273 lines
13 KiB
Python
Executable File
273 lines
13 KiB
Python
Executable File
# -*- coding:utf-8 -*-
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import cv2,os,torch,argparse
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import queue
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import time
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import threading
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from System_I import Check_start_fordataselect,Density,Detection_Y5,Fine_grained,Fine_grained_T,shishi,save_sigle_image,RealDeal
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from common import global_para as gp
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r_db = None
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import torch.multiprocessing as multiprocessing
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import sort.sort as SORT
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from imatovideo import images_to_video
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from multiprocessing import Array
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class DetFineVid():
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def __init__(self, VideoPath,SavePath):
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super(DetFineVid,self).__init__()
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# name
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self.VideoPath = VideoPath
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self.VideoResultSavePathroot = "/data1/Hatcher/model/project/data_MPKFNet/result_vedio/"
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self.VideoName = os.path.basename(self.VideoPath).split(".")[0]
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self.VideoResultSavePath = os.path.join(self.VideoResultSavePathroot,"{}.avi".format(self.VideoName))
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self.SavePath = os.path.join(SavePath,self.VideoName)
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if not os.path.exists(self.SavePath):
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os.makedirs(self.SavePath)
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self.root = "/home/wanghc/output_jiankong"
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self.DensityClass = None # Density(gp.devices, root)
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self.DetectionClass = Detection_Y5(gp.devices, self.root)
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self.Fine_grianedClass = Fine_grained_T(gp.devices, self.root)
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self.FineRestartFlag = True
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self.numrestart = 0
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def conVideo(self):
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images_to_video( self.SavePath,self.VideoResultSavePath)
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def StartRec(self):
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t1 = time.time()
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cap = cv2.VideoCapture(self.VideoPath)
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t2 = time.time()
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print("capture is ",(t2-t1)*1000)
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num_detection = 0
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num_cuntupian=0
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SortRestart = SORT.Sort(max_age=gp.max_age,
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min_hits=gp.min_hits,
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iou_threshold=gp.sortiou_threshold)
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realtimedeal = RealDeal("check_start",self.DetectionClass,self.Fine_grianedClass)
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if cap.isOpened():
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res, frame = cap.read()
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if res:
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shapex = frame.shape[1]
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shapey = frame.shape[0]
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gp.radioShowX = shapex / gp.radioShowX
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gp.radioShowY = shapey / gp.radioShowY
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gp.radioShowtext = gp.radioShowX
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if frame.shape[1] == 3840:
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gp.fontScale = 3
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elif frame.shape[1] == 2560:
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gp.fontScale = 1
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else:
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gp.fontScale = 1
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for i in range(20):
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res, frame = cap.read()
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print("skip{}".format(i))
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num_image = 0
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while True:
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if cap.isOpened():
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res, frame = cap.read()
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if res:
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closed = False
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failCnt = 0
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num_image += 1
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data_today = 8
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print(num_detection)
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# if num_detection <12974:
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# a=0
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# num_detection+=1
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# continue
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#cv2.imwrite("./test.jpg",frame)
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if (int(data_today) > 1) & (int(data_today) < 20):
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if (0xFF == ord('q')):
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print("restart")
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self.FineRestartFlag = True
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self.numrestart = 0
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SortRestart = SORT.Sort(max_age=gp.max_age,
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min_hits=gp.min_hits,
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iou_threshold=gp.sortiou_threshold)
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if self.FineRestartFlag :
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realtimedeal.realtimerestart(frame,num_image=num_image,num_detection=num_detection,SortRestart = SortRestart)
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self.numrestart += 1
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if self.numrestart >= 5:
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self.numrestart = 0
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self.FineRestartFlag = False
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FirstDetFineResult = realtimedeal.getrestartresult(gp.IndexMax)
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print("µÚÒ»´Îʶ±ðÄ¿±êÊýÁ¿ ",len(FirstDetFineResult))
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gp.SortLabel_Index = FirstDetFineResult
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# SortRealTime = SORT.Sort(max_age=gp.max_age,
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# min_hits=gp.min_hits,
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# iou_threshold=gp.sortiou_threshold)
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else:
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tm1 = time.time()
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#frame = cv2.imread("./test.jpg")
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img_show,num_image = realtimedeal.realtime(frame,num_image=num_image,num_detection=num_detection,SortRestart = SortRestart)
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tm2 = time.time()
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if gp.DebugPrint_flag:
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print("main1 time is ", (tm2 - tm1) * 1000)
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# cv2.imshow("AI-ECO", img_show)
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# #cv2.imwrite("../data_MPKFNet/RTSP3/" + "{}.jpg".format(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))),img_show)
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# if cv2.waitKey(1) & 0xFF == ord('r'):
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# break
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save_path =os.path.join(self.SavePath,"{}.jpg".format(str(num_detection).zfill(6)))
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cv2.imwrite(save_path,img_show)
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tm3 = time.time()
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if gp.DebugPrint_flag:
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print("main2 time is ", (tm3 - tm2) * 1000)
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else:
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print("time is out")
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if (num_detection >1000000) :
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# save_image = save_sigle_image(frame)
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# save_image.start()
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# num_detection=0
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num_detection = 0
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num_detection+=1
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num_cuntupian+=1
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else:
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break
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def commanddeal():
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SavePath = "/data1/Hatcher/model/project/data_MPKFNet/result/"
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print("starting ",gp.VideoPath)
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DFS = DetFineVid(gp.VideoPath,SavePath)
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DFS.StartRec()
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DFS.conVideo()
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def filedeal(rootPath): #deal all file in a folder
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SavePath = "/data1/Hatcher/model/project/data_MPKFNet/result/"
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i = 0
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for video_path in os.listdir(rootPath):
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if i == 1:
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print("111")
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# print("starting deal {}".format(str(video_path)))
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# VideoPath = os.path.join(rootPath,video_path)
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# DFS = DetFineVid(VideoPath,SavePath)
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# DFS.StartRec()
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# DFS.conVideo()
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P = multiprocessing.Process(target=func,args=(video_path,SavePath,rootPath))
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P.start()
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P.join()
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#globalInit()
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i += 1
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def func(video_path,SavePath,rootPath):
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print("starting deal {}".format(str(video_path)))
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VideoPath = os.path.join(rootPath, video_path)
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DFS = DetFineVid(VideoPath, SavePath)
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DFS.StartRec()
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DFS.conVideo()
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def globalInit(): #deal all file in a folder
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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',
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type=str,
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help='Video name.')
