From 96bf81b5e7a26e5f045d0ec314eb0defed032bce Mon Sep 17 00:00:00 2001 From: Qinchuanqi <2254943770@qq.com> Date: Thu, 5 Mar 2026 11:06:22 +0800 Subject: [PATCH] =?UTF-8?q?=E5=8D=95=E4=BA=BA=E5=8D=95=E6=A3=80=E5=92=8C?= =?UTF-8?q?=E6=97=A0=E4=BA=BA=E5=9C=A8=E5=9C=BA=E7=9A=84=E8=AD=A6=E5=91=8A?= =?UTF-8?q?=E5=8F=AA=E5=8F=91=E7=94=9F=E5=9C=A8=E6=9C=89=E8=BD=A6=E7=9A=84?= =?UTF-8?q?=E6=97=B6=E5=80=99;=E5=88=A0=E9=99=A4=E4=BA=86=E5=8D=95?= =?UTF-8?q?=E4=BA=BA=E5=8D=95=E6=A3=80=E5=92=8C=E6=97=A0=E4=BA=BA=E5=9C=A8?= =?UTF-8?q?=E5=9C=BA=E5=9C=A8=E8=A7=86=E9=A2=91=E4=B8=8A=E5=BC=B9=E5=87=BA?= =?UTF-8?q?=E7=9A=84=E8=AD=A6=E5=91=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/checkpoint/checkpoint_biz_02.py | 515 ++++++++++++++++++++++++++++ 1 file changed, 515 insertions(+) create mode 100644 biz/checkpoint/checkpoint_biz_02.py diff --git a/biz/checkpoint/checkpoint_biz_02.py b/biz/checkpoint/checkpoint_biz_02.py new file mode 100644 index 0000000..f0a372f --- /dev/null +++ b/biz/checkpoint/checkpoint_biz_02.py @@ -0,0 +1,515 @@ + +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