820 lines
33 KiB
Python
820 lines
33 KiB
Python
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import cv2
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import numpy as np
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from typing import Dict, Any
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import threading
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import queue
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from biz.base_frame_processor import BaseFrameProcessorWorker
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# -------------------------- Kadian 检测相关导入 --------------------------
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from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机）
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from algorithm.common.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX # Pose 专用模型
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from yolox.tracker.byte_tracker import BYTETracker
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from utils.logger import get_logger
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logger = get_logger(__name__)
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# ========================= 配置区 =========================
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# Kadian 模型路径与ROI（可根据实际情况修改）
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DETECT_MODEL_PATH = 'YOLO_Weight/car_opentrunk_person_phone.onnx'
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POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx'
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# 默认相对ROI（与原文件一致）
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#ROI_RELATIVE = np.array([
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# [0.10989583333333333, 0.006481481481481481],
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# [0.421875, 0.005555555555555556],
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# [0.9921875, 0.9888888888888889],
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# [0.3411458333333333, 0.9861111111111112]
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#])
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ROI_RELATIVE=np.array([
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[0.15,0.001],
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[0.5,0.001],
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[1.0,0.8],
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[0.35,1.0]
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])
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ALERT_PUSH_INTERVAL = 5.0
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# 输入尺寸
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PERSON_CAR_INPUT_SIZE = 640
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POSE_INPUT_SIZE = 640
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RTSP_TARGET_FPS = 10.0
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# ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
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class KadianDetector:
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def __init__(self, params=None):
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# 摄像头额外参数
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self.params = params if params is not None else {}
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# 模型加载
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self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.15, iou_threshold=0.65,
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input_size=PERSON_CAR_INPUT_SIZE)
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self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.45, iou_threshold=0.6,
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input_size=POSE_INPUT_SIZE)
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# Tracker
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class TrackerArgs:
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track_thresh = 0.2
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track_buffer = 60
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match_thresh = 0.9
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mot20 = True
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self.tracker = BYTETracker(TrackerArgs(), frame_rate=RTSP_TARGET_FPS)
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self.track_role = {}
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self.fps = RTSP_TARGET_FPS
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# ROI 处理：优先从 params 获取，否则使用默认值 ROI_RELATIVE
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roi_points = self.params.get('roi_points', ROI_RELATIVE)
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self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None
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# ==========================================
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# 超参数设置 (Hyperparameters)
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# ==========================================
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# 1. 业务判定时间阈值
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self.TIME_THRESHOLD_ONLY_ONE = 10.0 # 单人单检判定时长
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self.TIME_THRESHOLD_NOBODY = 10.0 # 无人检查判定时长
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# 后备箱检查判定阈值
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self.TIME_THRESHOLD_TRUNK_OPEN = 0.1
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# 新增：手机检测判定阈值
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self.TIME_THRESHOLD_PHONE = 3.0 # 手机检测持续1秒（30帧 @30fps）
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self.TIME_TOLERANCE_PHONE = 1.5 # 手机丢失缓冲时间（防抖动）
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# 新增：制服检测判定阈值
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self.TIME_THRESHOLD_UNIFORM = 2.0 # 制服不合规判定时长
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self.TIME_TOLERANCE_UNIFORM = 1.0 # 制服合规恢复缓冲时间
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# 2. Person 丢帧缓冲
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self.TIME_TOLERANCE_PERSON = 3.0
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# 车辆最小停留时间阈值 (小于此时间视为无人检查/直接通过)
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self.TIME_THRESHOLD_CAR_MIN_DURATION = 10.0
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# 3. Car 丢帧/ID维持缓冲
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self.TIME_TOLERANCE_CAR = 10.0
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# 4 OnlyOne 丢帧缓冲
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self.TIME_TOLERANCE_ONLY_ONE_DURATION = 3.0
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# --- 计算对应的帧数阈值 ---
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self.frame_thresh_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps)
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self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
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self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps)
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# 新增：手机检测帧数阈值
