text: #    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
  #
  #    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.
  
  import os
  from copy import deepcopy
  
  import numpy as np
  from batchgenerators.dataloading import MultiThreadedAugmenter
  from batchgenerators.transforms import DataChannelSelectionTransform, SegChannelSelectionTransform, SpatialTransform, \
      GammaTransform, MirrorTransform, Compose
  from batchgenerators.transforms.utility_transforms import RemoveLabelTransform, RenameTransform, NumpyToTensor
  from nnunet.training.data_augmentation.custom_transforms import Convert3DTo2DTransform, Convert2DTo3DTransform, \
      MaskTransform, ConvertSegmentationToRegionsTransform
  from nnunet.training.data_augmentation.pyramid_augmentations import MoveSegAsOneHotToData, \
      ApplyRandomBinaryOperatorTransform, \
      RemoveRandomConnectedComponentFromOneHotEncodingTransform
  
  try:
      from batchgenerators.dataloading.nondet_multi_threaded_augmenter import NonDetMultiThreadedAugmenter
  except ImportError as ie:
      NonDetMultiThreadedAugmenter = None
  
  
  default_3D_augmentation_params = {
      "selected_data_channels": None,
      "selected_seg_channels": None,
  
      "do_elastic": True,
      "elastic_deform_alpha": (0., 900.),
      "elastic_deform_sigma": (9., 13.),
      "p_eldef": 0.2,
  
      "do_scaling": True,
      "scale_range": (0.85, 1.25),
      "independent_scale_factor_for_each_axis": False,
      "p_independent_scale_per_axis": 1,
      "p_scale": 0.2,
  
      "do_rotation": True,
      "rotation_x": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
      "rotation_y": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
      "rotation_z": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
      "rotation_p_per_axis": 1,
      "p_rot": 0.2,
  
      "random_crop": False,
      "random_crop_dist_to_border": None,
  
      "do_gamma": True,
      "gamma_retain_stats": True,
      "gamma_range": (0.7, 1.5),
      "p_gamma": 0.3,
  
      "do_mirror": True,
      "mirror_axes": (0, 1, 2),
  
      "dummy_2D": False,
      "mask_was_used_for_normalization": None,
      "border_mode_data": "constant",
  
      "all_segmentation_labels": None,  # used for cascade
      "move_last_seg_chanel_to_data": False,  # used for cascade
      "cascade_do_cascade_augmentations": False,  # used for cascade
      "cascade_random_binary_transform_p": 0.4,
      "cascade_random_binary_transform_p_per_label": 1,
      "cascade_random_binary_transform_size": (1, 8),
      "cascade_remove_conn_comp_p": 0.2,
      "cascade_remove_conn_comp_max_size_percent_threshold": 0.15,
      "cascade_remove_conn_comp_fill_with_other_class_p": 0.0,
  
      "do_additive_brightness": False,
      "additive_brightness_p_per_sample": 0.15,
      "additive_brightness_p_per_channel": 0.5,
      "additive_brightness_mu": 0.0,
      "additive_brightness_sigma": 0.1,
  
      "num_threads": 12 if 'nnUNet_n_proc_DA' not in os.environ else int(os.environ['nnUNet_n_proc_DA']),
      "num_cached_per_thread": 1,
  }
  
  default_2D_augmentation_params = deepcopy(default_3D_augmentation_params)
  
  default_2D_augmentation_params["elastic_deform_alpha"] = (0., 200.)
  default_2D_augmentation_params["elastic_deform_sigma"] = (9., 13.)
  default_2D_augmentation_params["rotation_x"] = (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi)
  default_2D_augmentation_params["rotation_y"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)
  default_2D_augmentation_params["rotation_z"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)
  
  # sometimes you have 3d data and a 3d net but cannot augment them properly in 3d due to anisotropy (which is currently
  # not supported in batchgenerators). In that case you can 'cheat' and transfer your 3d data into 2d data and
  # transform them back after augmentation
  default_2D_augmentation_params["dummy_2D"] = False
  default_2D_augmentation_params["mirror_axes"] = (0, 1)  # this can be (0, 1, 2) if dummy_2D=True
  
  
  def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range):
      if isinstance(rot_x, (tuple, list)):
          rot_x = max(np.abs(rot_x))
      if isinstance(rot_y, (tuple, list)):
          rot_y = max(np.abs(rot_y))
      if isinstance(rot_z, (tuple, list)):
          rot_z = max(np.abs(rot_z))
      rot_x = min(90 / 360 * 2. * np.pi, rot_x)
      rot_y = min(90 / 360 * 2. * np.pi, rot_y)
      rot_z = min(90 / 360 * 2. * np.pi, rot_z)
      from batchgenerators.augmentations.utils import rotate_coords_3d, rotate_coords_2d
      coords = np.array(final_patch_size)
      final_shape = np.copy(coords)
      if len(coords) == 3:
          final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, rot_x, 0, 0)), final_shape)), 0)
          final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, rot_y, 0)), final_shape)), 0)
          final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, 0, rot_z)), final_shape)), 0)
      elif len(coords) == 2:
          final_shape = np.max(np.vstack((np.abs(rotate_coords_2d(coords, rot_x)), final_shape)), 0)
      final_shape /= min(scale_range)
      return final_shape.astype(int)
  
  
  def get_default_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params,
                               border_val_seg=-1, pin_memory=True,
                               seeds_train=None, seeds_val=None, regions=None):
      assert params.get('mirror') is None, "old version of params, use new keyword do_mirror"
      tr_transforms = []
  
      if params.get("selected_data_channels") is not None:
          tr_transforms.append(DataChannelSelectionTransform(params.get("selected_data_channels")))
  
      if params.get("selected_seg_channels") is not None:
          tr_transforms.append(SegChannelSelectionTransform(params.get("selected_seg_channels")))
  
      # don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
      if params.get("dummy_2D") is not None and params.get("dummy_2D"):
          tr_transforms.append(Convert3DTo2DTransform())
          patch_size_spatial = patch_size[1:]
      else:
          patch_size_spatial = patch_size
  
      tr_transforms.append(SpatialTransform(
          patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=params.get("do_elastic"),
          alpha=params.get("elastic_deform_alpha"), sigma=params.get("elastic_deform_sigma"),
          do_rotation=params.get("do_rotation"), angle_x=params.get("rotation_x"), angle_y=params.get("rotation_y"),
          angle_z=params.get("rotation_z"), do_scale=params.get("do_scaling"), scale=params.get("scale_range"),
          border_mode_data=params.get("border_mode_data"), border_cval_data=0, order_data=3, border_mode_seg="constant",
          border_cval_seg=border_val_seg,
          order_seg=1, random_crop=params.get("random_crop"), p_el_per_sample=params.get("p_eldef"),
          p_scale_per_sample=params.get("p_scale"), p_rot_per_sample=params.get("p_rot"),
          independent_scale_for_each_axis=params.get("independent_scale_factor_for_each_axis")
      ))
      if params.get("dummy_2D") is not None and params.get("dummy_2D"):
          tr_transforms.append(Convert2DTo3DTransform())
  
      if params.get("do_gamma"):
          tr_transforms.append(
              GammaTransform(params.get("gamma_range"), False, True, retain_stats=params.get("gamma_retain_stats"),
                             p_per_sample=params["p_gamma"]))
  
      if params.get("do_mirror"):
          tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
  
      if params.get("mask_was_used_for_normalization") is not None:
          mask_was_used_for_normalization = params.get("mask_was_used_for_normalization")
          tr_transforms.append(MaskTransform(mask_was_used_for_normalization, mask_idx_in_seg=0, set_outside_to=0))
  
      tr_transforms.append(RemoveLabelTransform(-1, 0))
  
