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imageEngine/invert_images/invert_cute.png create mode 100644 imageEngine/main.py delete mode 100644 imageEngine/my_images/7dd03be6-fc18-4c03-91e5-6d7da4b4a804.jpeg delete mode 100644 imageEngine/re_serie_7.py delete mode 100644 imageEngine/sobel/image_test.png diff --git a/imageEngine/filters/canny.py b/imageEngine/filters/canny.py new file mode 100644 index 0000000..f1469fc --- /dev/null +++ b/imageEngine/filters/canny.py @@ -0,0 +1,39 @@ +from usefull_func import * +from math import sqrt, atan2 + +#En_cours... +def filtre_canny(img): + + def norme_gradient(pixel1, pixel2): + color_x = pixel1[0] + color_y = pixel2[0] + + norm = round(sqrt(color_x**2 + color_y**2)) + norm = min(norm, 255) + + grad = atan2(color_y, color_x) + return norm, grad + + def liste_normGrad(im1, im2): + liste = [] + for j in range(len(im1)): + ligne = [] + for i in range(len(im1[0])): + normGrad = norme_gradient(im1[j][i], im2[j][i]) + ligne.append(normGrad) + liste.append(ligne) + return liste + + if not is_greyscale(img): + img = greyscale(img) + + mat_x = [[-1,0,1]] + mat_y = [[1],[0],[-1]] + + #lissage/suppression des bri + img_no_bruit = convolution_gauss(img) + Jx = convolution(img, mat_x) + Jy = convolution(img, mat_y) + normGrad = liste_normGrad(Jx, Jy) + #Suppresion des non-maximum + diff --git a/imageEngine/filters/sobel.py b/imageEngine/filters/sobel.py new file mode 100644 index 0000000..677ec51 --- /dev/null +++ b/imageEngine/filters/sobel.py @@ -0,0 +1,16 @@ +from usefull_func import * + + +def filtre_sobel(img): + + if not is_greyscale(img): + img = greyscale(img) + + mat_x = [[-1,0,1],[-2,0,2],[-1,0,1]] + mat_y = [[-1,-2,-1],[0,0,0],[1,2,1]] + Gx = convolution(img, mat_x) + Gy = convolution(img, mat_y) + + filtred_image = application_norme(Gx,Gy) + return filtred_image + diff --git a/imageEngine/filters/usefull_func.py b/imageEngine/filters/usefull_func.py new file mode 100644 index 0000000..af173a5 --- /dev/null +++ b/imageEngine/filters/usefull_func.py @@ -0,0 +1,105 @@ +from math import sqrt + +def greyscale(mat_img): + gray_img = [] + for ligne in mat_img: + lig = [] + for r,g,b in ligne: + v = int(r*0.2125 + g*0.7154 + b*0.0721) + lig.append((v,)*3) + gray_img.append(lig) + return gray_img + +def appliquer_convolution(img, mat, i, j): + somme = 0 + for y in range(len(mat)): + for x in range(len(mat[0])): + pixel_i = i - (len(mat[0]) // 2) + x + pixel_j = j - (len(mat) // 2) + y + pix = pixel(img, pixel_i, pixel_j) + somme += pix[0]*mat[y][x] + return min(max(somme,0), 255) + +def convolution(mat_img, mat): + return_img = [] + for j in range(len(mat_img)): + ligne = [] + for i in range(len(mat_img[0])): + val = appliquer_convolution(mat_img, mat, i, j) + ligne.append((val,)*3) + return_img.append(ligne) + return return_img + +def is_greyscale(img): + _greyscale = True + for ligne in img: + for r,g,b in ligne: + if not (r==g and g==b): + _greyscale = False + break + if not _greyscale: + break + return _greyscale + +def invert(img): + result_image = [] + for ligne in img: + result_ligne = [] + for r,g,b in ligne: + result_ligne.append((255-r, 255-g, 255-b)) + result_image.append(result_ligne) + return result_image + +def