Experimental counter and analyzer aquaculture animals with computer vision and machine learning assisted for minimizing error from human and maximizing the accuracy of counting
This project was designed to be a background process and needed a front-end application cragui to be fully functional
a short demo of progression in small prawn counter running on Nvidia Jetson Nano
- image processing time <50ms per frame without sacrifice accuracy
- minimal memory footprint enough for embedded
- extensible with front-end through UDS & TCP
- fast & secure local storage
- works well with BSD Unix or Linux with musl & glibc
- OpenCV4
- CMake
- sqlite3
sudo apt install libopencv-dev libsqlite3-dev cmakesudo pkg install opencv libsqlite3-dev cmake- consider alternative computer vision frameworks like
ccv - rewrite in c language for portability
[ ] reimplement image processing pipeline from previous project. [ ] add an additional functionally [ ] check and test FreeBSD compatibility with target hardware. [ ] build and test project against FreeBSD version of libraries.
See LICENSE