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
400 lines
13 KiB
Python
400 lines
13 KiB
Python
"""
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YOLO 格式的抽烟检测模型适配器（优化版）
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使用后台 Docker 容器，避免重复启动
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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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import time
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from typing import List, Dict, Optional
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class SmokingDetectionYOLO:
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"""
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模拟 YOLO 接口的抽烟检测模型（优化版）
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使用后台 Docker 容器，避免每次检测都启动新容器
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"""
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_container_name = "smoking-detection-server"
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_container_started = False
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def __init__(self, model_path=None):
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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
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# YOLO 兼容属性
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self.names = {0: 'cigarette'}
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self.model = self
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# 检查 Docker 并启动后台容器
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self._check_docker()
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if self.available:
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self._start_background_container()
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logger.info(f"抽烟检测模型初始化完成，Docker可用: {self.available}")
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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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self.available = result.returncode == 0
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if self.available:
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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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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 _start_background_container(self):
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"""启动后台容器"""
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try:
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# 检查容器是否已在运行
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result = subprocess.run(
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["docker", "ps", "-q", "-f", f"name={self._container_name}"],
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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.stdout.strip():
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logger.info(f"后台容器已在运行: {self._container_name}")
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SmokingDetectionYOLO._container_started = True
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return
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# 检查容器是否存在但已停止
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result = subprocess.run(
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["docker", "ps", "-aq", "-f", f"name={self._container_name}"],
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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.stdout.strip():
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# 启动已存在的容器
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logger.info(f"启动已存在的容器: {self._container_name}")
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subprocess.run(
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["docker", "start", self._container_name],
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capture_output=True,
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timeout=10
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)
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else:
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# 创建新容器
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logger.info(f"创建后台容器: {self._container_name}")
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subprocess.run(
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[
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"docker", "run", "-d",
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"--name", self._container_name,
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"-v", "/tmp:/workspace/input",
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"-v", "/tmp:/workspace/output",
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self.docker_image,
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"tail", "-f", "/dev/null"
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],
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capture_output=True,
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timeout=10
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)
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SmokingDetectionYOLO._container_started = True
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logger.info("后台容器启动成功")
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except Exception as e:
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logger.error(f"启动后台容器失败: {e}")
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SmokingDetectionYOLO._container_started = False
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def __call__(self, source, conf=0.1, iou=0.45, verbose=False, stream=False):
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"""模拟 YOLO 模型的调用接口"""
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if not self.available:
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logger.error("Docker 不可用，无法运行检测")
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return [YOLOResult([])]
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# 处理不同类型的输入
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if isinstance(source, str):
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image = cv2.imread(source)
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if image is None:
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logger.error(f"无法读取图片: {source}")
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return [YOLOResult([])]
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return self._detect_single(image, conf, verbose)
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elif isinstance(source, np.ndarray):
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return self._detect_single(source, conf, verbose)
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elif isinstance(source, list):
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results = []
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for img in source:
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if isinstance(img, str):
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img = cv2.imread(img)
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if img is not None:
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results.extend(self._detect_single(img, conf, verbose))
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return results
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else:
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logger.error(f"不支持的输入类型: {type(source)}")
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return [YOLOResult([])]
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def _detect_single(self, image: np.ndarray, conf: float, verbose: bool) -> List['YOLOResult']:
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"""检测单张图片（使用后台容器）"""
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start_time = time.time()
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try:
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# 创建临时文件
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input_filename = f"smoking_input_{int(time.time()*1000)}.jpg"
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output_filename = f"smoking_output_{int(time.time()*1000)}.jpg"
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temp_input = f"/tmp/{input_filename}"
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temp_output = f"/tmp/{output_filename}"
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# 保存输入图片
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cv2.imwrite(temp_input, image)
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if verbose:
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logger.info(f"正在检测: {temp_input}")
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# 使用后台容器执行检测
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if SmokingDetectionYOLO._container_started:
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# 使用 exec 在运行中的容器内执行
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cmd = [
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"docker", "exec",
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self._container_name,
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"python", "deploy/python/infer.py",
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f"--model_dir={self.model_dir}",
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f"--image_file=/workspace/input/{input_filename}",
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"--device=CPU",
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f"--output_dir=/workspace/output",
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f"--threshold={conf}"
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]
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else:
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# 回退到原来的方式
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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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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",
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f"--threshold={conf}"
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]
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# 执行检测
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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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elapsed = time.time() - start_time
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if verbose:
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logger.info(f"检测完成，耗时: {elapsed:.2f}秒")
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# 解析结果
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detections = self._parse_output(result.stdout)
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# 清理临时文件
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try:
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os.remove(temp_input)
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except:
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pass
