def __init__(self,
mode,
dataset_path='.././datasets',
split_idx=150,
crop_dim=(512, 512),
samples=100,
classes=7,
save=True):
"""
:param mode: 'train','val'
:param image_paths: image dataset paths
:param label_paths: label dataset paths
:param crop_dim: 2 element tuple to decide crop values
:param samples: number of sub-grids to create(patches of the input img)
"""
image_paths = sorted(
glob.glob(
dataset_path +
"/MICCAI_2019_pathology_challenge/Train Imgs/Train Imgs/*.jpg")
)
label_paths = sorted(
glob.glob(dataset_path +
"/MICCAI_2019_pathology_challenge/Labels/*.png"))
# TODO problem with random shuffle in val and train
# TODo cope with 3d affine and full volume for better generalization
image_paths, label_paths = utils.shuffle_lists(image_paths,
label_paths)
self.full_volume = None
self.affine = None
self.slices = 244 # dataset instances
self.mode = mode
self.crop_dim = crop_dim
self.sample_list = []
self.samples = samples
self.save = save
self.root = dataset_path
self.per_image_sample = int(self.samples / self.slices)
if self.per_image_sample < 1:
self.per_image_sample = 1
print("per image sampleeeeee", self.per_image_sample)
sub_grid = '_2dgrid_' + str(crop_dim[0]) + 'x' + str(crop_dim[1])
if self.save:
self.sub_vol_path = self.root + '/MICCAI_2019_pathology_challenge/generated/' + mode + sub_grid + '/'
utils.make_dirs(self.sub_vol_path)
if self.mode == 'train':
self.list_imgs = image_paths[0:split_idx]
self.list_labels = label_paths[0:split_idx]
elif self.mode == 'val':
self.list_imgs = image_paths[split_idx:]
self.list_labels = label_paths[split_idx:]
self.generate_samples()
def __init__(self,
mode,
dataset_path='./datasets',
classes=5,
crop_dim=(200, 200, 150),
split_idx=260,
samples=10,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param split_idx: 1 to 10 values
:param samples: number of sub-volumes that you want to create
"""
self.mode = mode
self.root = str(dataset_path)
self.training_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/'
self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/'
self.full_vol_dim = (240, 240, 155) # slice, width, height
self.crop_size = crop_dim
self.list = []
self.samples = samples
self.full_volume = None
self.classes = classes
self.save_name = self.root + '/brats2019/brats2019-list-' + mode + '-samples-' + str(
samples) + '.txt'
if load:
self.list = utils.load_list(self.save_name)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path,
'*GG/*/*t1.nii.gz')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
return
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz')))
list_IDsT1ce = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1ce.nii.gz')))
list_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t2.nii.gz')))
list_IDsFlair = sorted(
glob.glob(os.path.join(self.training_path,
'*GG/*/*_flair.nii.gz')))
labels = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*_seg.nii.gz')))
list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists(
list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
seed=17)
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
print('Brats2019, Total data:', len(list_IDsT1))
list_IDsT1 = list_IDsT1[:split_idx]
list_IDsT1ce = list_IDsT1ce[:split_idx]
list_IDsT2 = list_IDsT2[:split_idx]
list_IDsFlair = list_IDsFlair[:split_idx]
labels = labels[:split_idx]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_idx:]
list_IDsT1ce = list_IDsT1ce[split_idx:]
list_IDsT2 = list_IDsT2[split_idx:]
list_IDsFlair = list_IDsFlair[split_idx:]
labels = labels[split_idx:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz')))
self.list_IDsT1ce = sorted(
glob.glob(os.path.join(self.testing_path,
'*GG/*/*t1ce.nii.gz')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz')))
self.list_IDsFlair = sorted(
glob.glob(
os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz')))
self.labels = None
# Todo inference code here
utils.save_list(self.save_name, self.list)
def __init__(self,
args,
mode,
dataset_path='./datasets',
classes=4,
crop_dim=(100, 100, 100),
split_idx=204,
samples=10,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param split_idx: 1 to 10 values
:param samples: number of sub-volumes that you want to create
"""
self.mode = mode
self.root = str(dataset_path)
self.training_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/train_generated'
self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/'
self.full_vol_dim = (128, 128, 128) # slice, width, height
self.crop_size = crop_dim
self.threshold = args.threshold
self.normalization = args.normalization
self.augmentation = args.augmentation
self.list = []
self.samples = samples
self.full_volume = None
self.classes = classes
if self.augmentation:
self.transform = augment3D.RandomChoice(transforms=[
augment3D.GaussianNoise(mean=0, std=0.01),
augment3D.RandomFlip(),
augment3D.ElasticTransform()
],
p=0.5)
self.save_name = self.root + '/brats2019/brats2019-list-' + mode + '-samples-' + str(
samples) + '.txt'
if load:
## load pre-generated data
self.list = utils.load_list(self.save_name)
list_IDsT1 = sorted(
glob.glob(
os.path.join(self.training_path,
'***_imgs/***_imgs_t1.npy')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
return
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(
os.path.join(self.training_path, '***_imgs/***_imgs_t1.npy')))
list_IDsT1ce = sorted(
glob.glob(
os.path.join(self.training_path,
'***_imgs/***_imgs_t1gd.npy')))
list_IDsT2 = sorted(
glob.glob(
os.path.join(self.training_path, '***_imgs/***_imgs_t2.npy')))
list_IDsFlair = sorted(
glob.glob(
os.path.join(self.training_path,
'***_imgs/***_imgs_flair.npy')))
labels = sorted(
glob.glob(
os.path.join(self.training_path, '***_seg/***_seg_label.npy')))
list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists(
list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
seed=17)
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
print('Brats2019, Total data:', len(list_IDsT1))
list_IDsT1 = list_IDsT1[:split_idx]
list_IDsT1ce = list_IDsT1ce[:split_idx]
list_IDsT2 = list_IDsT2[:split_idx]
list_IDsFlair = list_IDsFlair[:split_idx]
labels = labels[:split_idx]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
th_percent=self.threshold)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_idx:]
list_IDsT1ce = list_IDsT1ce[split_idx:]
list_IDsT2 = list_IDsT2[split_idx:]
list_IDsFlair = list_IDsFlair[split_idx:]
labels = labels[split_idx:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
th_percent=self.threshold)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz')))
self.list_IDsT1ce = sorted(
glob.glob(os.path.join(self.testing_path,
'*GG/*/*t1ce.nii.gz')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz')))
self.list_IDsFlair = sorted(
glob.glob(
os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz')))
self.labels = None
# Todo inference code here
utils.save_list(self.save_name, self.list)