From 3cbc37feb634d731d8afe9f5a885cfa7c812d8cf Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Wed, 4 Mar 2026 19:13:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=90=8C=E6=AD=A5=E7=AE=97=E6=B3=95=E4=BF=AE?= =?UTF-8?q?=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/checkpoint/checkpoint_biz.py | 700 +++++++++---------------------- 1 file changed, 192 insertions(+), 508 deletions(-) diff --git a/biz/checkpoint/checkpoint_biz.py b/biz/checkpoint/checkpoint_biz.py index c77818b..f61ccd2 100644 --- a/biz/checkpoint/checkpoint_biz.py +++ b/biz/checkpoint/checkpoint_biz.py @@ -17,8 +17,8 @@ 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' +DETECT_MODEL_PATH = 'YOLO_Weight/Kadian.onnx' +#POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx' # 默认相对ROI（与原文件一致） #ROI_RELATIVE = np.array([ @@ -29,10 +29,12 @@ POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx' #]) ROI_RELATIVE=np.array([ - [0.15,0.001], - [0.5,0.001], - [1.0,0.8], - [0.35,1.0] + [0.12,0.0], + [0.3,0.0], + [0.5,0.2], + [1.0, 0.95], + [1.0,1.0], + [0.42,1.0] ]) @@ -40,7 +42,7 @@ ALERT_PUSH_INTERVAL = 5.0 # 输入尺寸 PERSON_CAR_INPUT_SIZE = 640 -POSE_INPUT_SIZE = 640 +#POSE_INPUT_SIZE = 640 RTSP_TARGET_FPS = 10.0 @@ -52,122 +54,77 @@ class KadianDetector: self.params = params if params is not None else {} # 模型加载 - self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.15, iou_threshold=0.65, - input_size=PERSON_CAR_INPUT_SIZE) - self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.45, iou_threshold=0.6, - input_size=POSE_INPUT_SIZE) + self.detector = YOLOv8_ONNX( + DETECT_MODEL_PATH, + conf_threshold=0.25, + iou_threshold=0.45, + input_size=PERSON_CAR_INPUT_SIZE + ) - # Tracker + # 跟踪器配置 class TrackerArgs: - track_thresh = 0.2 - track_buffer = 60 - match_thresh = 0.9 + track_thresh = 0.3 # 必须大于等于yolo的conf_threshold + track_buffer = 40 + match_thresh = 0.85 mot20 = True - self.tracker = BYTETracker(TrackerArgs(), frame_rate=RTSP_TARGET_FPS) - - self.track_role = {} - self.fps = RTSP_TARGET_FPS + self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) + self.track_role = {} # 跟踪ID到类别的映射 + # ROI 处理：优先从 params 获取，否则使用默认值 ROI_RELATIVE roi_points = self.params.get('roi_points', ROI_RELATIVE) 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 = 10.0 # 单人单检判定时长 - self.TIME_THRESHOLD_NOBODY = 10.0 # 无人检查判定时长 - + # ===================== 超参数设置 (仅保留车/后备箱相关) ===================== # 后备箱检查判定阈值 self.TIME_THRESHOLD_TRUNK_OPEN = 0.1 - - # 新增：手机检测判定阈值 - self.TIME_THRESHOLD_PHONE = 3.0 # 手机检测持续1秒（30帧 @30fps） - self.TIME_TOLERANCE_PHONE = 1.5 # 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 = 2.0 # 制服不合规判定时长 - self.TIME_TOLERANCE_UNIFORM = 1.0 # 制服合规恢复缓冲时间 - - # 2. Person 丢帧缓冲 - self.TIME_TOLERANCE_PERSON = 3.0 - # 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 = 10.0 + self.TIME_THRESHOLD_CAR_MIN_DURATION = 3.0 + # Car 丢帧/ID维持缓冲 + self.TIME_TOLERANCE_CAR = 2.0 - # 3. Car 丢帧/ID维持缓冲 - self.TIME_TOLERANCE_CAR = 10.0 - - # 4 OnlyOne 丢帧缓冲 - self.TIME_TOLERANCE_ONLY_ONE_DURATION = 3.0 + # police丢失阈值 + self.TIME_TOLERANCE_POLICE = 3.0 + # police状态判定阈值 (累计秒数) + self.TIME_THRESHOLD_NOBODY = 5.0 + self.TIME_THRESHOLD_ONLY_ONE = 5.0 # --- 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) - self.frame_buffer_limit_onlyOne = int(self.TIME_TOLERANCE_ONLY_ONE_DURATION * self.fps) + self.frame_buffer_limit_police = int(self.TIME_TOLERANCE_POLICE * self.fps) + self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) + self.frame_thresh_only_one = int(self.TIME_THRESHOLD_ONLY_ONE * 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' 最小停留: {self.frame_thresh_car_min_duration} 帧") - - self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - # self.onlyone_buffer_limit = self.frame_buffer_limit_person # 10帧（1秒） - self.onlyone_thresh = self.frame_thresh_one # 30帧（3秒） - - self.nobody_counter = 0 - self.nobody_present_counter = 0 - self.nobody_buffer_limit = self.frame_buffer_limit_onlyOne - self.nobody_thresh = self.frame_thresh_nobody # 20帧（2秒） + # 显示相关阈值 + self.ignore_show_seconds = 0.2 # 未检测的警告显示时长 + self.openTrunk_show_seconds = 0.2 # 打开后备箱的警告显示时长 + self.police_show_seconds = 0.2 # 警察在场警告显示时长 + # 状态变量初始化 self.current_frame_idx = 0 + self.width = 0 + self.height = 0 - self.ignore_show_seconds = 0.5 # 未检测的警告显示时长 - self.openTrunk_show_seconds = 0.5 # 打开后备箱的警告显示时长 - - # 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 + self.unchecked_trunk_alerts = {} # 后备箱未检 + self.fast_pass_alerts = {} # 通过过快 - # 违规车辆记录 (后备箱未检) - self.unchecked_trunk_alerts = {} + # 警察注册表 (字典) + self.roi_police_registry = {} + # 警察在场告警记录 + self.nobody_alerts = {} # 无人在场 + self.only_one_alerts = {} # 单人在场 + # 累计帧数计数器 + self.nobody_frames = 0 # 累计无人在场帧数 + self.only_one_frames = 0 # 累计单人在场帧数 - # 违规车辆记录 (通过过快 -> 归类为 Ignore) - self.fast_pass_alerts = {} def _get_roi_points(self, frame_width: int, frame_height: int): """ @@ -188,9 +145,11 @@ class KadianDetector: return roi_abs.astype(np.int32) def check_point_in_roi(self, roi_points, point): + """判断点是否在ROI内""" return cv2.pointPolygonTest(roi_points, point, False) >= 0 def compute_iou(self, boxA, boxB): + """计算两个框的IOU""" # box = [x1, y1, x2, y2] xA = max(boxA[0], boxB[0]) yA = max(boxA[1], boxB[1]) @@ -227,112 +186,51 @@ class KadianDetector: cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) def is_point_in_box(self, point, box): + """判断点是否在框内""" px, py = point x1, y1, x2, y2 = box return x1 < px < x2 and y1 < py < y2 - def is_pose_inside_detector_person(self, pose_bbox, dets_xyxy, dets_roles): - """ - 判断一个 pose 人是否位于 detector 的 person 框内部（中心点匹配） - - 参数： - pose_bbox: [x1, y1, x2, y2] - dets_xyxy: detector 输出的所有 bbox 列表 - dets_roles: 对应的类别列表（如 "person", "car"...） - - 返回： - True -> 在某个人体框内部 - False -> 不在任何人体框内部 - """ - - px1, py1, px2, py2 = pose_bbox - cx, cy = (px1 + px2) // 2, (py1 + py2) // 2 - - for box, role in zip(dets_xyxy, dets_roles): - if role != "person": - continue - - dx1, dy1, dx2, dy2 = map(int, box) - - # 判断中心点是否在 detector person 框内 - if dx1 <= cx <= dx2 and dy1 <= cy <= dy2: - return True - - return False - - def count_pose_inside_detector_person(self, pose_results, dets_xyxy, dets_roles): - """ - 统计有多少个pose框在detector person框内部 - - 参数： - pose_results: pose检测结果列表，每个元素为字典，包含'bbox'键，值为[x1, y1, x2, y2] - dets_xyxy: detector输出的所有bbox列表 - dets_roles: 对应的类别列表（如 "person", "car"...） - - 返回： - int: 在detector person框内部的pose框数量 - """ - count = 0 - for pose in pose_results: - pose_bbox = pose['bbox'] # [x1, y1, x2, y2] - if self.is_pose_inside_detector_person(pose_bbox, dets_xyxy, dets_roles): - count += 1 - return count - 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 + # ========= 主检测（删除pose检测）========= 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）========== + # ========= 