Merge branch 'master' of https://gitea.swiftsnake.cn/yipai-tech/SupervisorAI

biz/base_frame_processor.py

@@ -4,6 +4,9 @@
 
 import cv2
 import base64
+import json
+import os
+import subprocess
 import time
 import threading
 import queue
@@ -12,7 +15,9 @@ from typing import Dict, Any, Callable
 from concurrent.futures import ThreadPoolExecutor
 
 from common import constants
+from common.type_mapping import get_alert_label
 from utils.logger import get_logger
+from utils.hls_utils import get_segments_before_current, parse_segment_info
 
 logger = get_logger(__name__)
 
@@ -67,6 +72,12 @@ class BaseFrameProcessorWorker(threading.Thread):
             max_workers=post_workers,
             thread_name_prefix="alert_post"
         )
+        
+        # MP4缓存 {segment_path: mp4_path}
+        self._mp4_cache: Dict[str, str] = {}
+        
+        # 启动视频文件清理线程
+        self._start_cleanup_thread()
     
     def _encode_image_to_base64(self, img) -> str:
         """图像编码为 Base64"""
@@ -80,10 +91,10 @@ class BaseFrameProcessorWorker(threading.Thread):
         将 msg 中的 result_type 从数组展开为多个独立的 msg
         
         Args:
-            msg: 原始消息，result_type 为数组
+            msg: 原始消息，result_type 为 action code 字符串数组
             
         Returns:
-            msg 列表，每个 msg 的 result_type 为数组中的单个元素
+            msg 列表，每个 msg 的 result_type 为包含 action_code 和 action_name 的对象
         """
         result_types = msg.get("result_type", [])
         if not isinstance(result_types, list):
@@ -94,15 +105,18 @@ class BaseFrameProcessorWorker(threading.Thread):
             return [msg]
         
         result = []
-        for r_type in result_types:
+        for action_code in result_types:
             new_msg = msg.copy()
-            new_msg["result_type"] = r_type
+            new_msg["result_type"] = {
+                "action_code": action_code,
+                "action_name": get_alert_label(action_code)
+            }
             result.append(new_msg)
         
         return result
     
     def _post_alert(self, msg: dict):
-        """异步发送告警 POST 请求（在线程池中执行）"""
+        """异步发送告警 POST 请求（在线程池中执行）- 旧接口，保留备用"""
         try:
             response = requests.post(constants.ALERT_PUSH_URL, json=msg, timeout=5.0)
             if response.status_code == 200:
@@ -112,6 +126,218 @@ class BaseFrameProcessorWorker(threading.Thread):
         except Exception as e:
             print(f"[ERROR] POST alert request failed: {e}")
     
