{"id":300,"date":"2020-12-10T00:47:34","date_gmt":"2020-12-10T00:47:34","guid":{"rendered":"http:\/\/med-space.org\/youssef-zaz-master\/?page_id=300"},"modified":"2020-12-14T01:21:38","modified_gmt":"2020-12-14T01:21:38","slug":"matlab-traitement-dimages","status":"publish","type":"page","link":"https:\/\/yzaz.net\/master\/matlab-traitement-dimages\/","title":{"rendered":"Matlab – Traitement d’images"},"content":{"rendered":"

Histogramme<\/strong>
\nCalcul et affichage d’histogramme d’une image:<\/p>\n

\r\nImage=imread('cameraman.tif');\r\n[l,c]=size(Image);\r\nH=zeros(256,1);\r\nfor i=1:l\r\n   for j=1:c\r\n\t H(Image(i,j)+1)= H(Image(i,j)+1)+1;    \r\n    end \r\n  end\r\nbar(H)\r\n<\/pre>\n
 <\/p>\n
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\nAvec la fonction pr\u00e9definie:<\/p>\n
\r\nimhist(Image)\r\n<\/pre>\n
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Egalisation d’histogramme <\/strong>
\nl’\u00e9galisation d’histogramme est une m\u00e9thode d’ajustement du contraste d’une image num\u00e9rique qui utilise l’histogramme. Elle consiste \u00e0 appliquer une transformation sur chaque pixel de l’image, et donc d’obtenir une nouvelle image \u00e0 partir d’une op\u00e9ration ind\u00e9pendante sur chacun des pixels. Cette transformation est construite \u00e0 partir de l’histogramme cumul\u00e9 de l’image de d\u00e9part.<\/p>\n
\u00c9tapes \u00e0 suivre
\n1. Calcul de l\u2019histogramme h(k) avec k \u0404[0; 255]
\n2. Histogramme cumul\u00e9
\n3. Transformation des niveaux de gris de l\u2019image<\/p>\n
En Matlab, la fonction pr\u00e9d\u00e9finie histeq<\/strong> permet d’obtenir l’histogramme \u00e9galis\u00e9 d’une image.<\/p>\n
\r\nI = imread('pout.tif');\r\nimshow(I)\r\nimhist(I)\r\nI2 = histeq(I); \r\nimshow(I2)\r\nsubplot ( 2 , 2 , 1 ) ; imshow( I) ; title ( 'Image originale' ) ;\r\nsubplot ( 2 , 2 , 3 ) ; imhist(I) ; title ( 'Histogramme original' ) ;\r\nsubplot ( 2 , 2 , 2 ) ; imshow(I2) ; title ( 'Image trait\u00e9e (\u00e9galisation d''histogramme)' ) ;\r\nsubplot ( 2 , 2 , 4 ) ; imhist(I2) ; title ( 'Histogramme \u00e9galis\u00e9' ) ;\r\n<\/pre>\n
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Ajout de bruit \u00e0 une image:<\/h2>\n
\r\n% Read the test Image\r\nmygrayimg = imread('cameraman.tif');\r\nsubplot(2,3,1); \r\nimshow(mygrayimg); \r\ntitle('Original Image');\r\n\r\n% Add Salt and pepper noise with noise density 0.001\r\nsalt = imnoise(mygrayimg,'salt &amp; pepper',0.001);\r\nsubplot(2,3,2);\r\nimshow(salt);\r\ntitle('Salt &amp; Pepper Image (bruit impulsionnel faible)');\r\n\r\n% Add Salt and pepper noise with noise density 0.03\r\nsalt = imnoise(mygrayimg,'salt &amp; pepper',0.03);\r\nsubplot(2,3,3);\r\nimshow(salt);\r\ntitle('Salt &amp; Pepper Image (bruit impulsionnel fort)');\r\n\r\n\r\n% Add Gaussian noise with mean 0 and variance 0.003 \r\ngau = imnoise(mygrayimg, 'gaussian', 0, 0.003);\r\nsubplot(2,3,4);\r\nimshow(gau);\r\ntitle('Gaussian Image- mean 0 and variance 0.003');\r\n\r\n% Add Gaussian noise with mean 0 and variance 0.02 \r\ngau = imnoise(mygrayimg, 'gaussian', 0, 0.02);\r\nsubplot(2,3,5);\r\nimshow(gau);\r\ntitle('Gaussian Image- mean 0 and variance 0.02');\r\n\r\n% Original Image plus sinusoidal noise    \r\nsubplot(2,3,6);\r\n[x y] = meshgrid(1:256,1:256);\r\nmysinusoidalnoise = 15 * sin(2*pi\/14*x+2*pi\/14*y);\r\nmynoiseimg1 = double(mygrayimg) + mysinusoidalnoise;\r\nimshow(mynoiseimg1,[]);\r\ntitle('Generated Sinusoidal noise');\r\n<\/pre>\n
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Filtre Moyen:<\/strong><\/p>\n
\r\norg=imread('peppers.png');\r\nm = imnoise(org, 'gaussian', 0, 0.03); % Ajout d'un bruit blanc Gaussien, de moyenne nulle\r\nfilterWindow3 = ones(3) \/ 9;\r\nfilteredImage3 = imfilter(m, filterWindow3);\r\nfilterWindow5 = ones(5) \/ 25;\r\nfilteredImage5 = imfilter(m, filterWindow5);\r\nfilterWindow9 = ones(9) \/ 81;\r\nfilteredImage9 = imfilter(m, filterWindow9);\r\nfilterWindow15 = ones(15) \/ 225;\r\nfilteredImage15 = imfilter(m, filterWindow15);\r\nsubplot ( 2 , 3 , 1 ) ; imshow(org) ; title ( 'Image originale' ) ;\r\nsubplot ( 2 , 3 , 2 ) ; imshow(m) ; title ( 'Image bruit\u00e9e (bruit Gaussien)' ) ;\r\nsubplot ( 2 , 3 , 3 ) ; imshow(filteredImage3) ; title ( 'Image filtr\u00e9e (Moyen 3x3)');\r\nsubplot ( 2 , 3 , 4 ) ; imshow(filteredImage5) ; title ( 'Image filtr\u00e9e (Moyen 5x5)');\r\nsubplot ( 2 , 3 , 5 ) ; imshow(filteredImage9) ; title ( 'Image filtr\u00e9e (Moyen 9x9)');\r\nsubplot ( 2 , 3 , 6 ) ; imshow(filteredImage15) ; title ( 'Image filtr\u00e9e (Moyen 15x15)');\r\n<\/pre>\n
