.gitignore

@@ -0,0 +1,4 @@
+.vscode/
+imageEngine/images/
+imageEngine/test/
+imageEngine/__pycache__/

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.analysis.typeCheckingMode": "basic"
-}

imageEngine/filters/canny.py

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+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
+

imageEngine/filters/sobel.py

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+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
+

imageEngine/filters/usefull_func.py

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+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

imageEngine/main.py

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+

imageEngine/re_serie_7.py

@@ -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")
-"""