+    def _post_alert_with_video(self, msg: dict, video_path: str = None):
+        """
+        异步发送告警 POST 请求（带视频，multipart/form-data）
+        
+        Args:
+            msg: 消息内容
+            video_path: 视频文件路径（可选）
+        """
+        try:
+            if video_path and os.path.exists(video_path):
+                # 有视频，使用 multipart/form-data 上传
+                with open(video_path, 'rb') as f:
+                    files = {
+                        'video': f,
+                        'metadata': (None, json.dumps(msg))
+                    }
+                    response = requests.post(constants.ALERT_PUSH_URL, files=files, timeout=10.0)
+            else:
+                # 无视频，也使用 multipart/form-data
+                files = {
+                    'metadata': (None, json.dumps(msg))
+                }
+                response = requests.post(constants.ALERT_PUSH_URL, files=files, timeout=5.0)
+            
+            if response.status_code == 200:
+                logger.info(f"[INFO] POST alert sent successfully for actions: {msg.get('result_type')}")
+            else:
+                logger.warning(f"[WARN] POST alert failed with status: {response.status_code}")
+        except Exception as e:
+            logger.error(f"[ERROR] POST alert request failed: {e}")
+    
+    def _start_cleanup_thread(self):
+        """启动视频文件清理线程"""
+        def cleanup_loop():
+            while not self.stop_event.is_set():
+                try:
+                    self._cleanup_expired_files()
+                except Exception as e:
+                    logger.error(f"[ERROR] Cleanup thread error: {e}")
+                # 每10分钟检查一次
+                self.stop_event.wait(600)
+        
+        thread = threading.Thread(target=cleanup_loop, daemon=True, name="video_cleanup")
+        thread.start()
+        logger.info("[INFO] Video cleanup thread started")
+    
+    def _cleanup_expired_files(self):
+        """清理过期的视频文件"""
+        output_dir = constants.VIDEO_CLIP_OUTPUT_DIR
+        if not output_dir or not os.path.exists(output_dir):
+            return
+        
+        retention_seconds = constants.VIDEO_CLIP_RETENTION_SECONDS
+        current_time = time.time()
+        
+        try:
+            for filename in os.listdir(output_dir):
+                if not filename.endswith('.mp4'):
+                    continue
+                filepath = os.path.join(output_dir, filename)
+                if os.path.isfile(filepath):
+                    file_mtime = os.path.getmtime(filepath)
+                    if current_time - file_mtime > retention_seconds:
+                        try:
+                            os.remove(filepath)
+                            logger.info(f"[INFO] Cleaned up expired video: {filename}")
+                        except Exception as e:
+                            logger.error(f"[ERROR] Failed to delete {filename}: {e}")
+        except Exception as e:
+            logger.error(f"[ERROR] Cleanup expired files error: {e}")
+    
+    def _create_or_get_video_clip(self, segment_path: str, segment_duration: float = None) -> str | None:
+        """
+        创建或获取视频剪辑
+        
+        Args:
+            segment_path: 当前TS分片路径
+            segment_duration: 当前分片时长（秒）
+            
+        Returns:
+            MP4文件路径，失败返回 None
+        """
+        if not segment_path:
+            return None
+        
+        # 检查缓存
+        if segment_path in self._mp4_cache:
+            cached_path = self._mp4_cache[segment_path]
+            if os.path.exists(cached_path):
+                return cached_path
+            else:
+                # 缓存失效，移除
+                del self._mp4_cache[segment_path]
+        
+        # 解析分片信息，构建MP4路径
+        camera_id, timestamp, seq = parse_segment_info(segment_path)
+        if not camera_id:
+            logger.warning(f"[WARN] Failed to parse segment info: {segment_path}")
+            return None
+        
+        output_dir = constants.VIDEO_CLIP_OUTPUT_DIR
+        if not output_dir:
+            logger.warning("[WARN] VIDEO_CLIP_OUTPUT_DIR not configured")
+            return None
+        
+        # 确保输出目录存在
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # MP4文件名
+        mp4_filename = f"{camera_id}_{timestamp}_{seq}.mp4"
+        mp4_path = os.path.join(output_dir, mp4_filename)
+        
+        # 检查是否已存在
+        if os.path.exists(mp4_path):
+            self._mp4_cache[segment_path] = mp4_path
+            return mp4_path
+        
+        # 计算需要回溯的分片数量
+        clip_duration = constants.VIDEO_CLIP_DURATION_SECONDS
+        default_segment_duration = constants.VIDEO_CLIP_DEFAULT_SEGMENT_DURATION
