Функция OpenCV, соответствующая нескольким объектам

Мой источник для поиска нескольких объектов с использованием дескрипторов ORB

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

Изображения результатов

Для решения этой задачи могут быть использованы следующие подходы:

1. SIFT (ПОВЕРХ) + СРЕДНИЙ СДВИГ
2. Каскады Хаара
3. HOG + SVM