import cv2 import numpy as np import base64 from typing import Dict, Any import threading import time import queue # -------------------------- Kadian 检测相关导入 -------------------------- from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） from algorithm.checkpoint.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX # Pose 专用模型 from yolox.tracker.byte_tracker import BYTETracker from utils.logger import get_logger logger = get_logger(__name__) # ========================= 配置区 ========================= # Kadian 模型路径与ROI（可根据实际情况修改） DETECT_MODEL_PATH = 'YOLO_Weight/car_opentrunk_person_phone.onnx' POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx' # 默认相对ROI（与原文件一致） #ROI_RELATIVE = np.array([ # [0.10989583333333333, 0.006481481481481481], # [0.421875, 0.005555555555555556], # [0.9921875, 0.9888888888888889], # [0.3411458333333333, 0.9861111111111112] #]) ROI_RELATIVE=np.array([ [0.15,0.001], [0.5,0.001], [1.0,0.8], [0.35,1.0] ]) ALERT_PUSH_INTERVAL = 5.0 # 输入尺寸 PERSON_CAR_INPUT_SIZE = 640 POSE_INPUT_SIZE = 640 RTSP_TARGET_FPS = 10.0 # ========================= Kadian TrafficMonitor（精简版，专为服务设计） ========================= class KadianDetector: def __init__(self, roi_points=ROI_RELATIVE): # 模型加载 self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.25, iou_threshold=0.45, input_size=PERSON_CAR_INPUT_SIZE) self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.7, iou_threshold=0.6, input_size=POSE_INPUT_SIZE) # Tracker class TrackerArgs: track_thresh = 0.25 track_buffer = 30 match_thresh = 0.8 mot20 = False self.tracker = BYTETracker(TrackerArgs(), frame_rate=10.0) self.track_role = {} self.fps = RTSP_TARGET_FPS # ROI 处理（支持相对/绝对） #self.roi_points = roi_points.astype(np.int32) self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None # ========================================== # 超参数设置 (Hyperparameters) # ========================================== # 1. 业务判定时间阈值 self.TIME_THRESHOLD_ONLY_ONE = 3.0 # 单人单检判定时长 self.TIME_THRESHOLD_NOBODY = 2.0 # 无人检查判定时长 # 后备箱检查判定阈值 self.TIME_THRESHOLD_TRUNK_OPEN = 0.5 # 新增：手机检测判定阈值 self.TIME_THRESHOLD_PHONE = 1.0 # 手机检测持续1秒（30帧 @30fps） self.TIME_TOLERANCE_PHONE = 0.5 # 手机丢失缓冲时间（防抖动） # 新增：制服检测判定阈值 self.TIME_THRESHOLD_UNIFORM = 1.0 # 制服不合规判定时长 self.TIME_TOLERANCE_UNIFORM = 0.5 # 制服合规恢复缓冲时间 # 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) self.TIME_THRESHOLD_CAR_MIN_DURATION = 3.0 # 2. Person 丢帧缓冲 self.TIME_TOLERANCE_PERSON = 1.0 # 3. Car 丢帧/ID维持缓冲 self.TIME_TOLERANCE_CAR = 0.5 # --- 计算对应的帧数阈值 --- self.frame_thresh_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps) self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps) # 新增：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) # 新增：制服检测帧数阈值 self.frame_thresh_uniform = int(self.TIME_THRESHOLD_UNIFORM * self.fps) self.frame_buffer_uniform = int(self.TIME_TOLERANCE_UNIFORM * self.fps) self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps) self.frame_buffer_limit_person = int(self.TIME_TOLERANCE_PERSON * self.fps) self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * self.fps) logger.info(f"\n超参数设置:") logger.info(f" FPS: {self.fps:.2f}") logger.info(f" 判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧") logger.info(f" 判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧") logger.info(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧") logger.info(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧") logger.info(f" 判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧") logger.info(f" 制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧") logger.info(f" 判定 'Too Fast' (视为Nobody) 最小停留: {self.frame_thresh_car_min_duration} 帧") self.current_frame_idx = 0 self.cnt_frame_one_person = 0 self.cnt_frame_nobody = 0 self.cnt_missing_buffer_person = 0 # 手机检测状态变量（独立于车辆） 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 # 新增：制服检测状态变量 self.pose_person_count = 0 # 骨骼点模型检测的ROI内人员数量 self.uniform_alert_active = False # 制服报警是否激活 self.uniform_detection_frames = 0 # 连续检测到制服不合规的帧数 self.uniform_recovery_frames = 0 # 连续恢复合规的帧数 # 车辆注册表 (字典) self.roi_car_registry = {} # 违规车辆记录 (后备箱未检) self.unchecked_trunk_alerts = {} # 违规车辆记录 (通过过快 -> 归类为 Nobody) self.fast_pass_alerts = {} def _get_roi_points(self, frame_width: int, frame_height: int): """ 每帧动态计算正确的 ROI 绝对坐标，并确保类型为 np.int32 用于 pointPolygonTest 和 polylines """ if self.roi_points is None: # 使用默认相对坐标 default_rel = np.array([ [0.15, 0.01], [0.45, 0.01], [0.95, 0.95], [0.35, 0.95] ], dtype=np.float64) roi_abs = default_rel * np.array([frame_width, frame_height]) else: if self.roi_points.max() <= 1.0: # 相对坐标 → 转换为绝对 roi_abs = self.roi_points * np.array([frame_width, frame_height]) else: # 绝对坐标，直接使用 roi_abs = self.roi_points.copy() # 强制转为 int32（关键！