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self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps)
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self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps)
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# 新增：制服检测帧数阈值
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self.frame_thresh_uniform = int(self.TIME_THRESHOLD_UNIFORM * self.fps)
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self.frame_buffer_uniform = int(self.TIME_TOLERANCE_UNIFORM * self.fps)
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self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps)
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self.frame_buffer_limit_person = int(self.TIME_TOLERANCE_PERSON * self.fps)
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self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * self.fps)
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self.frame_buffer_limit_onlyOne = int(self.TIME_TOLERANCE_ONLY_ONE_DURATION * self.fps)
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logger.info(f"\n超参数设置:")
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logger.info(f" FPS: {self.fps:.2f}")
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logger.info(f" 判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧")
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logger.info(f" 判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧")
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logger.info(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧")
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logger.info(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧")
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logger.info(f" 判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧")
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logger.info(f" 制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧")
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logger.info(f" 判定 'Too Fast' 最小停留: {self.frame_thresh_car_min_duration} 帧")
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self.onlyone_counter = 0
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# self.onlyone_lost_counter = 0
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# self.onlyone_buffer_limit = self.frame_buffer_limit_person # 10帧（1秒）
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self.onlyone_thresh = self.frame_thresh_one # 30帧（3秒）
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self.nobody_counter = 0
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self.nobody_present_counter = 0
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self.nobody_buffer_limit = self.frame_buffer_limit_onlyOne
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self.nobody_thresh = self.frame_thresh_nobody # 20帧（2秒）
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self.current_frame_idx = 0
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self.ignore_show_seconds = 0.5 # 未检测的警告显示时长
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self.openTrunk_show_seconds = 0.5 # 打开后备箱的警告显示时长
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# 手机检测状态变量（独立于车辆）
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self.phone_detection_frames = 0 # 连续检测到手机的帧数
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self.phone_missing_frames = 0 # 连续未检测到手机的帧数
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self.phone_alert_active = False # 手机报警是否激活
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# 新增：制服检测状态变量
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self.pose_person_count = 0 # 骨骼点模型检测的ROI内人员数量
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self.uniform_alert_active = False # 制服报警是否激活
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self.uniform_detection_frames = 0 # 连续检测到制服不合规的帧数
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self.uniform_recovery_frames = 0 # 连续恢复合规的帧数
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# 车辆注册表 (字典)
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self.roi_car_registry = {}
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# 违规车辆记录 (后备箱未检)
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self.unchecked_trunk_alerts = {}
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# 违规车辆记录 (通过过快 -> 归类为 Ignore)
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self.fast_pass_alerts = {}
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def _get_roi_points(self, frame_width: int, frame_height: int):
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"""
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每帧动态计算正确的 ROI 绝对坐标，并确保类型为 np.int32
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用于 pointPolygonTest 和 polylines
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"""
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if self.roi_points is None:
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raise ValueError("ROI points must be provided; cannot be None.")
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if self.roi_points.max() <= 1.0:
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# 相对坐标 → 转换为绝对
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roi_abs = self.roi_points * np.array([frame_width, frame_height])
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else:
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# 绝对坐标，直接使用
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roi_abs = self.roi_points.copy()
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# 强制转为 int32（关键！解决 OpenCV 断言错误）
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return roi_abs.astype(np.int32)
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def check_point_in_roi(self, roi_points, point):
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return cv2.pointPolygonTest(roi_points, point, False) >= 0
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def compute_iou(self, boxA, boxB):
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# box = [x1, y1, x2, y2]
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xA = max(boxA[0], boxB[0])
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yA = max(boxA[1], boxB[1])
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xB = min(boxA[2], boxB[2])
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yB = min(boxA[3], boxB[3])
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interW = max(0, xB - xA)
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interH = max(0, yB - yA)
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interArea = interW * interH
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boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
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boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
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unionArea = boxAArea + boxBArea - interArea
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if unionArea == 0:
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return 0.0
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return interArea / unionArea
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def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0):
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"""在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)"""
