From 124672332fd02f110e726032cb8a9b5f0f952190 Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Fri, 6 Mar 2026 09:46:40 +0800 Subject: [PATCH] =?UTF-8?q?=E6=96=B0=E5=A2=9Eab=E9=97=A8biz?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/checkpoint/checkpoint_biz_02.py | 515 ---------------------------- biz/prison/ab_biz.py | 447 ++++++++++++++++++++++++ 2 files changed, 447 insertions(+), 515 deletions(-) delete mode 100644 biz/checkpoint/checkpoint_biz_02.py create mode 100644 biz/prison/ab_biz.py diff --git a/biz/checkpoint/checkpoint_biz_02.py b/biz/checkpoint/checkpoint_biz_02.py deleted file mode 100644 index f0a372f..0000000 --- a/biz/checkpoint/checkpoint_biz_02.py +++ /dev/null @@ -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 diff --git a/biz/prison/ab_biz.py b/biz/prison/ab_biz.py new file mode 100644 index 0000000..d43aa28 --- /dev/null +++ b/biz/prison/ab_biz.py @@ -0,0 +1,447 @@ +import cv2 +import numpy as np +import base64 +from typing import Dict, Any +import threading +import time +import queue +import requests + +# -------------------------- 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/prisoner_model.onnx' + +# 输入尺寸 +input_size = 1280 + +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 ZoulangDetector: + def __init__(self): + # 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) + + # ByteTracker + class TrackerArgs: + track_thresh = 0.25 + track_buffer = 30 + match_thresh = 0.8 + mot20 = False + + + + self.police_prisoner_track_role = {} + + self.fps = RTSP_TARGET_FPS + + self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) + + # ========================================== + # 超参数设置 (Hyperparameters) + # ========================================== + + # 1. 业务判定时间阈值 + # self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长 + + self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 + self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） + + self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 + self.TIME_TOLERANCE_PRISONER = 0.5 # 犯人丢失缓冲时间（防抖动） + + # 无人在场帧数阈值 + # self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) + + # 警察检测帧数阈值 + self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) + self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) + + # 犯人检测帧数阈值 + self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) + self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) + + print(f"\n超参数设置:") + print(f" FPS: {self.fps:.2f}") + # print(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") + print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") + print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") + print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") + print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") + + # ========================================== + # 状态变量初始化 + # ========================================== + + self.current_frame_idx = 0 + + # 无人在场检测状态变量 + self.cnt_frame_nobody = 0 + + # 警察检测状态变量 + self.police_detection_frames = 0 # 连续检测到警察的帧数 + self.police_missing_frames = 0 # 连续未检测到警察的帧数 + self.police_alert_active = False # 警察报警是否激活 + + # 犯人检测状态变量 + self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 + self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 + self.prisoner_alert_active = False # 犯人报警是否激活 + + + 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 + + # ========= 警察和犯人检测 ========= + police_prisoner_results = self.police_prisoner_detector(frame) + + police_prisoner_dets_xyxy = [] + police_prisoner_dets_roles = [] + police_prisoner_dets_for_tracker = [] + + # ========= 当前帧所有警告列表（关键改动）========== + current_frame_alerts = [] # 每帧清空，重新收集 + + if police_prisoner_results: + for det in police_prisoner_results: + x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 + police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) + police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) + if cls_id == 0: + police_prisoner_dets_roles.append("police") + elif cls_id == 1: + police_prisoner_dets_roles.append("prisoner") + + ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( + police_prisoner_dets_for_tracker) else np.empty((0, 5)) + + police_prisoner_dets_tracks = self.tracker.update( + ppolice_prisoner_dets, + [self.height, self.width], + [self.height, self.width] + ) + # ========= 单帧统计变量 ========= + current_police_count = 0 + current_prisoner_count = 0 + + # ========= 警察和犯人检测 ========= + for t in police_prisoner_dets_tracks: + # print("t: {}".format(t)) + tid = t.track_id + # cls_id = -1 + + # IoU 匹配角色 + + REVALIDATE_FRAME_INTERVAL = 10 + if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( + tid not in self.police_prisoner_track_role): + best_iou = 0 + best_role = "unknown" + + t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] + + for i, box in enumerate(police_prisoner_dets_xyxy): + iou_val = self.compute_iou(t_box, box) + if iou_val > best_iou: + best_iou = iou_val + best_role = police_prisoner_dets_roles[i] + if best_iou > 0.1: + self.police_prisoner_track_role[tid] = best_role + else: + self.police_prisoner_track_role[tid] = "unknown" + + role = self.police_prisoner_track_role.get(tid, "unknown") + cls_id = -1 + if role == "police": + cls_id = 0 + elif role == "prisoner": + cls_id = 1 + # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) + + x1, y1, x2, y2 = map(int, t.tlbr) + + cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 + + color = None + label = None + + if cls_id == 0: # Person + current_police_count += 1 + color = (255, 0, 255) + label = "police" + + elif cls_id == 1: # Phone（主模型已支持） + current_prisoner_count += 1 + color = (0, 0, 139) + label = "prisoner" + else: + color = (255, 255, 255) + label = "Unknown" + + # label = f"ID:{tid} IN" + + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) + cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) + + # ========================================== + # 犯人检测 + # ========================================== + if current_prisoner_count > 0: + # 检测到犯人框 + self.prisoner_detection_frames += 1 + self.prisoner_missing_frames = 0 # 重置丢失计数器 + + # 当检测累计达到阈值时，激活报警 + if self.prisoner_detection_frames >= self.frame_thresh_prisoner: + self.prisoner_alert_active = True + else: + # 未检测到犯人框 + self.prisoner_missing_frames += 1 + + # 如果之前检测到手机，重置检测计数器 + if self.prisoner_detection_frames > 0: + # 只有在连续丢失超过缓冲帧数时才重置 + if self.prisoner_missing_frames >= self.frame_buffer_prisoner: + self.prisoner_detection_frames = 0 + self.prisoner_alert_active = False + else: + # 从未检测到犯人，保持状态 + pass + # ========================================== + # 警察检测 + # ========================================== + if current_police_count > 0: + # 检测到犯人框 + self.police_detection_frames += 1 + self.police_missing_frames = 0 # 重置丢失计数器 + + # 当检测累计达到阈值时，激活报警 + if self.police_detection_frames >= self.frame_thresh_police: + self.police_alert_active = True + else: + # 未检测到犯人框 + self.police_missing_frames += 1 + + # 如果之前检测到手机，重置检测计数器 + if self.police_detection_frames > 0: + # 只有在连续丢失超过缓冲帧数时才重置 + if self.police_missing_frames >= self.frame_buffer_police: + self.police_detection_frames = 0 + self.police_alert_active = False + else: + # 从未检测到犯人，保持状态 + pass + + + + alert_offset = 0 + + # A. 有犯人 + if self.prisoner_alert_active: + duration_seconds = self.prisoner_detection_frames / self.fps + current_frame_alerts.append( + { + 'time': current_time_sec, + 'action': 'prisoner', + 'confidence': 1.0, # 固定为1.0（规则判定） + 'details': f"Detected for {duration_seconds:.1f}s" + } + ) + self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) + alert_offset += 100 + + # ========================================== + # 11. 统一显示当前帧所有警告（可替换原分层显示） + # ========================================== + debug_info = f" prisoner: {current_prisoner_count}" + cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) + + # 统一警告显示区 + alert_y_start = 150 + for i, alert in enumerate(current_frame_alerts): + action = alert['action'] + details = alert.get('details', '') + color = (0, 0, 255) # 默认红色警告 + + if action == 'prisoner': + color = (255, 255, 255) + + main_text = action + if details: + main_text += f" ({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(threading.Thread): + def __init__(self, + 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] = {} + + # 每个摄像头一个独立的 Kadian 检测器实例 + self.kadian_detectors: Dict[int, ZoulangDetector] = {} + + # 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() \ No newline at end of file