src/methods/Yosra_Jelassi/new_reconstruction.py

+"""A file containing a (pretty useless) reconstruction.
+It serves as example of how the project works.
+This file should NOT be modified.
+"""
+
+
+import numpy as np
+from scipy.signal import convolve2d
+
+from src.forward_model import CFA
+
+
+def new_interpolation(op: CFA, y: np.ndarray) -> np.ndarray:
+    """Performs a simple interpolation of the lost pixels.
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    z = op.adjoint(y)
+    if op.cfa == 'bayer':
+        demosaicked_red = np.empty_like(z[:,:,0])
+
+        weights_red = np.array([[0, 1],                      
+                                [0, 0]])   
+
+        for i in range(1, z.shape[0] - 1,2):
+            for j in range(1, z.shape[1] - 1,2):
+                demosaicked_red[i-1:i+1, j-1:j+1] = np.sum(z[i-1:i+1, j-1:j+1,0] * weights_red)
+   
+        demosaicked_green = np.empty_like(z[:,:,1])
+
+        weights_green = np.array([[1, 0],
+                                  [0, 1]]) 
+    
+        for i in range(1, z.shape[0] - 1,2):
+            for j in range(1, z.shape[1] - 1,2):
+                demosaicked_green[i-1:i+1, j-1:j+1] = np.sum(z[i-1:i+1, j-1:j+1,1] * weights_green)/2
+
+        weights_blue = np.array([[0, 0],                      
+                                 [1, 0]])  
+        demosaicked_blue = np.empty_like(z[:,:,2])
+
+        for i in range(1, z.shape[0] - 1,2):
+            for j in range(1, z.shape[1] - 1,2):
+                demosaicked_blue[i-1:i+1, j-1:j+1] = np.sum(z[i-1:i+1, j-1:j+1,2] * weights_blue)
+
+        demosaicked_image = np.dstack((demosaicked_red, demosaicked_green, demosaicked_blue))
+    else:
+        demosaicked_red = np.empty_like(z[:,:,0])
+
+        weights_red = np.array([[0, 0, 1, 1 ],                      
+                                [0, 0, 1, 1 ],
+                                [0, 0, 0, 0 ],
+                                [0, 0, 0, 0 ]])   
+
+        for i in range(2, z.shape[0] - 2, 4):
+            for j in range(2, z.shape[1] - 2, 4):
+                demosaicked_red[i-2:i+2, j-2:j+2] = np.sum(z[i-2:i+2, j-2:j+2,0] * weights_red)/4
+   
+        demosaicked_green = np.empty_like(z[:,:,1])
+
+        weights_green = np.array([[1, 1, 0, 0 ],                      
+                                  [1, 1, 0, 0 ],
+                                  [0, 0, 1, 1 ],
+                                  [0, 0, 1, 1 ]])   
+    
+        for i in range(2, z.shape[0] - 2, 4):
+            for j in range(2, z.shape[1] - 2, 4):
+                demosaicked_green[i-2:i+2, j-2:j+2] = np.sum(z[i-2:i+2, j-2:j+2,1] * weights_green)/8
+
+        weights_blue = np.array([[0, 0, 0, 0 ],                      
+                                 [0, 0, 0, 0 ],
+                                 [1, 1, 0, 0 ],
+                                 [1, 1, 0, 0 ]])  
+        demosaicked_blue = np.empty_like(z[:,:,2])
+
+        for i in range(2, z.shape[0] - 2, 4):
+            for j in range(2, z.shape[1] - 2, 4):
+                demosaicked_blue[i-2:i+2, j-2:j+2] = np.sum(z[i-2:i+2, j-2:j+2,2] * weights_blue)/4
+
+        demosaicked_image = np.dstack((demosaicked_red, demosaicked_green, demosaicked_blue))
+        
+    return demosaicked_image
+
+
+
+def extract_padded(M, size, i, j):
+    N_i, N_j = M.shape
+    res = np.zeros((size, size))
+    middle_size = int((size - 1) / 2)
+
+    for ii in range(- middle_size, middle_size + 1):
+        for jj in range(- middle_size, middle_size + 1):
+            if i + ii >= 0 and i + ii < N_i and j + jj >= 0 and j + jj < N_j:
+                res[middle_size + ii, middle_size + jj] = M[i + ii, j + jj]
+
+    return res
+
+
+def varying_kernel_convolution(M, K_list):
+    N_i, N_j = M.shape
+    res = np.zeros_like(M)
+
+    for i in range(N_i):
+        for j in range(N_j):
+            res[i, j] = np.sum(extract_padded(M, K_list[4 * (i % 4) + j % 4].shape[0], i, j) * K_list[4 * (i % 4) + j % 4])
+
+    np.clip(res, 0, 1, res)
+
+    return res
+
+
+K_identity = np.zeros((5, 5))
+K_identity[2, 2] = 1
+
+K_red_0 = np.zeros((5, 5))
+K_red_0[2, :] = np.array([-3, 13, 0, 0, 2]) / 12
+
+K_red_1 = np.zeros((5, 5))
+K_red_1[2, :] = np.array([2, 0, 0, 13, -3]) / 12
+
+K_red_8 = np.zeros((5, 5))
+K_red_8[:2, :2] = np.array([[-1, -1], [-1, 9]]) / 6
+
+K_red_9 = np.zeros((5, 5))
+K_red_9[:2, 3:] = np.array([[-1, -1], [9, -1]]) / 6
+
+K_red_10 = np.zeros((5, 5))
+K_red_10[:, 2] = np.array([-3, 13, 0, 0, 2]) / 12
+
+K_red_12 = np.zeros((5, 5))
+K_red_12[3:, :2] = np.array([[-1, 9], [-1, -1]]) / 6
+
+K_red_13 = np.zeros((5, 5))
+K_red_13[3:, 3:] = np.array([[9, -1], [-1, -1]]) / 6
+
+K_red_14 = np.zeros((5, 5))
+K_red_14[:, 2] = np.array([2, 0, 0, 13, -3]) / 12
+
+K_list_red = [K_red_0, K_red_1, K_identity, K_identity, K_red_0, K_red_1, K_identity, K_identity, K_red_8, K_red_9, K_red_10, K_red_10, K_red_12, K_red_13, K_red_14, K_red_14]
+
+
+K_green_2 = np.zeros((5, 5))
+K_green_2[2, :] = [-3, 13, 0, 0, 2]
+K_green_2[:, 2] = [-3, 13, 0, 0, 2]
+K_green_2 = K_green_2 / 24
+
+K_green_3 = np.zeros((5, 5))
+K_green_3[2, :] = [2, 0, 0, 13, -3]
+K_green_3[:, 2] = [-3, 13, 0, 0, 2]
+K_green_3 = K_green_3 / 24
+
+K_green_6 = np.zeros((5, 5))
+K_green_6[2, :] = [-3, 13, 0, 0, 2]
+K_green_6[:, 2] = [2, 0, 0, 13, -3]
+K_green_6 = K_green_6 / 24
+
+K_green_7 = np.zeros((5, 5))
+K_green_7[2, :] = [2, 0, 0, 13, -3]
+K_green_7[:, 2] = [2, 0, 0, 13, -3]
+K_green_7 = K_green_7 / 24
+
+K_list_green = [K_identity, K_identity, K_green_2, K_green_3, K_identity, K_identity, K_green_6, K_green_7, K_green_2, K_green_3, K_identity, K_identity, K_green_6, K_green_7, K_identity, K_identity]
+
+
+K_list_blue = [K_red_10, K_red_10, K_red_8, K_red_9, K_red_14, K_red_14, K_red_12, K_red_13, K_identity, K_identity, K_red_0, K_red_1, K_identity, K_identity, K_red_0, K_red_1]
+
+
+
+ker_bayer_red_blue = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 4
+
+ker_bayer_green = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]]) / 4
+
+
+

src/methods/Yosra_Jelassi/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.Yosra_Jelassi.new_reconstruction import new_interpolation
+
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """Performs demosaicking on y.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    # Performing the reconstruction.
+    # TODO
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA(cfa, input_shape)
+    res = new_interpolation(op, y)
+
+    return res
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/babacar/demoisaicing_fct.py

