srez_input.py 文件源码

def setup_test_inputs(sess, filenames, image_size=None, capacity_factor=3):
    if image_size is None:
        image_size = FLAGS.sampleSize

    # Read each JPEG file
    reader = tf.WholeFileReader()
    filename_queue = tf.train.string_input_producer(filenames)
    key, value = reader.read(filename_queue)
    channels = 3
    image = tf.image.decode_jpeg(value, channels=channels, name="dataset_image")
    image.set_shape([None, None, channels])

    crop_size = 128

    image = tf.random_crop(image, [crop_size, crop_size, 3])
    image = tf.reshape(image, [1, crop_size, crop_size, 3])
    image = tf.cast(image, tf.float32) / 255.0

    if crop_size != image_size:
        image = tf.image.resize_area(image, [image_size, image_size])

    # The feature is simply a Kx downscaled version
    K = 4
    downsampled = tf.image.resize_area(image, [image_size // K, image_size // K])

    feature = tf.reshape(downsampled, [image_size // K, image_size // K, 3])
    label = tf.reshape(image, [image_size, image_size, 3])

    # Using asynchronous queues
    features, labels = tf.train.batch([feature, label],
                                      batch_size=FLAGS.batch_test_size,
                                      num_threads=4,
                                      capacity=capacity_factor * FLAGS.batch_test_size,
                                      name='labels_and_features')

    tf.train.start_queue_runners(sess=sess)

    return features, labels