opencv python 使用特征匹配和单应性查找对象

摘要：为了解决这个问题，算法使用和可以通过参数来设定所以好的匹配提供的正确的估计被称为，剩下的被称为返回一个掩模，这个掩模确定了和点

Feature Matching + Homography to find Objects

联合使用特征提取和 calib3d 模块中的 findHomography 在复杂图像中查找已知对象.

之前在一张杂乱的图像中找到了一个对象（的某些部分）的位置.这些信息足以帮助我们在目标图像中准确的找到（查询图像）对象.为了达到这个目的可以使用 calib3d 模块中的cv2.findHomography()函数.如果将这两幅图像中的特征点集传给这个函数，他就会找到这个对象的透视图变换.然后就可以使用函数 cv2.perspectiveTransform() 找到这个对象了.至少要 4 个正确的点才能找到这种变换.在匹配过程可能会有一些错误，而这些错误会影响最终结果。为了解决这个问题，算法使用 RANSAC 和 LEAST_MEDIAN(可以通过参数来设定).所以好的匹配提供的正确的估计被称为 inliers，剩下的被称为outliers.cv2.findHomography() 返回一个掩模，这个掩模确定了 inlier 和outlier 点.

import numpy as np
import cv2
import matplotlib.pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread("img.jpg",0)          # queryImage
img2 = cv2.imread("img1.jpg",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 = 0
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)
if len(good)>MIN_MATCH_COUNT:
    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)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()

    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts,M)

    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)

else:
    print("Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))
    matchesMask = None

# Finally we draw our inliers (if successfully found the object) or matching keypoints (if failed).

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()