Merge branch 'master' into 'master'

Image analysis project CASA NOVA Vincent

See merge request !18

src/methods/CASA_NOVA_Vincent/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 functions as fu
+from src.forward_model import CFA
+import importlib 
+importlib.reload(fu)
+
+
+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
+
+    return fu.extrapolate_cfa(y,cfa)
+

src/methods/baseline/demo_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 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.
-    """
-    z = op.adjoint(y)
-
-    if op.cfa == 'bayer':
-        res = np.empty(op.input_shape)
-
-        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')
-
-    else:
-        res = np.empty(op.input_shape)
-
-        res[:, :, 0] = varying_kernel_convolution(z[:, :, 0], K_list_red)
-        res[:, :, 1] = varying_kernel_convolution(z[:, :, 1], K_list_green)
-        res[:, :, 2] = varying_kernel_convolution(z[:, :, 2], K_list_blue)
-
-    return res
-
-
-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
-
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-# 2023
-# Authors: Mauro Dalla Mura and Matthieu Muller

src/methods/baseline/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.baseline.demo_reconstruction import naive_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.
-    """
-    input_shape = (y.shape[0], y.shape[1], 3)
-    op = CFA(cfa, input_shape)
-
-    res = naive_interpolation(op, y)
-
-    return res
-
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-# 2023
-# Authors: Mauro Dalla Mura and Matthieu Muller