Automated Sample Alignment for MX using a YOLO Model
Project description
aicenter
A python based Soft IOC Server for Sample Alignment using a YOLO model.
Usage
python -m venv my-venv
source my-venv/bin/activate
pip install ai-center
- Create a directory for the IOC instance.
- Copy the start.sh file from the
deploydirectory into this directory. - Copy the ai-centering.service unit file from the
deployinto your /etc/systemd/system directory. - Edit the files from (2) and (3) above to reflect your environment and to set all the required instance parameters
- Enable the unit file using your system commands. For example,
systemctl enable ai-centering. - Start the init file using your system commands. For example
systemctl start ai-centering.
You can manage the instance daemon through procServ, by telneting to the configured port.
OpenCV
For best performance, a version of python-opencv compiled with support for CUDA and cuDNN along
with a compatible GPU should be used.
Testing
The test/inference.py file can be used to test the inference / model performance without running
a full IOC application. To obtain a copy of the trained model weights, contact the authors.
Create an environment with requirements-test.txt and install aicenter without dependencies:
pip install -r requirements-test.txt
pip install . --no-deps
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