Awesome
ST-CGAN: Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal with PyTorch
This repository is unofficial implementation of Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal [Wang+, CVPR 2018] with PyTorch.
Official Dataset and Code(coming soon...) is here.
Requirements
- Python3.x
- PyTorch 1.5.0
- pillow
- matplotlib
Usage
- Set datasets under
./dataset
. You can Download datasets from here.
Then,
Training
python3 train.py
Testing
When Testing images from ISTD dataset.
python3 test.py -l <checkpoint number>
When you would like to test your own image.
python3 test.py -l <checkpoint number> -i <image_path> -o <out_path>
Results
Here is a result from test sets. (Left to right: input, ground truth, shadow removal, ground truth shadow, shadow detection)
Shadow Detection
Here are some results from validation set. (Top to bottom: ground truth, shadow detection)
Shadow Removal
Here are some results from validation set. (Top to bottom: input, ground truth, shadow removal)
Trained model
You can download from here.
References
- Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal, Jifeng Wang<sup>∗</sup>, Xiang Li<sup>∗</sup>, Le Hui, Jian Yang, Nanjing University of Science and Technology, [arXiv]