+        
+        effective_duration = segment_duration if segment_duration else default_segment_duration
+        if effective_duration <= 0:
+            effective_duration = default_segment_duration
+        
+        n_segments = int(clip_duration / effective_duration) + 1
+        
+        # 获取需要合并的分片
+        ts_files = get_segments_before_current(segment_path, n_segments)
+        if not ts_files:
+            logger.warning(f"[WARN] No segments found for clip: {segment_path}")
+            return None
+        
+        # 合并TS为MP4
+        if self._merge_ts_to_mp4(ts_files, mp4_path):
+            self._mp4_cache[segment_path] = mp4_path
+            return mp4_path
+        
+        return None
+    
+    def _merge_ts_to_mp4(self, ts_files: list, output_path: str) -> bool:
+        """
+        使用 ffmpeg 合并 TS 分片为 MP4
+        
+        Args:
+            ts_files: TS文件路径列表（按时间顺序）
+            output_path: 输出MP4路径
+            
+        Returns:
+            是否成功
+        """
+        if not ts_files:
+            return False
+        
+        try:
+            # 构建 concat 字符串
+            concat_str = "|".join(ts_files)
+            
+            # ffmpeg 命令
+            cmd = [
+                'ffmpeg',
+                '-i', f'concat:{concat_str}',
+                '-c', 'copy',
+                '-y',  # 覆盖输出文件
+                output_path
+            ]
+            
+            # 执行命令
+            result = subprocess.run(
+                cmd,
+                capture_output=True,
+                timeout=60  # 60秒超时
+            )
+            
+            if result.returncode == 0:
+                logger.info(f"[INFO] Created video clip: {output_path}")
+                return True
+            else:
+                logger.error(f"[ERROR] ffmpeg failed: {result.stderr.decode()}")
+                return False
+                
+        except subprocess.TimeoutExpired:
+            logger.error(f"[ERROR] ffmpeg timeout for {output_path}")
+            return False
+        except FileNotFoundError:
+            logger.error("[ERROR] ffmpeg not found, please install ffmpeg")
+            return False
+        except Exception as e:
+            logger.error(f"[ERROR] Failed to merge TS files: {e}")
+            return False
+    
+    def _process_alert_with_video(self, msg: dict, segment_path: str, segment_duration: float):
+        """
+        处理告警（含视频剪辑）- 在线程池中执行
+        
+        Args:
+            msg: 基础消息
+            segment_path: 当前TS分片路径
+            segment_duration: 当前分片时长
+        """
+        # 尝试创建/获取视频剪辑
+        mp4_path = None
+        if segment_path:
+            mp4_path = self._create_or_get_video_clip(segment_path, segment_duration)
+        
+        # 展开 result_type
+        expanded_msgs = self._expand_msg_by_result_type(msg)
+        
+        # 发送每个展开后的消息
+        for expanded_msg in expanded_msgs:
+            self._post_alert_with_video(expanded_msg, mp4_path)
+    
     def _create_detector(self, params):
         """创建检测器实例"""
         # 使用 type(self) 访问类属性，避免 lambda 被绑定 self 参数
@@ -203,13 +429,24 @@ class BaseFrameProcessorWorker(threading.Thread):
                     try:
                         self.ws_queue.put(msg, timeout=1.0)
                         if push_actions and len(push_actions) > 0:
-                            # 异步发送 POST 请求（提交到线程池）
+                            # 构建消息
                             post_msg = msg.copy()
                             post_msg['type'] = self.POST_TYPE
-                            # 展开 result_type 为多个独立的 msg
+
-                            expanded_msgs = self._expand_msg_by_result_type(post_msg)
+                            #备用backup
-                            for expanded_msg in expanded_msgs:
+                            #self.post_executor.submit(self._post_alert, post_msg)
-                                self.post_executor.submit(self._post_alert, expanded_msg)
+
+                            # 获取视频相关信息（仅HLS模式有）
+                            segment_path = item.get("segment_path")
+                            segment_duration = item.get("segment_duration")
+                            
+                            # 提交到线程池执行（包含视频剪辑和POST）
+                            self.post_executor.submit(
+                                self._process_alert_with_video,
+                                post_msg,
+                                segment_path,
+                                segment_duration
+                            )
                     except queue.Full:
                         logger.warning("[WARN] ws_send_queue full, drop frame message")
 

