Update src/methods/dzik_erwan/__init__.py,...

Update src/methods/dzik_erwan/__init__.py, src/methods/dzik_erwan/functions.py, src/methods/dzik_erwan/reconstruct.py, src/methods/dzik_erwan/dzik_erwan.pdf, src/methods/dzik_erwan/output/bayer/reconstructed_img1.png, src/methods/dzik_erwan/output/bayer/reconstructed_img4.png, src/methods/dzik_erwan/output/bayer/reconstructed_img2.png, src/methods/dzik_erwan/output/bayer/reconstructed_img3.png, src/methods/dzik_erwan/output/quad_bayer/reconstructed_img1.png, src/methods/dzik_erwan/output/quad_bayer/reconstructed_img3.png, src/methods/dzik_erwan/output/quad_bayer/reconstructed_img4.png, src/methods/dzik_erwan/output/quad_bayer/reconstructed_img2.png

src/methods/dzik_erwan/functions.py

+import numpy as np 
+from scipy.ndimage import convolve 
+
+from src.forward_model import CFA
+
+def malvar2004(y,op): 
+    "Code inspiered by KelSolaar's GitHub repo:"
+    "https://github.com/colour-science/colour-demosaicing/tree/master"
+
+    z = op.adjoint(y)
+    mask = op.mask
+
+    R = z[:,:,0]
+    G = z[:,:,1]
+    B = z[:,:,2]
+
+    R_m, G_m, B_m = mask[:,:,0], mask[:,:,1], mask[:,:,2]
+
+    
+    G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(y, GR_GB), G)
+
+
+    #Logical array that verify the conditon of the different filters 
+    # Red rows.
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[None]) * np.ones(R.shape)
+    # Red columns.
+    R_c = np.any(R_m == 1, axis=0)[None] * np.ones(R.shape)
+    # Blue rows.
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[None]) * np.ones(B.shape)
+    # Blue columns
+    B_c = np.any(B_m == 1, axis=0)[None] * np.ones(B.shape)
+
+    #Results of the convolutions
+    RBg_RBBR = convolve(y, Rg_RB_Bg_BR)
+    RBg_BRRB = convolve(y, Rg_BR_Bg_RB)
+    RBgr_BBRR = convolve(y, Rb_BB_Br_RR)
+
+    
+    R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
+    R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
+
+    B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
+    B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
+
+    R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
+    B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
+
+    
+    res = np.clip(np.stack((R,G,B), axis = 2), 0, 1)
+
+
+    return res
+
+def quad_bayer_to_bayer(cfa_quad_bayer):
+    "Quad to Bayer by swapping method"
+
+    cfa_bayer = np.flip(np.copy(cfa_quad_bayer), 1)
+    h,w = cfa_bayer.shape
+    for col in range(1, w, 4):
+        temp = np.copy(cfa_bayer[:, col])
+        cfa_bayer[:, col] = cfa_bayer[:, col + 1]
+        cfa_bayer[:, col + 1] = temp
+    del temp
+
+
+    for row in range(1, h, 4):
+        temp = np.copy(cfa_bayer[row, :])
+        cfa_bayer[row, :] = cfa_bayer[row + 1, :]
+        cfa_bayer[row + 1, :] = temp
+    del temp
+
+    for row in range(1, h, 4):
+        for col in range(2, w, 4):
+            g1 = cfa_bayer[row, col]
+            cfa_bayer[row, col] = cfa_bayer[row + 1, col -1]
+            cfa_bayer[row + 1, col -1] = g1
+
+    return np.flip(cfa_bayer,1)
+
+# G at R locations or G at B locations
+GR_GB = (1/8) * np.asarray([
+    [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.],
+])
+
+#R at green in R row, B column or B at green in B row, R colunm
+Rg_RB_Bg_BR = (1/8) * np.asarray([
+    [0., 0., .5, 0., 0.],
+    [0., -1., 0., -1., 0.],
+    [-1., 4., 5., 4., -1.],
+    [0., -1., 0., -1., 0.],
+    [0., 0., .5, 0., 0.],
+])
+
+# R at green in B row, R column or B at green in R row, B column 
+Rg_BR_Bg_RB = (1/8) * np.asarray([
+    [0., 0., -1, 0., 0.],
+    [0., -1., 4., -1., 0.],
+    [.5, 0., 5., 0., .5],
+    [0., -1., 4., -1., 0.],
+    [0., 0., -1, 0., 0.],
+])
+
+#R at blue in B row, B column or B at red in R row, R column
+Rb_BB_Br_RR = (1/8) * np.asarray([
+    [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.],
+])
+

src/methods/dzik_erwan/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.dzik_erwan.functions import malvar2004, 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.
+    """
+    # Performing the reconstruction.
+    # TODO
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA(cfa, input_shape)
+
+    if cfa == 'bayer':
+        res = malvar2004(y,op)
+    else:
+        bayer_equivalent = quad_bayer_to_bayer(y)
+        res = malvar2004(bayer_equivalent, CFA("bayer", input_shape))
+    
+    return res
+
+
+####
+####
+####
+
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+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller