text: # Copyright 2020 - 2021 MONAI Consortium
  # 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 warnings
  from typing import Optional, Union
  
  import numpy as np
  import torch
  
  from monai.metrics.utils import do_metric_reduction, get_mask_edges, get_surface_distance, ignore_background
  from monai.utils import MetricReduction
  
  __all__ = ["HausdorffDistanceMetric", "compute_hausdorff_distance", "compute_percent_hausdorff_distance"]
  
  
  class HausdorffDistanceMetric:
      """
      Compute Hausdorff Distance between two tensors. It can support both multi-classes and multi-labels tasks.
      It supports both directed and non-directed Hausdorff distance calculation. In addition, specify the `percentile`
      parameter can get the percentile of the distance.
      Input `y_pred` (BNHW[D] where N is number of classes) is compared with ground truth `y` (BNHW[D]).
      `y_preds` is expected to have binarized predictions and `y` should be in one-hot format.
      You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.
  
      The implementation refers to `DeepMind's implementation <https://github.com/deepmind/surface-distance>`_.
  
      Args:
          include_background: whether to include distance computation on the first channel of
              the predicted output. Defaults to ``False``.
          distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
              the metric used to compute surface distance. Defaults to ``"euclidean"``.
          percentile: an optional float number between 0 and 100. If specified, the corresponding
              percentile of the Hausdorff Distance rather than the maximum result will be achieved.
              Defaults to ``None``.
          directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.
          reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
              ``"mean_channel"``, ``"sum_channel"``}
              Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``.
  
      """
  
      def __init__(
          self,
          include_background: bool = False,
          distance_metric: str = "euclidean",
          percentile: Optional[float] = None,
          directed: bool = False,
          reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
      ) -> None:
          super().__init__()
          self.include_background = include_background
          self.distance_metric = distance_metric
          self.percentile = percentile
          self.directed = directed
          self.reduction = reduction
  
      def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
          """
          Args:
              y_pred: input data to compute, typical segmentation model output.
                  It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
                  should be binarized.
              y: ground truth to compute the distance. It must be one-hot format and first dim is batch.
                  The values should be binarized.
  
          Raises:
              ValueError: when `y` is not a binarized tensor.
              ValueError: when `y_pred` has less than three dimensions.
          """
          if not torch.all(y_pred.byte() == y_pred):
              warnings.warn("y_pred is not a binarized tensor here!")
          if not torch.all(y.byte() == y):
              raise ValueError("y should be a binarized tensor.")
          dims = y_pred.ndimension()
          if dims < 3:
              raise ValueError("y_pred should have at least three dimensions.")
          # compute (BxC) for each channel for each batch
          f = compute_hausdorff_distance(
              y_pred=y_pred,
              y=y,
              include_background=self.include_background,
              distance_metric=self.distance_metric,
              percentile=self.percentile,
              directed=self.directed,
          )
  
          # do metric reduction
          f, not_nans = do_metric_reduction(f, self.reduction)
          return f, not_nans
  
  
  def compute_hausdorff_distance(
      y_pred: Union[np.ndarray, torch.Tensor],
      y: Union[np.ndarray, torch.Tensor],
      include_background: bool = False,
      distance_metric: str = "euclidean",
      percentile: Optional[float] = None,
      directed: bool = False,
  ):
      """
      Compute the Hausdorff distance.
  
      Args:
          y_pred: input data to compute, typical segmentation model output.
              It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
              should be binarized.
          y: ground truth to compute mean the distance. It must be one-hot format and first dim is batch.
              The values should be binarized.
          include_background: whether to skip distance computation on the first channel of
              the predicted output. Defaults to ``False``.
          distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
              the metric used to compute surface distance. Defaults to ``"euclidean"``.
          percentile: an optional float number between 0 and 100. If specified, the corresponding
              percentile of the Hausdorff Distance rather than the maximum result will be achieved.
              Defaults to ``None``.
          directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.
      """
  
      if not include_background:
          y_pred, y = ignore_background(
              y_pred=y_pred,
              y=y,
          )
      if isinstance(y, torch.Tensor):
          y = y.float()
      if isinstance(y_pred, torch.Tensor):
          y_pred = y_pred.float()
  
      if y.shape != y_pred.shape:
          raise ValueError("y_pred and y should have same shapes.")
  
      batch_size, n_class = y_pred.shape[:2]
      hd = np.empty((batch_size, n_class))
      for b, c in np.ndindex(batch_size, n_class):
          (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])
          distance_1 = compute_percent_hausdorff_distance(edges_pred, edges_gt, distance_metric, percentile)
          if directed:
              hd[b, c] = distance_1
          else:
              distance_2 = compute_percent_hausdorff_distance(edges_gt, edges_pred, distance_metric, percentile)
              hd[b, c] = max(distance_1, distance_2)
      return torch.from_numpy(hd)
  
  
  def compute_percent_hausdorff_distance(
      edges_pred: np.ndarray,
      edges_gt: np.ndarray,
      distance_metric: str = "euclidean",
      percentile: Optional[float] = None,
  ):
      """
      This function is used to compute the directed Hausdorff distance.
      """
  
      surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)
  
      # for both pred and gt do not have foreground
      if surface_distance.shape == (0,):
          return np.nan
  
      if not percentile:
          return surface_distance.max()
  
      if 0 <= percentile <= 100:
          return np.percentile(surface_distance, percentile)
      raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
  