OpenCV (Open Source Computer Vision) is a computer vision library that contains various functions to perform operations on Images or videos. OpenCV library can be used to perform multiple operations on videos.
Let’s see how to detect the corner in the image.
cv2.goodFeaturesToTrack() method finds N strongest corners in the image by Shi-Tomasi method. Note that the image should be a grayscale image. Specify the number of corners you want to find and the quality level (which is a value between 0-1). It denotes the minimum quality of corner below which everyone is rejected. Then provide the minimum Euclidean distance between corners detected.
Syntax :
cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]])
Image before corner detection:
# import the required library import numpy as np
import cv2
from matplotlib import pyplot as plt
# read the image img = cv2.imread('corner1.png')
# convert image to gray scale image gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# detect corners with the goodFeaturesToTrack function. corners = cv2.goodFeaturesToTrack(gray, 27, 0.01, 10)
corners = np.int0(corners)
# we iterate through each corner, # making a circle at each point that we think is a corner. for i in corners:
x, y = i.ravel()
cv2.circle(img, (x, y), 3, 255, -1)
plt.imshow(img), plt.show() |
Image after corner detection –