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a23d562 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | from matplotlib import pyplot as plt
import torchio as tio
from auto_detect_breast_mri.data.metadata import get_uka_metatensor
from auto_detect_breast_mri.data.transforms import RandomCropOrPad, ZNormalization, ImageToTensor
from auto_detect_breast_mri.data.breast_mri_dataset import BreastMRISubjects
from auto_detect_breast_mri.data.loaders import get_multiple_subjects_dataloader
from auto_detect_breast_mri.config import resolve_path
# Paths come from the site config (see config.example.yaml).
path_base = resolve_path(None, "data_root", "root folder of the NIfTI data")
subset_path = resolve_path(None, "split_root", "folder holding the split files")
subset_file = "fold0/stratified_training_set-f0-0.csv"
feature_path = resolve_path(None, "metadata_file", "metadata export")
pre_image_shape = (32, 512, 512)
protocol = ['Sub_1']
train_prop = 0.7
train_fraction = 0.5
batch_size = 4
transform = tio.Compose([
tio.RandomFlip((0,1,2), flip_probability=0.5),
RandomCropOrPad(pre_image_shape),
ZNormalization(per_channel=True, percentiles=(0.5, 99.5), masking_method=lambda x:x>0),
tio.RandomNoise(std=(0.25,0.5)),
ImageToTensor()
])
data_set = BreastMRISubjects(path_base, subset_path + subset_file, protocol=protocol, transform=transform)
feature_dataframe = get_uka_metatensor(0, feature_path)
train_loader, eval_loader, test_loader = get_multiple_subjects_dataloader(path_base, feature_dataframe,
pre_image_shape, transform, protocol,
subset_path, batch_size, stratified=True,
fraction=train_fraction, fold=0, subfold=0)
for i, batch in enumerate(train_loader):
image = batch['image']['data']
label = batch['label']
plt.imshow(image[0, 0, :, :, 14], cmap='gray', interpolation=None)
plt.show()
print(label[0])
print("-------")
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