CV / YOLOv8

YOLOv8n_seg

YOLOv8n-Seg Instantiesegmentatieserie lichtgewicht versie, compact en efficiënt, ontworpen voor objectsegmentatie op pixelniveau en maskervoorspelling, ideaal voor edge-deployment.

264 downloads

Grootte

~7.2MB

Geheugen

1GB+

Precisie

INT8

Kies het apparaat dat je gebruikt. De installatiehandleiding en documentatie worden dienovereenkomstig bijgewerkt.

Platform

Inferentie-engine

Aan de slag

Implementeren

sudo docker run --rm \
  --name rk3588-yolov8n-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/yolov8n_seg.rknn --video video/test.mp4

REST API

Gebruik de REST API om inferentie uit te voeren. Kopieer de onderstaande commando's.

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))

Modeldetails

Snelstart

1. Docker installeren

Voer de volgende commando's uit op het ontwikkelbord om Docker te installeren:

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. Het project uitvoeren (Een commando, dual-mode preview)

Dit project ondersteunt gelijktijdige preview via Lokale GUI en Webbrowser. Het programma detecteert automatisch de weergaveomgeving en schakelt terug naar Web-modus als er geen beeldscherm is aangesloten.

Stap A: Weergaverechten configureren (Optioneel)

Als u een monitor hebt aangesloten en het venster lokaal wilt zien:

bash

xhost +local:docker

Stap B: Uitvoeren met een klik

Voor 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

Voor 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

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

Let op: Als u aangepaste klassen nodig heeft, kunt u de -v $(pwd)/class_config.txt:/app/class_config.txt \ mount en de --class_path parameter toevoegen. Het programma gebruikt standaard de COCO 80-klassen.

Voorbeeld:

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

Let op: Als u met een lokale video wilt testen in plaats van een camera, gebruik dan de --video_path parameter:

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-documentatie

Dit project biedt RESTful-interfaces die compatibel zijn met de Ultralytics Cloud API-standaard, en ondersteunt instancesegmentatie via afbeelding-, videouploads of directe camera-aanroepen.

1. Modelinferentie-interface (Predict)

Endpoint: POST /api/models/yolov8_seg/predict (of /api/models/yolo11/predict afhankelijk van de exacte scripttoewijzing)

Verzoekparameters (Multipart/Form-Data):

file: (Optioneel) Afbeeldingsbestand dat moet worden gedetecteerd.
video: (Optioneel) MP4-videobestand dat moet worden gedetecteerd.
timestamp: (Optioneel) Tijdstempel in het videobestand (seconden), retourneert detectieresultaten voor het frame op dat punt. Standaard is 0.
realtime: (Optioneel) Boolean. Indien true of als er geen file/video parameters zijn opgegeven, retourneert detectieresultaten voor het huidige cameraframe.
conf: (Optioneel) Betrouwbaarheidsdrempel voor een enkel verzoek, bereik 0.0-1.0.
iou: (Optioneel) NMS IOU-drempel voor een enkel verzoek, bereik 0.0-1.0.

Gebruiksvoorbeelden:

1. Afbeeldingsdetectie:

bash

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

2. Detectie specifiek videoframe:

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. Huidig cameraframe detectie ophalen:

bash

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

Responsformaat (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. Systeemconfiguratie-interface (Config)

Wordt gebruikt om drempels voor realtime videostreams en standaardinferentie dynamisch aan te passen.

Huidige configuratie ophalen

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

Systeemconfiguratie bijwerken

Endpoint: POST /api/config
Verzoekbody (JSON): {"obj_thresh": 0.3, "nms_thresh": 0.5}
Respons: {"status": "success"}

3. Realtime videostream-interface (Video Feed)

Ontvang realtime MJPEG-videostream met detectiekaders en segmentatiemaskers ingetekend, kan direct worden ingesloten in HTML <img>-tags.

Endpoint: GET /api/video_feed
Gebruiksvoorbeeld: <img src="http://<Board_IP>:8000/api/video_feed">

🛠️ Ontwikkelaarsgids (Productie-aanbevelingen)

Codebeschrijving

web_detection.py:
- Dual-mode ondersteuning: Integreert FastAPI en ondersteunt zowel lokale weergave als MJPEG-streaminguitvoer.
- Omgevingsadaptief: Detecteert automatisch de DISPLAY-omgevingsvariabele en slaat GUI-initialisatie stilzwijgend over indien niet aanwezig.
- RKNN-inferentie: Omvat RKNN-initialisatie, modelladen en multi-core inferentielogica.
- Dynamisch laden: Ondersteunt dynamisch laden van klasseconfiguratie via --class_path.
- Nabewerking: Hoogwaardige Numpy-gebaseerde bounding box-decodering, NMS en contour-extractie (cv2.findContours).

Modellen wijzigen

Plaats het getrainde en geconverteerde .rknn-model in de model/-directory.
Voeg het --model_path-argument toe aan het uitvoercommando om naar het nieuwe model te verwijzen.