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font_scale = 1.5
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thickness = 3
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font = cv2.FONT_HERSHEY_SIMPLEX
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(text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness)
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x = self.width - text_w - 20
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y = 50 + text_h + offset_y # 增加 Y 轴偏移
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cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1)
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cv2.putText(frame, text, (x, y), font, font_scale, color, thickness)
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if sub_text:
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cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2)
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def is_point_in_box(self, point, box):
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px, py = point
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x1, y1, x2, y2 = box
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return x1 < px < x2 and y1 < py < y2
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def is_pose_inside_detector_person(self, pose_bbox, dets_xyxy, dets_roles):
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"""
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判断一个 pose 人是否位于 detector 的 person 框内部（中心点匹配）
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参数：
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pose_bbox: [x1, y1, x2, y2]
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dets_xyxy: detector 输出的所有 bbox 列表
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dets_roles: 对应的类别列表（如 "person", "car"...）
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返回：
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True -> 在某个人体框内部
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False -> 不在任何人体框内部
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"""
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px1, py1, px2, py2 = pose_bbox
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cx, cy = (px1 + px2) // 2, (py1 + py2) // 2
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for box, role in zip(dets_xyxy, dets_roles):
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if role != "person":
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continue
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dx1, dy1, dx2, dy2 = map(int, box)
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# 判断中心点是否在 detector person 框内
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if dx1 <= cx <= dx2 and dy1 <= cy <= dy2:
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return True
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return False
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def count_pose_inside_detector_person(self, pose_results, dets_xyxy, dets_roles):
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"""
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统计有多少个pose框在detector person框内部
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参数：
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pose_results: pose检测结果列表，每个元素为字典，包含'bbox'键，值为[x1, y1, x2, y2]
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dets_xyxy: detector输出的所有bbox列表
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dets_roles: 对应的类别列表（如 "person", "car"...）
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返回：
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int: 在detector person框内部的pose框数量
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"""
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count = 0
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for pose in pose_results:
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pose_bbox = pose['bbox'] # [x1, y1, x2, y2]
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if self.is_pose_inside_detector_person(pose_bbox, dets_xyxy, dets_roles):
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count += 1
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return count
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def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
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h, w = frame.shape[:2]
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self.width, self.height = w, h
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self.current_frame_idx += 1
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# 性能计时开始
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# total_start = time.time()
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# ========= 每帧动态获取正确的 ROI（int32）=========
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roi_points_int32 = self._get_roi_points(w, h) # shape: (4, 2), dtype: int32
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roi_points_draw = roi_points_int32.reshape((-1, 1, 2)) # shape: (4, 1, 2) 用于绘制
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current_time_sec = timestamp
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# ========= 骨骼检测 =========
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# pose_start = time.time()
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# 耗时操作
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pose_results = self.pose_detector(frame)
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# pose_time = (time.time() - pose_start) * 1000
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# ========= 主检测 =========
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# detect_start = time.time()
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# 耗时操作
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detections = self.detector(frame)
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# detect_time = (time.time() - detect_start) * 1000
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dets_xyxy = []
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dets_roles = []
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dets_for_tracker = []
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# ========= 当前帧所有警告列表（关键改动）==========
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current_frame_alerts = [] # 每帧清空，重新收集
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if detections:
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for det in detections:
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x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角
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dets_xyxy.append([x1, y1, x2, y2])
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dets_for_tracker.append([x1, y1, x2, y2, conf])
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if cls_id == 0:
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dets_roles.append("car")
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elif cls_id == 1:
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dets_roles.append("opentrunk")
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elif cls_id == 2:
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dets_roles.append("person")
