# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Data parser and processing for segmentation datasets."""

import tensorflow as tf
from official.vision.dataloaders import decoder
from official.vision.dataloaders import parser
from official.vision.ops import preprocess_ops


class Decoder(decoder.Decoder):
  """A tf.Example decoder for segmentation task."""

  def __init__(self):
    self._keys_to_features = {
        'image/encoded': tf.io.FixedLenFeature((), tf.string, default_value=''),
        'image/height': tf.io.FixedLenFeature((), tf.int64, default_value=0),
        'image/width': tf.io.FixedLenFeature((), tf.int64, default_value=0),
        'image/segmentation/class/encoded':
            tf.io.FixedLenFeature((), tf.string, default_value='')
    }

  def decode(self, serialized_example):
    return tf.io.parse_single_example(
        serialized_example, self._keys_to_features)


class Parser(parser.Parser):
  """Parser to parse an image and its annotations into a dictionary of tensors.
  """

  def __init__(self,
               output_size,
               crop_size=None,
               resize_eval_groundtruth=True,
               groundtruth_padded_size=None,
               ignore_label=255,
               aug_rand_hflip=False,
               preserve_aspect_ratio=True,
               aug_scale_min=1.0,
               aug_scale_max=1.0,
               dtype='float32'):
    """Initializes parameters for parsing annotations in the dataset.

    Args:
      output_size: `Tensor` or `list` for [height, width] of output image. The
        output_size should be divided by the largest feature stride 2^max_level.
      crop_size: `Tensor` or `list` for [height, width] of the crop. If
        specified a training crop of size crop_size is returned. This is useful
        for cropping original images during training while evaluating on
        original image sizes.
      resize_eval_groundtruth: `bool`, if True, eval groundtruth masks are
        resized to output_size.
      groundtruth_padded_size: `Tensor` or `list` for [height, width]. When
        resize_eval_groundtruth is set to False, the groundtruth masks are
        padded to this size.
      ignore_label: `int` the pixel with ignore label will not used for training
        and evaluation.
      aug_rand_hflip: `bool`, if True, augment training with random
        horizontal flip.
      preserve_aspect_ratio: `bool`, if True, the aspect ratio is preserved,
        otherwise, the image is resized to output_size.
      aug_scale_min: `float`, the minimum scale applied to `output_size` for
        data augmentation during training.
      aug_scale_max: `float`, the maximum scale applied to `output_size` for
        data augmentation during training.
      dtype: `str`, data type. One of {`bfloat16`, `float32`, `float16`}.
    """
    self._output_size = output_size
    self._crop_size = crop_size
    self._resize_eval_groundtruth = resize_eval_groundtruth
    if (not resize_eval_groundtruth) and (groundtruth_padded_size is None):
      raise ValueError('groundtruth_padded_size ([height, width]) needs to be'
                       'specified when resize_eval_groundtruth is False.')
    self._groundtruth_padded_size = groundtruth_padded_size
    self._ignore_label = ignore_label
    self._preserve_aspect_ratio = preserve_aspect_ratio

    # Data augmentation.
    self._aug_rand_hflip = aug_rand_hflip
    self._aug_scale_min = aug_scale_min
    self._aug_scale_max = aug_scale_max

    # dtype.
    self._dtype = dtype

  def _prepare_image_and_label(self, data):
    """Prepare normalized image and label."""
    image = tf.io.decode_image(data['image/encoded'], channels=3)
    label = tf.io.decode_image(data['image/segmentation/class/encoded'],
                               channels=1)
    height = data['image/height']
    width = data['image/width']
    image = tf.reshape(image, (height, width, 3))

    label = tf.reshape(label, (1, height, width))
    label = tf.cast(label, tf.float32)
    # Normalizes image with mean and std pixel values.
    image = preprocess_ops.normalize_image(image)

    if not self._preserve_aspect_ratio:
      label = tf.reshape(label, [data['image/height'], data['image/width'], 1])
      image = tf.image.resize(image, self._output_size, method='bilinear')
      label = tf.image.resize(label, self._output_size, method='nearest')
      label = tf.reshape(label[:, :, -1], [1] + self._output_size)

    return image, label

  def _parse_train_data(self, data):
    """Parses data for training and evaluation."""
    image, label = self._prepare_image_and_label(data)

    if self._crop_size:

      label = tf.reshape(label, [data['image/height'], data['image/width'], 1])
      # If output_size is specified, resize image, and label to desired
      # output_size.
      if self._output_size:
        image = tf.image.resize(image, self._output_size, method='bilinear')
        label = tf.image.resize(label, self._output_size, method='nearest')

      image_mask = tf.concat([image, label], axis=2)
      image_mask_crop = tf.image.random_crop(image_mask,
                                             self._crop_size + [4])
      image = image_mask_crop[:, :, :-1]
      label = tf.reshape(image_mask_crop[:, :, -1], [1] + self._crop_size)

    # Flips image randomly during training.
    if self._aug_rand_hflip:
      image, _, label = preprocess_ops.random_horizontal_flip(
          image, masks=label)

    train_image_size = self._crop_size if self._crop_size else self._output_size
    # Resizes and crops image.
    image, image_info = preprocess_ops.resize_and_crop_image(
        image,
        train_image_size,
        train_image_size,
        aug_scale_min=self._aug_scale_min,
        aug_scale_max=self._aug_scale_max)

    # Resizes and crops boxes.
    image_scale = image_info[2, :]
    offset = image_info[3, :]

    # Pad label and make sure the padded region assigned to the ignore label.
    # The label is first offset by +1 and then padded with 0.
    label += 1
    label = tf.expand_dims(label, axis=3)
    label = preprocess_ops.resize_and_crop_masks(
        label, image_scale, train_image_size, offset)
    label -= 1
    label = tf.where(tf.equal(label, -1),
                     self._ignore_label * tf.ones_like(label), label)
    label = tf.squeeze(label, axis=0)
    valid_mask = tf.not_equal(label, self._ignore_label)
    labels = {
        'masks': label,
        'valid_masks': valid_mask,
        'image_info': image_info,
    }

    # Cast image as self._dtype
    image = tf.cast(image, dtype=self._dtype)

    return image, labels

  def _parse_eval_data(self, data):
    """Parses data for training and evaluation."""
    image, label = self._prepare_image_and_label(data)
    # The label is first offset by +1 and then padded with 0.
    label += 1
    label = tf.expand_dims(label, axis=3)

    # Resizes and crops image.
    image, image_info = preprocess_ops.resize_and_crop_image(
        image, self._output_size, self._output_size)

    if self._resize_eval_groundtruth:
      # Resizes eval masks to match input image sizes. In that case, mean IoU
      # is computed on output_size not the original size of the images.
      image_scale = image_info[2, :]
      offset = image_info[3, :]
      label = preprocess_ops.resize_and_crop_masks(label, image_scale,
                                                   self._output_size, offset)
    else:
      label = tf.image.pad_to_bounding_box(
          label, 0, 0, self._groundtruth_padded_size[0],
          self._groundtruth_padded_size[1])

    label -= 1
    label = tf.where(tf.equal(label, -1),
                     self._ignore_label * tf.ones_like(label), label)
    label = tf.squeeze(label, axis=0)

    valid_mask = tf.not_equal(label, self._ignore_label)
    labels = {
        'masks': label,
        'valid_masks': valid_mask,
        'image_info': image_info
    }

    # Cast image as self._dtype
    image = tf.cast(image, dtype=self._dtype)

    return image, labels
