ShiftTolerant-LPIPS
Shift-tolerant Perceptual Similarity Metric
Abhijay Ghildyal, Feng Liu. In ECCV, 2022. [Arxiv]
from stlpips_pytorch import stlpips
from stlpips_pytorch import utils
path0 = "<dir>/ShiftTolerant-LPIPS/imgs/ex_p0.png"
path1 = "<dir>/ShiftTolerant-LPIPS/imgs/ex_ref.png"
img0 = utils.im2tensor(utils.load_image(path0))
img1 = utils.im2tensor(utils.load_image(path1))
stlpips_metric = stlpips.LPIPS(net="alex", variant="shift_tolerant")
stlpips_metric(img0,img1)
# 0.7777554988861084
Citation
If you find this repository useful for your research, please use the following.
@inproceedings{ghildyal2022stlpips,
title={Shift-tolerant Perceptual Similarity Metric},
author={Ghildyal, Abhijay and Liu, Feng},
booktitle={European Conference on Computer Vision},
year={2022}
}
Acknowledgements
This repository borrows from LPIPS, Anti-aliasedCNNs, and CNNsWithoutBorders. We thank the authors of these repositories for their incredible work and inspiration.
Metadata
Release files for stlpips-pytorch 0.0.2
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| stlpips_pytorch-0.0.2.tar.gz | 63.8 MB | Details |
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|---|---|---|---|---|
| stlpips_pytorch-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 127.6 MB
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