504 lines
20 KiB
Python
504 lines
20 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/Kadian.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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[0.12,0.0],
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[0.3,0.0],
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[0.5,0.2],
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[1.0, 0.95],
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[1.0,1.0],
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[0.42,1.0]
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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(
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DETECT_MODEL_PATH,
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conf_threshold=0.25,
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iou_threshold=0.45,
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input_size=PERSON_CAR_INPUT_SIZE
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)
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# 跟踪器配置
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class TrackerArgs:
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track_thresh = 0.3 # 必须大于等于yolo的conf_threshold
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track_buffer = 40
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match_thresh = 0.85
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mot20 = True
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self.fps = RTSP_TARGET_FPS
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self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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self.track_role = {} # 跟踪ID到类别的映射
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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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# ===================== 超参数设置 (仅保留车/后备箱相关) =====================
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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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# 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 = 3.0
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# Car 丢帧/ID维持缓冲
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self.TIME_TOLERANCE_CAR = 2.0
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# police丢失阈值
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self.TIME_TOLERANCE_POLICE = 3.0
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# police状态判定阈值 (累计秒数)
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self.TIME_THRESHOLD_NOBODY = 5.0
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self.TIME_THRESHOLD_ONLY_ONE = 5.0
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# --- 计算对应的帧数阈值 ---
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self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * 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_car = int(self.TIME_TOLERANCE_CAR * self.fps)
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self.frame_buffer_limit_police = int(self.TIME_TOLERANCE_POLICE * self.fps)
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self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
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self.frame_thresh_only_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps)
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# 显示相关阈值
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self.ignore_show_seconds = 0.2 # 未检测的警告显示时长
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self.openTrunk_show_seconds = 0.2 # 打开后备箱的警告显示时长
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self.police_show_seconds = 0.2 # 警察在场警告显示时长
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# 状态变量初始化
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self.current_frame_idx = 0
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self.width = 0
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self.height = 0
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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
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self.unchecked_trunk_alerts = {} # 后备箱未检
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self.fast_pass_alerts = {} # 通过过快
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# 警察注册表 (字典)
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self.roi_police_registry = {}
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# 警察在场告警记录
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self.nobody_alerts = {} # 无人在场
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self.only_one_alerts = {} # 单人在场
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# 累计帧数计数器
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self.nobody_frames = 0 # 累计无人在场帧数
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self.only_one_frames = 0 # 累计单人在场帧数
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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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"""判断点是否在ROI内"""
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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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"""计算两个框的IOU"""
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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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"""判断点是否在框内"""
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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 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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# ========= 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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# ========= 主检测（删除pose检测）=========
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detections = self.detector(frame)
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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 ==========
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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:左上角，x2,y2: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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# 更新类别映射：0=Car,1=OpenTrunk,2=Passerby,3=Police
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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("passerby") # 路人
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elif cls_id == 3:
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dets_roles.append("police") # 警察
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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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# 跟踪器更新
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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 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_trunk_count = 0 # 仅保留后备箱统计
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current_roi_police_count = 0 # ROI内警察数量
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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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# ========= 处理跟踪结果 =========
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for t in tracks:
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tid = t.track_id
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REVALIDATE_FRAME_INTERVAL = 10
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# 定期重新匹配跟踪ID的类别
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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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x1, y1, x2, y2 = map(int, t.tlbr)
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cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
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# 定义不同类别的颜色（仅标框，不告警）
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if role == "car":
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color = (0, 255, 0) # 绿色
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label = f"Car:{tid}"
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# 仅处理ROI内的车辆
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if self.check_point_in_roi(roi_points_int32, (cx, cy)):
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current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]})
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# 车辆注册表初始化
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if tid not in self.roi_car_registry:
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self.roi_car_registry[tid] = {
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'first_seen': self.current_frame_idx,
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'last_seen': self.current_frame_idx,
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'trunk_frames': 0,
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'is_checked': False,
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}
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else:
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self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx
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label += " IN"
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elif role == "opentrunk":
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color = (255, 165, 0) # 橙色
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label = "OpenTrunk"
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if self.check_point_in_roi(roi_points_int32, (cx, cy)):
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current_roi_trunk_count += 1
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current_trunks.append((cx, cy))
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label += " IN"
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elif role == "passerby":
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color = (255, 255, 0) # 黄色（仅标框，不告警）
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label = "Passerby"
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elif role == "police":
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color = (0, 255, 255) # 青色
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label = "Police"
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if self.check_point_in_roi(roi_points_int32, (cx, cy)):
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current_roi_police_count += 1
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# 警察注册表初始化
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if tid not in self.roi_police_registry:
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self.roi_police_registry[tid] = {
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'first_seen': self.current_frame_idx,
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'last_seen': self.current_frame_idx,
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}
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else:
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self.roi_police_registry[tid]['last_seen'] = self.current_frame_idx
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label += " IN"
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else:
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color = (255, 255, 255) # 白色
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label = "Unknown"
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# 绘制检测框和标签（所有类别都标框，仅车/后备箱有逻辑）
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
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cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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# ==========================================
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# 关联分析: 哪个后备箱属于哪辆车?
