Eagle-Eye-AI

Eagle-Eye-AI is a project designed for the Kria KR260 board that enables AI-driven camera tracking and face detection. The project integrates custom hardware and software, including a RS-485 PMOD module for camera rotator control and ROS2 nodes for real-time processing and communication. Follow our comprehensive guide on Hackster.io to build, test, and deploy the system, transforming your camera into an intelligent, autonomous tracking device.

Requirements

1. Kria KR260 Board: Ensure that your KRIA™ KR260 board has the official Ubuntu image installed and Docker set up. The board should be prepared for running official demo applications from AMD, such as the Smartcam demo application.

2. IP Camera: You will need an IP camera that supports RTSP streaming with a resolution of 1920x1080. The camera should be connected to the same local network as the Kria board. A recommended camera is the SIMICAM 4k Video Cam or similar.

3. Pan-Tilt Rotator: A Pan-Tilt Camera Rotator that supports RS-485 and the Pelco-P/D protocol is required for rotating the camera. A recommended rotator is the PTZ Rotator.

4. RS-485 PMOD Module: Required for camera rotator control. The Camera Rotator should support the Pelco-D protocol for Pan-Tilt through RS-485 interface. The module can be found here.

5. Network Connection: Connect the Ethernet cable to your local network with DHCP enabled.

Getting the Application Package

1. Clone the Repository:

   git clone https://github.com/s59mz/eagle-eye-ai.git
   cd eagle-eye-ai

Install Firmware Binaries

1. Install the firmware binaries:

   cp fpga-firmware/firmware-kr260-eagle-eye.deb /tmp
   sudo apt install /tmp/firmware-kr260-eagle-eye.deb

2. Dynamically load the firmware package:

   • Switch to the kr260-eagle-eye platform:

     sudo xmutil unloadapp
     sudo xmutil loadapp kr260-eagle-eye

   • Show the list and status of available acceleration platforms:

     sudo xmutil listapps

3. Disable the desktop environment:

   sudo xmutil desktop_disable

Building the Docker Image

1. Update the RTSP IP Camera URL:

   Edit the run_eagle_eye_ai.sh script file in the scripts directory and update the default_camera_url parameter:

   vi scripts/run_eagle_eye_ai.sh
   
   # Update line #18 with your IP camera URL:
   default_camera_url="rtsp://192.168.1.11:554/stream1"

2. Build the Docker Image:

   The build process will take about 8 minutes on the Kria board. This cannot be built on a host PC.

   ./build.sh

   For a visual guide on building and installing the Eagle-Eye-AI application, watch this YouTube video.

   

Launching the Docker Image

1. Launch the Docker Image:

   ./run.sh

   This will start the Eagle-Eye-AI Docker image in a new container:

   root@xlnx-docker/#

2. Optional: Run an Empty ROS2 Docker Image:

   To test your own ROS2/GStreamer applications:

   ./ros2_humble_run.sh

3. Manage Docker Storage:

   To remove the existing container if storage is limited:

   docker rmi --force eagle-eye-ai

Running the Application

1. In the running Eagle-Eye-AI Docker container:

   • Launch the application:

     ./run_eagle_eye_ai.sh

     You should see the camera’s captured images on the monitor connected to the board. When a face is detected, a blue box will appear around it, tracking the face as it moves. The camera rotator will also adjust to keep the detected face centered on the screen.

   • Press Ctrl-C to exit.

   • To change the RTSP IP camera URL, run the startup script with the new URL:

     ./run_eagle_eye_ai.sh rtsp://192.168.1.20:554/stream2

   For a visual guide on starting the Eagle-Eye-AI application, watch this YouTube video.

   

License

This project is licensed under the GPL-3.0. See the LICENSE file for details.

For further information or support, please refer to the project documentation on Hackster.io.