删除错误引用

biz/prison/indoor_biz.py

@@ -0,0 +1,236 @@
+import cv2
+import numpy as np
+import time
+import requests
+from biz.base_frame_processor import BaseFrameProcessorWorker
+from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX
+from yolox.tracker.byte_tracker import BYTETracker
+
+# ========================= 走廊场景专属配置 =========================
+MODEL_PATH = 'YOLO_Weight/kanshousuo.onnx'  # 犯人检测onnx模型路径
+INPUT_SIZE = 640  # 模型输入尺寸
+RTSP_FPS = 10  # 视频流目标FPS
+ALERT_PUSH_INTERVAL = 5  # 相同报警5秒内仅推送1次
+ALERT_PUSH_URL = "http://123.57.151.210:10000/picenter/websocket/test/process"
+# 消失判定：中心点在ROI内消失后，持续无检测的帧数（1.0秒，可微调）
+ROI_LOST_FRAMES_THRESH = int(0.5 * RTSP_FPS)
+
+# ========================= 5个ROI区域配置（相对坐标，适配任意分辨率） =========================
+# 格式：{ROI名称: [[x1,y1], [x2,y2], ...], ...} （多边形顶点，顺/逆时针均可）
+# 相对坐标：x/y 0~1（0=左/上，1=右/下），可直接根据场景调整
+ROI_CONFIG = {
+    "left_door_1": [[0.195, 0.242], [0.265, 0.17], [0.3, 0.63] ,[0.248, 0.8]],  # 左侧1门ROI
+    "left_door_2": [[0.3, 0.1], [0.34, 0.08], [0.35, 0.43], [0.322, 0.52]],      # 左侧2门 ROI
+    "left_door_3": [[0.355, 0.06], [0.42, 0], [0.42, 0.18], [0.362, 0.36]],  # 左侧3门ROI
+    "right_door_1": [[0.735, 0.142], [0.81, 0.22], [0.78, 0.8], [0.715, 0.65]],      # 右侧1门 ROI
+    "right_door_2": [[0.65, 0.06], [0.7, 0.09], [0.69, 0.5], [0.65, 0.4]]    # 右侧2门ROI
+}
+
+# ==================================================================================
+class PrisonerDoorDetector:
+    def __init__(self, params=None):
+        self.params = params or {}
+        # 1. 加载YOLO模型（仅提取犯人检测结果）
+        self.detector = YOLOv8_ONNX(
+            MODEL_PATH,
+            conf_threshold=0.5,  # 置信度阈值，可根据模型精度调整
+            iou_threshold=0.45,  # IOU阈值
+            input_size=INPUT_SIZE
+        )
+
+        # 2. 初始化ByteTracker跟踪器（适配走廊单/多犯人跟踪）
+        class TrackerArgs:
+            track_thresh = 0.25
+            track_buffer = 20  # 减小缓冲避免跟踪漂移
+            match_thresh = 0.75
+            mot20 = False
+        self.tracker = BYTETracker(TrackerArgs(), frame_rate=RTSP_FPS)
+
+        # 3. 状态变量初始化
+        self.last_alert_time = 0.0  # 最后报警时间（防重复推送）
+        # 犯人跟踪信息：{track_id: {'is_cx_in_roi': 中心点是否在ROI, 'lost_frames': 消失帧数, 'lost_roi': 消失的ROI名称, 'last_cxcy': 最后中心点坐标}}
+        self.prisoner_track_info = {}
+        self.frame_width = 0  # 帧宽度（动态获取）
+        self.frame_height = 0  # 帧高度（动态获取）
+        self.roi_abs_cache = {}  # ROI绝对坐标缓存：{roi_name: np.int32数组}
+
+    def compute_iou(self, boxA, boxB):
+        """IOU计算：匹配跟踪框与犯人检测框，过滤非犯人目标"""
+        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
+        return interArea / unionArea if unionArea > 0 else 0.0
+
+    def _get_roi_abs(self, roi_name):
+        """相对坐标转绝对像素坐标（适配当前帧分辨率，OpenCV要求int32）"""
+        if roi_name not in ROI_CONFIG:
+            return None
+        roi_rel = np.array(ROI_CONFIG[roi_name], dtype=np.float64)
+        roi_abs = roi_rel * np.array([self.frame_width, self.frame_height])
+        return roi_abs.astype(np.int32)
+
+    def is_cxcy_in_roi(self, cx, cy):
+        """判断犯人框**中心点(cx,cy)** 是否在任意ROI内，返回：(是否在ROI, 所在ROI名称)"""
+        for roi_name, roi_abs in self.roi_abs_cache.items():
+            # OpenCV点在多边形内判定：>=0 表示在内部/边上
+            if cv2.pointPolygonTest(roi_abs, (cx, cy), False) >= 0:
+                return (True, roi_name)
+        return (False, "outside")
+
+    def push_alert(self, camera_id, track_id, lost_roi, last_cxcy, timestamp):
+        """报警推送：带频率限制，携带消失ROI、最后中心点坐标"""
+        current_time = time.time()
+        if current_time - self.last_alert_time < ALERT_PUSH_INTERVAL:
+            return False
+        # 构造报警信息（可根据平台要求扩展字段）
+        alert_info = {
+            "camera_id": camera_id,
+            "alert_type": "prisoner_cx_disappear_in_roi",
