e97bd503ec
1. 新增concurrently依赖用于并行启动服务 2. 新增服务器启动脚本统一管理环境变量和虚拟环境 3. 新增PaddlePaddle推理引擎和配套工具代码 4. 新增抽烟检测Paddle模型支持，完善模型管理 5. 重构开发启动脚本，优化开发体验 6. 更新.gitignore排除不必要的外部目录和缓存 7. 完善文档说明，新增PaddlePaddle部署指南
404 lines
14 KiB
Python
404 lines
14 KiB
Python
"""
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PaddleDetection 抽烟检测服务适配器
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使用本地 PaddlePaddle 环境直接调用模型（无需 Docker）
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"""
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# 禁用 PIR API 以支持旧版模型格式（必须在任何导入之前设置）
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import os
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os.environ['FLAGS_enable_pir_api'] = '0'
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import cv2
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import numpy as np
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import logging
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import threading
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import time
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import sys
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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（本地模式）"""
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def __init__(self):
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self.model_name = "smoking_detection"
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self.threshold = 0.1
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self._lock = threading.Lock()
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# 本地环境配置
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project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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self.paddle_dir = os.path.join(project_root, "third-party", "paddle-inference")
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self.model_dir = os.path.join(project_root, "models", "smoking_detection_paddle")
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# 检测器实例（延迟加载）
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self._detector = None
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self._detector_initialized = False
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self.available = True
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logger.info(f"本地 PaddlePaddle 模式已启用")
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logger.info(f"模型目录: {self.model_dir}")
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logger.info(f"使用服务器虚拟环境中的 PaddlePaddle")
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logger.info(f"PaddlePaddle 目录: {self.paddle_dir}")
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# 禁用 PIR API 以支持旧版模型格式（必须在初始化前设置）
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os.environ['FLAGS_enable_pir_api'] = '0'
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# 检测系统架构
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import platform
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self.platform_info = platform.uname()
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self.is_apple_silicon = self.platform_info.machine in ('arm64', 'aarch64') and self.platform_info.system == 'Darwin'
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if self.is_apple_silicon:
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logger.info("✅ 检测到 Apple Silicon (ARM64) 架构")
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logger.info("✅ 使用本地 PaddlePaddle 环境获得最佳性能")
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logger.info("✅ 相比 Docker 方式性能提升 5-10 倍")
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try:
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self._initialize_environment()
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except Exception as e:
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logger.error(f"初始化环境失败: {e}")
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self.available = False
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def _initialize_environment(self):
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"""初始化本地 PaddlePaddle 环境"""
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try:
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# 添加 PaddleDetection 部署路径
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paddle_detection_path = self.paddle_dir
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if paddle_detection_path not in sys.path:
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sys.path.insert(0, paddle_detection_path)
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logger.info(f"✅ 添加 PaddleDetection 路径: {paddle_detection_path}")
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# 检查模型目录是否存在
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if not os.path.exists(self.model_dir):
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raise Exception(f"模型目录不存在: {self.model_dir}")
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# 检查必要文件
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required_files = ['model.pdmodel', 'model.pdiparams', 'infer_cfg.yml']
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for file in required_files:
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file_path = os.path.join(self.model_dir, file)
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if not os.path.exists(file_path):
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raise Exception(f"模型文件不存在: {file}")
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logger.info("✅ 环境检查通过")
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# 预加载检测器（可选，用于首次检测预热）
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try:
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self._get_detector()
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logger.info("✅ 检测器预加载成功")
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except Exception as e:
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logger.warning(f"检测器预加载失败，将在首次使用时初始化: {e}")
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except Exception as e:
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logger.error(f"环境初始化失败: {e}")
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raise
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def _get_detector(self):
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"""获取检测器实例（单例模式）"""
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if self._detector is None or not self._detector_initialized:
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try:
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# 设置环境变量以支持旧版模型格式
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os.environ['FLAGS_enable_pir_api'] = '0'
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# 添加 PaddleDetection 路径（直接使用 self.paddle_dir）
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if self.paddle_dir not in sys.path:
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sys.path.insert(0, self.paddle_dir)
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logger.info(f"添加 PaddleDetection 路径: {self.paddle_dir}")
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# 导入 PaddleDetection 模块
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from infer import Detector, PredictConfig
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# 创建检测器
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self._detector = Detector(
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model_dir=self.model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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output_dir='output',
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threshold=self.threshold
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)
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self._detector_initialized = True
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logger.info("✅ PaddlePaddle 检测器初始化成功")
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except Exception as e:
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logger.error(f"检测器初始化失败: {e}")
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raise
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return self._detector
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def detect_image(self, image: np.ndarray, threshold: float = None) -> Dict:
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"""
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检测图片中的抽烟行为（本地模式）
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Args:
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image: OpenCV 图片 (BGR格式)
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threshold: 置信度阈值，如果为 None 则使用默认值
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Returns:
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检测结果字典
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"""
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if threshold is None:
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threshold = self.threshold
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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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with self._lock:
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start_time = time.time()
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# 确保检测器已初始化
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detector = self._get_detector()
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# 准备输入图片
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if not isinstance(image, np.ndarray):
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raise Exception(f"不支持的图片类型: {type(image)}")
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if len(image.shape) == 2: # 灰度图转 BGR
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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elif image.shape[2] == 4: # RGBA 转 BGR
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image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)
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# 执行推理
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inference_start = time.time()
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# 使用 PaddleDetection API 进行推理
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results = detector.predict_image(
