Opencv实现程序-部分10

import numpy as np import cv2 from matplotlib import pyplot as plt imgname1 = './w2.jpg' imgname2 = './w1.jpg' # cv2.xfeatures2d.SURF_create()函数 surf = cv2.xfeatures2d.SURF_create() # FLANNbasedMatched FLANN_INDEX_KDTREE = 0 # 词典1 dict FLANN_INDEX_KDTREE # 词典2 dict index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks=50) # cv2.FlannBasedMatcher函数 flann = cv2.FlannBasedMatcher(index_params,search_params) img1 = cv2.imread(imgname1) img1 = cv2.resize(img1,(473, 316),interpolation = cv2.INTER_LINEAR) gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) #灰度处理图像 kp1, des1 = surf.detectAndCompute(img1,None)#des是描述子 img2 = cv2.imread(imgname2) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) kp2, des2 = surf.detectAndCompute(img2,None) /* 开发不易，整理也不易，如需要详细的说明文档和程序，以及完整的数据集，训练好的模型，或者进一步开发， 可加作者新联系方式咨询，WX：Q3101759565，QQ：3101759565 */ hmerge = np.hstack((img3, img4)) #水平拼接 cv2.imshow("point", hmerge) #拼接显示为gray cv2.waitKey(0) # flannbaedMatch算法 matches = flann.knnMatch(des1,des2,k=2) good = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append([m]) img5 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2) cv2.imshow("SURF", img5) cv2.waitKey(0) cv2.destroyAllWindows()