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