biz/checkpoint/checkpoint_biz.py

@@ -372,12 +372,12 @@ class KadianDetector:
 
             # 情况1：通过时间太短 -> Ignore (Too Fast)
             if duration_frames < self.frame_thresh_car_min_duration:
-                print(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!")
+                logger.info(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']:
-                print(f"ALARM: Car {car_id} left without checking trunk!")
+                logger.info(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)
 

biz/checkpoint/checkpoint_biz_02.py

@@ -1,515 +0,0 @@
-
-import cv2
-import numpy as np
-from typing import Dict, Any
-import threading
-import queue
-
-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）
-from algorithm.common.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX                 # Pose 专用模型
-from yolox.tracker.byte_tracker import BYTETracker
-
-from utils.logger import get_logger
-logger = get_logger(__name__)
-
-# ========================= 配置区 =========================
-# Kadian 模型路径与ROI（可根据实际情况修改）
-DETECT_MODEL_PATH = 'YOLO_Weight/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([
-#    [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]
-# ])
-
-ROI_RELATIVE=np.array([
-  [0.12,0.0],
-  [0.3,0.0],
-  [0.5,0.2],
-  [1.0, 0.95],
-  [1.0,1.0],
-  [0.42,1.0]
-])
-
-
-ALERT_PUSH_INTERVAL = 5.0
-
-# 输入尺寸
-PERSON_CAR_INPUT_SIZE = 640
-
-
-
-RTSP_TARGET_FPS = 10.0
-
-
-
-class KadianDetector:
-    def __init__(self, roi_points=ROI_RELATIVE):
-        # 模型加载 - 仅保留主检测器，删除pose_detector
-        self.detector = YOLOv8_ONNX(
-            DETECT_MODEL_PATH,
-            conf_threshold=0.25,
-            iou_threshold=0.45,
-            input_size=PERSON_CAR_INPUT_SIZE
-        )
-
-        # 跟踪器配置
-        class TrackerArgs:
-            track_thresh = 0.3  # 必须大于等于yolo的conf_threshold
-            track_buffer = 40
-            match_thresh = 0.85
-            mot20 = True
-
-        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（支持相对/绝对）
-        self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None
-
-        # ===================== 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
-        # 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
-        # Car 丢帧/ID维持缓冲
-        self.TIME_TOLERANCE_CAR = 2.0
-
-        # 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_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps)
-        self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps)
-        self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * 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)
-
-        # 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
-
-
-        # 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
-
-        # 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
-
-        # 打印超参数
-        print(f"\n超参数设置:")
-        print(f"  FPS: {self.fps:.2f}")
-        print(f"  判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧")
-        print(f"  判定 'Too Fast' 最小停留: {self.frame_thresh_car_min_duration} 帧")
-
-    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:
-            raise ValueError("ROI points must be provided; cannot be None.")
-
-        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):
-        """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])
-        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):
-        """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 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
-
-        # ========= 主检测（删除pose检测）=========
-        detections = self.detector(frame)
-
-        dets_xyxy = []
-        dets_roles = []
-        dets_for_tracker = []
-
-        # ========= 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: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("passerby")  # 路人
-                elif cls_id == 3:
-                    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],
-            [self.height, self.width]
-        )
-
-        # ========= 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_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:
-            tid = t.track_id
-            REVALIDATE_FRAME_INTERVAL = 10
-
-            # 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):
-                    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")
-            x1, y1, x2, y2 = map(int, t.tlbr)
-            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
-
-            # 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,
-                            '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 += " 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
-                    current_trunks.append((cx, cy))
-                    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 = (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)
-
-        # ==========================================
-        # 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
-
-        # ==========================================
-        # 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)
-
-        # 处理离场车辆，生成违规告警
-        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 -> Ignore (Too Fast)
-            if duration_frames < self.frame_thresh_car_min_duration:
-                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']:
-                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)
-
-            del self.roi_car_registry[car_id]
-
-        effective_car_count = len(active_car_ids)
-
-        # ==========================================
-        # 维护警察注册表
-        # ==========================================
-        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)
-
-        # ==========================================
-        # 显示调试信息和报警 (仅保留车/后备箱相关)
-        # ==========================================
-        # 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)
-
-        # 报警偏移量（防止重叠）
-        alert_offset = 0
-
-        # 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': "Trunk Checked",
-        #         })
-        #         self.draw_alert(frame, "Trunk Checked!!", (0, 255, 0), offset_y=alert_offset)
-        #         alert_offset += 100
-        #         break  # 只显示一次
-
-        # 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:
-            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
-
-        # 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:
-            del self.fast_pass_alerts[cid]
-
-        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",
-            })
-            self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
-            alert_offset += 100
-
-        # 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]
-
-        if effective_car_count > 0:
-            # 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
-        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
-
-        return {
-            "image": frame,
-            "alerts": current_frame_alerts,
-        }
-
-
-# ========================= 帧处理线程 =========================
-class FrameProcessorWorker(BaseFrameProcessorWorker):
-    """卡点检测帧处理线程"""
-    
-    # 子类配置
-    DETECTOR_FACTORY = lambda params: KadianDetector(params)
-    POST_TYPE = 1
-    TARGET_FPS = RTSP_TARGET_FPS

