facenet.py 文件源码

def read_and_augment_data(image_list, label_list, image_size, batch_size, max_nrof_epochs, 
        random_crop, random_flip, random_rotate, nrof_preprocess_threads, shuffle=True):

    images = ops.convert_to_tensor(image_list, dtype=tf.string)
    labels = ops.convert_to_tensor(label_list, dtype=tf.int32)

    # Makes an input queue
    input_queue = tf.train.slice_input_producer([images, labels],
        num_epochs=max_nrof_epochs, shuffle=shuffle)

    images_and_labels = []
    for _ in range(nrof_preprocess_threads):
        image, label = read_images_from_disk(input_queue)
        if random_rotate:
            image = tf.py_func(random_rotate_image, [image], tf.uint8)
        if random_crop:
            image = tf.random_crop(image, [image_size, image_size, 3])
        else:
            image = tf.image.resize_image_with_crop_or_pad(image, image_size, image_size)
        if random_flip:
            image = tf.image.random_flip_left_right(image)
        #pylint: disable=no-member
        image.set_shape((image_size, image_size, 3))
        image = tf.image.per_image_standardization(image)
        images_and_labels.append([image, label])

    image_batch, label_batch = tf.train.batch_join(
        images_and_labels, batch_size=batch_size,
        capacity=4 * nrof_preprocess_threads * batch_size,
        allow_smaller_final_batch=True)

    return image_batch, label_batch