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Commit e4985dbc authored by Aymane Khattabi's avatar Aymane Khattabi
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rapport and construct

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src/methods/khattabi/KHATTABI.pdf 0 → 100644
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src/methods/khattabi/reconstruct.py 0 → 100755
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"""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.khattabi.utils import HA
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)
y_adjoint=op.adjoint(y)
res = HA(y_adjoint)
return res
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# 2023
# Authors: Mauro Dalla Mura and Matthieu Muller
src/methods/khattabi/utils.py 0 → 100755
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"""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 HA(y_adjoint):
height, width = y_adjoint.shape[:2]
# Green channel interpolation
for i in range(2 , height-2):
for j in range(2, width-2):
# Red pixels
if y_adjoint[i,j,1] == 0 and y_adjoint[i,j,0] != 0:
grad_y = np.abs(y_adjoint[i-1,j,1] - y_adjoint[i+1,j,1]) + np.abs(2*y_adjoint[i,j,0] - y_adjoint[i-2,j,0] - y_adjoint[i+2,j,0])
grad_x = np.abs(y_adjoint[i,j-1,1] - y_adjoint[i,j+1,1]) + np.abs(2*y_adjoint[i,j,0] - y_adjoint[i,j-2,0] - y_adjoint[i,j+2,0])
if grad_x < grad_y:
y_adjoint[i,j,1] = (y_adjoint[i,j-1,1] + y_adjoint[i,j+1,1])/2 + (2*y_adjoint[i,j,0] - y_adjoint[i,j-2,0] - y_adjoint[i,j+2,0])/4
elif grad_x > grad_y:
y_adjoint[i,j,1] = (y_adjoint[i-1,j,1] + y_adjoint[i+1,j,1])/2 + (2*y_adjoint[i,j,0] - y_adjoint[i-2,j,0] - y_adjoint[i+2,j,0])/4
else:
y_adjoint[i,j,1] = (y_adjoint[i-1,j,1] + y_adjoint[i+1,j,1] + y_adjoint[i,j-1,1] + y_adjoint[i,j+1,1])/4 + \
(2*y_adjoint[i,j,0] - y_adjoint[i,j-2,0] - y_adjoint[i,j+2,0] + 2*y_adjoint[i,j,0] - y_adjoint[i-2,j,0] - y_adjoint[i+2,j,0])/8
# Blue Pixels
elif y_adjoint[i,j,1] == 0 and y_adjoint[i,j,2] != 0:
grad_y = np.abs(y_adjoint[i-1,j,1] - y_adjoint[i+1,j,1]) + np.abs(2*y_adjoint[i,j,2] - y_adjoint[i-2,j,2] - y_adjoint[i+2,j,2])
grad_x = np.abs(y_adjoint[i,j-1,1] - y_adjoint[i,j+1,1]) + np.abs(2*y_adjoint[i,j,2] - y_adjoint[i,j-2,2] - y_adjoint[i,j+2,2])
if grad_x < grad_y:
y_adjoint[i,j,1] = (y_adjoint[i,j-1,1] + y_adjoint[i,j+1,1])/2 + (2*y_adjoint[i,j,2] - y_adjoint[i,j-2,2] - y_adjoint[i,j+2,2])/4
elif grad_x > grad_y:
y_adjoint[i,j,1] = (y_adjoint[i-1,j,1] + y_adjoint[i+1,j,1])/2 + (2*y_adjoint[i,j,2] - y_adjoint[i-2,j,2] - y_adjoint[i+2,j,2])/4
else:
y_adjoint[i,j,1] = (y_adjoint[i-1,j,1] + y_adjoint[i+1,j,1] + y_adjoint[i,j-1,1] + y_adjoint[i,j+1,1])/4 + \
(2*y_adjoint[i,j,2] - y_adjoint[i,j-2,2] - y_adjoint[i,j+2,2] + 2*y_adjoint[i,j,2] - y_adjoint[i-2,j,2] - y_adjoint[i+2,j,2])/8
# Red and blue channel interpolation
for i in range(1 , height-1):
for j in range(1, width-1):
if y_adjoint[i,j,2] != 0 :
y_adjoint[i,j,0] = (y_adjoint[i-1,j-1,0] + y_adjoint[i-1,j+1,0] + y_adjoint[i+1,j-1,0] + y_adjoint[i+1,j+1,0]) / 4
elif y_adjoint[i,j,0] != 0:
y_adjoint[i,j,2] = (y_adjoint[i-1,j-1,2] + y_adjoint[i-1,j+1,2] + y_adjoint[i+1,j-1,2] + y_adjoint[i+1,j+1,2]) / 4
else:
y_adjoint[i,j] = y_adjoint[i,j]
for i in range(1 , height-1):
for j in range(1, width-1):
if y_adjoint[i,j,0] == y_adjoint[i,j,2]:
y_adjoint[i,j,0] = (y_adjoint[i-1,j,0] + y_adjoint[i,j-1,0] + y_adjoint[i+1,j,0] + y_adjoint[i,j+1,0]) / 4
y_adjoint[i,j,2] = (y_adjoint[i-1,j,2] + y_adjoint[i,j-1,2] + y_adjoint[i+1,j,2] + y_adjoint[i,j+1,2]) / 4
return y_adjoint
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# 2023
# Authors: Mauro Dalla Mura and Matthieu Muller
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