import cv2 import numpy as np import base64 from typing import Dict, Any import threading import time import queue import requests # -------------------------- 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 common.constants import ALERT_PUSH_URL 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 = 1280 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 ZoulangDetector: 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) # ========================================== # 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_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 = 0.5 # 犯人丢失缓冲时间（防抖动） # 无人在场帧数阈值 # self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) # 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 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") 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 # 无人在场检测状态变量 self.cnt_frame_nobody = 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 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 # ========= 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（关键改动）========== 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] ) # ========= 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: # print("t: {}".format(t)) tid = t.track_id # cls_id = -1 # 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 # 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_police_count += 1 color = (255, 0, 255) label = "police" elif cls_id == 1: # Phone（主模型已支持） current_prisoner_count += 1 color = (0, 0, 139) label = "prisoner" 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) # ========================================== # 犯人检测 # ========================================== if current_prisoner_count > 0: # 检测到犯人框 self.prisoner_detection_frames += 1 self.prisoner_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.prisoner_detection_frames >= self.frame_thresh_prisoner: self.prisoner_alert_active = True else: # 未检测到犯人框 self.prisoner_missing_frames += 1 # 如果之前检测到手机，重置检测计数器 if self.prisoner_detection_frames > 0: # 只有在连续丢失超过缓冲帧数时才重置 if self.prisoner_missing_frames >= self.frame_buffer_prisoner: self.prisoner_detection_frames = 0 self.prisoner_alert_active = False else: # 从未检测到犯人，保持状态 pass # ========================================== # 警察检测 # ========================================== if current_police_count > 0: # 检测到犯人框 self.police_detection_frames += 1 self.police_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.police_detection_frames >= self.frame_thresh_police: self.police_alert_active = True else: # 未检测到犯人框 self.police_missing_frames += 1 # 如果之前检测到手机，重置检测计数器 if self.police_detection_frames > 0: # 只有在连续丢失超过缓冲帧数时才重置 if self.police_missing_frames >= self.frame_buffer_police: self.police_detection_frames = 0 self.police_alert_active = False else: # 从未检测到犯人，保持状态 pass 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, # 固定为1.0（规则判定） 'details': f"Detected for {duration_seconds:.1f}s" } ) self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) alert_offset += 100 # ========================================== # 11. 统一显示当前帧所有警告（可替换原分层显示） # ========================================== debug_info = f" prisoner: {current_prisoner_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 == 'prisoner': color = (255, 255, 255) 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_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.kadian_detectors: Dict[int, ZoulangDetector] = {} # 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.kadian_detectors: self.kadian_detectors[cam_id] = ZoulangDetector() detector = self.kadian_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: # 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": item["camera_index"], "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: # 发送POST请求 post_msg = msg.copy() post_msg['type'] = 2 try: response = requests.post(ALERT_PUSH_URL, json=post_msg, timeout=5.0) if response.status_code == 200: print(f"[INFO] POST alert sent successfully for actions: {push_actions}") else: print(f"[WARN] POST alert failed with status: {response.status_code}") except Exception as e: print(f"[ERROR] POST alert request failed: {e}") except queue.Full: print("[WARN] ws_send_queue full, drop frame message") self.raw_queue.task_done()