def __init__(self, transforms=None) -> None:
if transforms is None:
transforms = []
if not isinstance(transforms, (list, tuple)):
raise ValueError("Parameters 'transforms' must be a list or tuple")
self.transforms = transforms
self.set_random_state(seed=get_seed())
def __init__(
self,
image_files,
seg_files=None,
labels=None,
as_closest_canonical: bool = False,
transform: Optional[Callable] = None,
seg_transform: Optional[Callable] = None,
image_only: bool = True,
dtype: Optional[np.dtype] = np.float32,
):
"""
Initializes the dataset with the image and segmentation filename lists. The transform `transform` is applied
to the images and `seg_transform` to the segmentations.
Args:
image_files (list of str): list of image filenames
seg_files (list of str): if in segmentation task, list of segmentation filenames
labels (list or array): if in classification task, list of classification labels
as_closest_canonical: if True, load the image as closest to canonical orientation
transform: transform to apply to image arrays
seg_transform: transform to apply to segmentation arrays
image_only: if True return only the image volume, other return image volume and header dict
dtype (np.dtype, optional): if not None convert the loaded image to this data type
"""
if seg_files is not None and len(image_files) != len(seg_files):
raise ValueError(
"Must have same number of image and segmentation files")
self.image_files = image_files
self.seg_files = seg_files
self.labels = labels
self.as_closest_canonical = as_closest_canonical
self.transform = transform
self.seg_transform = seg_transform
self.image_only = image_only
self.dtype = dtype
self.set_random_state(seed=get_seed())
self._seed = 0 # transform synchronization seed