Fast computation of Hausdorff distance in Python/Cython.

This code implements the algorithm presented in An Efficient Algorithm for Calculating the Exact Hausdorff Distance (DOI: 10.1109/TPAMI.2015.2408351) by Aziz and Hanbury.

To install the package I provide you a setup.py file. You must run:

python setup.py install

The main functions is:

hausdorff(np.ndarray[:,:] X, np.ndarray[:,:] Y)

Which computes the Hausdorff distance between the rows of X and Y using the Euclidean distance as metric. It receives the optional argument distance (string), which is the distance function used to compute the distance between the rows of X and Y. It could be any of the following: manhattan, euclidean (default), chebyshev and cosine.

Note: I will add more distances in the near future. If you need any distance in particular, open an issue.

import numpy as np
from hausdorff import hausdorff

# two random 2D arrays (second dimension must match)
np.random.seed(0)
X = np.random.random((1000,100))
Y = np.random.random((5000,100))

# Test computation of Hausdorff distance with different base distances
print("Hausdorff distance test: {0}".format( hausdorff(X, Y, distance="manhattan") ))
print("Hausdorff distance test: {0}".format( hausdorff(X, Y, distance="euclidean") ))
print("Hausdorff distance test: {0}".format( hausdorff(X, Y, distance="chebyshev") ))
print("Hausdorff distance test: {0}".format( hausdorff(X, Y, distance="cosine") ))