当前帧所有警告列表 ========== 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 + x1, y1, x2, y2, conf, cls_id = det # x1,y1:左上角，x2,y2:右下角 dets_xyxy.append([x1, y1, x2, y2]) dets_for_tracker.append([x1, y1, x2, y2, conf]) + + # 更新类别映射：0=Car,1=OpenTrunk,2=Passerby,3=Police if cls_id == 0: dets_roles.append("car") - elif cls_id == 1: dets_roles.append("opentrunk") - elif cls_id == 2: - dets_roles.append("person") - + dets_roles.append("passerby") # 路人 elif cls_id == 3: - dets_roles.append("phone") - # logger.debug(f'dets_roles: {dets_roles}') + dets_roles.append("police") # 警察 dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5)) + # 跟踪器更新 tracks = self.tracker.update( dets, [self.height, self.width], @@ -340,33 +238,26 @@ class KadianDetector: ) # 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 + current_roi_trunk_count = 0 # 仅保留后备箱统计 + current_roi_police_count = 0 # ROI内警察数量 # 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) + # ========= 处理跟踪结果 ========= 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: + + # 定期重新匹配跟踪ID的类别 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): @@ -380,30 +271,17 @@ class KadianDetector: 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) - + # 定义不同类别的颜色（仅标框，不告警） + if role == "car": + color = (0, 255, 0) # 绿色 + label = f"Car:{tid}" + # 仅处理ROI内的车辆 + if self.check_point_in_roi(roi_points_int32, (cx, cy)): current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]}) - + # 车辆注册表初始化 if tid not in self.roi_car_registry: self.roi_car_registry[tid] = { 'first_seen': self.current_frame_idx, @@ -413,176 +291,83 @@ class KadianDetector: } else: self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx - - label = f"Car:{tid} IN" - - elif cls_id == 1: # Opentrunk + label += " IN" + elif role == "opentrunk": + color = (255, 165, 0) # 橙色 + label = "OpenTrunk" + if self.check_point_in_roi(roi_points_int32, (cx, cy)): 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)" + label += " IN" + elif role == "passerby": + color = (255, 255, 0) # 黄色（仅标框，不告警） + label = "Passerby" + elif role == "police": + color = (0, 255, 255) # 青色 + label = "Police" + if self.check_point_in_roi(roi_points_int32, (cx, cy)): + current_roi_police_count += 1 + # 警察注册表初始化 + if tid not in self.roi_police_registry: + self.roi_police_registry[tid] = { + 'first_seen': self.current_frame_idx, + 'last_seen': self.current_frame_idx, + } + else: + self.roi_police_registry[tid]['last_seen'] = self.current_frame_idx + label += " IN" else: - color = (0, 0, 255) - label = "OUT" + color = (255, 255, 255) # 白色 + label = "Unknown" + # 绘制检测框和标签（所有类别都标框，仅车/后备箱有逻辑） 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 - - # 统计pose框在detector person框内部的数量 - pose_inside_count = self.count_pose_inside_detector_person(pose_results, dets_xyxy, dets_roles) - - # ========================================== - # 5. 