biz/prison/ab_biz.py

@@ -0,0 +1,259 @@
+import cv2
+import numpy as np
+import base64
+from typing import Dict, Any
+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）
+from common.constants import ALERT_PUSH_URL
+
+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/bag_model.onnx'
+
+# 输入尺寸
+input_size = 640
+
+RTSP_TARGET_FPS = 10.0
+
+# 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 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.detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45,
+                                    input_size=input_size)
+
+        # ByteTracker
+        class TrackerArgs:
+            track_thresh = 0.25
+            track_buffer = 30
+            match_thresh = 0.8
+            mot20 = False
+
+        self.track_role = {}
+
+        self.fps = RTSP_TARGET_FPS
+
+        self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
+
+        # ==========================================
+        # 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
+
+        # 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 '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
+
+        # 黑包检测状态
+        self.blackbag_detection_frames = 0
+        self.blackbag_missing_frames = 0
+        self.blackbag_alert_active = False
+
+        # 人员统计变量
+        self.current_person_count = 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 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
+
+        # ========= 检测推理（黑包+人）=========
+        detect_results = self.detector(frame)
+
+        # 初始化检测结果存储
+        dets_xyxy = []
+        dets_roles = []
+        dets_for_tracker = []
+        current_frame_alerts = []
+
+        # 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:
+                    dets_roles.append("black_bag")
+                elif cls_id == 1:
+                    dets_roles.append("person")
+
+        # 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
+
+            # IoU匹配跟踪ID和类别
+            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))
+                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]
+                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"
+
+            # 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)
+
+        # ==========================================
+        # 黑包状态更新
+        # ==========================================
+        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:
+            self.blackbag_missing_frames += 1
+            if self.blackbag_missing_frames >= self.frame_buffer_blackbag:
+                self.blackbag_detection_frames = 0
+                self.blackbag_alert_active = False
+
+        # ==========================================
+        # 警告信息收集
+        # ==========================================
+        if self.blackbag_alert_active:
+            duration_seconds = self.blackbag_detection_frames / self.fps
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': 'Black Bag',
+                    'details': f"Detected for {duration_seconds:.1f}s"
+                }
+            )
+            self.draw_alert(frame, "Black Bag Alert", (0, 0, 255), sub_text=f"Detected for {duration_seconds:.1f}s")
+
+        # ==========================================
+        # 绘制信息
+        # ==========================================
+        # 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)
+
+        # 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)  # 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)
+
+        return {
+            "image": frame,
+
+            "alerts":current_frame_alerts
+        }
+
+
+# ========================= 帧处理线程 =========================
+class FrameProcessorWorker(BaseFrameProcessorWorker):
+    """轨迹检测帧处理线程"""
+
+    # 子类配置
+    DETECTOR_FACTORY = lambda params: AbDetector(params)
+    POST_TYPE = 2
+    TARGET_FPS = RTSP_TARGET_FPS

biz/prison/prison_biz.py

@@ -7,6 +7,8 @@ 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）
 from common.constants import ALERT_PUSH_URL
@@ -19,7 +21,7 @@ from yolox.tracker.byte_tracker import BYTETracker
 detector_model_path = 'YOLO_Weight/prisoner_model.onnx'
 
 # 输入尺寸
-input_size = 1280
+input_size = 640
 
 RTSP_TARGET_FPS = 10.0
 
@@ -28,9 +30,11 @@ ALERT_PUSH_INTERVAL = 5.0  # 相同action 5秒内仅推送一次
 
 
 class ZoulangDetector:
-    def __init__(self):
+    def __init__(self, params=None):
-        # 模型加载
+        # 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)
 
@@ -41,8 +45,6 @@ class ZoulangDetector:
             match_thresh = 0.8
             mot20 = False
 
-
-
         self.police_prisoner_track_role = {}
 
         self.fps = RTSP_TARGET_FPS
@@ -334,114 +336,11 @@ class ZoulangDetector:
         }
 