+            "prisoner_track_id": track_id,
+            "disappear_roi": lost_roi,
+            "last_cx": round(last_cxcy[0], 2),
+            "last_cy": round(last_cxcy[1], 2),
+            "timestamp": timestamp,
+            "details": f"犯人框中心点在{lost_roi}区域内消失，触发报警"
+        }
+        # 推送报警请求
+        try:
+            requests.post(ALERT_PUSH_URL, json=alert_info, timeout=3)
+            print(f"[报警成功] {alert_info}")
+            self.last_alert_time = current_time
+            return True
+        except Exception as e:
+            print(f"[报警失败] 原因：{str(e)}")
+            return False
+
+    def process_frame(self, frame, camera_id: int, timestamp: float) -> dict:
+        """
+        核心帧处理：
+        1. 绘制5个ROI区域  2. 检测+跟踪犯人  3. 判定中心点是否在ROI内
+        4. 中心点在ROI内消失则累计帧数，达到阈值触发报警
+        """
+        self.frame_height, self.frame_width = frame.shape[:2]
+        current_frame_alerts = []  # 本帧报警信息
+
+        # ========================= 1. 初始化ROI绝对坐标并绘制5个ROI =========================
+        roi_colors = {  # 各ROI绘制颜色（自定义区分）
+            "left_door_1": (255, 0, 0), "left_door_2": (0, 255, 0),
+            "left_door_3": (0, 0, 255), "right_door_1": (255, 255, 0),
+            "right_door_2": (255, 165, 0)
+        }
+        self.roi_abs_cache.clear()
+        for roi_name, _ in ROI_CONFIG.items():
+            roi_abs = self._get_roi_abs(roi_name)
+            if roi_abs is None:
+                continue
+            self.roi_abs_cache[roi_name] = roi_abs
+            # 绘制ROI多边形（闭合）+ ROI名称标签
+            roi_draw = roi_abs.reshape((-1, 1, 2))  # OpenCV绘制要求形状 (n,1,2)
+            cv2.polylines(frame, [roi_draw], isClosed=True, color=roi_colors[roi_name], thickness=2)
+            cv2.putText(frame, roi_name, (roi_abs[0][0], roi_abs[0][1] - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, roi_colors[roi_name], 2)
+
+        # ========================= 2. 模型推理：仅提取犯人检测框 =========================
+        detect_results = self.detector(frame)
+        prisoner_dets_xyxy = []  # 仅存犯人检测框 [x1,y1,x2,y2]
+        dets_for_tracker = []    # 跟踪器输入 [x1,y1,x2,y2,conf]
+        if detect_results:
+            for det in detect_results:
+                x1, y1, x2, y2, conf, cls_id = det
+                dets_for_tracker.append([x1, y1, x2, y2, conf])
+                # 替换为你模型中「犯人」的实际类别ID，此处默认cls_id=1
+                if cls_id == 1:
+                    prisoner_dets_xyxy.append([x1, y1, x2, y2])
+
+        # ========================= 3. 目标跟踪：更新犯人跟踪结果 =========================
+        dets_np = np.array(dets_for_tracker, dtype=np.float32) if dets_for_tracker else np.empty((0, 5))
+        track_results = self.tracker.update(dets_np, [self.frame_height, self.frame_width],
+                                            [self.frame_height, self.frame_width])
+
+        # ========================= 4. 遍历跟踪结果：判定犯人中心点是否在ROI =========================
+        current_prisoner_tids = set()  # 本帧存在的犯人track_id
+        for track in track_results:
+            track_id = track.track_id
+            track_box = list(map(float, track.tlbr))  # 跟踪框 [x1,y1,x2,y2]
+            # IOU匹配：过滤非犯人目标，仅保留真正的犯人
+            is_prisoner = False
+            for p_box in prisoner_dets_xyxy:
+                if self.compute_iou(track_box, p_box) > 0.3:
+                    is_prisoner = True
+                    break
+            if not is_prisoner:
+                continue
+
+            # 计算犯人框**中心点坐标**（核心判定依据）
+            cx = (track_box[0] + track_box[2]) / 2
+            cy = (track_box[1] + track_box[3]) / 2
+            # 判定中心点是否在ROI内，返回(是否在ROI, 所在ROI名称)
+            is_cx_in_roi, current_roi = self.is_cxcy_in_roi(cx, cy)
+            # 更新犯人跟踪信息：记录中心点状态、所在ROI、最后坐标，重置消失帧数
+            self.prisoner_track_info[track_id] = {
