721 lines
27 KiB
Python
721 lines
27 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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import asyncio
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import websockets
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from dataclasses import dataclass
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from typing import Dict, Any, Tuple
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from datetime import datetime
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from utils.logger import setup_logger
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logger = setup_logger(__name__)
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# 导入人脸识别算法
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try:
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from api.routes.algorithm_router import video_face_prison_biz
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logger.info("[INFO] 成功导入人脸识别算法")
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except Exception as e:
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logger.error(f"[WARN] 无法导入人脸识别算法: {e}")
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# -------------------------- Kadian 检测相关导入 --------------------------
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from 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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# ========================= 配置区 =========================
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# Kadian 模型路径与ROI（可根据实际情况修改）
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police_prisoner_model_path = 'YOLO_Weight/prisoner_model.onnx'
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FACE_RECOGNITION_ENABLED = True # 是否启用人脸识别
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# 输入尺寸
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police_prisoner_input_size = 1280
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# RTSP 服务配置
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RTSP_TARGET_FPS = 30.0
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FRAMES_PER_SEGMENT = 300
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VIDEO_OUTPUT_DIR = "./videos"
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WS_HOST = "0.0.0.0"
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WS_PORT = 8765
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# WebSocket 客户端集合
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ws_clients = set()
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# ========================= 数据结构 =========================
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@dataclass
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class CameraConfig:
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id: int
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name: str
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rtsp_url: str
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# ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
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class KadianDetector:
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def __init__(self):
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# 模型加载
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self.police_prisoner_detector = YOLOv8_ONNX(police_prisoner_model_path, conf_threshold=0.5, iou_threshold=0.45,
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input_size=police_prisoner_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.police_prisoner_track_role = {}
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self.fps = RTSP_TARGET_FPS
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self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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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_THRESHOLD_POLICE = 1.0 # 警察判定时长
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self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动）
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self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长
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self.TIME_TOLERANCE_PRISONER = 0.5 # 犯人丢失缓冲时间（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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# 警察检测帧数阈值
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self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps)
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self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps)
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# 犯人检测帧数阈值
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self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps)
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self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps)
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logger.info(f"\n超参数设置:")
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logger.info(f" FPS: {self.fps:.2f}")
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# logger.info(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧")
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logger.info(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧")
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logger.info(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧")
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logger.info(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧")
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logger.info(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 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.cnt_frame_nobody = 0
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# 警察检测状态变量
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self.police_detection_frames = 0 # 连续检测到警察的帧数
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self.police_missing_frames = 0 # 连续未检测到警察的帧数
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self.police_alert_active = False # 警察报警是否激活
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# 犯人检测状态变量
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self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数
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self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数
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self.prisoner_alert_active = False # 犯人报警是否激活
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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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# ========= 警察和犯人检测 =========
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police_prisoner_results = self.police_prisoner_detector(frame)
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police_prisoner_dets_xyxy = []
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police_prisoner_dets_roles = []
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police_prisoner_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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if police_prisoner_results:
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for det in police_prisoner_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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police_prisoner_dets_xyxy.append([x1, y1, x2, y2])
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police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf])
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if cls_id == 0:
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police_prisoner_dets_roles.append("police")
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elif cls_id == 1:
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police_prisoner_dets_roles.append("prisoner")
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ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len(
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police_prisoner_dets_for_tracker) else np.empty((0, 5))
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police_prisoner_dets_tracks = self.tracker.update(
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ppolice_prisoner_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_police_count = 0
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current_prisoner_count = 0
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# ========= 警察和犯人检测 =========
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for t in police_prisoner_dets_tracks:
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# logger.info("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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if tid not in self.police_prisoner_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(police_prisoner_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 = police_prisoner_dets_roles[i]
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if best_iou > 0.1:
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self.police_prisoner_track_role[tid] = best_role
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else:
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self.police_prisoner_track_role[tid] = "unknown"
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role = self.police_prisoner_track_role.get(tid, "unknown")
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cls_id = -1
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if role == "police":
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cls_id = 0
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elif role == "prisoner":
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cls_id = 1
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# logger.info("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_police_count += 1
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color = (255, 0, 255)
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label = "police"
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elif cls_id == 1: # Phone（主模型已支持）
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current_prisoner_count += 1
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color = (0, 0, 139)
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label = "prisoner"
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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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# 犯人检测
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# ==========================================
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if current_prisoner_count > 0:
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# 检测到犯人框
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self.prisoner_detection_frames += 1
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self.prisoner_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.prisoner_detection_frames >= self.frame_thresh_prisoner:
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self.prisoner_alert_active = True
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else:
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# 未检测到犯人框
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self.prisoner_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.prisoner_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.prisoner_missing_frames >= self.frame_buffer_prisoner:
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self.prisoner_detection_frames = 0
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self.prisoner_alert_active = False
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else:
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# 从未检测到犯人，保持状态
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pass
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# ==========================================
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# 警察检测
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# ==========================================
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if current_police_count > 0:
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# 检测到犯人框
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self.police_detection_frames += 1
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self.police_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.police_detection_frames >= self.frame_thresh_police:
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self.police_alert_active = True
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else:
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# 未检测到犯人框
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self.police_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.police_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.police_missing_frames >= self.frame_buffer_police:
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self.police_detection_frames = 0
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self.police_alert_active = False
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else:
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# 从未检测到犯人，保持状态
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pass
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alert_offset = 0
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# A. 有犯人
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if self.prisoner_alert_active:
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duration_seconds = self.prisoner_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': 'prisoner',
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'confidence': 1.0, # 固定为1.0（规则判定）
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'details': f"Detected for {duration_seconds:.1f}s"
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}
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)
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self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset)
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alert_offset += 100
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# ==========================================
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# 11. 统一显示当前帧所有警告（可替换原分层显示）
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# ==========================================
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debug_info = f" prisoner: {current_prisoner_count}"
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cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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# 统一警告显示区
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alert_y_start = 150
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for i, alert in enumerate(current_frame_alerts):
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action = alert['action']
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details = alert.get('details', '')
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color = (0, 0, 255) # 默认红色警告
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if action == 'prisoner':