 
-
 # ========================= 帧处理线程 =========================
-class FrameProcessorWorker(threading.Thread):
+class FrameProcessorWorker(BaseFrameProcessorWorker):
-    def __init__(self,
+    """轨迹检测帧处理线程"""
-                 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
 
-        self.last_ts: Dict[int, float] = {}
+    # 子类配置
-
+    DETECTOR_FACTORY = lambda params: ZoulangDetector(params)
-        # 每个摄像头一个独立的 Kadian 检测器实例
+    POST_TYPE = 2
-        self.kadian_detectors: Dict[int, ZoulangDetector] = {}
+    TARGET_FPS = RTSP_TARGET_FPS
-
-        # 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()

common/constants.py

@@ -9,6 +9,12 @@ HLS_ROOT_PATH = ""
 
 HLS_SEGMENT_PATTERN = "segment_%09d.ts"  # TS文件命名模式
 
+# 视频剪辑配置
+VIDEO_CLIP_OUTPUT_DIR = ""
+VIDEO_CLIP_DURATION_SECONDS = 30
+VIDEO_CLIP_RETENTION_SECONDS = 3600
+VIDEO_CLIP_DEFAULT_SEGMENT_DURATION = 2
+
 
 def init_config(config_path: str = "config.yaml"):
     """
@@ -18,6 +24,7 @@ def init_config(config_path: str = "config.yaml"):
         config_path: 配置文件路径，默认为 config.yaml
     """
     global ALERT_PUSH_URL, HLS_ROOT_PATH
+    global VIDEO_CLIP_OUTPUT_DIR, VIDEO_CLIP_DURATION_SECONDS, VIDEO_CLIP_RETENTION_SECONDS, VIDEO_CLIP_DEFAULT_SEGMENT_DURATION
     
     try:
         with open(config_path, "r", encoding="utf-8") as f:
@@ -25,6 +32,13 @@ def init_config(config_path: str = "config.yaml"):
         
         ALERT_PUSH_URL = cfg.get("alert_push_url", "")
         # HLS_ROOT_PATH = cfg.get("hls_root_path", "")
+        
+        # 视频剪辑配置
+        VIDEO_CLIP_OUTPUT_DIR = cfg.get("video_clip_output_dir", "")
+        VIDEO_CLIP_DURATION_SECONDS = cfg.get("video_clip_duration_seconds", 30)
+        VIDEO_CLIP_RETENTION_SECONDS = cfg.get("video_clip_retention_seconds", 3600)
+        VIDEO_CLIP_DEFAULT_SEGMENT_DURATION = cfg.get("video_clip_default_segment_duration", 2)
+        
         logger.info(f"[INFO] Config initialized from {config_path}, alert_push_url={ALERT_PUSH_URL}")
         
     except Exception as e:

common/processor_factory.py

@@ -1,12 +1,17 @@
 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
+from biz.prison.prison_biz import FrameProcessorWorker as CorridorWorker
+
 # ... 其他导入
 
 PROCESSOR_MAP = {
     "checkpoint": CheckpointWorker,
     "trajectory": TrajectoryWorker,
     "supervision_room": SupervisionWorker,
+    "ab": AbWorker,
+    "corridor": CorridorWorker
 }
 
 def get_processor(processor_type: str):

