7aa71c5f83
本次提交实现了完整的人员行为分析系统，包括： 1. 新增基于位置和跟踪ID的两种行为检测算法 2. 新增徘徊检测服务与行为处理器模块 3. 前后端集成算法配置界面与告警展示 4. 支持图片和视频流场景下的行为分析 5. 新增算法配置接口与文档说明 具体改动： - 新增loitering_detection模型目录与算法实现 - 新增AlgorithmConfig组件实现可视化配置 - 扩展图片/视频检测接口支持算法参数传递 - 新增行为告警推送与前端展示页面 - 优化检测服务，集成行为分析逻辑 - 移除冗余日志输出，完善代码注释
237 lines
7.6 KiB
Python
237 lines
7.6 KiB
Python
"""
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基于位置的静止人员检测算法
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不依赖跟踪 ID，而是根据位置来关联人员
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适用于跟踪不稳定但人员相对静止的场景
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"""
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import time
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from typing import Dict, List, Tuple, Optional
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from dataclasses import dataclass, field
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from collections import defaultdict
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@dataclass
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class PositionRecord:
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"""位置记录"""
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first_seen: float
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last_seen: float
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center: Tuple[int, int]
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box: Tuple[int, int, int, int]
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duration: float = 0.0
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class PositionBasedStationaryDetector:
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"""
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基于位置的静止检测器
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特点：
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- 不依赖跟踪 ID，直接用位置关联人员
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- 适用于 SORT 等跟踪器不稳定的场景
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- 使用网格化位置 + 距离容差进行匹配
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"""
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def __init__(
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self,
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stationary_threshold: float = 10.0, # 静止阈值（秒）
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position_tolerance: int = 50, # 位置容差（像素）
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cleanup_interval: float = 5.0 # 清理间隔（秒）
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):
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self.stationary_threshold = stationary_threshold
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self.position_tolerance = position_tolerance
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self.cleanup_interval = cleanup_interval
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# 位置历史记录: {position_key: PositionRecord}
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self._position_history: Dict[Tuple[int, int], PositionRecord] = {}
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self._last_cleanup = time.time()
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def _get_position_key(self, center: Tuple[int, int]) -> Tuple[int, int]:
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"""
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将连续坐标转换为离散的位置键
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用于将相近位置归为一类
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"""
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x, y = center
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grid_x = int(x / self.position_tolerance)
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grid_y = int(y / self.position_tolerance)
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return (grid_x, grid_y)
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def _find_matching_position(
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self,
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center: Tuple[int, int]
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) -> Optional[Tuple[int, int]]:
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"""
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查找与当前位置匹配的历史位置
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返回匹配的位置键，如果没有则返回 None
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"""
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current_key = self._get_position_key(center)
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# 首先检查精确匹配
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if current_key in self._position_history:
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hist_center = self._position_history[current_key].center
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distance = ((center[0] - hist_center[0]) ** 2 +
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(center[1] - hist_center[1]) ** 2) ** 0.5
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if distance < self.position_tolerance:
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return current_key
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# 检查相邻网格
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for dx in [-1, 0, 1]:
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for dy in [-1, 0, 1]:
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if dx == 0 and dy == 0:
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continue
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neighbor_key = (current_key[0] + dx, current_key[1] + dy)
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if neighbor_key in self._position_history:
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hist_center = self._position_history[neighbor_key].center
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distance = ((center[0] - hist_center[0]) ** 2 +
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(center[1] - hist_center[1]) ** 2) ** 0.5
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if distance < self.position_tolerance:
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return neighbor_key
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return None
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def update(
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self,
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center: Tuple[int, int],
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box: Tuple[int, int, int, int]
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) -> Tuple[str, float, bool]:
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"""
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更新位置信息
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Args:
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center: (x, y) 中心点坐标
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box: (x1, y1, x2, y2) 边界框
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Returns:
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position_id: 位置 ID（用于关联）
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stationary_duration: 静止时长（秒）
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is_stationary: 是否静止超过阈值
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"""
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current_time = time.time()
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# 定期清理旧记录
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if current_time - self._last_cleanup > self.cleanup_interval:
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self.cleanup_old_positions()
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self._last_cleanup = current_time
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# 查找匹配的历史位置
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matching_key = self._find_matching_position(center)
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if matching_key is not None:
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# 更新已有位置
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record = self._position_history[matching_key]
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record.last_seen = current_time
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# 平滑更新中心位置（使用移动平均）
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old_center = record.center
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record.center = (
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int(0.7 * old_center[0] + 0.3 * center[0]),
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int(0.7 * old_center[1] + 0.3 * center[1])
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)
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record.box = box
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duration = current_time - record.first_seen
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record.duration = duration
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is_stationary = duration > self.stationary_threshold
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position_id = f"pos_{matching_key[0]}_{matching_key[1]}"
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return position_id, duration, is_stationary
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else:
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# 创建新位置记录
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new_key = self._get_position_key(center)
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self._position_history[new_key] = PositionRecord(
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first_seen=current_time,
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last_seen=current_time,
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center=center,
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box=box,
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duration=0.0
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)
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new_id = f"pos_{new_key[0]}_{new_key[1]}"
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return new_id, 0.0, False
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def cleanup_old_positions(self, max_age: float = 5.0) -> int:
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"""
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清理长时间未更新的位置记录
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Args:
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max_age: 最大保留时间（秒）
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Returns:
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清理的记录数量
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"""
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current_time = time.time()
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to_remove = [
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key for key, data in self._position_history.items()
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if current_time - data.last_seen > max_age
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]
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for key in to_remove:
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del self._position_history[key]
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return len(to_remove)
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def get_all_stationary(
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self,
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threshold: Optional[float] = None
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) -> List[Dict]:
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"""
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获取所有静止超过阈值的位置
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Args:
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threshold: 静止阈值（秒），默认使用初始化时的阈值
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Returns:
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list: [{position_id, duration, center, box}, ...]
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"""
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threshold = threshold or self.stationary_threshold
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result = []
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for key, data in self._position_history.items():
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if data.duration > threshold:
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result.append({
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'position_id': f"pos_{key[0]}_{key[1]}",
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'duration': data.duration,
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'center': data.center,
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'box': data.box
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})
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# 按时长排序
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result.sort(key=lambda x: x['duration'], reverse=True)
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return result
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def reset(self):
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"""重置所有跟踪数据"""
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self._position_history.clear()
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self._last_cleanup = time.time()
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def detect(
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self,
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detections: List[Dict]
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) -> List[Dict]:
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"""
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批量检测静止状态
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Args:
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detections: 检测结果列表，每项包含 'bbox': [x1, y1, x2, y2]
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Returns:
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添加 'stationary_info' 字段的检测结果
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"""
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results = []
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for det in detections:
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x1, y1, x2, y2 = det['bbox']
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center = ((x1 + x2) // 2, (y1 + y2) // 2)
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box = (x1, y1, x2, y2)
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position_id, duration, is_stationary = self.update(center, box)
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det_copy = det.copy()
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det_copy['stationary_info'] = {
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'position_id': position_id,
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'duration': round(duration, 2),
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'is_stationary': is_stationary,
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'threshold': self.stationary_threshold
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}
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results.append(det_copy)
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return results
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