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elif cls_id == 3:
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dets_roles.append("phone")
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# logger.debug(f'dets_roles: {dets_roles}')
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dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5))
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tracks = self.tracker.update(
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dets,
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[self.height, self.width],
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[self.height, self.width]
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)
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# logger.debug("tracks: {}".format(tracks))
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# ========= 绘制骨骼 =========
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frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results)
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# ========= 绘制 ROI =========
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cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3)
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# ========= 单帧统计变量 =========
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current_roi_person_count = 0
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current_roi_trunk_count = 0
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current_roi_phone_count = 0
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# 临时存储本帧的目标，用于后续关联分析
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current_cars = [] # {'id':, 'box':}
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current_trunks = [] # (cx, cy)
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for t in tracks:
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# logger.debug("t: {}".format(t))
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tid = t.track_id
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# cls_id = -1
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# IoU 匹配角色
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# if tid not in track_role and dets_xyxy:
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REVALIDATE_FRAME_INTERVAL = 10
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# if tid not in self.track_role:
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if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role):
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best_iou = 0
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best_role = "unknown"
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t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2]
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for i, box in enumerate(dets_xyxy):
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iou_val = self.compute_iou(t_box, box)
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if iou_val > best_iou:
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best_iou = iou_val
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best_role = dets_roles[i]
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if best_iou > 0.1:
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self.track_role[tid] = best_role
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else:
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self.track_role[tid] = "unknown"
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role = self.track_role.get(tid, "unknown")
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cls_id = -1
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if role == "car":
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cls_id = 0
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elif role == "opentrunk":
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cls_id = 1
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elif role == "person":
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cls_id = 2
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elif role == "phone":
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cls_id = 3
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# logger.debug("tid: {}, role: {}, cls: {}".format(tid, role,cls_id))
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x1, y1, x2, y2 = map(int, t.tlbr)
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cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
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color = None
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label = None
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if self.check_point_in_roi(roi_points_int32, (cx, cy)):
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if cls_id == 0: # Car
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color = (0, 255, 0)
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current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]})
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|
||
if tid not in self.roi_car_registry:
|
||
self.roi_car_registry[tid] = {
|
||
'first_seen': self.current_frame_idx,
|
||
'last_seen': self.current_frame_idx,
|
||
'trunk_frames': 0,
|
||
'is_checked': False,
|
||
}
|
||
else:
|
||
self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx
|
||
|
||
label = f"Car:{tid} IN"
|
||
|
||
elif cls_id == 1: # Opentrunk
|
||
current_roi_trunk_count += 1
|
||
color = (255, 165, 0)
|
||
current_trunks.append((cx, cy))
|
||
label = "OpenTrunk IN"
|
||
|
||
elif cls_id == 2: # Person
|
||
current_roi_person_count += 1
|
||
color = (255, 0, 255)
|
||
label = "Person IN"
|
||
|
||
elif cls_id == 3: # Phone（主模型已支持）
|
||
current_roi_phone_count += 1
|
||
color = (0, 0, 139)
|
||
|
||
else:
|
||
color = (255, 255, 255)
|
||
label = "Unknown"
|
||
|
||
# label = f"ID:{tid} IN"
|
||
|
||
# 特殊显示: 如果这辆车已经合格，框变蓝色
|
||
if cls_id == 0 and tid in self.roi_car_registry and self.roi_car_registry[tid][
|
||
'is_checked']:
|
||
color = (255, 255, 0) # Cyan for checked cars
|
||
label += " (Checked)"
|
||
else:
|
||
color = (0, 0, 255)
|
||
label = "OUT"
|
||
|
||
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
|
||
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
|
||
|
||
# ==========================================
|
||
# 4. 从骨骼点模型中统计ROI内人员数量
|
||
# ==========================================
|
||
self.pose_person_count = 0
|
||
# if pose_results[0].boxes is not None:
|
||
# pose_boxes = pose_results[0].boxes
|
||
# for box in pose_boxes:
|
||
# # 获取人体检测框的中心点
|
||
# x1, y1, x2, y2 = map(int, box.xyxy[0])
|
||
# cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
|
||
#
|
||
# # 判断中心点是否在ROI内
|
||
# if self.check_point_in_roi((cx, cy)):
|
||
# self.pose_person_count += 1
|
||
|
||
if pose_results:
|
||
for pose in pose_results:
|
||
|
||
x1, y1, x2, y2 = pose['bbox'][0], pose['bbox'][1], pose['bbox'][2], pose['bbox'][3]
|
||
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
|
||
# 判断中心点是否在ROI内
|
||
if self.check_point_in_roi(roi_points_int32, (cx, cy)):
|
||
self.pose_person_count += 1
|
||
|
||
# 统计pose框在detector person框内部的数量
|
||
pose_inside_count = self.count_pose_inside_detector_person(pose_results, dets_xyxy, dets_roles)
