Automated-Objects-Removal-Inpainter

Automated object remover Inpainter is a project that combines Semantic segmentation and EdgeConnect architectures with minor changes in order to remove specified objects from photos. For Semantic Segmentation, the code from pytorch has been adapted, whereas for EdgeConnect, the code has been adapted from https://github.com/knazeri/edge-connect.

This project is capable of removing objects from list of 20 different ones; currently, it only supports removing one category of objects at a time.

How does it work?

Semantic segmentator model of deeplabv3/fcn resnet 101 has been combined with EdgeConnect. A pre-trained segmentation network has been used for object segmentation (generating a mask around detected object), and its output is fed to a EdgeConnect network along with input image with portion of mask removed. EdgeConnect uses two stage adversarial architecture where first stage is edge generator followed by image completion network. EdgeConnect paper can be found here and code in this repo

License

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International.

Except where otherwise noted, this content is published under a CC BY-NC license, which means that you can copy, remix, transform and build upon the content as long as you do not use the material for commercial purposes and give appropriate credit and provide a link to the license.

Citation

@inproceedings{nazeri2019edgeconnect,
  title={EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning},
  author={Nazeri, Kamyar and Ng, Eric and Joseph, Tony and Qureshi, Faisal and Ebrahimi, Mehran},
  journal={arXiv preprint},
  year={2019},
}

@InProceedings{Nazeri_2019_ICCV,
  title = {EdgeConnect: Structure Guided Image Inpainting using Edge Prediction},
  author = {Nazeri, Kamyar and Ng, Eric and Joseph, Tony and Qureshi, Faisal and Ebrahimi, Mehran},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
  month = {Oct},
  year = {2019}
}