From 16a55fadd757d2e90516eba1da49e57aae0a4efd Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Fri, 27 Feb 2026 13:46:15 +0800 Subject: [PATCH] =?UTF-8?q?=E7=9B=91=E5=8C=BA=E8=B5=B0=E5=BB=8A=E6=96=B0?= =?UTF-8?q?=E7=AE=97=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/prison/trajectory02_biz.py | 561 +++++++++++++++++++++++++++++++++ 1 file changed, 561 insertions(+) create mode 100644 biz/prison/trajectory02_biz.py diff --git a/biz/prison/trajectory02_biz.py b/biz/prison/trajectory02_biz.py new file mode 100644 index 0000000..150b937 --- /dev/null +++ b/biz/prison/trajectory02_biz.py @@ -0,0 +1,561 @@ +# rtsp_service_kadian.py +# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py +# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 +# 修改为单一区域监控：犯人离开指定区域即报警 + +import cv2 +import numpy as np +import os +import time +import threading +import queue +import yaml +import json +import base64 +import asyncio +import websockets +from dataclasses import dataclass +from typing import Dict, Any, Tuple, List +from datetime import datetime +from common.contants import ALERT_PUSH_URL + +# -------------------------- Kadian 检测相关导入 -------------------------- +from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） +# from rtsp_service_ws_0108 import WS_PORT + +from yolox.tracker.byte_tracker import BYTETracker + +# ========================= 配置区 ========================= +# Kadian 模型路径与ROI（可根据实际情况修改） +detector_model_path = 'YOLO_Weight/prisoner_model.onnx' + +# 输入尺寸 +input_size = 640 + +# RTSP 服务配置 +RTSP_TARGET_FPS = 10.0 + +# 新增：告警推送频率限制（秒） +ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 + + +class TrajectoryDetector: + def __init__(self): + # 模型加载 + self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, + input_size=input_size) + + # ByteTracker + class TrackerArgs: + track_thresh = 0.25 + track_buffer = 30 + match_thresh = 0.8 + mot20 = False + + self.police_prisoner_track_role = {} + + self.fps = RTSP_TARGET_FPS + + self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) + + # ========================================== + # 超参数设置 (Hyperparameters) + # ========================================== + self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 + self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） + + self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 + self.TIME_TOLERANCE_PRISONER = 1.0 # 犯人丢失缓冲时间（防抖动） + + # 警察检测帧数阈值 + self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) + self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) + + # 犯人检测帧数阈值 + self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) + self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) + + print(f"\n超参数设置:") + print(f" FPS: {self.fps:.2f}") + print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") + print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") + print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") + print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") + + # ========================================== + # 状态变量初始化 + # ========================================== + self.current_frame_idx = 0 + + # 警察检测状态变量 + self.police_detection_frames = 0 # 连续检测到警察的帧数 + self.police_missing_frames = 0 # 连续未检测到警察的帧数 + self.police_alert_active = False # 警察报警是否激活 + + # 犯人检测状态变量 + self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 + self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 + self.prisoner_alert_active = False # 犯人报警是否激活 + + # ========================= + # 区域 ROI + 状态机初始化（修改为单一区域） + # ========================= + # ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1 + # 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208 + self.route_rois = [ + { + "name": "zone", # 单一区域，犯人离开即报警 + "polygon_rel": [(0.47, 0.35), (0.5, 0.35), (0.7, 1.0), (0.3, 1.0)] # 相对坐标，可自定义 + } + ] + + # 帧尺寸（动态更新） + self.width = 0 + self.height = 0 + + print(f"相对坐标 ROI: {self.route_rois}") + + # 每个犯人（track_id）一套状态 + self.prisoner_route_state = {} + + # 新增：记录所有曾经出现过的犯人track_id及其状态 + self.all_prisoner_tracks = {} + # 新增：记录已触发违规的track_id，避免重复告警 + self.violated_tracks = set() + + def _get_abs_polygon(self, rel_polygon): + """将相对坐标(0-1)转换为绝对像素坐标""" + return [ + (int(x * self.width), int(y * self.height)) + for x, y in rel_polygon + ] + + def compute_iou(self, boxA, boxB): + # box = [x1, y1, x2, y2] + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxB[3], boxB[3]) + + interW = max(0, xB - xA) + interH = max(0, yB - yA) + interArea = interW * interH + + boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) + boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) + + unionArea = boxAArea + boxBArea - interArea + if unionArea == 0: + return 0.0 + + return interArea / unionArea + + def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): + """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" + font_scale = 1.5 + thickness = 3 + font = cv2.FONT_HERSHEY_SIMPLEX + + (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) + x = self.width - text_w - 20 + y = 50 + text_h + offset_y # 增加 Y 轴偏移 + + cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) + cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) + + if sub_text: + cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) + + def _point_in_polygon(self, point, polygon): + """ + 判断点是否在多边形内 + polygon: 绝对像素坐标的多边形 + """ + return cv2.pointPolygonTest( + np.array(polygon, dtype=np.int32), + point, + False + ) >= 0 + + def _draw_route_rois(self, frame): + """ + 在画面中绘制路线 ROI（动态转换为绝对坐标） + """ + for idx, roi in enumerate(self.route_rois): + # 相对坐标转绝对坐标 + abs_polygon = self._get_abs_polygon(roi["polygon_rel"]) + pts = np.array(abs_polygon, np.int32).reshape((-1, 1, 2)) + + # ROI 边框 + cv2.polylines( + frame, + [pts], + isClosed=True, + color=(0, 255, 255), + thickness=2 + ) + + # 标注名称 + text_pos = abs_polygon[0] + cv2.putText( + frame, + f"{idx + 1}:{roi['name']}", + (text_pos[0], text_pos[1] - 5), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 255, 255), + 2 + ) + + def _update_prisoner_route(self, tid, point, timestamp): + """ + 区域监控状态机（修改为单一区域）： + 只监控一个区域，如果犯人进入过该区域，后来离开（连续多帧不在区域内或消失），则触发违规。 + """ + # 初始化状态 + if tid not in self.prisoner_route_state: + self.prisoner_route_state[tid] = { + "entered_zone": False, # 是否曾进入区域 + "in_zone": False, # 当前是否在区域内 + "out_frames": 0, # 连续不在区域内的帧数 + "violation": False, # 是否已触发离开违规 + "last_seen": timestamp # 最后出现时间 + } + # 记录所有犯人track + self.all_prisoner_tracks[tid] = self.prisoner_route_state[tid] + + state = self.prisoner_route_state[tid] + state["last_seen"] = timestamp + + # 如果已经触发违规，不再处理（可保留但不重复触发） + if state["violation"]: + return + + # 获取当前唯一区域的多边形（绝对坐标） + current_roi_rel = self.route_rois[0]["polygon_rel"] + current_roi_abs = self._get_abs_polygon(current_roi_rel) + + # 判断点是否在区域内 + in_zone = self._point_in_polygon(point, current_roi_abs) + + if in_zone: + # 在区域内 + state["in_zone"] = True + state["out_frames"] = 0 + if not state["entered_zone"]: + state["entered_zone"] = True + else: + # 不在区域内 + if state["entered_zone"]: + # 曾进入过区域，开始计数离开帧数 + state["out_frames"] += 1 + # 如果离开帧数超过阈值，触发违规 + # 使用 frame_buffer_prisoner 作为离开判定缓冲（可自定义） + if state["out_frames"] >= self.frame_buffer_prisoner: + state["violation"] = True + state["in_zone"] = False + # 如果还未进入区域，忽略 + + def _check_prisoner_violation(self, current_time): + """ + 检查消失的犯人是否违规（离开区域）： + 1. 曾进入过区域 + 2. 未触发过违规 + 3. 