pics_compare.py

import numpy as np
import cv2
img1=cv2.imread("/home/aastha/seminar/test/dog.jpg")   #source image
img2=cv2.imread("/home/aastha/seminar/test/ball.jpg")   #query image
img1=img1.astype('uint8')
img2=img2.astype('uint8')
gray1=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gray2=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches=flann.knnMatch(np.asarray(des1,np.float32),np.asarray(des2,np.float32),k=2)
good = []
for m,n in matches:
	if m.distance < 0.7*n.distance:
		good.append(m)
if len(good)>10:
	src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
	dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
	M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
	matchesMask = mask.ravel().tolist()
	print("match")
else:
	matchesMask = None
	print("no match")
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
cv2.imwrite('feature_matching.jpg',img3)