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Commit 05b16945 authored by Matthieu Muller's avatar Matthieu Muller
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Merge branch 'master' into 'master'

EL-MURR

See merge request mullemat/sicom_image_analysis_project!32
parents 91157269 aba74218
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import numpy as np
from scipy.signal import correlate2d
from src.forward_model import CFA
def malvar_he_cutler(y: np.ndarray, op: CFA ) -> np.ndarray:
"""Performs demosaicing using the malvar-he-cutler algorithm
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
red_mask, green_mask, blue_mask = [op.mask[:, :, 0], op.mask[:, :, 1], op.mask[:, :, 2]]
mosaicked_image = np.float32(y)
demosaicked_image = np.empty(op.input_shape)
if op.cfa == 'quad_bayer':
filters = get_quad_bayer_filters()
else:
filters = get_default_filters()
demosaicked_image = apply_demosaicking_filters(
mosaicked_image,demosaicked_image, red_mask, green_mask, blue_mask, filters
)
return demosaicked_image
def get_quad_bayer_filters():
coefficient_scale = 0.03125
return {
"G_at_R_and_B": np.array([
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, 2, 2, 0, 0, 0, 0],
[0, 0, 0, 0, 2, 2, 0, 0, 0, 0],
[-1, -1, 2, 2, 4, 4, 2, 2, -1, -1],
[-1, -1, 2, 2, 4, 4, 2, 2, -1, -1],
[0, 0, 0, 0, 2, 2, 0, 0, 0, 0],
[0, 0, 0, 0, 2, 2, 0, 0, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0]
]) * coefficient_scale,
"R_at_GR_and_B_at_GB": np.array([
[0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0],
[0, 0, -1, -1, 0, 0, -1, -1, 0, 0],
[0, 0, -1, -1, 0, 0, -1, -1, 0, 0],
[-1, -1, 4, 4, 5, 5, 4, 4, -1, -1],
[-1, -1, 4, 4, 5, 5, 4, 4, -1, -1],
[0, 0, -1, -1, 0, 0, -1, -1, 0, 0],
[0, 0, -1, -1, 0, 0, -1, -1, 0, 0],
[0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0]
]) * coefficient_scale,
"R_at_GB_and_B_at_GR": np.array([
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
[0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
[0.5, 0.5, 0, 0, 5, 5, 0, 0, 0.5, 0.5],
[0.5, 0.5, 0, 0, 5, 5, 0, 0, 0.5, 0.5],
[0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
[0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, -1, -1, 0, 0, 0, 0]
]) * coefficient_scale,
"R_at_B_and_B_at_R": np.array([
[0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
[0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
[-1.5, -1.5, 0, 0, 6, 6, 0, 0, -1.5, -1.5],
[-1.5, -1.5, 0, 0, 6, 6, 0, 0, -1.5, -1.5],
[0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
[0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
[0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0]
]) * coefficient_scale,
}
def get_default_filters():
coefficient_scale = 0.125
return {
"G_at_R_and_B": np.array([
[0, 0, -1, 0, 0],
[0, 0, 2, 0, 0],
[-1, 2, 4, 2, -1],
[0, 0, 2, 0, 0],
[0, 0, -1, 0, 0]
]) * coefficient_scale,
"R_at_GR_and_B_at_GB": np.array([
[0, 0, 0.5, 0, 0],
[0, -1, 0, -1, 0],
[-1, 4, 5, 4, -1],
[0, -1, 0, -1, 0],
[0, 0, 0.5, 0, 0]
]) * coefficient_scale,
"R_at_GB_and_B_at_GR": np.array([
[0, 0, -1, 0, 0],
[0, -1, 4, -1, 0],
[0.5, 0, 5, 0, 0.5],
[0, -1, 4, -1, 0],
[0, 0, -1, 0, 0]
]) * coefficient_scale,
"R_at_B_and_B_at_R": np.array([
[0, 0, -1.5, 0, 0],
[0, 2, 0, 2, 0],
[-1.5, 0, 6, 0, -1.5],
[0, 2, 0, 2, 0],
[0, 0, -1.5, 0, 0]
]) * coefficient_scale,
}
def apply_demosaicking_filters(image, res, red_mask, green_mask, blue_mask, filters):
red_channel = image * red_mask
green_channel = image * green_mask
blue_channel = image * blue_mask
# Create the green channel after applying a filter
green_channel = np.where(
np.logical_or(red_mask == 1, blue_mask == 1),
correlate2d(image, filters['G_at_R_and_B'], mode="same", boundary="symm"),
green_channel
)
# Define masks for extracting pixel values
red_row_mask = np.any(red_mask == 1, axis=1)[:, np.newaxis].astype(np.float32)
red_col_mask = np.any(red_mask == 1, axis=0)[np.newaxis].astype(np.float32)
blue_row_mask = np.any(blue_mask == 1, axis=1)[:, np.newaxis].astype(np.float32)
blue_col_mask = np.any(blue_mask == 1, axis=0)[np.newaxis].astype(np.float32)
def update_channel(channel, row_mask, col_mask, filter_key):
return np.where(
np.logical_and(row_mask == 1, col_mask == 1),
correlate2d(image, filters[filter_key], mode="same", boundary="symm"),
channel
)
# Update the red channel and blue channel
red_channel = update_channel(red_channel, red_row_mask, blue_col_mask, 'R_at_GR_and_B_at_GB')
red_channel = update_channel(red_channel, blue_row_mask, red_col_mask, 'R_at_GB_and_B_at_GR')
blue_channel = update_channel(blue_channel, blue_row_mask, red_col_mask, 'R_at_GR_and_B_at_GB')
blue_channel = update_channel(blue_channel, red_row_mask, blue_col_mask, 'R_at_GB_and_B_at_GR')
# Update R channel and B channel again
red_channel = update_channel(red_channel, blue_row_mask, blue_col_mask, 'R_at_B_and_B_at_R')
blue_channel = update_channel(blue_channel, red_row_mask, red_col_mask, 'R_at_B_and_B_at_R')
res[:, :, 0] = red_channel
res[:, :, 1] = green_channel
res[:, :, 2] = blue_channel
return res
\ No newline at end of file
"""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.el_murr.malvar import malvar_he_cutler
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.
input_shape = (y.shape[0], y.shape[1], 3)
op = CFA(cfa, input_shape)
res = malvar_he_cutler(y,op)
return res
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# 2023
# Authors: Mauro Dalla Mura and Matthieu Muller
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