From 0e74fa21675638b94d68932ccf164b7901688721 Mon Sep 17 00:00:00 2001 From: xdrm-brackets Date: Sun, 27 Sep 2015 21:35:28 +0200 Subject: [PATCH] =?UTF-8?q?Correction=20et=20am=C3=A9lioration=20unset=20a?= =?UTF-8?q?dditif,=20dossier?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/AdditiveNoise.bmp | Bin 0 -> 263222 bytes code/main.py | 9 +- code/new.bmp | Bin 0 -> 263222 bytes code/tests.py | 6 +- code/utility/Additive_Noise.py | 48 ++- docs/shapes/impulse_removal.pdf | Bin 0 -> 1468889 bytes dossier/.dossier.tex.swp | 290 ------------------ dossier/Additive/algo1_10.jpg | Bin 0 -> 204667 bytes dossier/Additive/algo1_10_ZOOM.jpg | Bin 0 -> 2798 bytes dossier/Additive/unset_algo1_10.jpg | Bin 0 -> 175919 bytes dossier/Additive/unset_algo1_10_ZOOM.jpg | Bin 0 -> 2551 bytes .../50.jpg} | Bin .../50_ZOOM.jpg} | Bin .../unset_50.jpg} | Bin .../unset_50_ZOOM.jpg} | Bin .../unset_50_liss.jpg} | Bin .../unset_50_liss_ZOOM.jpg} | Bin dossier/dossier.pdf | Bin 890897 -> 1357503 bytes dossier/dossier.tex | 176 ++++++++--- dossier/fileTree.jpg | Bin 0 -> 70863 bytes 20 files changed, 172 insertions(+), 357 deletions(-) create mode 100644 code/AdditiveNoise.bmp create mode 100644 code/new.bmp create mode 100644 docs/shapes/impulse_removal.pdf delete mode 100644 dossier/.dossier.tex.swp create mode 100644 dossier/Additive/algo1_10.jpg create mode 100644 dossier/Additive/algo1_10_ZOOM.jpg create mode 100644 dossier/Additive/unset_algo1_10.jpg create mode 100644 dossier/Additive/unset_algo1_10_ZOOM.jpg rename dossier/{SaltAndPepper_50.jpg => SaltAndPepper/50.jpg} (100%) rename dossier/{SaltAndPepper_50ZOOM.jpg => SaltAndPepper/50_ZOOM.jpg} (100%) rename dossier/{UnSaltAndPepper_50.jpg => SaltAndPepper/unset_50.jpg} (100%) rename dossier/{UnSaltAndPepper_50ZOOM.jpg => SaltAndPepper/unset_50_ZOOM.jpg} (100%) rename dossier/{UnSaltAndPepperLiss_50.jpg => SaltAndPepper/unset_50_liss.jpg} (100%) rename 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seuil=50) + FX.Additive.set(img.content.map, seuil=seuilA) print "%s |" % (t.get()) # Unparsing @@ -338,7 +338,7 @@ def testAdditiveNoise(): print "| Removing Additive |",; t.reset(); - FX.Additive.unset(img.content.map) + img.content.map = FX.Additive.unset(img.content.map, seuil=seuilB) print "%s |" % (t.get()) # Unparsing diff --git a/code/utility/Additive_Noise.py b/code/utility/Additive_Noise.py index 8ae75b8..445112c 100644 --- a/code/utility/Additive_Noise.py +++ b/code/utility/Additive_Noise.py @@ -3,6 +3,10 @@ import random import time +import sys +sys.path.append(sys.path[0]+'/..') +from BMPFile import RGBPixel + class Additive_Noise: # Applique le bruitage de type "Additif" sur la matrice de pixels # @@ -26,10 +30,10 @@ class Additive_Noise: if random.randint(0,1) == 1: maxColor = max(pixelMap[y][x].r, pixelMap[y][x].g, pixelMap[y][x].b) - randomAdd = random.randint(0, (255-maxColor) / 5 ) + randomAdd = random.randint(0, (255-maxColor) / 2 ) else: minColor = min(pixelMap[y][x].r, pixelMap[y][x].g, pixelMap[y][x].b) - randomAdd = - random.randint(0, minColor / 5 ) + randomAdd = - random.randint(0, minColor / 2 ) pixelMap[y][x].setRGB( pixelMap[y][x].r + randomAdd, @@ -42,23 +46,37 @@ class Additive_Noise: # @param pixelMap Matrice de pixel à traiter (modifier) # @param seuil Seuil à partir duquel on doit traiter les pixels (écart entre la moyenne des pixels avoisinant et le pixel concerné) # + # @return cleanMatrix matrice propre qui est retournée def unset(self, pixelMap, seuil=10): width = len( pixelMap[0] ) height = len( pixelMap ) + # matrice qui sera retournée + cleanMatrix = [] + if seuil < 0 or seuil > 255: # si le seuil est incohérent => valeur par défaut (5) seuil = 5; # on parcourt tout les pixels for y in range(0, len(pixelMap)): + cleanMatrix.append( [] ); for x in range(0, len(pixelMap[y])): + # on ajoute le pixel à la matrice "propre" + cleanMatrix[y].append( RGBPixel( + r = pixelMap[y][x].r, + g = pixelMap[y][x].g, + b = pixelMap[y][x].b, + x = pixelMap[y][x].x, + y = pixelMap[y][x].y, + bpp = pixelMap[y][x].bpp, + )); + # on calcule la moyenne des valeurs R G B du pixel courant pMoy = ( pixelMap[y][x].r + pixelMap[y][x].g + pixelMap[y][x].b ) / 3 - - xmin, ymin, xmax, ymap = x, y, x, y; # les bornes ducarré 3x3 autour du pixel + xmin, ymin, xmax, ymax = x, y, x, y; # les bornes ducarré 3x3 autour du pixel rMoy, gMoy, bMoy, count = 0.0, 0.0, 0.0, 0 # initialisation des variables de moyennes et