DeepNormals Save

Code and Dataset from Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters

Project README

Deep Normals

Example result Example result of a deep normal estimation from sketch. Original creation from David Revoy, only non-commercial research usage is allowed.

Overview

This code provides pre-trained models used in the following research paper:

   "Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters"
   Matis Hudon, Rafael Pagés, Mairéad Grogan, Aljosa Smolić
   Geometry Meets Deep Learning ECCV 2018 Workshop

Please refer to our project webpage for more detailed information. Please also see our video of results.

Please note that futher work was made on this project:

   "Augmenting Hand-Drawn Art with Global Illumination Effects through Surface Inflation" 
   Matis Hudon and Sebastian Lutz and Rafael Pages and Aljosa Smolic
   CVMP 2019

Also a new dataset with ground truth normal maps and depth maps was made available, see the project webpage

Prerequisites

With Docker

If you are familiar with docker this image has everything installed to run the code (please install and run with nvidia-docker): docker pull matishudon/dockerdeepn

This code was tested under ubuntu 16.04.

Locally

This code was tested under ubuntu 16.04.

  • Cuda 9

  • python3

  • Numpy

    ip3 install numpy
    
  • Tensorflow

    ip3 install -U tensorflow-gpu
    
  • opencv-python

    ip3 install opencv-python
    
  • tqdm

    ip3 install tqdm
    
  • TFlearn

    pip3 install tflearn
    

Usage

Generate Normal maps from line drawings

Before using the code, the model has to be downloaded from here Unzip in the Net/ folder.

To Run Locally:

You can test the code with:

python3 main.py

You should see a file Normal_Map.png You can specify your own images (as in the example folder) with:

python3 main.py --lineart_path PathToYourImage --mask_path PathToCorrespondingMask

Please see main.py for other commands.

To run with docker:

sudo nvidia-docker run -v CodeDirectory/:/container/directory/ -it matishudon/dockerdeepn python3 /container/directory/main.py --docker_path /container/directory/

Where CodeDirectory is the full path of the directory containing main.py.

Play with the "renderer"

We also provide a very simple and slow renderer Interactive_Rendering.py please look into the python file for commands. You need to provide an additional color image for this. See the example folder Pepper/.

Artistic image from David Revoy, only non-commercial research usage is allowed.

To Run Locally:

python3 Interactive_Rendering.py 

To run with docker:

First run on local machine:

xhost +local:docker

Then:

sudo nvidia-docker run -v CodeDirectory/:/container/directory/ -ti -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --env QT_X11_NO_MITSHM=1 matishudon/dockerdeepn python3 /container/directory/Interactive_Rendering.py --docker_path /container/directory/

Where CodeDirectory is the full path of the directory containing main.py.

Dataset

The Dataset used in this work (Training and Testing) can be downloaded here.

Citing

If you use this model, code or dataset please cite our paper:

@inproceedings{hudon2018deep,
  title={Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters},
  author={Hudon, Matis and Grogan, Mair{\'e}ad and Pag{\'e}s, Rafael and Smoli{\'c}, Aljo{\v{s}}a},
  booktitle={European Conference on Computer Vision},
  pages={246--262},
  year={2018},
  organization={Springer}
}

Acknowledgements

The authors would like to thank David Revoy and Ester Huete, for sharing their original creations. This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under the Grant Number 15/RP/2776. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

License

Copyright (c) 2018 Matis Hudon, Trinity College Dublin

Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You understand and agree that the authors are under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. The authors nevertheless reserve the right to update, modify, or discontinue the Software at any time.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. You agree to cite the Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters paper in documents and papers that report on research using this Software.

Open Source Agenda is not affiliated with "DeepNormals" Project. README Source: V-Sense/DeepNormals
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