Prerequisites

• Python 3
• NVIDIA GPU + CUDA 9.0 + cuDNN
• PyTorch 1.3.1
• Tensorflow 1.7.0
• Git-lfs

Packages Requirments

• numpy ~= 1.14.3
• scipy ~= 1.0.1
• future ~= 0.16.0
• matplotlib ~= 2.2.2
• pillow >= 6.2.0
• opencv-python ~= 3.4.0
• scikit-image ~= 0.14.0
• pyaml
• glob

Model Dataset

1) Images

We use our own dataset from Google Image and PictureSG

To run own your dataset, run scripts/flist.py to generate train, test and validation set file lists. For example, to generate the training set file list on your dataset run:

mkdir data
python ./scripts/flist.py --path PATH_TO_YOUR_DATA --output ./data/flist/XXX.flist

2) Masks

The model is trained on randomly generated irregular mask

Model Training

Our model is trained in four stages:

1. training the edge model
2. training the coarse inpaint model
3. training the fine inpaint model
4. training the joint model

To train the model:

python EIWA.py --mode 1 --model XXX

#Model Testing To test the model:

python EIWA.py --mode 2

Other Models

1. Context Encoder
2. GLCIC
3. Contextual Attention

References

[1] Nazeri, Kamyar, et al. "Edgeconnect: Generative image inpainting with adversarial edge learning." arXiv preprint arXiv:1901.00212 (2019).

[2] Iizuka, Satoshi, Edgar Simo-Serra, and Hiroshi Ishikawa. "Globally and locally consistent image completion." ACM Transactions on Graphics (ToG) 36.4 (2017): 107.

[3] Pathak, Deepak, et al. "Context encoders: Feature learning by inpainting." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

[4] Yu, Jiahui, et al. "Generative image inpainting with contextual attention." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.