CV

Anleitungen für Computer-Vision-Anwendungen auf reComputer RK3576/RK3588.

Unterstützte Plattformen

Plattform	Chip	Rechenleistung	Image-Name
RK3588	RK3588/RK3588S	6 TOPS	`rk3588-yolo`
RK3576	RK3576	6 TOPS	`rk3576-yolo`

Schnellstart

1. Docker installieren

Führe die folgenden Befehle auf dem Entwicklungsboard aus, um Docker zu installieren:

bash

# 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. Projekt ausführen (ein Befehl, Vorschau im Dual-Modus)

Dieses Projekt unterstützt die gleichzeitige Vorschau über die lokale GUI und einen Webbrowser. Wenn kein Monitor angeschlossen ist, erkennt das Programm dies automatisch und wechselt in den Web-Modus.

Schritt A: Anzeigeberechtigungen konfigurieren (optional)

Wenn ein Monitor angeschlossen ist und du das Fenster lokal sehen möchtest:

bash

xhost +local:docker

Schritt B: Images herunterladen

bash

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

Schritt C: Mit einem Klick ausführen

For RK3588:

bash

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:

bash

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

Aufruf über: 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:

bash

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-Dokumentation

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:

bash

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

2. Video Specific Frame Detection:

bash

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:

bash

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):

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:

Argument	Description	Default
`--model_path`	Path to RKNN model file	(Required)
`--camera_id`	Camera device ID (e.g., fill 1 for `/dev/video1`)	1
`--video_path`	Path to video file (overrides camera_id if provided)	None
`--class_path`	Path to custom class configuration file (class_config.txt)	None (Default COCO 80)
`--host`	Web server listening address	`0.0.0.0`
`--port`	Web server port	8000

Custom Class Configuration (class_config.txt) Format:

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

Echtzeit-Videostream-Schnittstelle (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">

Detaillierte Plattform-Dokumentation

Automatisierte Builds

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.

🛠️ Entwicklerhandbuch (Empfehlungen für die Produktion)

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

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

Echtzeit-YOLOv11-Objekterkennungsdemo auf reComputer RK3576

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