+"""A file containing a (pretty useless) reconstruction.
+It serves as example of how the project works.
+This file should NOT be modified.
+"""
+
+
+import numpy as np
+from scipy.signal import convolve2d
+
+from src.forward_model import CFA
+
+
+def High_Quality_Linear_Interpolation(op: CFA, y: np.ndarray) -> np.ndarray:
+    """Performs a High-Quality Linear Interpolation of the lost pixels.
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    z = op.adjoint(y)
+    #operation for the bayer pattern
+    if op.cfa == 'bayer':
+        res = np.empty(op.input_shape)
+        mask = op.mask
+        R  = 0
+        G = 1
+        B = 2
+
+        y_padded = np.pad(y,2, 'constant', constant_values=0)
+
+        for i in range(2,y_padded.shape[0]-2):
+            for j in range(2,y_padded.shape[1]-2):
+                for c in range(op.input_shape[2]):
+                    i_real = i-2
+                    j_real = j-2
+                    if (c==R and mask[i_real,j_real,R] == 1) or (c==G and mask[i_real,j_real,G] == 1)or (c==B and mask[i_real,j_real,B] == 1): #no need to find the pixel value
+                        res[i_real,j_real,c] = y_padded[i,j]
+                    elif (c == G) and (mask[i_real,j_real,B] == 1 or mask[i_real,j_real,R] == 1): #green channel and pixel value = B or R
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_g_r_and_b, mode='valid'))
+
+                    elif (c == R or c== B )and (mask[i_real,j_real,G] == 1) and (mask[i_real,j_real-1,c] == 0): #red or blue channel and pixel value = G and left pixel isn't in the recovered layer
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_and_b_g_brow_rcol, mode='valid'))
+
+                    elif (c == R or c == B) and (mask[i_real,j_real,G] == 1) and (mask[i_real-1,j_real,c] == 0): #red or blue channel and pixel value = G and up pixel isn't in the recovered layer
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_and_b_g_rrow_bcol, mode='valid'))
+
+                    elif (c == R and mask[i_real,j_real,B] == 1) or (c == B and mask[i_real,j_real,R] == 1) : #red channel and pixel value = B or inverse
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_b, mode='valid'))
+                    else : print("error")
+
+    #operation for the quad bayer pattern
+    else:
+        res = np.empty(op.input_shape)
+        mask = op.mask
+        R  = 0
+        G = 1
+        B = 2
+
+        y_padded = np.pad(y,4, 'constant', constant_values=0)
+
+        for i in range(4,y_padded.shape[0]-4, 2):
+            for j in range(4,y_padded.shape[1]-4, 2):
+                for c in range(op.input_shape[2]):
+                    i_real = i-4
+                    j_real = j-4
+                    if (c==R and mask[i_real,j_real,R] == 1) or (c==G and mask[i_real,j_real,G] == 1)or (c==B and mask[i_real,j_real,B] == 1): #no need to find the pixel value
+                        res[i_real:i_real+2,j_real:j_real+2,c] = y_padded[i:i+2,j:j+2]
+                        
+                    elif (c == G) and (mask[i_real,j_real,B] == 1 or mask[i_real,j_real,R] == 1): #green channel and pixel value = B or R
+                        # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_g_r_and_b, mode='valid'))
+
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_g_r_and_b, mode='valid'))
+                        res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_g_r_and_b, mode='valid'))
+                        res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_g_r_and_b, mode='valid'))
+                        res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_g_r_and_b, mode='valid'))
+
+                    elif (c == R or c== B )and (mask[i_real,j_real,G] == 1) and (mask[i_real,j_real-2,c] == 0): #red or blue channel and pixel value = G and left pixel isn't in the recovered layer
+                        # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+
+
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+                        res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+                        res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+                        res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+
+                    elif (c == R or c == B) and (mask[i_real,j_real,G] == 1) and (mask[i_real-2,j_real,c] == 0): #red or blue channel and pixel value = G and up pixel isn't in the recovered layer
+                        # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+                        res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+                        res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+                        res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+                        
+
+                    elif (c == R and mask[i_real,j_real,B] == 1) or (c == B and mask[i_real,j_real,R] == 1) : #red channel and pixel value = B or inverse
+                        # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_b, mode='valid'))
+
+                        res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_b, mode='valid'))
+                        res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_b, mode='valid'))
+                        res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_b, mode='valid'))
+                        res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_b, mode='valid'))
+                    else : print("error")
+    np.clip(res, 0, 1, res)
+    return res
+
+
+# Bayer masks
+
+hq_g_r_and_b = np.array([[0, 0, -1, 0, 0], [0, 0, 2, 0, 0], [-1, 2, 4, 2, -1], [0, 0, 2, 0, 0], [0, 0, -1, 0, 0]]) / 8
+
+hq_r_and_b_g_rrow_bcol = np.array([[0, 0, 1/2, 0, 0], [0, -1, 0, -1, 0], [-1, 4, 5, 4, -1], [0, -1, 0, -1, 0], [0, 0, 1/2, 0, 0]]) / 8
+
+hq_r_and_b_g_brow_rcol = hq_r_and_b_g_rrow_bcol.T
+
+hq_r_b = np.array([[0, 0, -3/2, 0, 0], [0, 2, 0, 2, 0], [-3/2, 0, 6, 0, -3/2], [0, 2, 0, 2, 0], [0, 0, -3/2, 0, 0]]) / 8
+
+
+# Quad bayer masks
+
+## Calcul the value of 4 pixels with one mask, then the value is apply for the 4 pixels
+
+
+hq_quad_g_r_and_b = np.array([[0,0, 0,0, -1,-1, 0,0, 0,0],[0,0, 0,0, -1,-1, 0,0, 0,0],
+                              [0,0,0, 0,2, 2, 0,0, 0,0],[0,0,0, 0,2, 2, 0,0, 0,0],
+                              [-1,-1, 2,2, 4,4, 2,2, -1,-1],[-1,-1, 2,2, 4,4, 2,2, -1,-1],
+                              [0,0, 0,0, 2,2, 0,0, 0,0],[0,0, 0,0, 2,2, 0,0, 0,0],
+                              [0,0, 0,0, -1,-1, 0,0, 0,0],[0,0, 0,0, -1,-1, 0,0, 0,0]]) / 16
+
+hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 1/2,1/2, 0,0, 0,0],[0,0, 0,0, 1/2,1/2, 0,0, 0,0],
+                                        [0,0, -1,-1, 0,0, -1,-1, 0,0],[0,0, -1,-1, 0,0, -1,-1, 0,0],
+                                        [-1,-1, 4,4, 5,5, 4,4, -1,-1],[-1,-1, 4,4, 5,5, 4,4, -1,-1],
+                                        [0,0, -1,-1, 0,0, -1,-1, 0,0],[0,0, -1,-1, 0,0, -1,-1, 0,0],
+                                        [0,0, 0,0, 1/2,1/2, 0,0, 0,0],[0,0, 0,0, 1/2,1/2, 0,0, 0,0]]) / 16
+
+hq_quad_r_and_b_g_brow_rcol = hq_quad_r_and_b_g_rrow_bcol.T
+
+hq_quad_r_b = np.array([[0,0, 0,0, -3/2,-3/2, 0,0, 0,0],[0,0, 0,0, -3/2,-3/2, 0,0, 0,0],
+                        [0,0, 2,2, 0,0, 2,2, 0,0],[0,0, 2,2, 0,0, 2,2, 0,0],
+                        [-3/2,-3/2, 0,0, 6,6, 0,0, -3/2,-3/2],[-3/2,-3/2, 0,0, 6,6, 0,0, -3/2,-3/2],
+                        [0,0, 2,2, 0,0, 2,2, 0,0],[0,0, 2,2, 0,0, 2,2, 0,0],
+                        [0,0, 0,0, -3/2,-3/2, 0,0, 0,0],[0,0, 0,0, -3/2,-3/2, 0,0, 0,0]]) / 16
+
+
+## Calcul the value of one pixel of the 2x2 matrix independentely
+# A = up left pixel / B = up right pixel / C = down left pixel / D = down right pixel
+
+A_hq_quad_g_r_and_b = np.array([[0,0, 0,0, -1,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, 2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [-1,0, 2,0, 4,0, 2,0, -1,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, 2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, -1,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,-1, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,-1, 0,2, 0,4, 0,2, 0,-1],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,-1, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+C_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -1,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 2,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[-1,0, 2,0, 4,0, 2,0, -1,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 2,0, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -1,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,2, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,-1, 0,2, 0,4, 0,2, 0,-1],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,2, 0,0, 0,0],
+                              [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1, 0,0, 0,0]]) / 8
+
+
+A_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 1/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, -1,0, 0,0, -1,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [-1,0, 4,0, 5,0, 4,0, -1,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, -1,0, 0,0, -1,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, 0,0, 1/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,1/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, 0,-1, 0,0, 0,-1, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,-1, 0,4, 0,5, 0,4, 0,-1],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, 0,-1, 0,0, 0,-1, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                                        [0,0, 0,0, 0,-1/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+C_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 1/2,0, 0,0, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, -1,0, 0,0, -1,0, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[-1,0, 4,0, 5,0, 4,0, -1,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, -1,0, 0,0, -1,0, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 1/2,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,1/2, 0,0, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,-1, 0,0, 0,-1, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,-1, 0,4, 0,5, 0,4, 0,-1],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,-1, 0,0, 0,-1, 0,0],
+                                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1/2, 0,0, 0,0]]) / 8
+
+
+A_hq_quad_r_and_b_g_brow_rcol = A_hq_quad_r_and_b_g_rrow_bcol.T
+B_hq_quad_r_and_b_g_brow_rcol = B_hq_quad_r_and_b_g_rrow_bcol.T
+C_hq_quad_r_and_b_g_brow_rcol = C_hq_quad_r_and_b_g_rrow_bcol.T
+D_hq_quad_r_and_b_g_brow_rcol = D_hq_quad_r_and_b_g_rrow_bcol.T
+
+
+
+
+A_hq_quad_r_b = np.array([[0,0, 0,0, -3/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 2,0, 0,0, 2,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [-3/2,0, 0,0, 6,0, 0,0, -3/2,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 2,0, 0,0, 2,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 0,0, -3/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_r_b = np.array([[0,0, 0,0, 0,-3/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 0,2, 0,0, 0,2, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,-3/2, 0,0, 0,6, 0,0, 0,-3/2],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 0,2, 0,0, 0,2, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+                        [0,0, 0,0, 0,-3/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+
+C_hq_quad_r_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -3/2,0, 0,0, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 2,0, 0,0, 2,0, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[-3/2,0, 0,0, 6,0, 0,0, -3/2,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 2,0, 0,0, 2,0, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -3/2,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_r_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-3/2, 0,0, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,2, 0,0, 0,2, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,-3/2, 0,0, 0,6, 0,0, 0,-3/2],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,2, 0,0, 0,2, 0,0],
+                        [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-3/2, 0,0, 0,0]]) / 8
+
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/babacar/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+import src.methods.babacar.demoisaicing_fct as b
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """Performs demosaicking on y.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    # Performing the reconstruction.
+
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA(cfa, input_shape)
+    res  = b.High_Quality_Linear_Interpolation(op, y)
+    return res
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/brice_convers/README.md

