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Phase 0: Color correction.
Adjusts image contrast by clipping extreme pixel values and normalizing
each color channel independently.
"""
import cv2
import math
import numpy as np
def correct_color(img, percent=5):
"""Apply percentile-based color correction to an image.
Args:
img: BGR image (numpy array) with 3 channels.
percent: Percentile range to clip (0-100).
Returns:
Color-corrected BGR image.
"""
assert img.shape[2] == 3
assert percent > 0 and percent < 100
half_percent = percent / 200.0
channels = cv2.split(img)
out_channels = []
for channel in channels:
assert len(channel.shape) == 2
height, width = channel.shape
vec_size = width * height
flat = channel.reshape(vec_size)
assert len(flat.shape) == 1
flat = np.sort(flat)
n_cols = flat.shape[0]
low_val = flat[math.floor(n_cols * half_percent)]
high_val = flat[math.ceil(n_cols * (1.0 - half_percent))]
thresholded = _apply_threshold(channel, low_val, high_val)
normalized = cv2.normalize(
thresholded, thresholded.copy(), 0, 255, cv2.NORM_MINMAX
)
out_channels.append(normalized)
return cv2.merge(out_channels)
def _apply_threshold(matrix, low_value, high_value):
"""Clip matrix values below low_value and above high_value."""
low_mask = matrix < low_value
matrix = _apply_mask(matrix, low_mask, low_value)
high_mask = matrix > high_value
matrix = _apply_mask(matrix, high_mask, high_value)
return matrix
def _apply_mask(matrix, mask, fill_value):
"""Fill masked positions with fill_value."""
masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
return masked.filled()
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