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color = (255, 255, 255)
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main_text = action
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if details:
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main_text += f" ({details})"
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y_pos = alert_y_start + i * 50
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cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1)
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cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2)
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return {
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"image": frame,
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"alerts":current_frame_alerts
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}
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# ========================= WebSocket 服务线程 =========================
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class WebSocketSender(threading.Thread):
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def __init__(self, send_queue: queue.Queue, stop_event: threading.Event):
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super().__init__(daemon=True)
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self.send_queue = send_queue
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self.stop_event = stop_event
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async def _ws_handler(self, websocket):
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ws_clients.add(websocket)
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try:
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async for _ in websocket:
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pass
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finally:
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ws_clients.discard(websocket)
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async def _broadcaster(self):
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while not self.stop_event.is_set():
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try:
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msg = await asyncio.to_thread(self.send_queue.get, timeout=0.5)
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except queue.Empty:
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continue
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data = json.dumps(msg)
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dead = []
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for ws in list(ws_clients):
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try:
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await ws.send(data)
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except:
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dead.append(ws)
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for ws in dead:
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ws_clients.discard(ws)
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self.send_queue.task_done()
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async def _run_async(self):
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async with websockets.serve(self._ws_handler, WS_HOST, WS_PORT):
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logger.info(f"[INFO] WebSocket server started at ws://{WS_HOST}:{WS_PORT}")
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await self._broadcaster()
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def run(self):
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asyncio.run(self._run_async())
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# ========================= RTSP 抓流线程 =========================
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class RTSPCaptureWorker(threading.Thread):
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def __init__(self, camera_cfg: CameraConfig, raw_queue: queue.Queue, stop_event: threading.Event):
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super().__init__(daemon=True)
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self.camera_cfg = camera_cfg
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self.raw_queue = raw_queue
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self.stop_event = stop_event
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# 添加重连计数器
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self.reconnect_count = 0
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self.max_reconnects = 10
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def run(self):
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while not self.stop_event.is_set() and self.reconnect_count < self.max_reconnects:
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try:
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# 方法1：使用TCP传输（更稳定）
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rtsp_url = self.camera_cfg.rtsp_url
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if "?" not in rtsp_url:
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rtsp_url += "?transport=tcp" # 强制TCP传输
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else:
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rtsp_url += "&transport=tcp"
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# 方法2：添加更多FFmpeg参数
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cap = cv2.VideoCapture(rtsp_url, cv2.CAP_FFMPEG)
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# 方法3：设置缓冲区大小
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 10) # 增加缓冲区
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||
# 方法4：设置超时和重连参数
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os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = \
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"rtsp_transport;tcp|buffer_size;1024000|max_delay;500000|stimeout;2000000"
|
||
|
||
# 方法5：设置解码器flags，忽略解码错误
|
||
# cap.set(cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_ANY)
|
||
|
||
if not cap.isOpened():
|
||
logger.error(f"[ERROR] Cannot open RTSP: {self.camera_cfg.rtsp_url}")
|
||
time.sleep(2)
|
||
self.reconnect_count += 1
|
||
continue
|
||
|
||
logger.info(f"[INFO] Successfully opened RTSP: {self.camera_cfg.name}")
|
||
self.reconnect_count = 0 # 重置重连计数
|
||
|
||
# # 设置帧率（可选）
|
||
# cap.set(cv2.CAP_PROP_FPS, 25)
|
||
|
||
while not self.stop_event.is_set():
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
# 检查流是否结束
|
||
logger.warning(f"[WARN] Failed to read frame from {self.camera_cfg.name}")
|
||
|
||
# 检查是否还有数据
|
||
time.sleep(0.1)
|
||
# 尝试几次后重连
|
||
break
|
||
|
||
item = {
|
||
"camera_id": self.camera_cfg.id,
|
||
"camera_name": self.camera_cfg.name,
|
||
"timestamp": time.time(),
|
||
"frame": frame,
|
||
}
|
||
|
||
try:
|
||
# 添加队列满时的处理
|
||
if self.raw_queue.full():
|
||
# 丢弃最旧的一帧
|
||
try:
|
||
self.raw_queue.get_nowait()
|
||
self.raw_queue.task_done()
|
||
except queue.Empty:
|
||
pass
|
||
|
||
self.raw_queue.put(item, timeout=0.5)
|
||
except queue.Full:
|
||
logger.warning(f"[WARN] Queue full, dropping frame from {self.camera_cfg.name}")
|
||
continue
|
||
|
||
# 控制读取速度，避免过快
|
||
time.sleep(0.02) # 约50ms间隔
|
||
|
||
cap.release()
|
||
|
||
except Exception as e:
|
||
logger.error(f"[ERROR] Error in RTSP capture for {self.camera_cfg.name}: {e}")
|
||
time.sleep(2)
|
||
self.reconnect_count += 1
|
||
|
||
if self.reconnect_count >= self.max_reconnects:
|
||
logger.error(f"[ERROR] Max reconnects reached for {self.camera_cfg.name}, stopping.")
