CV / YOLOv8

YOLOv8s_seg

Versión estándar de la serie YOLOv8s_Seg de segmentación de instancias, ligera y eficiente, diseñada para segmentación a nivel de píxel y predicción de máscaras con particionamiento regional preciso, ideal para despliegue en el borde.

264 descargas

Tamaño

~16MB

Memoria

1GB+

Precisión

INT8

Elige el dispositivo que estás usando. La guía de configuración y la documentación se actualizarán en consecuencia.

Plataforma

Motor de inferencia

Primeros pasos

Desplegar

sudo docker run --rm \
  --name rk3588-yolov8s-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/yolov8s_seg.rknn --video video/test.mp4

API REST

Usa la API REST para ejecutar inferencia. Copia los comandos siguientes.

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))

Detalles del modelo

Inicio rapido

1. Instalar Docker

Ejecute los siguientes comandos en la placa de desarrollo para instalar Docker:

bash

# Descargar script de instalacion
curl -fsSL https://get.docker.com -o get-docker.sh
# Instalar usando fuente espejo Aliyun
sudo sh get-docker.sh --mirror Aliyun
# Iniciar Docker y habilitar inicio automatico al arrancar
sudo systemctl enable docker
sudo systemctl start docker

2. Ejecutar el proyecto (Un comando, vista previa en modo dual)

Este proyecto admite vista previa simultanea via GUI local y Navegador web. El programa detecta automaticamente el entorno de visualizacion y cambia a modo Web si no hay pantalla conectada.

Paso A: Configurar permisos de pantalla (Opcional)

Si tiene un monitor conectado y desea ver la ventana localmente:

bash

xhost +local:docker

Paso B: Ejecucion con un clic

Para 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

Para 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

Acceda via: http://<Board_IP>:8000

Nota: Si necesita clases personalizadas, puede agregar el montaje -v $(pwd)/class_config.txt:/app/class_config.txt \ y el parametro --class_path. El programa usa por defecto las 80 clases COCO.

Ejemplo:

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

Nota: Si desea probar con un video local en lugar de una camara, use el parametro --video_path:

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

Documentacion de la API

Este proyecto proporciona interfaces RESTful compatibles con el estandar Ultralytics Cloud API, admitiendo segmentacion de instancias mediante carga de imagenes, videos o llamadas directas a la camara.

1. Interfaz de inferencia del modelo (Predict)

Endpoint: POST /api/models/yolov8_seg/predict (o /api/models/yolo11/predict segun el mapeo exacto del script)

Parametros de solicitud (Multipart/Form-Data):

file: (Opcional) Archivo de imagen a detectar.
video: (Opcional) Archivo de video MP4 a detectar.
timestamp: (Opcional) Marca de tiempo en el archivo de video (segundos), devuelve los resultados de deteccion para el fotograma en ese punto. El valor predeterminado es 0.
realtime: (Opcional) Booleano. Si es true o si no se proporcionan parametros file/video, devuelve los resultados de deteccion para el fotograma actual de la camara.
conf: (Opcional) Umbral de confianza para una sola solicitud, rango 0.0-1.0.
iou: (Opcional) Umbral NMS IOU para una sola solicitud, rango 0.0-1.0.

Ejemplos de uso:

1. Deteccion de imagen:

bash

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

2. Deteccion de fotograma especifico de video:

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. Obtener deteccion del fotograma actual de la camara:

bash

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

Formato de respuesta (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. Interfaz de configuracion del sistema (Config)

Se utiliza para ajustar dinamicamente los umbrales para transmisiones de video en tiempo real y la inferencia predeterminada.

Obtener configuracion actual

Endpoint: GET /api/config
Respuesta: {"obj_thresh": 0.25, "nms_thresh": 0.45}

Actualizar configuracion del sistema

Endpoint: POST /api/config
Cuerpo de solicitud (JSON): {"obj_thresh": 0.3, "nms_thresh": 0.5}
Respuesta: {"status": "success"}

3. Interfaz de transmision de video en tiempo real (Video Feed)

Obtenga transmision de video MJPEG en tiempo real con cuadros de deteccion y mascaras de segmentacion dibujadas, se puede incrustar directamente en etiquetas HTML <img>.

Endpoint: GET /api/video_feed
Ejemplo de uso: <img src="http://<Board_IP>:8000/api/video_feed">

Guia para desarrolladores (Recomendaciones de produccion)

Descripcion del codigo

web_detection.py:
- Soporte de modo dual: Integra FastAPI, admitiendo tanto renderizado local como salida de transmision MJPEG.
- Adaptacion al entorno: Detecta automaticamente la variable de entorno DISPLAY, omitiendo silenciosamente la inicializacion de GUI si no esta presente.
- Inferencia RKNN: Encapsula la inicializacion RKNN, carga de modelos y logica de inferencia multinucleo.
- Carga dinamica: Admite carga dinamica de configuracion de clases via --class_path.
- Post-procesamiento: Decodificacion de cuadros delimitadores basada en Numpy de alto rendimiento, NMS y extraccion de contornos (cv2.findContours).

Modificar modelos

Coloque el modelo .rknn entrenado y convertido en el directorio model/.
Agregue el argumento --model_path al comando de ejecucion para apuntar al nuevo modelo.