轉自 http://www.itnose.net/detail/6158653.html
先上效果圖.
其中
是logo標識,
是待檢測圖片.
#coding=utf-8 import cv2 import scipy as sp img1 = cv2.imread('x1.jpg',0) # queryImage img2 = cv2.imread('x2.jpg',0) # trainImage # Initiate SIFT detector sift = cv2.SIFT() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) # FLANN parameters FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks=50) # or pass empty dictionary flann = cv2.FlannBasedMatcher(index_params,search_params) matches = flann.knnMatch(des1,des2,k=2) print 'matches...',len(matches) # Apply ratio test good = [] for m,n in matches: if m.distance < 0.75*n.distance: good.append(m) print 'good',len(good) # ##################################### # visualization h1, w1 = img1.shape[:2] h2, w2 = img2.shape[:2] view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8) view[:h1, :w1, 0] = img1 view[:h2, w1:, 0] = img2 view[:, :, 1] = view[:, :, 0] view[:, :, 2] = view[:, :, 0] for m in good: # draw the keypoints # print m.queryIdx, m.trainIdx, m.distance color = tuple([sp.random.randint(0, 255) for _ in xrange(3)]) #print 'kp1,kp2',kp1,kp2 cv2.line(view, (int(kp1[m.queryIdx].pt[0]), int(kp1[m.queryIdx].pt[1])) , (int(kp2[m.trainIdx].pt[0] + w1), int(kp2[m.trainIdx].pt[1])), color) cv2.imshow("view", view) cv2.waitKey()
From : http://blog.csdn.net/handsomekang/article/details/41448697