content: # coding=utf-8
  # Copyright 2018 The DisentanglementLib 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
  # limitations under the License.
  
  """Cars3D data set."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from disentanglement_lib.data.ground_truth import ground_truth_data
  from disentanglement_lib.data.ground_truth import util
  import numpy as np
  import PIL
  import scipy.io as sio
  from six.moves import range
  from sklearn.utils import extmath
  from tensorflow.compat.v1 import gfile
  
  
  CARS3D_PATH = os.path.join(
      os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "cars")
  
  
  
  class Cars3D(ground_truth_data.GroundTruthData):
    """Cars3D data set.
  
    The data set was first used in the paper "Deep Visual Analogy-Making"
    (https://papers.nips.cc/paper/5845-deep-visual-analogy-making) and can be
    downloaded from http://www.scottreed.info/. The images are rescaled to 64x64.
  
    The ground-truth factors of variation are:
    0 - elevation (4 different values)
    1 - azimuth (24 different values)
    2 - object type (183 different values)
    """
  
    def __init__(self):
      self.factor_sizes = [4, 24, 183]
      features = extmath.cartesian(
          [np.array(list(range(i))) for i in self.factor_sizes])
      self.latent_factor_indices = [0, 1, 2]
      self.num_total_factors = features.shape[1]
      self.index = util.StateSpaceAtomIndex(self.factor_sizes, features)
      self.state_space = util.SplitDiscreteStateSpace(self.factor_sizes,
                                                      self.latent_factor_indices)
      self.data_shape = [64, 64, 3]
      self.images = self._load_data()
  
    @property
    def num_factors(self):
      return self.state_space.num_latent_factors
  
    @property
    def factors_num_values(self):
      return self.factor_sizes
  
  
    @property
    def observation_shape(self):
      return self.data_shape
  
    def sample_factors(self, num, random_state):
      """Sample a batch of factors Y."""
      return self.state_space.sample_latent_factors(num, random_state)
  
    def sample_observations_from_factors(self, factors, random_state):
      """Sample a batch of observations X given a batch of factors Y."""
      all_factors = self.state_space.sample_all_factors(factors, random_state)
      indices = self.index.features_to_index(all_factors)
      return self.images[indices].astype(np.float32)
  
    def _load_data(self):
      dataset = np.zeros((24 * 4 * 183, 64, 64, 3))
      all_files = [x for x in gfile.ListDirectory(CARS3D_PATH) if ".mat" in x]
      for i, filename in enumerate(all_files):
        data_mesh = _load_mesh(filename)
        factor1 = np.array(list(range(4)))
        factor2 = np.array(list(range(24)))
        all_factors = np.transpose([
            np.tile(factor1, len(factor2)),
            np.repeat(factor2, len(factor1)),
            np.tile(i,
                    len(factor1) * len(factor2))
        ])
        indexes = self.index.features_to_index(all_factors)
        dataset[indexes] = data_mesh
      return dataset
  
  
  def _load_mesh(filename):
    """Parses a single source file and rescales contained images."""
    with gfile.Open(os.path.join(CARS3D_PATH, filename), "rb") as f:
      mesh = np.einsum("abcde->deabc", sio.loadmat(f)["im"])
    flattened_mesh = mesh.reshape((-1,) + mesh.shape[2:])
    rescaled_mesh = np.zeros((flattened_mesh.shape[0], 64, 64, 3))
    for i in range(flattened_mesh.shape[0]):
      pic = PIL.Image.fromarray(flattened_mesh[i, :, :, :])
      pic.thumbnail((64, 64, 3), PIL.Image.ANTIALIAS)
      rescaled_mesh[i, :, :, :] = np.array(pic)
    return rescaled_mesh * 1. / 255
  