg2(1))*((nrows-1)/(arg2(my)-arg2(1)));
+  
+end
+
+if any([nrows ncols]<[2 2]), error('Z must be at least 2-by-2.'); end
+if any(size(s)~=size(t)),
+  error('XI and YI must be the same size.');
+end
+
+% Check for out of range values of s and set to 1
+sout = find((s<1)|(s>ncols));
+if length(sout)>0, s(sout) = ones(size(sout)); end
+
+% Check for out of range values of t and set to 1
+tout = find((t<1)|(t>nrows));
+if length(tout)>0, t(tout) = ones(size(tout)); end
+
+% Matrix element indexing
+ndx = floor(t)+floor(s-1)*nrows;
+
+% Compute intepolation parameters, check for boundary value.
+d = find(s==ncols); 
+s(:) = (s - floor(s));
+if length(d)>0, s(d) = s(d)+1; ndx(d) = ndx(d)-nrows; end
+
+% Compute intepolation parameters, check for boundary value.
+d = find(t==nrows);
+t(:) = (t - floor(t));
+if length(d)>0, t(d) = t(d)+1; ndx(d) = ndx(d)-1; end
+d = [];
+
+% Now interpolate, reuse u and v to save memory.
+if nargin==5,
+  F =  ( arg3(ndx).*(1-t) + arg3(ndx+1).*t ).*(1-s) + ...
+       ( arg3(ndx+nrows).*(1-t) + arg3(ndx+(nrows+1)).*t ).*s;
+else
+  F =  ( arg1(ndx).*(1-t) + arg1(ndx+1).*t ).*(1-s) + ...
+       ( arg1(ndx+nrows).*(1-t) + arg1(ndx+(nrows+1)).*t ).*s;
+end
+
+% Now set out of range values to NaN.
+if length(sout)>0, F(sout) = NaN*ones(size(sout)); end
+if length(tout)>0, F(tout) = NaN*ones(size(tout)); end
\ No newline at end of file

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/papers/cvpr97.ps b/THESE/codes/snake/gvf_dist_v4.2c/papers/cvpr97.ps
new file mode 100644 (file)
index 0000000..dc17977
--- /dev/null
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@@ -0,0 +1,53318 @@
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+%%EndDocument
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+% Stop using temporary dictionary
+end
+
+% Restore original state
+origstate restore
+
+%%EndDocument
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+neg exch neg exch translate
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+ currentpoint grestore moveto
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+ gsave currentpoint currentpoint translate -90 neg rotate neg exch
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+ 841 258 a 841 258
+a
+ currentpoint currentpoint translate 0.91936 0.91936 scale neg exch
+neg exch translate
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+%%BeginDocument: images/stream_gvf.epsf
+
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+
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+  {
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+    bcount 128 ge
+    {
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+      bcount 128 sub {
+        currentfile rgbval readhexstring pop pop
+        outrow count rgbval putinterval
+        /count count 1 add store
+      } repeat
+    }
+    {
+      % run block
+      currentfile rgbval readhexstring pop pop
+      bcount {
+        outrow count rgbval putinterval
+        /count count 1 add store
+      } repeat
+    } ifelse
+  } loop  % until end of row
+  outrow
+} bind def
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+
+showpage
+
+% Stop using temporary dictionary
+end
+
+% Restore original state
+origstate restore
+
+%%EndDocument
+ @endspecial 841 258 a
+ currentpoint currentpoint translate 1 0.91936 div 1 0.91936 div scale
+neg exch neg exch translate
+ 841 258 a 1691 258 a
+ currentpoint grestore moveto
+ 1691 258
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diff --git a/THESE/codes/snake/gvf_dist_v4.2c/papers/tip.ps b/THESE/codes/snake/gvf_dist_v4.2c/papers/tip.ps
new file mode 100644 (file)
index 0000000..ca50ba9
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+end
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+
+%%EndDocument
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+%%BeginDocument: images/stream_dist.epsf
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+    {
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+      } repeat
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+    {
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+      bcount {
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+      } repeat
+    } ifelse
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+showpage
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+% Stop using temporary dictionary
+end
+
+% Restore original state
+origstate restore
+
+%%EndDocument
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+neg exch neg exch translate
+ 1346 3323 a 2575 3323 a
+ currentpoint grestore moveto
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+a Fs(\(b\))p 2605 3298 V 2605 4576 4 1279 v 2633 3323
+a
+ gsave currentpoint currentpoint translate -90 neg rotate neg exch
+neg exch translate
+ 2633 3323 a 2633 3323 a
+ currentpoint currentpoint translate 1.32805 1.32805 scale neg exch
+neg exch translate
+ 2633 3323 a @beginspecial 250
+@llx 340 @lly 361 @urx 451 @ury 1110 @rwi @setspecial
+%%BeginDocument: images/stream_gvf_revise1.epsf
+
+/origstate save def
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+    bcount 128 ge
+    {
+      % not a run block
+      bcount 128 sub {
+        currentfile rgbval readhexstring pop pop
+        outrow count rgbval putinterval
+        /count count 1 add store
+      } repeat
+    }
+    {
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+      bcount {
+        outrow count rgbval putinterval
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+      } repeat
+    } ifelse
+  } loop  % until end of row
+  outrow
+} bind def
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+       dup  0 exch rlineto
+       exch 0 rlineto
+       neg  0 exch rlineto
+       closepath
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+/L1i {
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+       } bdef
+
+/tMatrix matrix def
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+       tMatrix currentmatrix pop
+       translate scale
+       0 0 1 0 360 arc
+       tMatrix setmatrix
+       } bdef
+/FO {
+       MakeOval
+       stroke
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+/PO {
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+       fill
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+
+/PD {
+       2 copy moveto lineto stroke
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+
+
+currentdict end def
+
+MathWorks begin
+
+0 cap
+
+end
+
+MathWorks begin
+bpage
+
+bplot
+
+/dpi2point 12 def
+portraitMode 2376 6048 csm
+
+  337   197  2058  2059 MR c np
+268 dict begin %Colortable dictionary
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+       dup  0 exch rlineto
+       exch 0 rlineto
+       neg  0 exch rlineto
+       closepath
+       } bdef
+/FR {
+       MR stroke
+       } bdef
+/PR {
+       MR fill
+       } bdef
+/L1i {
+       { currentfile picstr readhexstring pop } image
+       } bdef
+
+/tMatrix matrix def
+/MakeOval {
+       newpath
+       tMatrix currentmatrix pop
+       translate scale
+       0 0 1 0 360 arc
+       tMatrix setmatrix
+       } bdef
+/FO {
+       MakeOval
+       stroke
+       } bdef
+/PO {
+       MakeOval
+       fill
+       } bdef
+
+/PD {
+       2 copy moveto lineto stroke
+       } bdef
+
+
+currentdict end def
+
+MathWorks begin
+
+0 cap
+
+end
+
+MathWorks begin
+bpage
+
+bplot
+
+/dpi2point 12 def
+portraitMode 2376 6048 csm
+
+  337   197  2058  2059 MR c np
+268 dict begin %Colortable dictionary
+/c0 { 0 0 0 sr} bdef
+/c1 { 1 1 1 sr} bdef
+/c2 { 1 0 0 sr} bdef
+/c3 { 0 1 0 sr} bdef
+/c4 { 0 0 1 sr} bdef
+/c5 { 1 1 0 sr} bdef
+/c6 { 1 0 1 sr} bdef
+/c7 { 0 1 1 sr} bdef
+/Helvetica /ISOLatin1Encoding 144 FMSR
+
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+[2009 0 0 2010 337 245] cc
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+showpage
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+% Stop using temporary dictionary
+end
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+% Restore original state
+origstate restore
+
+%%EndDocument
+ @endspecial 5422 3142 a
+ currentpoint currentpoint translate 1 1.17308 div 1 1.15312 div scale
+neg exch neg exch translate
+ 5422 3142 a 5936 3254 a Fs(\(b\))6644
+3142 y
+ currentpoint currentpoint translate 1.06308 1.04666 scale neg exch
+neg exch translate
+ 6644 3142 a @beginspecial 242 @llx 331 @lly 370
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+%%BeginDocument: images/msphere.sf1.epsf
+
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+    {
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+        /count count 1 add store
+      } repeat
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+    {
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+      bcount {
+        outrow count rgbval putinterval
+        /count count 1 add store
+      } repeat
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+  outrow
+} bind def
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+%%EndDocument
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+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7f
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+ff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff7fff7fff7fff54ff
+
+showpage
+
+% Stop using temporary dictionary
+end
+
+% Restore original state
+origstate restore
+
+%%EndDocument
+ @endspecial 7867 4462 a
+ currentpoint currentpoint translate 1 1.215 div 1 1.19359 div scale
+neg exch neg exch translate
+ 7867 4462 a 8381 4575 a Fs(\(h\))3900
+4760 y(Figure)25 b(9:)30 b(\(a\))24 b(Isosurf)o(ace)j(of)d(a)g(3-D)g
+(object)h(de\002ned)g(on)g(a)e Fn(64)5913 4727 y Fj(3)5977
+4760 y Fs(grid;)i(\(b\))g(positions)i(of)d(planes)h(A)e(and)i(B)e(on)h
+(which)h(the)f(3-D)g(GVF)e(v)o(ectors)k(are)e(depicted)i(in)e(\(c\))h
+(and)3900 4873 y(\(d\),)e(respecti)n(v)o(ely;)k(\(e\))d(the)g(initial)g
+(con\002guration)j(of)c(a)h(deformable)h(surf)o(ace)h(using)e(GVF)e
+(and)i(its)f(positions)k(after)d(\(f\))f(10,)h(\(g\))f(40,)h(and)g
+(\(h\))f(100)i(iterations.)9300 5400 y
+ currentpoint grestore moveto
+ 9300 5400 a eop
+%%Trailer
+end
+userdict /end-hook known{end-hook}if
+%%EOF

