Python：特征匹配+单应性查找多个对象

Question

我正在尝试通过 python 使用 opencv 来查找火车图像中的多个对象，并将其与从查询图像中检测到的关键点进行匹配。对于我的情况，我正在尝试检测下面提供的图像中的网球场。我看了网上的教程，只发现它只能检测1个物体。我想过插入一个循环来查找多个对象，但我失败了。关于如何做有什么想法吗？*我使用 SIFT，因为 ORB 不太适合我的情况

这是代码和一组示例图像。

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

MIN_MATCH_COUNT = 10
img1 = cv2.imread('Image 11.jpg',0)          # queryImage
img2 = cv2.imread('Image 5.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 = 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)
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 - {}/{}".format(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)
plt.imshow(img3, 'gray'),plt.show()

火车形象

查询图片

提前致谢！

Answer 1

如果您多次拥有相同的图像，那么在查找单应性时就会遇到一些问题。即使使用循环，您的关键点描述也可能会混合在不同的相同图像周围。您可以进行预处理并重新组合关键点以进行多重匹配，但对于不同尺寸的不同图像可能会很复杂，我建议使用模板匹配，但困难在于尺度和旋转不变性。您可以阅读这篇文章以获得一些帮助https://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/

希望有帮助！