Open source implementation of an object tracking algorithm that takes an input of a video source and calculates different parameters from the objects in the frame (people, bikes, cars) then displays the parameters (number of objects, average speed, direction of moving) and sends them to be displayed on the Open Remote manager throw an HTTP API in a Json format.
- Install CUDA Toolkit (10.2 recommended) NVIDIA GPU required
- Install Python3 (3.8.5)
- Install OpenCV (pip install opencv-python)
- Install pyQT (pip install PyQt5)
- Install pyTorch (https://pytorch.org/get-started/locally/ -> select proper install instructions and use the command).
- Install torchVision (included with pytorch install above)
- Install numba (pip install numba)
- Install Scikit (pip install scikit-image)
- Install sklearn (pip install sklearn)
- Install filterpy (pip install filterpy)
- Go to the YOLO_DETECTION folder
- run python3 gui.py
- select the parameters and you video source
- click the start analyse button
- after the program closes run python3 start.py
It should now work.
If you are having problems with the GUI you can also manualy edit the main.py script and then run "python3 main.py" to start the detection.
If you are having problems starting the program:
- Make sure you have selected the right Camera input!
- Installed all packages
- Have a CUDA compatible GPU and that CUDA is working
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Install manager https://github.com/openremote/openremote/blob/master/README.md
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Setup HTTP API https://github.com/openremote/or-objectdetection/wiki/Editing-Seting-up-the-HTTP-API-connection-with-the-manager
THIS REPO IS WORK IN PROGRESS. THERE ARE BUGS! A DETAILED INSTRUCTIONS WILL COME LATER.