de total rInterval, gInterval, bInterval, rgbInterval = 0, 0, 0, 0 # initialisation des variables d'intervalles entre les couleurs @@ -79,14 +97,18 @@ class Additive_Noise: xmax = x+1 - pixels = [ pixelMap[y][xmin], pixelMap[y][xmax], pixelMap[ymin][x], pixelMap[ymax][x] ]; - for p in pixels: - if p != pixelMap[y][x]: - rMoy += p.r; - gMoy += p.g; - bMoy += p.b; - count += 1 + # on parcourt le carré de 3x3 + for j in pixelMap[ymin:ymax+1]: + for pix in j[xmin:xmax+1]: + # si le pixel n'est pas le pixel courant (mais ses voisins) et que sa couleur n'est pas trop éloignée des autres + if pix != pixelMap[y][x]: + # calcul de la moyenne autour du pixel + rMoy += pix.r; + gMoy += pix.g; + bMoy += pix.b; + count += 1 + # si il y a au moins un pixel autour (normalement tjs mais évite l'erreur div par zéro) if count > 0: @@ -105,4 +127,6 @@ class Additive_Noise: # si la couleur est trop "différente" (dépend du seuil) alors on remplace sa couleur par la moyenne des couleurs alentours if rgbInterval > seuil: - pixelMap[y][x].setRGB(rMoy, gMoy, bMoy); \ No newline at end of file + cleanMatrix[y][x].setRGB(rMoy, gMoy, bMoy); + + return cleanMatrix; \ No newline at end of file diff --git a/docs/shapes/impulse_removal.pdf b/docs/shapes/impulse_removal.pdf new file mode 100644 index 0000000000000000000000000000000000000000..781424f82de7081a3b1d3be87712057f25ca9c16 GIT binary patch literal 1468889 zcmag_Q*bU!7p{%Qwr$(CZQHhO+cUOpTTeJ+Co{I~b)}c;(jVZ#j?wlEHf&KVbSddU)+l`CIJh_BN935RIf?ta=TM 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Une problématique qui se pose est le débruitage des images obtenues. Nous proposons dans ce dossier d'aborder certaines techniques utilisées.\\ + \textbf{Image numérique}\par + Une image numérique de taille \begin{math}n_{x} \times n_{y}\end{math} est composée de pixels (le nombre de pixels est \begin{math}n = n_{x}.n_{y}\end{math}), et pour chaque pixel on dispose (selon les cas) :\\ + - une valeur entière comprise entre 0 et 255 (image en niveaux de gris codée en 8 bits)\\ + - trois valeurs entières comprises entre 0 et 255, correspondant aux trois canaux de couleur (RGB)\\ + - une valeur entière codée sur 12 ou 16 bits, ou même sur 1 seul, mais ces cas ne seront pas traités ici.\\ + \textbf{Bruit}\par + Dans une image numérique, les processus de dégradation peuvent provenir de plusieurs sources, et donner du bruit qui peut avoir différentes expressions mathématiques. Notons \emph{u} l'image d'origine (l'image <>) et \begin{math}u^{OBS}\end{math} l'image mesurée. Les exemples traités dans ce dossiers sont décrits ci-dessous.\par + \indent{ + - l'image \begin{math}u^{OBS}\end{math} est mesurée par des appareils électroniques qui sont perturbés par des fluctuations aléatoires, et ces perturbations viennent s'ajouter au signal mesuré. On parle de \emph{bruit addifit}, et on a \begin{math}u^{OBS} = u + n,\end{math} où \emph{n} est le bruit ajouté. + }\\ + \indent{ + - l'image \begin{math}u^{OBS}\end{math} comporte certains pixels où la valeur est aberrante (par exemple à cause d'un capteur défectueux, ou de défaut de transmission de l'information). Pour fixer les idées, on parle de \emph{bruit <>} lorsque certains pixels (au hasard) ont un niveau 0, et certains ont un niveau 255. + }\\ + \indent{ + - lorsque le processus de formation de l'image comporte des compteurs de photons ou des photomultiplicateurs, lerreur commise peut être proportionnelle au signal mesurée. On parle alors de \emph{bruit multiplicatif} et on a \begin{math}u^{OBS} = u.(1+n)\end{math}. + }\\ + + + \hrulefill\\ + Cette étude vise à améliorer les méthodes de \emph{nettoyage d'image}, et donc de développer un programme universel de débruitage, c'est-à-dire qu'il réduirait convenablement tout type de bruit, dépendant toutefois de paramètres adaptés à chacun. \begin{center} \emph{Avant d'essayer d'améliorer quelque chose, il faut d'abord le comprendre.