diff --git a/src/methods/anouk_enz/Report_Enz.pdf b/src/methods/anouk_enz/Report_Enz.pdf
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diff --git a/src/methods/anouk_enz/reconstruct.py b/src/methods/anouk_enz/reconstruct.py
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+"""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 as cv
+from skimage.filters import rank
+from src.forward_model import CFA
+from skimage.morphology import disk
+import matplotlib.pyplot as plt
+
+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)
+
+ z = op.adjoint(y)
+
+
+ ################# MY CODE ############################
+ if op.cfa == 'quad_bayer' :
+ # Transform into a Bayer-patterned image
+ y = QuadBayer_to_Bayer(y, op, vizualize=False)
+
+ # Demosaïck the image
+ res = demosaicking_bayer(y)
+
+ if op.cfa == 'bayer':
+ res = demosaicking_bayer(y)
+ ####################################################
+
+ return res
+
+def get_color(mask, i, j):
+ """
+ Returns the index of the chanel that contains already a color at a pixel localization.
+
+ Args :
+ mask (np.ndarray) : mask of the used cfa.
+ i (int): index of the line of the pixel.
+ j (int): index of the column of the pixel.
+
+ Returns
+ int: the index of the color avaible at the indicated pixel.
+ """
+ if mask[i,j,0] == 1:
+ return 0
+ if mask[i,j,1] == 1:
+ return 1
+ if mask[i,j,2] == 1:
+ return 2
+ return None
+
+def demosaicking_bayer(y):
+ """
+ Performs High-Quality Linear Interpolation on a Bayer-patterned image.
+
+ Args :
+ y (np.ndarray): Mosaicked image to be reconstructed.
+
+ Returns
+ np.ndarray: Demosaicked image.
+ """
+ input_shape = (y.shape[0], y.shape[1], 3)
+ op = CFA("bayer", input_shape)
+ res = np.empty(op.input_shape)
+
+ for i in range (2, input_shape[0]-2):
+ for j in range (2, input_shape[0]-2):
+ patch = y[i-2:i+3, j-2:j+3]
+ channel = get_color(op.mask, i, j)
+ # If the Red value is avaible
+ if channel == 0:
+ res[i, j, 0] = y[i,j]
+ # Interpolate the Green value
+ res[i, j, 1] = np.sum(patch*G_at_R)/8
+ #Interpolate the Blue value
+ res[i, j, 2] = np.sum(patch* B_at_R)/8
+
+ # If the Green value is avaible
+ if channel == 1 :
+ res[i, j, 1] = y[i,j]
+ # Interpolation of the Red value
+ if get_color(op.mask, i, j+1) == 0:
+ res[i,j,0] = np.sum(patch*R_at_G_row)/8
+ else :
+ res[i,j,0] = np.sum(patch*R_at_G_column)/8
+
+ # Interpolation of the Blue value
+ if get_color(op.mask, i, j+1) == 2:
+ res[i,j,2] = np.sum(patch* B_at_G_row)/8
+ else :
+ res[i,j,2] = np.sum(patch * B_at_G_column)/8
+
+ # If the Blue value is avaible
+ if channel == 2:
+ res[i, j, 2] = y[i,j]
+ # Interpolate the Red value
+ res[i, j, 1] = np.sum(patch* G_at_B)/8
+ #Interpolate the Red value
+ res[i, j, 0] = np.sum(patch* R_at_B)/8
+ return res
+
+def QuadBayer_to_Bayer(y, op, vizualize=False):
+ """
+ Applies the swapping method to transform a QuadBayer-patterned image into a Bayer-patterned image
+
+ Args :
+ y (np.ndarray): Mosaicked image to be tranformed
+ op (CFA) : the CFA object
+ vizualize (Boolean) : show the evolution of the mask if True
+
+ Returns :
+ (np.ndarray): a bayer-patterned image
+ """
+ input_shape = (y.shape[0], y.shape[1], 3)
+
+ if vizualize :
+ fig, axs = plt.subplots(1, 4, figsize=(15,15))
+ axs[0].imshow(op.mask[0:10, 0:10, :])
