607 lines
23 KiB
Python
607 lines
23 KiB
Python
# rtsp_service_kadian.py
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# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py
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# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警
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import cv2
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import numpy as np
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import os
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import time
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import threading
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import queue
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import yaml
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import json
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import base64
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from typing import Dict, Any, Tuple, List
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# -------------------------- Kadian 检测相关导入 --------------------------
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from algorithm.checkpoint.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 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'D:\Python_Save\PoliceProject\Yolo_Weight\person_phone_model.onnx' # 人和手机的检测模型
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Smoke_Model = r'D:\Python_Save\PoliceProject\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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# 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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class ZhihuishiDetector:
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def __init__(self):
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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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# 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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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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self.person_phone_track_role = {}
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self.smoke_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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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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# ==========================================
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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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# 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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xB = min(boxA[2], boxB[2])
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yB = min(boxA[3], boxB[3])
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interW = max(0, xB - xA)
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interH = max(0, yB - yA)
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interArea = interW * interH
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boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
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boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
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unionArea = boxAArea + boxBArea - interArea
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if unionArea == 0:
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return 0.0
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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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"""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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font = cv2.FONT_HERSHEY_SIMPLEX
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(text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness)
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x = self.width - text_w - 20
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y = 50 + text_h + offset_y # 增加 Y 轴偏移
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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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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
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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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# ========= 抽烟检测 =========
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smoke_results = self.smoke_detector(frame)
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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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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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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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[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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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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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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# ========= 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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# ========= 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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# 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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t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2]
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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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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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x1, y1, x2, y2 = map(int, t.tlbr)
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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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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))
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tid = t.track_id
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# cls_id = -1
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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.smoke_track_role):
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#if tid not in self.smoke_track_role:
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best_iou = 0
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best_role = "unknown"
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t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2]
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for i, box in enumerate(smoke_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 = smoke_dets_roles[i]
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# self.smoke_track_role[tid] = best_role
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if best_iou > 0.1:
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self.smoke_track_role[tid] = best_role
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else:
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self.smoke_track_role[tid] = "unknown"
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role = self.smoke_track_role.get(tid, "unknown")
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cls_id = -1
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if role == "smoke":
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cls_id = 0
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x1, y1, x2, y2 = map(int, t.tlbr)
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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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if cls_id == 0: # 抽烟
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current_smoke_count += 1
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color = (255, 255, 0)
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label = "Smoke"
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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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# ==========================================