+# Image Analysis Project
+
+Numerous RGB cameras in the commercial sector employ Color Filter Array (CFA) technology.
+This technology involves an array of red, green, or blue filters placed atop the sensors, usually
+organized in periodic patterns. The incident light is consequently filtered by each filter, before
+being captured by the sensor. The typical process of acquiring images utilizes a predefined CFA
+pattern to allocate a color to each pixel of the sensor. The goal of this code project is to demosaicing these CFA Image.
+
+You can check the report called **Image_Analysis_Project_Report_Brice_Convers** to find more information about it.
+
+## How to use
+
+To use the code project you can move the **main_template.py** outside the **src** file and execute it.
+Or you can also call the **run_reconstruction** method in you programme.
+
+```Python
+
+import src.methods.brice_convers.dataHandler as DataHandler
+import src.methods.brice_convers.dataEvaluation as DataEvaluation
+import time
+
+WORKING_DIRECOTRY_PATH = "SICOM_Image_Analysis/sicom_image_analysis_project/"
+
+DataHandler = DataHandler.DataHandler(WORKING_DIRECOTRY_PATH)
+DataEvaluation = DataEvaluation.DataEvaluation(DataHandler)
+
+def main(DataHandler):
+
+    IMAGE_PATH = WORKING_DIRECOTRY_PATH + "images/"
+
+    CFA_NAME = "quad_bayer"
+
+    METHOD = "menon"
+
+    startTime = time.time()
+
+    DataHandler.list_images(IMAGE_PATH)
+
+    DataHandler.print_list_images()
+
+    DataHandler.compute_CFA_images(CFA_NAME)
+
+    DataHandler.compute_reconstruction_images(METHOD, {"cfa": CFA_NAME})
+
+    #The first agurment (ex: 3) is the image index in the list print by "DataHandler.print_list_images()"
+    DataHandler.plot_reconstructed_image(3, METHOD, {"cfa": CFA_NAME}, zoomSize="large")
+
+    DataEvaluation.print_metrics(3, METHOD)
+
+    endTime = time.time()
+
+    print("[INFO] Elapsed time: " + str(endTime - startTime) + "s")
+
+    print("[INFO] End")
+
+if __name__ == "__main__":
+    main(DataHandler)
+
+```
+
+## TODO List:
+
+- Fix menon method pour quad bayer pattern with landscape picture
+
+
+
+
+
+
+
+## References:
+
+[1] [*Research Paper:*](https://ieeexplore.ieee.org/document/4032820) Used for Menon Method.
+
+## Authors
+- [Brice Convers](https://briceconvers.com)

