opencv python 使用特征匹配和单应性查找对象

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()