semvec/pypi-packages huggingface dataset:
SimpleCRF 0.2.1.1
An open-source toolkit for conditional random field (CRF) and dense CRF
License: BSD
Requires Python: >=3.6

# SimpleCRF
Matlab and Python wrap of Conditional Random Field (CRF) and fully connected (dense) CRF for 2D and 3D image segmentation, according to the following papers:

[1] Yuri Boykov and Vladimir Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision", IEEE TPAMI, 2004.

[2] Philipp Krähenbühl and Vladlen Koltun, "Efficient inference in fully connected crfs with gaussian edge potentials", in NIPS, 2011.

[3] Kamnitsas et al in "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation", Medical Image Analysis, 2017.

![maxflow](./data/maxflow.png)
![densecrf1](./data/densecrf1.png)
![densecrf2](./data/densecrf2.png)

### Dependency
This repository depends on the following packages:
[`Maxflow`](https://vision.cs.uwaterloo.ca/code/),
[`DenceCRF`](http://graphics.stanford.edu/projects/drf/) and 
[`3D Dense CRF`](https://github.com/deepmedic/dense3dCrf)

### Installation
1. Install by: [`pip install SimpleCRF`](https://github.com/taigw/SimpleCRF)

2. Alternatively, you can compile the source files by the following two steps:
```bash
python setup.py build
python setup.py install
```

### Examples
Some demos of using this package are:

* `examples/demo_maxflow.py`: using maxflow for automatic and interactive segmentation of 2D and 3D images.

* `examples/demo_densecrf.py`: using dense CRF for 2D gray scale and RGB image segmentation.

* `examples/demo_densecrf3d.py`: using 3D dense CRF for 3D multi-modal image segmentation.

### Modules
1. `maxflow` has four functions as follows. Note that the current version only supports binary segmentation.

* `maxflow.maxflow2d()` for 2D automatic segmentation.

* `maxflow.interactive_maxflow2d()` for 2D interactive segmentation.

* `maxflow.maxflow3d()` for 3D automatic segmentation.

* `maxflow.interactive_maxflow3d()` for 3D interactive segmentation.

2. `denseCRF` has the following function. It can deal with multi-class segmentation, and only supports RGB images.

* `denseCRF.densecrf()` for 2D automatic segmentation.

3. `denseCRF3D` has the following function. It can deal with multi-class segmentation. The input channel number can be 1-5.

* `denseCRF3D.densecrf3d()` for 3D automatic segmentation.