From 845814e6c1a3167bd303a8a1a35707e9b1915185 Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Fri, 6 Mar 2026 11:11:09 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90ab=E9=97=A8=E8=BF=81=E7=A7=BB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/prison/ab_biz.py | 386 +++++++++--------------------------- common/processor_factory.py | 3 + 2 files changed, 102 insertions(+), 287 deletions(-) diff --git a/biz/prison/ab_biz.py b/biz/prison/ab_biz.py index d43aa28..6af6432 100644 --- a/biz/prison/ab_biz.py +++ b/biz/prison/ab_biz.py @@ -6,6 +6,7 @@ import threading import time import queue import requests +from biz.base_frame_processor import BaseFrameProcessorWorker # -------------------------- Kadian 检测相关导入 -------------------------- from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） @@ -16,10 +17,10 @@ from yolox.tracker.byte_tracker import BYTETracker # ========================= 配置区 ========================= # Kadian 模型路径与ROI（可根据实际情况修改） -detector_model_path = 'YOLO_Weight/prisoner_model.onnx' +detector_model_path = 'YOLO_Weight/bag_model.onnx' # 输入尺寸 -input_size = 1280 +input_size = 640 RTSP_TARGET_FPS = 10.0 @@ -27,12 +28,15 @@ RTSP_TARGET_FPS = 10.0 ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 -class ZoulangDetector: - def __init__(self): +class AbDetector: + def __init__(self, params=None): + # 摄像头额外参数 + self.params = params if params is not None else {} + # 模型加载 - self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, - input_size=input_size) + self.detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, + input_size=input_size) # ByteTracker class TrackerArgs: @@ -41,9 +45,7 @@ class ZoulangDetector: match_thresh = 0.8 mot20 = False - - - self.police_prisoner_track_role = {} + self.track_role = {} self.fps = RTSP_TARGET_FPS @@ -52,53 +54,30 @@ class ZoulangDetector: # ========================================== # 超参数设置 (Hyperparameters) # ========================================== + self.TIME_THRESHOLD_BLACKBAG = 1.0 # 黑包判定时长（秒） + self.TIME_TOLERANCE_BLACKBAG = 0.5 # 黑包丢失缓冲时间 - # 1. 业务判定时间阈值 - # self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长 - - self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 - self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） - - self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 - self.TIME_TOLERANCE_PRISONER = 0.5 # 犯人丢失缓冲时间（防抖动） - - # 无人在场帧数阈值 - # self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) - - # 警察检测帧数阈值 - self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) - self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) - - # 犯人检测帧数阈值 - self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) - self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) + # 转换为帧数阈值 + self.frame_thresh_blackbag = int(self.TIME_THRESHOLD_BLACKBAG * self.fps) + self.frame_buffer_blackbag = int(self.TIME_TOLERANCE_BLACKBAG * self.fps) print(f"\n超参数设置:") print(f" FPS: {self.fps:.2f}") - # print(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") - print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") - print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") - print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") - print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") + print(f" 判定 'BlackBag Detected' 需累计检测: {self.frame_thresh_blackbag} 帧") + print(f" 黑包丢失缓冲帧数: {self.frame_buffer_blackbag} 帧") # ========================================== # 状态变量初始化 # ========================================== - self.current_frame_idx = 0 - # 无人在场检测状态变量 - self.cnt_frame_nobody = 0 + # 黑包检测状态 + self.blackbag_detection_frames = 0 + self.blackbag_missing_frames = 0 + self.blackbag_alert_active = False - # 警察检测状态变量 - self.police_detection_frames = 0 # 连续检测到警察的帧数 - self.police_missing_frames = 0 # 连续未检测到警察的帧数 - self.police_alert_active = False # 警察报警是否激活 - - # 犯人检测状态变量 - self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 - self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 - self.prisoner_alert_active = False # 犯人报警是否激活 + # 人员统计变量 + self.current_person_count = 0 def compute_iou(self,boxA, boxB): @@ -145,184 +124,120 @@ class ZoulangDetector: current_time_sec = timestamp - # ========= 警察和犯人检测 ========= - police_prisoner_results = self.police_prisoner_detector(frame) + # ========= 检测推理（黑包+人）========= + detect_results = self.detector(frame) - police_prisoner_dets_xyxy = [] - police_prisoner_dets_roles = [] - police_prisoner_dets_for_tracker = [] + # 初始化检测结果存储 + dets_xyxy = [] + dets_roles = [] + dets_for_tracker = [] + current_frame_alerts = [] - # ========= 当前帧所有警告列表（关键改动）========== - current_frame_alerts = [] # 每帧清空，重新收集 - - if police_prisoner_results: - for det in police_prisoner_results: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) - police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) + # 解析检测结果（黑包cls_id=0，人员cls_id=1） + if detect_results: + for det in detect_results: + x1, y1, x2, y2, conf, cls_id = det + dets_xyxy.append([x1, y1, x2, y2]) + dets_for_tracker.append([x1, y1, x2, y2, conf]) if cls_id == 0: - police_prisoner_dets_roles.append("police") + dets_roles.append("black_bag") elif cls_id == 1: - police_prisoner_dets_roles.append("prisoner") + dets_roles.append("person") - ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( - police_prisoner_dets_for_tracker) else np.empty((0, 5)) - - police_prisoner_dets_tracks = self.tracker.update( - ppolice_prisoner_dets, + # 跟踪器更新 + 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] ) - # ========= 单帧统计变量 ========= - current_police_count = 0 - current_prisoner_count = 0 - # ========= 警察和犯人检测 ========= - for t in police_prisoner_dets_tracks: - # print("t: {}".format(t)) + # ========= 单帧统计初始化 ========= + self.current_person_count = 0 + current_blackbag_count = 0 + + # ========= 跟踪结果绘制与统计 ========= + for t in tracks: tid = t.track_id - # cls_id = -1 - # IoU 匹配角色 + # IoU匹配跟踪ID和类别 + REVALIDATE_FRAME_INTERVAL = 10 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( - tid not in self.police_prisoner_track_role): + + #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(police_prisoner_dets_xyxy): + t_box = list(map(float, t.tlbr)) + 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 = police_prisoner_dets_roles[i] - if best_iou > 0.1: - self.police_prisoner_track_role[tid] = best_role - else: - self.police_prisoner_track_role[tid] = "unknown" - - role = self.police_prisoner_track_role.get(tid, "unknown") - cls_id = -1 - if role == "police": - cls_id = 0 - elif role == "prisoner": - cls_id = 1 - # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) + best_role = dets_roles[i] + self.track_role[tid] = best_role if best_iou > 0.1 else "unknown" + role = self.track_role.get(tid, "unknown") x1, y1, x2, y2 = map(int, t.tlbr) + color = (255, 255, 255) + label = "Unknown" - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # Person - current_police_count += 1 - color = (255, 0, 255) - label = "police" - - elif cls_id == 1: # Phone（主模型已支持） - current_prisoner_count += 1 - color = (0, 0, 139) - label = "prisoner" - else: - color = (255, 255, 255) - label = "Unknown" - - # label = f"ID:{tid} IN" + # 人员检测（cls_id=1） + if role == "person": + self.current_person_count += 1 + color = (255, 0, 255) # 紫色框 + label = "Person" + # 黑包检测（cls_id=0） + elif role == "black_bag": + current_blackbag_count += 1 + color = (0, 128, 0) # 绿色框 + label = "Black Bag" + # 绘制检测框和标签 cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) # ========================================== - # 犯人检测 + # 黑包状态更新 # ========================================== - if current_prisoner_count > 0: - # 检测到犯人框 - self.prisoner_detection_frames += 1 - self.prisoner_missing_frames = 0 # 重置丢失计数器 - - # 当检测累计达到阈值时，激活报警 - if self.prisoner_detection_frames >= self.frame_thresh_prisoner: - self.prisoner_alert_active = True + if current_blackbag_count > 0: + self.blackbag_detection_frames += 1 + self.blackbag_missing_frames = 0 + if self.blackbag_detection_frames >= self.frame_thresh_blackbag: + self.blackbag_alert_active = True else: - # 未检测到犯人框 - self.prisoner_missing_frames += 1 + self.blackbag_missing_frames += 1 + if self.blackbag_missing_frames >= self.frame_buffer_blackbag: + self.blackbag_detection_frames = 0 + self.blackbag_alert_active = False - # 如果之前检测到手机，重置检测计数器 - if self.prisoner_detection_frames > 0: - # 只有在连续丢失超过缓冲帧数时才重置 - if self.prisoner_missing_frames >= self.frame_buffer_prisoner: - self.prisoner_detection_frames = 0 - self.prisoner_alert_active = False - else: - # 从未检测到犯人，保持状态 - pass # ========================================== - # 警察检测 + # 警告信息收集 # ========================================== - if current_police_count > 0: - # 检测到犯人框 - self.police_detection_frames += 1 - self.police_missing_frames = 0 # 重置丢失计数器 - - # 当检测累计达到阈值时，激活报警 - if self.police_detection_frames >= self.frame_thresh_police: - self.police_alert_active = True - else: - # 未检测到犯人框 - self.police_missing_frames += 1 - - # 如果之前检测到手机，重置检测计数器 - if self.police_detection_frames > 0: - # 只有在连续丢失超过缓冲帧数时才重置 - if self.police_missing_frames >= self.frame_buffer_police: - self.police_detection_frames = 0 - self.police_alert_active = False - else: - # 从未检测到犯人，保持状态 - pass - - - - alert_offset = 0 - - # A. 