Skip to main content

Minimal path extraction using the fast marching method

Project description

scikit-mpe

PyPI version Build status Documentation Status Coverage Status Supported Python versions License

scikit-mpe is a package for extracting a minimal path in N-dimensional Euclidean space (on regular Cartesian grids) using the fast marching method.

Quickstart

Installing

pip install -U scikit-mpe

Usage

Here is a simple example that demonstrates how to extract the path passing through the retina vessels.

from matplotlib import pyplot as plt

from skimage.data import retina
from skimage.color import rgb2gray
from skimage.transform import rescale
from skimage.filters import sato

from skmpe import mpe

image = rescale(rgb2gray(retina()), 0.5)
speed_image = sato(image)

start_point = (76, 388)
end_point = (611, 442)
way_points = [(330, 98), (554, 203)]

path_info = mpe(speed_image, start_point, end_point, way_points)

px, py = path_info.path[:, 1], path_info.path[:, 0]

plt.imshow(image, cmap='gray')
plt.plot(px, py, '-r')

plt.plot(*start_point[::-1], 'oy')
plt.plot(*end_point[::-1], 'og')
for p in way_points:
    plt.plot(*p[::-1], 'ob')

plt.show()

retina_vessel_path

Documentation

The full documentation can be found at scikit-mpe.readthedocs.io

References

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-mpe-0.2.4.tar.gz (12.8 kB view hashes)

Uploaded Source

Built Distribution

scikit_mpe-0.2.4-py3-none-any.whl (13.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page