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单人单检和无人在场的警告只发生在有车的时候;删除了单人单检和无人在场在视频上弹出的警告

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Qinchuanqi
2026-03-05 11:06:22 +08:00
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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