CV / YOLOv8

YOLOv8m_seg

YOLOv8m-Segインスタンスセグメンテーションシリーズの標準バージョン。軽量で高効率、ピクセルレベルのセグメンテーションとマスク予測、精密な領域分割向けに設計され、エッジデプロイに最適です。

264 ダウンロード

サイズ

~35MB

メモリ

1GB+

精度

INT8

使用しているデバイスを選択してください。セットアップガイドとドキュメントがそれに応じて更新されます。

プラットフォーム

推論エンジン

はじめる

デプロイ

sudo docker run --rm \
  --name rk3588-yolov8m-seg \
  --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-yolov8_seg:latest \
  python web_detection.py --model_path model/yolov8m_seg.rknn --video video/test.mp4

REST API

REST API を使用して推論を実行します。以下のコマンドをコピーしてください。

Curl

curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict" -F "realtime=true"
# Or without file parameters
curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict"

Python

import requests
import json
resp = requests.post(
    "http://127.0.0.1:8000/api/models/yolov8_seg/predict",
    json={},
    timeout=30
)
result = resp.json()
print(json.dumps(result, indent=2, ensure_ascii=False))

モデル詳細

クイックスタート

1. Dockerのインストール

開発ボードで以下のコマンドを実行してDockerをインストールします：

bash

# インストールスクリプトをダウンロード
curl -fsSL https://get.docker.com -o get-docker.sh
# Aliyunミラーソースを使用してインストール
sudo sh get-docker.sh --mirror Aliyun
# Dockerを起動し、起動時の自動起動を有効化
sudo systemctl enable docker
sudo systemctl start docker

2. プロジェクトの実行（ワンコマンド、デュアルモードプレビュー）

このプロジェクトはローカルGUIとWebブラウザの両方からの同時プレビューをサポートしています。プログラムはディスプレイ環境を自動検出し、ディスプレイが接続されていない場合はWebモードにダウングレードします。

ステップA: ディスプレイ権限の設定（オプション）

モニターが接続されており、ローカルでウィンドウを表示したい場合：

bash

xhost +local:docker

ステップB: ワンクリックで実行

RK3588の場合：

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/rk3588-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n_seg.rknn --video video/test.mp4

RK3576の場合：

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-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n_seg.rknn --video video/test.mp4

アクセス: http://<Board_IP>:8000

注意: カスタムクラスが必要な場合は、-v $(pwd)/class_config.txt:/app/class_config.txt \ マウントと --class_path パラメータを追加できます。プログラムはデフォルトでCOCO 80クラスを使用します。

例：

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/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-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n-seg.rknn --video video/test.mp4 --class_path class_config.txt

注意: カメラの代わりにローカル動画でテストしたい場合は、--video_pathパラメータを使用します：

bash

sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    -v $(pwd)/video:/app/video \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n-seg.rknn --video_path video/test.mp4

🔌 APIドキュメント

このプロジェクトはUltralytics Cloud API標準に準拠したRESTfulインターフェースを提供し、画像、動画のアップロードまたは直接カメラ呼び出しによるインスタンスセグメンテーションをサポートします。

1. モデル推論インターフェース（Predict）

エンドポイント: POST /api/models/yolov8_seg/predict（または正確なスクリプトマッピングに応じて /api/models/yolo11/predict）

リクエストパラメータ（Multipart/Form-Data）：

file: （オプション）検出する画像ファイル。
video: （オプション）検出するMP4動画ファイル。
timestamp: （オプション）動画ファイル内のタイムスタンプ（秒）、その時点のフレームの検出結果を返します。デフォルトは0です。
realtime: （オプション）ブール値。trueの場合、またはfile/videoパラメータが提供されていない場合、現在のカメラフレームの検出結果を返します。
conf: （オプション）単一リクエストの信頼度しきい値、範囲0.0〜1.0。
iou: （オプション）単一リクエストのNMS IOUしきい値、範囲0.0〜1.0。

使用例：

1. 画像検出：

bash

curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict" -F "file=@/home/cat/001.jpg"

2. 動画の特定フレーム検出：

bash

curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict" -F "video=@/home/cat/test.mp4" -F "timestamp=5.5"

3. 現在のカメラフレーム検出の取得：

bash

curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict" -F "realtime=true"

レスポンス形式（JSON）：

json

{
  "success": true,
  "source": "realtime camera frame",
  "predictions": [
    {
      "class": "cup",
      "confidence": 0.9840346574783325,
      "box": { "x1": 100, "y1": 200, "x2": 300, "y2": 500 },
      "polygons": [
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      ]
    }
  ],
  "image": { "width": 1280, "height": 720 }
}

2. システム設定インターフェース（Config）

リアルタイムビデオストリームとデフォルト推論のしきい値を動的に調整するために使用します。

現在の設定の取得

エンドポイント: GET /api/config
レスポンス: {"obj_thresh": 0.25, "nms_thresh": 0.45}

システム設定の更新

エンドポイント: POST /api/config
リクエストボディ（JSON）： {"obj_thresh": 0.3, "nms_thresh": 0.5}
レスポンス: {"status": "success"}

3. リアルタイムビデオストリームインターフェース（Video Feed）

検出ボックスとセグメンテーションマスクが描画されたリアルタイムMJPEGビデオストリームを取得し、HTMLの<img>タグに直接埋め込むことができます。

エンドポイント: GET /api/video_feed
使用例: <img src="http://<Board_IP>:8000/api/video_feed">

🛠️ 開発者ガイド（本番環境推奨事項）

コード説明

web_detection.py:
- デュアルモードサポート: FastAPIを統合し、ローカルレンダリングとMJPEGストリーミング出力の両方をサポートします。
- 環境適応: DISPLAY環境変数を自動検出し、存在しない場合はGUI初期化を静かにスキップします。
- RKNN推論: RKNN初期化、モデル読み込み、マルチコア推論ロジックをカプセル化します。
- 動的読み込み: --class_pathによる動的なクラス設定読み込みをサポートします。
- 後処理: 高性能Numpyベースのバウンディングボックスデコード、NMS、および輪郭抽出（cv2.findContours）。

モデルの変更

学習・変換済みの.rknnモデルをmodel/ディレクトリに配置します。
実行コマンドに--model_path引数を追加して新しいモデルを指定します。