def __init__(self, opt):
self.typeID = DatasetTypeIDs['microtubule']
train_crop_size1 = opt.fineSize * 2
train_crop_size2 = opt.fineSize + 200
train_crop_size3 = opt.fineSize
test_size = opt.fineSize
self.input_clip = (0, 5)
self.output_clip = (2, 100)
# prepare the transforms
self.iMerge = Merge()
self.iElastic = ElasticTransform(alpha=1000, sigma=40)
self.iSplit = Split([0, 1], [1, 2])
self.iRot = RandomRotate()
self.iRCropTrain = RandomCropNumpy(size=(train_crop_size2,
train_crop_size2))
self.iCropFTrain = CenterCropNumpy(size=(train_crop_size1,
train_crop_size1))
self.iCropTrain = CenterCropNumpy(size=(train_crop_size3,
train_crop_size3))
self.iCropTest = CenterCropNumpy(size=(test_size, test_size))
self.ptrain = './datasets/wei-tubulin-ctrl-20170520-images/train'
self.ptest = './datasets/wei-tubulin-ctrl-20170520-images/test'
self.dim_ordering = opt.dim_ordering
self.opt = opt
self.repeat = 30
def __init__(self, opt):
self.typeID = DatasetTypeIDs['microtubule']
train_crop_size1 = opt.fineSize * 2
train_crop_size2 = opt.fineSize + 200
train_crop_size3 = opt.fineSize
test_size = opt.fineSize
self.input_clip = (0, 5)
self.output_clip = (2, 100)
# prepare the transforms
self.iMerge = Merge()
self.iElastic = ElasticTransform(alpha=1000, sigma=40)
self.iSplit = Split([0, 1], [1, 2])
self.iRot = RandomRotate()
self.iRCropTrain = RandomCropNumpy(size=(train_crop_size2, train_crop_size2))
self.iCropFTrain = CenterCropNumpy(size=(train_crop_size1, train_crop_size1))
self.iCropTrain = CenterCropNumpy(size=(train_crop_size3, train_crop_size3))
self.iCropTest = CenterCropNumpy(size=(test_size, test_size))
self.ptrain = '../anet-lite/src/datasets/Christian-TMR-IF-v0.1/train'
self.ptest = '../anet-lite/src/datasets/Christian-TMR-IF-v0.1/test'
self.dim_ordering = opt.dim_ordering
self.opt = opt
self.repeat = 30
self.folder_filter = '*'
self.drift_correction = False
self.scale_LR = True
def __init__(self, opt):
self.typeID = DatasetTypeIDs['tubulin']
self.iRot = RandomRotate()
self.iMerge = Merge()
self.iSplit = Split([0, 1], [1, 2])
self.irCropTrain = RandomCropNumpy(size=(opt.fineSize+100, opt.fineSize+100))
self.ioCropTrain = CenterCropNumpy(size=[opt.fineSize, opt.fineSize])
self.iCropTest = CenterCropNumpy(size=(1024, 1024))
self.iElastic = ElasticTransform(alpha=1000, sigma=40)
self.iBlur = GaussianBlurring(sigma=1.5)
self.iPoisson = PoissonSubsampling(peak=['lognormal', -0.5, 0.001])
self.iBG = AddGaussianPoissonNoise(sigma=25, peak=0.06)
self.train_count = 0
self.test_count = 0
self.dim_ordering = opt.dim_ordering
self.repeat = 1
self.opt = opt
def __init__(self, opt):
train_crop_size1 = int(opt.fineSize * 1.45) #pre-crop
train_crop_size2 = opt.fineSize
train_crop_size3 = opt.fineSize
test_size = opt.fineSize
self.ptrain = os.path.join(opt.workdir, 'train') #'./datasets/Christian-TMR-IF-v0.1/train'
self.pvalid = os.path.join(opt.workdir, 'valid')
self.ptest = os.path.join(opt.workdir, 'test') #'./datasets/Christian-TMR-IF-v0.1/test'
self.input_channels = []
for ch in opt.input_channels.split(','):
name, filter = ch.split('=')
self.input_channels.append((name, {'filter':filter, 'loader':ImageLoader()}, ))
self.output_channels = []
for ch in opt.output_channels.split(','):
name, filter = ch.split('=')
self.output_channels.append((name, {'filter':filter, 'loader':ImageLoader()}, ))
# prepare the transforms
self.iMerge = Merge()
self.iElastic = ElasticTransform(alpha=1000, sigma=40)
self.iSplit = Split([0, len(self.input_channels)], [len(self.input_channels), len(self.input_channels)+len(self.output_channels)])
self.iRCropTrain1 = RandomCropNumpy(size=(train_crop_size1, train_crop_size1))
self.iRot = RandomRotate()
self.iCropTrain2 = CenterCropNumpy(size=(train_crop_size2, train_crop_size2))
self.iCropTest = CenterCropNumpy(size=(test_size, test_size))
self.dim_ordering = opt.dim_ordering
self.opt = opt
self.repeat = 30
self.input_channel_names = [n for n, _ in self.input_channels]
self.output_channel_names = [n for n, _ in self.output_channels]