148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
import warnings
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import numpy as np
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from scipy.spatial import cKDTree
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def hausdorff_distance(image0, image1, method="standard"):
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"""Calculate the Hausdorff distance between nonzero elements of given images.
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Parameters
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----------
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image0, image1 : ndarray
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Arrays where ``True`` represents a point that is included in a
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set of points. Both arrays must have the same shape.
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method : {'standard', 'modified'}, optional, default = 'standard'
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The method to use for calculating the Hausdorff distance.
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``standard`` is the standard Hausdorff distance, while ``modified``
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is the modified Hausdorff distance.
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Returns
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-------
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distance : float
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The Hausdorff distance between coordinates of nonzero pixels in
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``image0`` and ``image1``, using the Euclidean distance.
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Notes
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-----
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The Hausdorff distance [1]_ is the maximum distance between any point on
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``image0`` and its nearest point on ``image1``, and vice-versa.
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The Modified Hausdorff Distance (MHD) has been shown to perform better
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than the directed Hausdorff Distance (HD) in the following work by
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Dubuisson et al. [2]_. The function calculates forward and backward
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mean distances and returns the largest of the two.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Hausdorff_distance
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.. [2] M. P. Dubuisson and A. K. Jain. A Modified Hausdorff distance for object
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matching. In ICPR94, pages A:566-568, Jerusalem, Israel, 1994.
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:DOI:`10.1109/ICPR.1994.576361`
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.8155
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Examples
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--------
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>>> points_a = (3, 0)
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>>> points_b = (6, 0)
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>>> shape = (7, 1)
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>>> image_a = np.zeros(shape, dtype=bool)
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>>> image_b = np.zeros(shape, dtype=bool)
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>>> image_a[points_a] = True
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>>> image_b[points_b] = True
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>>> hausdorff_distance(image_a, image_b)
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3.0
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"""
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if method not in ('standard', 'modified'):
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raise ValueError(f'unrecognized method {method}')
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a_points = np.transpose(np.nonzero(image0))
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b_points = np.transpose(np.nonzero(image1))
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# Handle empty sets properly:
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# - if both sets are empty, return zero
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# - if only one set is empty, return infinity
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if len(a_points) == 0:
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return 0 if len(b_points) == 0 else np.inf
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elif len(b_points) == 0:
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return np.inf
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fwd, bwd = (
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cKDTree(a_points).query(b_points, k=1)[0],
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cKDTree(b_points).query(a_points, k=1)[0],
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)
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if method == 'standard': # standard Hausdorff distance
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return max(max(fwd), max(bwd))
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elif method == 'modified': # modified Hausdorff distance
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return max(np.mean(fwd), np.mean(bwd))
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def hausdorff_pair(image0, image1):
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"""Returns pair of points that are Hausdorff distance apart between nonzero
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elements of given images.
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The Hausdorff distance [1]_ is the maximum distance between any point on
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``image0`` and its nearest point on ``image1``, and vice-versa.
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Parameters
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----------
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image0, image1 : ndarray
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Arrays where ``True`` represents a point that is included in a
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set of points. Both arrays must have the same shape.
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Returns
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-------
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point_a, point_b : array
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A pair of points that have Hausdorff distance between them.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Hausdorff_distance
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Examples
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--------
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>>> points_a = (3, 0)
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>>> points_b = (6, 0)
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>>> shape = (7, 1)
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>>> image_a = np.zeros(shape, dtype=bool)
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>>> image_b = np.zeros(shape, dtype=bool)
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>>> image_a[points_a] = True
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>>> image_b[points_b] = True
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>>> hausdorff_pair(image_a, image_b)
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(array([3, 0]), array([6, 0]))
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"""
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a_points = np.transpose(np.nonzero(image0))
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b_points = np.transpose(np.nonzero(image1))
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# If either of the sets are empty, there is no corresponding pair of points
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if len(a_points) == 0 or len(b_points) == 0:
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warnings.warn("One or both of the images is empty.", stacklevel=2)
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return (), ()
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nearest_dists_from_b, nearest_a_point_indices_from_b = cKDTree(a_points).query(
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b_points
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)
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nearest_dists_from_a, nearest_b_point_indices_from_a = cKDTree(b_points).query(
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a_points
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)
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max_index_from_a = nearest_dists_from_b.argmax()
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max_index_from_b = nearest_dists_from_a.argmax()
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max_dist_from_a = nearest_dists_from_b[max_index_from_a]
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max_dist_from_b = nearest_dists_from_a[max_index_from_b]
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if max_dist_from_b > max_dist_from_a:
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return (
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a_points[max_index_from_b],
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b_points[nearest_b_point_indices_from_a[max_index_from_b]],
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)
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else:
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return (
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a_points[nearest_a_point_indices_from_b[max_index_from_a]],
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b_points[max_index_from_a],
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)
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