EmmaSRH / py-hausdorff

Fast computation of Hausdorff distance in Python

py-hausdorff

Fast computation of Hausdorff distance in Python.

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.

Installation

Via PyPI:

pip install hausdorff

Or you can clone this repository and install it manually:

python setup.py install

Example Usage

The main functions is:

hausdorff_distance(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.

Note: The haversine distance is calculated assuming lat, lng coordinate ordering and assumes the first two coordinates of each point are latitude and longitude respectively.

import numpy as np
from hausdorff import hausdorff_distance

# 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_distance(X, Y, distance="manhattan") ))
print("Hausdorff distance test: {0}".format( hausdorff_distance(X, Y, distance="euclidean") ))
print("Hausdorff distance test: {0}".format( hausdorff_distance(X, Y, distance="chebyshev") ))
print("Hausdorff distance test: {0}".format( hausdorff_distance(X, Y, distance="cosine") ))

# For haversine, use 2D lat, lng coordinates
def rand_lat_lng(N):
    lats = np.random.uniform(-90, 90, N)
    lngs = np.random.uniform(-180, 180, N)
    return np.stack([lats, lngs], axis=-1)
        
X = rand_lat_lng(100)
Y = rand_lat_lng(250)
print("Hausdorff haversine test: {0}".format( hausdorff_distance(X, Y, distance="haversine") ))