Development Setup¶
Using OWL software on your laptop/desktop or other non-Raspberry Pi system is a great way to test, develop and learn more about how it works.
This method has been successfully tested on PyCharm with Anaconda environments.
Quick Start¶
Clone the repository:
git clone https://github.com/geezacoleman/OpenWeedLocator
cd OpenWeedLocator
Activate your virtual environment, then install the non-RPi requirements:
pip install -r non_rpi_requirements.txt
Run OWL with display:
python owl.py --show-display
Virtual Environments¶
If you’re unsure about virtual environments, these resources explain them well:
PyImageSearch blog on configuring an Ubuntu environment - skip to the virtual environment step
Testing and Development¶
Once installed, you can:
Change command line flags (see Configuration)
Modify detection parameters
Test with images and videos using
--inputVisualise detection with
--show-display
# Run with custom input
python owl.py --input /path/to/test/images --show-display
# Focus mode
python owl.py --focus
Next Steps¶
Configuration Guide - Adjust parameters
Green-on-Green - Experimental deep learning detection