opencv学习——cv2.findHomography()

# 第三个参数 Method used to computed a homography matrix. The following methods are possible:        #0 - a regular method using all the points        #CV_RANSAC - RANSAC-based robust method        #CV_LMEDS - Least-Median robust method        # 第四个参数取值范围在 1 到 10 , 绝一个点对的阈值。原图像的点经过变换后点与目标图像上对应点的误差        # 超过误差就认为是 outlier        # 返回值中 H 为变换矩阵。mask是掩模，online的点        H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)

画出匹配点的连线，以及拼接图。
代码：

import cv2import numpy as npMIN_MATCH_COUNT = 10img1 = cv2.imread('images/1.jpg')          # queryImageimg2 = cv2.imread('images/2.jpg')          # trainImagedef SIFT():    # Initiate SIFT detector    sift = cv2.xfeatures2d.SIFT_create()    # find the keypoints and descriptors with SIFT    kp1, des1 = sift.detectAndCompute(img2,None)    kp2, des2 = sift.detectAndCompute(img1,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.    good_2 = np.expand_dims(good, 1)    matching = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good_2[:20],None, flags=2)    if len(good)>MIN_MATCH_COUNT:        # 获取关键点的坐标        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)        H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)        wrap = cv2.warpPerspective(img2, H, (img2.shape[1]+img2.shape[1] , img2.shape[0]+img2.shape[0]))        wrap[0:img2.shape[0], 0:img2.shape[1]] = img1        rows, cols = np.where(wrap[:,:,0] !=0)        min_row, max_row = min(rows), max(rows) +1        min_col, max_col = min(cols), max(cols) +1        result = wrap[min_row:max_row,min_col:max_col,:]#去除黑色无用部分        return matching, resultif __name__ == '__main__':    matching, result = SIFT()    cv2.imshow('img3.jpg',matching)    cv2.imshow('result.jpg',result)    cv2.waitKey(0)    cv2.destroyAllWindows()     cv2.waitKey(1)    cv2.waitKey(1)    cv2.waitKey(1)    cv2.waitKey(1)