} \end{center} Nous avons donc commencé par lire des études sur débruitage, la détection de contours, de formes, et la vectorisation d'images. \noindent Pour cela, nous avont étudié: \begin{description} \item[ - Le débruitage standard,]c'est à dire les méthodes déjà existantes. - \item[ - Le filtrage par convolution,]afin de mettre en valeur les contours et formes. + \item[ - Le filtrage par convolution,]afin de mettre en valeur les contours et formes et d'effectuer des "lissages". \item[ - La détection de formes,]afin d'effectuer un débruitage sans <>. \item[ - La détection de contours,]afin d'optimiser la détection de formes. \end{description} @@ -50,35 +71,37 @@ % =========================== % \section{Architecture et structure du programme} + \begin{figure}[!ht] + \centering + \includegraphics{fileTree.jpg} + \end{figure} + \begin{description} - \item \textbf{racine/} + \item \textbf{main.py} \hfill \\ + Programme principal, il affiche une interface (en console) permettant d'éxécuter une des opérations disponibles. + \item \textbf{tests.py} \hfill \\ + Contient les corps de toutes le opérations pouvant être choisies dans le programme principal. + \item \textbf{BMPFile.py} \hfill \\ + Contient les structures \emph{BMPFile}, \emph{BMPContent}, \emph{BMPHeader}, permettant d'encoder et de décoder un fichier BMP. Ainsi que la strucure \emph{RGBPixel} permettant de coder un pixel. + \item \textbf{Noise.py} \hfill \\ + Contient tout les traitements tels que les bruitages et débruitages, les filtres, et les détections de formes et contours. (il importe tout le dossier \emph{utility}) + \item \textbf{utility/} \begin{description} - \item \textbf{main.py} \hfill \\ - Programme principal, il affiche une interface (en console) permettant d'éxécuter une des opérations disponibles. - \item \textbf{tests.py} \hfill \\ - Contient les corps de toutes le opérations pouvant être choisies dans le programme principal. - \item \textbf{BMPFile.py} \hfill \\ - Contient les structures \emph{BMPFile}, \emph{BMPContent}, \emph{BMPHeader}, permettant d'encoder et de décoder un fichier BMP. Ainsi que la strucure \emph{RGBPixel} permettant de coder un pixel. - \item \textbf{Noise.py} \hfill \\ - Contient tout les traitements tels que les bruitages et débruitages, les filtres, et les détections de formes et contours. (il importe tout le dossier \emph{utility}) - \item \textbf{utility/} - \begin{description} - \item \textbf{SaltAndPepper\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{Poivre \& Sel}. - \item \textbf{Additive\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{additif}. - \item \textbf{Gaussian\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{gaussien}. - \item \textbf{Multiplicative\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multiplicatif}. - \item \textbf{Color\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multicolore}. - \item \textbf{Filter.py} \hfill \\ - Contient les méthodes de filtrage (standard et par convolution). - \item \textbf{Shape.py} \hfill \\ - Contient les méthodes de détection de formes et de contours - - \end{description} + \item \textbf{SaltAndPepper\_Noise.py} \hfill \\ + Contient les méthodes de bruitage et débruitage du bruit de type \emph{Poivre \& Sel}. + \item \textbf{Additive\_Noise.py} \hfill \\ + Contient les méthodes de bruitage et débruitage du bruit de type \emph{additif}. + \item \textbf{Gaussian\_Noise.py} \hfill \\ + Contient les méthodes de bruitage et débruitage du bruit de type \emph{gaussien}. + \item \textbf{Multiplicative\_Noise.py} \hfill \\ + Contient les méthodes de bruitage et débruitage du bruit de type \emph{multiplicatif}. + \item \textbf{Color\_Noise.py} \hfill \\ + Contient les méthodes de bruitage et débruitage du bruit de type \emph{multicolore}. + \item \textbf{Filter.py} \hfill \\ + Contient les méthodes de filtrage (standard et par convolution). + \item \textbf{Shape.py} \hfill \\ + Contient les méthodes de détection de formes et de contours + \end{description} \end{description} @@ -91,15 +114,15 @@ % === SECT 2 - VARIABLE === % % ========================= % \section{Variables globales} - Ces valeurs seront utilisées pour la majeure partie des calculs et correspondent à des notations générales à l'image ou aux autres notations utilisées.\\ - Soit \begin{math}(width, height)\in\N^2\end{math}, respectivement la largeur et la hauteur de l'image en pixels.\\ - Soit la matrice \begin{math}I_{height, width}\end{math} correspondant à la répartition des pixels dans l'image. Il est à noter qu'en accord avec le langage de programmation, les indices de la matrices prendront leurs valeurs dans [0;width-1] et [0;height-1] et non dans [1;width] et [1;height].\\ - Soit le couple \begin{math}(x,y)\in\N^2\end{math}, les coordonnés d'un pixel dans I, tel que \begin{math}x\in[0;width[,\ et\ y\in[0;height[\end{math}.\\ + Ces valeurs seront utilisées pour la majeure partie des calculs et correspondent à des notations générales à l'image ou aux autres notations utilisées.