From f8e933cbdc76fa3f329c2fdcb3dd5e9cacc23e23 Mon Sep 17 00:00:00 2001
From: Andy K <donfackandy@gmail.com>
Date: Sat, 5 Nov 2022 20:48:53 +0100
Subject: [PATCH] Add re_serie_7

---
 re_serie_7.py | 164 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 164 insertions(+)
 create mode 100644 re_serie_7.py

diff --git a/re_serie_7.py b/re_serie_7.py
new file mode 100644
index 0000000..7730459
--- /dev/null
+++ b/re_serie_7.py
@@ -0,0 +1,164 @@
+import umage as um
+from math import sqrt, atan2
+
+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):
+    return_img = []
+    for j in range(len(mat_img)):
+        ligne = []
+        for i in range(len(mat_img[0])):
+            val = reduction_bruit(mat_img, mat, 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 = int(round(somme / (1/159)))
+    return min(max(normalise,0), 255)
+
+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 = int(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_gauss = [
+        [2, 4, 5, 4,2],
+        [4, 9,12, 9,4],
+        [5,12,15,12,5],
+        [4, 9,12, 9,4],
+        [2, 4, 5, 4,2]
+    ]
+    mat_x = [[-1,0,1]]
+    mat_y = [[1],[0],[-1]]
+
+    #lissage
+    img = convolution_gauss(img, mat_gauss)
+    Jx = convolution(img, mat_x)
+    Jy = convolution(img, mat_y)
+    normGrad = liste_normGrad(Jx, Jy)
+