kanul / glcic

Globally and Locally Consistent Image Completion with TensorFlow

Globally and Locally Consistent Image Completion (GLCIC)

This is an implementation of the image completion model proposed in the paper (Globally and Locally Consistent Image Completion) with TensorFlow.

Requirements

• Python 3
• TensorFlow 1.3
• OpenCV

Results

Usage

I. Prepare the training data

Put the images for training the "data/images" directory and convert images to npy format.

$ cd data
$ python to_npy.py

When you use the face images for training, it is recommended that you align the face landmarks with dlib before training. If you have no time for preprocessing, utilize mirror padding.

II. Train the GLCIC model

$ cd src
$ python train.py

You can download the trained model file: glcic_model.tar.gz

Approach

This implementation uses 128x128 images as training data unlike paper. So the both discriminators have 1 conv layer fewer; that is, the local and global discriminator have 4 and 5 conv layers, respectively.

I trained the GLCIC model using 5,434 face images collected from the Internet. The paper says the training should be split into three phases, but I skipped the second step. The completion network is trained with the MSE loss for 100 interatinos; then both the completion network and discriminator are trained to reach the total of 400 iterations. The entire training procedure takes roughly 16 hours on a single machine equipped with a GTX 1070.

Future Work

The result is not good when marginal area is missing.

License

MIT License

Copyright (c) 2018 tadax

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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.