# 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 import cv2 import numpy as np import os import time import threading import queue import yaml import json import base64 from typing import Dict, Any, Tuple, List # -------------------------- 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 yolox.tracker.byte_tracker import BYTETracker from utils.logger import get_logger logger = get_logger(__name__) # ========================= 配置区 ========================= Person_Phone_Model = r'Yolo_Weight/person_phone_model.onnx' # 人和手机的检测模型 Smoke_Model = r'Yolo_Weight/smoke_model.onnx' # 抽烟检测模型 person_phone_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 smoke_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 # RTSP 服务配置 RTSP_TARGET_FPS = 5.0 # 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 ZhihuishiDetector: def __init__(self, params=None): # 模型加载 # 人和手机检测模型 print(f"加载人和手机检测模型: {Person_Phone_Model}") self.person_phone_detector = YOLOv8_ONNX(Person_Phone_Model, conf_threshold=0.6, iou_threshold=0.45, input_size=person_phone_input_size) # 抽烟检测模型 print(f"加载抽烟检测模型: {Smoke_Model}") self.smoke_detector = YOLOv8_ONNX(Smoke_Model, conf_threshold=0.4, iou_threshold=0.65, input_size=smoke_input_size) # ByteTracker class TrackerArgs: track_thresh = 0.25 track_buffer = 30 match_thresh = 0.8 mot20 = False self.fps = RTSP_TARGET_FPS self.person_phone_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) self.smoke_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) self.person_phone_track_role = {} self.smoke_track_role = {} # ========================================== # 超参数设置 (Hyperparameters) # ========================================== # 1. 业务判定时间阈值 self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长 self.TIME_TOLERANCE_NOBODY = 2.0 # 人丢失缓冲时间 self.TIME_THRESHOLD_SMOKE = 1.0 # 抽烟判定时长 self.TIME_TOLERANCE_SMOKE = 0.5 # 烟丢失缓冲时间（防抖动） self.TIME_THRESHOLD_PHONE = 1.0 # 玩手机判定时长 self.TIME_TOLERANCE_PHONE = 0.5 # 手机丢失缓冲时间（防抖动） # 无人在场帧数阈值 self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) self.frame_buffer_nobody = int(self.TIME_TOLERANCE_NOBODY * self.fps) # 抽烟检测帧数阈值 self.frame_thresh_smoke = int(self.TIME_THRESHOLD_SMOKE * self.fps) self.frame_buffer_smoke = int(self.TIME_TOLERANCE_SMOKE * self.fps) # 手机检测帧数阈值 self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps) self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps) print(f"\n超参数设置:") print(f" FPS: {self.fps:.2f}") print(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") print(f" 判定 'Smoke Detected' 需累计检测: {self.frame_thresh_smoke} 帧") print(f" 抽烟丢失缓冲帧数: {self.frame_buffer_smoke} 帧") print(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧") print(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧") # ========================================== # 状态变量初始化 # ========================================== self.current_frame_idx = 0 # 无人在场检测状态变量 self.nobody_detection_frames = 0 self.nobody_missing_frames = 0 # 连续未检测到手机的帧数 self.nobody_alert_active = False # 手机报警是否激活 # 手机检测状态变量 self.phone_detection_frames = 0 # 连续检测到手机的帧数 self.phone_missing_frames = 0 # 连续未检测到手机的帧数 self.phone_alert_active = False # 手机报警是否激活 # 抽烟检测状态变量 self.smoke_detection_frames = 0 # 连续检测到手机的帧数 self.smoke_missing_frames = 0 # 连续未检测到手机的帧数 self.smoke_alert_active = False # 手机报警是否激活 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(boxA[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 process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: h, w = frame.shape[:2] self.width, self.height = w, h self.current_frame_idx += 1 current_time_sec = timestamp # ========= 人和手机检测 ========= person_phone_results = self.person_phone_detector(frame) # ========= 抽烟检测 ========= smoke_results = self.smoke_detector(frame) person_phone_dets_xyxy = [] person_phone_dets_roles = [] person_phone_dets_for_tracker = [] smoke_dets_xyxy = [] smoke_dets_roles = [] smoke_dets_for_tracker = [] # ========= 当前帧所有警告列表（关键改动）========== current_frame_alerts = [] # 每帧清空，重新收集 # 收集 人和手机的检测结果 if person_phone_results: for det in person_phone_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 person_phone_dets_xyxy.append([x1, y1, x2, y2]) person_phone_dets_for_tracker.append([x1, y1, x2, y2, conf]) if cls_id == 0: person_phone_dets_roles.append("phone") elif cls_id == 1: person_phone_dets_roles.append("police") person_phone_dets = np.array(person_phone_dets_for_tracker, dtype=np.float32) if len( person_phone_dets_for_tracker) else np.empty((0, 5)) person_phone_tracks = self.person_phone_tracker.update( person_phone_dets, [self.height, self.width], [self.height, self.width] ) # 收集 抽烟的检测结果 if smoke_results: for det in smoke_results: x1, y1, x2, y2, conf, cls_id = det smoke_dets_xyxy.append([x1, y1, x2, y2]) smoke_dets_for_tracker.append([x1, y1, x2, y2, conf]) if cls_id == 0: smoke_dets_roles.append("smoke") smoke_dets = np.array(smoke_dets_for_tracker, dtype=np.float32) if len( smoke_dets_for_tracker) else np.empty((0, 5)) smoke_tracks = self.smoke_tracker.update( smoke_dets, [self.height, self.width], [self.height, self.width] ) # ========= 单帧统计变量 ========= current_person_count = 0 current_phone_count = 0 current_smoke_count = 0 # ========= 人和手机检测 ========= for t in person_phone_tracks: # print("t: {}".format(t)) tid = t.track_id # cls_id = -1 # IoU 匹配角色 # IoU匹配跟踪ID和类别 REVALIDATE_FRAME_INTERVAL = 10 if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.person_phone_track_role): #if tid not in self.person_phone_track_role: best_iou = 0 best_role = "unknown" t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] for i, box in enumerate(person_phone_dets_xyxy): iou_val = self.compute_iou(t_box, box) if iou_val > best_iou: best_iou = iou_val best_role = person_phone_dets_roles[i] if best_iou > 0.1: self.person_phone_track_role[tid] = best_role else: self.person_phone_track_role[tid] = "unknown" role = self.person_phone_track_role.get(tid, "unknown") cls_id = -1 if role == "phone": cls_id = 0 elif role == "police": cls_id = 1 # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) x1, y1, x2, y2 = map(int, t.tlbr) cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 color = None label = None if cls_id == 0: # Person current_phone_count += 1 color = (255, 0, 255) label = "Phone" elif cls_id == 1: # Phone（主模型已支持） current_person_count += 1 color = (0, 0, 139) label = "Person" else: color = (255, 255, 255) label = "Unknown" # label = f"ID:{tid} IN" cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) # ========= 抽烟检测 ========= for t in smoke_tracks: # print("t: {}".format(t)) tid = t.track_id # cls_id = -1 # IoU 匹配角色 # IoU匹配跟踪ID和类别 REVALIDATE_FRAME_INTERVAL = 10 if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.smoke_track_role): #if tid not in self.smoke_track_role: best_iou = 0 best_role = "unknown" t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] for i, box in enumerate(smoke_dets_xyxy): iou_val = self.compute_iou(t_box, box) if iou_val > best_iou: best_iou = iou_val best_role = smoke_dets_roles[i] # self.smoke_track_role[tid] = best_role if best_iou > 0.1: self.smoke_track_role[tid] = best_role else: self.smoke_track_role[tid] = "unknown" role = self.smoke_track_role.get(tid, "unknown") cls_id = -1 if role == "smoke": cls_id = 0 x1, y1, x2, y2 = map(int, t.tlbr) cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 color = None label = None if cls_id == 0: # 抽烟 current_smoke_count += 1 color = (255, 255, 0) label = "Smoke" else: color = (255, 255, 255) label = "Unknown" # label = f"ID:{tid} IN" cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) # ========================================== # 手机检测 # ========================================== if current_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 # ========================================== # 抽烟检测 # ========================================== if current_smoke_count > 0: # 检测到抽烟框 self.smoke_detection_frames += 1 self.smoke_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.smoke_detection_frames >= self.frame_thresh_smoke: self.smoke_alert_active = True else: # 未检测到抽烟框 self.smoke_missing_frames += 1 # 如果之前检测到抽烟，重置检测计数器 if self.smoke_detection_frames > 0: # 只有在连续丢失超过缓冲帧数时才重置 if self.smoke_missing_frames >= self.frame_buffer_smoke: self.smoke_detection_frames = 0 self.smoke_alert_active = False else: # 从未检测到抽烟，保持状态 pass # ========================================== # 9. 