DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network
International Conference on Computer Vision (ICCV'2021)

Yeying Jin, Aashish Sharma and Robby T. Tan

[Paper] [Supplementary] [Poster] [Slides] [Video] [Zhihu]

git clone https://github.com/jinyeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal.git
cd DC-ShadowNet-Hard-and-Soft-Shadow-Removal/
conda create -n shadow python=3.7
conda activate shadow

conda install pytorch=1.10.2 torchvision torchaudio cudatoolkit=11.3 -c pytorch
python3 -m pip install -r requirements.txt

1. SRD Train|BaiduPan, Test. Shadow Masks

2. AISTD|ISTD+ [link]

3. ISTD [link]

4. USR: Unpaired Shadow Removal Dataset [link]

5. LRSS: Soft Shadow Dataset [link]

1. [Update main_test_single.py and DCShadowNet_test_single.py]
   Put the test images in test_input, results in: results/output/
   

${DC-ShadowNet-Hard-and-Soft-Shadow-Removal}
|-- test_input           ## Shadow
|-- results
    |-- output           ## result

CUDA_VISIBLE_DEVICES='1' python main_test_single.py

2. For the test SRD dataset /dataset/SRD/testA/, results in: results/SRD/500000(iteration)/outputB/

${DC-ShadowNet-Hard-and-Soft-Shadow-Removal}
|-- dataset
    |-- SRD
      |-- testA           ## Shadow
    |-- AISTD
      |-- testA           ## Shadow
    |-- USR
      |-- testA           ## Shadow
|-- results
    |-- SRD
      |-- model           ## SRD_params_0500000.pt
      |-- 500000/outputB/ ## result
    |-- AISTD
      |-- model           ## AISTD_params_0500000.pt
      |-- 500000/outputB/ ## result
    |-- ISTD
      |-- model           ## ISTD_params_0600000.pt
      |-- 600000/outputB/ ## result
    |-- USR
      |-- model           ## USR_params_0600000.pt
      |-- 600000/outputB/ ## result

rename to the original name, please change the suffix of test images accordingly (.jpg or .png)

CUDA_VISIBLE_DEVICES='1' python main_test.py --dataset SRD --datasetpath /home1/yeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal/dataset/SRD --use_original_name True --im_suf_A .jpg

CUDA_VISIBLE_DEVICES='1' python main_test.py --dataset AISTD --datasetpath /home1/yeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal/dataset/AISTD --use_original_name True --im_suf_A .png

CUDA_VISIBLE_DEVICES='1' python main_test.py --dataset ISTD --datasetpath /home1/yeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal/dataset/ISTD --use_original_name True --im_suf_A .png

CUDA_VISIBLE_DEVICES='1' python main_test.py --dataset USR --datasetpath /home1/yeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal/dataset/USR --use_original_name True --im_suf_A .jpg

The root mean squared error (RMSE) evaluation code used by all methods (including ours) computes mean absolute error (MAE).

set the paths of the shadow removal result and the dataset in evaluation/demo_srd_release.m and then run it.

demo_srd_release.m

Get the following Table 1 in the main paper on the SRD (size: 256x256):

For SRD (size: 640x840):

set the paths of the shadow removal result and the dataset in evaluation/demo_aistd_release.m and then run it.

demo_aistd_release.m

Get the following Table 2 in the main paper on the AISTD (size: 256x256):

For AISTD (size: 480x640):

set the paths of the shadow removal result and the dataset in evaluation/demo_lrss_release.m and then run it.

demo_lrss_release.m

Get the following Table 3 in the main paper on the LRSS dataset (size: 256x256):

1. Implement On the removal of shadows from images (TPAMI,05) and Recovery of Chromaticity Image Free from Shadows via Illumination Invariance (ICCV,03)
   [Update] We have released our MATLAB and Python implementations on Sep 8, 2023. We recommend the MATLAB.

1.1 MATLAB: inputs are in 0_Shadow-Free_Chromaticity_matlab/input/, outputs are in 0_Shadow-Free_Chromaticity_matlab/sfchroma/.

0_Shadow-Free_Chromaticity_matlab/physics_all.m

1.2 Python: inputs are in 0_Shadow-Free_Chromaticity_python/input/, outputs are in 0_Shadow-Free_Chromaticity_python/sfchroma/.

0_Shadow-Free_Chromaticity_python/physics_all.py

2. Download datasets and run 0_Shadow-Free_Chromaticity_matlab/physics_all.m to get the Shadow-Free Chromaticity Maps after Illumination Compensation, and put them in the trainC folder, you should see the following directory structure.

${DC-ShadowNet-Hard-and-Soft-Shadow-Removal}
|-- dataset
    |-- SRD
      |-- trainA ## Shadow 
      |-- trainB ## Shadow-free 
      |-- trainC ## Shadow-Free Chromaticity Maps after Illumination Compensation
      |-- testA  ## Shadow 
      |-- testB  ## Shadow-free 

3. python main_train.py --dataset SRD --datasetpath [path_to_SRD dataset] --iteration [iteration]
   [Update] We have released DCShadowNet_train.py on Dec 7, 2022.

Get the following Figure 5 in the main paper, VGG feature visualization code is in the feature_release folder,

python test_VGGfeatures.py

Results in: ./results_VGGfeatures/shadow_VGGfeatures/layernumber/imagenumber/visual_featurenumber_RMSE.jpg

Code is implemented based U-GAT-IT, we would like to thank them.
One trick used in networks.py is to change out = self.UpBlock2(x) to out = (self.UpBlock2(x)+input).tanh() to learn a residual.

The code and models in this repository are licensed under the MIT License for academic and other non-commercial uses.
For commercial use of the code and models, separate commercial licensing is available. Please contact:

• Yeying Jin (jinyeying@u.nus.edu)
• Robby T. Tan (tanrobby@gmail.com)
• Jonathan Tan (jonathan_tano@nus.edu.sg)

If this work is useful for your research, please cite our paper.

@inproceedings{jin2021dc,
  title={DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network},
  author={Jin, Yeying and Sharma, Aashish and Tan, Robby T},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5027--5036},
  year={2021}
}