"""Implementation of sample defense.

This defense loads inception v3 checkpoint and classifies all images
using loaded checkpoint.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import numpy as np
from scipy.misc import imread

import tensorflow as tf
from tensorflow.contrib.slim.nets import inception

slim = tf.contrib.slim


tf.flags.DEFINE_string("master", "", "The address of the TensorFlow master to use.")

tf.flags.DEFINE_string(
    "checkpoint_path", "", "Path to checkpoint for inception network."
)

tf.flags.DEFINE_string("input_dir", "", "Input directory with images.")

tf.flags.DEFINE_string("output_file", "", "Output file to save labels.")

tf.flags.DEFINE_integer("image_width", 299, "Width of each input images.")

tf.flags.DEFINE_integer("image_height", 299, "Height of each input images.")

tf.flags.DEFINE_integer("batch_size", 16, "How many images process at one time.")

FLAGS = tf.flags.FLAGS


def load_images(input_dir, batch_shape):
    """Read png images from input directory in batches.

    Args:
      input_dir: input directory
      batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]

    Yields:
      filenames: list file names without path of each image
        Lenght of this list could be less than batch_size, in this case only
        first few images of the result are elements of the minibatch.
      images: array with all images from this batch
    """
    images = np.zeros(batch_shape)
    filenames = []
    idx = 0
    batch_size = batch_shape[0]
    for filepath in tf.gfile.Glob(os.path.join(input_dir, "*.png")):
        with tf.gfile.Open(filepath) as f:
            image = imread(f, mode="RGB").astype(np.float) / 255.0
        # Images for inception classifier are normalized to be in [-1, 1] interval.
        images[idx, :, :, :] = image * 2.0 - 1.0
        filenames.append(os.path.basename(filepath))
        idx += 1
        if idx == batch_size:
            yield filenames, images
            filenames = []
            images = np.zeros(batch_shape)
            idx = 0
    if idx > 0:
        yield filenames, images


def main(_):
    """Run the sample defense"""
    batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
    nb_classes = 1001

    tf.logging.set_verbosity(tf.logging.INFO)

    with tf.Graph().as_default():
        # Prepare graph
        x_input = tf.placeholder(tf.float32, shape=batch_shape)

        with slim.arg_scope(inception.inception_v3_arg_scope()):
            _, end_points = inception.inception_v3(
                x_input, num_classes=nb_classes, is_training=False
            )

        predicted_labels = tf.argmax(end_points["Predictions"], 1)

        # Run computation
        saver = tf.train.Saver(slim.get_model_variables())
        session_creator = tf.train.ChiefSessionCreator(
            scaffold=tf.train.Scaffold(saver=saver),
            checkpoint_filename_with_path=FLAGS.checkpoint_path,
            master=FLAGS.master,
        )

        with tf.train.MonitoredSession(session_creator=session_creator) as sess:
            with tf.gfile.Open(FLAGS.output_file, "w") as out_file:
                for filenames, images in load_images(FLAGS.input_dir, batch_shape):
                    labels = sess.run(predicted_labels, feed_dict={x_input: images})
                    for filename, label in zip(filenames, labels):
                        out_file.write("{0},{1}\n".format(filename, label))


if __name__ == "__main__":
    tf.app.run()