|
||
|
||
# ==========================================
|
||
# 5. 关联分析: 哪个后备箱属于哪辆车?
|
||
# ==========================================
|
||
for car_info in current_cars:
|
||
c_id = car_info['id'] # 车的id
|
||
c_box = car_info['box'] # 车的框
|
||
|
||
trunk_found_for_this_car = False # 开后备箱标记
|
||
for t_pt in current_trunks:
|
||
if self.is_point_in_box(t_pt, c_box): # 如果开后备箱的框在车的框内,就设置开后备箱标记为true
|
||
trunk_found_for_this_car = True
|
||
break
|
||
|
||
if trunk_found_for_this_car: # 如果当前车辆的开后备箱标记为true了,就设置开了后备箱的帧数+1,凑够了判断“开后备箱”这个动作的帧数之后,就设置该车"已检查"
|
||
self.roi_car_registry[c_id]['trunk_frames'] += 1
|
||
if self.roi_car_registry[c_id]['trunk_frames'] >= self.frame_thresh_trunk_valid:
|
||
self.roi_car_registry[c_id]['is_checked'] = True
|
||
|
||
# ==========================================
|
||
# 6. 独立的手机检测逻辑（不与车辆绑定）
|
||
# ==========================================
|
||
if current_roi_phone_count > 0:
|
||
# 检测到手机框
|
||
self.phone_detection_frames += 1
|
||
self.phone_missing_frames = 0 # 重置丢失计数器
|
||
|
||
# 当检测累计达到阈值时，激活报警
|
||
if self.phone_detection_frames >= self.frame_thresh_phone:
|
||
self.phone_alert_active = True
|
||
else:
|
||
# 未检测到手机框
|
||
self.phone_missing_frames += 1
|
||
|
||
# 如果之前检测到手机，重置检测计数器
|
||
if self.phone_detection_frames > 0:
|
||
# 只有在连续丢失超过缓冲帧数时才重置
|
||
if self.phone_missing_frames >= self.frame_buffer_phone:
|
||
self.phone_detection_frames = 0
|
||
self.phone_alert_active = False
|
||
else:
|
||
# 从未检测到手机，保持状态
|
||
pass
|
||
|
||
# ==========================================
|
||
# 7. 制服检测逻辑（比较两个模型的人员数量）
|
||
# ==========================================
|
||
# 比较骨骼点模型和业务检测模型的人员数量
|
||
uniform_invalid = False
|
||
|
||
if self.pose_person_count > current_roi_person_count:
|
||
# 骨骼点模型检测到的人员多于业务检测模型
|
||
# 说明有人没穿执勤制服
|
||
uniform_invalid = True
|
||
self.uniform_detection_frames += 1
|
||
self.uniform_recovery_frames = 0 # 重置恢复计数器
|
||
|
||
# 当连续检测不合规达到阈值时，激活报警
|
||
if self.uniform_detection_frames >= self.frame_thresh_uniform:
|
||
self.uniform_alert_active = True
|
||
else:
|
||
# 人员数量匹配或业务模型检测更多（理论上不会）
|
||
self.uniform_recovery_frames += 1
|
||
|
||
# 如果之前有不合规检测，检查是否需要关闭报警
|
||
if self.uniform_detection_frames > 0:
|
||
# 只有在连续合规超过缓冲帧数时才重置
|
||
if self.uniform_recovery_frames >= self.frame_buffer_uniform:
|
||
self.uniform_detection_frames = 0
|
||
self.uniform_alert_active = False
|
||
else:
|
||
# 从未检测到不合规，保持状态
|
||
pass
|
||
|
||
# ==========================================
|
||
# 8. 维护车辆注册表 & 生成离场报警
|
||
# ==========================================
|
||
active_car_ids = []
|
||
cars_to_remove = []
|
||
|
||
for car_id, info in self.roi_car_registry.items():
|
||
# 遍历所有车辆，如果当前帧时间-该车辆最后可见的时间得到的值大于车辆消失时间阈值的话,就把该车添加到移除列表中,否则添加到活跃列表中
|
||
last_seen = info['last_seen']
|
||
|
||
if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_car:
|
||
active_car_ids.append(car_id)
|
||
else:
|
||
cars_to_remove.append(car_id)
|
||
|
||
# 执行删除 并 检查违规
|
||
for car_id in cars_to_remove:
|
||
# 遍历所有移除列表中的车辆,
|
||
# 如果该车辆最后出现时间-最早出现时间的值小于车辆最小存在时间,则判断为ignore,
|
||
# 如果该车辆的“已检查”标记为true,则
|
||
# 最后在所有车辆列表中删除该车辆
|
||
|
||
car_info = self.roi_car_registry[car_id]
|
||
|
||
duration_frames = car_info['last_seen'] - car_info['first_seen']
|
||
|
||
# 情况1：通过时间太短 -> 归类为 Ignore (Too Fast)
|
||
if duration_frames < self.frame_thresh_car_min_duration:
|
||
logger.warning(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!")