|
||
|
||
|
||
# ========================= 帧处理线程 =========================
|
||
class FrameProcessorWorker(threading.Thread):
|
||
def __init__(self,
|
||
raw_frame_queue: "queue.Queue[Dict[str, Any]]",
|
||
ws_send_queue: "queue.Queue[Dict[str, Any]]",
|
||
stop_event: threading.Event):
|
||
super().__init__(daemon=True)
|
||
self.raw_queue = raw_frame_queue
|
||
self.ws_queue = ws_send_queue
|
||
self.stop_event = stop_event
|
||
|
||
self.video_writers: Dict[int, cv2.VideoWriter] = {}
|
||
self.video_counts: Dict[int, int] = {}
|
||
self.last_ts: Dict[int, float] = {}
|
||
self.video_files: Dict[int, str] = {}
|
||
|
||
|
||
os.makedirs(VIDEO_OUTPUT_DIR, exist_ok=True)
|
||
|
||
# 每个摄像头一个独立的 Kadian 检测器实例
|
||
self.kadian_detectors: Dict[int, KadianDetector] = {}
|
||
|
||
def _get_writer(self, camera_id: int, frame) -> Tuple[cv2.VideoWriter, str]:
|
||
if camera_id in self.video_writers:
|
||
return self.video_writers[camera_id], self.video_files[camera_id]
|
||
|
||
h, w = frame.shape[:2]
|
||
ts_str = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
filepath = os.path.join(VIDEO_OUTPUT_DIR, f"{ts_str}_cam{camera_id}.mp4")
|
||
writer = cv2.VideoWriter(filepath, cv2.VideoWriter_fourcc(*"mp4v"), RTSP_TARGET_FPS, (w, h))
|
||
|
||
self.video_writers[camera_id] = writer
|
||
self.video_files[camera_id] = filepath
|
||
self.video_counts[camera_id] = 0
|
||
logger.info(f"[INFO] New segment: {filepath}")
|
||
return writer, filepath
|
||
|
||
def _close_segment_if_needed(self, camera_id: int):
|
||
count = self.video_counts.get(camera_id, 0)
|
||
if count >= FRAMES_PER_SEGMENT:
|
||
writer = self.video_writers.get(camera_id)
|
||
if writer is not None:
|
||
writer.release()
|
||
logger.info(f"[INFO] Close segment: camera={camera_id}, file={self.video_files[camera_id]}")
|
||
|
||
self.video_writers.pop(camera_id, None)
|
||
self.video_counts.pop(camera_id, None)
|
||
self.last_ts.pop(camera_id, None)
|
||
self.video_files.pop(camera_id, None)
|
||
|
||
def _encode_image_to_base64(self, image) -> str:
|
||
ok, buf = cv2.imencode(".jpg", image)
|
||
if not ok:
|
||
raise RuntimeError("Failed to encode image to JPEG")
|
||
return base64.b64encode(buf.tobytes()).decode("ascii")
|
||
|
||
def run(self):
|
||
target_interval = 1.0 / RTSP_TARGET_FPS
|
||
# last_processed_time = {} # 记录每个摄像头上次处理时间
|
||
while not self.stop_event.is_set():
|
||
try:
|
||
item = self.raw_queue.get(timeout=0.5)
|
||
except queue.Empty:
|
||
continue
|
||
|
||
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] = KadianDetector()
|
||
detector = self.kadian_detectors[cam_id]
|
||
|
||
# # 计算距离上次处理的时间间隔
|
||
# current_time = time.time()
|
||
# time_since_last = 0
|
||
# if cam_id in last_processed_time:
|
||
# time_since_last = (current_time - last_processed_time[cam_id]) * 1000 # 转换为毫秒
|
||
# last_processed_time[cam_id] = current_time
|
||
#
|
||
# if time_since_last > 0:
|
||
# logger.info(f"[DEBUG] 摄像头{cam_id} - 距离上次处理间隔: {time_since_last:.1f}ms")
|
||
|
||
# 2) 进行人脸识别（如果启用）
|
||
current_face_alert = None
|
||
face_results = []
|
||
face_processing_time = 0
|
||
if video_face_prison_biz is not None and FACE_RECOGNITION_ENABLED:
|
||
try:
|
||
# 处理当前帧 - 获取人脸识别结果
|
||
processed_frame_for_face, face_results, face_processing_time = video_face_prison_biz.process_frame(
|
||
frame.copy())
|
||
|
||
for result in face_results:
|
||
if result['has_passed']:
|
||
