get_hof.py 文件源码

def get_hof(name):
    print name

    FLOW_DIR = 'data/of_' + args.domain + '/' + name + '/'
    BOXES_DIR = 'data/feature_' + args.domain + \
        '_' + str(args.n_boxes) + 'boxes/' + name + '/'

    n_frames = len(glob.glob(FLOW_DIR + '*.png'))

    # init boxes
    clip_boxes_index = 1
    clip_boxes = np.load(BOXES_DIR + 'roislist{:04d}.npy'.format(clip_boxes_index))

    # init hof
    hof_shape = (50, args.n_boxes, 12)
    hof = np.zeros(hof_shape)

    for i in xrange(1, n_frames+1):
        print "{}, Flow {}, ".format(name, i)
        # boxes
        new_clip_boxes_index = (i-1) / 50 + 1
        if clip_boxes_index != new_clip_boxes_index:
            # 1.1 save hof and init a new one
            np.save(BOXES_DIR + 'hof{:04d}.npy'.format(clip_boxes_index), hof)
            hof = np.zeros(hof_shape)

            # 2.1 update clip_boxes
            clip_boxes_index = new_clip_boxes_index
            clip_boxes = np.load(BOXES_DIR + 'roislist{:04d}.npy'.format(clip_boxes_index))

        flow_img = np.array(cv2.imread(FLOW_DIR + '{:06d}.png'.format(i)), dtype=np.float32)

        frame_boxes = clip_boxes[(i-1) % 50].astype(int)
        for box_id, (xmin, ymin, xmax, ymax) in enumerate(frame_boxes):
            xmin, ymin, xmax, ymax = preprocess_box(xmin, ymin, xmax, ymax)
            box_flow_img = flow_img[ymin:ymax, xmin:xmax, :]
            hof[(i-1) % 50][box_id], _ = flow_to_hist(box_flow_img)

    # save latest hof
    np.save(BOXES_DIR + 'hof{:04d}.npy'.format(clip_boxes_index), hof)