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorEnsure.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorEnsure.m
new file mode 100644 (file)
index 0000000..6afbd60
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorEnsure.m
@@ -0,0 +1,39 @@
+function B = BoundMirrorEnsure(A)
+% Ensure mirror boundary condition
+%
+% The number of rows and columns of A must be greater than 2
+%
+% for example (X means value that is not of interest)
+% 
+% A = [
+%     X  X  X  X  X   X
+%     X  1  2  3  11  X
+%     X  4  5  6  12  X 
+%     X  7  8  9  13  X 
+%     X  X  X  X  X   X
+%     ]
+%
+% B = BoundMirrorEnsure(A) will yield
+%
+%     5  4  5  6  12  6
+%     2  1  2  3  11  3
+%     5  4  5  6  12  6 
+%     8  7  8  9  13  9 
+%     5  4  5  6  12  6
+%
+
+% Chenyang Xu and Jerry L. Prince, 9/9/1999
+% http://iacl.ece.jhu.edu/projects/gvf
+
+[m,n] = size(A);
+
+if (m<3 | n<3) 
+    error('either the number of rows or columns is smaller than 3');
+end
+
+yi = 2:m-1;
+xi = 2:n-1;
+B = A;
+B([1 m],[1 n]) = B([3 m-2],[3 n-2]);  % mirror corners
+B([1 m],xi) = B([3 m-2],xi);          % mirror left and right boundary
+B(yi,[1 n]) = B(yi,[3 n-2]);          % mirror top and bottom boundary

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorExpand.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorExpand.m
new file mode 100644 (file)
index 0000000..a226c6b
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorExpand.m
@@ -0,0 +1,31 @@
+function B = BoundMirrorExpand(A)
+% Expand the matrix using mirror boundary condition
+% 
+% for example 
+%
+% A = [
+%     1  2  3  11
+%     4  5  6  12
+%     7  8  9  13
+%     ]
+%
+% B = BoundMirrorExpand(A) will yield
+%
+%     5  4  5  6  12  6
+%     2  1  2  3  11  3
+%     5  4  5  6  12  6 
+%     8  7  8  9  13  9 
+%     5  4  5  6  12  6
+%
+
+% Chenyang Xu and Jerry L. Prince, 9/9/1999
+% http://iacl.ece.jhu.edu/projects/gvf
+
+[m,n] = size(A);
+yi = 2:m+1;
+xi = 2:n+1;
+B = zeros(m+2,n+2);
+B(yi,xi) = A;
+B([1 m+2],[1 n+2]) = B([3 m],[3 n]);  % mirror corners
+B([1 m+2],xi) = B([3 m],xi);          % mirror left and right boundary
+B(yi,[1 n+2]) = B(yi,[3 n]);          % mirror top and bottom boundary

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorShrink.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorShrink.m
new file mode 100644 (file)
index 0000000..519d2c9
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/BoundMirrorShrink.m
@@ -0,0 +1,27 @@
+function B = BoundMirrorShrink(A)
+% Shrink the matrix to remove the padded mirror boundaries
+%
+% for example 
+%
+% A = [
+%     5  4  5  6  12  6
+%     2  1  2  3  11  3
+%     5  4  5  6  12  6 
+%     8  7  8  9  13  9 
+%     5  4  5  6  12  6
+%     ]
+% 
+% B = BoundMirrorShrink(A) will yield
+%
+%     1  2  3  11
+%     4  5  6  12
+%     7  8  9  13
+
+% Chenyang Xu and Jerry L. Prince, 9/9/1999
+% http://iacl.ece.jhu.edu/projects/gvf
+
+[m,n] = size(A);
+yi = 2:m-1;
+xi = 2:n-1;
+B = A(yi,xi);
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/Contents.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/Contents.m
new file mode 100644 (file)
index 0000000..2823bf3
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/Contents.m
@@ -0,0 +1,30 @@
+% GVF snake (active contour) toolbox
+% Version 1.0 17-June-1997
+% Copyright (c) 1996-1997 by Chenyang Xu and Jerry L. Prince 
+%
+%
+%  Image input/output
+%    rawread       - Read a Portable Bitmap file, or a raw file
+%    rawwrite      - Write a Portable Bitmap file, or a raw file
+% 
+%  Image Display
+%    imdisp        - Display an image
+% 
+%  Active Contour
+%    snakeinit     - Initialize the snake manually
+%    snakedeform   - Deform snake in the given external force field
+%    snakedeform2  - Deform snake in the given external force field with
+%                    pressure force
+%    snakedisp     - Display a snake
+%    snakeinterp   - Interpolate the snake adaptively
+%    snakeinterp1  - Interpolate the snake at a fixed resolution
+%                    (better implemented than snakeinterp)
+% 
+%  Gradient Vector Flow
+%    GVF           - Compute the gradient vector flow field
+% 
+%  Other
+%    dt            - Simple distance transform
+%    gaussianBlur  - Blurring an image using gaussian kernel   
+%    gaussianMask  - Generate a discrete gaussian mask
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/GVF.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/GVF.m
new file mode 100644 (file)
index 0000000..999d92e
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/GVF.m
@@ -0,0 +1,45 @@
+function [u,v] = GVF(f, mu, ITER)
+%GVF Compute gradient vector flow.
+%   [u,v] = GVF(f, mu, ITER) computes the
+%   GVF of an edge map f.  mu is the GVF regularization coefficient
+%   and ITER is the number of iterations that will be computed.  
+
+%   Chenyang Xu and Jerry L. Prince 6/17/97
+%   Copyright (c) 1996-99 by Chenyang Xu and Jerry L. Prince
+%   Image Analysis and Communications Lab, Johns Hopkins University
+
+%   modified on 9/9/99 by Chenyang Xu
+%   MATLAB do not implement the boundary condition for gradient and del2 
+%   consistently between MATLAB 4.2 and MATLAB 5. Hence I modify
+%   the function to take care of this issue by the code itself.
+%   Also, in the previous version, the input "f" is assumed to have been
+%   normalized to the range [0,1] before the function is called. 
+%   In this version, "f" is normalized inside the function to avoid 
+%   potential error of inputing an unnormalized "f".
+
+[m,n] = size(f);
+fmin  = min(f(:));
+fmax  = max(f(:));
+f = (f-fmin)/(fmax-fmin);  % Normalize f to the range [0,1]
+
+f = BoundMirrorExpand(f);  % Take care of boundary condition
+[fx,fy] = gradient(f);     % Calculate the gradient of the edge map
+u = fx; v = fy;            % Initialize GVF to the gradient
+SqrMagf = fx.*fx + fy.*fy; % Squared magnitude of the gradient field
+
+% Iteratively solve for the GVF u,v
+for i=1:ITER,
+  u = BoundMirrorEnsure(u);
+  v = BoundMirrorEnsure(v);
+  u = u + mu*4*del2(u) - SqrMagf.*(u-fx);
+  v = v + mu*4*del2(v) - SqrMagf.*(v-fy);
+  fprintf(1, '%3d', i);
+  if (rem(i,20) == 0)
+     fprintf(1, '\n');
+  end 
+end
+fprintf(1, '\n');
+
+u = BoundMirrorShrink(u);
+v = BoundMirrorShrink(v);
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/dt.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/dt.m
new file mode 100644 (file)
index 0000000..ffcabe8
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/dt.m
@@ -0,0 +1,23 @@
+function D = dt(B)
+% DT apply Eucledian distance transform
+%    D = dt(B) compute the Eucledian distance transform of B
+%    B must be a binary map. 
+%
+% NOTE: this is not an efficient way to implement distance transform. 
+%    If one is interested in using DT, one may want to implement its
+%    own DT. 
+
+%    Chenyang Xu and Jerry L. Prince 6/17/97
+%    Copyright (c) 1996-97 by Chenyang Xu and Jerry L. Prince
+
+[i,j] = find(B);
+
+[n,m] = size(B);
+for x = 1:n,
+    for y = 1:m,
+        dx = i-x;
+        dy = j-y;
+        dmag = sqrt(dx.*dx+dy.*dy);
+       D(x,y) = min(dmag);
+    end
+end

