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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 https://scikit-mpe.readthedocs.io/en/latest

(The documentation is being written)

References

License

MIT

Project details


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