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Last active June 26, 2023 00:52
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Simple color balance algorithm using Python 2.7.8 and OpenCV 2.4.10. Ported from:
import cv2
import math
import numpy as np
import sys
def apply_mask(matrix, mask, fill_value):
masked =, mask=mask, fill_value=fill_value)
return masked.filled()
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
def simplest_cb(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))]
print "Lowval: ", low_val
print "Highval: ", high_val
# 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)
return cv2.merge(out_channels)
if __name__ == '__main__':
img = cv2.imread(sys.argv[1])
out = simplest_cb(img, 1)
cv2.imshow("before", img)
cv2.imshow("after", out)
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what is used of below line:
half_percent = percent / 200.0.
why percent is divided by 200.

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why percent is divided by 200.

The percent parameter is expressed in natural language (number 5 means 5%) thus there is a division by 100 to express it at a fraction of 1, which is easier to work with (you do not need to normalize at each computation). The division by 2 is to get its half (half is used to cut the darks, half to cut the brights).
When you combine the two divisions, you get a division by 200.

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bulatnv commented Dec 1, 2020

@DavidYKay, @JackDesBwa, @jbonyun.
Thank you, guys. Excelent work. Thumb up!

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