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mindspore.train.HausdorffDistance

class mindspore.train.HausdorffDistance(distance_metric='euclidean', percentile=None, directed=False, crop=True)

Calculates the Hausdorff distance. Hausdorff distance is the maximum and minimum distance between two point sets. Given two feature sets A and B, the Hausdorff distance between two point sets A and B is defined as follows:

$$\begin{array}{r}\begin{array}{l}\\ H(A,B)=\text{max}[h(A,B),h(B,A)]\\ h(A,B)=\underset{a\in A}{\text{max}}\{\underset{b\in B}{\text{min}}\Vert a-b\Vert \}\\ h(B,A)=\underset{b\in B}{\text{max}}\{\underset{a\in A}{\text{min}}\Vert b-a\Vert \}\end{array}\end{array}$$

where $h(A,B)$ is the maximum distance of a set A to the nearest point in the set B, $h(B,A)$ is the maximum distance of a set B to the nearest point in the set A. The distance calculation is oriented, which means that most of times $h(A,B)$ is not equal to $h(B,A)$. $H(A,B)$ is the two-way Hausdorff distance.

Parameters

• distance_metric (string) – Three distance measurement methods are supported: "euclidean" (Euclidean Distance) , "chessboard" (Chessboard Distance, Chebyshev Distance) or "taxicab" (Taxicab Distance, Manhattan Distance). Default: "euclidean" .

• percentile (float) – Floating point numbers between 0 and 100. Specify the percentile parameter to get the percentile of the Hausdorff distance. Default: None .

• directed (bool) – If True, it only calculates h(y_pred, y) distance, otherwise, max(h(y_pred, y), h(y, y_pred)) will be returned. Default: False .

• crop (bool) – Crop input images and only keep the foregrounds. In order to maintain two inputs' shapes, here the bounding box is achieved by (y_pred | y) which represents the union set of two images. Default: True .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import HausdorffDistance
>>>
>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
>>> metric = HausdorffDistance()
>>> metric.clear()
>>> metric.update(x, y, 0)
>>> mean_average_distance = metric.eval()
>>> print(mean_average_distance)
1.4142135623730951

clear()

Clears the internal evaluation result.

eval()

Calculate the no-directed or directed Hausdorff distance.

Returns

numpy.float64, the hausdorff distance.

Raises

RuntimeError – If the update method is not called first, an error will be reported.

update(*inputs)

Updates the internal evaluation result with the inputs: 'y_pred', 'y' and 'label_idx'.

Parameters

inputs – Input 'y_pred', 'y' and 'label_idx'. 'y_pred' and 'y' are a Tensor, list or numpy.ndarray. 'y_pred' is the predicted binary image. 'y' is the actual binary image. Data type of 'label_idx' is int or float.

Raises

• ValueError – If the number of the inputs is not 3.

• TypeError – If the data type of label_idx is not int or float.

• ValueError – If the value of label_idx is not in y_pred or y.

• ValueError – If y_pred and y have different shapes.