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianBlur.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianBlur.m
new file mode 100644 (file)
index 0000000..d453cf2
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianBlur.m
@@ -0,0 +1,13 @@
+function GI = gaussianBlur(I,s)
+% GAUSSIANBLUR blur the image with a gaussian kernel
+%     GI = gaussianBlur(I,s) 
+%     I is the image, s is the standard deviation of the gaussian
+%     kernel, and GI is the gaussian blurred image.
+
+%    Chenyang Xu and Jerry L. Prince 6/17/97
+%    Copyright (c) 1996-97 by Chenyang Xu and Jerry L. Prince
+
+M = gaussianMask(1,s);
+M = M/sum(sum(M));   % normalize the gaussian mask so that the sum is
+                     % equal to 1
+GI = xconv2(I,M);

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianMask.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianMask.m
new file mode 100644 (file)
index 0000000..2792d97
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/gaussianMask.m
@@ -0,0 +1,11 @@
+function M = gaussianMask(k,s)
+% k: the scaling factor
+% s: standard variance
+
+R = ceil(3*s); % cutoff radius of the gaussian kernal  
+for i = -R:R,
+    for j = -R:R,
+        M(i+ R+1,j+R+1) = k * exp(-(i*i+j*j)/2/s/s)/(2*pi*s*s);
+    end
+end
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/imdisp.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/imdisp.m
new file mode 100644 (file)
index 0000000..4c9e36e
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/imdisp.m
@@ -0,0 +1,12 @@
+function imdisp(I)
+% imdisp(I) - scale the dynamic range of an image and display it.
+
+x = (0:255)./255;
+grey = [x;x;x]';
+minI = min(min(I));
+maxI = max(max(I));
+I = (I-minI)/(maxI-minI)*255;
+image(I);
+axis('square','off');
+colormap(grey);
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/rawread.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/rawread.m
new file mode 100644 (file)
index 0000000..cd6643d
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/rawread.m
@@ -0,0 +1,106 @@
+function [X,map] = rawread(filename,n,m);
+% RAWREAD Read a Portable Bitmap file, or a raw file.
+%       RAWREAD('imagefile.raw', xsize, ysize) reads a "raw" image file
+%       RAWREAD('imagefile.pgm') reads a "pgm" (portable gray map) image
+%       [X,map] = RAWREAD('imagefile.raw') returns both the image and a
+%       color map, so that
+%               [X,map] = rawread('imagefile.raw',sx,sy);
+%       or      [X,map] = rawread('imagefile.pgm');
+%               image(X)
+%               colormap(map)
+%       will display the result with the proper colors.
+%
+%       NOTE : map is optional and could be replaced during the display by
+%              the "colormap('gray')" command
+%
+%       See also IMWRITE, IMREAD, IMAGE, COLORMAP.
+
+dot = max(find(filename == '.'));
+suffix = filename(dot+1:dot+3);
+
+if strcmp(suffix,'pgm') | strcmp(suffix,'raw')
+
+   disp(sprintf('\nopens %s file\n',filename));
+   fp = fopen(filename,'rb','b');  % "Big-endian" byte order.
+   
+   if (fp<0)
+      error(['Cannot open ' filename '.']);
+   end
+
+   if strcmp(suffix,'pgm')
+   % Read and crack the header
+   
+      head = fread(fp,2,'uchar'); % pgm magic number : P5
+      if ~strcmp(head,'P5'),
+         fprintf(1,'\n Magic Number : %s\n',head);
+      else
+         fprintf(1,'\n Bad Magic Number : %s\n',head);
+         error('cannot continue this way, good bye cruel world');
+      end
+
+      c = fread(fp,1,'uchar'); %reads the carriage return separating P5 from the creator
+
+      precreator = fread(fp,1,'uchar'); % look for a '#' character preceeding a creator signature
+      if precreator == '#',
+         c = setstr(20);  % any character except carriage return
+         cr = setstr(10); % defines a carriage return
+         while c ~= cr,
+               c = fread(fp,1,'uchar');
+               creator = [creator,c];
+         end;
+         fprintf(1,'\n creator : %s\n',creator);
+      else
+         fprintf('\n No creator signature\n');
+         fseek(fp,-1,'cof'); % return one char before
+      end;
+
+end
+
+   if nargin <2,
+      if strcmp(suffix,'raw')
+      % assume image size is 256x256
+         disp('RAW file without size : assume image size is 256x256');
+         n = 256;
+         m = 256;
+      else % for PGM files
+      % reads the size and depth
+          disp(' reads sizes');
+          n = fscanf(fp,'%d',1);
+          tn = num2str(n);
+          disp(['  xsize = ' tn]);
+          m = fscanf(fp,'%d',1);
+          tm = num2str(m);
+          disp(['  ysize = ' tm]);
+          p = fscanf(fp,'%d',1);
+          tp = num2str(p);
+          disp(['  depth = ' tp]);
+          c = fread(fp,1,'uchar'); %reads the last carriage return
+      end;
+   end
+
+
+   % Creates a gray palette and scale it to [0,1].
+   disp(' create gray palette');
+   for i=1:256,
+       map(i,[1:3])=[i/256,i/256,i/256];
+   end;
+   
+  
+   % Read the image
+   disp(' Reads image data ...');
+   [X,l] = fread(fp,[n,m],'uchar');
+   if l ~= m*n, l, error('HSI image file is wrong length'), end
+   % Image elements are colormap indices, so start at 1.
+   X = X'+1;
+   
+   fclose(fp);
+   
+   disp('end');
+
+else
+   error('Image file name must end in ''raw'' or ''pgm''.')
+end
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/rawwrite.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/rawwrite.m
new file mode 100644 (file)
index 0000000..0bc07e0
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/rawwrite.m
@@ -0,0 +1,54 @@
+function rawwrite(X,MAX,filename);
+% RAWWRITE Write a Portable Bitmap file, or a raw file.
+%       RAWWRITE(X,MAX,'imagefile.raw') writes a "raw" image file
+%       RAWWRITE(X,MAX,'imagefile.pgm') writes a "pgm" (portable gray map) image
+%       MAX is the maximum intensity value used, must be smaller or
+%       equal to 255. If bigger, 255 is automatically used. 
+%
+%       See also RAWREAD, IMWRITE, IMREAD, IMAGE, COLORMAP.
+
+%      Chenyang Xu and Jerry L. Prince, 4/1/95, 6/17/97
+%      Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%      Image Analysis and Communications Lab, Johns Hopkins University
+
+disp('RawWrite');
+dot = max(find(filename == '.'));
+suffix = filename(dot+1:dot+3);
+
+if strcmp(suffix,'pgm') | strcmp(suffix,'raw')
+
+   disp(sprintf('\nopens %s file\n',filename));
+   fp = fopen(filename,'wb','b');  % "Big-endian" byte order.
+   
+   if (fp<0)
+      error(['Cannot open ' filename '.']);
+   end
+
+   [height,width] = size(X);
+
+   if strcmp(suffix,'pgm')
+   % Write and crack the header
+   
+      fprintf(fp,'P5\n'); % pgm magic number : P5
+
+      fprintf(fp, '%d %d\n', [height width]);
+      if (MAX>255)
+       MAX = 255;
+      end
+      fprintf(fp, '%d\n', MAX);
+   end
+
+   % Read the image
+   disp(' Writes image data ...');
+   l = fwrite(fp,X,'uchar');
+   if l ~= height*width, l, error('HSI image file is wrong length'), end
+   
+   fclose(fp);
+   
+   disp('end');
+
+else
+   error('Image file name must end in ''raw'' or ''pgm''.')
+end
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform.m
new file mode 100644 (file)
index 0000000..e71e3e1
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform.m
@@ -0,0 +1,59 @@
+function [x,y] = snakedeform(x,y,alpha,beta,gamma,kappa,fx,fy,ITER)
+% SNAKEDEFORM  Deform snake in the given external force field
+%     [x,y] = snakedeform(x,y,alpha,beta,gamma,kappa,fx,fy,ITER)
+%
+%     alpha:   elasticity parameter
+%     beta:    rigidity parameter
+%     gamma:   viscosity parameter
+%     kappa:   external force weight
+%     fx,fy:   external force field
+
+%      Chenyang Xu and Jerry L. Prince, 4/1/95, 6/17/97
+%      Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%      Image Analysis and Communications Lab, Johns Hopkins University
+
+% generates the parameters for snake
+
+N = length(x);
+
+alpha = alpha* ones(1,N); 
+beta = beta*ones(1,N);
+
+% produce the five diagnal vectors
+alpham1 = [alpha(2:N) alpha(1)];
+alphap1 = [alpha(N) alpha(1:N-1)];
+betam1 = [beta(2:N) beta(1)];
+betap1 = [beta(N) beta(1:N-1)];
+
+a = betam1;
+b = -alpha - 2*beta - 2*betam1;
+c = alpha + alphap1 +betam1 + 4*beta + betap1;
+d = -alphap1 - 2*beta - 2*betap1;
+e = betap1;
+
+% generate the parameters matrix
+A = diag(a(1:N-2),-2) + diag(a(N-1:N),N-2);
+A = A + diag(b(1:N-1),-1) + diag(b(N), N-1);
+A = A + diag(c);
+A = A + diag(d(1:N-1),1) + diag(d(N),-(N-1));
+A = A + diag(e(1:N-2),2) + diag(e(N-1:N),-(N-2));
+
+invAI = inv(A + gamma * diag(ones(1,N)));
+
+for count = 1:ITER,
+   vfx = interp4(fx,x,y);
+   vfy = interp4(fy,x,y);
+
+   % deform snake
+   x = invAI * (gamma* x + kappa*vfx);
+   y = invAI * (gamma* y + kappa*vfy);
+end
+
+
+
+
+
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform2.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform2.m
new file mode 100644 (file)
index 0000000..6076a64
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedeform2.m
@@ -0,0 +1,70 @@
+function [x,y] = snakedeform(x,y,alpha,beta,gamma,kappa, kappap, fx,fy,ITER)
+% SNAKEDEFORM2  Deform snake in the given external force field with
+%     pressure force added
+%     [x,y] = snakedeform(x,y,alpha,beta,gamma,kappa,kappap,fx,fy,ITER)
+%
+%     alpha:   elasticity parameter
+%     beta:    rigidity parameter
+%     gamma:   viscosity parameter
+%     kappa:   external force weight
+%     kappap:  pressure force weight
+%     fx,fy:   external force field
+
+%      Chenyang Xu and Jerry L. Prince, 4/1/95, 6/17/97
+%      Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%      Image Analysis and Communications Lab, Johns Hopkins University
+
+
+% generates the parameters for snake
+
+N = length(x);
+
+alpha = alpha* ones(1,N); 
+beta = beta*ones(1,N);
+
+% produce the five diagnal vectors
+alpham1 = [alpha(2:N) alpha(1)];
+alphap1 = [alpha(N) alpha(1:N-1)];
+betam1 = [beta(2:N) beta(1)];
+betap1 = [beta(N) beta(1:N-1)];
+
+a = betam1;
+b = -alpha - 2*beta - 2*betam1;
+c = alpha + alphap1 +betam1 + 4*beta + betap1;
+d = -alphap1 - 2*beta - 2*betap1;
+e = betap1;
+
+% generate the parameters matrix
+A = diag(a(1:N-2),-2) + diag(a(N-1:N),N-2);
+A = A + diag(b(1:N-1),-1) + diag(b(N), N-1);
+A = A + diag(c);
+A = A + diag(d(1:N-1),1) + diag(d(N),-(N-1));
+A = A + diag(e(1:N-2),2) + diag(e(N-1:N),-(N-2));
+
+invAI = inv(A + gamma * diag(ones(1,N)));
+
+for count = 1:ITER,
+   vfx = interp4(fx,x,y);
+   vfy = interp4(fy,x,y);
+  
+   % adding the pressure force         
+   xp = [x(2:N);x(1)];    yp = [y(2:N);y(1)]; 
+   xm = [x(N);x(1:N-1)];  ym = [y(N);y(1:N-1)]; 
+   
+   qx = xp-xm;  qy = yp-ym;
+   pmag = sqrt(qx.*qx+qy.*qy);
+   px = qy./pmag;     py = -qx./pmag;
+
+   % deform snake
+   x = invAI * (gamma* x + kappa*vfx + kappap.*px);
+   y = invAI * (gamma* y + kappa*vfy + kappap.*py);
+end
+
+
+
+
+
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedisp.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedisp.m
new file mode 100644 (file)
index 0000000..39f4793
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakedisp.m
@@ -0,0 +1,21 @@
+function snakedisp(x,y,style)
+% SNAKEDISP  Initialize the snake 
+%      snakedisp(x,y,line)
+%       
+%      style is same as the string for plot
+
+%      Chenyang Xu and Jerry L. Prince, 5/15/95, 6/17/97
+%      Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%      Image Analysis and Communications Lab, Johns Hopkins University
+
+hold on
+
+% convert to column data
+x = x(:); y = y(:);
+
+if nargin == 3
+   plot([x;x(1,1)],[y;y(1,1)],style);
+   hold off
+else
+   disp('snakedisp.m: The input parameter is not correct!'); 
+end

