diff --git a/biz/prison/indoor_biz.py b/biz/prison/indoor_biz.py index e4527fa..a4d8eac 100644 --- a/biz/prison/indoor_biz.py +++ b/biz/prison/indoor_biz.py @@ -2,243 +2,446 @@ import cv2 import numpy as np import time import requests +from collections import deque 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 -from common.constants import MODEL_ROOT_PATH # ========================= 走廊场景专属配置 ========================= -DETECT_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 +MODEL_PATH = 'YOLO_Weight/kanshousuo.onnx' +INPUT_SIZE = 640 +RTSP_FPS = 10 +ALERT_PUSH_INTERVAL = 10 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) # todo: 从frame改为时间 +ROI_LOST_FRAMES_THRESH = int(1 * RTSP_FPS) -# ========================= 默认ROI区域配置（当config.yaml未配置时使用） ========================= -DEFAULT_DOOR_ROIS = { - "left_door_1": { - "points": [[0.195, 0.242], [0.265, 0.17], [0.3, 0.63], [0.248, 0.8]], - "color": [255, 0, 0] - } +# ========================= ROI区域配置 ========================= +ROI_CONFIG = { + "left": [[0.195, 0.245], [0.42, 0], [0.421, 0.185], [0.248, 0.8]], + "right": [[0.575, 0.], [0.81, 0.22], [0.78, 0.8], [0.575, 0.185]], } -# ================================================================================== + class PrisonerDoorDetector: def __init__(self, params=None): self.params = params or {} - # 0. 从params解析ROI配置，无则使用默认值 - door_rois_config = self.params.get('door_rois', DEFAULT_DOOR_ROIS) - self.roi_config = {} - self.roi_colors = {} - for door_name, door_cfg in door_rois_config.items(): - self.roi_config[door_name] = door_cfg['points'] - self.roi_colors[door_name] = tuple(door_cfg['color']) - - model_path = self.params.get('model_path') - if model_path: - full_model_path = f"{MODEL_ROOT_PATH}/{model_path}" - else: - full_model_path = DETECT_MODEL_PATH - + # 1. 加载YOLO模型 - 降低阈值提高检测率 self.detector = YOLOv8_ONNX( - full_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 + MODEL_PATH, + conf_threshold=0.57, # 进一步降低，捕获更多检测 + iou_threshold=0.4, input_size=INPUT_SIZE ) - # 2. 初始化ByteTracker跟踪器（适配走廊单/多犯人跟踪） + # 2. ByteTracker参数优化 class TrackerArgs: - track_thresh = 0.25 - track_buffer = 20 # 减小缓冲避免跟踪漂移 - match_thresh = 0.75 + track_thresh = 0.65 # 更低的跟踪阈值 + track_buffer = 60 # 更大的缓冲，应对短暂消失 + match_thresh = 0.5 # 更宽松的匹配 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} + # 3. 状态变量 + self.last_alert_time = 0.0 + self.frame_width = 0 + self.frame_height = 0 + self.roi_abs_cache = {} + self.entry_frame_cache = {} + + # 【核心改进】基于位置的跟踪状态管理 + self.active_targets = {} # {target_id: {...}} + self.next_target_id = 0 + self.position_history = {} # {target_id: deque of positions} + + # 距离阈值（用于匹配检测框和已有目标） + self.distance_threshold = 100 # 像素距离 + + def compute_center_distance(self, box1, box2): + """计算两个框中心点的欧氏距离""" + cx1 = (box1[0] + box1[2]) / 2 + cy1 = (box1[1] + box1[3]) / 2 + cx2 = (box2[0] + box2[2]) / 2 + cy2 = (box2[1] + box2[3]) / 2 + return np.sqrt((cx1 - cx2) ** 2 + (cy1 - cy2) ** 2) 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""" + """IOU计算""" 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]) + + boxAArea = max(0, (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])) + boxBArea = max(0, (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 self.roi_config: + """相对坐标转绝对像素坐标""" + if roi_name not in ROI_CONFIG: return None - roi_rel = np.array(self.roi_config[roi_name], dtype=np.float64) + 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)""" + """判断中心点是否在ROI内""" 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 match_detection_to_target(self, detection_box, detection_conf): + """ + 【核心】将检测框匹配到已有目标 + 返回: (matched_target_id, match_score) + """ + best_match_id = None + best_match_score = 0 + + det_center = np.array([(detection_box[0] + detection_box[2]) / 2, + (detection_box[1] + detection_box[3]) / 2]) + + for target_id, target_info in self.active_targets.items(): + # 计算与目标最后已知位置的距离 + last_box = target_info['last_box'] + last_center = np.array([(last_box[0] + last_box[2]) / 2, + (last_box[1] + last_box[3]) / 2]) + + distance = np.linalg.norm(det_center - last_center) + + # 计算IOU（如果目标最近刚更新） + time_since_update = time.time() - target_info['last_update_time'] + iou_score = self.compute_iou(detection_box, last_box) if time_since_update < 1.0 else 0 + + # 综合评分：距离近 + IOU高 + distance_score = max(0, 1 - distance / self.distance_threshold) + match_score = 0.3 * distance_score + 0.7 * iou_score + + # 考虑位置预测（如果目标在移动中） + if target_id in self.position_history and len(self.position_history[target_id]) >= 2: + # 简单的线性预测 + hist = list(self.position_history[target_id]) + if len(hist) >= 2: + velocity = hist[-1] - hist[-2] + predicted_pos = last_center + velocity + pred_distance = np.linalg.norm(det_center - predicted_pos) + pred_score = max(0, 1 - pred_distance / self.distance_threshold) + match_score = 0.7 * match_score + 0.3 * pred_score + + if match_score > best_match_score and match_score > 0.3: # 阈值可调 + best_match_score = match_score + best_match_id = target_id + + return best_match_id, best_match_score + + def push_alert(self, camera_id, target_id, lost_roi, last_cxcy, timestamp, entry_frame): + """报警推送""" + current_time = time.time() + if current_time - self.last_alert_time < ALERT_PUSH_INTERVAL: + return False + + _, frame_encoded = cv2.imencode('.jpg', entry_frame) + frame_base64 = frame_encoded.tobytes() + + alert_info = { + "camera_id": camera_id, + "alert_type": "prisoner_cx_disappear_in_roi", + "prisoner_track_id": target_id, + "disappear_roi": lost_roi, + "last_cx": round(last_cxcy[0], 2), + "last_cy": round(last_cxcy[1], 2), + "timestamp": timestamp, + "entry_frame_base64": frame_base64, + "details": f"犯人框中心点在{lost_roi}区域内消失" + } + + try: + requests.post(ALERT_PUSH_URL, json=alert_info, timeout=3) + print(f"[报警成功] target_id={target_id}, roi={lost_roi}") + self.last_alert_time = current_time + return True + except Exception as e: + print(f"[报警失败] {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 - """ + """核心帧处理 - 增强检测版""" self.frame_height, self.frame_width = frame.shape[:2] - current_frame_alerts = [] # 本帧报警信息 + current_frame_alerts = [] + frame_copy = frame.copy() + current_time = time.time() - # ========================= 1. 初始化ROI绝对坐标并绘制ROI ========================= + # ========================= 1. 绘制ROI区域 ========================= + roi_colors = {"left": (255, 0, 0), "right": (255, 0, 0)} self.roi_abs_cache.clear() - for roi_name in self.roi_config: + 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) - color = self.roi_colors.get(roi_name, (255, 255, 255)) - cv2.polylines(frame, [roi_draw], isClosed=True, color=color, thickness=2) + roi_draw = roi_abs.reshape((-1, 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, color, 2) + cv2.FONT_HERSHEY_SIMPLEX, 0.5, roi_colors[roi_name], 2) - # ========================= 2. 模型推理：仅提取犯人检测框 ========================= + # ========================= 2. 