指挥室新需求: 仅检查 Nobody 这一种违规
This commit is contained in:
@@ -12,117 +12,75 @@ import json
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from typing import Dict, Any, Tuple, List
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from openxlab.model.commands import Model
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from biz.base_frame_processor import BaseFrameProcessorWorker
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# -------------------------- Kadian 检测相关导入 --------------------------
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from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机）
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from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机）
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from yolox.tracker.byte_tracker import BYTETracker
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from rtsp_service_ws_prison import TrackerArgs
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from utils.logger import get_logger
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logger = get_logger(__name__)
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# ========================= 配置区 =========================
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Person_Phone_Model = r'YOLO_Weight/person_phone_model.onnx' # 人和手机的检测模型
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Smoke_Model = r'YOLO_Weight/smoke_model.onnx' # 抽烟检测模型
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person_phone_input_size = 1280 # 模型输入尺寸，与训练时的模型一致
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smoke_input_size = 1280 # 模型输入尺寸，与训练时的模型一致
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# RTSP 服务配置
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RTSP_TARGET_FPS = 5.0
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RTSP_TARGET_FPS = 10.0
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# 新增：告警推送频率限制（秒）
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ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次
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Model_Path = 'YOLO_Weight/zhihuishi.onnx'
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Model_size = 640 # yolo模型尺寸
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Label_Map = {
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-1: 'Unknown',
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0: 'Police'
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}
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Color_Map = {
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-1: (255, 255, 255), # 白
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0: (0, 255, 0) # 绿
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}
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NOBODY_THRESHOLD = 5.0 * RTSP_TARGET_FPS # 当屏幕中的人消失了开始计数，如果累计够时长判定Nobody，如果中间又检测到了人，则立即清空计数
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class ZhihuishiDetector:
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def __init__(self, params=None):
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# 模型加载
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# 人和手机检测模型
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print(f"加载人和手机检测模型: {Person_Phone_Model}")
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self.person_phone_detector = YOLOv8_ONNX(Person_Phone_Model, conf_threshold=0.6, iou_threshold=0.45,
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input_size=person_phone_input_size)
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# 抽烟检测模型
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print(f"加载抽烟检测模型: {Smoke_Model}")
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self.smoke_detector = YOLOv8_ONNX(Smoke_Model, conf_threshold=0.4, iou_threshold=0.65,
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input_size=smoke_input_size)
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# person检测模型
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self.detector = YOLOv8_ONNX(Model_Path, conf_threshold=0.6, iou_threshold=0.6, input_size=Model_size)
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# ByteTracker
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class TrackerArgs:
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track_thresh = 0.25
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track_buffer = 30
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track_thresh = 0.61
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track_buffer = RTSP_TARGET_FPS * 3 # 3 秒未见该目标，则判定该目标消失，在字典中删除
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match_thresh = 0.8
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mot20 = False
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self.fps = RTSP_TARGET_FPS
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self.person_phone_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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self.smoke_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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# 当前帧的ID
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self.current_frame_idx = 0
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self.person_phone_track_role = {}
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self.smoke_track_role = {}
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# 设置 ByteTrack跟踪器
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self.ByteTracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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# 用来保存历史跟踪目标的字典，当目标消失了之后，就在该字典中清除该目标
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self.track_role = {}
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# ==========================================
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# 超参数设置 (Hyperparameters)
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# ==========================================
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# 1. 业务判定时间阈值
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self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长