\\\\ + \noindent + Soit \begin{math}(n_x, n_y)\in\N^2\end{math}, respectivement la largeur et la hauteur de l'image en pixels.\\ + Soit la matrice \begin{math}I_{n_y, n_x}\end{math} correspondant à la répartition des pixels dans l'image.\\ + Soit le couple \begin{math}(x,y)\in\N^2\end{math}, les coordonnés d'un pixel dans I, tel que \begin{math}x\in[0;n_x[,\ et\ y\in[0;n_y[\end{math}.\\ Soit \begin{math}\vec{u}\end{math} le vecteur associé à une unité de contour, tel que \begin{math}||\vec{u}|| = 1,\ et\ sa\ direction\ d =\frac{n\pi}{2}[2\pi]\ rad,\ avec\ n\in\N\end{math}.\\ Soit \begin{math}K_n\end{math}, la matrice carré d'ordre \emph{n}. Par exemple \begin{math}K_3 = \begin{pmatrix}k_1&k_2&k_3\\k_4&k_5&k_6\\k_7&k_8&k_9\\\end{pmatrix}\end{math}\\\\ - \newpage @@ -233,29 +256,29 @@ % =================== % \section{Le bruit de type <>} \textbf{Définition graphique: }L'image est parsemée de pixels ayant une teinte extrême (blancs ou noirs) sans rapport avec leur contexte (voisinage).\\ - \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \emph{(x, y)} aléatoires qui sont en noir ou blanc (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ + \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \begin{math}(x, y)\end{math} aléatoires qui sont en noir ou blanc (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ \textbf{Débruitage: }L'image est débruitée en fonction de deux paramètres \emph{seuil} et \emph{borne}, tout deux compris dans \begin{math}[0;255]\end{math}. Chaque pixel est traité si et seulement si il est proche du noir ou du blanc, c'est-à-dire compris dans \begin{math}[0;borne]\end{math} pour le noir et dans \begin{math}[255-borne;255]\end{math} pour le blanc. Chaque pixel traité est comparé à la couleur moyenne de ses 8 voisins, si la différence avec sa couleur est supérieure à \emph{seuil}, le pixel se voit attribuer la couleur moyenne de ses voisins. Il est à noter que si borne vaut 255, tout les pixels seront traités, de même que si seuil vaut 255, tout les pixels traités se verront affecter la couleur moyenne de leurs voisins.\\\\ % effet 50% \begin{figure}[!ht] \centering - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{lena.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{lena.jpg} Image originale \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{SaltAndPepper_50.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/50.jpg} Bruitée, seuil de 0.5 \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepper_50.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50.jpg} Débruitée sans lissage \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepperLiss_50.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_liss.jpg} Débruitée avec lissage \end{minipage} \end{figure} @@ -263,28 +286,79 @@ % effet 50% (zoom) \begin{figure}[!ht] \centering - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{lenaZOOM.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{lenaZOOM.jpg} Image originale (zoom) \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{SaltAndPepper_50ZOOM.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/50_ZOOM.jpg} Bruitée, seuil de 0.5 (zoom) \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepper_50ZOOM.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_ZOOM.jpg} Débruitée sans lissage (zoom) \end{minipage} - \begin{minipage}[t]{3cm} + \begin{minipage}[t]{3.5cm} \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepperLiss_50ZOOM.jpg} + \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_liss_ZOOM.jpg} Débruitée avec lissage (zoom) \end{minipage} \end{figure} On remarque bien ici que les contours subissent un <> ce qui dégrade en majeure partie le rendu. Par contre, les couleurs sont convenablement restituées, de même pour les teintes et contrastes. Il suffirait de traiter l'image en prenant en compte les contours, ce qui permettrait de gagner en qualité. - + \newpage + + + % =================== % + % === SECT2 - ADD === % + % =================== % + \section{Le bruit additif} + \textbf{Définition graphique: }L'image est parsemée de pixels ayant une teinte plus ou moins incohérente avec leurs contexte (voisinage).