flownet_tools.py 文件源码

def get_data_kitti(datadir, shuffle_all, batchs):
    """Construct input data lists for Kitti 2012 Evaluation"""

    sintel_imgs_1 = "image_2_crop/"
    sintel_flows = "flow_occ_crop/"
    with tf.name_scope('Input'):
        # after number 154 image sizes change
        list_0 = sorted(glob.glob(datadir + sintel_imgs_1 + '/*10.png'))
        list_1 = sorted(glob.glob(datadir + sintel_imgs_1 + '/*11.png'))
        flow_list = sorted(glob.glob(datadir + sintel_flows + '/*.png'))
        print(len(list_0), len(list_1), len(flow_list))
        print("Number of input length: " + str(len(list_0)))
        assert len(list_0) == len(list_1) == len(
            flow_list) != 0, ('Input Lengths not correct')

        if shuffle_all:
            p = np.random.permutation(len(list_0))
        else:
            p = np.arange(len(list_0))
        list_0 = [list_0[i] for i in p]
        list_1 = [list_1[i] for i in p]
        flow_list = [flow_list[i] for i in p]

        input_queue = tf.train.slice_input_producer(
            [list_0, list_1, flow_list],
            shuffle=False)  # shuffled before
        # image reader
        content_0 = tf.read_file(input_queue[0])
        content_1 = tf.read_file(input_queue[1])
        content_flow = tf.read_file(input_queue[2])

        imgs_0 = tf.image.decode_png(content_0, channels=3)
        imgs_1 = tf.image.decode_png(content_1, channels=3)
        imgs_0 = tf.image.convert_image_dtype(imgs_0, dtype=tf.float32)
        imgs_1 = tf.image.convert_image_dtype(imgs_1, dtype=tf.float32)
        flows = tf.cast(tf.image.decode_png(
            content_flow, channels=3, dtype=tf.uint16), tf.float32)
        # set shape

        imgs_0.set_shape(FLAGS.img_shape)
        imgs_1.set_shape(FLAGS.img_shape)
        flows.set_shape(FLAGS.img_shape)

        return tf.train.batch([imgs_0, imgs_1, flows],
                              batch_size=batchs
                              #,num_threads=1
                              )