CV

Guides d'applications de vision par ordinateur pour reComputer RK3576/RK3588.

Plateformes prises en charge

Plateforme	Puce	Puissance de calcul	Nom de l'image
RK3588	RK3588/RK3588S	6 TOPS	`rk3588-yolo`
RK3576	RK3576	6 TOPS	`rk3576-yolo`

Démarrage rapide

1. Installer Docker

Exécutez les commandes suivantes sur la carte de développement pour installer Docker :

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. Exécuter le projet (une commande, aperçu en double mode)

Ce projet prend en charge la prévisualisation simultanée via GUI locale et navigateur web. Le programme détecte automatiquement l'environnement graphique et bascule en mode Web si aucun écran n'est connecté.

Étape A : configurer les permissions d'affichage (facultatif)

Si vous avez un moniteur connecté et souhaitez voir la fenêtre localement :

bash

xhost +local:docker

Étape B : récupérer les images

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

Étape C : exécuter en un clic

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

Accédez 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:

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

🔌 Documentation de l'API

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"

Interface de flux vidéo en temps réel (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">

Documentation détaillée par plateforme

Build automatisé

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.

🛠️ Guide développeur (recommandations pour la production)

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).

Démo de détection d'objets YOLOv11 en temps réel sur reComputer RK3576

PreviousConfigurer

NextLLM