模型推理 ========================= 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] + prisoner_detections = [] + 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]) + # 确保坐标在图像范围内 + x1 = max(0, min(x1, self.frame_width - 1)) + y1 = max(0, min(y1, self.frame_height - 1)) + x2 = max(0, min(x2, self.frame_width - 1)) + y2 = max(0, min(y2, self.frame_height - 1)) - # ========================= 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]) + if cls_id == 1 and x2 > x1 and y2 > y1 and (x2 - x1) * (y2 - y1) > 100: # 过滤太小的框 + prisoner_detections.append([x1, y1, x2, y2, conf, cls_id]) + + # ========================= 3. ByteTracker跟踪 ========================= + prisoner_det_boxes = np.array( + [[x1, y1, x2, y2, conf] for x1, y1, x2, y2, conf, cls_id in prisoner_detections], + dtype=np.float32) if prisoner_detections else np.empty((0, 5)) + + if len(prisoner_det_boxes) > 0: + track_results = self.tracker.update( + prisoner_det_boxes, + [self.frame_height, self.frame_width], + [self.frame_height, self.frame_width] + ) + else: + track_results = [] + + # ========================= 4. 【核心改进】融合跟踪和检测 ========================= + # 4.1 先处理跟踪结果 + tracked_detections = {} # {track_id: detection_box} + used_det_indices = set() - # ========================= 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 + t_box = [float(x) for x in track.tlbr] - # 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) + # 寻找匹配的检测框 + best_iou = 0.0 # 最低阈值 + best_det_idx = -1 - # 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) + for det_idx, det in enumerate(prisoner_detections): + if det_idx in used_det_indices: + continue + iou = self.compute_iou(t_box, det[:4]) + if iou > best_iou: + best_iou = iou + best_det_idx = det_idx + + if best_det_idx != -1: + # 跟踪框有对应的检测框，使用检测框（更准确） + tracked_detections[f"track_{track_id}"] = { + 'box': prisoner_detections[best_det_idx][:4], + 'conf': prisoner_detections[best_det_idx][4], + 'source': 'tracked' + } + used_det_indices.add(best_det_idx) + else: + # 跟踪框没有对应的检测框，但仍保留跟踪框 + tracked_detections[f"track_{track_id}"] = { + 'box': t_box, + 'conf': 0.5, # 给个中等置信度 + 'source': 'track_only' + } + + # 4.2 处理未被跟踪的检测框 + for det_idx, det in enumerate(prisoner_detections): + if det_idx not in used_det_indices: + tracked_detections[f"det_{det_idx}"] = { + 'box': det[:4], + 'conf': det[4], + 'source': 'det_only' + } + + # ========================= 5. 匹配到已有目标 ========================= + current_target_ids = set() + matched_det_keys = set() + + for det_key, det_info in tracked_detections.items(): + det_box = det_info['box'] + det_conf = det_info['conf'] + + # 计算中心点 + cx = (det_box[0] + det_box[2]) / 2 + cy = (det_box[1] + det_box[3]) / 2 + + # 匹配到已有目标 + matched_target_id, match_score = self.match_detection_to_target(det_box, det_conf) + + if matched_target_id is not None and match_score > 0.3: + # 更新已有目标 + target_id = matched_target_id + target_info = self.active_targets[target_id] + + # 更新位置历史 + if target_id not in self.position_history: + self.position_history[target_id] = deque(maxlen=10) + self.position_history[target_id].append(np.array([cx, cy])) + + # 判断是否在ROI内 + is_cx_in_roi, current_roi = self.is_cxcy_in_roi(cx, cy) + + # 首次进入ROI缓存帧 + if not target_info.get('in_roi', False) and is_cx_in_roi: + self.entry_frame_cache[target_id] = frame_copy.copy() + target_info['lost_frames'] = 0 + + # 更新目标信息 + target_info.update({ + 'last_box': det_box, + 'last_cxcy': (cx, cy), + 'last_conf': det_conf, + 'last_update_time': current_time, + 'in_roi': is_cx_in_roi, + 'current_roi': current_roi if is_cx_in_roi else target_info.get('current_roi', 'outside'), + 'detection_source': det_info['source'] + }) + + current_target_ids.add(target_id) + matched_det_keys.add(det_key) + + else: + # 创建新目标 + target_id = self.next_target_id + self.next_target_id += 1 + + is_cx_in_roi, current_roi = self.is_cxcy_in_roi(cx, cy) + + self.active_targets[target_id] = { + 'first_seen': current_time, + 'last_box': det_box, + 'last_cxcy': (cx, cy), + 'last_conf': det_conf, + 'last_update_time': current_time, + 'in_roi': is_cx_in_roi, + 'current_roi': current_roi if is_cx_in_roi else 'outside', + 'lost_frames': 0, + 'detection_source': det_info['source'] + } + + self.position_history[target_id] = deque(maxlen=10) + self.position_history[target_id].append(np.array([cx, cy])) + + if is_cx_in_roi: + self.entry_frame_cache[target_id] = frame_copy.copy() + + current_target_ids.add(target_id) + matched_det_keys.add(det_key) + + # ========================= 6. 