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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=0.4, type=float,
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help='GPU.')
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parser1.add_argument("--red", default=0, type=int,
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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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gp.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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gp.devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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gp.camera_address = args.add
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gp.VideoPath = args.video_num
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gp.red_flag = args.red
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gp.detect_thresh = args.thresh
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# fine_thresh2 = torch.tensor(fine_thresh2).cuda()
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gp.birdIndex = 99999
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# birdIndex = torch.tensor(birdIndex).cuda()
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gp.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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gp.Density_thread_flag = False # ÃܶȹÀ¼ÆÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
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gp.Detection_thread_flag = False # Ä¿±ê¼ì²âºÍϸÁ£¶Èʶ±ðÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
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gp.Vedio_thread_flag = False # ´æ´¢ÊÓƵÏ̱߳ê־룬´æ´¢30s
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gp.SaveImage_thread_flag = False # ±£´æÔͼÏ̱߳ê־룬´æ´¢30ÕÅÔͼ
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gp.ALL_thread_flag = False # ËùÓÐÏ̵߳ıê־룬µ¥¶À¿ªÉèÒ»¸öỊ̈߳¬¼à¿ØËùÓÐÏß³ÌÊÇ·ñ¶¼½áÊø£¬Èç¹û¶¼½áÊø£¬ÔòÖÃΪFalse£¬ºóÐøÌí¼ÓÏ̹߳ÜÀí£¬¿ÉÒÔ·ÅÔÚÕâÀïÃæ
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gp.ShowAllNumFlag = False
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gp.DebugPrint_flag = False # ´òÓ¡±ê־λ
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gp.num_count_old = 0
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gp.boxes_fine = None
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gp.labels_fine = None
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gp.showThresh = 25
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gp.destory_image = False
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gp.Camera_device = args.video_num # ÉãÏñÍ·±àºÅ£¬0001
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gp.image_time = None # µ±Ç°´¦ÀíµÄ30ÊÓƵµÄ±àºÅ
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gp.ImgDealQueue = [] # 750Ö¡ÊÓƵ£¬»º´æ¿Õ¼ä
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gp.img_show = None
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# event = threading.Event()
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gp.eventDensity = threading.Event()
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gp.eventDetection = threading.Event()
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gp.eventSaveimage = threading.Event()
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gp.max_age = 2
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gp.min_hits = 3
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gp.sortiou_threshold = 0.3
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gp.Finerestartboxes = []
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gp.IndexMax = 0
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gp.radioShowX = 2560.0
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gp.radioShowY = 1440.0
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gp.radioShowtext = gp.radioShowX
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gp.SortLabel_Index = None # ±êÇ©ºÍË÷Òý¶ÔÓ¦¹Øϵ[[4,1],[3,2]]
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gp.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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gp.FineCountThresh = 5
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gp.img_show = None
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gp.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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gp.DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/KunMingCuiHu-Fish2/weights/best.pt"
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gp.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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gp.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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gp.FineClassNum = 4
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gp.color_show = (255, 255, 255)
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gp.color_box = (255, 0, 0)
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gp.NoneLabel = "鱼" # "鹤类"
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gp.fine_thresh1 = 6000
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gp.fine_thresh2 = 10000
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gp.fontScale = 1
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torch.zero_(gp.Species)
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if __name__ == '__main__':
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multiprocessing.set_start_method("spawn")
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rootPath = "/data/DataManagement/Project/005_kunmingdianchi/0002_20220412_800_200/研究院-鱼视频/"
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#filedeal(rootPath)
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commanddeal()
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