Python | Harris Corner Detection using OpenCV

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 neighborhood 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:

# Python program for illustrators and
# angle detection with
# Harris angle detection method

# organizing imports

import cv2

import numpy as np

# path to the specified input image and
# image is loaded using imread command

image = cv2.imread ( `GeekforGeeks.jpg` )

# convert input image to
Grayscale color space

operatedImage = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)

# change data type
# setting 32-bit floating point

operatedImage = np.float32 (operatedImage)

# apply cv2.cornerHarris method
# to define corners with matching
# values as input

dest = cv2.cornerHarris (operatedImage, 2 , 5 , 0.07 )

# Results are marked with wide corners

dest = cv2.dilate (dest , None )

# Returning to the original image # with optimal threshold


  image [dest & gt;   0.01   *   dest.   max   ()]   =   [  0  ,   0  ,   255  ] 
  
  # window with output image with corners  
  cv2.imshow (  `Image with Borders`  , image) 
   
  # Unselect any associated memory usage  
  if   cv2.waitKey (  0  ) & amp;   0xff   =   =   27  : 
   cv2.destroyAllWindows ()

Input data :

Exit:

Python | Harris Corner Detection using OpenCV

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