+ axs[0].set_title('Original mask')
+
+ # Step 1 : Swap 2 columns every 2 columns
+ temp = np.zeros((input_shape[0], 1))
+ temp_mask = np.zeros((input_shape[0], 1, 3))
+ for col in range (1, input_shape[0]-1, 4):
+ temp = np.copy(y[:, col])
+ y[:, col] = np.copy(y[:, col+1])
+ y[:, col+1] = np.copy(temp)
+
+ if vizualize:
+ temp_mask = np.copy(op.mask[:, col,:])
+ op.mask[:, col,:] = np.copy(op.mask[:, col+1,:])
+ op.mask[:, col+1,:] = np.copy(temp_mask)
+ if vizualize:
+ axs[1].imshow(op.mask[0:10, 0:10, :])
+ axs[1].set_title('Mask after first step')
+
+ #Step 2 : Swap 2 lines every 2 lines
+ temp = np.zeros((1, input_shape[1]))
+ temp_mask = np.zeros((1, input_shape[1],3))
+ for line in range (1, input_shape[1], 4):
+ temp = np.copy(y[line, :])
+ y[line, :] = np.copy(y[line+1, :])
+ y[line+1, :] = np.copy(temp)
+
+ if vizualize:
+ temp_mask = np.copy(op.mask[line, :, :])
+ op.mask[line, :, :] = np.copy(op.mask[line+1, :, :])
+ op.mask[line+1, :, :] = np.copy(temp_mask)
+ if vizualize:
+ axs[2].imshow(op.mask[0:10, 0:10, :])
+ axs[2].set_title('Mask after second step')
+
+ # 3: Swap back some diagonal greens
+ temp_mask = np.zeros((1,1,3))
+ for i in range(0, input_shape[0], 4):
+ for j in range(2, input_shape[1], 4):
+ temp = y[i, j]
+ y[i, j] = y[i+1, j-1]
+ y[i+1, j-1] = temp
+ if vizualize:
+ temp_mask = op.mask[i, j, :]
+ op.mask[i, j, :] = np.copy(op.mask[i+1, j-1, :])
+ op.mask[i+1, j-1, :] = np.copy(temp_mask)
+ if vizualize:
+ axs[3].imshow(op.mask[0:10, 0:10, :])
+ axs[3].set_title('Mask after third step')
+ plt.tight_layout()
+ plt.show()
+
+ return y
+
+
+# Defining the mask
+
+# Interpolate the Green
+# Green at Red
+G_at_R = [[ 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]]
+# Green at Blue
+G_at_B = [[ 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]]
+
+#Interpolate the Red
+# R at G in R row, B column
+R_at_G_row = [[ 0, 0, 0.5, 0, 0],
+ [0, -1, 0 ,-1, 0],
+ [-1, 4, 5 , 4,-1],
+ [0, -1, 0 ,-1, 0],
+ [0, 0, 0.5, 0 , 0]]
+# R at G in B row, R column
+R_at_G_column = [[0, 0,-1, 0, 0],
+ [0,-1, 4,-1, 0],
+ [0.5,0, 5,0,0.5],
+ [ 0 ,-1,4,-1, 0],
+ [0,0,-1,0,0]]
+# Red at Blue
+R_at_B = [[0, 0, -1.5, 0, 0],
+ [0, 2, 0, 2, 0],
+ [-1.5, 0, 6, 0, -1.5],
+ [0, 2, 0, 2, 0],
+ [0, 0, -1.5, 0, 0]]
+
+#Intelropation for Blue
+# Blue at Green in B row, R column
+B_at_G_row = [[ 0, 0, 0.5, 0, 0],
+ [0, -1, 0 ,-1, 0],
+ [-1, 4, 5 , 4,-1],
+ [0, -1, 0 ,-1, 0],
+ [0, 0, 0.5, 0 , 0]]
+
+B_at_G_column = [[0, 0,-1, 0, 0],
+ [0,-1, 4,-1, 0],
+ [0.5,0, 5,0,0.5],
+ [ 0 ,-1,4,-1, 0],
+ [0,0,-1,0,0]]
+
+B_at_R = [[0, 0, -1.5, 0, 0],
+ [0, 2, 0, 2, 0],
+ [-1.5, 0, 6, 0, -1.5],
+ [0, 2, 0, 2, 0],
+ [0, 0, -1.5, 0, 0]]
+
+
+####
+####
+####
+
+#### #### #### #############
+#### ###### #### ##################
+#### ######## #### ####################
+#### ########## #### #### ########
+#### ############ #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ## ###### #### #### ######
+#### #### #### ## #### #### ############
+#### #### ###### #### #### ##########
+#### #### ########## #### #### ########
+#### #### ######## #### ####
+#### #### ############ ####
+#### #### ########## ####
+#### #### ######## ####
+#### #### ###### ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller