srcnn.py 文件源码

def process_data(sess, filenames):
    """
    This script gen the input images(downsample) and labels(origin images)
    """
    images_size = FLAGS.input_image_size
    reader = tf.WholeFileReader()
    filename_queue = tf.train.string_input_producer(filenames)
    _, value = reader.read(filename_queue)
    channels = FLAGS.image_channels
    image = tf.image.decode_jpeg(
        value, channels=channels, name="dataset_image")
    # add data augmentation here
    image.set_shape([None, None, channels])
    image = tf.reshape(image, [1, images_size, images_size, 3])
    image = tf.cast(image, tf.float32) / 255.0
    K = FLAGS.scale
    downsampled = tf.image.resize_area(
        image, [images_size // K, images_size // K])
    upsampled = tf.image.resize_area(downsampled, [images_size, images_size])

    feature = tf.reshape(upsampled, [images_size, images_size, 3])
    label = tf.reshape(image, [images_size, images_size, 3])
    features, labels = tf.train.shuffle_batch(
        [feature, label], batch_size=FLAGS.batch_size, num_threads=4, capacity=5000, min_after_dequeue=1000, name='labels_and_features')
    tf.train.start_queue_runners(sess=sess)
    print 'tag31', features.eval(), labels.get_shape()
    return features, labels