InstanceShadowDetection
Instance shadow detection aims to find shadow instances paired with object instances. We present a dataset, a deep framework, and an evaluation metric to approach this new task. This repo is implemented on Detectron2.
Installation
Install LISA and pysobatools
$ cd InstanceShadowDetection
$ python setup.py install
$ cd PythonAPI
$ python setup.py install
Docker
$ cd InstanceShadowDetection/docker
$ docker build --tag="instanceshadow" ./Dockerfile .
Model, dataset and our results
Please dowload from Google Drive. Put the model in projects/LISA/output_light/.
Demo
$ cd projects/LISA/
$ python demo.py --input ./demo/web-shadow0573.jpg --output ./ --config ./config/LISA_101_FPN_3x_demo.yaml
Train
$ python train_net.py --num-gpus 2 --config-file ./config/LISA_101_FPN_3x.yaml
Evaluation
$ python train_net.py --num-gpus 2 --config-file ./config/LISA_101_FPN_3x.yaml --eval-only --resume
$ python SOAP.py
Visualize
python visualize_json_results.py --ins_input ./output_light/inference/soba_instances_results.json --ass_input ./output_light/inference/soba_association_results.json --output ./output_light/results --dataset soba_cast_shadow_val_full
Citation
If you use LISA, SOBA, or SOAP, please use the following BibTeX entry.
@InProceedings{Wang_2020_CVPR,
author = {Wang, Tianyu and Hu, Xiaowei and Wang, Qiong and Heng, Pheng-Ann and Fu, Chi-Wing},
title = {Instance Shadow Detection},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
