Harris Corner Detector - Python(OpenCV)

1.      -  determne which windows produce very large variatons in intensity when moved in both X and Y directions.

2.       - With each such windows found, a score R is computed.

3.       - After applying a threshold to this score, important corners are selected & marked.

Img: imput 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.

[Red Marks Detected Corners]

import numpy as np
import cv2 as cv

img = cv.imread('chessboard.png')

cv.imshow('img', img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

gray = np.float32(gray)
dst = cv.cornerHarris(gray, 2, 3, 0.04)
dst = cv.dilate(dst, None)

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

if cv.waitKey(0) & 0xff == 27:
    cv.destroyAllWindows()