|
||
self.fast_pass_alerts[car_id] = self.current_frame_idx + int(self.ignore_show_seconds * self.fps)
|
||
|
||
# 情况2：时间够长，但没检查后备箱 -> Unchecked Trunk
|
||
elif not car_info['is_checked']:
|
||
logger.warning(f"ALARM: Car {car_id} left without checking trunk!")
|
||
self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int(
|
||
self.openTrunk_show_seconds * self.fps)
|
||
|
||
del self.roi_car_registry[car_id]
|
||
|
||
effective_car_count = len(active_car_ids)
|
||
|
||
# ==========================================
|
||
# 9. 业务逻辑判定 (Only One / Nobody)
|
||
# ==========================================
|
||
# status_text = ""
|
||
#
|
||
# if effective_car_count > 0:
|
||
# # --- Only One ---
|
||
# if current_roi_person_count == 1:
|
||
# self.cnt_frame_one_person += 1
|
||
# self.cnt_missing_buffer_person = 0
|
||
# self.cnt_frame_nobody = 0
|
||
#
|
||
# # --- Nobody ---
|
||
# elif current_roi_person_count == 0:
|
||
# if self.cnt_frame_one_person > 0 and self.cnt_missing_buffer_person < self.frame_buffer_limit_person:
|
||
# self.cnt_frame_one_person += 1
|
||
# self.cnt_missing_buffer_person += 1
|
||
# self.cnt_frame_nobody = 0
|
||
# status_text = f"Person Buffer ({self.cnt_missing_buffer_person}/{self.frame_buffer_limit_person})"
|
||
# else:
|
||
# self.cnt_frame_one_person = 0
|
||
# self.cnt_missing_buffer_person = 0
|
||
# self.cnt_frame_nobody += 1
|
||
# else:
|
||
# self.cnt_frame_one_person = 0
|
||
# self.cnt_missing_buffer_person = 0
|
||
# self.cnt_frame_nobody = 0
|
||
# else:
|
||
# self.cnt_frame_one_person = 0
|
||
# self.cnt_missing_buffer_person = 0
|
||
# self.cnt_frame_nobody = 0
|
||
|
||
# ==========================================
|
||
# 9. 业务逻辑判定 (Only One / Nobody) - 重构版
|
||
# ==========================================
|
||
if effective_car_count >= 0: # 只要没人就检测,不用等到来了车再检测
|
||
# ----- 定义条件 -----
|
||
onlyone_condition = (pose_inside_count == 1)
|
||
nobody_condition = (current_roi_person_count == 0 and self.pose_person_count == 0)
|
||
|
||
# ----- Onlyone 计数器更新 -----
|
||
if onlyone_condition: # 如果骨骼点和检测框都检测到了只有一个人时,onlyone+1,当onlyone累计够了之后触发报警
|
||
self.onlyone_counter += 1
|
||
# self.onlyone_lost_counter = 0
|
||
elif current_roi_person_count > 1 or self.pose_person_count > 1:
|
||
self.onlyone_counter = 0
|
||
# if self.onlyone_counter > 0:
|
||
# self.onlyone_lost_counter += 1
|
||
# if self.onlyone_lost_counter > self.onlyone_buffer_limit:
|
||
# self.onlyone_counter = 0
|
||
# self.onlyone_lost_counter = 0
|
||
|
||
# ----- Nobody 计数器更新 -----
|
||
if nobody_condition:
|
||
self.nobody_counter += 1
|
||
# self.nobody_present_counter = 0
|
||
elif current_roi_person_count > 0 or self.pose_person_count > 0:
|
||
self.nobody_counter = 0
|
||
# if self.nobody_counter > 0:
|
||
# self.nobody_present_counter += 1
|
||
# if self.nobody_present_counter > self.nobody_buffer_limit:
|
||
# self.nobody_counter = 0
|
||
# self.nobody_present_counter = 0
|
||
|
||
else:
|
||
# 无活跃车辆，清零所有计数器
|
||
self.onlyone_counter = 0
|
||
# self.onlyone_lost_counter = 0
|
