This is the inference codes of Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation using Tensorflow (paper link). Given an image and its trimap, it estimates the alpha matte and foreground color.

System: Ubuntu

Tensorflow version: tf1.8, tf1.12 and tf1.13 (It might also work for other versions.)

GPU memory: >= 12G

System RAM: >= 64G

1, Clone Context-aware Matting repository

git clone https://github.com/hqqxyy/Context-Aware-Matting.git

2, Download our models at here. Unzip them and move it to root of this repository.

tar -xvf model.tgz

After moving, it should be like

.
├── conmat
│   ├── common.py
│   ├── core
│   ├── demo.py
│   ├── model.py
│   └── utils
├── examples
│   ├── img
│   └── trimap
├── model
│   ├── lap
│   ├── lap_fea_da
│   └── lap_fea_da_color
└── README.md

You can first set the image and trimap path by:

export IMAGEPATH=./examples/img/2848300_93d0d3a063_o.png
export TRIMAPPATH=./examples/trimap/2848300_93d0d3a063_o.png

For the model(3) ME+CE+lap in the paper,

python conmat/demo.py \
--checkpoint=./model/lap/model.ckpt \
--vis_logdir=./log/lap/ \
--fgpath=$IMAGEPATH \
--trimappath=$TRIMAPPATH \
--model_parallelism=True

You can find the result at ./log/

For the model(5) ME+CE+lap+fea+DA in the paper. (Please use this model for the real world images)

python conmat/demo.py \
--checkpoint=./model/lap_fea_da/model.ckpt \
--vis_logdir=./log/lap_fea_da/ \
--fgpath=$IMAGEPATH \
--trimappath=$TRIMAPPATH \
--model_parallelism=True

You can find the result at ./log/

For the model(7) ME+CE+lap+fea+color+DA in the paper.

python conmat/demo.py \
--checkpoint=./model/lap_fea_da_color/model.ckpt \
--vis_logdir=./log/lap_fea_da_color/ \
--fgpath=$IMAGEPATH \
--trimappath=$TRIMAPPATH \
--branch_vis=1 \
--branch_vis=1 \
--model_parallelism=True

You can find the result at ./log/

Please note that since the input image is high resolution. You might need to use gpu whose memory is bigger or equal to 12G. You can set the --model_parallelism=True in order to further save the GPU memory.

If you still meet problems, you can run the codes in CPU by disable GPU

export CUDA_VISIBLE_DEVICES=''

, and you need to set --model_parallelism=False. Otherwise, you can resize the image and trimap to a smaller size and then change the vis_comp_crop_size and vis_patch_crop_size accordingly.

You can download our results of Compisition-1k dataset and the real-world image dataset at here.

The provided implementation is strictly for academic purposes only. Should you be interested in using our technology for any commercial use, please feel free to contact us.

If you find this code is helpful, please consider to cite our paper.

@inproceedings{hou2019context,
  title={Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation},
  author={Hou, Qiqi and Liu, Feng},
  booktitle = {IEEE International Conference on Computer Vision},
  year = {2019}
}

If you find any bugs of the code, feel free to send me an email: qiqi2 AT pdx DOT edu. You can find more information in my homepage.

This projects employs functions from Deeplab V3+ to implement our network. The source images in the demo figure are used under a Creative Commons license from Flickr users Robbie Sproule, MEGA PISTOLO and Jeff Latimer. The background images are from the MS-COCO dataset. The images in the examples are from Composition-1k dataset and the real-world image. We thank them for their help.