已经消失（超过track buffer时间） + """ + violations = [] + # 遍历所有曾经出现过的犯人track + for tid, state in list(self.all_prisoner_tracks.items()): + # 跳过已违规或未进入区域的track + if state["violation"] or not state["entered_zone"]: + continue + + # 检查是否已消失（超过track buffer时间，这里用2秒作为消失判定） + if current_time - state["last_seen"] > 2.0 and tid not in self.violated_tracks: + state["violation"] = True + self.violated_tracks.add(tid) + violations.append({ + 'time': current_time, + 'action': 'violation', + 'confidence': 1.0, + 'details': "Prisoner left zone (disappeared)" + }) + return violations + + def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: + h, w = frame.shape[:2] + self.width, self.height = w, h # 更新帧尺寸 + + self.current_frame_idx += 1 + current_time_sec = timestamp + + # ========= 警察和犯人检测 ========= + police_prisoner_results = self.police_prisoner_detector(frame) + + police_prisoner_dets_xyxy = [] + police_prisoner_dets_roles = [] + police_prisoner_dets_for_tracker = [] + + # ========= 当前帧所有警告列表 ========== + current_frame_alerts = [] # 每帧清空，重新收集 + + if police_prisoner_results: + for det in police_prisoner_results: + 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 + police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) + police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) + if cls_id == 0: + police_prisoner_dets_roles.append("police") + elif cls_id == 1: + police_prisoner_dets_roles.append("prisoner") + + ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( + police_prisoner_dets_for_tracker) else np.empty((0, 5)) + + police_prisoner_dets_tracks = self.tracker.update( + ppolice_prisoner_dets, + [self.height, self.width], + [self.height, self.width] + ) + + # 重置当前帧的犯人track标记 + current_frame_prisoner_tids = set() + + # ========= 单帧统计变量 ========= + current_police_count = 0 + current_prisoner_count = 0 + + # ========= 警察和犯人检测 ========= + for t in police_prisoner_dets_tracks: + tid = t.track_id + + # IoU 匹配角色 + REVALIDATE_FRAME_INTERVAL = 10 + if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( + tid not in self.police_prisoner_track_role): + best_iou = 0 + best_role = "unknown" + + t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] + + for i, box in enumerate(police_prisoner_dets_xyxy): + iou_val = self.compute_iou(t_box, box) + if iou_val > best_iou: + best_iou = iou_val + best_role = police_prisoner_dets_roles[i] + if best_iou > 0.1: + self.police_prisoner_track_role[tid] = best_role + else: + self.police_prisoner_track_role[tid] = "unknown" + + role = self.police_prisoner_track_role.get(tid, "unknown") + cls_id = -1 + if role == "police": + cls_id = 0 + elif role == "prisoner": + cls_id = 1 + + x1, y1, x2, y2 = map(int, t.tlbr) + cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 + + color = None + label = None + + if cls_id == 0: # police + current_police_count += 1 + color = (255, 0, 255) + label = "police" + + elif cls_id == 1: # prisoner + current_prisoner_count += 1 + color = (0, 0, 139) + label = "prisoner" + current_frame_prisoner_tids.add(tid) + # ===== 区域状态机更新 ===== + self._update_prisoner_route( + tid=tid, + point=(cx, cy), + timestamp=current_time_sec + ) + else: + color = (255, 255, 255) + label = "Unknown" + + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) + cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) + + # ========================================== + # 检查犯人违规（进入区域后离开） + # ========================================== + violation_alerts = self._check_prisoner_violation(current_time_sec) + + # 遍历所有状态，收集刚刚触发的 violation（那些在更新中被标记但尚未加入 violated_tracks 的） + for tid, state in self.prisoner_route_state.items(): + if state["violation"] and tid not in self.violated_tracks: + self.violated_tracks.add(tid) + violation_alerts.append({ + 'time': current_time_sec, + 'action': 'violation', + 'confidence': 1.0, + 'details': "Prisoner left zone" + }) + + current_frame_alerts.extend(violation_alerts) + + # ========================================== + # 犯人检测 + # ========================================== + if current_prisoner_count > 0: + self.prisoner_detection_frames += 1 + self.prisoner_missing_frames = 0 + if self.prisoner_detection_frames >= self.frame_thresh_prisoner: + self.prisoner_alert_active = True + else: + self.prisoner_missing_frames += 1 + if self.prisoner_detection_frames > 0: + if self.prisoner_missing_frames >= self.frame_buffer_prisoner: + self.prisoner_detection_frames = 0 + self.prisoner_alert_active = False + + # ========================================== + # 警察检测 + # ========================================== + if current_police_count > 0: + self.police_detection_frames += 1 + self.police_missing_frames = 0 + if self.police_detection_frames >= self.frame_thresh_police: + self.police_alert_active = True + else: + self.police_missing_frames += 1 + if self.police_detection_frames > 0: + if self.police_missing_frames >= self.frame_buffer_police: + self.police_detection_frames = 0 + self.police_alert_active = False + + alert_offset = 0 + + # A. 