`spateo.tools.live_wire`#

This file implements the LiveWire segmentation algorithm. The code is ported from:

https://github.com/pdyban/livewire: LiveWireSegmentation and compute_shortest_path functions/class.
https://github.com/Usama3627/live-wire: include the _compute_graph and compute_shortest_path functions.

Module Contents#

Classes#

LiveWireSegmentation

Functions#

`compute_shortest_path`(→ List)	Inline function for easier computation of shortest_path in an image.
`live_wire`(→ List[numpy.ndarray])	Use LiveWire segmentation algorithm for image segmentation aka intelligent scissors. The general idea of the

class spateo.tools.live_wire.LiveWireSegmentation(image: Optional = None, smooth_image: bool = False, threshold_gradient_image: bool = False)[source]#

Bases: object

property image[source]#

_smooth_image()[source]#

_compute_gradient_image()[source]#

_threshold_gradient_image()[source]#

_compute_graph()[source]#

compute_shortest_path(startPt, endPt)[source]#

spateo.tools.live_wire.compute_shortest_path(image: numpy.ndarray, startPt: Tuple[float, float], endPt: Tuple[float, float]) → List[source]#

Inline function for easier computation of shortest_path in an image. This function will create a new instance of LiveWireSegmentation class every time it is called, calling for a recomputation of the gradient image and the shortest path graph. If you need to compute the shortest path in one image more than once, use the class-form initialization instead.

Parameters:

image: image on which the shortest path should be computed
startPt: starting point for path computation
endPt: target point for path computation

Returns:

shortest path as a list of tuples (x, y), including startPt and endPt

Return type:

path

spateo.tools.live_wire.live_wire(image: numpy.ndarray, smooth_image: bool = False, threshold_gradient_image: bool = False, interactive: bool = True) → List[numpy.ndarray][source]#

Use 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.

Now let’s display the image using matplotlib front end. A click on the image starts livewire segmentation. The suggestion for the best segmentation will appear as you will be moving mouse across the image. To submit a suggestion, click on the image for the second time. To finish the segmentation, press Escape key.