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeindex.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeindex.m
new file mode 100644 (file)
index 0000000..f497c8b
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeindex.m
@@ -0,0 +1,12 @@
+function y = snakeindex(IDX)
+% SNAKEINDEX  Create index for adpative interpolating the snake 
+%     y = snakeindex(IDX)
+%
+
+N = length(IDX);
+y=1:0.5:N+0.5;
+x=1:N;
+y(2*x(IDX==0))=[];
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinit.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinit.m
new file mode 100644 (file)
index 0000000..b2785ef
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinit.m
@@ -0,0 +1,46 @@
+function [x,y] = snakeinit(delta)
+%SNAKEINIT  Manually initialize a 2-D, closed snake 
+%   [x,y] = SNAKEINIT(delta)
+%
+%   delta: interpolation step
+
+%   Chenyang Xu and Jerry L. Prince, 4/1/95, 6/17/97
+%   Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%   Image Analysis and Communications Lab, Johns Hopkins University
+
+hold on
+
+x = [];
+y = [];
+n =0;
+
+% Loop, picking up the points
+disp('Left mouse button picks points.')
+disp('Right mouse button picks last point.')
+
+but = 1;
+while but == 1
+      [s, t, but] = ginput(1);
+      n = n + 1;
+      x(n,1) = s;
+      y(n,1) = t;
+      plot(x, y, 'r-');
+end   
+
+plot([x;x(1,1)],[y;y(1,1)],'r-');
+hold off
+
+% sampling and record number to N
+x = [x;x(1,1)];
+y = [y;y(1,1)];
+t = 1:n+1;
+ts = [1:delta:n+1]';
+xi = interp1(t,x,ts);
+yi = interp1(t,y,ts);
+n = length(xi);
+x = xi(1:n-1);
+y = yi(1:n-1);
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp.m
new file mode 100644 (file)
index 0000000..5ebe622
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp.m
@@ -0,0 +1,67 @@
+function [xi,yi] = snakeinterp(x,y,dmax,dmin)
+%SNAKEINTERP  Interpolate the snake adaptively
+%   [xi,yi] = snakeinterp(x,y,dmax,dmin)
+%
+%   dmax: the maximum distance between two snake points
+%   dmin: the maximum distance between two snake points
+%   d(i,i+1)>dmax, then a new point is added between i and i+1
+%   d(i,i+1)<dmin, then either i or i+1 is removed 
+%  
+%   NOTE: the spacing of original curve must be close to the 
+%         range defined by dmax and dmin. For arbitrary spacing,
+%         try snakeinterp1.
+% 
+%   See also SNAKEINTERP1
+
+%    there is a bug in the program for points removal
+
+%   Chenyang Xu and Jerry L. Prince, 4/1/95, 6/17/97
+%   Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%   Image Analysis and Communications Lab, Johns Hopkins University
+
+% convert to column vector
+x = x(:); y = y(:);
+
+N = length(x);
+
+d = abs(x([2:N 1])- x(:)) + abs(y([2:N 1])- y(:));
+
+% remove the points which distance to neighbor points is shorter than dmin
+IDX = (d<dmin);
+
+idx = find(IDX==0);
+x = x(idx);
+y = y(idx);
+
+N = length(x);
+d = abs(x([2:N 1])- x(:)) + abs(y([2:N 1])- y(:));
+
+IDX = (d>dmax);
+
+z = snakeindex(IDX);
+
+p = 1:N+1;
+
+xi = interp1(p,[x;x(1)],z');
+yi = interp1(p,[y;y(1)],z');
+
+N = length(xi);
+d = abs(xi([2:N 1])- xi(:)) + abs(yi([2:N 1])- yi(:));
+
+while (max(d)>dmax),
+
+    IDX = (d>dmax);
+    z = snakeindex(IDX);
+
+    p = 1:N+1;
+
+    xi = interp1(p,[xi;xi(1)],z');
+    yi = interp1(p,[yi;yi(1)],z');
+
+    N = length(xi);
+    d = abs(xi([2:N 1])- xi(:)) + abs(yi([2:N 1])- yi(:));
+end
+
+
+
+