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self.TIME_TOLERANCE_NOBODY = 2.0 # 人丢失缓冲时间
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self.TIME_THRESHOLD_SMOKE = 1.0 # 抽烟判定时长
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self.TIME_TOLERANCE_SMOKE = 0.5 # 烟丢失缓冲时间（防抖动）
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self.TIME_THRESHOLD_PHONE = 1.0 # 玩手机判定时长
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self.TIME_TOLERANCE_PHONE = 0.5 # 手机丢失缓冲时间（防抖动）
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# 无人在场帧数阈值
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self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
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self.frame_buffer_nobody = int(self.TIME_TOLERANCE_NOBODY * self.fps)
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# 抽烟检测帧数阈值
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self.frame_thresh_smoke = int(self.TIME_THRESHOLD_SMOKE * self.fps)
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self.frame_buffer_smoke = int(self.TIME_TOLERANCE_SMOKE * self.fps)
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# 手机检测帧数阈值
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self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps)
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self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps)
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# 记录无人的帧数
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self.nobody_frames = 0
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print(f"\n超参数设置:")
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print(f" FPS: {self.fps:.2f}")
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print(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧")
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print(f" 判定 'Smoke Detected' 需累计检测: {self.frame_thresh_smoke} 帧")
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print(f" 抽烟丢失缓冲帧数: {self.frame_buffer_smoke} 帧")
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print(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧")
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print(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧")
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print(f" 判定 'Nobody' 需连续: {NOBODY_THRESHOLD} 帧")
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# ==========================================
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# 状态变量初始化
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# ==========================================
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self.current_frame_idx = 0
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# 无人在场检测状态变量
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self.nobody_detection_frames = 0
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self.nobody_missing_frames = 0 # 连续未检测到手机的帧数
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self.nobody_alert_active = False # 手机报警是否激活
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# 手机检测状态变量
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self.phone_detection_frames = 0 # 连续检测到手机的帧数
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self.phone_missing_frames = 0 # 连续未检测到手机的帧数
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self.phone_alert_active = False # 手机报警是否激活
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# 抽烟检测状态变量
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self.smoke_detection_frames = 0 # 连续检测到手机的帧数
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self.smoke_missing_frames = 0 # 连续未检测到手机的帧数
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self.smoke_alert_active = False # 手机报警是否激活
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def compute_iou(self,boxA, boxB):
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def compute_iou(self, boxA, boxB):
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# box = [x1, y1, x2, y2]
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xA = max(boxA[0], boxB[0])
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yA = max(boxA[1], boxB[1])
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@@ -142,7 +100,7 @@ class ZhihuishiDetector:
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return interArea / unionArea
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def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0):
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def draw_alert(self, frame, text, color=(0, 0, 255), offset_y=0):
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"""在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)"""
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font_scale = 1.5
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thickness = 3
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@@ -155,340 +113,107 @@ class ZhihuishiDetector:
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cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1)
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cv2.putText(frame, text, (x, y), font, font_scale, color, thickness)
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if sub_text:
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cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2)
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def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
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# =================================== 收集检测结果 ===================================
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h, w = frame.shape[:2]
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self.width, self.height = w, h
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self.current_frame_idx += 1
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current_time_sec = timestamp # 当前时间戳
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current_time_sec = timestamp
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# yolo 的检测结果
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detect_results = self.detector(frame)
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# ========= 人和手机检测 =========
