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import numpy as np
from src.forward_model import CFA
from src.methods.ELAMRANI_Mouna.functions import *
def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
cfa_name = 'bayer' # bayer or quad_bayer
input_shape = (y.shape[0], y.shape[1], 3)
op = CFA(cfa_name, input_shape)
img_res = op.adjoint(y)
N = img_res[:,:,0].shape[0]
M = img_res[:,:,0].shape[1]
def interpolate_channel(img_res, channel, first_pass, N, M):
for i in range(N):
for j in range(M):
if first_pass and ((channel == 0 and i % 2 == 1 and j % 2 == 0) or
(channel == 2 and i % 2 == 0 and j % 2 == 1)):
neighbors = find_Knearest_neighbors(img_res, channel, i, j, N, M)
neighbors_G = find_Knearest_neighbors(img_res, 1, i, j, N, M)
dir_deriv = calculate_directional_gradients(neighbors_G)
weights = calculate_adaptive_weights(img_res, neighbors_G, dir_deriv, 1, i, j, N, M)
img_res[i, j, channel] = interpolate_RedBlue(neighbors, neighbors_G, weights)
elif not first_pass and img_res[i, j, channel] == 0:
neighbors = find_Knearest_neighbors(img_res, channel, i, j, N, M)
dir_deriv = calculate_directional_gradients(neighbors)
weights = calculate_adaptive_weights(img_res, neighbors, dir_deriv, channel, i, j, N, M)
img_res[i, j, channel] = interpolate_pixel(neighbors, weights)
return img_res
# Interpolation pour chaque canal
img_res = interpolate_channel(img_res, 1, False, N, M) # Interpolation du canal vert
img_res = interpolate_channel(img_res, 0, True, N, M) # Première interpolation du canal rouge
img_res = interpolate_channel(img_res, 0, False, N, M) # Seconde interpolation du canal rouge
img_res = interpolate_channel(img_res, 2, True, N, M) # Première interpolation du canal bleu
img_res = interpolate_channel(img_res, 2, False, N, M) # Seconde interpolation du canal bleu
img_res[img_res > 1] = 1
img_res[img_res < 0] = 0
return img_res