Metadata-Version: 2.1
Name: pytorch-hed
Version: 0.4
Summary: pytorch-hed - Holistically-Nested Edge Detection based on Pytorch
Home-page: https://github.com/Davidelanz/pytorch-hed
Author: Davide Lanza
Author-email: lanza.davide.it@gmail.com
License: GNU General Public License v3.0
Description: # Pytorch Holistically-Nested Edge Detection (HED)
        
        [![CodeFactor](https://www.codefactor.io/repository/github/davidelanz/pytorch-hed/badge)](https://www.codefactor.io/repository/github/davidelanz/pytorch-hed)
        [![travisCI](https://travis-ci.com/Davidelanz/pytorch-hed.svg?branch=master)](https://travis-ci.com/github/Davidelanz/pytorch-hed)
        [![codecov](https://codecov.io/gh/Davidelanz/pytorch-hed/branch/master/graph/badge.svg?token=MAAZAVSORH)](https://codecov.io/gh/Davidelanz/pytorch-hed)
        [![Pypi](https://img.shields.io/badge/Pypi-pytorch–hed-yellow)](https://pypi.org/project/pytorch-hed/)
        
        > This is a reimplementation in the form of a python package of Holistically-Nested Edge Detection [[1]](#references) using PyTorch based on the previous pytorch implementation by [sniklaus](https://github.com/sniklaus) [[2]](#references). If you would like to use of this work, please cite the paper accordingly. Also, make sure to adhere to the licensing terms of the authors. Moreover, if you will be making use of this particular implementation, please acknowledge the present [[3]](#references) implementation.
        
        <a href="https://arxiv.org/abs/1504.06375" rel="Paper"><img src="http://www.arxiv-sanity.com/static/thumbs/1504.06375v2.pdf.jpg" alt="Paper" width="100%"></a>
        
        |   |GitHub|Ref|
        |---|---|---|
        |Original version based on Caffe | https://github.com/s9xie/hed | [[1]](#references) |
        |Another reimplementation based on Caffe | https://github.com/zeakey/hed |
        |Original reimplementation based on PyTorch | https://github.com/sniklaus/pytorch-hed | [[2]](#references)|
        
        ## Usage
        
        First, you have to install the package with 
        ```
        pip install pytorch-hed
        ```
        or
        ```
        git clone https://github.com/Davidelanz/pytorch-hed.git
        pip install ./pytorch-hed
        ```
        
        To run it on your own image, use the following function:
        
        ``` python
        import torchHED
        torchHED.process_img("path/to/input/image.png", "path/to/output/image.png")
        ```
        
        ## Results
        
        |Input | Original Caffe Implementation [[1]](#references) | pytorch-hed [[3]](#references) |
        |---|---|---|
        | ![sample](https://github.com/Davidelanz/pytorch-hed/blob/master/images/sample.png?raw=true) | ![sample](https://github.com/Davidelanz/pytorch-hed/blob/master/images/official_caffe.png?raw=true) | ![sample](https://github.com/Davidelanz/pytorch-hed/blob/master/images/torchHED.png?raw=true) |
        
        ## References
        
        ```
        [1]  @inproceedings{Xie_ICCV_2015,
                 author = {Saining Xie and Zhuowen Tu},
                 title = {Holistically-Nested Edge Detection},
                 booktitle = {IEEE International Conference on Computer Vision},
                 year = {2015}
             }
        ```
        
        ```
        [2]  @misc{pytorch-hed,
                 author = {Simon Niklaus},
                 title = {A Reimplementation of {HED} Using {PyTorch}},
                 year = {2018},
                 howpublished = {\url{https://github.com/sniklaus/pytorch-hed}}
            }
        ```
        
        
        ```
        [3]  @misc{pytorch-hed-2,
                 author = {Davide Lanza},
                 title = {The {pytorch-hed} Python Package},
                 year = {2020},
                 howpublished = {\url{https://github.com/Davidelanz/pytorch-hed}}
            }
        ```
        
Keywords: edge-detection pytorch
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Natural Language :: English
Requires-Python: >=3.7.*
Description-Content-Type: text/markdown
