Merge branch 'master' into 'master'

merge request dai

See merge request !16

src/methods/dai/function.py

+import numpy as np
+from scipy.signal import convolve2d
+
+from src.forward_model import CFA
+
+
+def naive_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.
+    """
+    
+
+    res = np.empty(op.input_shape)
+    z = op.adjoint(y)
+
+    res[:, :, 0] = convolve2d(z[:, :, 0], ker_bayer_red_blue, mode='same')
+    res[:, :, 1] = convolve2d(z[:, :, 1], ker_bayer_green, mode='same')
+    res[:, :, 2] = convolve2d(z[:, :, 2], ker_bayer_red_blue, mode='same')
+    
+    return res
+
+
+
+
+def Spectral_difference(op,y):
+
+    z = op.adjoint(y)
+    y_hat  = naive_interpolation(op,y)
+    res = np.empty(op.input_shape)
+    for l in range(3):
+        res[:,:,0]+=z[:,:,l]+convolve2d(z[:,:,0]-y_hat[:,:,l]*op.mask[:,:,0], ker_bayer_red_blue, mode='same')*op.mask[:,:,l]
+        res[:,:,1]+=z[:,:,l]+convolve2d(z[:,:,1]-y_hat[:,:,l]*op.mask[:,:,1], ker_bayer_green, mode='same')*op.mask[:,:,l]
+        res[:,:,2]+=z[:,:,l]+convolve2d(z[:,:,2]-y_hat[:,:,l]*op.mask[:,:,2], ker_bayer_red_blue, mode='same')*op.mask[:,:,l]
+
+    return res
+
+def quad_bayer_to_bayer(y_quad):
+    y_bayer=np.copy(y_quad)
+
+    for i in range(1,y_quad.shape[0],4):
+        temp = np.copy(y_bayer[i,:])
+        y_bayer[i,:]=y_bayer[i+1,:]
+        y_bayer[i+1,:]=temp
+    
+    for j in range(1,y_quad.shape[1],4):
+        temp = np.copy(y_bayer[:,j])
+        y_bayer[:,j]=y_bayer[:,j+1]
+        y_bayer[:,j+1]=temp
+
+
+
+    return y_bayer
+
+
+
+
+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
\ No newline at end of file

src/methods/dai/reconstruct.py

+"""The main file for the baseline reconstruction.
+This file should NOT be modified.
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.dai.function import Spectral_difference, 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(cfa, input_shape)
+
+    if op.cfa == 'bayer':
+        res = Spectral_difference(op, y)
+
+    else: 
+        op.mask = quad_bayer_to_bayer(op.mask)
+        res = Spectral_difference(CFA('bayer',input_shape),quad_bayer_to_bayer(y))
+
+    return res
+
+
+####a
+####
+####
+
+####      ####                ####        #############
+####      ######              ####      ##################
+####      ########            ####      ####################
+####      ##########          ####      ####        ########
+####      ############        ####      ####            ####
+####      ####  ########      ####      ####            ####
+####      ####    ########    ####      ####            ####
+####      ####      ########  ####      ####            ####
+####      ####  ##    ######  ####      ####          ######
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+####      ####  ######        ####      ####    ##########
+####      ####  ##########    ####      ####    ########
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+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller