# AutoPortraitMatting

**Repository Path**: ssfantasy/AutoPortraitMatting

## Basic Information

- **Project Name**: AutoPortraitMatting
- **Description**: Tensorflow implementation of Automatic Portrait Matting on paper "Automatic Portrait Segmentation for Image Stylization"
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-01-07
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# AutoPortraitMatting
Tensorflow implementation of Automatic Portrait Matting on paper "Automatic Portrait Segmentation for Image Stylization"

## Requirement

* Tensorflow
* scipy
* six
* PIL
* numpy



## Data

mkdir Model_zoo and download http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

download data from http://xiaoyongshen.me/webpage_portrait/index.html

mkdir data and copy the following folders and files into data/:

  * images_mask/
  * images_data/
  * images_data_crop/
  * images_tracker/
  * portraitFCN_data/
  * portraitFCN+_data/
  * meanmask.png
  * testlist.mat
  * trainlist.mat

Some of the folders data above, will be obtained by additional scripting. To obtain additional data, run the `data_download.m` and `generate_data_file.m` respectively

## How to run

FCN.py and FCN_puls.py implements the two models in that paper.
To train:

`
  if __name__ == "__main__":
      tf.app.run()
      #pred()
`

After train, predict:

`
  if __name__ == "__main__":
      #tf.app.run()
      pred()
`
## Datasets
由于shenxiaoyong网站崩溃，提供数据的百度云下载链接
Link: http://pan.baidu.com/s/1dE14537 
Password: ndg8
仅供学术交流，如有引用，请通知原作者。

## Ackownledge

Thanks for Xiaoyong Shen and his paper

"Deep Automatic Portrait Matting" Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, Jiaya Jia. European Conference on Computer Vision (ECCV), 2016

I can't reach the performance in this paper. So please modify it as you want and inform me if any improvement.