common/type_mapping.py

@@ -0,0 +1,96 @@
+# common/type_mapping.py
+"""
+告警类型映射配置模块
+从 config.yaml 加载告警 code 到 label 的映射关系
+"""
+
+import yaml
+from typing import Dict, Optional
+from utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class TypeMapping:
+    """类型映射类"""
+    
+    def __init__(self, mapping: Dict[str, str], name: str = ""):
+        self._mapping = mapping or {}
+        self._name = name
+    
+    def get(self, code: str, default: Optional[str] = None) -> str:
+        """获取label，支持自定义默认值"""
+        if default is None:
+            default = f"{code}"
+        return self._mapping.get(code, default)
+    
+    def __getitem__(self, code: str) -> str:
+        """支持 [] 语法访问"""
+        return self.get(code)
+    
+    def __contains__(self, code: str) -> bool:
+        """支持 in 操作符"""
+        return code in self._mapping
+    
+    def all(self) -> Dict[str, str]:
+        """获取所有映射"""
+        return self._mapping.copy()
+    
+    def codes(self) -> list:
+        """获取所有code"""
+        return list(self._mapping.keys())
+    
+    def labels(self) -> list:
+        """获取所有label"""
+        return list(self._mapping.values())
+
+
+# 全局映射实例
+_alert_types: Optional[TypeMapping] = None
+
+
+def init_type_mappings(config_path: str = "config.yaml"):
+    """
+    从配置文件初始化类型映射
+    
+    Args:
+        config_path: 配置文件路径
+    """
+    global _alert_types
+    
+    try:
+        with open(config_path, "r", encoding="utf-8") as f:
+            cfg = yaml.safe_load(f)
+        
+        _alert_types = TypeMapping(
+            cfg.get("alert_types", {}),
+            name="告警类型"
+        )
+        
+        logger.info(f"[INFO] Alert type mappings initialized from {config_path}")
+        logger.info(f"  - alert_types: {len(_alert_types.codes())} items")
+        
+    except Exception as e:
+        logger.error(f"[ERROR] Failed to load type mappings from {config_path}: {e}")
+        _alert_types = TypeMapping({}, "告警类型")
+
+
+def alert_types() -> TypeMapping:
+    """获取告警类型映射"""
+    if _alert_types is None:
+        init_type_mappings()
+    return _alert_types
+
+
+def get_alert_label(code: str, default: str = None) -> str:
+    """
+    快捷获取告警类型label
+    
+    Args:
+        code: 告警类型代码
+        default: 默认值，未提供时返回 "未知告警类型(code)"
+        
+    Returns:
+        告警类型中文名称
+    """
+    return alert_types().get(code, default)

config.yaml

@@ -41,6 +41,12 @@ hls_downloader_daily_rotate_hour: 3        # 凌晨轮换时间
 hls_downloader_retention_days: 3           # 文件保留天数
 hls_downloader_retry_interval_seconds: 10  # 重试等待秒数
 
+# 视频剪辑配置
+video_clip_output_dir: "D:/ProjectDoc/Police/data/video_clips"  # 视频剪辑输出目录
+video_clip_duration_seconds: 30                                 # 回溯时长（秒）
+video_clip_retention_seconds: 3600                              # 视频文件保留时长（秒）
+video_clip_default_segment_duration: 2                          # 默认分片时长fallback（秒）
+
 service_groups:
 - name: "kadian_group"           # 服务组名称
   video_source_type: "hls"
@@ -49,7 +55,7 @@ service_groups:
   algorithm: "checkpoint"            # 算法类型
   cameras:                       # 该组下的摄像头列表
   - id: 8
-    index: 12345
+    index: "12345"
     name: Entrance
     params:
       roi_points:
@@ -72,3 +78,20 @@ service_groups:
 #        - [0.5, 0.001]
 #        - [1.0, 0.8]
 #        - [0.35, 1.0]
+
+# 告警类型映射 (code -> 中文名称)
+alert_types:
+  # 卡点检测 (checkpoint)
+  "Unchecked Trunk": "未检查后备箱"
+  "Ignore": "漏检"
+  "Nobody": "无人在场"
+  "Only One": "单人单检"
+
+  # 监狱检测 (prison)
+  "prisoner": "带出犯人"
+  "violation": "路线违规"
+  
+  # 监控室检测 (supervision_room)
+  "Playing Phone": "玩手机"
+  "Smoke": "吸烟"
+  "Nobody Checking": "无人在场"