有犯人 - if self.prisoner_alert_active: - duration_seconds = self.prisoner_detection_frames / self.fps + if self.blackbag_alert_active: + duration_seconds = self.blackbag_detection_frames / self.fps current_frame_alerts.append( { 'time': current_time_sec, - 'action': 'prisoner', - 'confidence': 1.0, # 固定为1.0（规则判定） + 'action': 'Black Bag', 'details': f"Detected for {duration_seconds:.1f}s" } ) - self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 + self.draw_alert(frame, "Black Bag Alert", (0, 0, 255), sub_text=f"Detected for {duration_seconds:.1f}s") # ========================================== - # 11. 统一显示当前帧所有警告（可替换原分层显示） + # 绘制信息 # ========================================== - debug_info = f" prisoner: {current_prisoner_count}" + # 实时统计 + debug_info = f"Person: {self.current_person_count} | BlackBag: {current_blackbag_count}" cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - # 统一警告显示区 + # 警告信息 alert_y_start = 150 for i, alert in enumerate(current_frame_alerts): action = alert['action'] details = alert.get('details', '') - color = (0, 0, 255) # 默认红色警告 - - if action == 'prisoner': - color = (255, 255, 255) - - main_text = action - if details: - main_text += f" ({details})" - + color = (0, 0, 255) # 红色警告 + main_text = f"{action} ({details})" y_pos = alert_y_start + i * 50 cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1) cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) @@ -334,114 +249,11 @@ class ZoulangDetector: } - # ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, - raw_frame_queue: "queue.Queue[Dict[str, Any]]", - ws_send_queue: "queue.Queue[Dict[str, Any]]", - stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_frame_queue - self.ws_queue = ws_send_queue - self.stop_event = stop_event +class FrameProcessorWorker(BaseFrameProcessorWorker): + """轨迹检测帧处理线程""" - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.kadian_detectors: Dict[int, ZoulangDetector] = {} - - # 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}} - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - - - def _encode_image_to_base64(self, image) -> str: - ok, buf = cv2.imencode(".jpg", image) - if not ok: - raise RuntimeError("Failed to encode image to JPEG") - return base64.b64encode(buf.tobytes()).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.kadian_detectors: - self.kadian_detectors[cam_id] = ZoulangDetector() - detector = self.kadian_detectors[cam_id] - - # 执行检测 - result = detector.process_frame(frame.copy(), cam_id, ts) - - result_img = result["image"] - result_type = result["alerts"] - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - try: - img_b64 = self._encode_image_to_base64(result_img) - except Exception as e: - print(f"[ERROR] Encode image failed: {e}") - img_b64 = None - - if img_b64 is not None: - # 将abnormal_actions对象数组转换为字符串数组 - #action_names = [action_info['action'] for action_info in push_actions] - - msg = { - "msg_type": "frame", - "camera_id": item["camera_index"], - "timestamp": ts, - #"result_type": action_names, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - if push_actions and len(push_actions) > 0: - # 发送POST请求 - post_msg = msg.copy() - post_msg['type'] = 2 - try: - response = requests.post(ALERT_PUSH_URL, json=post_msg, timeout=5.0) - if response.status_code == 200: - print(f"[INFO] POST alert sent successfully for actions: {push_actions}") - else: - print(f"[WARN] POST alert failed with status: {response.status_code}") - except Exception as e: - print(f"[ERROR] POST alert request failed: {e}") - except queue.Full: - print("[WARN] ws_send_queue full, drop frame message") - - self.raw_queue.task_done() \ No newline at end of file + # 子类配置 + DETECTOR_FACTORY = lambda params: AbDetector(params) + POST_TYPE = 2 + TARGET_FPS = RTSP_TARGET_FPS diff --git a/common/processor_factory.py b/common/processor_factory.py index c404bd8..fd70ce4 100644 --- a/common/processor_factory.py +++ b/common/processor_factory.py @@ -1,12 +1,15 @@ from biz.checkpoint.checkpoint_biz import FrameProcessorWorker as CheckpointWorker from biz.prison.trajectory02_biz import FrameProcessorWorker as TrajectoryWorker from biz.prison.supervision_room_biz import FrameProcessorWorker as SupervisionWorker +from biz.prison.ab_biz import FrameProcessorWorker as AbWorker + # ... 其他导入 PROCESSOR_MAP = { "checkpoint": CheckpointWorker, "trajectory": TrajectoryWorker, "supervision_room": SupervisionWorker, + "ab": AbWorker } def get_processor(processor_type: str):