Merge branch 'master' of https://gitea.swiftsnake.cn/yipai-tech/SupervisorAI

biz/checkpoint/checkpoint_biz.py

@@ -372,12 +372,12 @@ class KadianDetector:
 
             # 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!")
+                logger.info(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!")
+                logger.info(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)
 

biz/checkpoint/checkpoint_biz_02.py

@@ -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