deep-needs/opencv_transform/dress_to_correct.py

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()