CV

Gidsen voor computer-visiontoepassingen op reComputer RK3576/RK3588.

Ondersteunde platformen

Snelstart

1. Docker installeren

Voer de volgende commandos uit op het ontwikkelbord om Docker te installeren:

# Download installation script
curl -fsSL https://get.docker.com -o get-docker.sh
# Install using Aliyun mirror source
sudo sh get-docker.sh --mirror Aliyun
# Start Docker and enable auto-start on boot
sudo systemctl enable docker
sudo systemctl start docker

2. Het project uitvoeren (één commando, dubbele preview)

Dit project ondersteunt tegelijkertijd een lokale GUI en een webbrowser als preview. Het programma detecteert automatisch de schermomgeving en schakelt over naar webmodus als er geen scherm is aangesloten.

Stap A: schermrechten configureren (optioneel)

Als je een monitor hebt aangesloten en het venster lokaal wilt bekijken:

xhost +local:docker

Stap B: images ophalen

sudo docker pull ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest
sudo docker pull ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3576-yolo:latest

Stap C: met één klik uitvoeren

For RK3588:

sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video1:/dev/video1 \
    --device /dev/dri/renderD129:/dev/dri/renderD129 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 1

For RK3576:

sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video0:/dev/video0 \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3576-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 0

Te bereiken via: http://<Board_IP>:8000

Note: If you need custom classes, you can add -v $(pwd)/class_config.txt:/app/class_config.txt \ mount and --class_path parameter. The program defaults to COCO 80 classes.

Example:

sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    -v $(pwd)/class_config.txt:/app/class_config.txt \
    --device /dev/video1:/dev/video1 \
    --device /dev/dri/renderD129:/dev/dri/renderD129 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 1 --class_path class_config.txt

🔌 API-documentatie

This project provides RESTful interfaces compatible with the Ultralytics Cloud API standard, supporting object detection via image uploads using HTTP POST requests.

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/yolo11/predict

Request Parameters (Multipart/Form-Data):

• file: (Optional) Image file to be detected.
• video: (Optional) MP4 video file to be detected.
• timestamp: (Optional) Timestamp in the video file (seconds), returns detection results for the frame at that point. Default is 0.
• realtime: (Optional) Boolean. If true or if no file/video parameters are provided, returns detection results for the current camera frame.
• conf: (Optional) Confidence threshold for a single request, range 0.0-1.0.
• iou: (Optional) NMS IOU threshold for a single request, range 0.0-1.0.

Usage Examples:

1. Image Detection:

curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "file=@/home/cat/001.jpg"

2. Video Specific Frame Detection:

curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "video=@/home/cat/test.mp4" -F "timestamp=5.5"

3. Get Current Camera Frame Detection:

curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "realtime=true"
# Or without file parameters
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict"

Response Format (JSON):

{
  "success": true,
  "source": "video frame at 5.5s",
  "predictions": [
    {
      "class": "person",
      "confidence": 0.92,
      "box": { "x1": 100, "y1": 200, "x2": 300, "y2": 500 }
    }
  ],
  "image": { "width": 1280, "height": 720 }
}

2. System Configuration Interface (Config)

Used to dynamically adjust thresholds for real-time video streams and default inference.

Get Current Configuration

• Endpoint: GET /api/config
• Response: {"obj_thresh": 0.25, "nms_thresh": 0.45}

Update System Configuration

• Endpoint: POST /api/config
• Request Body (JSON): {"obj_thresh": 0.3, "nms_thresh": 0.5}
• Response: {"status": "success"}

3. Command Line Arguments

web_detection.py supports the following arguments:

Custom Class Configuration (class_config.txt) Format:

Name classes with double quotes, separated by commas, for example: "person", "bicycle", "car", "motorbike"

Realtime videostream-interface (Video Feed)

Get real-time MJPEG video stream with detection boxes drawn, can be directly embedded in HTML <img> tags.

• Endpoint: GET /api/video_feed
• Example Usage: <img src="http://<Board_IP>:8000/api/video_feed">

Gedetailleerde platformdocumentatie

• RK3588 Guide
• RK3576 Guide

Geautomatiseerde build

This project supports automated multi-platform image building via GitHub Actions.

• Modifying the src/rk3588/ directory automatically triggers the rk3588-yolo image build.
• Modifying the src/rk3576/ directory automatically triggers the rk3576-yolo image build.
• Manual trigger is supported, with the option to specify image_tag.

🛠️ Ontwikkelaarsgids (productie-aanbevelingen)

Code Description

• web_detection.py:
  • Dual-mode Support: Integrates FastAPI, supporting both local rendering and MJPEG streaming output.
  • Environment Adaptive: Automatically detects the DISPLAY environment variable, silently skipping GUI initialization if not present.
  • RKNN Inference: Encapsulates RKNN initialization, model loading, and multi-core inference logic.
  • Dynamic Loading: Supports dynamic class configuration loading via --class_path.
  • Post-processing: YOLOv11 specific Box decoding and NMS logic.

Modifying Models

1. Place the trained and converted .rknn model into the model/ directory of the corresponding platform.
2. Add the --model_path argument to the running command to point to the new model (default already configured in Dockerfile).

Realtime YOLOv11-objectdetectiedemo op reComputer RK3576