pixel(img, i, j, default=(0,0,0)): + #i la colone et j la ligne + if 0 <= i < len(img[0]) and 0 <= j < len(img): + return img[j][i] + else: + return default + +def reduction_bruit(img, mat, i, j): + somme = 0 + for y in range(len(mat)): + for x in range(len(mat[0])): + pixel_i = i - (len(mat[0]) // 2) + x + pixel_j = j - (len(mat) // 2) + y + pix = pixel(img, pixel_i, pixel_j) + somme += pix[0]*mat[y][x] + normalise = round(somme) + return normalise + +def convolution_gauss(mat_img): + mat_gauss = [ + [2/159, 4/159, 5/159, 4/159,2/159], + [4/159, 9/159,12/159, 9/159,4/159], + [5/159,12/159,15/159,12/159,5/159], + [4/159, 9/159,12/159, 9/159,4/159], + [2/159, 4/159, 5/159, 4/159,2/159] + ] + + return_img = [] + for j in range(len(mat_img)): + ligne = [] + for i in range(len(mat_img[0])): + val = reduction_bruit(mat_img, mat_gauss, i, j) + ligne.append((val,)*3) + return_img.append(ligne) + return return_img + +def calcul_norme(pixel1, pixel2): + valeur = pixel1[0]**2 + pixel2[0]**2 + norm = round(sqrt(valeur)) + norm = int(min(norm, 255)) + return norm + +def application_norme(im_x, im_y): + result_image = [] + for j in range(len(im_x)): + ligne = [] + for i in range(len(im_x[0])): + pixel1 = im_x[j][i] + pixel2 = im_y[j][i] + norme = calcul_norme(pixel1, pixel2) + ligne.append((norme,)*3) + result_image.append(ligne) + return result_image \ No newline at end of file diff --git a/imageEngine/gray_images/cute_gray.png b/imageEngine/gray_images/cute_gray.png deleted file mode 100644 index a9f57ab1848834fadf61726f94c809c59bf8f4ed..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1012293 zcmce-_dnI|A3y#)%Q@CDGmgC}*(>uH3E86}aYQn*C7I{gGLlisI!0!NkSH7pDYCP& zqU@EuKTo~h-|y}77kvD}&CR$T*Y&vWkNf@pcwDi@hF7R5Fcbg)Q0wYwnF0X9764G5 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z*B9R90+8b6EU+b7H=zRFXOZ3i0LG$c(T=@2M}PVrro9dYg1o@!QGu7ECwRGpJd-7| zB+By*b!ePFM%mdj+vt5(b&Jw4k1*WcKS1=_Ydzr@Z4*0{)98H}a^=Q4_3P?($A9_} z^y}%T62jXH#K2%Lct&(}h^njIi*3j1zpa-pK94Zm+vyLa9)_6n7X3Q?40g+%V-p^| XeNNc_0P1}@{T9=hvW7qV{{a8lI-@iD diff --git a/imageEngine/re_serie_7.py b/imageEngine/re_serie_7.py deleted file mode 100644 index 6f6f3e4..0000000 --- a/imageEngine/re_serie_7.py +++ /dev/null @@ -1,294 +0,0 @@ -import umage as um -from math import sqrt, atan2, sin, cos, pi - -def greyscale(mat_img): - gray_img = [] - for ligne in mat_img: - lig = [] - for r,g,b in ligne: - v = int(r*0.2125 + g*0.7154 + b*0.0721) - lig.append((v,)*3) - gray_img.append(lig) - return gray_img - -def convolution(mat_img, mat): - return_img = [] - for j in range(len(mat_img)): - ligne = [] - for i in range(len(mat_img[0])): - val = appliquer_convolution(mat_img, mat, i, j) - ligne.append((val,)*3) - return_img.append(ligne) - return return_img - -def filtre_sobel(img): - - def calcul_norme(pixel1, pixel2): - valeur = pixel1[0]**2 + pixel2[0]**2 - norm = round(sqrt(valeur)) - norm = int(min(norm, 255)) - return norm - - def application_norme(im_x, im_y): - result_image = [] - for j in range(len(im_x)): - ligne = [] - for i in range(len(im_x[0])): - pixel1 = im_x[j][i] - pixel2 = im_y[j][i] - norme = calcul_norme(pixel1, pixel2) - ligne.append((norme,)*3) - result_image.append(ligne) - return result_image - - if not is_greyscale(img): - img = greyscale(img) - - mat_x = [[-1,0,1],[-2,0,2],[-1,0,1]] - mat_y = [[-1,-2,-1],[0,0,0],[1,2,1]] - Gx = convolution(img, mat_x) - Gy = convolution(img, mat_y) - - filtred_image = application_norme(Gx,Gy) - return filtred_image - - - -######################################################################### -########################Exercices Supplémentaires######################## -######################################################################### - -def is_greyscale(img): - _greyscale = True - for ligne in img: - for r,g,b in ligne: - if not (r==g and g==b): - _greyscale = False - break - if not _greyscale: - break - return _greyscale - -def invert(img): - result_image = [] - for ligne in img: - result_ligne = [] - for r,g,b in ligne: - result_ligne.append((255-r, 255-g, 255-b)) - result_image.append(result_ligne) - return result_image - -def pixel(img, i, j, default=(0,0,0)): - #i la colone et j la ligne - if 0 <= i < len(img[0]) and 0 <= j < len(img): - return img[j][i] - else: - return default - -def appliquer_convolution(img, mat, i, j): - somme = 0 - for y in range(len(mat)): - for x in range(len(mat[0])): - pixel_i = i - (len(mat[0]) // 2) + x - pixel_j = j - (len(mat) // 2) + y - pix = pixel(img, pixel_i, pixel_j) - somme += pix[0]*mat[y][x] - return min(max(somme,0), 255) - - - -###################################################################### -########################Exercices personnelles######################## -###################################################################### -def convolution_gauss(mat_img): - mat_gauss = [ - [2/159, 4/159, 5/159, 4/159,2/159], - [4/159, 9/159,12/159, 9/159,4/159], - [5/159,12/159,15/159,12/159,5/159], - [4/159, 9/159,12/159, 9/159,4/159], - [2/159, 4/159, 5/159, 4/159,2/159] - ] - - return_img = [] - for j in range(len(mat_img)): - ligne = [] - for i in range(len(mat_img[0])): - val = reduction_bruit(mat_img, mat_gauss, i, j) - ligne.append((val,)*3) - return_img.append(ligne) - return return_img - -def reduction_bruit(img, mat, i, j): - somme = 0 - for y in range(len(mat)): - for x in range(len(mat[0])): - pixel_i = i - (len(mat[0]) // 2) + x - pixel_j = j - (len(mat) // 2) + y - pix = pixel(img, pixel_i, pixel_j) - somme += pix[0]*mat[y][x] - normalise = round(somme) - return normalise - -def filtre_canny(img): - - def norme_gradient(pixel1, pixel2): - color_x = pixel1[0] - color_y = pixel2[0] - - norm = round(sqrt(color_x**2 + color_y**2)) - norm = min(norm, 255) - - grad = atan2(color_y, color_x) - return norm, grad - - def liste_normGrad(im1, im2): - liste = [] - for j in range(len(im1)): - ligne = [] - for i in range(len(im1[0])): - normGrad = norme_gradient(im1[j][i], im2[j][i]) - ligne.append(normGrad) - liste.append(ligne) - return liste - - if not is_greyscale(img): - img = greyscale(img) - - mat_x = [[-1,0,1]] - mat_y = [[1],[0],[-1]] - - #lissage/suppression des bri - img_no_bruit = convolution_gauss(img) - Jx = convolution(img, mat_x) - Jy = convolution(img, mat_y) - normGrad = liste_normGrad(Jx, Jy) - - #Suppresion des non-maximum - - -#temp -def norme_gradient(pixel1, pixel2): - color_x = pixel1[0] - color_y = pixel2[0] - - norm = round(sqrt(color_x**2 + color_y**2)) - norm = min(norm, 255) - - grad = atan2(color_y, color_x) - return norm, grad - -#temp -def liste_normGrad(im1, im2): - liste = [] - for j in range(len(im1)): - ligne = [] - for i in range(len(im1[0])): - normGrad = norme_gradient(im1[j][i], im2[j][i]) - ligne.append(normGrad) - liste.append(ligne) - return liste - -mat_x = [[-1,0,1]] -mat_y = [[1],[0],[-1]] -#temp -#lissage -img = um.load("imageEngine\\images\\valve.png") -img = convolution_gauss(img) -Jx = convolution(img, mat_x) -Jy = convolution(img, mat_y) -normGrad = liste_normGrad(Jx, Jy) -########### - - - -def find_neighbord_norm(mat, i, j, rad): - x = 0 - y = 0 - if sin(pi/8) <= abs(sin(rad)): - y = 1 - if cos(3*pi/8)>abs(cos(rad)): - x = 1 - - norm_pix1 = -1 - norm_pix2 = -1 - if 0 <= j-y < len(mat): - if 0 <= i-x < len(mat[0]): - norm_pix1 = mat[j-y][i-x][0] - if 0 <= j+y < len(mat): - if 0 <= i+x < len(mat[0]): - norm_pix2 = mat[j+y][i+x][0] - - return norm_pix1, norm_pix2 - -def delete_pixel(mat_img, mat): - img_to_return = [] - for j in range(len(mat)): - ligne = [] - for i in range(len(mat[0])): - rad = mat[j][i][1] - norms = find_neighbord_norm(mat, i, j, rad) - if rad < norms[0] or rad < norms[1]: - ligne.append((0,)*3) - else: - ligne.append(mat_img[j][i]) - img_to_return.append(ligne) - return img_to_return - - -""" -def hysteresis(mat_img, mat_norm, Th): - Tl = Th / 2 - mat_img = yesOrNo(mat_img, Th, Tl) - result_image = [] - for j in range(len(mat_img)): - ligne = [] - for i in range(len(mat_img[0])): - rad = mat_norm[j][i][1] - color1, color2 = find_neighbord_pixel(mat_img, i, j, rad+(pi/2)) - if color1 == 255 or color2 == 255: - ligne.append((255,)*3) - else: - ligne.append((0,)*3) - result_image.append(ligne) - return result_image - -def find_neighbord_pixel(mat_image, i, j, rad): - x = 0 - y = 0 - if sin(pi/8) <= abs(sin(rad)): - y = 1 - if cos(3*pi/8)>abs(cos(rad)): - x = 1 - - color_pix1 = 0 - color_pix2 = 0 - if 0 <= j-y < len(mat_image): - if 0 <= i-x < len(mat_image[0]): - color_pix1 = mat_image[j-y][i-x][0] - if 0 <= j+y < len(mat_image): - if 0 <= i+x < len(mat_image[0]): - color_pix2 = mat_image[j+y][i+x][0] - - return color_pix1, color_pix2 - -def yesOrNo(mat_img, Th, Tl): - result_image = [] - for j in range(len(mat_img)): - ligne = [] - for i in range(len(mat_img[0])): - pix = mat_img[j][i] - if Th <= pix[0]: - ligne.append((255,)*3) - elif pix[0] < Tl: - ligne.append((0,)*3) - else: - ligne.append(pix) - result_image.append(ligne) - return result_image - - -zt_no_maxima = delete_pixel(img, normGrad) -zt_hysteresis = hysteresis(zt_no_maxima, normGrad, 200) - -um.save(zt_hysteresis, "imageEngine\\test\\valve", "png") -""" \ No newline at end of file diff --git a/imageEngine/sobel/image_test.png b/imageEngine/sobel/image_test.png deleted file mode 100644 index b16cde2847ace1cb30ddc41719a7c1fe455d4a02..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 401120 zcmXtgbyU>d_ch%u;7FIW2uKV#bazTgmq@pCcXxLPh?KN+gGhIGiAZ<-?(==u>mM%H z2*YRYJ+aT;`w*_ID20tdf&m8yhb<#5t_lZ-;0Ff>zmA3gUin!oR|^LZ2PY#gqVAUU z_ff{$RNHk8e(icwqBqi=0)oYvF0Y}er>AFGzsOA>0!Gf~jyvtDxf-p%&r3?D&w|g#=l}n&=gaT{#t51@ zmqJEIvOT-UQ7^&R0>)(7#|mG;%n{=Ya=jKmQGW3b3 zZmk;SBj&Y3rayGqQiqE<=(80|rVcjjoAj%01D*M^rRgLKRUjd^UOrqTuS^6VFQzoB 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