https://github.com/buswinka/piefs

Python Instance (map) Eikonal Function Solver
https://github.com/buswinka/piefs

cellpose distance-calculation eikonal eikonal-solver instance-segmentation omnipose pytorch

Last synced: about 2 months ago
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Python Instance (map) Eikonal Function Solver

• Host: GitHub
• URL: https://github.com/buswinka/piefs
• Owner: buswinka
• License: gpl-3.0
• Created: 2023-07-25T18:51:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-02T18:58:11.000Z (over 1 year ago)
• Last Synced: 2024-11-15T07:14:30.990Z (2 months ago)
• Topics: cellpose, distance-calculation, eikonal, eikonal-solver, instance-segmentation, omnipose, pytorch
• Language: Python
• Homepage:
• Size: 59.6 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Python Instance Eikonal Function Solver (PIEFS) 

PIEFS is a library which solves the Eikonal Function for 2D and 3D instance masks, using the Fast Iterative Method.

It achieves memory efficiency through a fused kernel written in OpenAI Triton.

It also provides functionality to calculate gradients of the eikonal field.

This implementation uses convolutions to calculate affinity masks, which may be faster and

can occur on cuda, however uses dense representations of the affinity masks and therefore is 

memory intensive. It may be possible for much of the mask operations to occur with sparse tensors

for memory efficiency. 

A big thanks to Kevin Cutler (Original Omnipose Author) for helping me create this. 

Example:

```python

import torch

from piefs import solve_eikonal, gradient_from_eikonal

image = torch.load('path/to/my/image.pt')  # An image with shape (B, C=1, X, Y, Z)

eikonal = solve_eikonal(image, eps=1e-5, min_steps=200, use_triton=True)  # A Distance map with shape (B, C=1, X, Y, Z)

gradients = gradient_from_eikonal(eikonal)  # A Gradient tensor with shape (B, C=3, X, Y, Z)

```