Livewire Segmentation Algorithm¶
Contents:
This package implements Livewire segmentation algorithm for image segmentation aka intelligent scissors. The general idea of the algorithm is to use image information for segmentation and avoid crossing object boundaries. A gradient image highlights the boundaries, and Dijkstra’s shortest path algorithm computes a path using gradient differences as segment costs. Thus the line avoids strong gradients in the gradient image, which corresponds to following object boundaries in the original image.
How to use¶
The package is easy to use. First, read/import/generate an image:
from skimage import data
image = data.coins()
Then compute the shortest path. The short version:
from livewire import compute_shortest_path
path_standalone = compute_shortest_path(image, (0, 0), (10, 25))
or a longer version:
from livewire import LiveWireSegmentation
algorithm = LiveWireSegmentation(image)
path = algorithm.compute_shortest_path((0, 0), (10, 25))
Installation¶
This package is hosted at github. You can clone it, or download a zipped version of source code.
Requirements¶
This package requires scikit-image of version at least 0.11.3.
Contribute¶
If you think you can contribute to this project, simply fork it on github, then publish a pull request when you are done.
Support¶
If yu are having issues with this code, let me know. You can create an issue on github. I will get back to you.
License¶
The project is licensed under the BSD license.