45 lines
2.4 KiB
Python
45 lines
2.4 KiB
Python
#进入线程先把标志位置为True
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# from System_I import Detection
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# import os,torch
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# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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# devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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# root = "/home/wanghc/output"
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import os,torch, threading,argparse
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class global_para():
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parser1 = argparse.ArgumentParser(description="GYD")
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parser1.add_argument("video_num", default='0001', type=str,
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help='Video name.')
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parser1.add_argument("GPU", default='5', type=str,
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help='GPU.')
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parser1.add_argument("thresh", default=0.6, type=float,
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help='GPU.')
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parser1.add_argument("red", default=0, type=int,
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help='red.')
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args = parser1.parse_args()
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print(args)
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red_flag = args.red
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detect_thresh = args.thresh
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is_ex_bird = False # 目标检测标志位，每个一秒检测一次目标检测，如果有鸟，则置为True，为True的时候说明， 处理30s的线程还在继续，直到标志位变为了False再开启新的线程
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Density_thread_flag = False # 密度估计线程标志位，为True说明开启了密度估计线程，处理完750帧为止，标志位标为False，
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Detection_thread_flag = False # 目标检测和细粒度识别线程标志位，为True说明开启了密度估计线程，处理完750帧为止，标志位标为False，
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Vedio_thread_flag = False # 存储视频线程标志位，存储30s
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SaveImage_thread_flag = False # 保存原图线程标志位，存储30张原图
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ALL_thread_flag = False # 所有线程的标志位，单独开设一个线程，监控所有线程是否都结束，如果都结束，则置为False，后续添加线程管理，可以放在这里面
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DebugPrint_flag = True # 打印标志位
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destory_image = False
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Camera_device = args.video_num # 摄像头编号，0001
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image_time = None # 当前处理的30视频的编号
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ImgDealQueue = [] # 750帧视频，缓存空间
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root = "/data/Hatcher/output2"
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#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
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devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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#event = threading.Event()
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eventDensity = threading.Event()
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eventDetection = threading.Event()
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eventSaveimage = threading.Event()
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# Detection2 = Detection(devices,root)
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