有犯人 + if self.prisoner_alert_active: + duration_seconds = self.prisoner_detection_frames / self.fps + current_frame_alerts.append( + { + 'time': current_time_sec, + 'action': 'prisoner', + 'confidence': 1.0, + 'details': f"Detected for {duration_seconds:.1f}s" + } + ) + self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) + alert_offset += 100 + + # C. 区域违规告警（离开区域） + for violation in violation_alerts: + self.draw_alert(frame, "ZONE VIOLATION!", (0, 0, 255), + sub_text=violation['details'], offset_y=alert_offset) + alert_offset += 100 + + # ========================= + # 绘制区域 ROI（始终显示） + # ========================= + self._draw_route_rois(frame) + + return { + "image": frame, + "alerts": current_frame_alerts + } + + +# ========================= 帧处理线程 ========================= +class FrameProcessorWorker(threading.Thread): + def __init__(self, + raw_frame_queue: "queue.Queue[Dict[str, Any]]", + ws_send_queue: "queue.Queue[Dict[str, Any]]", + stop_event: threading.Event): + super().__init__(daemon=True) + self.raw_queue = raw_frame_queue + self.ws_queue = ws_send_queue + self.stop_event = stop_event + + self.last_ts: Dict[int, float] = {} + + # 每个摄像头一个独立的 Kadian 检测器实例 + self.trajectory_detectors: Dict[int, TrajectoryDetector] = {} + + # 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}} + self.last_alert_push_time: Dict[int, Dict[str, float]] = {} + + def _encode_image_to_base64(self, image) -> str: + ok, buf = cv2.imencode(".jpg", image) + if not ok: + raise RuntimeError("Failed to encode image to JPEG") + return base64.b64encode(buf.tobytes()).decode("ascii") + + def run(self): + target_interval = 1.0 / RTSP_TARGET_FPS + while not self.stop_event.is_set(): + try: + item = self.raw_queue.get(timeout=0.5) + except queue.Empty: + continue + + cam_id = item["camera_id"] + ts = item["timestamp"] + frame = item["frame"] + + # 抽帧控制 + if ts - self.last_ts.get(cam_id, 0) < target_interval: + self.raw_queue.task_done() + continue + self.last_ts[cam_id] = ts + + # 获取检测器实例 + if cam_id not in self.trajectory_detectors: + self.trajectory_detectors[cam_id] = TrajectoryDetector() + detector = self.trajectory_detectors[cam_id] + + # 执行检测 + result = detector.process_frame(frame.copy(), cam_id, ts) + + result_img = result["image"] + result_type = result["alerts"] + + # ========= 核心修改：过滤5秒内重复的action ========= + # 初始化当前摄像头的推送时间记录 + if cam_id not in self.last_alert_push_time: + self.last_alert_push_time[cam_id] = {} + + # 筛选出符合推送条件的action（5秒内未推送过） + push_actions = [] + current_time = time.time() + for alert in result_type: + action = alert['action'] + last_push = self.last_alert_push_time[cam_id].get(action, 0) + # 检查是否超过推送间隔 + if current_time - last_push >= ALERT_PUSH_INTERVAL: + push_actions.append(action) + # 更新该action的最后推送时间 + self.last_alert_push_time[cam_id][action] = current_time + + # 通过 WebSocket 发送帧结果 + try: + img_b64 = self._encode_image_to_base64(result_img) + except Exception as e: + print(f"[ERROR] Encode image failed: {e}") + img_b64 = None + + if img_b64 is not None: + msg = { + "msg_type": "frame", + "camera_id": 1, + "timestamp": ts, + "result_type": push_actions, + "image_base64": img_b64, + } + try: + self.ws_queue.put(msg, timeout=1.0) + # if push_actions and len(push_actions) > 0: + # self.ws_queue_2.put(msg, timeout=1.0) + except queue.Full: + print("[WARN] ws_send_queue full, drop frame message") + + self.raw_queue.task_done() \ No newline at end of file