2019-9-10 opencv特征检测和描述10-特征匹配+单应（Homography，投影映射）查找对象

    # -*- coding: cp936 -*-
    import cv2 
    import numpy as np
    from matplotlib import pyplot as plt
    
    MIN_MATCH_COUNT = 10
    img1 = cv2.imread('box.png',0)          # queryImage
    img2 = cv2.imread('box_in_scene.png',0) # trainImage
    # 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)
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
    search_params = dict(checks = 50)
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1,des2,k=2)
    # store all the good matches as per Lowe's ratio test.
    good = []
    for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    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 计算单应矩阵所使用的方法
    #0 - 利用所有点的常规方法
    #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，?将点对视为内点的最大允许重投影错误阈值。
    #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M 为变换矩阵。    
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()
    
    # 获得原图像的高和宽
    h,w= img1.shape #官网为 h,w,d = img1.shape，运行报错
    
    # 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,M)
    
    # 原图像为灰度图
    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
    else:
    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
    matchesMask = None
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    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)
    plt.imshow(img3, 'gray'),plt.show()
    
    
      
      
      
      
      
      
    
    代码解读