# Copyright 2020 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.
from typing import Optional, Union
import numpy as np
import torch
from .utils import get_mask_edges, get_surface_distance
[docs]def compute_hausdorff_distance(
seg_pred: Union[np.ndarray, torch.Tensor],
seg_gt: Union[np.ndarray, torch.Tensor],
label_idx: int,
distance_metric: str = "euclidean",
percentile: Optional[float] = None,
directed: bool = False,
):
"""
Compute the Hausdorff distance. The user has the option to calculate the
directed or non-directed Hausdorff distance. By default, the non-directed
Hausdorff distance is calculated. In addition, specify the `percentile`
parameter can get the percentile of the distance.
Args:
seg_pred: the predicted binary or labelfield image.
seg_gt: the actual binary or labelfield image.
label_idx: for labelfield images, convert to binary with
`seg_pred = seg_pred == label_idx`.
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: calculate directed Hausdorff distance. Defaults to ``False``.
"""
(edges_pred, edges_gt) = get_mask_edges(seg_pred, seg_gt, label_idx)
hd = compute_percent_hausdorff_distance(edges_pred, edges_gt, label_idx, distance_metric, percentile)
if directed:
return hd
hd2 = compute_percent_hausdorff_distance(edges_gt, edges_pred, label_idx, distance_metric, percentile)
return max(hd, hd2)
def compute_percent_hausdorff_distance(
edges_pred: np.ndarray,
edges_gt: np.ndarray,
label_idx: int,
distance_metric: str = "euclidean",
percentile: Optional[float] = None,
):
"""
This function is used to compute the directed Hausdorff distance.
Args:
edges_pred: the edge of the predictions.
edges_gt: the edge of the ground truth.
label_idx: for labelfield images, convert to binary with
`seg_pred = seg_pred == label_idx`.
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``.
"""
surface_distance = get_surface_distance(edges_pred, edges_gt, label_idx, distance_metric=distance_metric)
# for input without foreground
if surface_distance.shape == (0,):
return np.inf
if not percentile:
return surface_distance.max()
elif 0 <= percentile <= 100:
return np.percentile(surface_distance, percentile)
else:
raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")