# Author - Nikhil Kumar Jha

"""Routine for decoding the CIFAR10 binary file format."""

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

import os

from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

# Process images of this size. Note that this differs from the original CIFAR image size of 32*32.
# If one alters this number, then the entire model architecture will
# change and any model would need to be retrained.
IMAGE_SIZE = 24

# Global constants describing the CIFAR10 dataset
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000


def read_cifar10(filename_queue):
    """
    Reads and parses examples form CIFAR10 data files.

    Recommendation: if you want N-way read parallelism, call this function N times.
    This will give you N independent readers reading different files and positions within those files,
    which will give better mixing of examples.

    Args :
      filename_queue: A queue of strings with the filenames to read from.

    Returns:
      An object representing a single example, with the following fields:
        height: number of rows in the result - 32
        width: number of columns in the result - 32
        depth: number of color channels in the result - 3
        key: a scalar string Tensor with the label in the range 0 - 9.
        uint8image: a [heigh, width, depth] uint8 Tensor with the image data.
    """

    class CIFAR10Record(object):
        pass

    result = CIFAR10Record()

    # Dimensions of the images in the CIFAR10 dataset. See online for description of the input format.
    label_bytes = 1  # 2 for CIFAR100
    result.height = 32
    result.width = 32
    result.depth = 3
    image_bytes = result.height * result.width * result.depth
    # Every record consists of the label followed by the image, with a fixed number of bytes for each.
    record_bytes = label_bytes + image_bytes

    # Read a record, getting filenames from the filename_queue.
    # No header or footer in the CIFAR10 format, so we leave header_bytes
    # and footer_bytes at their deafult of 0.
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    result.key, value = reader.read(filename_queue)

    # Convert from a string to a vector of uint8 that is record_bytes long.
    record_bytes = tf.decode_raw(value, tf.uint8)

    # The first bytes represent the label, which we convert from uint8->int32
    result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)

    # The remaining bytes after the label represent the image,
    # which we reshape from [depth * height * width] to [depth, height, width]
    depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]),
                             [result.depth, result.height, result.width])

    # Convert from [depth, height, width] to [height, width, depth]
    result.uint8image = tf.transpose(depth_major, [1, 2, 0])

    return result


def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle):
    """
    Construct a queued batch of images and labels.

    Args:
      image: 3D Tensor of [height, width, 3] of type.float32
      label: 1D Tensor of type.int32
      min_queue_examples: int32, minimum number of samples to retain in the queue that provides batches of examples.
      batch_size: Number of images per batch.
      shuffle: boolean indicating whether to use a shuffling queue.

    Returns:
      images: Images. 4D tensor of [batch_size, height, width, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """

    # Create a queue that shuffles the examples, and then read the
    # 'batch_size' images + labels from the the example queue.
    num_preprocess_threads = 16
    if shuffle:
        images, label_batch = tf.train.shuffle_batch([image, label], batch_size=batch_size,
                                                     num_threads=num_preprocess_threads,
                                                     capacity=min_queue_examples + 3 * batch_size,
                                                     min_after_dequeue=min_queue_examples)
    else:
        images, label_batch = tf.train.batch([image, label], batch_size=batch_size, num_threads=num_preprocess_threads,
                                             capacity=min_queue_examples + 3 * batch_size)

    # Display the training images in the visualizer
    tf.summary.image('images', images)

    return images, tf.reshape(label_batch, [batch_size])


def distorted_inputs(data_dir, batch_size):
    """
    Construct distorted input for CIFAR training using the Reader ops.

    Args:
      data_dir: Path to the CIFAR10 data directory
      batch_size: Number of images per batch

    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size
      labels: Labels. 1D tensor of [batch_size] size
    """

    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)

    # Create a queue that produces the filename to read
    filename_queue = tf.train.string_input_producer(filenames)

    with tf.name_scope('data_augmentation'):
        # Read examples from files in the filename queue.
        read_input = read_cifar10(filename_queue)
        reshaped_image = tf.cast(read_input.uint8image, tf.float32)

        height = IMAGE_SIZE
        width = IMAGE_SIZE

        # Image processing for training the network. Note the many random distortions applied to image.

        # Randomly crop a [height, width] section of the image.
        distorted_image = tf.random_crop(reshaped_image, [height, width, 3])

        # Randomly flip the image horizontally.
        distorted_image = tf.image.random_flip_left_right(distorted_image)

        # Because these operations are not commutative, consider randomizing the order of their operation.
        # NOTE: since per_image_standardization zeros the mean and makes the stddev unit, this likely has no effect.
        distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)
        distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)

        # Subtract off the mean and divide by the variance of the pixels
        float_image = tf.image.per_image_standardization(distorted_image)

        # Set the shapes of tensors
        float_image.set_shape([height, width, 3])
        read_input.label.set_shape([1])

        # Ensure that the random shuffling has good mixing properties
        min_fraction_of_examples_in_queue = 0.4
        min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue)
        print('Filling queue with %d CIFAR images before starting to train. '
              'This will take few minutes.' % min_queue_examples)

    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True)


def inputs(eval_data, data_dir, batch_size):
    """
    Construct input for CIFAR training using the Reader ops.

    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
      data_dir: Path to the CIFAR10 data directory
      batch_size: Number of images per batch

    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size
      labels: Labels. 1D tensor of [batch_size] size
    """

    if not eval_data:
        filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
    else:
        filenames = [os.path.join(data_dir, 'test_batch.bin')]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)

    with tf.name_scope('input'):
        # Create a queue that produces the filenames to read
        filename_queue = tf.train.string_input_producer(filenames)

        # Read examples from files in the filename queue.
        read_input = read_cifar10(filename_queue)
        reshaped_image = tf.cast(read_input.uint8image, tf.float32)

        height = IMAGE_SIZE
        width = IMAGE_SIZE

        # Image processing for evaluation.
        # Crop the central [height, width] of the image.
        resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, height, width)

        # Subtract off the mean and divide by the variance of the pixels
        float_image = tf.image.per_image_standardization(resized_image)

        # Set the shapes of tensors
        float_image.set_shape([height, width, 3])
        read_input.label.set_shape([1])

        # Ensure that the random shuffling has good mixing properties
        min_fraction_of_examples_in_queue = 0.4
        min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue)

    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)
