python-3.x - python : Feature Matching + Homography to find Multiple Objects

Question

我正在尝试通过 python 使用 opencv 在火车图像中查找多个对象，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，发现只能检测1个物体。我想在其中插入一个循环以查找多个对象，但我没有这样做。关于如何做的任何想法？*我使用 SIFT，因为 ORB 对我的情况效果不佳

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()

Train Image

Query Image

提前致谢!

Answer 1

如果您多次使用相同的图像，您在寻找单应性时会遇到一些问题。即使有循环，您的关键点描述也可能围绕不同的相同图像混合。您可以进行预处理并重新组合关键点以进行多重匹配，但对于不同大小的不同图像可能会很复杂我建议使用模板匹配，但困难在于缩放和旋转不变性。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/

希望对您有所帮助!