dataset.py 文件源码

def csv_inputs_test(self, csv_file_path):
        filename_queue = tf.train.string_input_producer([csv_file_path], shuffle=False)
        reader = tf.TextLineReader()
        _, serialized_example = reader.read(filename_queue)
        filename, depth_filename, depthMeters_filename = tf.decode_csv(serialized_example, [["path"], ["annotation"], ["meters"]])
        # input
        rgb_png = tf.read_file(filename)
        image = tf.image.decode_png(rgb_png, channels=3)
        image = tf.cast(image, tf.float32)
        # target
        depth_png = tf.read_file(depth_filename)
        depth = tf.image.decode_png(depth_png, channels=1)
        depth = tf.cast(depth, tf.float32)
        depth = tf.div(depth, [255.0])
        # resize
        image = tf.image.resize_images(image, (IMAGE_HEIGHT, IMAGE_WIDTH))
        depth = tf.image.resize_images(depth, (TARGET_HEIGHT, TARGET_WIDTH))
        invalid_depth = tf.sign(depth)
        # generate batch
        images, depths, invalid_depths, filenames, depth_filenames = tf.train.batch(
            [image, depth, invalid_depth, filename, depth_filename],
            batch_size=self.batch_size,
            num_threads=4,
            capacity= 50 + 3 * self.batch_size,
        )
        return images, depths, invalid_depths, filenames, depth_filenames