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# 手机检测
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# ==========================================
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if current_phone_count > 0:
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# 检测到手机框
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self.phone_detection_frames += 1
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self.phone_missing_frames = 0 # 重置丢失计数器
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# 当检测累计达到阈值时，激活报警
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if self.phone_detection_frames >= self.frame_thresh_phone:
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self.phone_alert_active = True
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else:
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# 未检测到手机框
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self.phone_missing_frames += 1
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# 如果之前检测到手机，重置检测计数器
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if self.phone_detection_frames > 0:
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# 只有在连续丢失超过缓冲帧数时才重置
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if self.phone_missing_frames >= self.frame_buffer_phone:
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self.phone_detection_frames = 0
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self.phone_alert_active = False
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else:
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# 从未检测到手机，保持状态
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pass
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# ==========================================
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# 抽烟检测
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# ==========================================
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if current_smoke_count > 0:
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# 检测到抽烟框
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self.smoke_detection_frames += 1
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self.smoke_missing_frames = 0 # 重置丢失计数器
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# 当检测累计达到阈值时，激活报警
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if self.smoke_detection_frames >= self.frame_thresh_smoke:
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self.smoke_alert_active = True
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else:
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# 未检测到抽烟框
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self.smoke_missing_frames += 1
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# 如果之前检测到抽烟，重置检测计数器
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if self.smoke_detection_frames > 0:
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# 只有在连续丢失超过缓冲帧数时才重置
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if self.smoke_missing_frames >= self.frame_buffer_smoke:
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self.smoke_detection_frames = 0
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self.smoke_alert_active = False
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else:
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# 从未检测到抽烟，保持状态
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pass
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# ==========================================
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# 9. 业务逻辑判定 (Only One / Nobody)
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# ==========================================
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status_text = ""
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if current_person_count == 0:
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self.nobody_detection_frames += 1
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self.nobody_missing_frames = 0
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if self.nobody_detection_frames >= self.frame_thresh_nobody:
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self.nobody_alert_active = True
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else:
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self.nobody_missing_frames += 1
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if self.nobody_detection_frames > 0:
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if self.nobody_missing_frames >= self.frame_buffer_nobody:
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self.nobody_detection_frames = 0
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self.nobody_alert_active = False
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else:
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pass
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# if current_person_count == 0:
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# self.cnt_frame_nobody += 1
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# else:
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# self.cnt_frame_nobody = 0
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# ==========================================
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# 10. 收集并生成结构化警告（核心改动）
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# ==========================================
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alert_offset = 0
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# A. Playing Phone
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if self.phone_alert_active:
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duration_seconds = self.phone_detection_frames / self.fps
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current_frame_alerts.append(
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{
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'time': current_time_sec,
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||
'action': 'Playing Phone',
|
||
'confidence': 1.0, # 固定为1.0（规则判定）
|
||
'details': f"Detected for {duration_seconds:.1f}s"
|
||
}
|
||
)
|
||
|
||
# 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)
|
||
|
||
return {
|
||
"image": frame,
|
||
|
||
"alerts":current_frame_alerts
|
||
}
|
||
|
||
|
||
# ========================= 帧处理线程 =========================
|
||
class FrameProcessorWorker(threading.Thread):
|
||
def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event):
|
||
super().__init__(daemon=True)
|
||
self.raw_queue = raw_queue
|
||
self.ws_queue = ws_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, ZhihuishiDetector] = {}
|
||
|
||
self.last_alert_push_time: Dict[int, Dict[str, float]] = {}
|
||
|
||
def _encode_base64(self, img):
|
||
_, buf = cv2.imencode(".jpg", img)
|
||
return base64.b64encode(buf).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
|
||
|
||
try:
|
||
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] = ZhihuishiDetector()
|
||
detector = self.kadian_detectors[cam_id]
|
||
|
||
# 执行检测
|
||
# detect_start = time.time()
|
||
result = detector.process_frame(frame.copy(), cam_id, ts)
|
||
# detect_time = (time.time() - detect_start) * 1000
|
||
|
||
result_img = result["image"]
|
||
result_type = result["alerts"]
|
||
# logger.debug(f"alerts: {result_type}")
|
||
|
||
# ========= 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
|
||
# encode_start = time.time()
|
||
try:
|
||
img_b64 = self._encode_base64(result_img)
|
||
except Exception as e:
|
||
logger.error(f"[ERROR] Encode image failed: {e}")
|
||
img_b64 = None
|
||
# encode_time = (time.time() - encode_start) * 1000
|
||
|
||
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": 0,
|
||
"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:
|
||
# self.ws_queue_2.put(msg, timeout=1.0)
|
||
except queue.Full:
|
||
logger.warning("[WARN] ws_send_queue full, drop frame message")
|
||
|
||
# # 打印关键操作的耗时
|
||
# total_time = detect_time + encode_time
|
||
# logger.info(f"[PERF] Camera {cam_id} - Total: {total_time:.1f}ms | "
|
||
# f"Detect: {detect_time:.1f}ms | "
|
||
# f"Encode: {encode_time:.1f}ms | ")
|
||
|
||
except Exception as e:
|
||
logger.error(
|
||
f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}")
|
||
logger.exception("Exception details:") # 打印完整的堆栈跟踪
|
||
# 继续处理下一帧，不要退出循环
|
||
finally:
|
||
self.raw_queue.task_done()
|
||
|