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp1.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp1.m
new file mode 100644 (file)
index 0000000..ac857f0
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/snakeinterp1.m
@@ -0,0 +1,50 @@
+function [xi,yi] = snakeinterp1(x,y,RES)
+% SNAKEINTERP1  Interpolate the snake to have equal distance RES
+%     [xi,yi] = snakeinterp(x,y,RES)
+%
+%     RES: resolution desired
+
+%     update on snakeinterp after finding a bug
+
+%      Chenyang Xu and Jerry L. Prince, 3/8/96, 6/17/97
+%      Copyright (c) 1996-97 by Chenyang Xu and Jerry L. Prince
+%      Image Analysis and Communications Lab, Johns Hopkins University
+
+% convert to column vector
+x = x(:); y = y(:);
+
+N = length(x);  
+
+% make it a circular list since we are dealing with closed contour
+x = [x;x(1)];
+y = [y;y(1)];
+
+dx = x([2:N+1])- x(1:N);
+dy = y([2:N+1])- y(1:N);
+d = sqrt(dx.*dx+dy.*dy);  % compute the distance from previous node for point 2:N+1
+
+d = [0;d];   % point 1 to point 1 is 0 
+
+% now compute the arc length of all the points to point 1
+% we use matrix multiply to achieve summing 
+M = length(d);
+d = (d'*uppertri(M,M))';
+
+% now ready to reparametrize the closed curve in terms of arc length
+maxd = d(M);
+
+if (maxd/RES<3)
+   error('RES too big compare to the length of original curve');
+end
+
+di = 0:RES:maxd;
+
+xi = interp1(d,x,di);
+yi = interp1(d,y,di);
+
+N = length(xi);
+
+if (maxd - di(length(di)) <RES/2)  % deal with end boundary condition
+   xi = xi(1:N-1);
+   yi = yi(1:N-1);
+end

diff --git a/THESE/codes/snake/gvf_dist_v4.2c/snake/xconv2.m b/THESE/codes/snake/gvf_dist_v4.2c/snake/xconv2.m
new file mode 100644 (file)
index 0000000..bc1ef08
--- /dev/null
+++ b/THESE/codes/snake/gvf_dist_v4.2c/snake/xconv2.m
@@ -0,0 +1,26 @@
+function Y = xconv2(I,G)
+% function Y = xconv2(I,G)
+%   I: the original image
+%   G: the mask to be convoluted
+%   Y: the convoluted result (by taking fft2, multiply and ifft2)
+% 
+%   a similar version of the MATLAB conv2(I,G,'same'),  7/10/95
+%   implemented by fft instead of doing direct convolution as in conv2
+%   the result is almost same , differences are under 1e-10.
+%   However, the speed of xconv2 is much faster than conv2 when
+%   gaussian kernel has large standard variation.
+
+%   Chenyang Xu and Jerry L. Prince, 7/10/95, 6/17/97
+%   Copyright (c) 1995-97 by Chenyang Xu and Jerry L. Prince
+%   Image Analysis and Communications Lab, Johns Hopkins University
+%
+
+[n,m] = size(I);
+[n1,m1] = size(G);
+FI = fft2(I,n+n1-1,m+m1-1);  % avoid aliasing
+FG = fft2(G,n+n1-1,m+m1-1);
+FY = FI.*FG;
+YT = real(ifft2(FY));
+nl = floor(n1/2);
+ml = floor(m1/2);
+Y = YT(1+nl:n+nl,1+ml:m+ml);
\ No newline at end of file

