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CV / YOLOv8

YOLOv8m_seg

YOLOv8m 实例分割，集成在 R20 HailoRT 演示中，支持像素级分割。

264 次下载
大小
~35MB
内存
1GB+
精度
INT8

选择你正在使用的设备，设置指南和文档将相应更新。

快速开始

部署
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

使用 REST API 进行推理。复制以下命令。

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))

模型详情

快速开始

1. 安装 Docker

在开发板上运行以下命令安装 Docker：

bash
# 下载安装脚本
curl -fsSL https://get.docker.com -o get-docker.sh
# 使用阿里云镜像源安装
sudo sh get-docker.sh --mirror Aliyun
# 启动 Docker 并设置开机自启
sudo systemctl enable docker
sudo systemctl start docker

2. 运行项目（一条命令，双模式预览）

本项目支持同时通过 本地 GUI 和 Web 浏览器 进行预览。程序会自动检测显示环境，如果没有连接显示器，则会降级为 Web 模式。

步骤 A：配置显示权限（可选）

如果您连接了显示器并希望在本地看到窗口：

bash
xhost +local:docker

步骤 B：一键运行

适用于 RK3588：

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

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

通过以下地址访问：http://<Board_IP>:8000

注意：如果需要自定义类别，可以添加 -v $(pwd)/class_config.txt:/app/class_config.txt \ 挂载和 --class_path 参数。程序默认使用 COCO 80 个类别。

示例：

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：如果想使用本地视频而不是摄像头进行测试，请使用 --video_path 参数：

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 文档

本项目提供兼容 Ultralytics Cloud API 标准的 RESTful 接口，支持通过图像、视频上传或直接调用摄像头进行实例分割。

1. 模型推理接口（Predict）

端点： POST /api/models/yolov8_seg/predict（或 /api/models/yolo11/predict，取决于具体脚本映射）

请求参数（Multipart/Form-Data）：

  • file：（可选）待检测的图像文件。
  • video：（可选）待检测的 MP4 视频文件。
  • timestamp：（可选）视频文件中的时间戳（秒），返回该时间点对应帧的检测结果。默认为 0。
  • realtime：（可选）布尔值。如果为 true 或未提供 file/video 参数，则返回当前摄像头帧的检测结果。
  • conf：（可选）单次请求的置信度阈值，范围 0.0-1.0。
  • iou：（可选）单次请求的 NMS IOU 阈值，范围 0.0-1.0。

使用示例：

1. 图像检测：

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

2. 视频特定帧检测：

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. 获取当前摄像头帧检测：

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

响应格式（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. 系统配置接口（Config）

用于动态调整实时视频流和默认推理的阈值。

获取当前配置

  • 端点： GET /api/config
  • 响应： {"obj_thresh": 0.25, "nms_thresh": 0.45}

更新系统配置

  • 端点： POST /api/config
  • 请求体（JSON）： {"obj_thresh": 0.3, "nms_thresh": 0.5}
  • 响应： {"status": "success"}

3. 实时视频流接口（Video Feed）

获取带有检测框和分割掩码绘制的实时 MJPEG 视频流，可直接嵌入 HTML <img> 标签中。

  • 端点： GET /api/video_feed
  • 使用示例： <img src="http://<Board_IP>:8000/api/video_feed">

🛠️ 开发者指南（生产环境建议）

代码说明

  • web_detection.py：
    • 双模式支持：集成 FastAPI，同时支持本地渲染和 MJPEG 流输出。
    • 环境自适应：自动检测 DISPLAY 环境变量，如果不存在则静默跳过 GUI 初始化。
    • RKNN 推理：封装了 RKNN 初始化、模型加载和多核推理逻辑。
    • 动态加载：支持通过 --class_path 动态加载类别配置。
    • 后处理：基于 Numpy 的高性能边界框解码、NMS 和轮廓提取（cv2.findContours）。

修改模型

  1. 将训练并转换后的 .rknn 模型放入 model/ 目录。
  2. 在运行命令中添加 --model_path 参数以指向新模型。