def view_patient_raw_data(patient, width=400, height=400):
import batchviewer
a = []
a.append(patient['ed_data'][None])
a.append(patient['ed_gt'][None])
a.append(patient['es_data'][None])
a.append(patient['es_gt'][None])
batchviewer.view_batch(np.vstack(a), width, height)
num_splits=5,
random_state=12345)
patch_size = (128, 128, 128)
batch_size = 2
# I recommend you don't use 'iteration oder all training data' as epoch because in patch based training this is
# really not super well defined. If you leave all arguments as default then each batch sill contain randomly
# selected patients. Since we don't care about epochs here we can set num_threads_in_multithreaded to anything.
dataloader = BraTS2017DataLoader3D(train, batch_size, patch_size, 1)
batch = next(dataloader)
try:
from batchviewer import view_batch
# batch viewer can show up to 4d tensors. We can show only one sample, but that should be sufficient here
view_batch(batch['data'][0], batch['seg'][0])
except ImportError:
view_batch = None
print(
"you can visualize batches with batchviewer. It's a nice and handy tool. You can get it here: "
"https://github.com/FabianIsensee/BatchViewer")
# now we have some DataLoader. Let's go an get some augmentations
# first let's collect all shapes, you will see why later
shapes = [
BraTS2017DataLoader3D.load_patient(i)[0].shape[1:] for i in patients
]
max_shape = np.max(shapes, 0)
max_shape = np.max((max_shape, patch_size), 0)
self.strength, float) else np.random.uniform(
*self.strength)
if len(data.shape) == 4:
filter_here = self.filter_2d * strength_here
filter_here[1, 1] += 1
else:
filter_here = self.filter_3d * strength_here
filter_here[1, 1, 1] += 1
data[b, c] = convolve(data[b, c],
filter_here,
mode='same')
data[b, c] = np.clip(data[b, c], mn, mx)
return data_dict
if __name__ == '__main__':
from copy import deepcopy
from skimage.data import camera
# just some playing around with BrightnessGradientAdditiveTransform
data = {
'data':
np.vstack((camera()[None], camera()[None],
camera()[None]))[None, None].astype(np.float32)
}
tr = MedianFilterTransform((1, 20), True)
transformed = tr(**deepcopy(data))['data']
from batchviewer import view_batch
view_batch(*data['data'][0], *transformed[0])
intens_corrected = np.clip(ret * (filter_orig / filter_aug), data.min(), data.max())
#import IPython;IPython.embed()
data_dict['data'] = intens_corrected
return data_dict"""
if __name__ == '__main__':
from copy import deepcopy
from skimage.data import camera
# just some playing around with BrightnessGradientAdditiveTransform
data = {
'data':
np.vstack((camera()[None], camera()[None],
camera()[None]))[None].astype(np.float32)
}
tr = LocalContastTransform(
lambda x, y: np.random.uniform(x[y] // 10, x[y] // 4),
#lambda x, y: np.random.uniform(-1, 0) if np.random.uniform() < 0.5 else np.random.uniform(1, 2),
(0, 1),
#lambda: np.random.uniform(0.0001, 0.01) if np.random.uniform() < 0.5 else np.random.uniform(1, 10),
1e-90,
same_for_all_channels=False)
transformed = tr(**deepcopy(data))['data']
from batchviewer import view_batch
data['data'][0][:, 0:2, 0] = np.array((0, 255))
transformed[0][:, 0:2, 0] = np.array((0, 255))
diff = [i - j for i, j in zip(data['data'][0], transformed[0])]
[print(i[10, 10]) for i in diff]
view_batch(*data['data'][0], *transformed[0],
*[i - j for i, j in zip(data['data'][0], transformed[0])])
Created on Tue Jul 28 12:30:15 2020
@author: rtgun
"""
from batchviewer import view_batch
import numpy as np
import nibabel as nib
data = nib.load("d010_pre0_dat.nii.gz")
data = data.get_fdata()
print(data.shape)
data = data[None]
data1 = np.transpose(data, axes=(0, 3, 1, 2))
print(data1.shape)
data2 = np.transpose(data, axes=(0, 3, 2, 1))
print(data2.shape)
data3 = np.rot90(data1, k=2, axes=(-2, -1))
print(data3.shape)
data4 = np.rot90(data2, k=2, axes=(-1, -2))
print(data4.shape)
data_f = np.concatenate((data1, data2, data3, data4))
print(data_f.shape)
view_batch(data1, width=500, height=500)
train, val = get_split_deterministic(patients, fold=0, num_splits=5, random_state=12345)
patch_size = (160, 160)
batch_size = 48
# I recommend you don't use 'iteration oder all training data' as epoch because in patch based training this is
# really not super well defined. If you leave all arguments as default then each batch sill contain randomly
# selected patients. Since we don't care about epochs here we can set num_threads_in_multithreaded to anything.
dataloader = BraTS2017DataLoader2D(train, batch_size, patch_size, 1)
batch = next(dataloader)
try:
from batchviewer import view_batch
# batch viewer can show up to 4d tensors. We can show only one sample, but that should be sufficient here
view_batch(np.concatenate((batch['data'][0], batch['seg'][0]), 0)[:, None])
except ImportError:
view_batch = None
print("you can visualize batches with batchviewer. It's a nice and handy tool. You can get it here: "
"https://github.com/FabianIsensee/BatchViewer")
# now we have some DataLoader. Let's go an get some augmentations
# first let's collect all shapes, you will see why later
shapes = [BraTS2017DataLoader2D.load_patient(i)[0].shape[2:] for i in patients]
max_shape = np.max(shapes, 0)
max_shape = np.max((max_shape, patch_size), 0)
# we create a new instance of DataLoader. This one will return batches of shape max_shape. Cropping/padding is
# now done by SpatialTransform. If we do it this way we avoid border artifacts (the entire brain of all cases will
# be in the batch and SpatialTransform will use zeros which is exactly what we have outside the brain)