diff --git a/THESE/codes/snake/gvfc/gvfc.c b/THESE/codes/snake/gvfc/gvfc.c
new file mode 100644 (file)
index 0000000..32bfb27
--- /dev/null
+++ b/THESE/codes/snake/gvfc/gvfc.c
@@ -0,0 +1,354 @@
+/* NOTE:\r
+ * \r
+ * You must have "mex" command working in your matlab.  In matlab, \r
+ * type "mex gvfc.c" to compile the code. See usage for details of \r
+ * calling this function.\r
+ *\r
+ * Replace GVF(...) with GVFC in the snakedeform.m. The speed is \r
+ * significantly faster than the Matlab version.\r
+ */\r
+\r
+/***************************************************************************\r
+Copyright (c) 1996-1999 Chenyang Xu and Jerry Prince.\r
+\r
+This software is copyrighted by Chenyang Xu and Jerry Prince.  The\r
+following terms apply to all files associated with the software unless\r
+explicitly disclaimed in individual files.\r
+\r
+The authors hereby grant permission to use this software and its\r
+documentation for any purpose, provided that existing copyright\r
+notices are retained in all copies and that this notice is included\r
+verbatim in any distributions. Additionally, the authors grant\r
+permission to modify this software and its documentation for any\r
+purpose, provided that such modifications are not distributed without\r
+the explicit consent of the authors and that existing copyright\r
+notices are retained in all copies.\r
+\r
+IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY FOR\r
+DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT\r
+OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY DERIVATIVES THEREOF,\r
+EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\r
+\r
+THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES, INCLUDING,\r
+BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A\r
+PARTICULAR PURPOSE, AND NON-INFRINGEMENT.  THIS SOFTWARE IS PROVIDED ON AN\r
+"AS IS" BASIS, AND THE AUTHORS AND DISTRIBUTORS HAVE NO OBLIGATION TO PROVIDE\r
+MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.\r
+****************************************************************************/\r
+\r
+#include "mex.h"\r
+#include <stdio.h>\r
+#include <stdlib.h>\r
+#include <math.h>\r
+#include <string.h>\r
+\r
+/* \r
+ * [u,v] = GVFC(f, mu, ITER)\r
+ * \r
+ * assuming dx = dy = 1, and dt = 1, \r
+ * need to figure out CFL\r
+ *\r
+ * Gradient Vector Flow\r
+ *\r
+ * Chenyang Xu and Jerry L. Prince \r
+ * Copyright (c) 1996-99 by Chenyang Xu and Jerry L. Prince\r
+ * Image Analysis and Communications Lab, Johns Hopkins University\r
+ *\r
+ */\r
+\r
+/* PG Macros */\r
+#define PG_MAX(a,b)  (a>b? a: b)\r
+#define PG_MIN(a,b)  (a<b? a: b)\r
+\r
+typedef unsigned char  PGbyte;\r
+typedef          char  PGchar;\r
+typedef unsigned short PGushort;\r
+typedef          short PGshort; \r
+typedef unsigned int   PGuint;\r
+typedef          int   PGint;\r
+typedef float          PGfloat;\r
+typedef double         PGdouble;\r
+typedef void           PGvoid;\r
+\r
+/* Prints error message: modified to work in mex environment */\r
+PGvoid pgError(char error_text[])\r
+{\r
+   char buf[255];\r
+   \r
+   sprintf(buf, "GVFC.MEX: %s", error_text);\r
+   mexErrMsgTxt(buf);\r
+   \r
+   return;\r
+}\r
+\r
+\r
+/* Allocates a matrix of doubles with range [nrl..nrh][ncl..nch] */\r
+PGdouble **pgDmatrix(nrl,nrh,ncl,nch)\r
+     int nrl,nrh,ncl,nch;\r
+{\r
+   int j;\r
+   long bufsize,bufptr;\r
+   PGdouble **m;\r
+   \r
+   bufsize = (nrh-nrl+1)*sizeof(PGdouble*)\r
+      + (nrh-nrl+1)*(nch-ncl+1)*sizeof(PGdouble);\r
+   \r
+   m=(PGdouble **) malloc(bufsize);\r
+   if (!m) pgError("allocation failure 1 in pgDmatrix()");\r
+   m -= nrl;\r
+   \r
+   bufptr = ((long) (m+nrl)) + (nrh-nrl+1)*sizeof(PGdouble*);\r
+   for(j=nrl;j<=nrh;j++) {\r
+      m[j] = ((PGdouble *) (bufptr+(j-nrl)*(nch-ncl+1)*sizeof(PGdouble)));\r
+      m[j] -= ncl;\r
+   }\r
+   \r
+   return m;\r
+}\r
+\r
+\r
+/* Frees a matrix allocated by dmatrix */\r
+PGvoid pgFreeDmatrix(m,nrl,nrh,ncl,nch)\r
+     PGdouble **m;\r
+     int nrl,nrh,ncl,nch;\r
+{\r
+   free((char*) (m+nrl));\r
+   return;\r
+}\r
+\r
+\r
+void GVFC(int YN, int XN, double *f, double *ou, double *ov, \r
+         double mu, int ITER)\r
+{\r
+   double mag2, temp, tempx, tempy, fmax, fmin;\r
+   int count, x, y, XN_1, XN_2, YN_1, YN_2;\r
+   \r
+   PGdouble **fx, **fy, **u, **v, **Lu, **Lv, **g, **c1, **c2, **b;\r
+   \r
+   /* define constants and create row-major double arrays */\r
+   XN_1 = XN - 1;\r
+   XN_2 = XN - 2;\r
+   YN_1 = YN - 1;\r
+   YN_2 = YN - 2;\r
+   fx = pgDmatrix(0,YN_1,0,XN_1);\r
+   fy = pgDmatrix(0,YN_1,0,XN_1);\r
+   u  = pgDmatrix(0,YN_1,0,XN_1);\r
+   v  = pgDmatrix(0,YN_1,0,XN_1);\r
+   Lu = pgDmatrix(0,YN_1,0,XN_1);\r
+   Lv = pgDmatrix(0,YN_1,0,XN_1);\r
+   g  = pgDmatrix(0,YN_1,0,XN_1);\r
+   c1 = pgDmatrix(0,YN_1,0,XN_1);\r
+   c2 = pgDmatrix(0,YN_1,0,XN_1);\r
+   b  = pgDmatrix(0,YN_1,0,XN_1);\r
+   \r
+\r
+   /************** I: Normalize the edge map to [0,1] **************/\r
+   fmax = -1e10;\r
+   fmin = 1e10;\r
+   for (x=0; x<=YN*XN-1; x++) {\r
+      fmax = PG_MAX(fmax,f[x]);\r
+      fmin = PG_MIN(fmin,f[x]);\r
+   }\r
+\r
+   if (fmax==fmin) \r
+      mexErrMsgTxt("Edge map is a constant image.");      \r
+\r
+   for (x=0; x<=YN*XN-1; x++) \r
+      f[x] = (f[x]-fmin)/(fmax-fmin);\r
+\r
+   /**************** II: Compute edge map gradient *****************/\r
+   /* I.1: Neumann boundary condition: \r
+    *      zero normal derivative at boundary \r
+    */\r
+   /* Deal with corners */\r
+   fx[0][0] = fy[0][0] = fx[0][XN_1] = fy[0][XN_1] = 0;\r
+   fx[YN_1][XN_1] = fy[YN_1][XN_1] = fx[YN_1][0] = fy[YN_1][0] = 0;\r
+\r
+   /* Deal with left and right column */\r
+   for (y=1; y<=YN_2; y++) {\r
+      fx[y][0] = fx[y][XN_1] = 0;\r
+      fy[y][0] = 0.5 * (f[y+1] - f[y-1]);\r
+      fy[y][XN_1] = 0.5 * (f[y+1 + XN_1*YN] - f[y-1 + XN_1*YN]);\r
+   }\r
+\r
+   /* Deal with top and bottom row */\r
+   for (x=1; x<=XN_2; x++) {\r
+      fy[0][x] = fy[YN_1][x] = 0;\r
+      fx[0][x] = 0.5 * (f[(x+1)*YN] - f[(x-1)*YN]);\r
+      fx[YN_1][x] = 0.5 * (f[YN_1 + (x+1)*YN] - f[YN_1 + (x-1)*YN]);\r
+   }\r
+   \r
+   /* I.2: Compute interior derivative using central difference */\r
+   for (y=1; y<=YN_2; y++)\r
+      for (x=1; x<=XN_2; x++) {\r
+        /* NOTE: f is stored in column major */\r
+        fx[y][x] = 0.5 * (f[y + (x+1)*YN] - f[y + (x-1)*YN]);   \r
+        fy[y][x] = 0.5 * (f[y+1 + x*YN] - f[y-1 + x*YN]);\r
+      }\r
+   \r
+   /******* III: Compute parameters and initializing arrays **********/\r
+   temp = -1.0/(mu*mu);\r
+   for (y=0; y<=YN_1; y++)\r
+      for (x=0; x<=XN_1; x++) {\r
+        tempx = fx[y][x];\r
+        tempy = fy[y][x];\r
+        /* initial GVF vector */\r
+        u[y][x] = tempx;\r
+        v[y][x] = tempy;\r
+        /* gradient magnitude square */\r
+        mag2 = tempx*tempx + tempy*tempy; \r
+        \r
+        g[y][x] = mu;\r
+        b[y][x] = mag2;\r
+\r
+        c1[y][x] = b[y][x] * tempx;\r
+        c2[y][x] = b[y][x] * tempy;\r
+      }\r
+   \r
+   /* free memory of fx and fy */\r
+   pgFreeDmatrix(fx,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(fy,0,YN_1,0,XN_1);\r
+\r
+   /************* Solve GVF = (u,v) iteratively ***************/\r
+   for (count=1; count<=ITER; count++) {\r
+      /* IV: Compute Laplace operator using Neuman condition */\r
+      /* IV.1: Deal with corners */\r
+      Lu[0][0] = (u[0][1] + u[1][0])*0.5 - u[0][0]; \r
+      Lv[0][0] = (v[0][1] + v[1][0])*0.5 - v[0][0];\r
+      Lu[0][XN_1] = (u[0][XN_2] + u[1][XN_1])*0.5 - u[0][XN_1];\r
+      Lv[0][XN_1] = (v[0][XN_2] + v[1][XN_1])*0.5 - v[0][XN_1];\r
+      Lu[YN_1][0] = (u[YN_1][1] + u[YN_2][0])*0.5 - u[YN_1][0];\r
+      Lv[YN_1][0] = (v[YN_1][1] + v[YN_2][0])*0.5 - v[YN_1][0];\r
+      Lu[YN_1][XN_1] = (u[YN_1][XN_2] + u[YN_2][XN_1])*0.5 - u[YN_1][XN_1];\r
+      Lv[YN_1][XN_1] = (v[YN_1][XN_2] + v[YN_2][XN_1])*0.5 - v[YN_1][XN_1];\r
+      \r
+      /* IV.2: Deal with left and right columns */\r
+      for (y=1; y<=YN_2; y++) { \r
+        Lu[y][0] = (2*u[y][1] + u[y-1][0] + u[y+1][0])*0.25 - u[y][0];\r
+        Lv[y][0] = (2*v[y][1] + v[y-1][0] + v[y+1][0])*0.25 - v[y][0];\r
+        Lu[y][XN_1] = (2*u[y][XN_2] + u[y-1][XN_1] \r
+                       + u[y+1][XN_1])*0.25 - u[y][XN_1];\r
+        Lv[y][XN_1] = (2*v[y][XN_2] + v[y-1][XN_1] \r
+                       + v[y+1][XN_1])*0.25 - v[y][XN_1];\r
+      }\r
+      \r
+      /* IV.3: Deal with top and bottom rows */\r
+      for (x=1; x<=XN_2; x++) { \r
+        Lu[0][x] = (2*u[1][x] + u[0][x-1] + u[0][x+1])*0.25 - u[0][x];\r
+        Lv[0][x] = (2*v[1][x] + v[0][x-1] + v[0][x+1])*0.25 - v[0][x];\r
+        Lu[YN_1][x] = (2*u[YN_2][x] + u[YN_1][x-1] \r
+                       + u[YN_1][x+1])*0.25 - u[YN_1][x];\r
+        Lv[YN_1][x] = (2*v[YN_2][x] + v[YN_1][x-1] \r
+                       + v[YN_1][x+1])*0.25 - v[YN_1][x];\r
+      }\r
+      \r
+      /* IV.4: Compute interior */\r
+      for (y=1; y<=YN_2; y++)\r
+        for (x=1; x<=XN_2; x++) {\r
+           Lu[y][x] = (u[y][x-1] + u[y][x+1] \r
+                       + u[y-1][x] + u[y+1][x])*0.25 - u[y][x];\r
+           Lv[y][x] = (v[y][x-1] + v[y][x+1]\r
+                       + v[y-1][x] + v[y+1][x])*0.25 - v[y][x];\r
+        }\r
+      \r
+      /******** V: Update GVF ************/\r
+      for (y=0; y<=YN_1; y++)\r
+        for (x=0; x<=XN_1; x++) {\r
+           u[y][x] = (1- b[y][x])*u[y][x] + g[y][x]*Lu[y][x]*4 + c1[y][x];\r
+           v[y][x] = (1- b[y][x])*v[y][x] + g[y][x]*Lv[y][x]*4 + c2[y][x];\r
+        }\r
+      \r
+      /* print iteration number */\r
+      printf("%5d",count);\r
+      if (count%15 == 0)\r
+        printf("\n");\r
+   }\r
+   printf("\n");\r
+   \r
+   /* copy u,v to the output in column major order */\r
+   for (y=0; y<=YN_1; y++)\r
+      for (x=0; x<=XN_1; x++) {\r
+        ou[x*YN+y] = u[y][x];\r
+        ov[x*YN+y] = v[y][x];\r
+      }\r
+\r
+   /* free all the array memory */\r
+   pgFreeDmatrix(u,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(v,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(Lu,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(Lv,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(g,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(c1,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(c2,0,YN_1,0,XN_1);\r
+   pgFreeDmatrix(b,0,YN_1,0,XN_1);\r
+\r
+   return;\r
+}\r
+\r
+void mexFunction( int nlhs, mxArray *plhs[], \r
+                int nrhs, const mxArray *prhs[] )\r
+{\r
+   double  *f, *u, *v;\r
+   int mrows, ncols;\r
+   int m, n;\r
+   double mu;\r
+   int ITER;\r
+   char buf[80];\r
+\r
+   mexUnlock();\r
+\r
+   /* Check for proper number of arguments */\r
+   if ( nrhs < 1 || nrhs > 3) \r
+      mexErrMsgTxt("GVFC.MEX: wrong number of input arguments.");\r
+   if ( nlhs > 2 )\r
+      mexErrMsgTxt("GVFC.MEX: wrong number of output arguments.");\r
+   \r
+   /* Obtain the dimension information of inputs */\r
+   mrows = mxGetM(prhs[0]);   ncols = mxGetN(prhs[0]);\r
+\r
+   /* Create matrix for the return argument */\r
+   plhs[0] = mxCreateDoubleMatrix(mrows, ncols, mxREAL);\r
+   plhs[1] = mxCreateDoubleMatrix(mrows, ncols, mxREAL);\r
+   \r
+   /* Assign pointers to each input and output */\r
+   f = (double *)mxGetPr(prhs[0]); \r
+   u = (double *)mxGetPr(plhs[0]);\r
+   v = (double *)mxGetPr(plhs[1]);\r
+   \r
+   if (nrhs >= 2 && nrhs <= 3) {\r
+      /* The input must be a noncomplex scalar double.*/\r
+      m = mxGetM(prhs[1]);\r
+      n = mxGetN(prhs[1]);\r
+      if( !mxIsDouble(prhs[1]) || mxIsComplex(prhs[1]) ||\r
+        !(m==1 && n==1) ) {\r
+        mexErrMsgTxt("Input mu must be a noncomplex scalar double.");\r
+      }\r
+      mu = mxGetScalar(prhs[1]);\r
+   }\r
+\r
+   if (nrhs == 3) {\r
+      /* The input must be an integer.*/\r
+      m = mxGetM(prhs[2]);\r
+      n = mxGetN(prhs[2]);\r
+      if( !mxIsDouble(prhs[2]) || mxIsComplex(prhs[2]) ||\r
+        !(m==1 && n==1) ) {\r
+        mexErrMsgTxt("Input mu must be an integer.");\r
+      }\r
+      ITER = mxGetScalar(prhs[2]);\r
+   }\r
+\r
+\r
+   /* set default mu and ITER */\r
+   if (nrhs <= 1) \r
+        mu = 0.2;\r
+   if (nrhs <= 2)\r
+      ITER = PG_MAX(mrows, ncols);\r
+      \r
+   /* Call the distedt subroutine. */\r
+   GVFC(mrows, ncols, f, u, v, mu, ITER);\r
+\r
+   /* done */\r
+   return;\r
+}\r
+\r
+\r

