Histogram equalization gives me all black image-CodePudding

does anyone can help me? I am studying image processing and I am writing a code to do a Histogram equalization, but the output always give me an all black image.

import numpy as np
import scipy.misc, math
from PIL import Image


img = Image.open('/home/thaidy/Desktop/ex.jpg').convert('L')
#converting to ndarray
img1 = np.asarray(img)

#converting to 1D
fl = img1.flatten()

#histogram and the bins are computed
hist, bins = np.histogram(img1,256,[0,255])

#cdf computed
cdf = hist.cumsum()

#places where cdf = 0 is ignored
#rest stored in cdf_m
cdf_m = np.ma.masked_equal(cdf,0)

#histogram eq is performed
num_cdf_m = (cdf_m - cdf_m.min())*255
den_cdf_m =  (cdf_m.max()-cdf_m.min())*255
cdf_m = num_cdf_m/den_cdf_m

#the masked places are now 0
cdf = np.ma.filled(cdf_m,0).astype('uint8')
#cdf values assigned in the flattened array
im2 = cdf[fl]
#transformin in 2D
im3 = np.reshape(im2,img1.shape)
im4 = Image.fromarray(im3)
im4.save('/home/thaidy/Desktop/output.jpg')
im4.show()

This is my code

CodePudding user response：

The cdf needs to be normalized before the equalization.

One way to do that is to set the optional parameter density of np.histogram to True:

hist, bins = np.histogram(img1,256,[0,255],density=True)

Other way is to divide cdf after computation by total pixel count:

cdf = hist.cumsum()
cdf /= cdf[-1]

I would also change the equalization part to simply:

T = (255 * cdf).astype(np.uint8)
im2 = T[fl]

Wikipedia suggests to use this transformation formula instead:

T = (np.ceil(256 * cdf) - 1).astype(np.uint8)