8fb58c75fe
Features: - Fire detection (YOLOv10) - Helmet detection (YOLOv8) - Crowd detection (YOLOv8) - Smoking detection (YOLOv8) - Loitering detection (YOLOv8) Tech Stack: - Frontend: Vue 3 + Vite + Element Plus - Backend: FastAPI + WebSocket - Monorepo: pnpm workspace + Turbo - Docker support included
148 lines
6.1 KiB
Python
148 lines
6.1 KiB
Python
import os
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import logging
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from ultralytics import YOLO
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from typing import Dict, List, Optional
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logger = logging.getLogger(__name__)
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class ModelService:
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def __init__(self):
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self.models: Dict[str, YOLO] = {}
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self.model_configs = {
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'fire_detection': {
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'path': os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
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'fire_detection', 'models', 'best.pt'),
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'type': 'yolov10',
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'classes': ['Fire', 'Smoke'],
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'labels': {'Fire': '火焰', 'Smoke': '烟雾'},
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'size': '61MB',
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'description': '基于YOLOv10的火灾烟雾检测模型',
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'name': '火灾检测'
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},
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'helmet_detection': {
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'path': os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
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'yolov', 'yolov8n.pt'),
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'type': 'yolov8',
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'classes': ['person', 'helmet'],
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'labels': {'person': '人员', 'helmet': '安全帽'},
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'size': '6MB',
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'description': '基于YOLOv8的安全帽检测模型',
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'name': '安全帽检测'
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},
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'crowd_detection': {
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'path': os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
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'behavior_detection', 'Crowd-Gathering', 'models', 'yolov8l.pt'),
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'type': 'yolov8',
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'classes': ['person'],
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'labels': {'person': '人员'},
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'size': '100MB',
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'description': '基于YOLOv8的人群聚集检测模型',
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'name': '人群检测'
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},
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'smoking_detection': {
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'path': 'PADDLE_DETECTION', # 特殊标记，表示使用 PaddleDetection
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'type': 'paddle',
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'classes': ['cigarette'],
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'labels': {'cigarette': '香烟'},
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'size': '27MB',
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'description': '基于PP-YOLOE的抽烟检测模型',
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'name': '抽烟检测',
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'docker_image': 'smoking-detection:test',
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'model_dir': 'output_inference/ppyoloe_crn_s_80e_smoking_visdrone'
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}
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}
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def get_available_models(self) -> List[Dict]:
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available_models = []
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for model_id, config in self.model_configs.items():
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# 对于 PaddleDetection 模型，不需要检查本地文件
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if config.get('type') == 'paddle':
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# 检查 Docker 是否可用
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import subprocess
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try:
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result = subprocess.run(
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["docker", "image", "inspect", config['docker_image']],
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capture_output=True,
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timeout=5
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)
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if result.returncode == 0:
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available_models.append({
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'id': model_id,
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'name': config['name'],
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'description': config['description'],
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'classes': config['classes'],
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'labels': config['labels'],
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'size': config['size'],
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'type': config['type']
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})
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else:
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logger.warning(f"PaddleDetection Docker 镜像不可用: {config['docker_image']}")
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except Exception as e:
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logger.warning(f"检查 PaddleDetection Docker 镜像失败: {e}")
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elif os.path.exists(config['path']):
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available_models.append({
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'id': model_id,
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'name': config['name'],
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'description': config['description'],
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'classes': config['classes'],
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'labels': config['labels'],
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'size': config['size'],
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'type': config['type']
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})
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else:
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logger.warning(f"模型文件不存在: {config['path']}")
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return available_models
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async def load_model(self, model_id: str):
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if model_id not in self.model_configs:
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logger.error(f"未知模型ID: {model_id}")
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return None
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# 如果已经加载，直接返回
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if model_id in self.models:
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logger.info(f"模型已加载: {model_id}")
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return self.models[model_id]
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config = self.model_configs[model_id]
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# 处理 PaddleDetection 模型
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if config.get('type') == 'paddle':
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try:
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from .paddle_detection_service import SmokingDetectionModel
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logger.info(f"正在加载 PaddleDetection 抽烟检测模型...")
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model = SmokingDetectionModel()
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self.models[model_id] = model
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logger.info(f"PaddleDetection 模型加载成功: {model_id}")
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return model
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except Exception as e:
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logger.error(f"PaddleDetection 模型加载失败: {e}")
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return None
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# 处理 YOLO 模型
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model_path = config['path']
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if not os.path.exists(model_path):
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logger.error(f"模型文件不存在: {model_path}")
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return None
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try:
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logger.info(f"正在加载模型: {model_id} from {model_path}")
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model = YOLO(model_path)
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self.models[model_id] = model
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logger.info(f"模型加载成功: {model_id}")
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return model
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except Exception as e:
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logger.error(f"模型加载失败: {model_id}, 错误: {e}")
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return None
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def get_model(self, model_id: str):
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return self.models.get(model_id)
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async def unload_model(self, model_id: str) -> bool:
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if model_id in self.models:
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del self.models[model_id]
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logger.info(f"模型已卸载: {model_id}")
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return True
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return False
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