Python-opencv3 特征匹配match和drawMatches的使用

The result of matches = bf.match(des1,des2) line is a list of DMatch objects. This DMatch object has following attributes:    DMatch.distance - Distance between descriptors. The lower, the better it is.    DMatch.trainIdx - Index of the descriptor in train descriptors    DMatch.queryIdx - Index of the descriptor in query descriptors    DMatch.imgIdx - Index of the train image.

import cv2img1 = cv2.imread('s1.jpg')          # queryImageimg2 = cv2.imread('s2.jpg') # trainImage#%% BF+ORB methoddef BF_ORB():  # Initiate ORB detector  orb = cv2.ORB_create()  # find the keypoints and descriptors with SIFT  kp1, des1 = orb.detectAndCompute(img1,None)  kp2, des2 = orb.detectAndCompute(img2,None)   # create BFMatcher object  bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)  # Match descriptors.  matches = bf.match(des1,des2)  # Sort them in the order of their distance.  matches = sorted(matches, key = lambda x:x.distance)  # Draw first 10 matches.  img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[0:10], None,flags=2)  cv2.imshow('result.jpg',img3)  cv2.waitKey(0)  cv2.destroyAllWindows()  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  #%%BF+SIFTdef BF_SIFT():   # Initiate SIFT detector  sift = cv2.xfeatures2d.SIFT_create()  # find the keypoints and descriptors with SIFT  kp1, des1 = sift.detectAndCompute(img1,None)  kp2, des2 = sift.detectAndCompute(img2,None)  # BFMatcher with default params  bf = cv2.BFMatcher()  matches = bf.knnMatch(des1,des2, k=2)  # Apply ratio test  good = []  for m,n in matches:    if m.distance < 0.75*n.distance:      good.append([m])  # cv2.drawMatchesKnn expects list of lists as matches.  img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good[:10],None, flags=2)  cv2.imshow('result.jpg',img3)  cv2.waitKey(0)  cv2.destroyAllWindows()  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  def FLANN():  '''  FLANN stands for Fast Library for Approximate Nearest Neighbors.   It contains a collection of algorithms optimized for fast nearest   neighbor search in large datasets and for high dimensional features.   It works more faster than BFMatcher for large datasets.   We will see the second example with FLANN based matcher.  For FLANN based matcher, we need to pass two dictionaries which specifies   the algorithm to be used, its related parameters etc. First one is IndexParams.   For various algorithms, the information to be passed is explained   in FLANN docs. As a summary, for algorithms like SIFT, SURF etc.   you can pass following:      index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)  While using ORB, you can pass the following. The commented values   are recommended as per the docs, but it didn't provide required results  in some cases. Other values worked fine:    index_params= dict(algorithm = FLANN_INDEX_LSH,                       table_number = 6, # 12                       key_size = 12,     # 20                       multi_probe_level = 1) #2  Second dictionary is the SearchParams. It specifies the number of   times the trees in the index should be recursively traversed.   Higher values gives better precision, but also takes more time.  If you want to change the value, pass search_params = dict(checks=100).  '''  sift = cv2.xfeatures2d.SIFT_create()  # 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)  # Need to draw only good matches, so create a mask  matchesMask = [[0,0] for i in range(len(matches))]  # ratio test as per Lowe's paper  for i,(m,n) in enumerate(matches):    if m.distance < 0.7*n.distance:        matchesMask[i]=[1,0]  draw_params = dict(matchColor = (0,255,0),                   singlePointColor = (255,0,0),                  matchesMask = matchesMask,                  flags = 0)  img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)  cv2.imshow('result.jpg',img3)  cv2.waitKey(0)  cv2.destroyAllWindows()  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  cv2.waitKey(1)  #%%   if __name__ == '__main__':  FLANN()