[FIXED] contrast normalization of image python

Issue

i want to ask how to get the image result (Icon) with python code as indicated in

where ishade is a preprocessed image and std(Ishade) is the standard deviation of this image

result = ndimage.median_filter(blur, size=68)
std=cv2.meanStdDev(result)

Solution

I tried to follow the article in the reference you posted and the reference in that post to the original. But I do not get exactly what they do. Nevertheless, here is my interpretation (apart from the initial CLAHE). You can adjust the mean and median filter sizes as desired.

Input:

import cv2
import numpy as np
import skimage.exposure

# load image
img = cv2.imread("lena.jpg")

# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Gaussian blurred gray image
mean = cv2.GaussianBlur(gray, (0,0), sigmaX=5, sigmaY=5)

# apply median filter to mean image
median = cv2.medianBlur(mean, 25)

# divide mean by median
division = cv2.divide(mean.astype(np.float64)/255, median.astype(np.float64)/255)

# get global standard deviation of division
std = np.std(division)
print(std)

# divide the division by the std and normalize to range 0 to 255 as unint8
result = np.divide(division, std)
result = skimage.exposure.rescale_intensity(result, in_range='image', out_range=(0,255)).astype(np.uint8)
    
# write result to disk
cv2.imwrite("lena_std_division2.jpg", result)

# display it
cv2.imshow("mean", mean)
cv2.imshow("median", median)
cv2.imshow("division", division)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result:

Answered By - fmw42