Metadata-Version: 2.1
Name: numpymaxflow
Version: 0.0.2
Summary: numpymaxflow: Max-flow/Min-cut in Numpy for 2D images and 3D volumes
Home-page: https://github.com/masadcv/numpymaxflow
Author: Muhammad Asad
Author-email: masadcv@gmail.com
License: BSD-3-Clause License
Description: # numpymaxflow: Max-flow/Min-cut in numpy for 2D images and 3D volumes
        [![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
        [![CI Build](https://github.com/masadcv/numpymaxflow/actions/workflows/build.yml/badge.svg)](https://github.com/masadcv/numpymaxflow/actions/workflows/build.yml)
        [![PyPI version](https://badge.fury.io/py/numpymaxflow.svg)](https://badge.fury.io/py/numpymaxflow)
        <img src="https://img.shields.io/badge/Python-3.6%20|%203.7%20|%203.8%20|%203.9-3776ab.svg"/>
        <img src="https://img.shields.io/badge/numpy-%3E%3D%201.19.2-brightgreen.svg"/>
        
        Numpy-based implementation of Max-flow/Min-cut based on the following paper:
        
        - Boykov, Yuri, and Vladimir Kolmogorov. "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision." IEEE transactions on pattern analysis and machine intelligence 26.9 (2004): 1124-1137.
        
        Based on similar [PyTorch-based implementation](https://github.com/masadcv/torchmaxflow)
        
        ## Citation
        If you use this code in your research, then please consider citing:
        
         **Asad, Muhammad, Lucas Fidon, and Tom Vercauteren. ["ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation."](https://openreview.net/pdf?id=9xtE2AgD_Cc) Medical Imaging with Deep Learning (MIDL), 2022.**
        
        ## Installation instructions
        `pip install numpymaxflow`
        
        or 
        
        
        ```
        # Clone and install from github repo
        
        $ git clone https://github.com/masadcv/numpymaxflow
        $ cd numpymaxflow
        $ pip install -r requirements.txt
        $ python setup.py install
        ```
        
        ## Example outputs
        Maxflow2d
        
        ![./figures/numpymaxflow_maxflow2d.png](https://raw.githubusercontent.com/masadcv/numpymaxflow/main/figures/numpymaxflow_maxflow2d.png)
        
        Interactive maxflow2d
        
        ![./figures/numpymaxflow_intmaxflow2d.png](https://raw.githubusercontent.com/masadcv/numpymaxflow/main/figures/numpymaxflow_intmaxflow2d.png)
        
        
        ![figures/figure_numpymaxflow.png](https://raw.githubusercontent.com/masadcv/numpymaxflow/main/figures/figure_numpymaxflow.png)
        
        
        ## Example usage
        
        The following demonstrates a simple example showing numpymaxflow usage:
        ```
        image = np.asarray(Image.open('data/image2d.png').convert('L'), np.float32)
        image = numpy.from_numpy(image).unsqueeze(0).unsqueeze(0)
        
        prob = np.asarray(Image.open('data/image2d_prob.png'), np.float32)
        prob = numpy.from_numpy(prob).unsqueeze(0)
        
        lamda = 20.0
        sigma = 10.0
        
        post_proc_label = numpymaxflow.maxflow(image, prob, lamda, sigma)
        ```
        
        For more usage examples see: 
        
        **2D and 3D maxflow and interactive maxflow examples**: [`demo_maxflow.py`](https://raw.githubusercontent.com/masadcv/numpymaxflow/main/demo_maxflow.py) 
         
        ## References
        - [OpenCV's Graphcut implementation](https://github.com/opencv/opencv/blob/4.x/modules/imgproc/include/opencv2/imgproc/detail/gcgraph.hpp)
        - [SimpleCRF's maxflow implementation](https://github.com/HiLab-git/SimpleCRF)
        - [torchmaxflow's implementation](https://github.com/masadcv/torchmaxflow)
        
        This repository depends on the code for [maxflow from latest version of OpenCV](https://github.com/opencv/opencv/blob/4.x/modules/imgproc/include/opencv2/imgproc/detail/gcgraph.hpp), which has been included.
        
        <!-- BibTeX:
        ```
        @inproceedings{
        asad2022econet,
        title={{ECON}et: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation},
        author={Muhammad Asad and Lucas Fidon and Tom Vercauteren},
        booktitle={Medical Imaging with Deep Learning},
        year={2022},
        url={https://openreview.net/forum?id=9xtE2AgD_Cc}
        }
        ``` -->
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
