Metadata-Version: 2.1
Name: SimpleCRF
Version: 0.0.6
Summary: An open-source toolkit for conditional random field (CRF) and dense CRF
Home-page: https://github.com/HiLab-git/SimpleCRF
Author: Guotai Wang
Author-email: wguotai@gmail.com
License: BSD
Description: # 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.
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
