This project implements real-time object detection using a YOLO v8 model, a Rust gRPC service (Tonic + Ort) for inference, and an Axum-based middleware that streams video from a webcam, collects predictions, and serves them via Http. A simple HTML page is available to visualize the detections live from the webcam.
- Tonic, Ort for running YOLO model inference via gRPC using ONNX runtime
- Axum, Tonic for handling video streaming and client Http requests
- OpenCV for processing video frames in the middleware
- HTML + JavaScript for real-time visualization
To run the project, simply execute the following command at the root directory:
make allThis will download the necessary images, runs the service and automatically open a webpage for visualization.
If you have set up CUDA correctly, you can run the project with GPU acceleration by executing:
make all-cudaCurrently, the project only works on Linux as it relies on mounting the /dev/video1 device.
If you are using a different video input, you may need to modify the source code accordingly.
This project utilizes the YOLOv8m model, which is distributed under the AGPL-3.0 license. Please refer to the Ultralytics website for the full license details.
This project is intended for educational and demonstration purposes. Its licensing is subject to the licensing terms of the YOLOv8m model.
Disclaimer: Users are responsible for ensuring their compliance with the YOLOv8m model's license.