From cf456a397d0cb46e9f921b1f20f9d15f63f04000 Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Mon, 2 Feb 2026 12:49:02 +0800 Subject: [PATCH] =?UTF-8?q?=E5=B0=9D=E8=AF=95=E4=BB=8E=E5=8D=A1=E7=82=B9rt?= =?UTF-8?q?sp=E4=B8=AD=E7=A7=BB=E9=99=A4=E7=AE=97=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- algorithm/checkpoint/__init__.py | 0 biz/checkpoint/__init__.py | 0 biz/checkpoint/checkpoint_biz.py | 667 ++++++++++++++++++++++++++++ rtsp_service_ws_kadian.py | 722 +------------------------------ 4 files changed, 674 insertions(+), 715 deletions(-) create mode 100644 algorithm/checkpoint/__init__.py create mode 100644 biz/checkpoint/__init__.py create mode 100644 biz/checkpoint/checkpoint_biz.py diff --git a/algorithm/checkpoint/__init__.py b/algorithm/checkpoint/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/biz/checkpoint/__init__.py b/biz/checkpoint/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/biz/checkpoint/checkpoint_biz.py b/biz/checkpoint/checkpoint_biz.py new file mode 100644 index 0000000..18998e0 --- /dev/null +++ b/biz/checkpoint/checkpoint_biz.py @@ -0,0 +1,667 @@ + +import cv2 +import numpy as np +from typing import Dict, Any + +# -------------------------- 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 + + +# ========================= 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) + + print(f"\n超参数设置:") + print(f" FPS: {self.fps:.2f}") + print(f" 判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧") + print(f" 判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 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") + print(f" 判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧") + print(f" 制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧") + print(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 + # ========= 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 + + pose_results = self.pose_detector(frame) + + # ========= 主检测 ========= + detections = self.detector(frame) + + 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") + # print(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] + ) + # print("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: + # print("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 + # 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 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: + print(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']: + print(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 + + return { + "image": frame, + + "alerts":current_frame_alerts + } \ No newline at end of file diff --git a/rtsp_service_ws_kadian.py b/rtsp_service_ws_kadian.py index 24a4ad8..cf83e90 100644 --- a/rtsp_service_ws_kadian.py +++ b/rtsp_service_ws_kadian.py @@ -3,7 +3,7 @@ # 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 @@ -15,50 +15,15 @@ import asyncio import websockets from dataclasses import dataclass from typing import Dict, Any + +from biz.checkpoint.checkpoint_biz import KadianDetector, RTSP_TARGET_FPS, ALERT_PUSH_INTERVAL from test_cam import get_camera_preview_url -# -------------------------- 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 - - -# ========================= 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 服务配置 -RTSP_TARGET_FPS = 10.0 WS_HOST = "0.0.0.0" WS_PORT = 8765 -WS_PORT_2 = 8764 # 新增：第二个WebSocket端口 # WebSocket 客户端集合 ws_clients = set() -ws_clients_2 = set() # 新增：第二个WebSocket客户端集合 # ========================= 数据结构 ========================= @@ -70,633 +35,6 @@ class CameraConfig: rtsp_url: str -# ========================= 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) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧") - print(f" 判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 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") - print(f" 判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧") - print(f" 制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧") - print(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 - # ========= 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 - - pose_results = self.pose_detector(frame) - - # ========= 主检测 ========= - detections = self.detector(frame) - - 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") - # print(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] - ) - # print("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: - # print("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 - # 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 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: - print(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']: - print(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 - - return { - "image": frame, - - "alerts":current_frame_alerts - } - # ========================= WebSocket 服务线程 ========================= class WebSocketSender(threading.Thread): def __init__(self, send_queue: queue.Queue, stop_event: threading.Event): @@ -738,46 +76,6 @@ class WebSocketSender(threading.Thread): asyncio.run(self._run_async()) -# ========================= WebSocket 服务线程2 ========================= -class WebSocketSender2(threading.Thread): - def __init__(self, send_queue: queue.Queue, stop_event: threading.Event): - super().__init__(daemon=True) - self.send_queue = send_queue - self.stop_event = stop_event - - async def _ws_handler(self, websocket): - ws_clients_2.add(websocket) - try: - async for _ in websocket: - pass - finally: - ws_clients_2.discard(websocket) - - async def _broadcaster(self): - while not self.stop_event.is_set(): - try: - msg = await asyncio.to_thread(self.send_queue.get, timeout=0.5) - except queue.Empty: - continue - data = json.dumps(msg) - dead = [] - for ws in list(ws_clients_2): - try: - await ws.send(data) - except: - dead.append(ws) - for ws in dead: - ws_clients_2.discard(ws) - self.send_queue.task_done() - - async def _run_async(self): - async with websockets.serve(self._ws_handler, WS_HOST, WS_PORT_2): - print(f"[INFO] WebSocket server 2 started at ws://{WS_HOST}:{WS_PORT_2}") - await self._broadcaster() - - def run(self): - asyncio.run(self._run_async()) - # ========================= RTSP 抓流线程 ========================= class RTSPCaptureWorker(threading.Thread): @@ -924,11 +222,10 @@ class RTSPCaptureWorker(threading.Thread): # ========================= 帧处理线程 ========================= class FrameProcessorWorker(threading.Thread): - def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, ws_queue_2: queue.Queue, stop_event: threading.Event): + 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.ws_queue_2 = ws_queue_2 # 新增：第二个WebSocket队列 self.stop_event = stop_event self.last_ts: Dict[int, float] = {} @@ -1011,8 +308,8 @@ class FrameProcessorWorker(threading.Thread): } 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) + # 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") @@ -1030,16 +327,13 @@ class RTSPService: self.stop_event = threading.Event() self.raw_queue = queue.Queue(maxsize=500) self.ws_queue = queue.Queue(maxsize=1000) - self.ws_queue_2 = queue.Queue(maxsize=1000) # 新增：第二个WebSocket队列 self.capture_workers = [] - self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.ws_queue_2, self.stop_event) + self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.stop_event) self.ws_sender = WebSocketSender(self.ws_queue, self.stop_event) - self.ws_sender_2 = WebSocketSender2(self.ws_queue_2, self.stop_event) # 新增：第二个WebSocket发送器 def start(self): self.ws_sender.start() - self.ws_sender_2.start() # 新增：启动第二个WebSocket服务 self.processor.start() for cam in self.cameras: w = RTSPCaptureWorker(cam, self.raw_queue, self.stop_event) @@ -1051,12 +345,10 @@ class RTSPService: self.stop_event.set() self.raw_queue.join() self.ws_queue.join() - self.ws_queue_2.join() # 新增：等待第二个WebSocket队列 for w in self.capture_workers: w.join(timeout=2.0) self.processor.join(timeout=2.0) self.ws_sender.join(timeout=2.0) - self.ws_sender_2.join(timeout=2.0) # 新增：等待第二个WebSocket发送器 print("[INFO] Service stopped")