src/methods/brice_convers/configuration.py

+
+PIXEL_PATTERN = "RGB"
+REFINING_STEP = True

src/methods/brice_convers/dataEvaluation.py

+from src.methods.brice_convers.dataHandler import DataHandler
+from src.utils import psnr, ssim
+from sklearn.metrics import f1_score, mean_squared_error
+import numpy as np
+
+class DataEvaluation:
+    def __init__(self, DataHandler: DataHandler):
+        DataEvaluation.DataHandler = DataHandler
+
+    def print_metrics(self, indexImage, method):
+        DataEvaluation.DataHandler.indexImageExists(indexImage)
+
+        img = DataEvaluation.DataHandler.load_image(indexImage)
+        res = DataEvaluation.DataHandler.get_reconstructed_image(indexImage, method)
+
+        ssimMetric = ssim(img, res)
+        psnrMetrc = psnr(img, res)
+        mse = mean_squared_error(img.flatten(), res.flatten())
+        mseRedPixels = mean_squared_error(img[:,:,0], res[:,:,0])
+        mseGreenPixels = mean_squared_error(img[:,:,1], res[:,:,1])
+        mseBluePixels = mean_squared_error(img[:,:,2], res[:,:,2])
+        miMetric = DataEvaluation.MI(img, res)
+        ccMetric = DataEvaluation.CC(img, res)
+        sadMetric = DataEvaluation.SAD(img, res)
+        lsMetric = DataEvaluation.LS(img, res)
+
+        print("[INFO] Metrics for image {}".format(indexImage))
+        print("#" * 30)
+        print(" SSIM: {:.6}  ".format(ssimMetric))
+        print(" PSNR: {:.6}   ".format(psnrMetrc))
+        print(" MSE : {:.3e}    ".format(mse))
+        print(" MSE (R): {:.3e}    ".format(mseRedPixels))
+        print(" MSE (G): {:.3e}    ".format(mseGreenPixels))
+        print(" MSE (B): {:.3e}    ".format(mseBluePixels))
+        print(" MI: {:.6}     ".format(miMetric))
+        print(" CC: {:.6}    ".format(ccMetric))
+        print(" SAD: {:.6} ".format(sadMetric))
+        print(" LS: {:.3e} ".format(lsMetric))
+        print("#" * 30)
+
+    #Mutual Information
+    def MI(img_mov, img_ref):
+        hgram, x_edges, y_edges = np.histogram2d(img_mov.ravel(), img_ref.ravel(), bins=20)
+        pxy = hgram / float(np.sum(hgram))
+        px = np.sum(pxy, axis=1) # marginal for x over y
+        py = np.sum(pxy, axis=0) # marginal for y over x
+        px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals
+        # Now we can do the calculation using the pxy, px_py 2D arrays
+        nzs = pxy > 0 # Only non-zero pxy values contribute to the sum
+        return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))
+
+    # Cross Correlation
+    def CC(img_mov, img_ref):
+        # Vectorized versions of c,d,e
+        a = img_mov.astype('float64')
+        b = img_ref.astype('float64')
+
+        # Calculating mean values
+        AM = np.mean(a)
+        BM = np.mean(b)
+
+        c_vect = (a - AM) * (b - BM)
+        d_vect = (a - AM) ** 2
+        e_vect = (b - BM) ** 2
+
+        # Finally get r using those vectorized versions
+        r_out = np.sum(c_vect) / float(np.sqrt(np.sum(d_vect) * np.sum(e_vect)))
+        return r_out
+
+    #Sum of Absolute Differences
+    def SAD(img_mov, img_ref):
+        img1 = img_mov.astype('float64')
+        img2 = img_ref.astype('float64')
+        ab = np.abs(img1 - img2)
+        sav = np.sum(ab.ravel())
+        sav /= ab.ravel().shape[0]
+        return sav
+
+    #Sum of Least Squared Errors
+    def LS(img_mov, img_ref):
+        img1 = img_mov.astype('float64')
+        img2 = img_ref.astype('float64')
+        r = (img1 - img2)**2
+        sse = np.sum(r.ravel())
+        sse /= r.ravel().shape[0]
+        return sse
+