diff --git a/THESE/codes/snake/gvfc/gvfc.m b/THESE/codes/snake/gvfc/gvfc.m
new file mode 100644 (file)
index 0000000..1ea60e9
--- /dev/null
+++ b/THESE/codes/snake/gvfc/gvfc.m
@@ -0,0 +1,11 @@
+% GVFC Generalized Gradient Vector Flow (C mex version)\r
+% \r
+% [u,v] = GVFC(f, mu, ITER)\r
+% \r
+% recommended value for mu to get stable solution:  mu in [0, 0.2]\r
+%\r
+% Chenyang Xu and Jerry L. Prince \r
+% Copyright (c) 1996-99 by Chenyang Xu and Jerry L. Prince\r
+% Image Analysis and Communications Lab, Johns Hopkins University\r
+%\r
+\r

diff --git a/THESE/these.aux b/THESE/these.aux
index 331819feec89d9c118568edd5db019a242234ac5..c83dbe85f4a7eaddf2b4f686c4cda6fd08b21ea2 100644 (file)
--- a/THESE/these.aux
+++ b/THESE/these.aux
@@ -49,8 +49,8 @@
 \newlabel{sub@fig-histo-cochon-e}{{(e)}{16}{Subfigure 2 2.1(e)\relax }{subfigure.2.1.5}{}}
 \newlabel{fig-histo-cochon-f}{{2.1(f)}{16}{Subfigure 2 2.1(f)\relax }{subfigure.2.1.6}{}}
 \newlabel{sub@fig-histo-cochon-f}{{(f)}{16}{Subfigure 2 2.1(f)\relax }{subfigure.2.1.6}{}}
 \newlabel{sub@fig-histo-cochon-e}{{(e)}{16}{Subfigure 2 2.1(e)\relax }{subfigure.2.1.5}{}}
 \newlabel{fig-histo-cochon-f}{{2.1(f)}{16}{Subfigure 2 2.1(f)\relax }{subfigure.2.1.6}{}}
 \newlabel{sub@fig-histo-cochon-f}{{(f)}{16}{Subfigure 2 2.1(f)\relax }{subfigure.2.1.6}{}}

-\@writefile{lof}{\contentsline {figure}{\numberline {2.1}{\ignorespaces Segmentation par analyse simple d'histogramme. Colonne de gauche : image d'entr\IeC {\'e}e. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : r\IeC {\'e}sultat de la segmentation.}}{16}{figure.2.1}}
-\newlabel{fig-histo-cochon}{{2.1}{16}{Segmentation par analyse simple d'histogramme. Colonne de gauche : image d'entrée. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : résultat de la segmentation}{figure.2.1}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2.1}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par analyse simple d'histogramme. Colonne de gauche : image d'entr\IeC {\'e}e. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : r\IeC {\'e}sultat de la segmentation.}}{16}{figure.2.1}}
+\newlabel{fig-histo-cochon}{{2.1}{16}{Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par analyse simple d'histogramme. Colonne de gauche : image d'entrée. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : résultat de la segmentation}{figure.2.1}{}}

