alignment.py 文件源码

def find_label_clusters(kitti_base, kittiLabels, shape, num_clusters, descriptors=None):
    if descriptors is None:
        progressbar = ProgressBar('Computing descriptors', max=len(kittiLabels))
        descriptors = []
        for label in kittiLabels:
            progressbar.next()
            img = getCroppedSampleFromLabel(kitti_base, label)
            # img = cv2.resize(img, (shape[1], shape[0]), interpolation=cv2.INTER_AREA)
            img = resizeSample(img, shape, label)
            hist = get_hog(img)
            descriptors.append(hist)
        progressbar.finish()
    else:
        print 'find_label_clusters,', 'Using supplied descriptors.'
        print len(kittiLabels), len(descriptors)
        assert(len(kittiLabels) == len(descriptors))

    # X = np.random.randint(25,50,(25,2))
    # Y = np.random.randint(60,85,(25,2))
    # Z = np.vstack((X,Y))

    # convert to np.float32
    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(kittiLabels, label)
    return clusters
    # # Plot the data
    # from matplotlib import pyplot as plt
    # plt.scatter(A[:,0],A[:,1])
    # plt.scatter(B[:,0],B[:,1],c = 'r')
    # plt.scatter(center[:,0],center[:,1],s = 80,c = 'y', marker = 's')
    # plt.xlabel('Height'),plt.ylabel('Weight')
    # plt.show()