内容简介:Preprint:Set of Machine Learning Python plugins for GIMP.The plugins have been tested with GIMP 2.10 on the following machines:
GIMP-ML
Preprint: Link
Set of Machine Learning Python plugins for GIMP.
The plugins have been tested with GIMP 2.10 on the following machines:
[1] macOS Catalina 10.15.3
[2] ubuntu 18.04 LTS
Screenshot of Menu
Installation Steps
[1] Install GIMP .
[2] Clone this repository: git clone https://github.com/kritiksoman/GIMP-ML.git
[3] Open GIMP and go to Preferences -> Folders -> Plug-ins, add the folder gimp-plugins and close GIMP.
[4] Download weights.zip (1.22 GB) and save it in gimp-plugins folder.
[5] Open terminal and run :
bash installGimpML-mac.sh
bash moveWeights.sh
[6] Open GIMP.
Demo videos on YouTube
Paper References
[1] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network ( https://arxiv.org/abs/1609.04802 )
[2] DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better ( https://arxiv.org/abs/1908.03826 )
[3] Digging into Self-Supervised Monocular Depth Prediction ( https://arxiv.org/abs/1806.01260 )
[4] BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation ( https://arxiv.org/abs/1808.00897 )
[5] MaskGAN: Towards Diverse and Interactive Facial Image Manipulation ( https://arxiv.org/abs/1907.11922 )
[6] Perceptual Losses for Real-Time Style Transfer and Super-Resolution ( https://cs.stanford.edu/people/jcjohns/eccv16/ )
[7] Rethinking Atrous Convolution for Semantic Image Segmentation ( https://arxiv.org/abs/1706.05587 )
Code References
The following have been ported :
[1] https://github.com/switchablenorms/CelebAMask-HQ
[2] https://github.com/TAMU-VITA/DeblurGANv2
[3] https://github.com/zllrunning/face-parsing.PyTorch
[4] https://github.com/nianticlabs/monodepth2
[5] https://github.com/richzhang/colorization
[6] https://github.com/twtygqyy/pytorch-SRResNet
Citation
Please cite using the following bibtex entry:
@article{soman2020GIMPML, title={GIMP-ML: Python Plugins for using Computer Vision Models in GIMP}, author={Soman, Kritik}, journal={arXiv preprint arXiv:2004.13060}, year={2020} }
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
Beginning Java Objects中文版从概念到代码
巴克 / 万波 / 人民邮电出版社 / 2007-1 / 78.00元
《Beginning Java Objects中文版从概念到代码(第2版)》是关于软件对象和Java的,但并不是纯粹地介绍Java语言,而是强调如何从对象模型转换到功能完整的Java应用程序。书中讲述了对象基础、对象建模和模型的实现。《Beginning Java Objects中文版从概念到代码(第2版)》除了用学生注册系统(SRS)示例贯穿全书之外,还在附录中给出三个附加的案例,这些案例是每章......一起来看看 《Beginning Java Objects中文版从概念到代码》 这本书的介绍吧!