knnMatch.py

import numpy as np
import cv2
#import random

if __name__ == '__main__':
	img1 = cv2.imread('Univ4.jpg')
	gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
	img2 = cv2.imread('Univ3.jpg')
	gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
	dst = gray1

	sift = cv2.xfeatures2d.SIFT_create()
	kp1, desc1 = sift.detectAndCompute(gray1, None)
	kp2, desc2 = sift.detectAndCompute(gray2, None)

	bf = cv2.BFMatcher()
	matches = bf.knnMatch(desc1, desc2, k=2)
	good_matches = []

	# Apply ratio test
	ratio = 0.5
	pt1 = []
	pt2 = []
	for m, n in matches:
		if m.distance < ratio * n.distance:
			good_matches.append([m])
			pt1.append(kp1[m.queryIdx].pt)
			pt2.append(kp2[m.trainIdx].pt)

	'''
	for index in range(len(pt1)):
		b = random.randint(0, 255)
		g = random.randint(0, 255)
		r = random.randint(0, 255)
		cv2.circle(img1, (int(pt1[index][0]), int(pt1[index][1])), 3, (b, g, r), thickness=-1, lineType=cv2.LINE_AA)
		cv2.circle(img2, (int(pt2[index][0]), int(pt2[index][1])), 3, (b, g, r), thickness=-1, lineType=cv2.LINE_AA)
	'''

	draw = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good_matches, None, flags=2)
	cv2.imshow('draw', draw)
	cv2.waitKey(0)
	cv2.destroyAllWindows()