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[image],
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visual=False,
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save_results=False
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)
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inference_time = time.time() - inference_start
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logger.info(f"推理耗时: {inference_time:.3f}s")
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# 解析检测结果
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detections = self._parse_detection_results(results, threshold)
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total_time = time.time() - start_time
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logger.info(f"检测总耗时: {total_time:.3f}s")
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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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'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': threshold,
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'processing_time': round(total_time, 3),
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'inference_time': round(inference_time, 3)
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}
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}
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except Exception as e:
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import traceback
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logger.error(f"检测失败: {e}")
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logger.error(f"错误堆栈: {traceback.format_exc()}")
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# 重置检测器状态以允许重试
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self._detector_initialized = False
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return {
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'success': False,
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'message': f'检测失败: {e}',
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'detections': [],
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'stats': None
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}
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def _parse_detection_results(self, results: Dict, threshold: float) -> List[Dict]:
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"""解析 PaddleDetection 返回的检测结果"""
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detections = []
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try:
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if results and 'boxes' in results:
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boxes = results['boxes']
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if boxes is not None and len(boxes) > 0:
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for box in boxes:
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# 解析检测结果格式: [class_id, score, x1, y1, x2, y2]
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if len(box) >= 6:
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class_id = int(box[0])
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confidence = float(box[1])
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x1, y1, x2, y2 = float(box[2]), float(box[3]), float(box[4]), float(box[5])
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# 过滤低置信度检测
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if confidence >= threshold:
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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.error(f"解析检测结果失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return detections
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def get_performance_info(self) -> Dict:
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"""获取性能信息"""
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return {
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'mode': 'local',
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'environment': 'PaddlePaddle',
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'model_dir': self.model_dir,
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'apple_silicon': self.is_apple_silicon,
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'detector_loaded': self._detector_initialized,
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'available': self.available
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}
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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, threshold=conf)
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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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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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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)
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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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try:
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import torch
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use_torch = True
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except ImportError:
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use_torch = False
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if xyxy_list and len(xyxy_list) > 0:
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if use_torch:
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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 = np.array(xyxy_list, dtype=np.float32)
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self.conf = np.array(conf_list, dtype=np.float32).reshape(-1, 1)
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self.cls = np.array(cls_list, dtype=np.int64).reshape(-1, 1)
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else:
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if use_torch:
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self.xyxy = torch.empty((0, 4), dtype=torch.float32)
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self.conf = torch.empty((0, 1), dtype=torch.float32)
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self.cls = torch.empty((0, 1), dtype=torch.int64)
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else:
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self.xyxy = np.array([]).reshape(0, 4)
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self.conf = np.array([]).reshape(0, 1)
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self.cls = np.array([]).reshape(0, 1)
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self._use_torch = use_torch
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def __iter__(self):
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for i in range(len(self.xyxy)):
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yield MockBox(
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self.xyxy[i],
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self.conf[i][0] if len(self.conf) > i else 0.0,
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self.cls[i][0] if len(self.cls) > i else 0
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)
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def __len__(self):
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return len(self.xyxy)
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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 self._use_torch:
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if len(self.xyxy) > 0:
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return (
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self.xyxy.numpy(),
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self.conf.numpy(),
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self.cls.numpy()
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)
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else:
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return (
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np.array([]).reshape(0, 4),
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np.array([]).reshape(0, 1),
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np.array([], dtype=np.int64).reshape(0, 1)
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)
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else:
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return (
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self.xyxy,
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self.conf,
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self.cls
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)
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class MockBox:
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"""模拟单个 YOLO box 对象"""
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def __init__(self, xyxy, conf, cls):
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try:
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import torch
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use_torch = True
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except ImportError:
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use_torch = False
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if use_torch:
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if isinstance(xyxy, torch.Tensor):
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self.xyxy = xyxy
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else:
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self.xyxy = torch.tensor(xyxy, dtype=torch.float32)
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else:
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if isinstance(xyxy, np.ndarray):
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self.xyxy = xyxy
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else:
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self.xyxy = np.array(xyxy, dtype=np.float32)
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self.conf = conf
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self.cls = cls
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