utils/hls_utils.py

@@ -1,5 +1,6 @@
 import os
 import glob
+import re
 
 from common import constants
 
@@ -67,3 +68,128 @@ def get_latest_n_segments_by_camera_id(camera_id: str, n: int) -> list:
     """
     camera_root_dir = os.path.join(constants.HLS_ROOT_PATH, camera_id)
     return get_latest_n_segments(camera_root_dir, n)
+
+
+def parse_segment_info(segment_path: str) -> tuple:
+    """
+    从TS分片路径解析出 camera_id, timestamp, sequence
+    
+    Args:
+        segment_path: TS分片路径，格式如: hls_root_path/camera_id/timestamp/segment_xxxxx.ts
+        
+    Returns:
+        (camera_id, timestamp, sequence) 或 (None, None, None) 解析失败时
+    """
+    try:
+        # 获取文件名和目录结构
+        # 路径格式: .../camera_id/timestamp/segment_00001.ts
+        abs_path = os.path.abspath(segment_path)
+        parts = abs_path.split(os.sep)
+        
+        # 从后往前找
+        # parts[-1] = segment_00001.ts
+        # parts[-2] = timestamp
+        # parts[-3] = camera_id
+        
+        if len(parts) < 3:
+            return None, None, None
+        
+        # 解析序号
+        filename = parts[-1]
+        match = re.search(r'segment_(\d+)\.ts$', filename)
+        if not match:
+            return None, None, None
+        sequence = match.group(1)
+        
+        # 时间戳
+        timestamp = parts[-2]
+        
+        # camera_id
+        camera_id = parts[-3]
+        
+        return camera_id, timestamp, sequence
+        
+    except Exception:
+        return None, None, None
+
+
+def get_segments_before_current(current_segment_path: str, n: int) -> list:
+    """
+    获取当前分片之前的n个分片（包括当前分片）
+    
+    Args:
+        current_segment_path: 当前TS分片路径
+        n: 需要获取的分片数量
+        
+    Returns:
+        分片路径列表（按时间顺序，旧的在前），如果不够n个则返回现有的
+    """
+    if not current_segment_path or not os.path.exists(current_segment_path):
+        return []
+    
+    # 解析当前分片信息
+    camera_id, timestamp, current_seq = parse_segment_info(current_segment_path)
+    if not camera_id:
+        return []
+    
+    # 构建摄像头根目录和时间戳目录
+    camera_root_dir = os.path.join(constants.HLS_ROOT_PATH, camera_id)
+    timestamp_dir = os.path.join(camera_root_dir, timestamp)
+    
+    if not os.path.exists(timestamp_dir):
+        return []
+    
+    # 获取当前时间戳文件夹下的所有分片
+    pattern = os.path.join(timestamp_dir, "segment_*.ts")
+    segment_files = glob.glob(pattern)
+    
+    # 按分片序号排序
+    segment_files.sort(key=lambda x: int(os.path.basename(x).split('_')[-1].split('.')[0]))
+    
+    # 找到当前分片的位置
+    try:
+        current_index = segment_files.index(current_segment_path)
+    except ValueError:
+        # 当前分片不在列表中
+        return []
+    
+    # 计算起始位置
+    start_index = max(0, current_index - n + 1)
+    
+    # 返回从起始位置到当前位置的所有分片
+    result = segment_files[start_index:current_index + 1]
+    
+    # 如果不够n个，需要从之前的时间戳文件夹获取
+    if len(result) < n:
+        # 获取所有时间戳文件夹
+        timestamp_folders = []
+        for folder_name in os.listdir(camera_root_dir):
+            folder_path = os.path.join(camera_root_dir, folder_name)
+            if os.path.isdir(folder_path):
+                timestamp_folders.append(folder_name)
+        
+        # 排序，找到当前时间戳之前的时间戳
+        timestamp_folders.sort()
+        try:
+            current_ts_index = timestamp_folders.index(timestamp)
+        except ValueError:
+            current_ts_index = len(timestamp_folders)
+        
+        # 从之前的时间戳文件夹获取分片
+        needed_count = n - len(result)
+        for i in range(current_ts_index - 1, -1, -1):
+            prev_ts_dir = os.path.join(camera_root_dir, timestamp_folders[i])
+            prev_pattern = os.path.join(prev_ts_dir, "segment_*.ts")
+            prev_segments = glob.glob(prev_pattern)
+            prev_segments.sort(key=lambda x: int(os.path.basename(x).split('_')[-1].split('.')[0]))
+            
+            # 取最后 needed_count 个
+            take_count = min(needed_count, len(prev_segments))
+            if take_count > 0:
+                result = prev_segments[-take_count:] + result
+                needed_count -= take_count
+            
+            if needed_count <= 0:
+                break
+    
+    return result