
**Histogram equalization** is a process where the intensity values of an image are adjusted to create a higher overall contrast. 

**Digital Image Processing** is a significant aspect of data science. It is used to enhance and modify images so that their attributes are more easily understandable. 

>Artificial Intelligence also makes use of digital image processing to make for a consistent and advantageous dataset.

**Image histograms** are largely used to obtain information about image attributes. **Image contrast** can be determined by looking at the range of pixel intensity values that the histogram bars are spread over. 

If the range is low, image contrast is also low. Meanwhile, a higher range of pixel intensity values means a higher contrast. 

An example of a low contrast image is shown below.



import cv2
import matplotlib.pyplot as plt

img = cv2.imread('/image_1.jpg',0)
hist1 = cv2.calcHist([img],[0],None,[256],[0,256])
plt.subplot(221),plt.imshow(img);
plt.subplot(222),plt.plot(hist1);

python

Histogram equalization is done by using the following formula:
$S_k=T(r_k)=\sum_{j=0}^kP_{in}(r_j)=\frac{(L-1)}{MN}\sum_{j=0}^kn_j$\
 where 
$k=0,1,2,......,L-1$
* `L`: The maximum intensity level of the image. For a 8-bit image, L is 256.
* `M`: The width of the image.
* `N`: The height of the image.
* `n`: The frequency corresponding to each intensity level.
* `rj`: The range of values from 0 to L-1.
* `pin`: The total frequency that corresponds to a specific value of rj.
* `rk`: The new frequencies.
* `sk`: The new equalized histogram.

#   
## Code example

import cv2
import matplotlib.pyplot as plt

img = cv2.imread('/image_1.jpg',0)
hist1 = cv2.calcHist([img],[0],None,[256],[0,256])
img_2 = cv2.equalizeHist(img)
hist2 = cv2.calcHist([img_2],[0],None,[256],[0,256])
plt.subplot(221),plt.imshow(img);
plt.subplot(222),plt.plot(hist1);
plt.subplot(223),plt.imshow(img_2);
plt.subplot(224),plt.plot(hist2);

## Explanation
  


* **Line 1:** import `cv2 `imports the OpenCV library into the python file.

* **Line 2:** `import matplotlib.pyplot as plt `imports the matplotlib library into the python file.

* **Line 4** The function `cv2.imread()` reads the image and returns the image data in `img`.
* **Line 5** The function `cv2.calcHist()` calculates the intensity distribution of the image.
* **Line 6** The function `cv2.equalizeHist()` stretch out the intensity range of the image in order to improve contrast.
* **Line 8-11:** Plotting the images and their histogram.

project.tar.gz

What is histogram equalization in Python?

Histogram equalization adjusts image intensity values for enhanced contrast, crucial in digital image processing and AI.