处理消失和报警 ========================= + for target_id in list(self.active_targets.keys()): + target_info = self.active_targets[target_id] + + if target_id not in current_target_ids: + # 目标在当前帧未出现 + if target_info['in_roi']: + # 在ROI内消失 + target_info['lost_frames'] += 1 + + if target_info['lost_frames'] >= ROI_LOST_FRAMES_THRESH: + # 触发报警 + entry_frame = self.entry_frame_cache.get(target_id, frame_copy) + self.push_alert( + camera_id=camera_id, + target_id=target_id, + lost_roi=target_info['current_roi'], + last_cxcy=target_info['last_cxcy'], + timestamp=timestamp, + entry_frame=entry_frame + ) - # ========================= 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": "Indoor Violation", - "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) + "action": "prisoner_cx_disappear_in_door", + "prisoner_track_id": target_id, + "disappear_roi": target_info['current_roi'], + "last_cx": round(target_info['last_cxcy'][0], 2), + "last_cy": round(target_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） ========================= + # 清理 + del self.active_targets[target_id] + if target_id in self.position_history: + del self.position_history[target_id] + if target_id in self.entry_frame_cache: + del self.entry_frame_cache[target_id] + else: + # 不在ROI内消失，直接清理 + del self.active_targets[target_id] + if target_id in self.position_history: + del self.position_history[target_id] + if target_id in self.entry_frame_cache: + del self.entry_frame_cache[target_id] + else: + # 目标仍在，但可能已离开ROI + if not target_info['in_roi']: + target_info['lost_frames'] = 0 + + # ========================= 7. 清理超时目标 ========================= + timeout_threshold = 5.0 # 5秒无更新就清理 + for target_id in list(self.active_targets.keys()): + if current_time - self.active_targets[target_id]['last_update_time'] > timeout_threshold: + del self.active_targets[target_id] + if target_id in self.position_history: + del self.position_history[target_id] + if target_id in self.entry_frame_cache: + del self.entry_frame_cache[target_id] + + # ========================= 8. 绘制可视化 ========================= + for target_id, target_info in self.active_targets.items(): + box = target_info['last_box'] + cx, cy = target_info['last_cxcy'] + in_roi = target_info['in_roi'] + current_roi = target_info['current_roi'] + source = target_info.get('detection_source', 'unknown') + + # 根据状态选择颜色 + if in_roi: + color = (0, 0, 255) # 绿色：在ROI内 + else: + color = (0, 255, 0) # 橙色：不在ROI内 + + # 根据来源选择线型 + thickness = 3 if source == 'tracked' else 2 + + cv2.rectangle(frame, (int(box[0]), int(box[1])), + (int(box[2]), int(box[3])), color, thickness) + cv2.circle(frame, (int(cx), int(cy)), 5, color, -1) + + status = f"T{target_id}_{current_roi[:2]}" + if source == 'det_only': + status += "_DET" + cv2.putText(frame, status, (int(box[0]), int(box[1]) - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + # ========================= 9. 统计信息 ========================= 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.putText(frame, f"Active Targets: {len(self.active_targets)}", + (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） ========================= + +# ========================= 帧处理线程 ========================= 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 + POST_TYPE = 3 TARGET_FPS = RTSP_FPS \ No newline at end of file