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# ==========================================
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for car_info in current_cars:
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c_id = car_info['id']
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c_box = car_info['box']
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trunk_found_for_this_car = False
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for t_pt in current_trunks:
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if self.is_point_in_box(t_pt, c_box):
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trunk_found_for_this_car = True
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break
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if trunk_found_for_this_car:
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self.roi_car_registry[c_id]['trunk_frames'] += 1
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if self.roi_car_registry[c_id]['trunk_frames'] >= self.frame_thresh_trunk_valid:
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self.roi_car_registry[c_id]['is_checked'] = True
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# ==========================================
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# 维护车辆注册表 & 生成离场报警
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# ==========================================
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active_car_ids = []
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cars_to_remove = []
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for car_id, info in self.roi_car_registry.items():
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last_seen = info['last_seen']
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if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_car:
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active_car_ids.append(car_id)
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else:
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cars_to_remove.append(car_id)
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# 处理离场车辆，生成违规告警
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for car_id in cars_to_remove:
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car_info = self.roi_car_registry[car_id]
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duration_frames = car_info['last_seen'] - car_info['first_seen']
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# 情况1：通过时间太短 -> Ignore (Too Fast)
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if duration_frames < self.frame_thresh_car_min_duration:
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print(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!")
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self.fast_pass_alerts[car_id] = self.current_frame_idx + int(self.ignore_show_seconds * self.fps)
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# 情况2：时间够长，但没检查后备箱 -> Unchecked Trunk
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elif not car_info['is_checked']:
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print(f"ALARM: Car {car_id} left without checking trunk!")
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self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int(
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self.openTrunk_show_seconds * self.fps)
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del self.roi_car_registry[car_id]
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effective_car_count = len(active_car_ids)
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# ==========================================
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# 维护警察注册表
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# ==========================================
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active_police_ids = []
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polices_to_remove = []
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for police_id, info in self.roi_police_registry.items():
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last_seen = info['last_seen']
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if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_police:
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active_police_ids.append(police_id)
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else:
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polices_to_remove.append(police_id)
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for police_id in polices_to_remove:
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del self.roi_police_registry[police_id]
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effective_police_count = len(active_police_ids)
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# ==========================================
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# 显示调试信息和报警 (仅保留车/后备箱相关)
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# ==========================================
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# 调试信息
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debug_info = f"Cars: {len(active_car_ids)} | Trunk: {current_roi_trunk_count} | Police: {effective_police_count} | Nobody:{self.nobody_frames}/{self.frame_thresh_nobody} | OnlyOne:{self.only_one_frames}/{self.frame_thresh_only_one}"
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cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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# 报警偏移量（防止重叠）
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alert_offset = 0
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# A. 显示 Trunk Checked (车辆已检查后备箱)
|
||
# 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
|
||
|
||
# B. 显示 Unchecked Trunk (离场未检查后备箱)
|
||
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
|
||
|
||
# C. 显示 Ignore (通过过快)
|
||
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
|
||
|
||
# D. 显示警察在场状态 (Nobody/Only One)
|
||
# 清理过期的 Nobody 告警
|
||
expired_nobody = [k for k, v in self.nobody_alerts.items() if self.current_frame_idx > v]
|
||
for k in expired_nobody:
|
||
del self.nobody_alerts[k]
|
||
|
||
# 清理过期的 Only One 告警
|
||
expired_only_one = [k for k, v in self.only_one_alerts.items() if self.current_frame_idx > v]
|
||
for k in expired_only_one:
|
||
del self.only_one_alerts[k]
|
||
|
||
# 更新累计帧数
|
||
if effective_police_count == 0:
|
||
self.nobody_frames += 1
|
||
self.only_one_frames = 0
|
||
elif effective_police_count == 1:
|
||
self.only_one_frames += 1
|
||
self.nobody_frames = 0
|
||
else:
|
||
self.nobody_frames = 0
|
||
self.only_one_frames = 0
|
||
|
||
if effective_police_count == 0 and self.nobody_frames >= self.frame_thresh_nobody:
|
||
alert_text = "Nobody"
|
||
if "Nobody" not in self.nobody_alerts:
|
||
self.nobody_alerts["Nobody"] = self.current_frame_idx + int(self.police_show_seconds * self.fps)
|
||
current_frame_alerts.append({
|
||
'time': current_time_sec,
|
||
'action': "Nobody",
|
||
})
|
||
self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
|
||
alert_offset += 100
|
||
elif effective_police_count == 1 and self.only_one_frames >= self.frame_thresh_only_one:
|
||
alert_text = "Only One"
|
||
if "Only One" not in self.only_one_alerts:
|
||
self.only_one_alerts["Only One"] = self.current_frame_idx + int(self.police_show_seconds * self.fps)
|
||
current_frame_alerts.append({
|
||
'time': current_time_sec,
|
||
'action': "Only One",
|
||
})
|
||
self.draw_alert(frame, alert_text, (255, 165, 0), offset_y=alert_offset)
|
||
alert_offset += 100
|
||
|
||
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
|
||
DETECTOR_FACTORY = lambda params: KadianDetector(params)
|
||
POST_TYPE = 1
|
||
TARGET_FPS = RTSP_TARGET_FPS
|