Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Usage

Dataset Process

Option a: Process data from raw TUM dataset
- Download TUM Rolling Shutter Dataset with Euroc/DSO format, modify data_path in tum_process/tum_process.py accordingly
- Download PWC-Net weights, modify ckpt_path in tum_process/pwcnet.py accordingly
- Process the dataset (specify save_path in tum_process/tum_process.py if necessary)
```
python3 tum_process/tum_process.py
```
Option b: Download the processed data directly

Training (modify data_path in data_loader.py to the save_path or the processed data)

python3 -m train_depth
python3 -m train_anchor --anchor=1
python3 -m train_anchor --anchor=2
python3 -m train_anchor --anchor=4
...

Testing

python3 -m test  --anchor=1
python3 -m test  --anchor=2
python3 -m test  --anchor=4 --rectify_img=1
...

Plot errors

python3 -m view_errs

Check recitified images in /test_results/images

datomi79/unrolling

Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption

Usage