src/methods/brice_convers/dataHandler.py

+import os
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from src.forward_model import CFA
+from pathlib import Path
+from src.methods.baseline.reconstruct import run_reconstruction
+from src.methods.brice_convers.reconstruct import run_reconstruction as run_reconstruction_brice_convers
+from src.methods.brice_convers.utilities import folderExists
+from src.utils import normalise_image, save_image, psnr, ssim
+from skimage.io import imread
+import numpy as np
+
+class DataHandler:
+    def __init__(self, workingDirectoryPath = ""):
+        DataHandler.imagePaths =[]
+        DataHandler.imagePathsLabels = []
+        DataHandler.CFA_images = {}
+        DataHandler.reconstruction_images = {"interpolation": {}, "menon": {}}
+
+        DataHandler.IMAGE_TYPES = (".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff")
+        DataHandler.WD_PATH = workingDirectoryPath
+
+    def list_files(self, basePath, interval, validExts=None, contains=None):
+        sum = 0
+
+        if interval is not None:
+            counter = interval[0]
+            lastFile = interval[1]
+        else:
+            counter = 0
+            lastFile = -1
+
+        # loop over the directory structure
+        for (rootDir, dirNames, filenames) in os.walk(basePath):
+            # loop over the filenames in the current directory
+            for filename in filenames:
+                # if the contains string is not none and the filename does not contain
+                # the supplied string, then ignore the file
+                if contains is not None and filename.find(contains) == -1:
+                    continue
+
+                # determine the file extension of the current file
+                ext = filename[filename.rfind("."):].lower()
+
+                # check to see if the file is an image and should be processed
+                if validExts is None or ext.endswith(validExts):
+                    if sum >= counter and (sum <= lastFile) or lastFile == -1:
+                        # construct the path to the image and yield it
+                        imagePath = os.path.join(rootDir, filename)
+                        imageName = Path(imagePath).stem
+                        DataHandler.imagePaths.append(imagePath)
+                        DataHandler.imagePathsLabels.append(imageName)
+
+                    sum += 1
+
+    def list_images(self, basePath, interval = None, contains=None):
+        # return the set of files that are valid
+        DataHandler.list_files(self, basePath, interval, validExts=DataHandler.IMAGE_TYPES, contains=contains)
+        print("[INFO] There are {} images.".format(len(DataHandler.imagePaths)))
+
+    def print_list_images(self):
+        print("[INFO] This is the order or your image in the list. IndexImage is the index in this table:")
+        print(DataHandler.imagePathsLabels)
+
+    def plot_input_images(self, rows=3, cols=3, figsize=(15, 5), max_images = 6, title="Input Images"):
+        fig = plt.figure(figsize=figsize)
+        fig.suptitle(title, fontsize=16)
+
+        for i, imagePath in enumerate(DataHandler.imagePaths):
+            if i >= max_images:
+                break
+
+            img = mpimg.imread(imagePath)
+            fig.add_subplot(rows, cols, i+1)
+            # image title
+            plt.title(imagePath.split(os.path.sep)[-1])
+            plt.text(0, -0.1, f"Shape of the image: {img.shape}.", fontsize=12, transform=plt.gca().transAxes)
+            plt.imshow(img)
+
+        plt.show()
+
+    def plot_raw_transformation(self, zoom = True, specificIndex = None, rows=8, cols=3, figsize=(15, 5), max_images = 24, title="Raw Transformation"):
+        fig, axs = plt.subplots(rows, cols, figsize=figsize)
+        fig.suptitle(title, fontsize=16)
+
+        for i, imagePath in enumerate(DataHandler.imagePaths):
+            if i >= max_images and max_images != -1:
+                break
+
+            if specificIndex is not None:
+                if i != specificIndex:
+                    continue
+
+            nameImage = Path(imagePath).stem
+
+            if DataHandler.CFA_images.get(nameImage) is None:
+                print("[ERROR] There is no CFA image for the image {}.".format(nameImage))
+                continue
+
+            img = mpimg.imread(imagePath)
+
+            y = DataHandler.CFA_images[nameImage].direct(img)
+            z = DataHandler.CFA_images[nameImage].adjoint(y)
+
+            if specificIndex is None:
+                line = 2*i
+                subLine = 2*i+1
+            else:
+                line = 0
+                subLine = 1
+
+            axs[line, 0].imshow(img)
+            axs[line, 0].set_title('Input image')
+            axs[line, 1].imshow(y, cmap='gray')
+            axs[line, 1].set_title('Output image')
+            axs[line, 2].imshow(z)
+            axs[line, 2].set_title('Adjoint image')
+
+            if zoom:
+                axs[subLine, 0].imshow(img[800:864, 450:514])
+                axs[subLine, 0].set_title('Zoomed input image')
+                axs[subLine, 1].imshow(y[800:864, 450:514], cmap='gray')
+                axs[subLine, 1].set_title('Zoomed output image')
+                axs[subLine, 2].imshow(z[800:864, 450:514])
+                axs[subLine, 2].set_title('Zoomed adjoint image')
+
+        print("[INFO] There are {} ploted images.".format(max_images))
+
+    def plot_specific_raw_transformation(self, indexImage, zoom = True, rows=2, cols=3, figsize=(15, 5), title="Raw Transformation"):
+        DataHandler.plot_raw_transformation(self, zoom, indexImage, rows, cols, figsize, -1, title)
+
+    def compute_CFA_images(self, CFA_NAME):
+        for i, imagePath in enumerate(DataHandler.imagePaths):
+            nameImage = Path(imagePath).stem
+            DataHandler.compute_CFA_image(self, CFA_NAME, nameImage, i)
+
+        print("[INFO] There are {} CFA images.".format(len(DataHandler.CFA_images)))
+
+    def compute_reconstruction_image(self, method, indexImage, options = None):
+        if len(DataHandler.imagePaths) == 0:
+            print("[ERROR] There is no image in imagePaths")
+            return
+
+        # Test key method
+        if method not in DataHandler.reconstruction_images.keys():
+            print("[ERROR] The method {} is not valid.".format(method))
+            exit(1)
+
+        nameImage = Path(DataHandler.imagePaths[indexImage]).stem
+
+        if DataHandler.CFA_images.get(nameImage) is None:
+            print("[ERROR] There is no CFA image for the image {}.".format(nameImage))
+            exit(1)
+
+        img = DataHandler.load_image(self, indexImage)
+
+        img_CFA = DataHandler.CFA_images[nameImage].direct(img)
+
+        cfa = options.get("cfa")
+
+        if cfa is None:
+            print("[ERROR] You must specify the cfa.")
+            exit(1)
+
+        if method == "interpolation":
+
+            DataHandler.reconstruction_images[method].setdefault(nameImage, run_reconstruction(img_CFA, cfa))
+
+        if method == "menon":
+
+            DataHandler.reconstruction_images[method].setdefault(nameImage, run_reconstruction_brice_convers(img_CFA, cfa))
+
+    def compute_reconstruction_images(self, method, options = None):
+
+        for i in range(len(DataHandler.imagePaths)):
+            DataHandler.compute_reconstruction_image(self, method, i, options)
+
+        print("[INFO] There are {} images which have been reconstructed.".format(len(DataHandler.reconstruction_images[method])))
+
+    def plot_reconstructed_image(self, indexImage, method, cfa = {"cfa": "bayer"}, zoomSize = "small", rows=1, cols=4, figsize=(15, 5)):
+        # Test key method
+        if method not in DataHandler.reconstruction_images.keys():
+            print("[ERROR] The method {} is not valid.".format(method))
+            exit(1)
+
+        res = DataHandler.get_reconstructed_image(self, indexImage, method)
+
+        fig, axs = plt.subplots(rows, cols, figsize=figsize)
+        fig.suptitle("Reconstructed Image with method: {} and pattern type: {}".format(method, cfa["cfa"]), fontsize=16)
+
+        axs[0].imshow(DataHandler.load_image(self, indexImage))
+        axs[0].set_title('Original Image')
+        axs[1].imshow(res)
+        axs[1].set_title('Reconstructed Image')
+
+        if zoomSize == "small":
+            axs[2].imshow(DataHandler.load_image(self, indexImage)[800:864, 450:514])
+            axs[2].set_title('Zoomed Input Image')
+            axs[3].imshow(res[800:864, 450:514])
+            axs[3].set_title('Zoomed Reconstructed Image')
+        else:
+            axs[2].imshow(DataHandler.load_image(self, indexImage)[2000:2064, 2000:2064])
+            axs[2].set_title('Zoomed Input Image')
+            axs[3].imshow(res[2000:2064, 2000:2064])
+            axs[3].set_title('Zoomed Reconstructed Image')
+
+        outputPath = os.path.join(DataHandler.WD_PATH, "output")
+        folderExists(outputPath)
+
+        imageName = Path(DataHandler.imagePaths[indexImage]).stem
+        plotCompImagePath = os.path.join(outputPath, "Compararison_" + imageName + "_" + method + "_" + cfa["cfa"] + ".png")
+
+        #save_image( plotReconstructedImagePath, res)
+        fig.savefig(plotCompImagePath)
+
+
+    def get_reconstructed_image(self, indexImage, method):
+        # Test key method
+        if method not in DataHandler.reconstruction_images.keys():
+            print("[ERROR] The method {} is not valid.".format(method))
+            exit(1)
+
+        DataHandler.indexImageExists(self, indexImage)
+
+        nameImage = Path(DataHandler.imagePaths[indexImage]).stem
+
+        if DataHandler.reconstruction_images[method].get(nameImage) is None:
+            print("[ERROR] There is no reconstruction image for the image {} and for the method {}.".format(nameImage, method))
+            exit(1)
+
+        return DataHandler.reconstruction_images[method][nameImage]
+
+    def shape_of_recontructed_image(self, indexImage, method):
+        if DataHandler.reconstruction_images[method].get(Path(DataHandler.imagePaths[indexImage]).stem) is None:
+            print("[ERROR] There is no reconstruction image for the image {} and for the method {}.".format(Path(DataHandler.imagePaths[indexImage]).stem, method))
+            exit(1)
+
+        return DataHandler.reconstruction_images[method][Path(DataHandler.imagePaths[indexImage]).stem].shape
+
+    def compute_CFA_image(self, CFA_NAME, nameImage, indexImage):
+        DataHandler.CFA_images.setdefault(nameImage, CFA(CFA_NAME, DataHandler.shape_of_image(self, indexImage)))
+
+    def shape_of_image(self, indexImage):
+        img = mpimg.imread(DataHandler.imagePaths[indexImage])
+        return img.shape
+
+    def load_image(self, indexImage):
+        img = imread(DataHandler.imagePaths[indexImage])
+        img = normalise_image(img)
+        return img
+
+    def indexImageExists(self, indexImage):
+        if indexImage >= len(DataHandler.imagePaths):
+            print("[ERROR] The index {} is not valid.".format(indexImage))

src/methods/brice_convers/main_template.py

+import src.methods.brice_convers.dataHandler as DataHandler
+import src.methods.brice_convers.dataEvaluation as DataEvaluation
+import time
+
+WORKING_DIRECOTRY_PATH = "SICOM_Image_Analysis/sicom_image_analysis_project/"
+
+DataHandler = DataHandler.DataHandler(WORKING_DIRECOTRY_PATH)
+DataEvaluation = DataEvaluation.DataEvaluation(DataHandler)
+
+def main(DataHandler):
+
+    IMAGE_PATH = WORKING_DIRECOTRY_PATH + "images/"
+
+    CFA_NAME = "quad_bayer"
+
+    METHOD = "menon"
+
+    startTime = time.time()
+
+    DataHandler.list_images(IMAGE_PATH)
+
+    DataHandler.print_list_images()
+
+    DataHandler.compute_CFA_images(CFA_NAME)
+
+    DataHandler.compute_reconstruction_images(METHOD, {"cfa": CFA_NAME})
+
+    DataHandler.plot_reconstructed_image(0, METHOD, {"cfa": CFA_NAME}, zoomSize="large")
+
+    DataEvaluation.print_metrics(0, METHOD)
+
+    endTime = time.time()
+
+    print("[INFO] Elapsed time: " + str(endTime - startTime) + "s")
+
+    print("[INFO] End")
+
+if __name__ == "__main__":
+    main(DataHandler)