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person_phone_results = self.person_phone_detector(frame)
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detect_xyxy = [] # 存储 yolo检测出来的所有检测框的角点坐标，x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角
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detect_roles = [] # 存储 yolo检测出来的所有检测框的标签类别，用id的形式保存
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detect_bytetrack = [] # 从 yolo的检测结果中提取出来用于ByteTrack追踪检测框所需的信息，保存在这里面
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# ========= 抽烟检测 =========
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smoke_results = self.smoke_detector(frame)
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# 累计在当前帧里每个标签类别被检测到的次数,存储格式为 类别id:次数
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current_labels_count = {id: 0 for id in Label_Map}
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person_phone_dets_xyxy = []
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person_phone_dets_roles = []
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person_phone_dets_for_tracker = []
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smoke_dets_xyxy = []
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smoke_dets_roles = []
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smoke_dets_for_tracker = []
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# ========= 当前帧所有警告列表（关键改动）==========
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# ========= 存储当前帧所有警告 ==========
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current_frame_alerts = [] # 每帧清空，重新收集
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# 收集 人和手机的检测结果
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if person_phone_results:
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for det in person_phone_results:
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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
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person_phone_dets_xyxy.append([x1, y1, x2, y2])
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person_phone_dets_for_tracker.append([x1, y1, x2, y2, conf])
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if cls_id == 0:
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person_phone_dets_roles.append("phone")
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elif cls_id == 1:
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person_phone_dets_roles.append("police")
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# 遍历 yolo 的检测结果，对 detect_xyxy detect_roles detect_bytetrack 进行填充
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if detect_results:
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for result in detect_results: # yolo检测结果返回 x1, y1, x2, y2, conf, cls_id
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detect_xyxy.append(result[:-2])
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detect_roles.append(result[-1])
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detect_bytetrack.append(result[:-1])
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person_phone_dets = np.array(person_phone_dets_for_tracker, dtype=np.float32) if len(
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person_phone_dets_for_tracker) else np.empty((0, 5))
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person_phone_tracks = self.person_phone_tracker.update(
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person_phone_dets,
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# 根据收集到的 detect_bytetrack 确定追踪的检测框目标
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tracks = self.ByteTracker.update(
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np.array(detect_bytetrack, dtype=np.float32) if len(detect_bytetrack) else np.empty((0, 5)),
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# np.empty((0,5)) 表示一个 0 行、5 列 的二维空数组
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[self.height, self.width],
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[self.height, self.width]
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)
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# 收集 抽烟的检测结果
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if smoke_results:
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for det in smoke_results:
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x1, y1, x2, y2, conf, cls_id = det
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smoke_dets_xyxy.append([x1, y1, x2, y2])
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smoke_dets_for_tracker.append([x1, y1, x2, y2, conf])
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if cls_id == 0:
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smoke_dets_roles.append("smoke")
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# 匹配每个跟踪目标的正确类别
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# 为什么要用track的结果来统计标签类别的出现次数以及绘制检测框，而不是仅用yolo的检测结果来统计及绘制，是因为yolo的检测结果是针对单帧，而bytetrack可以实现跨帧处理，bytetrack的track_id会给每个目标设置一个唯一的id
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current_track_ids = []
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for track in tracks:
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track_id = track.track_id
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current_track_ids.append(track_id)
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smoke_dets = np.array(smoke_dets_for_tracker, dtype=np.float32) if len(
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smoke_dets_for_tracker) else np.empty((0, 5))
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reIdentify_frame_interval = 10 # 重新匹配每个跟踪目标的类别的帧间隔
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if (current_time_sec % reIdentify_frame_interval == 0) or track_id not in self.track_role:
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best_iou = 0.0
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best_role = -1
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smoke_tracks = self.smoke_tracker.update(
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smoke_dets,
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[self.height, self.width],
