The official repository for Deep Image Matting with Flexible Guidance Input.
Paper: https://arxiv.org/abs/2110.10898
- easydict
- numpy
- opencv-python
- Pillow
- PyQt5
- scikit-image
- scipy
- toml
- torch>=1.5.0
- torchvision
Google drive: https://drive.google.com/drive/folders/13qnlXUSKS5HfkfvzdMKAv7FvJ6YV_wPK?usp=sharing
百度网盘： https://pan.baidu.com/s/1ZYcbwyCIrL6G9t7pkCIBYw 提取码: zjtj
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Weight_DIM.pthThe model trained with Adobe matting dataset. -
Weight_D646.pthThe model trained with Distincions-646 dataset. -
DIM_test_supp_data.zipScribblemaps and Clickmaps for DIM test set. -
D-646_test_supp_data.zipScribblemaps and Clickmaps for Distinctions-646 test set.
Place Weight_DIM.pth and Weight_D646.pth in ./checkpoints.
Edit ./config/FGI_config to modify the path of the testset and choose the checkpoint name.
| Methods | SAD | MSE | Grad | Conn |
|---|---|---|---|---|
| Trimap test | 30.19 | 0.0061 | 13.07 | 26.66 |
| Scribblemap test | 32.86 | 0.0090 | 14.18 | 29.09 |
| Clickmap test | 34.67 | 0.0112 | 15.45 | 30.96 |
| No guidance test | 36.36 | 0.0141 | 15.23 | 32.76 |
"checkpoint" in ./config/FGI_config.toml should be "Weight_DIM".
bash test.sh
Modify "guidancemap_phase" in ./config/FGI_config.toml to test on trimap, scribblemap, clickmap and No_guidance.
For further test, please use the code in ./DIM_evaluation_code and the predicted alpha mattes in ./alpha_pred.
| Methods | SAD | MSE | Grad | Conn |
|---|---|---|---|---|
| Trimap test | 28.90 | 0.0105 | 24.67 | 27.40 |
| Scribblemap test | 33.22 | 0.0131 | 26.93 | 31.38 |
| Clickmap test | 34.97 | 0.0146 | 27.60 | 33.11 |
| No guidance test | 36.83 | 0.0156 | 28.28 | 34.90 |
"checkpoint" in ./config/FGI_config.toml should be "Weight_D646".
bash test.sh
Modify "guidancemap_phase" in ./config/FGI_config.toml to test on trimap, scribblemap, clickmap and No_guidance.
For further test, please use the code in ./DIM_evaluation_code and the predicted alpha mattes in ./alpha_pred.
Copy one of the pth file and rename it "Weight_qt_in_use.pth", also place it in ./checkpoints.
Run test_one_img_qt.py.
Try images in ./testimg. It will use GPU if avaliable, otherwise it will use CPU.
I recommend to use the one trained on DIM dataset.
Have fun :D
GCA-Matting: https://github.com/Yaoyi-Li/GCA-Matting