解决 OpenCV 断言错误） return roi_abs.astype(np.int32) def check_point_in_roi(self,roi_points, point): return cv2.pointPolygonTest(roi_points, point, False) >= 0 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 is_point_in_box(self, point, box): px, py = point x1, y1, x2, y2 = box return x1 < px < x2 and y1 < py < y2 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 # 性能计时开始 # total_start = time.time() # ========= 每帧动态获取正确的 ROI（int32）========= roi_points_int32 = self._get_roi_points(w, h) # shape: (4, 2), dtype: int32 roi_points_draw = roi_points_int32.reshape((-1, 1, 2)) # shape: (4, 1, 2) 用于绘制 current_time_sec = timestamp # ========= 骨骼检测 ========= # pose_start = time.time() # 耗时操作 pose_results = self.pose_detector(frame) # pose_time = (time.time() - pose_start) * 1000 # ========= 主检测 ========= # detect_start = time.time() # 耗时操作 detections = self.detector(frame) # detect_time = (time.time() - detect_start) * 1000 dets_xyxy = [] dets_roles = [] 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 detections: for det in detections: 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 dets_xyxy.append([x1, y1, x2, y2]) dets_for_tracker.append([x1, y1, x2, y2, conf]) if cls_id == 0: dets_roles.append("car") elif cls_id == 1: dets_roles.append("opentrunk") elif cls_id == 2: dets_roles.append("person") elif cls_id == 3: dets_roles.append("phone") # logger.debug(f'dets_roles: {dets_roles}') dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5)) tracks = self.tracker.update( dets, [self.height, self.width], [self.height, self.width] ) # logger.debug("tracks: {}".format(tracks)) # ========= 绘制骨骼 ========= frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results) # ========= 绘制 ROI ========= cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3) # ========= 单帧统计变量 ========= current_roi_person_count = 0 current_roi_trunk_count = 0 current_roi_phone_count = 0 # 临时存储本帧的目标，用于后续关联分析 current_cars = [] # {'id':, 'box':} current_trunks = [] # (cx, cy) for t in tracks: # logger.debug("t: {}".format(t)) tid = t.track_id # cls_id = -1 # IoU 匹配角色 # if tid not in track_role and dets_xyxy: REVALIDATE_FRAME_INTERVAL = 10 #if tid not in self.track_role: if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role): best_iou = 0 best_role = "unknown" t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] for i, box in enumerate(dets_xyxy): iou_val = self.compute_iou(t_box, box) if iou_val > best_iou: best_iou = iou_val best_role = dets_roles[i] if best_iou > 0.1: self.track_role[tid] = best_role else: self.track_role[tid] = "unknown" role = self.track_role.get(tid, "unknown") cls_id = -1 if role == "car": cls_id = 0 elif role == "opentrunk": cls_id = 1 elif role == "person": cls_id = 2 elif role == "phone": cls_id = 3 # logger.debug("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 self.check_point_in_roi(roi_points_int32,(cx, cy)): if cls_id == 0: # Car color = (0, 255, 0) current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]}) 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 # ========================================== # 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'] c_box = car_info['box'] trunk_found_for_this_car = False for t_pt in current_trunks: if self.is_point_in_box(t_pt, c_box): trunk_found_for_this_car = True break if trunk_found_for_this_car: 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(): 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: 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 Nobody (Too Fast) if duration_frames < self.frame_thresh_car_min_duration: logger.warning(f"ALARM: Car {car_id} passed too fast -> Regarded as Nobody Checked!") self.fast_pass_alerts[car_id] = self.current_frame_idx + int(3.0 * 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(3.0 * 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 # ========================================== # 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 # 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), sub_text, 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), sub_text, 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 Nobody (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"Nobody (ID:{list(self.fast_pass_alerts.keys())})" 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 # # ========= 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(threading.Thread): def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event): super().__init__(daemon=True) self.raw_queue = raw_queue self.ws_queue = ws_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, KadianDetector] = {} 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: self.kadian_detectors[cam_id] = KadianDetector() 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 | ") self.raw_queue.task_done() 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