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return [YOLOResult(detections)]
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except subprocess.TimeoutExpired:
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logger.error("检测超时")
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return [YOLOResult([])]
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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 [YOLOResult([])]
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def _parse_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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import re
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pattern = r'class_id:\d+,\s*confidence:([\d.]+),\s*left_top:\[([\d.]+),\s*([\d.]+)\],\s*right_bottom:\[([\d.]+),\s*([\d.]+)\]'
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for line in output.split('\n'):
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match = re.search(pattern, line)
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if match:
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try:
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confidence = float(match.group(1))
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x1 = float(match.group(2))
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y1 = float(match.group(3))
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x2 = float(match.group(4))
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y2 = float(match.group(5))
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detections.append({
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'bbox': [int(x1), int(y1), int(x2), int(y2)],
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'confidence': confidence,
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'class': 0,
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'name': 'cigarette'
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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}, line: {line}")
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continue
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return detections
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def predict(self, source, **kwargs):
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"""兼容 predict 方法"""
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return self.__call__(source, **kwargs)
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@classmethod
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def stop_background_container(cls):
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"""停止后台容器"""
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try:
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subprocess.run(
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["docker", "stop", cls._container_name],
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capture_output=True,
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timeout=10
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)
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logger.info("后台容器已停止")
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except Exception as e:
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logger.error(f"停止后台容器失败: {e}")
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# 复制 YOLOResult, Boxes, Box 类（与原版相同）
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class YOLOResult:
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"""模拟 YOLO 检测结果对象"""
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def __init__(self, detections: List[Dict]):
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self.detections = detections
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self.names = {0: 'cigarette'}
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self.boxes = Boxes(detections)
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self.probs = None
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self.keypoints = None
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self.obb = None
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self.speed = {'preprocess': 0, 'inference': 0, 'postprocess': 0}
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def __len__(self):
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return len(self.detections)
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def __getitem__(self, idx):
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if idx < len(self.detections):
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return YOLOResult([self.detections[idx]])
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return YOLOResult([])
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def plot(self, **kwargs):
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return None
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class Boxes:
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"""模拟 YOLO boxes 对象"""
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def __init__(self, detections: List[Dict]):
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self.detections = detections
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try:
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import torch
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if detections:
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xyxy_list = [[d['bbox'][0], d['bbox'][1], d['bbox'][2], d['bbox'][3]] for d in detections]
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conf_list = [[d['confidence']] for d in detections]
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cls_list = [[d['class']] for d in detections]
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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)
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self.cls = torch.tensor(cls_list, dtype=torch.int64)
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self.id = None
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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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self.id = None
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except ImportError:
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import numpy as np
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if detections:
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xyxy_list = [[d['bbox'][0], d['bbox'][1], d['bbox'][2], d['bbox'][3]] for d in detections]
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conf_list = [[d['confidence']] for d in detections]
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cls_list = [[d['class']] for d in detections]
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self.xyxy = np.array(xyxy_list, dtype=np.float32)
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self.conf = np.array(conf_list, dtype=np.float32)
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self.cls = np.array(cls_list, dtype=np.int64)
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self.id = None
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else:
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self.xyxy = np.empty((0, 4), dtype=np.float32)
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self.conf = np.empty((0, 1), dtype=np.float32)
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self.cls = np.empty((0, 1), dtype=np.int64)
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self.id = None
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def __len__(self):
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return len(self.detections)
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def __iter__(self):
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for i in range(len(self.detections)):
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yield Box(self, i)
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def cpu(self):
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return self
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def numpy(self):
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if hasattr(self.xyxy, 'numpy'):
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return type('Boxes', (), {
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'xyxy': self.xyxy.numpy(),
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'conf': self.conf.numpy(),
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'cls': self.cls.numpy(),
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'id': self.id
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})()
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return self
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class Box:
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"""模拟单个检测框对象"""
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def __init__(self, boxes: Boxes, index: int):
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self._boxes = boxes
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self._index = index
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@property
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def xyxy(self):
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import torch
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import numpy as np
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coords = self._boxes.xyxy[self._index]
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if isinstance(coords, torch.Tensor):
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return coords.unsqueeze(0)
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else:
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return np.array([coords])
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@property
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def conf(self):
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import torch
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import numpy as np
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conf_val = self._boxes.conf[self._index]
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if isinstance(conf_val, torch.Tensor):
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return conf_val.view(1)
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else:
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return np.array([conf_val])
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@property
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def cls(self):
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import torch
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import numpy as np
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cls_val = self._boxes.cls[self._index]
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if isinstance(cls_val, torch.Tensor):
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return cls_val.view(1)
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else:
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return np.array([cls_val])
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