## Install & Requirements
The code has been tested on pytorch=0.4.0 and python3.6. Please refer to `requirements.txt` for detailed information.
Alternatively, you can run it with the provided [Docker image](docker/README.md).
**To Install python packages**
```
pip install -r requirements.txt
```
**To Install liteflownet modules**
The correlation layer for LiteFlowNet is implemented in CUDA using CuPy. Install it using `pip install cupy` or install one of the provided binaries (listed [here](https://docs-cupy.chainer.org/en/stable/install.html#install-cupy)).
## Componets
There exist three components in this repo:
* Video Inpainting Tool: DFVI
* Extract Flow: LiteFlowNet([Pytorch version](https://github.com/sniklaus/pytorch-liteflownet) reimplemented from [LiteFlowNet](https://github.com/twhui/LiteFlowNet))
* Image Inpainting(reimplemented from [Deepfillv1](https://github.com/JiahuiYu/generative_inpainting))
## Usage
* To use our video inpainting tool for object removing, we recommend that the frames should be put into `xxx/video_name/frames`
and the mask of each frame should be put into `xxx/video_name/masks`.
And please download the resources of the demo and model weights from [here](https://drive.google.com/drive/folders/1a2FrHIQGExJTHXxSIibZOGMukNrypr_g?usp=sharing).
An example demo containing frames and masks has been put into the demo and running the following command will get the result:
```
python tools/video_inpaint.py --frame_dir ./demo/frames --MASK_ROOT ./demo/masks --img_size 512 832 --LiteFlowNet --DFC --ResNet101 --Propagation
```
You can just change the **th_warp** param for getting better results in your video.
* To extract flow for videos:
```
python tools/infer_liteflownet.py --frame_dir xxx/video_name/frames
```
* To use the Deepfillv1-Pytorch model for image inpainting,
```
python tools/frame_inpaint.py --test_img xxx.png --test_mask xxx.png --image_shape 512 512
```
## Update
* More results can be found and downloaded [here](https://www.dropbox.com/sh/jxcl4he5bgsmk7t/AADxnfqHj-PGcjxd02Bil56ya?dl=0).
* **Support for PyTorch>1.0:** Sorry for the late update and the pre-release verison for supporting PyTorch>1.0 has been integrated into our new [v1.1 branch](https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting/tree/v1.1).
* The frames and masks of our movie demo have been put into [Google Drive](https://drive.google.com/drive/folders/1z2n1LzVY8gjvy7ezF_tuuMgVouR_pFcz?usp=sharing).
* The weights of DAVIS's refined stages have been released and you can download from [here](https://drive.google.com/drive/folders/1zIamN-DzvknZLf5QAGCfvWs7a6qUqaaC?usp=sharing).
Please refer to [Usage](#Usage) for using the Multi-Scale models.
## FAQ
* Errors when running install_scripts.sh
if you meet some problem about gcc when compiling, pls check if the following commands will help:
```
export CXXFLAGS="-std=c++11"
export CFLAGS="-std=c99"
```
## Citation
```
@InProceedings{Xu_2019_CVPR,
author = {Xu, Rui and Li, Xiaoxiao and Zhou, Bolei and Loy, Chen Change},
title = {Deep Flow-Guided Video Inpainting},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
```
# inpainting_colab
