From 5c7f74ce37b2e4f11f1f110df9cde858442777f1 Mon Sep 17 00:00:00 2001 From: Matthieu Muller <matthieu.muller@gipsa-lab.grenoble-inp.fr> Date: Wed, 31 Jan 2024 16:24:24 +0100 Subject: [PATCH] Post merge changes --- src/methods/baseline/demo_reconstruction.py | 153 ++++++++++++++++++++ src/methods/baseline/reconstruct.py | 53 +++++++ 2 files changed, 206 insertions(+) create mode 100644 src/methods/baseline/demo_reconstruction.py create mode 100644 src/methods/baseline/reconstruct.py diff --git a/src/methods/baseline/demo_reconstruction.py b/src/methods/baseline/demo_reconstruction.py new file mode 100644 index 0000000..dc2e5dc --- /dev/null +++ b/src/methods/baseline/demo_reconstruction.py @@ -0,0 +1,153 @@ +"""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 + + +#### +#### +#### + +#### #### #### ############# +#### ###### #### ################## +#### ######## #### #################### +#### ########## #### #### ######## +#### ############ #### #### #### +#### #### ######## #### #### #### +#### #### ######## #### #### #### +#### #### ######## #### #### #### +#### #### ## ###### #### #### ###### +#### #### #### ## #### #### ############ +#### #### ###### #### #### ########## +#### #### ########## #### #### ######## +#### #### ######## #### #### +#### #### ############ #### +#### #### ########## #### +#### #### ######## #### +#### #### ###### #### + +# 2023 +# Authors: Mauro Dalla Mura and Matthieu Muller diff --git a/src/methods/baseline/reconstruct.py b/src/methods/baseline/reconstruct.py new file mode 100644 index 0000000..fbc7e05 --- /dev/null +++ b/src/methods/baseline/reconstruct.py @@ -0,0 +1,53 @@ +"""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 + + +#### +#### +#### + +#### #### #### ############# +#### ###### #### ################## +#### ######## #### #################### +#### ########## #### #### ######## +#### ############ #### #### #### +#### #### ######## #### #### #### +#### #### ######## #### #### #### +#### #### ######## #### #### #### +#### #### ## ###### #### #### ###### +#### #### #### ## #### #### ############ +#### #### ###### #### #### ########## +#### #### ########## #### #### ######## +#### #### ######## #### #### +#### #### ############ #### +#### #### ########## #### +#### #### ######## #### +#### #### ###### #### + +# 2023 +# Authors: Mauro Dalla Mura and Matthieu Muller -- GitLab