      if params.get("move_last_seg_chanel_to_data") is not None and params.get("move_last_seg_chanel_to_data"):
          tr_transforms.append(MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"), 'seg', 'data'))
          if params.get("cascade_do_cascade_augmentations") and not None and params.get(
                  "cascade_do_cascade_augmentations"):
              tr_transforms.append(ApplyRandomBinaryOperatorTransform(
                  channel_idx=list(range(-len(params.get("all_segmentation_labels")), 0)),
                  p_per_sample=params.get("cascade_random_binary_transform_p"),
                  key="data",
                  strel_size=params.get("cascade_random_binary_transform_size")))
              tr_transforms.append(RemoveRandomConnectedComponentFromOneHotEncodingTransform(
                  channel_idx=list(range(-len(params.get("all_segmentation_labels")), 0)),
                  key="data",
                  p_per_sample=params.get("cascade_remove_conn_comp_p"),
                  fill_with_other_class_p=params.get("cascade_remove_conn_comp_max_size_percent_threshold"),
                  dont_do_if_covers_more_than_X_percent=params.get("cascade_remove_conn_comp_fill_with_other_class_p")))
  
      tr_transforms.append(RenameTransform('seg', 'target', True))
  
      if regions is not None:
          tr_transforms.append(ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
  
      tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
  
      tr_transforms = Compose(tr_transforms)
      # from batchgenerators.dataloading import SingleThreadedAugmenter
      # batchgenerator_train = SingleThreadedAugmenter(dataloader_train, tr_transforms)
      # import IPython;IPython.embed()
  
      batchgenerator_train = MultiThreadedAugmenter(dataloader_train, tr_transforms, params.get('num_threads'),
                                                    params.get("num_cached_per_thread"), seeds=seeds_train,
                                                    pin_memory=pin_memory)
  
      val_transforms = []
      val_transforms.append(RemoveLabelTransform(-1, 0))
      if params.get("selected_data_channels") is not None:
          val_transforms.append(DataChannelSelectionTransform(params.get("selected_data_channels")))
      if params.get("selected_seg_channels") is not None:
          val_transforms.append(SegChannelSelectionTransform(params.get("selected_seg_channels")))
  
      if params.get("move_last_seg_chanel_to_data") is not None and params.get("move_last_seg_chanel_to_data"):
          val_transforms.append(MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"), 'seg', 'data'))
  
      val_transforms.append(RenameTransform('seg', 'target', True))
  
      if regions is not None:
          val_transforms.append(ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
  
      val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
      val_transforms = Compose(val_transforms)
  
      # batchgenerator_val = SingleThreadedAugmenter(dataloader_val, val_transforms)
      batchgenerator_val = MultiThreadedAugmenter(dataloader_val, val_transforms, max(params.get('num_threads') // 2, 1),
                                                  params.get("num_cached_per_thread"), seeds=seeds_val,
                                                  pin_memory=pin_memory)
      return batchgenerator_train, batchgenerator_val
  
  
  if __name__ == "__main__":
      from nnunet.training.dataloading.dataset_loading import DataLoader3D, load_dataset
      from nnunet.paths import preprocessing_output_dir
      import os
      import pickle
  
      t = "Task002_Heart"
      p = os.path.join(preprocessing_output_dir, t)
      dataset = load_dataset(p, 0)
      with open(os.path.join(p, "plans.pkl"), 'rb') as f:
          plans = pickle.load(f)
  
      basic_patch_size = get_patch_size(np.array(plans['stage_properties'][0].patch_size),
                                        default_3D_augmentation_params['rotation_x'],
                                        default_3D_augmentation_params['rotation_y'],
                                        default_3D_augmentation_params['rotation_z'],
                                        default_3D_augmentation_params['scale_range'])
  
      dl = DataLoader3D(dataset, basic_patch_size, np.array(plans['stage_properties'][0].patch_size).astype(int), 1)
      tr, val = get_default_augmentation(dl, dl, np.array(plans['stage_properties'][0].patch_size).astype(int))
  