src/methods/brice_convers/menon.py

+"""
+DDFAPD - Menon (2007) Bayer CFA Demosaicing
+===========================================
+
+*Bayer* CFA (Colour Filter Array) DDFAPD - *Menon (2007)* demosaicing.
+
+References
+----------
+-   :cite:`Menon2007c` : Menon, D., Andriani, S., & Calvagno, G. (2007).
+    Demosaicing With Directional Filtering and a posteriori Decision. IEEE
+    Transactions on Image Processing, 16(1), 132-141.
+    doi:10.1109/TIP.2006.884928
+"""
+
+import numpy as np
+from colour.hints import ArrayLike, Literal, NDArrayFloat
+from colour.utilities import as_float_array, ones, tsplit, tstack
+from scipy.ndimage.filters import convolve, convolve1d
+from src.forward_model import CFA
+
+def tensor_mask_to_RGB_mask(mask: ArrayLike, pixelPattern: str = "RGB"):
+        # We extract image chanels from mask
+    for i, letter in enumerate(pixelPattern):
+        if letter == "R":
+            R_m = mask[:, :, i]
+        elif letter == "G":
+            G_m = mask[:, :, i]
+        elif letter == "B":
+            B_m = mask[:, :, i]
+
+    return R_m, G_m, B_m
+
+def _cnv_h(x: ArrayLike, y: ArrayLike) -> NDArrayFloat:
+    """Perform horizontal convolution."""
+    # we go through the rows because axis = -1
+    return convolve1d(x, y, mode="mirror")
+
+
+def _cnv_v(x: ArrayLike, y: ArrayLike) -> NDArrayFloat:
+    """Perform vertical convolution."""
+
+    return convolve1d(x, y, mode="mirror", axis=0)
+
+
+def demosaicing_CFA_Bayer_Menon2007(
+    rawImage: ArrayLike,
+    mask: ArrayLike,
+    pixelPattern: str = "RGB",
+    refining_step: bool = True,
+):
+    """
+    Return the demosaiced *RGB* colourspace array from given *Bayer* CFA using
+    DDFAPD - *Menon (2007)* demosaicing algorithm.
+
+    Parameters
+    ----------
+    CFA
+        *Bayer* CFA.
+    pattern
+        Arrangement of the colour filters on the pixel array.
+    refining_step
+        Perform refining step.
+
+    Returns
+    -------
+    :class:`numpy.ndarray`
+        *RGB* colourspace array.
+
+    Notes
+    -----
+    -   The definition output is not clipped in range [0, 1] : this allows for
+        direct HDRI image generation on *Bayer* CFA data and post
+        demosaicing of the high dynamic range data as showcased in this
+        `Jupyter Notebook <https://github.com/colour-science/colour-hdri/\
+blob/develop/colour_hdri/examples/\
+examples_merge_from_raw_files_with_post_demosaicing.ipynb>`__.
+
+    References
+    ----------
+    :cite:`Menon2007c`
+    """
+
+    # We extract image chanels from mask
+    R_m, G_m, B_m = tensor_mask_to_RGB_mask(mask, pixelPattern)
+
+    # We extract known pixel intensities: when we have a zero in the mask, we have an unknown pixel intensity for the color
+    R = rawImage * R_m
+    G = rawImage * G_m
+    B = rawImage * B_m
+
+    # We define the horizontal and vertical filters
+    h_0 = as_float_array([0.0, 0.5, 0.0, 0.5, 0.0])
+    h_1 = as_float_array([-0.25, 0.0, 0.5, 0.0, -0.25])
+
+    # Green components interpolation along both horizontal and veritcal directions:
+    # For each unkown green pixel, we compute the gradient along both horizontal and vertical directions
+    G_H = np.where(G_m == 0, _cnv_h(rawImage, h_0) + _cnv_h(rawImage, h_1), G)
+    G_V = np.where(G_m == 0, _cnv_v(rawImage, h_0) + _cnv_v(rawImage, h_1), G)
+
+    # We calculate the chrominance differences along both horizontal and vertical directions
+    # For each unknown red and blue pixel, we compute the difference between the pixel intensity and the horizontal green component
+    C_H = np.where(R_m == 1, R - G_H, 0)
+    C_H = np.where(B_m == 1, B - G_H, C_H)
+
+    # Sale method with vertical green component
+    C_V = np.where(R_m == 1, R - G_V, 0)
+    C_V = np.where(B_m == 1, B - G_V, C_V)
+
+    # We compute the directional gradients along both horizontal and vertical directions
+    # First we pad our arrayes with zeros to avoid boundary effects. Acxtually, we pad with the last value of the array
+    # We add two columns to the right of the horizontal array and two rows at the bottom of the  vertical array, with the reflect mode.
+    # Then we remove the first two columns of the horizontal array and the first two rows of the vertical array.
+    paded_D_H = np.pad(C_H, ((0, 0), (0, 2)), mode="reflect")[:, 2:]
+    paded_D_V = np.pad(C_V, ((0, 2), (0, 0)), mode="reflect")[2:, :]
+
+    # We compute the difference between the original array and the padded array.
+    # With the paded array, we have a difference between each pixel and the right neigborhood. We do not have issue with boundaries.
+    # It gives a measure of pixel intensity variation along the horizontal and vertical directions.
+    D_H = np.abs(C_H - paded_D_H)
+    D_V = np.abs(C_V - paded_D_V)
+
+    del h_0, h_1, C_V, C_H, paded_D_V, paded_D_H
+
+    # We define a sufficiently large neighborhood with a size of (5, 5).
+    k = as_float_array(
+        [
+            [0.0, 0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 3.0, 0.0, 3.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0, 1.0],
+        ]
+    )
+
+    # We convolve the difference component with the neighborhood. This method is used to highlight directional variations in the image, in two direction.
+    d_H = convolve(D_H, k, mode="constant")
+    d_V = convolve(D_V, np.transpose(k), mode="constant")
+
+    del D_H, D_V
+
+    # We estimate the green channel with our classifier
+    mask = d_V >= d_H
+    G = np.where(mask, G_H, G_V)
+    # We estimate the mask which represents the best directional reconstruction
+    M = np.where(mask, 1, 0)
+
+    del d_H, d_V, G_H, G_V
+
+    ## The, we estimate the red and blue channels
+
+    # We arrays with ones at the line where there is at least one red (blue) pixel in the red (blue) mask
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[None]) * ones(R.shape)
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[None]) * ones(B.shape)
+
+    # We define a new filter
