How to do it...

1. Import the necessary packages:

import sys 
import cv2 
import numpy as np 

2. Load the input image:

in_file = sys.argv[1] 
image = cv2.imread(in_file) 
cv2.imshow('Input image', image) 
image_gray1 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
image_gray2 = np.float32(image_gray1) 

3. Implement the Harris corner detection scheme:

image_harris1 = cv2.cornerHarris(image_gray2, 7, 5, 0.04) 

4. Dilate the input image and construct the corners:

image_harris2 = cv2.dilate(image_harris1, None) 

5. Implement image thresholding:

image[image_harris2 > 0.01 * image_harris2.max()] = [0, 0, 0] 

6. Display the input image:

cv2.imshow('Harris Corners', image) 

7. Wait for the instruction from the operator:

cv2.waitKey() 

8. The command used to execute the Detecting_corner.py Python program file, along with the input image (box.jpg), is shown here:

9. The input image used to execute the Detecting_corner.py file is shown here:

10. Harris Corners obtained after executing the Detecting_corner.py file are shown here:

In order to learn how it works for an input image, refer to the following:

• Image corner detection involves finding the edges/corners in the given picture. It can be used to extract the vital shape features from grayscale and RGB pictures. Refer to this survey paper on edge and corner detection:

https://pdfs.semanticscholar.org/24dd/6c2c08f5601e140aad5b9170e0c7485f6648.pdf.