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Filtre Gaussien:<\/strong><\/p>\n
\r\nI = imread('cameraman.tif');\r\nIblur = imgaussfilt(I,2); % Image filtr\u00e9e avec un filtre Gaussiin- Sigma = 2\r\nsubplot ( 1 , 2 , 1 ) ; imshow(I) ; title ( 'Image originale');\r\nsubplot ( 1 , 2 , 2 ) ; imshow(Iblur) ; title ( 'image filtr\u00e9e avec un filtre Gaussien');\r\n<\/pre>\n
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Filtre m\u00e9dian:<\/h2>\n
\r\nI = imread('eight.tif');\r\nJ = imnoise(I,'salt &amp; pepper',0.02);\r\nK = medfilt2(J);\r\nsubplot ( 1 , 3 , 1 ) ; imshow(I) ; title ( 'Image originale');\r\nsubplot ( 1 , 3 , 2 ) ; imshow(J) ; title ( 'Image bruit\u00e9e');\r\nsubplot ( 1 , 3 , 3 ) ; imshow(K) ; title ( 'image filtr\u00e9e avec un filtre m\u00e9dian');\r\n<\/pre>\n
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D\u00e9tection de contour:<\/h2>\n
\r\nI = imread('coins.png');\r\nBW1 = edge(I,'sobel');\r\nBW2 = edge(I,'prewitt');\r\nBW3 = edge(I,'canny');\r\nsubplot ( 2 , 2 , 1 ) ; imshow(I) ; title ( 'Image originale' ) ;\r\nsubplot ( 2 , 2 , 2 ) ; imshow(BW1) ; title ( 'Contour avec Sobel' ) ;\r\nsubplot ( 2 , 2 , 3 ) ; imshow(BW2) ; title ( 'Contour avec prewitt' ) ;\r\nsubplot ( 2 , 2 , 4 ) ; imshow(BW3) ; title ( 'Contour avec Canny' ) ;\r\n<\/pre>\n
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Filtre morphologique (\u00e9rosion):<\/h2>\n
\r\na=[\r\n0   0   0   1   0   0   0\r\n0   1   1   1   1   1   0\r\n0   1   0   1   1   1   0\r\n1   1   1   1   1   1   0\r\n0   0   1   1   1   1   0\r\n0   1   1   1   1   1   0\r\n0   0   0   0   0   1   0];\r\n \r\nst1=[1 1 1 \r\n     1 1 1\r\n     1 1 1];\r\nst2=[0 1 0 \r\n     1 1 1\r\n     0 1 0];\r\nst3=[0 1 0 \r\n     0 1 0\r\n     0 1 0]; \r\nst4=[0 0 0 \r\n     1 1 1\r\n     0 0 0]; \r\ni1= imerode(a,st1);\r\n i2= imerode(a,st2);\r\n i3= imerode(a,st3);\r\n i4= imerode(a,st4);\r\n subplot (2,3,1), imshow(a), title('original');\r\nsubplot (2,3,2), imshow(i1),  title ('erosion cube');\r\nsubplot (2,3,3), imshow(i2),  title ('erosion cercle');\r\nsubplot (2,3,4), imshow(i3),  title ('erosion |');\r\nsubplot (2,3,5), imshow(i4),  title ('erosion ---');\r\n<\/pre>\n
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Transform\u00e9e de Fourier Rapide sur l’image:<\/strong><\/h2>\n
\r\n[f, p]=uigetfile('*','selection de fichier');\r\nS=imread(fullfile(p,f));\r\nsubplot(1,3,1); imshow (S);  title ( 'Image originale');\r\nFS=fft2(double(S)); \r\nmodule=abs(fftshift(FS)); \r\nMax=max(max(max(abs(module))));\r\nsubplot(1,3,2), imshow(module *255 \/Max);colormap gray ; title ( 'Spectre d''amplitude');\r\nphase=angle(fftshift(FS)); \r\nsubplot(1,3,3); imshow(phase,[-pi,pi]); colormap gray; title ( 'Spectre de phase');\r\n<\/pre>\n
\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"
Histogramme Calcul et affichage d’histogramme d’une image:   Avec la fonction pr\u00e9definie: Egalisation d’histogramme l’\u00e9galisation d’histogramme est une m\u00e9thode d’ajustement du contraste d’une image num\u00e9rique […]<\/a><\/p>\n<\/div>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":221,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/pages\/300"}],"collection":[{"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/comments?post=300"}],"version-history":[{"count":14,"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/pages\/300\/revisions"}],"predecessor-version":[{"id":611,"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/pages\/300\/revisions\/611"}],"wp:attachment":[{"href":"https:\/\/yzaz.net\/master\/wp-json\/wp\/v2\/media?parent=300"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}