CV

reComputer RK3576/RK3588 向けコンピュータビジョンアプリケーションガイド。

対応プラットフォーム

クイックスタート

1. Docker のインストール

開発ボード上で以下のコマンドを実行して Docker をインストールします:

# 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. プロジェクトの実行(1 つのコマンドでデュアルモードプレビュー)

本プロジェクトは ローカル GUI と ウェブブラウザ の同時プレビューに対応しています。ディスプレイが接続されていない場合は自動的に検出して Web モードへ切り替わります。

ステップ A:ディスプレイ権限の設定(任意)

モニタが接続されており、ローカルでウィンドウを確認したい場合:

xhost +local:docker

ステップ B:イメージの取得

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

ステップ C:ワンクリックで実行

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

アクセス先: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 ドキュメント

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"

リアルタイムビデオストリームインターフェース(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">

プラットフォーム別ドキュメント

• RK3588 Guide
• RK3576 Guide

自動ビルド

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.

🛠️ 開発者ガイド(本番運用の推奨事項)

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).

reComputer RK3576 上のリアルタイム YOLOv11 物体検出デモ