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 generate_image_pairs_from_csv(csv_folder, output_dir, image_per_file=10, A_frame=150, B_frame=0.85, file_filter='*.csv',
top_left=(0, 0), input_size_nm=512*106, pixel_size=20, A_frame_limit=[0, 1.0], B_frame_limit=[0, 1.0],
output_clip = (0, 255), input_clip = (0, 20), target_size=(2560, 2560), center_crop=None, zero_offset=False):
lCropTrain = LocalizationCrop(fit_data=True, top_left=top_left)
fSamplerInTest = LocalizationFrameSampler(frame_num=A_frame, frame_limit=A_frame_limit, zero_offset=zero_offset)
fSamplerOutTest = LocalizationFrameSampler(frame_num=B_frame, frame_limit=B_frame_limit)
hRender = HistogramRendering(pixel_size=pixel_size, value_range= (0, 255), target_size=target_size)
if center_crop:
cropTest = CenterCropNumpy(size=center_crop)
def transform_train(imgDict):
table = imgDict['table']
repeat = imgDict['table.repeat']
table = lCropTrain(table)
tableout = fSamplerOutTest(table, index=repeat)
tablein = fSamplerInTest(table, index=repeat)
histout = hRender(tableout)
histin = hRender(tablein)
if center_crop:
histin = cropTest(histin)
histout = cropTest(histout)
histin = np.clip(histin, 0, 255)
histout = np.clip(histout, 0, 255)
return histin, histout, imgDict['table.path'], tablein.f_range, tableout.f_range
csvLoader = ThunderstormCSVLoader([0, input_size_nm, 0, input_size_nm])
source_train = FolderDataset(csv_folder,
channels = {'table': {'filter':file_filter, 'loader': csvLoader} },
transform = transform_train,
recursive=False,
repeat=image_per_file)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print('generating images...')
for i in range(len(source_train)):
(aa, bb, p, inf_range, outf_range) = source_train[i]
print(i, inf_range, outf_range)
name = os.path.split(p)[1]
fpath = os.path.join(output_dir, name)
if not os.path.exists(fpath):
os.makedirs(fpath)
ha = aa[:,:,0].astype('uint8')
imA = Image.fromarray(ha)
imA.save(os.path.join(fpath, 'A_{}_{}.png'.format(i%image_per_file, str(inf_range))))
hb = bb[:,:,0].astype('uint8')
imB = Image.fromarray(hb)
if B_frame == 1.0:
imB.save(os.path.join(fpath, 'B_{}_{}.png'.format('all', str(outf_range))))
else:
imB.save(os.path.join(fpath, 'B_{}_{}.png'.format(i%image_per_file, str(outf_range))))
lr_path = p.replace('.csv', '.png')
lr_target_path = os.path.join(fpath, 'LR_'+ name.replace('.csv', '.png'))
if os.path.exists(lr_path) and not os.path.exists(lr_target_path):
copyfile(lr_path, lr_target_path)
print('done')
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]
def generate_images_from_csv(csv_folder, output_dir, frame=1.0, image_per_file=1, file_filter='*.csv', zero_offset=False,
top_left=(0, 0), input_size_nm=512*106, pixel_size=20, frame_limit=[0, 1.0],
output_clip = (0, 255), input_clip = (0, 20), target_size=(2560, 2560), center_crop=None):
lCropTrain = LocalizationCrop(fit_data=True, top_left=top_left)
fSamplerOutTest = LocalizationFrameSampler(frame_num=frame, frame_limit=frame_limit, zero_offset=zero_offset)
hRender = HistogramRendering(pixel_size=pixel_size, value_range= (0, 255), target_size=target_size)
if center_crop:
cropTest = CenterCropNumpy(size=center_crop)
def transform_train(imgDict):
table = imgDict['table']
table = lCropTrain(table)
repeat = imgDict['table.repeat']
path = imgDict['table.path']
tableout = fSamplerOutTest(table, index=repeat)
path = path + '_' + str(repeat)+ '_' + str(tableout.f_range)
histout = hRender(tableout)
if center_crop:
histout = cropTest(histout)
histout = np.clip(histout, 0, 255)
return histout, path
csvLoader = ThunderstormCSVLoader([0, input_size_nm, 0, input_size_nm])
source_train = FolderDataset(csv_folder,
channels = {'table': {'filter':file_filter, 'loader': csvLoader} },
transform = transform_train,
repeat=image_per_file)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print('generating images...')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for i in range(len(source_train)):
(bb, p) = source_train[i]
print(i, p)
name = os.path.split(p)[1]
fpath = os.path.join(output_dir, name + '_{}.png'.format(frame))
hb = bb[:,:,0].astype('uint8')
imB = Image.fromarray(hb)
imB.save(fpath)
print('done')
