diff --git a/biz/prison/indoor_biz.py b/biz/prison/indoor_biz.py new file mode 100644 index 0000000..c58d8c6 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/common/processor_factory.py b/common/processor_factory.py index 565adb7..557a5fe 100644 --- a/common/processor_factory.py +++ b/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):