From 6c8a51c10df7e25bec2df0975e6281fedb1c5749 Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Fri, 27 Feb 2026 14:59:18 +0800 Subject: [PATCH] =?UTF-8?q?=E5=BC=95=E5=85=A5=E7=9B=91=E6=8E=A7=E5=AE=A4bi?= =?UTF-8?q?z?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/prison/supervision_room_biz.py | 606 +++++++++++++++++++++++++++++ 1 file changed, 606 insertions(+) create mode 100644 biz/prison/supervision_room_biz.py diff --git a/biz/prison/supervision_room_biz.py b/biz/prison/supervision_room_biz.py new file mode 100644 index 0000000..b6b8973 --- /dev/null +++ b/biz/prison/supervision_room_biz.py @@ -0,0 +1,606 @@ +# rtsp_service_kadian.py +# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py +# 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 +import queue +import yaml +import json +import base64 + +from typing import Dict, Any, Tuple, List + + +# -------------------------- 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 yolox.tracker.byte_tracker import BYTETracker +from utils.logger import get_logger +logger = get_logger(__name__) + +# ========================= 配置区 ========================= +Person_Phone_Model = r'D:\Python_Save\PoliceProject\Yolo_Weight\person_phone_model.onnx' # 人和手机的检测模型 +Smoke_Model = r'D:\Python_Save\PoliceProject\Yolo_Weight\smoke_model.onnx' # 抽烟检测模型 + +person_phone_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 +smoke_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 + +# RTSP 服务配置 +RTSP_TARGET_FPS = 5.0 + + +# 新增：告警推送频率限制（秒） +ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 + + +class ZhihuishiDetector: + def __init__(self): + # 模型加载 + + # 人和手机检测模型 + print(f"加载人和手机检测模型: {Person_Phone_Model}") + self.person_phone_detector = YOLOv8_ONNX(Person_Phone_Model, conf_threshold=0.6, iou_threshold=0.45, + input_size=person_phone_input_size) + + # 抽烟检测模型 + print(f"加载抽烟检测模型: {Smoke_Model}") + self.smoke_detector = YOLOv8_ONNX(Smoke_Model, conf_threshold=0.4, iou_threshold=0.65, + input_size=smoke_input_size) + + # ByteTracker + class TrackerArgs: + track_thresh = 0.25 + track_buffer = 30 + match_thresh = 0.8 + mot20 = False + + self.fps = RTSP_TARGET_FPS + + self.person_phone_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) + self.smoke_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) + + self.person_phone_track_role = {} + self.smoke_track_role = {} + + + # ========================================== + # 超参数设置 (Hyperparameters) + # ========================================== + + # 1. 业务判定时间阈值 + self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长 + self.TIME_TOLERANCE_NOBODY = 2.0 # 人丢失缓冲时间 + + self.TIME_THRESHOLD_SMOKE = 1.0 # 抽烟判定时长 + self.TIME_TOLERANCE_SMOKE = 0.5 # 烟丢失缓冲时间（防抖动） + + self.TIME_THRESHOLD_PHONE = 1.0 # 玩手机判定时长 + self.TIME_TOLERANCE_PHONE = 0.5 # 手机丢失缓冲时间（防抖动） + + # 无人在场帧数阈值 + self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) + self.frame_buffer_nobody = int(self.TIME_TOLERANCE_NOBODY * self.fps) + + # 抽烟检测帧数阈值 + self.frame_thresh_smoke = int(self.TIME_THRESHOLD_SMOKE * self.fps) + self.frame_buffer_smoke = int(self.TIME_TOLERANCE_SMOKE * self.fps) + + # 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) + + 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 'Smoke Detected' 需累计检测: {self.frame_thresh_smoke} 帧") + print(f" 抽烟丢失缓冲帧数: {self.frame_buffer_smoke} 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") + + # ========================================== + # 状态变量初始化 + # ========================================== + + self.current_frame_idx = 0 + + # 无人在场检测状态变量 + self.nobody_detection_frames = 0 + self.nobody_missing_frames = 0 # 连续未检测到手机的帧数 + self.nobody_alert_active = False # 手机报警是否激活 + + # 手机检测状态变量 + 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 + + # 抽烟检测状态变量 + self.smoke_detection_frames = 0 # 连续检测到手机的帧数 + self.smoke_missing_frames = 0 # 连续未检测到手机的帧数 + self.smoke_alert_active = False # 手机报警是否激活 + + + def compute_iou(self,boxA, boxB): + # box = [x1, y1, x2, y2] + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + + interW = max(0, xB - xA) + interH = max(0, yB - yA) + interArea = interW * interH + + boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) + boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) + + unionArea = boxAArea + boxBArea - interArea + if unionArea == 0: + return 0.0 + + return interArea / unionArea + + def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): + """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" + font_scale = 1.5 + thickness = 3 + font = cv2.FONT_HERSHEY_SIMPLEX + + (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) + x = self.width - text_w - 20 + y = 50 + text_h + offset_y # 增加 Y 轴偏移 + + cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) + cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) + + if sub_text: + cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) + + def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: + h, w = frame.shape[:2] + self.width, self.height = w, h + + self.current_frame_idx += 1 + + current_time_sec = timestamp + + # ========= 人和手机检测 ========= + person_phone_results = self.person_phone_detector(frame) + + # ========= 抽烟检测 ========= + smoke_results = self.smoke_detector(frame) + + person_phone_dets_xyxy = [] + person_phone_dets_roles = [] + person_phone_dets_for_tracker = [] + + smoke_dets_xyxy = [] + smoke_dets_roles = [] + smoke_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 + + # 收集 人和手机的检测结果 + if person_phone_results: + for det in person_phone_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 + person_phone_dets_xyxy.append([x1, y1, x2, y2]) + person_phone_dets_for_tracker.append([x1, y1, x2, y2, conf]) + if cls_id == 0: + person_phone_dets_roles.append("phone") + elif cls_id == 1: + person_phone_dets_roles.append("police") + + person_phone_dets = np.array(person_phone_dets_for_tracker, dtype=np.float32) if len( + person_phone_dets_for_tracker) else np.empty((0, 5)) + + person_phone_tracks = self.person_phone_tracker.update( + person_phone_dets, + [self.height, self.width], + [self.height, self.width] + ) + + # 收集 抽烟的检测结果 + if smoke_results: + for det in smoke_results: + x1, y1, x2, y2, conf, cls_id = det + smoke_dets_xyxy.append([x1, y1, x2, y2]) + smoke_dets_for_tracker.append([x1, y1, x2, y2, conf]) + if cls_id == 0: + smoke_dets_roles.append("smoke") + + smoke_dets = np.array(smoke_dets_for_tracker, dtype=np.float32) if len( + smoke_dets_for_tracker) else np.empty((0, 5)) + + smoke_tracks = self.smoke_tracker.update( + smoke_dets, + [self.height, self.width], + [self.height, self.width] + ) + + # ========= 单帧统计变量 ========= + current_person_count = 0 + current_phone_count = 0 + current_smoke_count = 0 + + # ========= 人和手机检测 ========= + for t in person_phone_tracks: + # print("t: {}".format(t)) + 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 + if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.person_phone_track_role): + #if tid not in self.person_phone_track_role: + best_iou = 0 + best_role = "unknown" + + t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] + + for i, box in enumerate(person_phone_dets_xyxy): + iou_val = self.compute_iou(t_box, box) + if iou_val > best_iou: + best_iou = iou_val + best_role = person_phone_dets_roles[i] + if best_iou > 0.1: + self.person_phone_track_role[tid] = best_role + else: + self.person_phone_track_role[tid] = "unknown" + + role = self.person_phone_track_role.get(tid, "unknown") + cls_id = -1 + if role == "phone": + cls_id = 0 + elif role == "police": + cls_id = 1 + # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) + + x1, y1, x2, y2 = map(int, t.tlbr) + + cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 + + color = None + label = None + + if cls_id == 0: # Person + current_phone_count += 1 + color = (255, 0, 255) + label = "Phone" + + elif cls_id == 1: # Phone（主模型已支持） + current_person_count += 1 + color = (0, 0, 139) + label = "Person" + + else: + color = (255, 255, 255) + label = "Unknown" + + # label = f"ID:{tid} IN" + + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) + cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) + + # ========= 抽烟检测 ========= + for t in smoke_tracks: + # print("t: {}".format(t)) + 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 + if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.smoke_track_role): + #if tid not in self.smoke_track_role: + best_iou = 0 + best_role = "unknown" + + t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] + + for i, box in enumerate(smoke_dets_xyxy): + iou_val = self.compute_iou(t_box, box) + if iou_val > best_iou: + best_iou = iou_val + best_role = smoke_dets_roles[i] + # self.smoke_track_role[tid] = best_role + if best_iou > 0.1: + self.smoke_track_role[tid] = best_role + else: + self.smoke_track_role[tid] = "unknown" + + role = self.smoke_track_role.get(tid, "unknown") + cls_id = -1 + if role == "smoke": + cls_id = 0 + + x1, y1, x2, y2 = map(int, t.tlbr) + + cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 + + color = None + label = None + + if cls_id == 0: # 抽烟 + current_smoke_count += 1 + color = (255, 255, 0) + label = "Smoke" + + else: + color = (255, 255, 255) + label = "Unknown" + + # label = f"ID:{tid} IN" + + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) + cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) + + # ========================================== + # 手机检测 + # ========================================== + if current_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 + + # ========================================== + # 抽烟检测 + # ========================================== + if current_smoke_count > 0: + # 检测到抽烟框 + self.smoke_detection_frames += 1 + self.smoke_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.smoke_detection_frames >= self.frame_thresh_smoke: + self.smoke_alert_active = True + else: + # 未检测到抽烟框 + self.smoke_missing_frames += 1 + + # 如果之前检测到抽烟，重置检测计数器 + if self.smoke_detection_frames > 0: + # 只有在连续丢失超过缓冲帧数时才重置 + if self.smoke_missing_frames >= self.frame_buffer_smoke: + self.smoke_detection_frames = 0 + self.smoke_alert_active = False + else: + # 从未检测到抽烟，保持状态 + pass + + # ========================================== + # 9. 业务逻辑判定 (Only One / Nobody) + # ========================================== + status_text = "" + + if current_person_count == 0: + self.nobody_detection_frames += 1 + self.nobody_missing_frames = 0 + + if self.nobody_detection_frames >= self.frame_thresh_nobody: + self.nobody_alert_active = True + else: + self.nobody_missing_frames += 1 + + if self.nobody_detection_frames > 0: + if self.nobody_missing_frames >= self.frame_buffer_nobody: + self.nobody_detection_frames = 0 + self.nobody_alert_active = False + else: + pass + + + # if current_person_count == 0: + # self.cnt_frame_nobody += 1 + # else: + # self.cnt_frame_nobody = 0 + + # ========================================== + # 10. 收集并生成结构化警告（核心改动） + # ========================================== + + alert_offset = 0 + + # A. Playing Phone + if self.phone_alert_active: + duration_seconds = self.phone_detection_frames / self.fps + current_frame_alerts.append( + { + 'time': current_time_sec, + 'action': 'Playing Phone', + 'confidence': 1.0, # 固定为1.0（规则判定） + 'details': f"Detected for {duration_seconds:.1f}s" + } + ) + + # A. Playing Phone + if self.smoke_alert_active: + duration_seconds = self.smoke_detection_frames / self.fps + current_frame_alerts.append( + { + 'time': current_time_sec, + 'action': 'Smoke', + 'confidence': 1.0, # 固定为1.0（规则判定） + 'details': f"Detected for {duration_seconds:.1f}s" + } + ) + + + # D. Nobody Checking + if self.nobody_alert_active: + duration_seconds = self.nobody_detection_frames / self.fps + current_frame_alerts.append({ + 'time': current_time_sec, + 'action': 'Nobody Checking', + 'confidence': 1.0, + 'details': 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） + # ========================================== + debug_info = f"Person: {current_person_count} | Phone: {current_phone_count} | Smoke: {current_smoke_count}" + cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) + + # 统一警告显示区 + alert_y_start = 150 + for i, alert in enumerate(current_frame_alerts): + action = alert['action'] + details = alert.get('details', '') + color = (0, 0, 255) # 默认红色警告 + + if action == 'Nobody Checking': + color = (255, 255, 255) + elif action == 'Smoke': + color = (0, 0, 255) + elif action == 'Playing Phone': + color = (255, 0, 0) + + main_text = action + if details: + main_text += f" ({details})" + + y_pos = alert_y_start + i * 50 + cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1) + cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) + + return { + "image": frame, + + "alerts":current_frame_alerts + } + + +# ========================= 帧处理线程 ========================= +class FrameProcessorWorker(threading.Thread): + def __init__(self, raw_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, ZhihuishiDetector] = {} + + 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] = ZhihuishiDetector() + 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 | ") + + 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 + finally: + self.raw_queue.task_done() +