||
self.nobody_counter = 0
|
||
self.nobody_present_counter = 0
|
||
|
||
# ==========================================
|
||
# 10. 显示报警 (UI分层优化)
|
||
# ==========================================
|
||
|
||
# 更新调试信息，包含所有检测状态
|
||
phone_status = f"Phone: {current_roi_phone_count}"
|
||
if self.phone_alert_active:
|
||
phone_status += " (ALERT)"
|
||
|
||
uniform_status = f"Uniform: Pose={self.pose_person_count}, Model={current_roi_person_count}"
|
||
if self.uniform_alert_active:
|
||
uniform_status += " (INVALID!)"
|
||
|
||
debug_info = f"Cars: {len(active_car_ids)} | Person: {current_roi_person_count} | Trunk: {current_roi_trunk_count} | {phone_status}"
|
||
cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||
cv2.putText(frame, uniform_status, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||
|
||
# 使用 offset 实现报警堆叠，防止遮挡
|
||
alert_offset = 0
|
||
|
||
# 第一层：实时状态 (Real-time Status)
|
||
# ------------------------------------------------
|
||
# A. 显示 Only One
|
||
# if self.cnt_frame_one_person >= self.frame_thresh_one:
|
||
# current_frame_alerts.append(
|
||
# {
|
||
# 'time': current_time_sec,
|
||
# 'action': "Only One",
|
||
# }
|
||
# )
|
||
# self.draw_alert(frame, "Only One", (0, 255, 255), status_text, offset_y=alert_offset)
|
||
# alert_offset += 100
|
||
#
|
||
# # B. 显示 Nobody (实时状态)
|
||
# elif self.cnt_frame_nobody >= self.frame_thresh_nobody:
|
||
# current_frame_alerts.append(
|
||
# {
|
||
# 'time': current_time_sec,
|
||
# 'action': "Nobody",
|
||
# }
|
||
# )
|
||
# self.draw_alert(frame, "Nobody", (0, 0, 255), offset_y=alert_offset)
|
||
# alert_offset += 100
|
||
|
||
# A. 显示 Only One（当累积帧数达到阈值时）
|
||
if self.onlyone_counter >= self.onlyone_thresh:
|
||
current_frame_alerts.append({'time': current_time_sec, 'action': "Only One"})
|
||
self.draw_alert(frame, "Only One", (0, 255, 255), None, offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
# B. 显示 Nobody（当累积帧数达到阈值时）
|
||
elif self.nobody_counter >= self.nobody_thresh:
|
||
current_frame_alerts.append({'time': current_time_sec, 'action': "Nobody"})
|
||
self.draw_alert(frame, "Nobody", (0, 0, 255), None, offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
# C. 显示 Trunk Checked (在车辆存活期间)
|
||
for car_id in active_car_ids:
|
||
if car_id in self.roi_car_registry and self.roi_car_registry[car_id]['is_checked']:
|
||
current_frame_alerts.append(
|
||
{
|
||
'time': current_time_sec,
|
||
'action': "Trunk Checked",
|
||
}
|
||
)
|
||
self.draw_alert(frame, "Trunk Checked!!", (0, 255, 0), offset_y=alert_offset)
|
||
alert_offset += 100
|
||
break # 只显示一次
|
||
|
||
# D. 显示 Playing Phone（独立检测，不与车辆绑定）
|
||
if self.phone_alert_active:
|
||
# 可以显示检测的持续时间
|
||
duration_seconds = self.phone_detection_frames / self.fps
|
||
# sub_text = f"Detected for {duration_seconds:.1f}s"
|
||
current_frame_alerts.append(
|
||
{
|
||
'time': current_time_sec,
|
||
'action': "Playing Phone",
|
||
}
|
||
)
|
||
self.draw_alert(frame, "Playing Phone", (255, 0, 0), None, offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
