Python | Corner detection with Harris Corner Detection method using OpenCV

Last Updated : 04 Jan, 2023

Harris Corner detection algorithm was developed to identify the internal corners of an image. The corners of an image are basically identified as the regions in which there are variations in large intensity of the gradient in all possible dimensions and directions. Corners extracted can be a part of the image features, which can be matched with features of other images, and can be used to extract accurate information. Harris Corner Detection is a method to extract the corners from the input image and to extract features from the input image.
About the function used:

Syntax: cv2.cornerHarris(src, dest, blockSize, kSize, freeParameter, borderType)
Parameters:
src – Input Image (Single-channel, 8-bit or floating-point)
dest – Image to store the Harris detector responses. Size is same as source image
blockSize – Neighborhood size ( for each pixel value blockSize * blockSize neighbourhood is considered )
ksize – Aperture parameter for the Sobel() operator
freeParameter – Harris detector free parameter
borderType – Pixel extrapolation method ( the extrapolation mode used returns the coordinate of the pixel corresponding to the specified extrapolated pixel )

Below is the Python implementation :

Python3

# Python program to illustrate 
# corner detection with 
# Harris Corner Detection Method 
  
# organizing imports 
import cv2 
import numpy as np 
  
# path to input image specified and  
# image is loaded with imread command 
image = cv2.imread('GeekforGeeks.jpg') 
  
# convert the input image into 
# grayscale color space 
operatedImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
  
# modify the data type 
# setting to 32-bit floating point 
operatedImage = np.float32(operatedImage) 
  
# apply the cv2.cornerHarris method 
# to detect the corners with appropriate 
# values as input parameters 
dest = cv2.cornerHarris(operatedImage, 2, 5, 0.07) 
  
# Results are marked through the dilated corners 
dest = cv2.dilate(dest, None) 
  
# Reverting back to the original image, 
# with optimal threshold value 
image[dest > 0.01 * dest.max()]=[0, 0, 255] 
  
# the window showing output image with corners 
cv2.imshow('Image with Borders', image) 
  
# De-allocate any associated memory usage  
if cv2.waitKey(0) & 0xff == 27: 
    cv2.destroyAllWindows() 