# clone project git clone https://github.com/lus105/StereoSense.git # change directory cd StereoSense # create conda environment conda create --name StereoSense python=3.11 # activate conda environment conda activate StereoSense # install pytorch pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126 # install requirements pip install -r requirements.txt
# navigate to root cd StereoSense # camera configuration files gdown https://drive.google.com/drive/folders/1mpDP1PHbM-xMh-Wwjx0DMOjTdmwnsyYU?usp=drive_link --folder # data (if crashes, try to download folder via link) gdown https://drive.google.com/drive/folders/1p_mfaFk4_bPl_JFaC41rpq9V0vVR02iw?usp=drive_link --folder # models gdown https://drive.google.com/drive/folders/15HT1PC70Jcmr9OSBmgRcslL31b8EEJld?usp=drive_link --folder # output (generated pointclouds) gdown https://drive.google.com/drive/folders/1N9eJ53YJbRBLYQ5ItHnJdAaVqU10zd-i?usp=drive_link --folder
Note: for using complete flow (main.py), install basler pylon.
- Gather calibration data with basler cameras:
python src/stereo_grab_basler.py - Run stereo camera calibration:
notebooks/1.0_Calibrate.ipynb. Change constants to your specific ones. - Download model and place inside models/ directory.
- Run
python main.py - Once the configs are loaded, press 'c' to capture frames. Results will be saved in output/ directory.
- Create your own camera calibration files (refer to
notebooks/1.0_Calibrate.ipynb) - Download model and place inside models/ directory.
- Grab sample images (left and right) and place inside data/samples directory.
- Run
notebooks/2.0_Stereo_inference.ipynb
The input size of the model is 800x640 (hxw).
- Model used: https://github.com/NVlabs/FoundationStereo
- Point Cloud Viewer using OpenCV Viz: https://github.com/rytisss/anaglyph3D