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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()
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