+                "is_cx_in_roi": is_cx_in_roi,
+                "lost_frames": 0,
+                "lost_roi": current_roi,
+                "last_cxcy": (cx, cy)
+            }
+            current_prisoner_tids.add(track_id)
+
+            # 绘制犯人框+中心点+状态标签（可视化调试）
+            x1, y1, x2, y2 = map(int, track_box)
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)  # 红色犯人框
+            cv2.circle(frame, (int(cx), int(cy)), 5, (0, 255, 255), -1)  # 黄色中心点
+            cv2.putText(frame, f"Prisoner_{track_id}({current_roi})", (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+
+        # ========================= 5. 核心判定：中心点在ROI内消失则报警 =========================
+        for track_id in list(self.prisoner_track_info.keys()):
+            if track_id not in current_prisoner_tids:
+                # 犯人本帧消失，获取其最后状态
+                track_info = self.prisoner_track_info[track_id]
+                # 仅处理「**中心点原本在ROI内**」的消失情况
+                if track_info["is_cx_in_roi"]:
+                    track_info["lost_frames"] += 1  # 累计消失帧数
+                    # 消失帧数达到阈值，触发报警
+                    if track_info["lost_frames"] >= ROI_LOST_FRAMES_THRESH:
+                        self.push_alert(
+                            camera_id=camera_id,
+                            track_id=track_id,
+                            lost_roi=track_info["lost_roi"],
+                            last_cxcy=track_info["last_cxcy"],
+                            timestamp=timestamp
+                        )
+                        # 记录本帧报警信息
+                        current_frame_alerts.append({
+                            "time": timestamp,
+                            "camera_id": camera_id,
+                            "action": "prisoner_cx_disappear_in_door",
+                            "prisoner_track_id": track_id,
+                            "disappear_roi": track_info["lost_roi"],
+                            "last_cx": round(track_info["last_cxcy"][0], 2),
+                            "last_cy": round(track_info["last_cxcy"][1], 2)
+                        })
+                        del self.prisoner_track_info[track_id]  # 报警后清除状态，避免重复触发
+                else:
+                    del self.prisoner_track_info[track_id]  # 中心点不在ROI的消失，直接清除
+
+        # ========================= 6. 绘制辅助信息（摄像头ID、在押犯人数） =========================
+        cv2.putText(frame, f"Camera: {camera_id}", (20, self.frame_height - 20),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        cv2.putText(frame, f"Prisoners: {len(current_prisoner_tids)}", (20, self.frame_height - 50),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
+
+        return {"image": frame, "alerts": current_frame_alerts}
+
+# ========================= 帧处理线程（对接原有框架，直接复用） =========================
+class FrameProcessorWorker(BaseFrameProcessorWorker):
+    """看守所走廊犯人检测 - 5ROI+中心点消失判定"""
+    DETECTOR_FACTORY = lambda params: PrisonerDoorDetector(params)
+    POST_TYPE = 3  # 与原有业务区分，自定义即可
+    TARGET_FPS = RTSP_FPS

biz/prison/supervision_room_biz.py

@@ -12,8 +12,6 @@ import json
 
 from typing import Dict, Any, Tuple, List
 
-from openxlab.model.commands import Model
-
 from biz.base_frame_processor import BaseFrameProcessorWorker
 
 # -------------------------- Kadian 检测相关导入 --------------------------

common/processor_factory.py

@@ -3,6 +3,7 @@ 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
+from biz.prison.indoor_biz import FrameProcessorWorker as IndoorWorker
 
 # ... 其他导入
 
@@ -11,7 +12,8 @@ PROCESSOR_MAP = {
     "trajectory": TrajectoryWorker,
     "supervision_room": SupervisionWorker,
     "ab": AbWorker,
-    "corridor": CorridorWorker
+    "corridor": CorridorWorker,
+    "indoor": IndoorWorker
 }
 
 def get_processor(processor_type: str):