\\ + \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \begin{math}(x, y)\end{math} aléatoires qui sont en noir ou blanc (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ + \textbf{Débruitage: }L'image est débruitée en fonction de deux paramètres \emph{seuil} et \emph{borne}, tout deux compris dans \begin{math}[0;255]\end{math}. Chaque pixel est traité si et seulement si il est proche du noir ou du blanc, c'est-à-dire compris dans \begin{math}[0;borne]\end{math} pour le noir et dans \begin{math}[255-borne;255]\end{math} pour le blanc. Chaque pixel traité est comparé à la couleur moyenne de ses 8 voisins, si la différence avec sa couleur est supérieure à \emph{seuil}, le pixel se voit attribuer la couleur moyenne de ses voisins. Il est à noter que si borne vaut 255, tout les pixels seront traités, de même que si seuil vaut 255, tout les pixels traités se verront affecter la couleur moyenne de leurs voisins.\\\\ + % effet 50% + \begin{figure}[!ht] + \centering + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{lena.jpg} + Image originale + \end{minipage} + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{Additive/algo1_10.jpg} + Bruitée, seuil de 0.1 + \end{minipage} + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10.jpg} + Débruitée, seuil de .35 + \end{minipage} + \end{figure} + + % effet 50% (zoom) + \begin{figure}[!ht] + \centering + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{lenaZOOM.jpg} + Image originale (zoom) + \end{minipage} + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{Additive/algo1_10_ZOOM.jpg} + Bruitée, seuil de 0.1 (zoom) + \end{minipage} + \begin{minipage}[t]{5cm} + \centering + \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10_ZOOM.jpg} + Débruitée, seuil de .35 (zoom) + \end{minipage} + \end{figure} + On remarque bien ici que les contours subissent un <> ce qui dégrade en majeure partie le rendu. Par contre, les couleurs sont convenablement restituées, de même pour les teintes et contrastes. 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-\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} - -% Définition des symboles -\newcommand{\N}{\mathbb{N}} % \N pour les entiers naturels -\newcommand{\R}{\mathbb{R}} % \R pour les réels -\newcommand\V[1]{\overrightarrow{#1}} % $\V{AB}$ pour les vecteurs - -\title{TRAITEMENT D'IMAGE\\Débruitage} -\author{Adrien MARQUÈS - Alexis HELSON } -\date{Septembre 2015} - -\begin{document} - - \maketitle - - % ======================== % - % === PART 1 - PRELUDE === % - % ======================== % - \part*{Prélude} - - % ====================== % - % === SECT 1 - INTRO === % - % ====================== % - \section{Introduction} - Cette étude vise à améliorer les méthodes de \emph{nettoyage d'image}, et donc de développer un programme universel de débruitage, c'est-à-dire qu'il réduirait convenablement tout type de bruit, dépendant toutefois de paramètres adaptés à chacun. - \begin{center} \emph{Avant d'essayer d'améliorer quelque chose, il faut d'abord le comprendre.} \end{center} - Nous avons donc commencé par lire des études sur débruitage, la détection de contours, de formes, et la vectorisation d'images. - \noindent Pour cela, nous avont étudié: - \begin{description} - \item[ - Le débruitage standard,]c'est à dire les méthodes déjà existantes. - \item[ - Le filtrage par convolution,]afin de mettre en valeur les contours et formes. - \item[ - La détection de formes,]afin d'effectuer un débruitage sans <>. - \item[ - La détection de contours,]afin d'optimiser la détection de formes. - \end{description} - Le programme a été développé en Python2 pour la simplicité et efficacité du langage. Le code est disponible sur Github à l'adresse suivante: - \begin{center} \textcolor{blue}{https://github.com/xdrm-brackets/bmp\_python} \end{center} - - - - - - % =========================== % - % === SECT 2 - ARCHI PROG === % - % =========================== % - \section{Architecture et structure du programme} - - \begin{description} - \item \textbf{racine/} - \begin{description} - \item \textbf{main.py} \hfill \\ - Programme principal, il affiche une interface (en console) permettant d'éxécuter une des opérations disponibles. - \item \textbf{tests.py} \hfill \\ - Contient les corps de toutes le opérations pouvant être choisies dans le programme principal. - \item \textbf{BMPFile.py} \hfill \\ - Contient les structures \emph{BMPFile}, \emph{BMPContent}, \emph{BMPHeader}, permettant d'encoder et de décoder un fichier BMP. Ainsi que la strucure \emph{RGBPixel} permettant de coder un pixel. - \item \textbf{Noise.py} \hfill \\ - Contient tout les traitements tels que les bruitages et débruitages, les