Mercurial > repos > perssond > coreograph

comparison UNet2DtCycifTRAINCoreograph.py @ 1:57f1260ca94e draft

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"planemo upload commit fec9dc76b3dd17b14b02c2f04be9d30f71eba1ae"
author watsocam
date Fri, 11 Mar 2022 23:40:51 +0000
parents 99308601eaa6
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0:99308601eaa6 1:57f1260ca94e
522 # UNet2D.train(imPath,logPath,modelPath,pmPath,500,100,40,True,20000,1,0) 522 # UNet2D.train(imPath,logPath,modelPath,pmPath,500,100,40,True,20000,1,0)
523 UNet2D.setup(128, 1, 2, 20, 2, 2, 3, 2, 0.03, 4, 32) 523 UNet2D.setup(128, 1, 2, 20, 2, 2, 3, 2, 0.03, 4, 32)
524 UNet2D.train(imPath, logPath, modelPath, pmPath, 2053, 513 , 641, True, 10, 1, 1) 524 UNet2D.train(imPath, logPath, modelPath, pmPath, 2053, 513 , 641, True, 10, 1, 1)
525 UNet2D.deploy(imPath,100,modelPath,pmPath,1,1) 525 UNet2D.deploy(imPath,100,modelPath,pmPath,1,1)
526 526
527 # I = im2double(tifread('/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/SinemSaka_NucleiSegmentation_SingleImageInferenceTest3.tif')) 527
528 # UNet2D.singleImageInferenceSetup(modelPath,0) 528
529 # J = UNet2D.singleImageInference(I,'accumulate',0) 529
530 # UNet2D.singleImageInferenceCleanup()
531 # # imshowlist([I,J])
532 # # sys.exit(0)
533 # # tifwrite(np.uint8(255*I),'/home/mc457/Workspace/I1.tif')
534 # # tifwrite(np.uint8(255*J),'/home/mc457/Workspace/I2.tif')
535 # K = np.zeros((2,I.shape[0],I.shape[1]))
536 # K[0,:,:] = I
537 # K[1,:,:] = J
538 # tifwrite(np.uint8(255*K),'/home/mc457/Workspace/Sinem_NucSeg.tif')
539
540 # UNet2D.singleImageInferenceSetup(modelPath,0)
541 # imagePath = 'Y://sorger//data//RareCyte//Connor//Topacio_P2_AF//ashlar//C0078'
542 #
543 # fileList = glob.glob(imagePath + '//registration//C0078.ome.tif')
544 # print(fileList)
545 # for iFile in fileList:
546 # fileName = os.path.basename(iFile)
547 # fileNamePrefix = fileName.split(os.extsep, 1)
548 # I = im2double(tifffile.imread(iFile, key=0))
549 # hsize = int((float(I.shape[0])*float(0.75)))
550 # vsize = int((float(I.shape[1])*float(0.75)))
551 # I = resize(I,(hsize,vsize))
552 # J = UNet2D.singleImageInference(I,'accumulate',1)
553 # K = np.zeros((3,I.shape[0],I.shape[1]))
554 # K[2,:,:] = I
555 # K[0,:,:] = J
556 # J = UNet2D.singleImageInference(I, 'accumulate', 2)
557 # K[1, :, :] = J
558 # outputPath = imagePath + '//prob_maps'
559 # if not os.path.exists(outputPath):
560 # os.makedirs(outputPath)
561 # tifwrite(np.uint8(255*K),outputPath + '//' + fileNamePrefix[0] +'_NucSeg.tif')
562 # UNet2D.singleImageInferenceCleanup()
563
564
565 # ----- test 2 -----
566
567 # imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/ClarenceYapp_NucleiSegmentation'
568 # UNet2D.setup(128,1,2,8,2,2,3,1,0.1,3,4)
569 # UNet2D.train(imPath,logPath,modelPath,pmPath,800,100,100,False,10,1)
570 # UNet2D.deploy(imPath,100,modelPath,pmPath,1)
571
572
573 # ----- test 3 -----
574
575 # imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/CarmanLi_CellTypeSegmentation'
576 # # UNet2D.setup(256,1,2,8,2,2,3,1,0.1,3,4)
577 # # UNet2D.train(imPath,logPath,modelPath,pmPath,1400,100,164,False,10000,1)
578 # UNet2D.deploy(imPath,164,modelPath,pmPath,1)
579
580
581 # ----- test 4 -----
582
583 # imPath = '/home/cicconet/Downloads/TrainSet1'
584 # UNet2D.setup(64,1,2,8,2,2,3,1,0.1,3,4)
585 # UNet2D.train(imPath,logPath,modelPath,pmPath,200,8,8,False,2000,1,0)
586 # # UNet2D.deploy(imPath,164,modelPath,pmPath,1)