业务逻辑判定 (Only One / Nobody) # ========================================== status_text = "" if current_person_count == 0: self.nobody_detection_frames += 1 self.nobody_missing_frames = 0 if self.nobody_detection_frames >= self.frame_thresh_nobody: self.nobody_alert_active = True else: self.nobody_missing_frames += 1 if self.nobody_detection_frames > 0: if self.nobody_missing_frames >= self.frame_buffer_nobody: self.nobody_detection_frames = 0 self.nobody_alert_active = False else: pass # if current_person_count == 0: # self.cnt_frame_nobody += 1 # else: # self.cnt_frame_nobody = 0 # ========================================== # 10. 收集并生成结构化警告（核心改动） # ========================================== alert_offset = 0 # A. Playing Phone if self.phone_alert_active: duration_seconds = self.phone_detection_frames / self.fps current_frame_alerts.append( { 'time': current_time_sec, 'action': 'Playing Phone', 'confidence': 1.0, # 固定为1.0（规则判定） 'details': f"Detected for {duration_seconds:.1f}s" } ) # A. Playing Phone if self.smoke_alert_active: duration_seconds = self.smoke_detection_frames / self.fps current_frame_alerts.append( { 'time': current_time_sec, 'action': 'Smoke', 'confidence': 1.0, # 固定为1.0（规则判定） 'details': f"Detected for {duration_seconds:.1f}s" } ) # D. Nobody Checking if self.nobody_alert_active: duration_seconds = self.nobody_detection_frames / self.fps current_frame_alerts.append({ 'time': current_time_sec, 'action': 'Nobody Checking', 'confidence': 1.0, 'details': f"Detected for {duration_seconds:.1f}s" }) # ========================================== # 11. 统一显示当前帧所有警告（可替换原分层显示） # ========================================== debug_info = f"Person: {current_person_count} | Phone: {current_phone_count} | Smoke: {current_smoke_count}" cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) # 统一警告显示区 alert_y_start = 150 for i, alert in enumerate(current_frame_alerts): action = alert['action'] details = alert.get('details', '') color = (0, 0, 255) # 默认红色警告 if action == 'Nobody Checking': color = (255, 255, 255) elif action == 'Smoke': color = (0, 0, 255) elif action == 'Playing Phone': color = (255, 0, 0) main_text = action if details: main_text += f" ({details})" y_pos = alert_y_start + i * 50 cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1) cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) return { "image": frame, "alerts":current_frame_alerts } # ========================= 帧处理线程 ========================= class FrameProcessorWorker(threading.Thread): def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event, cameras=None): super().__init__(daemon=True) self.raw_queue = raw_queue self.ws_queue = ws_queue self.stop_event = stop_event self.cameras = {cam.id: cam for cam in cameras} if cameras is not None else {} self.last_ts: Dict[int, float] = {} # 每个摄像头一个独立的 Kadian 检测器实例 self.kadian_detectors: Dict[int, ZhihuishiDetector] = {} self.last_alert_push_time: Dict[int, Dict[str, float]] = {} def _encode_base64(self, img): _, buf = cv2.imencode(".jpg", img) return base64.b64encode(buf).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 try: 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.kadian_detectors: camera_config = self.cameras.get(cam_id) params = camera_config.params if camera_config else None self.kadian_detectors[cam_id] = ZhihuishiDetector(params) detector = self.kadian_detectors[cam_id] # 执行检测 # detect_start = time.time() result = detector.process_frame(frame.copy(), cam_id, ts) # detect_time = (time.time() - detect_start) * 1000 result_img = result["image"] result_type = result["alerts"] # logger.debug(f"alerts: {result_type}") # ========= 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 # encode_start = time.time() try: img_b64 = self._encode_base64(result_img) except Exception as e: logger.error(f"[ERROR] Encode image failed: {e}") img_b64 = None # encode_time = (time.time() - encode_start) * 1000 if img_b64 is not None: # 将abnormal_actions对象数组转换为字符串数组 # action_names = [action_info['action'] for action_info in push_actions] msg = { "msg_type": "frame", "camera_id": 0, "timestamp": ts, # "result_type": action_names, "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: logger.warning("[WARN] ws_send_queue full, drop frame message") # # 打印关键操作的耗时 # total_time = detect_time + encode_time # logger.info(f"[PERF] Camera {cam_id} - Total: {total_time:.1f}ms | " # f"Detect: {detect_time:.1f}ms | " # f"Encode: {encode_time:.1f}ms | ") except Exception as e: logger.error( f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}") logger.exception("Exception details:") # 打印完整的堆栈跟踪 # 继续处理下一帧，不要退出循环 finally: self.raw_queue.task_done()