opencv学习——特征匹配和单应性查找图片

"""特征匹配及查找对象"""import cv2import numpy as npimport matplotlib.pylab as pltMIN_MATCH_COUNT = 10img1 = cv2.imread('images/111.jpg')          # queryImageimg2 = cv2.imread('images/11.jpg')          # trainImagedef 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)    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)        # 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。        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        matchesMask = mask.ravel().tolist()#        # 获得原图像的高和宽        h,w,_ = img1.shape#        # 使用得到的变换矩阵对原图像的四个角进行变换,获得在目标图像上对应的坐标。        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)        dst = cv2.perspectiveTransform(pts,H)        # 在原图中画出目标所在位置框, cv2.LINE_AA表示闭合框        cv2.polylines(img2,[np.int32(dst)],True,255,10, cv2.LINE_AA)    else:        print ("Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))        matchesMask = None#    draw_params = dict(matchColor = (0,255,0), # draw matches in green color#    singlePointColor = None,#    matchesMask = matchesMask, # draw only inliers#    flags = 2)#    img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)#    img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,matchesMask = matchesMask,flags=2)    good = np.expand_dims(good,1)    img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good[:20],None, flags=2)    return img3if __name__ == '__main__':    result = SIFT()    cv2.imshow('result.py',result)    cv2.waitKey(0)    cv2.destroyAllWindows()     cv2.waitKey(1)    cv2.waitKey(1)    cv2.waitKey(1)    cv2.waitKey(1)

查找结果图：
1、只画出所有inliers的点对,cv2.drawMatches()

2、画出最匹配的前20个，cv2.drawMatchesKnn()