关联分析: 哪个后备箱属于哪辆车? + # 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'] # 车的id - c_box = car_info['box'] # 车的框 + c_id = car_info['id'] + c_box = car_info['box'] + trunk_found_for_this_car = False - trunk_found_for_this_car = False # 开后备箱标记 for t_pt in current_trunks: - if self.is_point_in_box(t_pt, c_box): # 如果开后备箱的框在车的框内,就设置开后备箱标记为true + if self.is_point_in_box(t_pt, c_box): trunk_found_for_this_car = True break - if trunk_found_for_this_car: # 如果当前车辆的开后备箱标记为true了,就设置开了后备箱的帧数+1,凑够了判断“开后备箱”这个动作的帧数之后,就设置该车"已检查" + 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 + # 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(): - # 遍历所有车辆，如果当前帧时间-该车辆最后可见的时间得到的值大于车辆消失时间阈值的话,就把该车添加到移除列表中,否则添加到活跃列表中 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 + # 处理离场车辆，生成违规告警 for car_id in cars_to_remove: - # 遍历所有移除列表中的车辆, - # 如果该车辆最后出现时间-最早出现时间的值小于车辆最小存在时间,则判断为ignore, - # 如果该车辆的“已检查”标记为true,则 - # 最后在所有车辆列表中删除该车辆 - 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 Ignore (Too Fast) + # 情况1：通过时间太短 -> Ignore (Too Fast) if duration_frames < self.frame_thresh_car_min_duration: - logger.warning(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!") + print(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!") self.fast_pass_alerts[car_id] = self.current_frame_idx + int(self.ignore_show_seconds * 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!") + print(f"ALARM: Car {car_id} left without checking trunk!") self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int( self.openTrunk_show_seconds * self.fps) @@ -591,177 +376,45 @@ class KadianDetector: effective_car_count = len(active_car_ids) # ========================================== - # 9. 业务逻辑判定 (Only One / Nobody) + # 维护警察注册表 # ========================================== - # 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 + active_police_ids = [] + polices_to_remove = [] + + for police_id, info in self.roi_police_registry.items(): + last_seen = info['last_seen'] + if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_police: + active_police_ids.append(police_id) + else: + polices_to_remove.append(police_id) + + for police_id in polices_to_remove: + del self.roi_police_registry[police_id] + + effective_police_count = len(active_police_ids) # ========================================== - # 9. 业务逻辑判定 (Only One / Nobody) - 重构版 + # 显示调试信息和报警 (仅保留车/后备箱相关) # ========================================== - if effective_car_count >= 0: # 只要没人就检测,不用等到来了车再检测 - # ----- 定义条件 ----- - onlyone_condition = (pose_inside_count == 1) - nobody_condition = (current_roi_person_count == 0 and self.pose_person_count == 0) - - # ----- Onlyone 计数器更新 ----- - if onlyone_condition: # 如果骨骼点和检测框都检测到了只有一个人时,onlyone+1,当onlyone累计够了之后触发报警 - self.onlyone_counter += 1 - # self.onlyone_lost_counter = 0 - elif current_roi_person_count > 1 or self.pose_person_count > 1: - self.onlyone_counter = 0 - # if self.onlyone_counter > 0: - # self.onlyone_lost_counter += 1 - # if self.onlyone_lost_counter > self.onlyone_buffer_limit: - # self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - - # ----- Nobody 计数器更新 ----- - if nobody_condition: - self.nobody_counter += 1 - # self.nobody_present_counter = 0 - elif current_roi_person_count > 0 or self.pose_person_count > 0: - self.nobody_counter = 0 - # if self.nobody_counter > 0: - # self.nobody_present_counter += 1 - # if self.nobody_present_counter > self.nobody_buffer_limit: - # self.nobody_counter = 0 - # self.nobody_present_counter = 0 - - else: - # 无活跃车辆，清零所有计数器 - self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - self.nobody_counter = 0 - self.nobody_present_counter = 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}" + # 调试信息 + debug_info = f"Cars: {len(active_car_ids)} | Trunk: {current_roi_trunk_count} | Police: {effective_police_count} | Nobody:{self.nobody_frames}/{self.frame_thresh_nobody} | OnlyOne:{self.only_one_frames}/{self.frame_thresh_only_one}" 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 + # 报警偏移量（防止重叠） 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( - # { + # A. 