### Histogram comparison with histogram equalization method
### To evaluate equalization
import cv2
import cv
import sys
import numpy as np
from matplotlib import pyplot as plt
def generate_histogram(img):
hist,bins = np.histogram(img.flatten(),256,[0,256])
#cumulative distribution function calculation
cdf = hist.cumsum()
cdf_normalized = cdf *hist.max()/ cdf.max() # this line not necessary.
plt.plot(cdf_normalized, color = 'b')
plt.hist(img.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histograma'), loc = 'upper left')
plt.show()
return hist
def main():
imagePath = "img.jpg"
img = cv2.imread(imagePath)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
generate_histogram(gray)
cv2.imwrite("before.jpg", gray)
gray = cv2.equalizeHist(gray)
generate_histogram(gray)
cv2.imwrite("after.jpg",gray)
return 0
if __name__ == '__main__':
main()
