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| import cv2 | |
| import math | |
| import numpy as np | |
| import os | |
| # create_correct =============================================================== | |
| # return: | |
| # (<Boolean> True/False), depending on the transformation process | |
| def create_correct(cv_dress): | |
| #Production dir: | |
| return correct_color(cv_dress, 5) | |
| # correct_color ============================================================================== | |
| # return: | |
| # <RGB> image corrected | |
| def correct_color(img, percent): | |
| 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 | |
| # find the low and high precentile values (based on the input percentile) | |
| 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))] | |
| # saturate below the low percentile and above the high percentile | |
| thresholded = apply_threshold(channel, low_val, high_val) | |
| # scale the channel | |
| normalized = cv2.normalize(thresholded, thresholded.copy(), 0, 255, cv2.NORM_MINMAX) | |
| out_channels.append(normalized) | |
| return cv2.merge(out_channels) | |
| #Color correction utils | |
| def apply_threshold(matrix, low_value, 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 | |
| #Color correction utils | |
| def apply_mask(matrix, mask, fill_value): | |
| masked = np.ma.array(matrix, mask=mask, fill_value=fill_value) | |
| return masked.filled() | |