# # E. 新增：显示 Unvaild Uniform!!
|
||
# if self.uniform_alert_active:
|
||
# # 显示具体数量差异
|
||
# # diff = self.pose_person_count - current_roi_person_count
|
||
# #sub_text = f"Missing {diff} uniform(s)"
|
||
# current_frame_alerts.append(
|
||
# {
|
||
# 'time': current_time_sec,
|
||
# 'action': "Unvaild Uniform!!",
|
||
# }
|
||
# )
|
||
# self.draw_alert(frame, "Unvaild Uniform!!", (255, 165, 0), None, offset_y=alert_offset)
|
||
# alert_offset += 100
|
||
|
||
# 第二层：离场违规 (Post-Event Alerts)
|
||
# ------------------------------------------------
|
||
|
||
# F. 显示 Unchecked Trunk
|
||
expired_alerts = [cid for cid, end_frame in self.unchecked_trunk_alerts.items() if
|
||
self.current_frame_idx > end_frame]
|
||
for cid in expired_alerts:
|
||
del self.unchecked_trunk_alerts[cid]
|
||
|
||
if len(self.unchecked_trunk_alerts) > 0:
|
||
alert_text = f"Unchecked Trunk! (ID:{list(self.unchecked_trunk_alerts.keys())})"
|
||
current_frame_alerts.append(
|
||
{
|
||
'time': current_time_sec,
|
||
'action': "Unchecked Trunk",
|
||
}
|
||
)
|
||
self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
# G. 显示 Ignore (离场结果)
|
||
expired_fast_alerts = [cid for cid, end_frame in self.fast_pass_alerts.items() if
|
||
self.current_frame_idx > end_frame]
|
||
for cid in expired_fast_alerts:
|
||
del self.fast_pass_alerts[cid]
|
||
|
||
if len(self.fast_pass_alerts) > 0:
|
||
alert_text = f"Ignore: (ID:{list(self.fast_pass_alerts.keys())})"
|
||
current_frame_alerts.append(
|
||
{
|
||
'time': current_time_sec,
|
||
'action': "Ignore",
|
||
}
|
||
)
|
||
self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
# # ========= 性能统计和输出 =========
|
||
# total_time = (time.time() - total_start) * 1000
|
||
|
||
# logger.info(f"[PERF_DETAIL] Camera {camera_id} - ProcessFrame Total: {total_time:.1f}ms | "
|
||
# f"PoseDetect: {pose_time:.1f}ms | "
|
||
# f"MainDetect: {detect_time:.1f}ms | "
|
||
# )
|
||
|
||
return {
|
||
"image": frame,
|
||
"alerts": current_frame_alerts,
|
||
}
|
||
|
||
|
||
# ========================= 帧处理线程 =========================
|
||
class FrameProcessorWorker(BaseFrameProcessorWorker):
|
||
"""卡点检测帧处理线程"""
|
||
|
||
# 子类配置
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DETECTOR_FACTORY = lambda params: KadianDetector(params)
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POST_TYPE = 1
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TARGET_FPS = RTSP_TARGET_FPS
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