- Website: davidelanz.github.io/pytorch-hed
- Documentation: pytorch-hed.readthedocs.io
This is a reimplementation in the form of a python package of Holistically-Nested Edge Detection [1] using PyTorch based on the previous pytorch implementation by sniklaus [2]. 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[3], please acknowledge it.
GitHub | Ref | |
---|---|---|
Original version based on Caffe | https://github.com/s9xie/hed | [1] |
Another reimplementation based on Caffe | https://github.com/zeakey/hed | |
Original reimplementation based on PyTorch | https://github.com/sniklaus/pytorch-hed | [2] |
First, you have to install the package (stable) with
pip install pytorch-hed
or, for the current (unstable) version
pip install git+https://github.com/Davidelanz/pytorch-hed.git
Usage:
import torchHED
# process a single image file
torchHED.process_file("./images/sample.png", "./images/sample_processed.png")
# process all images in a folder
torchHED.process_folder("./input_folder", "./output_folder")
# process a PIL.Image loaded in memory and return a new PIL.Image
# img = PIL.Image.open("./images/sample.png")
img_hed = torchHED.process_img(img)
Input | Original Caffe Implementation [1] | pytorch-hed [3] |
---|---|---|
[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}}
}