Python : Feature Matching + Homography to find Multiple Objects

Question

I'm trying to use opencv via python to find multiple objects in a train image and match it with the key points detected from query image.For my case, i'm trying to detect the tennis courts in the image provided below. II looked at the online tutorials,and only figured that it can only detect 1 object. I thought of inserting a loop in for it to find multiple objects but i failed to do so. Any idea on how to do it ? *I Used SIFT as ORB does not work that well for my case

Here's the code and a sample set of images.

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

Thanks in advance!

Answer 1

If you have the same image multiple times you will have some problems finding the homography. Even with a loop, Your Keypoints descriptions might be mix around the different identical image. You could do a pretreatment and regroup the keypoint to do multiple matching but it might be complex for different image with different size I would suggest using template matching, but the difficulty is the scale and rotation invariance. You could read this article for some help https://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/

Hope it help !