How to Detect Corner in OpenCV with CornerHarris

In this OpenCV article iam going to show you How to Detect Corner in OpenCV with CornerHarris. basically in this article we are using CornerHarris algorithm.

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So now this is the complete code for How to Detect Corner in OpenCV with CornerHarris

import	cv2
import	numpy	as	np
img  =	cv2.imread('chess_board.png')
gray =	cv2.cvtColor(img,	cv2.COLOR_BGR2GRAY)
gray =	np.float32(gray)
dst  =	cv2.cornerHarris(gray,	2,	3,	0.04)
img[dst>0.01 *	dst.max()]   =	[0,	0,	255]

cv2.imshow("HarrisCorner Detection",	img)
cv2.waitKey()
cv2.destroyAllWindows()

import cv2

import numpy as np

img = cv2.imread('chess_board.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)

dst = cv2.cornerHarris(gray, 2, 3, 0.04)

img[dst>0.01 * dst.max()] = [0, 0, 255]

cv2.imshow("HarrisCorner Detection", img)

cv2.waitKey()

cv2.destroyAllWindows()

In this article iam using this image

OK in this code first of all we have loaded the image and also we have done conversion on our image, make sure that you have added the image in your working directory.

img  =	cv2.imread('chess_board.png')
gray =	cv2.cvtColor(img,  cv2.COLOR_BGR2GRAY)
gray =	np.float32(gray)

img = cv2.imread('chess_board.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)

So this is the important point and our CornerHarris algorithm

OpenCV has the function cv2.cornerHarris() for this purpose. Its arguments are :

img – Input image, it should be grayscale and float32 type.
blockSize – It is the size of neighbourhood considered for corner detection

ksize – Aperture parameter of Sobel derivative used.
k – Harris detector free parameter in the equation.

dst  =	cv2.cornerHarris(gray,	2,	3,	0.04)

1	dst = cv2.cornerHarris(gray, 2, 3, 0.04)

So now run the complete code and this will be the result

How to Detect Corner in OpenCV with CornerHarris

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