 \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Image initiale comportant deux zones : le fond et le cochon (la cible)}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {Histogramme des niveaux de gris}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(c)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 101 apr\IeC {\`e}s 4 it\IeC {\'e}rations.}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Image initiale comportant deux zones : le fond et le cochon (la cible)}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {Histogramme des niveaux de gris}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(c)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 101 apr\IeC {\`e}s 4 it\IeC {\'e}rations.}}}{16}{figure.2.1}}


@@ -58,21 +58,39 @@
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(e)}{\ignorespaces {Histogramme des niveaux de gris}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(f)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 99 apr\IeC {\`e}s 5 it\IeC {\'e}rations.}}}{16}{figure.2.1}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.4.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{17}{subsection.2.4.3}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(e)}{\ignorespaces {Histogramme des niveaux de gris}}}{16}{figure.2.1}}
 \@writefile{lof}{\contentsline {subfigure}{\numberline{(f)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 99 apr\IeC {\`e}s 5 it\IeC {\'e}rations.}}}{16}{figure.2.1}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.4.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{17}{subsection.2.4.3}}

-\@writefile{toc}{\contentsline {subsection}{\numberline {2.4.4}Les contours actifs, ou \textit  {snakes}}{18}{subsection.2.4.4}}
-\@writefile{toc}{\contentsline {subsection}{\numberline {2.4.5}M\IeC {\'e}thodes hybrides}{19}{subsection.2.4.5}}
-\@writefile{toc}{\contentsline {section}{\numberline {2.5}L'\IeC {\'e}tat de l'art des impl\IeC {\'e}mentations GPU}{20}{section.2.5}}
-\@writefile{toc}{\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{21}{chapter.3}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2.2}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par simplification de graphe de type \textit  {Normalized cut} pour un nombre $s$ de segments variant de 2 \IeC {\`a} 5.}}{18}{figure.2.2}}
+\newlabel{fig-graph-cochon}{{2.2}{18}{Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par simplification de graphe de type \textit {Normalized cut} pour un nombre $s$ de segments variant de 2 à 5}{figure.2.2}{}}
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 and I'll forget about whatever was undefined.
 
 

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 .
 
 
 .
 
 

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-[19]) [20]
+) [21] [22]

 Chapitre 3.
 Chapitre 3.

-[21
+[23

 
 

-] [22
+] [24

 
 ]
 Chapitre 4.
 
 ]
 Chapitre 4.

-[23] [24
+[25] [26

 
 ]
 Chapitre 5.
 No file these.bbl.
 
 ]
 Chapitre 5.
 No file these.bbl.

-[25] [26
+[27] [28

 
 ] (./these.lof)
 \tf@lof=\write5
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 ] (./these.lof)
 \tf@lof=\write5
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- [27] [28
+ [29] [30

 
 ] (./these.lot)
 \tf@lot=\write6
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 ] (./these.lot)
 \tf@lot=\write6
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- [29] [30
+ [31] [32

 
 
 
 

-] [31]
+] [33]

 
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diff --git a/THESE/these.pdf b/THESE/these.pdf
index a6a8f67b616d3b16b92294ef3bd27f94abb1ff28..6a369a4557424a66942024ee38f37faf611804ad 100644 (file)
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diff --git a/THESE/these.tex b/THESE/these.tex
index c93980ed73339a050dc8bc12d7d9866bf6256e9c..741a0676d25ab85c7841e18382aa9d888d70e79f 100644 (file)
--- a/THESE/these.tex
+++ b/THESE/these.tex
@@ -1,10 +1,10 @@
 \documentclass[french]{spimufcphdthesis}
 \usepackage[utf8]{inputenc}
 \documentclass[french]{spimufcphdthesis}
 \usepackage[utf8]{inputenc}

-  
+   

 \usepackage{graphicx}
 \usepackage{color, xcolor}
 \usepackage[ruled,lined,linesnumbered]{algorithm2e}
 \usepackage{graphicx}
 \usepackage{color, xcolor}
 \usepackage[ruled,lined,linesnumbered]{algorithm2e}

-
+    

 %%--------------------
 %% Set the title, subtitle, defense date, and
 %% the registration number of the PhD thesis.
 %%--------------------
 %% Set the title, subtitle, defense date, and
 %% the registration number of the PhD thesis.


@@ -74,12 +74,12 @@
 \begin{document}
  
 \tableofcontents
 \begin{document}
  
 \tableofcontents

-                             
+                               

 %--------------------
 % The content of the PhD thesis
 % objectifs, cadre
 % annonce du plan
 %--------------------
 % The content of the PhD thesis
 % objectifs, cadre
 % annonce du plan

-\chapter{Introduction}
+\chapter{Introduction}        

 \input{Chapters/chapter1/chapter1.tex}
 \chapter{Le traitement des images bruitées}
 \input{Chapters/chapter2/chapter2.tex}
 \input{Chapters/chapter1/chapter1.tex}
 \chapter{Le traitement des images bruitées}
 \input{Chapters/chapter2/chapter2.tex}

diff --git a/THESE/these.toc b/THESE/these.toc
index 8bbf155844d35cc0370d19b5ecd55c951b20f1eb..9241416d35e2ea2bdeaa020c5fe9f0b03c76a369 100644 (file)
--- a/THESE/these.toc
+++ b/THESE/these.toc
@@ -12,14 +12,14 @@
 \contentsline {subsection}{\numberline {2.4.1}Analyse d'histogramme}{14}{subsection.2.4.1}
 \contentsline {subsection}{\numberline {2.4.2}Analyse de graphe}{15}{subsection.2.4.2}
 \contentsline {subsection}{\numberline {2.4.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{17}{subsection.2.4.3}
 \contentsline {subsection}{\numberline {2.4.1}Analyse d'histogramme}{14}{subsection.2.4.1}
 \contentsline {subsection}{\numberline {2.4.2}Analyse de graphe}{15}{subsection.2.4.2}
 \contentsline {subsection}{\numberline {2.4.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{17}{subsection.2.4.3}

-\contentsline {subsection}{\numberline {2.4.4}Les contours actifs, ou \textit {snakes}}{18}{subsection.2.4.4}
-\contentsline {subsection}{\numberline {2.4.5}M\IeC {\'e}thodes hybrides}{19}{subsection.2.4.5}
-\contentsline {section}{\numberline {2.5}L'\IeC {\'e}tat de l'art des impl\IeC {\'e}mentations GPU}{20}{section.2.5}
-\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{21}{chapter.3}
-\contentsline {section}{\numberline {3.1}Pr\IeC {\'e}sentation - existant}{21}{section.3.1}
-\contentsline {section}{\numberline {3.2}La parall\IeC {\`e}lisation du snake polygonal}{21}{section.3.2}
-\contentsline {chapter}{\numberline {4}Le filtrage des images sur GPU}{23}{chapter.4}
-\contentsline {section}{\numberline {4.1}Algorithme de r\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{23}{section.4.1}
-\contentsline {section}{\numberline {4.2}Filtre m\IeC {\'e}dian}{23}{section.4.2}
-\contentsline {section}{\numberline {4.3}Filtres de convolution}{23}{section.4.3}
-\contentsline {chapter}{\numberline {5}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{25}{chapter.5}
+\contentsline {subsection}{\numberline {2.4.4}Les contours actifs, ou \textit {snakes}}{20}{subsection.2.4.4}
+\contentsline {subsection}{\numberline {2.4.5}M\IeC {\'e}thodes hybrides}{21}{subsection.2.4.5}
+\contentsline {section}{\numberline {2.5}L'\IeC {\'e}tat de l'art des impl\IeC {\'e}mentations GPU}{22}{section.2.5}
+\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{23}{chapter.3}
+\contentsline {section}{\numberline {3.1}Pr\IeC {\'e}sentation - existant}{23}{section.3.1}
+\contentsline {section}{\numberline {3.2}La parall\IeC {\`e}lisation du snake polygonal}{23}{section.3.2}
+\contentsline {chapter}{\numberline {4}Le filtrage des images sur GPU}{25}{chapter.4}
+\contentsline {section}{\numberline {4.1}Algorithme de r\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{25}{section.4.1}
+\contentsline {section}{\numberline {4.2}Filtre m\IeC {\'e}dian}{25}{section.4.2}
+\contentsline {section}{\numberline {4.3}Filtres de convolution}{25}{section.4.3}
+\contentsline {chapter}{\numberline {5}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{27}{chapter.5}