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[self.height, self.width]
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)
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track_box = list(map(float, track.tlbr))
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# ========= 单帧统计变量 =========
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current_person_count = 0
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current_phone_count = 0
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current_smoke_count = 0
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for i, box in enumerate(detect_xyxy):
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iou = self.compute_iou(track_box, box)
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if iou > best_iou:
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best_iou = iou
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best_role = detect_roles[i]
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# ========= 人和手机检测 =========
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for t in person_phone_tracks:
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# print("t: {}".format(t))
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tid = t.track_id
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# cls_id = -1
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self.track_role[track_id] = best_role
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# IoU 匹配角色
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# IoU匹配跟踪ID和类别
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REVALIDATE_FRAME_INTERVAL = 10
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if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.person_phone_track_role):
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#if tid not in self.person_phone_track_role:
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best_iou = 0
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best_role = "unknown"
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role = self.track_role[track_id]
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t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2]
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current_labels_count[role] += 1
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for i, box in enumerate(person_phone_dets_xyxy):
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iou_val = self.compute_iou(t_box, box)
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if iou_val > best_iou:
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best_iou = iou_val
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best_role = person_phone_dets_roles[i]
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if best_iou > 0.1:
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self.person_phone_track_role[tid] = best_role
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else:
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self.person_phone_track_role[tid] = "unknown"
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# 当 role 不等于 unknown 的时候，绘制检测框
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if role != -1:
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x1, y1, x2, y2 = map(int, track.tlbr)
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cv2.rectangle(frame, (x1, y1), (x2, y2), Color_Map[role], 2)
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cv2.putText(frame, Label_Map[role], (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, Color_Map[role], 2)
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role = self.person_phone_track_role.get(tid, "unknown")
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cls_id = -1
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if role == "phone":
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cls_id = 0
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elif role == "police":
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cls_id = 1
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# print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id))
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# 处理过期的 track_role 里的role,如果 track_role 里包含 tracks 里没有的 role ，直接删了即可
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for role in list(self.track_role.keys()): # 遍历字典的时候不能直接删元素,用 list() 先复制一份 key，再遍历删除，才安全
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if role not in current_track_ids:
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del self.track_role[role]
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x1, y1, x2, y2 = map(int, t.tlbr)
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# ========================= 业务逻辑判断 ===========================
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nobody_alter_flag = False # 无人在场业务逻辑是否成立
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cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
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color = None
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label = None
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# Nobody 业务逻辑判断
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if current_labels_count[0] == 0:
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self.nobody_frames += 1
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if cls_id == 0: # Person
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current_phone_count += 1
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color = (255, 0, 255)
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label = "Phone"
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elif cls_id == 1: # Phone（主模型已支持）
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current_person_count += 1
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color = (0, 0, 139)
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label = "Person"
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else:
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color = (255, 255, 255)
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label = "Unknown"
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# label = f"ID:{tid} IN"