+    k_b = as_float_array([0.5, 0, 0.5])
+
+    # We fill R array with the condition: if we are in a line where there is at least one red pixel in the red mask and we are on a green pixel in the green mask, we apply the filter horizontaly to the red channel.
+    # If not it means we are on a red pixel (only two possiblity) in the red mask, so we do not apply the filter because we know the red pixel
+    R = np.where(
+        np.logical_and(G_m == 1, R_r == 1),
+        G + _cnv_h(R, k_b) - _cnv_h(G, k_b),
+        R,
+    )
+
+    # Same but we test only the line where there is at least one blue pixel in the blue mask.
+    # When the condition is true, we apply the filter vertically because this time red pixel are aline vertically.
+    R = np.where(
+        np.logical_and(G_m == 1, B_r == 1) == 1,
+        G + _cnv_v(R, k_b) - _cnv_v(G, k_b),
+        R,
+    )
+
+    # It is the same logic for the blue image
+    B = np.where(
+        np.logical_and(G_m == 1, B_r == 1),
+        G + _cnv_h(B, k_b) - _cnv_h(G, k_b),
+        B,
+    )
+
+    B = np.where(
+        np.logical_and(G_m == 1, R_r == 1) == 1,
+        G + _cnv_v(B, k_b) - _cnv_v(G, k_b),
+        B,
+    )
+
+    # To finish R image we need to interpolate blue pixel. We use M to know wich direction is the best and then we interpolate the blue pixel with the filter.
+    R_b = np.where(
+            M == 1,
+            B + _cnv_h(R, k_b) - _cnv_h(B, k_b),
+            B + _cnv_v(R, k_b) - _cnv_v(B, k_b),
+        )
+
+    # Then we put the condition: if we are on a line where there is at least one blue pixel and we are on a blue pixel we take the previous interpolated value.
+    # If not we know the red pixel value and we keep it.
+    R = np.where(
+        np.logical_and(B_r == 1, B_m == 1),
+        R_b,
+        R,
+    )
+
+    # Same idea for the blue image.
+    B = np.where(
+        np.logical_and(R_r == 1, R_m == 1),
+        np.where(
+            M == 1,
+            R + _cnv_h(B, k_b) - _cnv_h(R, k_b),
+            R + _cnv_v(B, k_b) - _cnv_v(R, k_b),
+        ),
+        B,
+    )
+
+    # We stack the channels in the last dimension to get the final image
+    RGB = tstack([R, G, B])
+
+    del R, G, B, k_b, R_r, B_r
+
+    # We optionally perform the refining step
+    if refining_step:
+        RGB = refining_step_Menon2007(RGB, tstack([R_m, G_m, B_m]), M)
+
+    del M, R_m, G_m, B_m
+
+    return RGB
+
+
+def refining_step_Menon2007(
+    RGB: ArrayLike, RGB_m: ArrayLike, M: ArrayLike
+) -> NDArrayFloat:
+    """
+    Perform the refining step on given *RGB* colourspace array.
+
+    Parameters
+    ----------
+    RGB
+        *RGB* colourspace array.
+    RGB_m
+        *Bayer* CFA red, green and blue masks.
+    M
+        Estimation for the best directional reconstruction.
+
+    Returns
+    -------
+    :class:`numpy.ndarray`
+        Refined *RGB* colourspace array.
+    """
+    # Unpacking the RGB and RGB_m arrays.
+    R, G, B = tsplit(RGB)
+    R_m, G_m, B_m = tsplit(RGB_m)
+    M = as_float_array(M)
+
+    del RGB, RGB_m
+
+    # Updating of the green component.
+    R_G = R - G
+    B_G = B - G
+
+    # Definition of the low-pass filter.
+    FIR = ones(3) / 3
+
+    # When we are on a blue pixel, we convolve the pixel with the filter in function of the best direction.
+    B_G_m = np.where(
+        B_m == 1,
+        np.where(M == 1, _cnv_h(B_G, FIR), _cnv_v(B_G, FIR)),
+        0,
+    )
+
+    # Same for the red pixel.
+    R_G_m = np.where(
+        R_m == 1,
+        np.where(M == 1, _cnv_h(R_G, FIR), _cnv_v(R_G, FIR)),
+        0,
+    )
+
+    del B_G, R_G
+
+    # We update the green component for known red and blue pixels with the difference between the red or blue pixel intensity and the filtered pixel intensity.
+    G = np.where(R_m == 1, R - R_G_m, G)
+    G = np.where(B_m == 1, B - B_G_m, G)
+
+    # Updating of the red and blue components in the green locations.
+
+    # R_r is an array with ones at the line where there is at least one red pixel in the red mask.
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[None]) * ones(R.shape)
+    # R_c is an array with ones at the column where there is at least one red pixel in the red mask.
+    R_c = np.any(R_m == 1, axis=0)[None] * ones(R.shape)
+    # B_r is an array with ones at the line where there is at least one blue pixel in the blue mask.
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[None]) * ones(B.shape)
+    # B_c is an array with ones at the column where there is at least one blue pixel in the blue mask.
+    B_c = np.any(B_m == 1, axis=0)[None] * ones(B.shape)
+
+    R_G = R - G
+    B_G = B - G
+
+    k_b = as_float_array([0.5, 0.0, 0.5])
+
+    R_G_m = np.where(
+        np.logical_and(G_m == 1, B_r == 1),
+        _cnv_v(R_G, k_b),
+        R_G_m,
+    )
+    R = np.where(np.logical_and(G_m == 1, B_r == 1), G + R_G_m, R)
+    R_G_m = np.where(
+        np.logical_and(G_m == 1, B_c == 1),
+        _cnv_h(R_G, k_b),
+        R_G_m,
+    )
+    R = np.where(np.logical_and(G_m == 1, B_c == 1), G + R_G_m, R)
+
+    del B_r, R_G_m, B_c, R_G
+
+    B_G_m = np.where(
+        np.logical_and(G_m == 1, R_r == 1),
+        _cnv_v(B_G, k_b),
+        B_G_m,
+    )
+    B = np.where(np.logical_and(G_m == 1, R_r == 1), G + B_G_m, B)
+    B_G_m = np.where(
+        np.logical_and(G_m == 1, R_c == 1),
+        _cnv_h(B_G, k_b),
+        B_G_m,
+    )
+    B = np.where(np.logical_and(G_m == 1, R_c == 1), G + B_G_m, B)
+
+    del B_G_m, R_r, R_c, G_m, B_G
+
+    # Updating of the red (blue) component in the blue (red) locations.
+    R_B = R - B
+    R_B_m = np.where(
+        B_m == 1,
+        np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)),
+        0,
+    )
+    R = np.where(B_m == 1, B + R_B_m, R)
+
+    R_B_m = np.where(
+        R_m == 1,
+        np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)),
+        0,
+    )
+    B = np.where(R_m == 1, R - R_B_m, B)
+
+    del R_B, R_B_m, R_m
+
+    return tstack([R, G, B])