filtres, et les détections de formes et contours. (il importe tout le dossier \emph{utility}) - \item \textbf{utility/} - \begin{description} - \item \textbf{SaltAndPepper\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{Poivre \& Sel}. - \item \textbf{Additive\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{additif}. - \item \textbf{Gaussian\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{gaussien}. - \item \textbf{Multiplicative\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multiplicatif}. - \item \textbf{Color\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multicolore}. - \item \textbf{Filter.py} \hfill \\ - Contient les méthodes de filtrage (standard et par convolution). - \item \textbf{Shape.py} \hfill \\ - Contient les méthodes de détection de formes et de contours - - \end{description} - \end{description} - \end{description} - - Le fichier \textbf{tests.py} utilise la structure \emph{BMPFile} afin de lire/écrire les fichiers ainsi que la structure \emph{Noise} qui contient toutes les méthodes de bruitage et de débruitage, les filtres et les détections de formes et de contours. Le fichier \emph{\textbf{Noise.py}} (qui contient la structure de même nom) appelle les différents fichiers contenus dans le dossier \textbf{utility}. Les fichiers contenus dans \textbf{utility} peuvent être:\\ - - Un fichier qui contient toutes les méthodes pour un type de bruit (bruitage et débruitage)\\ - - Le fichier \emph{Filters.py} qui contient toutes les méthodes de filtrage\\ - - Le fichier \emph{Shapes.py} qui contient toutes les méthodes de détection de formes et contours - - % ========================= % - % === SECT 2 - VARIABLE === % - % ========================= % - \section{Variables globales} - Ces valeurs seront utilisées pour la majeure partie des calculs et correspondent à des notations générales à l'image ou aux autres notations utilisées.\\ - Soit \begin{math}(width, height)\in\N^2\end{math}, respectivement la largeur et la hauteur de l'image en pixels.\\ - Soit la matrice \begin{math}I_{height, width}\end{math} correspondant à la répartition des pixels dans l'image. Il est à noter qu'en accord avec le langage de programmation, les indices de la matrices prendront leurs valeurs dans [0;width-1] et [0;height-1] et non dans [1;width] et [1;height].\\ - Soit le couple \begin{math}(x,y)\in\N^2\end{math}, les coordonnés d'un pixel dans I, tel que \begin{math}x\in[0;width[,\ et\ y\in[0;height[\end{math}.\\ - Soit \begin{math}\vec{u}\end{math} le vecteur associé à une unité de contour, tel que \begin{math}||\vec{u}|| = 1,\ et\ sa\ direction\ d =\frac{n\pi}{2}[2\pi]\ rad,\ avec\ n\in\N\end{math}.\\ - Soit \begin{math}K_n\end{math}, la matrice carré d'ordre \emph{n}. Par exemple \begin{math}K_3 = \begin{pmatrix}k_1&k_2&k_3\\k_4&k_5&k_6\\k_7&k_8&k_9\\\end{pmatrix}\end{math}\\\\ - - - - \newpage - - - - % ========================= % - % === PART 2 - ENCODAGE === % - % ========================= % - \part*{Encodage et décodage de fichiers} - - % =============================== % - % === SECT 1 - FONCTIONNEMENT === % - % =============================== % - \section{Fonctionnement global} - La lecture et l'écriture des fichiers se fait octet par octet, la structure \emph{BMPFile} permet de transformer les octets d'un fichier BMP en matrice de pixels, et inversement, de transformer une matrice de pixels en fichier BMP. Les types d'encodage des couleurs pris en charge sont 8 (nuance de gris), et 24 (rouge, vert, bleu) bits par pixels. Les pixels sont du type \emph{RGBPixel}, ce type contient les coordonnées du pixel (x, y), ainsi que sa couleur au format RGB (rouge, vert, bleu) chaque nuance prend sa valeur entre 0 et 255. - \\\par Le seul format pris en charge est le format BMP. Pour que le programme fonctionne avec d'autres formats (jpeg, png, gif, etc), il suffirait de récupérer une matrice de \emph{RGBPixel} à partir d'une image. Toutes les méthodes de traitement d'image du programme agissent uniquement sur cette matrice. Il serait ainsi possible de convertir une image vers un autre format. Le fichier BMP peut se décomposer en 3 parties: - \begin{description} - \item - Le \emph{header} donne des informations sur le fichier et son contenu. \hfill\\ - Sa taille est de 54 octets. - \item - La \emph{palette} donne des informations sur l'encodage des couleurs. \hfill\\ - Sa taille est variable, elle est donnée dans le header. - \item - Le \emph{bitmap} est la matrice de pixels, c'est le <> du fichier. \hfill\\ - Sa taille est variable, elle est donnée dans le header. - \end{description} - - - % ====================== % - % === SUBS 1 - HEADE === % - % ====================== % - \subsection{Le header} - Le header fournit des informations sur le fichier, sur l'image, sur la palette et sur les tailles et positions des différentes parties. Il est - \begin{center}\begin{tabular}{|c|c|l|} - \hline \textbf{Position (octets)} & \textbf{Taille} & \textbf{Libellé}\\ - \hline 0 & 2 octets & signature, toujours 4D42\\ - \hline 2 & 4 octets & taille du fichier (octets)\\ - \hline 6 & 2 octets & réservé (défaut: 0)\\ - \hline 8 & 2 octets & réservé (défaut: 0)\\ - \hline 10 & 4 octets & offset de la bitmap (octets)\\ - \hline 10 & 4 octets & taille du BITMAPINFOHEADER (octets)\\ - \hline 14 & 4 octets & largeur de l'image (pixels)\\ - \hline 18 & 4 octets & hauteur de l'image (pixels)\\ - \hline 22 & 2 octets & nombre de plans (défaut: 0001)\\ - \hline 26 & 2 octets & nombre de bits par pixels (1, 4, 8, ou 24)\\ - \hline 30 & 4 octets & type de compression (0=rien, 1=RLE-8, 2=RLE-4)\\ - \hline 34 & 4 octets & taille de l'image padding inclus (octets)\\ - \hline 38 & 4 octets & résolution horizontale (pixels)\\ - \hline 42 & 4 octets & résolution verticale (pixels)\\ - \hline 46 & 4 octets & nombre de couleurs ou 0\\ - \hline 50 & 4 octets & nombre de couleurs importantes ou 0\\ - - \hline - \end{tabular}\newline\newline Figure 1 - BMP Header\end{center} - - % ====================== % - % === SUBS 2 - PALET === % - % ====================== % - \subsection{La palette} - La palette permet de définir les différentes couleurs d'une image. Elle correspond à une liste de couleurs ou bien à un <> associé à un encodage spécifique. Elle dépend en premier lieu du nombre de bits par pixels. Cette valeur nous informe sur combien de bits est codée chaque couleur, les valeurs possibles sont 1, 4, 8, et 24. Le nombre de couleurs possibles dépend directement du \emph{bpp} (bits par pixels), plus il est grand, plus il y a de nuances. \begin{math}nbcouleurs = 2^{bpp}\end{math}. Les différents types de palette correspondant à un \emph{bpp} sont décrites ci-dessous. - - % ======================== % - % === S2SECT 1 - 8 BPP === % - % ======================== % - \subsubsection{Encodage en 8 bits par pixels} - Pour une image encodée en 8 bits par pixels, chaque pixel est codé sur 1 octet. Il y a donc une possibilité de \begin{math}2^8 = 256\end{math} couleurs. Ces 256 couleurs sont définies dans la \emph{palette} sur 4 octets chacunes. Les 3 premiers octets contiennent les couleurs au format RGB, et le dernier octet est à "0", c'est un octet de bourrage car la taille du fichier doit être multiple de 4. Par exemple si l'on veut que la couleur "0" corresponde à du rouge (R=255,G=0,B=0), et la couleur 1 à du rose (R=255,G=0,B=255), la palette commencera par: \\\\ - \begin{tabular}{|c|c|c|c|c|c|} - \hline - \textbf{rang}&\textbf{couleur}&\textbf{octet1}&\textbf{octet2}&\textbf{octet3}&\textbf{octet4}\\\hline - 0 & rouge & 255 & 0 & 0 & 0\\\hline - 1 & rose & 255 & 0 & 255 & 0\\\hline - .. & .. & .. & .. & .. & ..\\\hline - 255 & .. & .. & .. & .. & 0\\\hline - \end{tabular}\\\\ - Par défaut, nous utilisons uniquement le codage en 8 bits par pixels pour des nuances de gris, notre palette a donc comme valeurs:\\\\ - \begin{tabular}{|c|c|c|c|c|c|} - \hline - \textbf{rang}&\textbf{couleur}&\textbf{octetS}&\textbf{octet2}&\textbf{octet3}&\textbf{octet4}\\\hline - 0 & noir & 0 & 0 & 0 & 0\\\hline - 1 & noir & 1 & 1 & 1 & 0\\\hline - 2 & noir & 2 & 2 & 2 & 0\\\hline - .. & .. & .. & .. & .. & ..\\\hline - 254 & blanc & 254 & 254 & 254 & 0\\\hline - 255 & blanc & 255 & 255 & 255 & 0\\\hline - \end{tabular} * \begin{math}4\times256 = 1024\ octets\end{math} au total - - - % ========================= % - % === S2SECT 2 - 24 BPP === % - % ========================= % - \subsubsection{Encodage en 24 bits par pixels} - Pour une image encodée en 24 bits par pixels, chaque pixel est codé sur 3 octets au format BGR, ce format est équivalent au format RGB, mis à part l'ordre des octets qui est inversé, celà correspond à \begin{math}2^24 = 16.777.216\end{math} couleurs par pixels. La \emph{palette} associée est définie par défaut comme suit:\\\\ - \begin{tabular}{|c|c|c|c|} - \hline - \textbf{Position (octets)}&\textbf{Taille (octets)}&\textbf{Valeur