显示 Trunk Checked (车辆已检查后备箱) + # 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': "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 + # '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 - # A. 显示 Only One（当累积帧数达到阈值时） - if self.onlyone_counter >= self.onlyone_thresh: - current_frame_alerts.append({'time': current_time_sec, 'action': "Only One"}) - self.draw_alert(frame, "Only One", (0, 255, 255), None, offset_y=alert_offset) - alert_offset += 100 - - # B. 显示 Nobody（当累积帧数达到阈值时） - elif self.nobody_counter >= self.nobody_thresh: - current_frame_alerts.append({'time': current_time_sec, 'action': "Nobody"}) - self.draw_alert(frame, "Nobody", (0, 0, 255), None, 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), None, 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), None, offset_y=alert_offset) - # alert_offset += 100 - - # 第二层：离场违规 (Post-Event Alerts) - # ------------------------------------------------ - - # F. 显示 Unchecked Trunk + # B. 显示 Unchecked Trunk (离场未检查后备箱) 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: @@ -769,16 +422,14 @@ class KadianDetector: 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", - } - ) + 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 Ignore (离场结果) + # C. 显示 Ignore (通过过快) 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: @@ -786,23 +437,56 @@ class KadianDetector: if len(self.fast_pass_alerts) > 0: alert_text = f"Ignore: (ID:{list(self.fast_pass_alerts.keys())})" - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Ignore", - } - ) + current_frame_alerts.append({ + 'time': current_time_sec, + 'action': "Ignore", + }) 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 + # D. 显示警察在场状态 (Nobody/Only One) + # 清理过期的 Nobody 告警 + expired_nobody = [k for k, v in self.nobody_alerts.items() if self.current_frame_idx > v] + for k in expired_nobody: + del self.nobody_alerts[k] + + # 清理过期的 Only One 告警 + expired_only_one = [k for k, v in self.only_one_alerts.items() if self.current_frame_idx > v] + for k in expired_only_one: + del self.only_one_alerts[k] + + # 更新累计帧数 + if effective_police_count == 0: + self.nobody_frames += 1 + self.only_one_frames = 0 + elif effective_police_count == 1: + self.only_one_frames += 1 + self.nobody_frames = 0 + else: + self.nobody_frames = 0 + self.only_one_frames = 0 + + if effective_police_count == 0 and self.nobody_frames >= self.frame_thresh_nobody: + alert_text = "Nobody" + if "Nobody" not in self.nobody_alerts: + self.nobody_alerts["Nobody"] = self.current_frame_idx + int(self.police_show_seconds * self.fps) + 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 + elif effective_police_count == 1 and self.only_one_frames >= self.frame_thresh_only_one: + alert_text = "Only One" + if "Only One" not in self.only_one_alerts: + self.only_one_alerts["Only One"] = self.current_frame_idx + int(self.police_show_seconds * self.fps) + current_frame_alerts.append({ + 'time': current_time_sec, + 'action': "Only One", + }) + self.draw_alert(frame, alert_text, (255, 165, 0), offset_y=alert_offset) + alert_offset += 100 - # 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,