src/methods/brice_convers/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+import cv2
+from src.forward_model import CFA
+from src.methods.brice_convers.menon import demosaicing_CFA_Bayer_Menon2007
+import src.methods.brice_convers.configuration as configuration
+from src.methods.brice_convers.utilities import quad_bayer_to_bayer
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """Performs demosaicking on y.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA("bayer", input_shape)
+
+    if cfa == "quad_bayer":
+        y = quad_bayer_to_bayer(y)
+        op = CFA("bayer", y.shape)
+
+    reconstructed_image = demosaicing_CFA_Bayer_Menon2007(y, op.mask, configuration.PIXEL_PATTERN, configuration.REFINING_STEP)
+
+    if cfa == "quad_bayer":
+        return cv2.resize(reconstructed_image, input_shape[:2], interpolation=cv2.INTER_CUBIC)
+    else:
+        return reconstructed_image
+
+####
+####
+####
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/brice_convers/utilities.py

+import os
+import cv2
+
+def folderExists(path):
+	CHECK_FOLDER = os.path.isdir(path)
+
+	# If folder doesn't exist, it creates it.
+	if not CHECK_FOLDER:
+		os.makedirs(path)
+		print("[DATA] You created a new folder : " +  str(path))
+
+import numpy as np
+
+def quad_bayer_to_bayer(quad_bayer_pattern):
+    # We test that thee quad bayer size fit with a multiple of 2 for width and height). If not, we pad it.
+	if quad_bayer_pattern.shape[0] % 2 != 0 or quad_bayer_pattern.shape[1] % 2 != 0:
+		print("[INFO] The quad bayer pattern size is not valid. We need to pad it.")
+
+		pad_schema = []
+
+		if quad_bayer_pattern.shape[0] % 2 != 0:
+				pad_schema.append([0, 1])
+
+		if quad_bayer_pattern.shape[1] % 2 != 0:
+				pad_schema.append([0, 1])
+		else:
+				pad_schema.append([0, 0])
+
+		quad_bayer_pattern = np.pad(quad_bayer_pattern, pad_schema, mode="reflect")[:, 2:]
+
+	# we create a new bayer pattern with the good size
+	bayer_pattern = np.zeros((quad_bayer_pattern.shape[0] // 2, quad_bayer_pattern.shape[1] // 2))
+
+	# We combine adjacent pixels to create the Bayer pattern
+	for i in range(0, quad_bayer_pattern.shape[0], 2):
+		for j in range(0, quad_bayer_pattern.shape[1], 2):
+			bayer_pattern[i // 2, j // 2] = (
+			quad_bayer_pattern[i, j] +
+			quad_bayer_pattern[i, j + 1] +
+			quad_bayer_pattern[i + 1, j] +
+			quad_bayer_pattern[i + 1, j + 1]
+		) / 4
+
+	# We resize bayer iamge to the original image size
+
+	#return cv2.resize(bayer_pattern, quad_bayer_pattern.shape, interpolation=cv2.INTER_CUBIC)
+	return bayer_pattern

src/methods/chaari_mohamed/fonctions.py

+import numpy as np
+from scipy.signal import convolve2d
+from src.forward_model import CFA
+
+def bilinear_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:
+    """
+    Bilinear demosaicing method.
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    # Copie des valeurs directement connues pour chaque canal
+    red = y[:, :, 0]
+    green = y[:, :, 1]
+    blue = y[:, :, 2]
+
+    # Création des masques pour chaque couleur selon le motif CFA
+    mask_red = (op.mask == 0)  # Supposons que 0 correspond au rouge dans le masque
+    mask_green = (op.mask == 1)  # Supposons que 1 correspond au vert
+    mask_blue = (op.mask == 2)  # Supposons que 2 correspond au bleu
+
+    # Interpolation bilinéaire pour le rouge et le bleu
+    # Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *
+    red_interp = convolve2d(np.multiply(red, mask_red), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+    blue_interp = convolve2d(np.multiply(blue, mask_blue), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+
+    # Interpolation bilinéaire pour le vert
+    # Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts
+    green_interp = convolve2d(np.multiply(green, mask_green), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')
+
+    # Création de l'image interpolée
+    demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)
+
+    # Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou
+    demosaicked_image[:, :, 0][mask_red] = red[mask_red]
+    demosaicked_image[:, :, 1][mask_green] = green[mask_green]
+    demosaicked_image[:, :, 2][mask_blue] = blue[mask_blue]
+
+    # Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]
+    demosaicked_image = np.clip(demosaicked_image, 0, 1)
+
+    return demosaicked_image
+
+
+def quad_bayer_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:
+    """
+    Demosaicing method for Quad Bayer CFA pattern.
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    
+    # Interpolation bilinéaire pour chaque canal
+    red_interp = convolve2d(np.multiply(y[:, :, 0], op.mask == 0), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+    green_interp = convolve2d(np.multiply(y[:, :, 1], op.mask == 1), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')
+    blue_interp = convolve2d(np.multiply(y[:, :, 2], op.mask == 2), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+
+    # Assemblage de l'image interpolée
+    demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)
+
+    # Réapplication des valeurs connues
+    demosaicked_image[:, :, 0][op.mask == 0] = y[:, :, 0][op.mask == 0]
+    demosaicked_image[:, :, 1][op.mask == 1] = y[:, :, 1][op.mask == 1]
+    demosaicked_image[:, :, 2][op.mask == 2] = y[:, :, 2][op.mask == 2]
+
+    # Clip des valeurs pour les maintenir dans la plage appropriée
+    demosaicked_image = np.clip(demosaicked_image, 0, 1)
+
+    return demosaicked_image

src/methods/chaari_mohamed/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.chaari_mohamed.fonctions import bilinear_demosaicing,quad_bayer_demosaicing
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """
+    Performs demosaicking on y based on the CFA pattern.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA pattern. Can be 'bayer' or 'quad_bayer'.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    input_shape = (y.shape[0], y.shape[1], 3)
+    
+    op = CFA(cfa, input_shape)
+    
+    if cfa == 'bayer':
+        res = bilinear_demosaicing(op, y)
+    elif cfa == 'quad_bayer':
+        res = quad_bayer_demosaicing(op, y)
+    else:
+        raise ValueError("Unsupported CFA pattern. Supported patterns are 'bayer' and 'quad_bayer'.")
+    
+    return res
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/charpentier_laurine/functions.py

+import numpy as np
+import cv2
+from scipy.signal import convolve2d
+
+from src.forward_model import CFA
+
+
+def superpixel(op: CFA, y: np.ndarray) -> np.ndarray:
+    """Performs the method of variable number of gradients
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    z = op.adjoint(y)
+
+    if op.cfa == 'bayer':
+        res = np.empty((op.input_shape[0]//2, op.input_shape[1]//2, op.input_shape[2]))
+
+        for i in range(1,op.input_shape[0],2):
+            for j in range(1, op.input_shape[1],2):
+                res[i//2,j//2,0] = z[ i-1,j ,0]
+                res[i//2,j//2,1] = (z[i,j,1] + z[i-1,j-1,1]) / 2
+                res[i//2,j//2,2] = z[ i,j-1 ,2]
+
+    else:
+        res = np.empty((op.input_shape[0]//2, op.input_shape[1]//2, op.input_shape[2]))
+
+        print()
+
+        for i in range(2,op.input_shape[0]-5,4):
+            for j in range(2, op.input_shape[1]-5,4):
+
+                res[i//2,j//2,0] = z[ i-2,j ,0]
+                res[i//2,j//2,1] = (z[i,j,1] + z[i-2,j-2,1]) / 2
+                res[i//2,j//2,2] = z[ i,j-2 ,2]
+
+                res[i//2+1,j//2,0] = z[ i-2+1,j ,0]
+                res[i//2+1,j//2,1] = (z[i+1,j,1] + z[i-2+1,j-2,1]) / 2
+                res[i//2+1,j//2,2] = z[ i+1,j-2 ,2]
+
+                res[i//2+1,j//2+1,0] = z[ i-2+1,j+1 ,0]
+                res[i//2+1,j//2+1,1] = (z[i+1,j+1,1] + z[i-2+1,j-2+1,1]) / 2
+                res[i//2+1,j//2+1,2] = z[ i+1,j-2+1 ,2]
+
+                res[i//2,j//2+1,0] = z[ i-2,j+1 ,0]
+                res[i//2,j//2+1,1] = (z[i,j+1,1] + z[i-2,j-2+1,1]) / 2
+                res[i//2,j//2+1,2] = z[ i,j-2+1 ,2]
+
+    return cv2.resize(res, (op.input_shape[0], op.input_shape[1]))
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/charpentier_laurine/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.charpentier_laurine.functions import superpixel
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """Performs demosaicking on y.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA(cfa, input_shape)
+
+    res = superpixel(op, y)
+
+    return res
+
+
+####
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
+####      ####  ####      ##  ####      ####    ############
+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
+####      ####      ########  ####      ####
+####      ####        ############      ####
+####      ####          ##########      ####
+####      ####            ########      ####
+####      ####              ######      ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller