alignment.py 文件源码

def find_sample_clusters(pos_reg_generator, window_dims, hog, num_clusters):
    regions = list(pos_reg_generator)
    descriptors = trainhog.compute_hog_descriptors(hog, regions, window_dims, 1)

    # convert to np.float32
    descriptors = [rd.descriptor for rd in descriptors]
    Z = np.float32(descriptors)

    # define criteria and apply kmeans()
    K = num_clusters
    print 'find_label_clusters,', 'kmeans:', K
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    attempts = 10
    ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS)
    # ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS)

    print 'ret:', ret
    # print 'label:', label
    # print 'center:', center

    # # Now separate the data, Note the flatten()
    # A = Z[label.ravel()==0]
    # B = Z[label.ravel()==1]

    clusters = partition(regions, label)
    return clusters