Python+OpenCV学习（12）---特征匹配

利用python学习OpenCV，个人感觉比较方便。函数的形式与C++基本相同，所以切换过来还是比较好的，对于像我这种对python不太熟练的人，使用python的集成开发环境PyCharm进行学习，可以设置断点调试，有助于我这类初学者理解掌握。

下面是利用python语言结合OpenCV进行SIFT特征进行匹配的代码：

# -*- coding:utf-8 -*-__author__ = 'Microcosm'import cv2#from find_obj import filter_matches,explore_matchimport numpy as npdef filter_matches(kp1, kp2, matches, ratio = 0.75):    mkp1, mkp2 = [], []    for m in matches:        if len(m) == 2 and m[0].distance < m[1].distance * ratio:            m = m[0]            mkp1.append( kp1[m.queryIdx] )            mkp2.append( kp2[m.trainIdx] )    p1 = np.float32([kp.pt for kp in mkp1])    p2 = np.float32([kp.pt for kp in mkp2])    kp_pairs = zip(mkp1, mkp2)    return p1, p2, kp_pairsdef explore_match(win, img1, img2, kp_pairs, status = None, H = None):    h1, w1 = img1.shape[:2]    h2, w2 = img2.shape[:2]    vis = np.zeros((max(h1, h2), w1+w2), np.uint8)    vis[:h1, :w1] = img1    vis[:h2, w1:w1+w2] = img2    vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)    if H is not None:        corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])        corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )        cv2.polylines(vis, [corners], True, (255, 255, 255))    if status is None:        status = np.ones(len(kp_pairs), np.bool)    p1 = np.int32([kpp[0].pt for kpp in kp_pairs])    p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)    green = (0, 255, 0)    red = (0, 0, 255)    white = (255, 255, 255)    kp_color = (51, 103, 236)    for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):        if inlier:            col = green            cv2.circle(vis, (x1, y1), 2, col, -1)            cv2.circle(vis, (x2, y2), 2, col, -1)        else:            col = red            r = 2            thickness = 3            cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)            cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)            cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)            cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)    vis0 = vis.copy()    for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):        if inlier:            cv2.line(vis, (x1, y1), (x2, y2), green)    cv2.imshow(win, vis)img1 = cv2.imread("E:\python\Python Project\opencv_showimage\images\multi_view_big.jpg")img2 = cv2.imread("E:\python\Python Project\opencv_showimage\images\multi_view_small.jpg")img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)sift = cv2.SIFT()kp1,des1 = sift.detectAndCompute(img1_gray, None)kp2,des2 = sift.detectAndCompute(img2_gray, None)# BFmatcher with default parmsbf = cv2.BFMatcher(cv2.NORM_L2)matches = bf.knnMatch(des1, des2, k=2)p1,p2,kp_pairs = filter_matches(kp1,kp2,matches,ratio=0.5)explore_match('matches', img1_gray,img2_gray,kp_pairs)#img3 = cv2.drawMatchesKnn(img1_gray,kp1,img2_gray,kp2,good[:10],flag=2)cv2.waitKey(0)cv2.destroyAllWindows()

运行结果为：