Image Processing
Consider an image whose pixel values are confined to some specific range of values only. For eg, brighter image will have all pixels confined to high values. But a good image will have pixels from all regions of the image. So you need to stretch this histogram to either ends (as given in below image) and that is what Histogram Equalization does (in simple words). This normally improves the contrast of the image.
#Histogram Equalization
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('image.jpg',0)
hist,bins = np.histogram(img.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * float(hist.max()) / cdf.max()
plt.plot(cdf_normalized, color = 'b')
plt.hist(img.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()
You can see histogram lies in brighter region. We need the full spectrum. For that, we need a transformation function which maps the input pixels in brighter region to output pixels in full region. That is what histogram equalization does.
Now we find the minimum histogram value (excluding 0) and apply the histogram equalization equation as given in wiki page. But I have used here, the masked array concept array from Numpy. For masked array, all operations are performed on non-masked elements. You can read more about it from Numpy docs on masked arrays.
cdf_m = np.ma.masked_equal(cdf,0)
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0).astype('uint8')Now we have the look-up table that gives us the information on what is the output pixel value for every input pixel value. So we just apply the transform.
img2 = cdf[img]Now we calculate its histogram and cdf as before ( you do it) and result looks like below :
Another important feature is that, even if the image was a darker image (instead of a brighter one we used), after equalization we will get almost the same image as we got. As a result, this is used as a "reference tool" to make all images with same lighting conditions. This is useful in many cases. For example, in face recognition, before training the face data, the images of faces are histogram equalized to make them all with same lighting conditions.
OpenCV has a function to do this, cv.equalizeHist(). Its input is just grayscale image and output is our histogram equalized image.
Below is a simple code snippet showing its usage for same image we used :
So now you can take different images with different light conditions, equalize it and check the results.
Histogram equalization is good when histogram of the image is confined to a particular region. It won't work good in places where there is large intensity variations where histogram covers a large region, ie both bright and dark pixels are present.
References:
1. https://docs.opencv.org/4.2.0/d5/daf/tutorial_py_histogram_equalization.html