Awesome
STICT for Video Shadow Detection
Code and Dataset for our CVPR 2022 paper "Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training"
VIdeo ShAdow Detection dataset (VISAD)
VISAD is consisted of 82 videos and was divided into two parts according to semantic of scenes: the Driving Scenes (VISAD-DS)
part and the Moving Object Scenes (VISAD-MOS)
part, denoted as DS and MOS respectively.
It is available at Google Drive.
scenes | videos/annotated | frames/annotated | resolution |
---|---|---|---|
DS-all | 47 / 17 | 7953 / 2881 | 1280×720 |
DS-test | 13 / 13 | 2190 / 2190 | 1280×720 |
MOS-all | 34 / 16 | 4613 / 1307 | (530-1920)×(360-1080) |
MOS-test | 13 / 13 | 873 / 873 | 1920×1080,1600×900 |
evaluation over predictions
Run python evaluate.py
important arguments:
-gp, --gt_path ground truth path
-pp, --pred_path your predicitons path
Our pretrained shadow maps is available here(DS, MOS, ViSha)
Spatio-Temporal Interpolation Consistency Training
Requirement
- cuda (10.0)
- Python (3.6)
- PyTorch (1.1.0)
- spatial-correlation-sampler (0.0.8)
- Flownet (2.0)
Download dataset
Download the following datasets and unzip them into ./data
folder
- SBU (it can refer MTMT)
- DS
- MOS
Testing
Our pretrained model is available here
- Run
python test.py
important arguments:
--trained_model trained model path (default:'./DS')
--dataset_path your test set path (default: './data/DS/test/')
--dataset_txt_path your test set list path (default: './data/DS/test/test.txt')
Training
important arguments:
--target_domain (options: 'DS_U', 'MOS_U', 'ViSha') (default: 'DS_U')
--dataset_U_path your video domain dataset path (default: './data/DS/train/')