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
275 lines
8.9 KiB
Python
275 lines
8.9 KiB
Python
"""
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PaddleDetection 抽烟检测服务适配器
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通过 Docker 调用 Paddle 模型
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"""
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import os
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import cv2
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import numpy as np
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import subprocess
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import tempfile
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import logging
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from typing import Dict, List, Optional
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class PaddleDetectionService:
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"""PaddleDetection 服务适配器"""
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def __init__(self):
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self.model_name = "smoking_detection"
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self.docker_image = "smoking-detection:test"
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self.model_dir = "output_inference/ppyoloe_crn_s_80e_smoking_visdrone"
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self.threshold = 0.1 # 抽烟检测需要较低的阈值
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# 检查 Docker 和镜像
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self._check_docker()
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def _check_docker(self):
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"""检查 Docker 环境"""
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try:
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result = subprocess.run(
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["docker", "info"],
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capture_output=True,
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text=True,
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timeout=5
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)
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if result.returncode != 0:
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logger.error("Docker 未运行")
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self.available = False
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return
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# 检查镜像
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result = subprocess.run(
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["docker", "image", "inspect", self.docker_image],
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capture_output=True,
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text=True,
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timeout=5
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)
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self.available = result.returncode == 0
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if self.available:
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logger.info(f"PaddleDetection 服务已就绪: {self.docker_image}")
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else:
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logger.error(f"Docker 镜像不存在: {self.docker_image}")
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except Exception as e:
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logger.error(f"Docker 检查失败: {e}")
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self.available = False
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def detect_image(self, image: np.ndarray) -> Dict:
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"""
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检测图片中的抽烟行为
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Args:
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image: OpenCV 图片 (BGR格式)
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Returns:
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检测结果字典
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"""
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if not self.available:
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return {
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'success': False,
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'message': 'PaddleDetection 服务不可用',
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'detections': [],
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'stats': None
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}
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try:
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# 创建临时文件
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with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as f:
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temp_input = f.name
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with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as f:
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temp_output = f.name
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# 保存输入图片
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cv2.imwrite(temp_input, image)
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# 构建 Docker 命令
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cmd = [
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"docker", "run", "--rm",
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"-v", f"{temp_input}:/workspace/input.jpg",
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"-v", f"{os.path.dirname(temp_output)}:/workspace/output",
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self.docker_image,
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"python", "deploy/python/infer.py",
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f"--model_dir={self.model_dir}",
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"--image_file=/workspace/input.jpg",
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"--device=CPU",
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"--output_dir=/workspace/output",
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f"--threshold={self.threshold}"
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]
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# 执行检测
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logger.info(f"执行抽烟检测: {temp_input}")
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=60
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)
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# 解析结果
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detections = self._parse_detection_output(result.stdout)
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# 读取输出图片
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output_image = None
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output_path = temp_output.replace('.jpg', '') + '_result.jpg'
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if os.path.exists(output_path):
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output_image = cv2.imread(output_path)
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# 清理临时文件
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self._cleanup_temp_files([temp_input, temp_output, output_path])
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return {
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'success': True,
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'message': '检测完成',
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'detections': detections,
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'output_image': output_image,
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'stats': {
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'total_detections': len(detections),
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'model_used': 'ppyoloe_crn_s_80e_smoking_visdrone',
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'threshold': self.threshold
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}
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}
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except subprocess.TimeoutExpired:
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logger.error("检测超时")
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return {
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'success': False,
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'message': '检测超时',
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'detections': [],
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'stats': None
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}
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except Exception as e:
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logger.error(f"检测失败: {e}")
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return {
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'success': False,
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'message': f'检测失败: {str(e)}',
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'detections': [],
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'stats': None
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}
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def _parse_detection_output(self, output: str) -> List[Dict]:
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"""解析检测输出"""
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detections = []
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# 查找检测结果行
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for line in output.split('\n'):
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if 'class_id:' in line and 'confidence:' in line:
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try:
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# 解析: class_id:0, confidence:0.8921, left_top:[268.66,231.64],right_bottom:[351.87,258.66]
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parts = line.split(',')
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# 提取置信度
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conf_part = [p for p in parts if 'confidence:' in p][0]
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confidence = float(conf_part.split(':')[1])
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# 提取坐标
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left_top_part = [p for p in parts if 'left_top:' in p][0]
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right_bottom_part = [p for p in parts if 'right_bottom:' in p][0]
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# 解析坐标
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left_top = eval(left_top_part.split(':')[1])
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right_bottom = eval(right_bottom_part.split(':')[1])
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x1, y1 = left_top
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x2, y2 = right_bottom
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detections.append({
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'class': 'cigarette',
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'label': '香烟',
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'confidence': round(confidence, 3),
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'bbox': [int(x1), int(y1), int(x2), int(y2)]
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})
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except Exception as e:
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logger.warning(f"解析检测结果失败: {e}")
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continue
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return detections
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def _cleanup_temp_files(self, files: List[str]):
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"""清理临时文件"""
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for f in files:
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try:
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if os.path.exists(f):
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os.remove(f)
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except Exception as e:
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logger.warning(f"清理临时文件失败: {f}, {e}")
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# 兼容性包装，保持与 YOLO 模型相同的接口
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class SmokingDetectionModel:
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"""抽烟检测模型包装器，兼容 YOLO 接口"""
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def __init__(self):
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self.service = PaddleDetectionService()
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self.names = {0: 'cigarette'}
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def __call__(self, image, conf=0.1, iou=0.45, verbose=False):
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"""
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模拟 YOLO 模型的调用接口
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Args:
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image: OpenCV 图片
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conf: 置信度阈值
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iou: IoU 阈值
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verbose: 是否输出详细信息
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Returns:
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模拟 YOLO 结果的对象
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"""
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result = self.service.detect_image(image)
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# 创建模拟的 YOLO 结果对象
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return [PaddleDetectionResult(result, self.names)]
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class PaddleDetectionResult:
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"""模拟 YOLO 检测结果对象"""
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def __init__(self, detection_result: Dict, names: Dict):
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self.detection_result = detection_result
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self.names = names
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# 创建模拟的 boxes 对象
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self.boxes = self._create_boxes()
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def _create_boxes(self):
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"""创建模拟的 boxes 对象"""
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detections = self.detection_result.get('detections', [])
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if not detections:
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return MockBoxes([])
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# 转换为 YOLO 格式
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xyxy = []
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conf = []
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cls = []
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for det in detections:
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xyxy.append(det['bbox'])
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conf.append(det['confidence'])
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cls.append(0) # cigarette 类别
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return MockBoxes(xyxy, conf, cls)
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class MockBoxes:
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"""模拟 YOLO boxes 对象"""
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def __init__(self, xyxy_list, conf_list=None, cls_list=None):
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import torch
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if xyxy_list:
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self.xyxy = torch.tensor(xyxy_list, dtype=torch.float32)
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self.conf = torch.tensor(conf_list, dtype=torch.float32).reshape(-1, 1)
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self.cls = torch.tensor(cls_list, dtype=torch.int64).reshape(-1, 1)
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else:
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self.xyxy = torch.empty((0, 4))
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self.conf = torch.empty((0, 1))
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self.cls = torch.empty((0, 1), dtype=torch.int64)
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