nexar_large_speed.py 文件源码

def visualize(self, net_inputs, net_outputs):
        # input a batch of training examples of form [Tensor1, Tensor2, ... Tensor_n]
        # net_inputs: usually images; net_outputs: usually the labels
        # this function visualize the data that is read in, do not return anything but use tf.summary

        # visualize the video using multiple images
        # their is no way to visualize time sequence now, so isvalid and isstop couldn't be visualized
        if not FLAGS.no_image_input:
            decoded = net_inputs[0]
            visualize = tf.cast(decoded[0,:,:,:,:], tf.uint8)
            tf.image_summary("video_seq", visualize, max_images=FLAGS.n_sub_frame)