entière}&\textbf{Valeur ASCII}\\\hline - 0 & 1 & 66 & "B" \\\hline - 1 & 1 & 71 & "G" \\\hline - 2 & 1 & 82 & "R" \\\hline - 3 & 1 & 115 & "s" \\\hline - 4 & 48 & 0 & inconnu\\\hline - 52 & 1 & 2 & inconnu\\\hline - 53 & 15 & 0 & inconnu \\\hline - \end{tabular} * 68 octets au total - - - % ====================== % - % === SUBS 3 - BITMA === % - % ====================== % - \subsection{Le bitmap} - - Le \emph{bitmap} du fichier (matrice de pixels) peut avoir plusieurs formats qui sont propres au nombre de bits par pixels et dépendent de la \emph{palette}. De plus la matrice est dans l'ordre inverse, c'est à dire que le premier pixel est en fait le dernier, il convient donc d'inverser les lignes et les colonnes. De manière générale les pixels sont enregistrés en une succession de lignes. Le nombre d'octets de chaque ligne doit être un multiple de 4, est donc ajouté en fin de ligne un <> composé de 0 à 3 octets ayant pour valeur zéro. - - - - - - - \newpage - - - - - - % ====================== % - % === PART 3 - BRUIT === % - % ====================== % - \part*{Bruits} - - - - - % =================== % - % === SECT1 - P&S === % - % =================== % - \section{Le bruit de type <>} - \textbf{Définition graphique: }L'image est parsemée de pixels ayant une teinte extrême (blancs ou noirs) sans rapport avec leur contexte (voisinage).\\ - \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \emph{(x, y)} aléatoires qui sont en noir ou blanc (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ - \textbf{Débruitage: }L'image est débruitée en fonction de deux paramètres \emph{seuil} et \emph{borne}, tout deux compris dans \begin{math}[0;255]\end{math}. Chaque pixel est traité si et seulement si il est proche du noir ou du blanc, c'est-à-dire compris dans \begin{math}[0;borne]\end{math} pour le noir et dans \begin{math}[255-borne;255]\end{math} pour le blanc. Chaque pixel traité est comparé à la couleur moyenne de ses 8 voisins, si la différence avec sa couleur est supérieure à \emph{seuil}, le pixel se voit attribuer la couleur moyenne de ses voisins. Il est à noter que si borne vaut 255, tout les pixels seront traités, de même que si seuil vaut 255, tout les pixels traités se verront affecter la couleur moyenne de leurs voisins.\\\\ - % effet 50% - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{lena.jpg} - Image originale - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{SaltAndPepper_50.jpg} - Bruitée, seuil de 0.5 - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepper_50.jpg} - Débruitée sans lissage - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepperLiss_50.jpg} - Débruitée avec lissage - \end{minipage} - \end{figure} - - % effet 50% (zoom) - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{lenaZOOM.jpg} - Image originale (zoom) - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{SaltAndPepper_50ZOOM.jpg} - Bruitée, seuil de 0.5 (zoom) - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepper_50ZOOM.jpg} - Débruitée sans lissage (zoom) - \end{minipage} - \begin{minipage}[t]{3cm} - \centering - \includegraphics[width=3cm,height=3cm]{UnSaltAndPepperLiss_50ZOOM.jpg} - Débruitée avec lissage (zoom) - \end{minipage} - \end{figure} - On remarque bien ici que les contours subissent un <> ce qui dégrade en majeure partie le rendu. Par contre, les couleurs sont convenablement restituées, de même pour les teintes et contrastes. Il suffirait de traiter l'image en prenant en compte les contours, ce qui permettrait de gagner en qualité. - - -\end{document} \ No newline at end of file diff --git a/dossier/Additive/algo1_10.jpg b/dossier/Additive/algo1_10.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e1560e4c7f5d4c89f061accb90867bc3d85a5165 GIT binary patch literal 204667 zcmbq(Wm6nX(Cy+H9D=*M1b26bg#{LO_u#?Z-QC^Y-4=ICf;&Nj=Y8&5_x^%=rfRCD zx~9MNobEH{-^RaR01O2gc^Lo{6cj-2zXkZW4Uqglr~fbf8wQ{tLIa@xON0sdHwqvH z0H9!D{!0n?e}IF9M?i#zL4pDx|Jwo}{%-`T(MGnI^G+6g;TC6Wp9hD_L|5c$K^U-p zo^;V#Gx@#SD6lD~lY z&#tSj7F(8o02|fo8y^3C`{au)?yS@UdJ45d5r~MHbN)Ldlw^ZxwZnCTvDni^rcTzg zgW`4Rfaat^kp$GhrepVa*SkzJTC%dO@CB3QmlSvDRMGq~)L#nuex;V#Jq;Y14%hWB z`cfpG*d%K2ggTarq?eP?W(f3Fy)$3jLu@#`TwNYwRZYA|@~&HkueHZz?Tn4XQCd%u%bNuQ}ZCJswqFQ1v$1 z=%dv=8#T3ZSXrO?10TQY(|Nwa+o2$WbVluQ-o}?ZDdI^8Y78&4O}>_>!zz=N36bit zRqe1?t zSh3r^*8Id!F&b6@a&bValQ(*G zu=~kd{j-88-u2O}+KWOG=9$2YFp1w0eX?IZxTvO2&EqRY?~p@tnXe~}uJ@b+VR}#g 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