logger.info(f"[INFO] 犯人带出: {result['passed_person_id']}")
|
||
|
||
# 记录当前帧人脸告警信息
|
||
current_face_alert = {
|
||
"person_name": result['passed_person_id'],
|
||
"timestamp": ts
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"[WARN] 人脸识别处理失败: {e}")
|
||
|
||
# 执行检测
|
||
result = detector.process_frame(frame.copy(), cam_id, ts)
|
||
|
||
result_img = result["image"]
|
||
result_type = result["alerts"]
|
||
|
||
# 绘制人脸识别结果
|
||
if video_face_prison_biz is not None and face_results:
|
||
result_img = video_face_prison_biz.draw_detections(result_img, face_results)
|
||
|
||
# 添加人脸识别统计信息
|
||
match_count = sum(1 for r in face_results if r['is_match'])
|
||
face_info_text = f"Faces: {len(face_results)} | Matches: {match_count}"
|
||
cv2.putText(result_img, face_info_text, (10, result_img.shape[0] - 20),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
|
||
|
||
# 写视频
|
||
writer, video_path = self._get_writer(cam_id, frame)
|
||
writer.write(result_img)
|
||
self.video_counts[cam_id] = self.video_counts.get(cam_id, 0) + 1
|
||
|
||
# 5) 通过 WebSocket 发送帧结果
|
||
try:
|
||
img_b64 = self._encode_image_to_base64(result_img)
|
||
except Exception as e:
|
||
logger.error(f"[ERROR] Encode image failed: {e}")
|
||
img_b64 = None
|
||
|
||
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 result_type]
|
||
|
||
if current_face_alert is not None:
|
||
action_names.append("face")
|
||
|
||
msg = {
|
||
"msg_type": "frame",
|
||
"camera_id": cam_id,
|
||
"timestamp": ts,
|
||
"result_type": action_names,
|
||
"image_base64": img_b64,
|
||
}
|
||
try:
|
||
self.ws_queue.put(msg, timeout=1.0)
|
||
except queue.Full:
|
||
logger.warning("[WARN] ws_send_queue full, drop frame message")
|
||
|
||
|
||
|
||
self._close_segment_if_needed(cam_id)
|
||
self.raw_queue.task_done()
|
||
|
||
self.video_counts[cam_id] = self.video_counts.get(cam_id, 0) + 1
|
||
|
||
# 清理
|
||
for w in self.video_writers.values():
|
||
w.release()
|
||
|
||
|
||
# ========================= 服务主类 =========================
|
||
class RTSPService:
|
||
def __init__(self, config_path: str = "config.yaml"):
|
||
with open(config_path, "r", encoding="utf-8") as f:
|
||
cfg = yaml.safe_load(f)
|
||
self.cameras = [CameraConfig(id=c["id"], name=c.get("name", f"cam_{c['id']}"), rtsp_url=c["rtsp_url"])
|
||
for c in cfg.get("cameras", [])]
|
||
|
||
self.stop_event = threading.Event()
|
||
self.raw_queue = queue.Queue(maxsize=500)
|
||
self.ws_queue = queue.Queue(maxsize=1000)
|
||
|
||
self.capture_workers = []
|
||
self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.stop_event)
|
||
self.ws_sender = WebSocketSender(self.ws_queue, self.stop_event)
|
||
|
||
def start(self):
|
||
self.ws_sender.start()
|
||
self.processor.start()
|
||
for cam in self.cameras:
|
||
w = RTSPCaptureWorker(cam, self.raw_queue, self.stop_event)
|
||
w.start()
|
||
self.capture_workers.append(w)
|
||
logger.info("[INFO] Kadian RTSP Service started")
|
||
|
||
def stop(self):
|
||
self.stop_event.set()
|
||
self.raw_queue.join()
|
||
self.ws_queue.join()
|
||
for w in self.capture_workers:
|
||
w.join(timeout=2.0)
|
||
self.processor.join(timeout=2.0)
|
||
self.ws_sender.join(timeout=2.0)
|
||
logger.info("[INFO] Service stopped")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
service = RTSPService("config.yaml")
|
||
service.start()
|
||
try:
|
||
while True:
|
||
time.sleep(1)
|
||
except KeyboardInterrupt:
|
||
service.stop() |