# 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.
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# http://www.apache.org/licenses/LICENSE-2.0
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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"]
[docs]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.
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
[docs]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}.")