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@@ -1,4 +1,4 @@
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#进入线程先把标志位置为True
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#½øÈëÏß³ÌÏȰѱê־λÖÃΪTrue
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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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@@ -6,39 +6,114 @@
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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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#check()
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parser1 = argparse.ArgumentParser(description="CUI")
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parser1.add_argument("--video_num", default='/data1/Hatcher/model/project/data_MPKFNet/rawdata/PYH-DEMO.mp4', 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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parser1.add_argument("--GPU", default='6', 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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parser1.add_argument("--thresh", default=0.4, type=float,
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help='GPU.')
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parser1.add_argument("red", default=0, type=int,
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parser1.add_argument("--red", default=0, type=int,
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help='red.')
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parser1.add_argument("--add", default=0, type=str,
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help='add.')
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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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#print(args)
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root = "/home/gyd/output1"
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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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camera_address = args.add
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VideoPath = args.video_num
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red_flag = args.red
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detect_thresh = args.thresh
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#fine_thresh2 = torch.tensor(fine_thresh2).cuda()
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birdIndex = 99999
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#birdIndex = torch.tensor(birdIndex).cuda()
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is_ex_bird = False # Ä¿±ê¼ì²â±ê־λ£¬Ã¿¸öÒ»Ãë¼ì²âÒ»´ÎÄ¿±ê¼ì²â£¬Èç¹ûÓÐÄñ£¬ÔòÖÃΪTrue£¬ÎªTrueµÄʱºò˵Ã÷£¬ ´¦Àí30sµÄÏ̻߳¹ÔÚ¼ÌÐø£¬Ö±µ½±ê־λ±äΪÁËFalseÔÙ¿ªÆôеÄÏß³Ì
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Density_thread_flag = False # ÃܶȹÀ¼ÆÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
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Detection_thread_flag = False # Ä¿±ê¼ì²âºÍϸÁ£¶Èʶ±ðÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
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Vedio_thread_flag = False # ´æ´¢ÊÓƵÏ̱߳ê־룬´æ´¢30s
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SaveImage_thread_flag = False # ±£´æÔͼÏ̱߳ê־룬´æ´¢30ÕÅÔͼ
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ALL_thread_flag = False # ËùÓÐÏ̵߳ıê־룬µ¥¶À¿ªÉèÒ»¸öỊ̈߳¬¼à¿ØËùÓÐÏß³ÌÊÇ·ñ¶¼½áÊø£¬Èç¹û¶¼½áÊø£¬ÔòÖÃΪFalse£¬ºóÐøÌí¼ÓÏ̹߳ÜÀí£¬¿ÉÒÔ·ÅÔÚÕâÀïÃæ
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ShowAllNumFlag = False
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DebugPrint_flag = False # ´òÓ¡±ê־λ
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Species = torch.zeros(8).cuda()
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num_count_old =0
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boxes_fine = None
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labels_fine = None
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showThresh = 25
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destory_image = False
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Camera_device = args.video_num # ÉãÏñÍ·±àºÅ£¬0001
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image_time = None # µ±Ç°´¦ÀíµÄ30ÊÓƵµÄ±àºÅ
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ImgDealQueue = [] # 750Ö¡ÊÓƵ£¬»º´æ¿Õ¼ä
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img_show = None
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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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max_age = 2
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min_hits = 3
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sortiou_threshold = 0.3
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Finerestartboxes = []
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IndexMax = 0
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radioShowX = 2560.0
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radioShowY = 1440.0
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radioShowtext = radioShowX
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SortLabel_Index = None #±êÇ©ºÍË÷Òý¶ÔÓ¦¹Øϵ[[4,1],[3,2]]
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SortLabel_Indexpre = [] #±êÇ©ºÍË÷ÒýµÄ¶ÔÓ¦¹Øϵ£¬ÓÃÔÚ×·×Ù¹ý³ÌÖÐгöÏÖµÄÄ¿±ê£¬[[label,Index,Count]]
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# Detection2 = Detection(devices,root)
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FineCountThresh = 5
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img_show = None
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Fine_thresh = 0.4
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# DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/exp21/weights/best.pt"
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# FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-PYH-3-20.7/_42522_PYH-3/best.pth"
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DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/KunMingCuiHu-Fish2/weights/best.pt"
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FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-DianChiFish-3-3-0.7/_4289_DianChiFish-4/best.pth"
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# DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/BeijingCuiHu-Bird26/weights/best.pt"
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# FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-cui90_1model0.7/_42111_CUI-94-1/best.pth"
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# BHG_chineseName = ["kong", '白鹤', '白头鹤','白枕鹤']#["kong","鲫鱼","麦穗鱼","鳑鲏"]
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BHG_chineseName = ["kong","鲫鱼","麦穗鱼","鳑鲏"]
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# BHG_chineseName = ['Kong', '白额雁', '白骨顶', '苍鹭', '苍鹭', '白秋沙鸭', '白腰草鹬', '斑头雁', '斑嘴鸭', '苍鹭', '赤膀鸭', '赤颈鸭', '苍鹭', '大麻鳽',
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# '豆雁', '反嘴鹬', '凤头鸊鷉', '凤头麦鸡', '凤头潜鸭', '黑鹳', '黑水鸡', '红嘴鸥', '鸿雁', '灰鹤', '灰雁', '绿翅鸭', '绿头鸭', '琵嘴鸭',
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# '普通鸬鹚', '普通秋沙鸭', '翘鼻麻鸭', '鹊鸭', '扇尾沙锥', '小白额雁', '小天鹅', '针尾鸭', '苍鹭', '暗绿绣眼鸟', '八哥', '白腹鹞', '白鹡鸰',
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# '白眼潜鸭', '草鹭', '池鹭', '戴胜', '黑翅长脚鹬', '黑鸢', '红隼', '花脸鸭', '环颈雉', '灰头绿啄木鸟', '灰头麦鸡', '矶鹬', '栗苇鳽',
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# '罗纹鸭', '牛背鹭', '普通翠鸟', '雀鹰', '喜鹊', '苍鹭', '鸳鸯', '棕背伯劳', '红胸秋沙鸭', '普通燕鸥', '小白额燕鸥', '丘鹬', '凤头蜂鹰',
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# '大天鹅', '大鸨', '小田鸡', '山斑鸠', '帆背潜鸭', '日本松雀鹰', '普通鵟', '松雀鹰', '毛脚鵟', '灰斑鸠', '白头鹞', '白尾鹞', '白眉鸭',
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# '白鹈鹕', '红头潜鸭', '苍鹰', '蒙古银鸥', '西方秧鸡', '赤嘴潜鸭', '阔嘴鹬', '雕鸮', '鹌鹑', '鹗', '黄苇鳽', '黑颈鹤', '小䴙䴘', '赤麻鸭']
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# BHG_chineseName =["kong","绿头鸭","斑嘴鸭","灰雁","鸿雁","kong","小鷿鷈"]
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spiec_number_old = [0 for i in range(len(BHG_chineseName))]
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FineClassNum = 4
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color_show = (255, 255, 255)
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color_box = (255, 0, 0)
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NoneLabel = "鱼" # "鹤类"
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fine_thresh1 = 6000
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fine_thresh2 = 10000
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fontScale = 1
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"""
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1.change weight
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2.change BHG_chineseName
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3.change FineClassNum
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4.change color_box (0, 115, 51)green (255, 0, 0) red
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5.change parser1
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6.change NoneLabel
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7. change main
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8. change fine_thresh2
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9. GPU
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fontScale plot file para to adjust chinese 400W ---2
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200W ---1
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800W ---3
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"""
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