Hello, I would like to implement a feature-matching-approach for multiple object detection. In related questions [http://answers.opencv.org/question/17985/detecting-multiple-instances-of-same-object-with-keypoint-matching-approach/] [http://answers.opencv.org/question/45888/detection-of-multiple-instance-of-object-with-keypoint-descriptors-brisk-orb/] a meanshift clustering for the feature points is recommended. In SO a python implementation is given. Is there a c++-equivalent for the approach from V. Gai. Especially the MeanShift part?
import cv2
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
img1 = cv2.imread('box.png', 0) # queryImage
img2 = cv2.imread('box1.png', 0) # trainImage
orb = cv2.ORB_create(10000, 1.2, nlevels=8, edgeThreshold = 5)
# find the keypoints and descriptors with ORB
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
x = np.array([kp2[0].pt])
for i in xrange(len(kp2)):
x = np.append(x, [kp2[i].pt], axis=0)
x = x[1:len(x)]
bandwidth = estimate_bandwidth(x, quantile=0.1, n_samples=500)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True, cluster_all=True)
ms.fit(x)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print("number of estimated clusters : %d" % n_clusters_)
s = [None] * n_clusters_
for i in xrange(n_clusters_):
l = ms.labels_
d, = np.where(l == i)
print(d.__len__())
s[i] = list(kp2[xx] for xx in d)
des2_ = des2
for i in xrange(n_clusters_):
kp2 = s[i]
l = ms.labels_
d, = np.where(l == i)
des2 = des2_[d, ]
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)
des1 = np.float32(des1)
des2 = np.float32(des2)
matches = flann.knnMatch(des1, des2, 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)>3:
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, 2)
if M is None:
print ("No Homography")
else:
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)
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()
else:
print ("Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))
matchesMask = None