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
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cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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# ========= 抽烟检测 =========
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for t in smoke_tracks:
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# print("t: {}".format(t))
|
||||
tid = t.track_id
|
||||
# cls_id = -1
|
||||
|
||||
# IoU 匹配角色
|
||||
# IoU匹配跟踪ID和类别
|
||||
REVALIDATE_FRAME_INTERVAL = 10
|
||||
if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.smoke_track_role):
|
||||
#if tid not in self.smoke_track_role:
|
||||
best_iou = 0
|
||||
best_role = "unknown"
|
||||
|
||||
t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2]
|
||||
|
||||
for i, box in enumerate(smoke_dets_xyxy):
|
||||
iou_val = self.compute_iou(t_box, box)
|
||||
if iou_val > best_iou:
|
||||
best_iou = iou_val
|
||||
best_role = smoke_dets_roles[i]
|
||||
# self.smoke_track_role[tid] = best_role
|
||||
if best_iou > 0.1:
|
||||
self.smoke_track_role[tid] = best_role
|
||||
else:
|
||||
self.smoke_track_role[tid] = "unknown"
|
||||
|
||||
role = self.smoke_track_role.get(tid, "unknown")
|
||||
cls_id = -1
|
||||
if role == "smoke":
|
||||
cls_id = 0
|
||||
|
||||
x1, y1, x2, y2 = map(int, t.tlbr)
|
||||
|
||||
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
|
||||
|
||||
color = None
|
||||
label = None
|
||||
|
||||
if cls_id == 0: # 抽烟
|
||||
current_smoke_count += 1
|
||||
color = (255, 255, 0)
|
||||
label = "Smoke"
|
||||
|
||||
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)
|
||||
|
||||
# ==========================================
|
||||
# 手机检测
|
||||
# ==========================================
|
||||
if current_phone_count > 0:
|
||||
# 检测到手机框
|
||||
self.phone_detection_frames += 1
|
||||
self.phone_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.phone_detection_frames >= self.frame_thresh_phone:
|
||||
self.phone_alert_active = True
|
||||
if self.nobody_frames >= NOBODY_THRESHOLD:
|
||||
nobody_alter_flag = True
|
||||
else:
|
||||
# 未检测到手机框
|
||||
self.phone_missing_frames += 1
|
||||
|
||||
# 如果之前检测到手机，重置检测计数器
|
||||
if self.phone_detection_frames > 0:
|
||||
# 只有在连续丢失超过缓冲帧数时才重置
|
||||
if self.phone_missing_frames >= self.frame_buffer_phone:
|
||||
self.phone_detection_frames = 0
|
||||
self.phone_alert_active = False
|
||||
else:
|
||||
# 从未检测到手机，保持状态
|
||||
pass
|
||||
|
||||
# ==========================================
|
||||
# 抽烟检测
|
||||
# ==========================================
|
||||
if current_smoke_count > 0:
|
||||
# 检测到抽烟框
|
||||
self.smoke_detection_frames += 1
|
||||
self.smoke_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.smoke_detection_frames >= self.frame_thresh_smoke:
|
||||
self.smoke_alert_active = True
|
||||
else:
|
||||
# 未检测到抽烟框
|
||||
self.smoke_missing_frames += 1
|
||||
|
||||
# 如果之前检测到抽烟，重置检测计数器
|
||||
if self.smoke_detection_frames > 0:
|
||||
# 只有在连续丢失超过缓冲帧数时才重置
|
||||
if self.smoke_missing_frames >= self.frame_buffer_smoke:
|
||||
self.smoke_detection_frames = 0
|
||||
self.smoke_alert_active = False
|
||||
else:
|
||||
# 从未检测到抽烟，保持状态
|
||||
pass
|
||||
|
||||
# ==========================================
|
||||
# 9. 业务逻辑判定 (Only One / Nobody)
|
||||
# ==========================================
|
||||
status_text = ""
|
||||
|
||||
if current_person_count == 0:
|
||||
self.nobody_detection_frames += 1
|
||||
self.nobody_missing_frames = 0
|
||||
|
||||
if self.nobody_detection_frames >= self.frame_thresh_nobody:
|
||||
self.nobody_alert_active = True
|
||||
else:
|
||||
self.nobody_missing_frames += 1
|
||||
|
||||
if self.nobody_detection_frames > 0:
|
||||
if self.nobody_missing_frames >= self.frame_buffer_nobody:
|
||||
self.nobody_detection_frames = 0
|
||||
self.nobody_alert_active = False
|
||||
else:
|
||||
pass
|
||||
self.nobody_frames = 0
|
||||
|
||||
|
||||
# if current_person_count == 0:
|
||||
# self.cnt_frame_nobody += 1
|
||||
# else:
|
||||
# self.cnt_frame_nobody = 0
|
||||
|
||||
# ==========================================
|
||||
# 10. 收集并生成结构化警告（核心改动）
|
||||
# ==========================================
|
||||
|
||||
alert_offset = 0
|
||||
|
||||
# A. Playing Phone
|
||||
if self.phone_alert_active:
|
||||
duration_seconds = self.phone_detection_frames / self.fps
|
||||
if nobody_alter_flag:
|
||||
action_text = 'Nobody Checking'
|
||||
current_frame_alerts.append(
|
||||
{
|
||||
'time': current_time_sec,
|
||||
'action': 'Playing Phone',
|
||||
'confidence': 1.0, # 固定为1.0（规则判定）
|
||||
'details': f"Detected for {duration_seconds:.1f}s"
|
||||
'action': action_text,
|
||||
}
|
||||
)
|
||||
|
||||
# A. Playing Phone
|
||||
if self.smoke_alert_active:
|
||||
duration_seconds = self.smoke_detection_frames / self.fps
|
||||
current_frame_alerts.append(
|
||||
{
|
||||
'time': current_time_sec,
|
||||
'action': 'Smoke',
|
||||
'confidence': 1.0, # 固定为1.0（规则判定）
|
||||
'details': f"Detected for {duration_seconds:.1f}s"
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# D. Nobody Checking
|
||||
if self.nobody_alert_active:
|
||||
duration_seconds = self.nobody_detection_frames / self.fps
|
||||
current_frame_alerts.append({
|
||||
'time': current_time_sec,
|
||||
'action': 'Nobody Checking',
|
||||
'confidence': 1.0,
|
||||
'details': f"Detected for {duration_seconds:.1f}s"
|
||||
})
|
||||
|
||||
# ==========================================
|
||||
# 11. 统一显示当前帧所有警告（可替换原分层显示）
|
||||
# ==========================================
|
||||
debug_info = f"Person: {current_person_count} | Phone: {current_phone_count} | Smoke: {current_smoke_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 == 'Nobody Checking':
|
||||
color = (255, 255, 255)
|
||||
elif action == 'Smoke':
|
||||
color = (0, 0, 255)
|
||||
elif action == 'Playing Phone':
|
||||
color = (255, 0, 0)
|
||||
|
||||
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)
|
||||
self.draw_alert(frame, action_text, offset_y=0)
|
||||
|
||||
return {
|
||||
"image": frame,
|
||||
|
||||
"alerts":current_frame_alerts
|
||||
"alerts": current_frame_alerts
|
||||
}
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user