I/O

input/output management

ScalarImage

 ScalarImage (*args, **kwargs)

Image whose pixel values represent scalars.

See :class:~torchio.Image for more information.

Image Writers

write_image

 write_image (data, file_path, dimension_order='TCZYX')

Writes an image to a file.

:param data: Image data (numpy array, tensor, or AICSImage) :param file_path: Path to save the image :param format: Format to save the image in (default is png)

# Example usage:
numpy_array = np.random.rand(3, 100, 100)
write_image(numpy_array, './data_examples/output_from_numpy.tiff')

tensor = torch.rand(3, 100, 100)
write_image(tensor, './data_examples/output_from_tensor.tiff')

aics_image = AICSImage('./data_examples/example_tiff.tiff')
write_image(aics_image, './data_examples/output_from_tiff.png')
Image successfully saved to ./data_examples/output_from_numpy.tiff
Image successfully saved to ./data_examples/output_from_tensor.tiff
Image successfully saved to ./data_examples/output_from_tiff.png

Image Readers

It uses helper functions such as _load_and_preprocess and _multi_sequence to load and preprocess biological images.

tiff2torch

 tiff2torch (file_path:str)

Load tiff into pytorch tensor

string2dict

 string2dict (input_string:str)

split_path

 split_path (file_path,
             exts:(<class'fastcore.foundation.L'>,<class'list'>)=['.ome.ti
             ff', '.tiff', '.tif', '.png'])
Type Default Details
file_path The path to the file to split
exts (<class ‘fastcore.foundation.L’>, <class ‘list’>) [‘.ome.tiff’, ‘.tiff’, ‘.tif’, ‘.png’] List of filename extensions

aics_image_reader

 aics_image_reader (ind_dict=None)

Initialize self. See help(type(self)) for accurate signature.

Type Default Details
ind_dict NoneType None Dictionary indicating the channels to load 
file_path = 'data_examples/example_tiff.tiff'
test_img, _ = aics_image_reader({'Z': 0})(file_path)
test_img.shape
(1, 1, 512, 512)

Hierarchical Data Format

split_hdf_path

 split_hdf_path (file_path,
                 hdf5_exts:(<class'fastcore.foundation.L'>,<class'list'>)=
                 ['.h5', '.hdf5'])
Type Default Details
file_path The path to the HDF5 file to split
hdf5_exts (<class ‘fastcore.foundation.L’>, <class ‘list’>) [‘.h5’, ‘.hdf5’] List of filename extensions

hdf5_reader

 hdf5_reader (dataset=None, patch=0,
              hdf5_exts:(<class'fastcore.foundation.L'>,<class'list'>)=['.
              h5', '.hdf5'])

Initialize self. See help(type(self)) for accurate signature.

Type Default Details
dataset NoneType None The dataset to load
patch int 0 The patch to load from the dataset
hdf5_exts (<class ‘fastcore.foundation.L’>, <class ‘list’>) [‘.h5’, ‘.hdf5’] List of filename extensions

Images can be loaded by explicitly writing dataset name and path number…

from bioMONAI.visualize import plot_image
file_path = './data_examples/0450_1.hdf5'
dataset_name='clean'
patch_num=10

im , _ = hdf5_reader(dataset=dataset_name, patch=patch_num)(file_path)
plot_image(im[0])

… or enconding them in the path, where datasets are subfolders and patches the image files. The latter being compatible with image_reader syntaxis.

f = file_path + '/' + dataset_name + '/' + '%d'%(patch_num)
im , _ = hdf5_reader()(f)
plot_image(im[0])

Preprocessing

Load and preprocess

org_img, _, _ = _load_and_preprocess(f)

test_eq(org_img.data[0].shape, im.shape)

Read multichannel data

t = _multi_sequence([f], only_tensor=True);
test_eq(t[0].shape, im.shape)
t.shape
torch.Size([1, 1, 96, 96])

Image reader

image_reader

 image_reader (file_path:(<class'str'>,<class'pathlib.Path'>,<class'fastco
               re.foundation.L'>,<class'list'>), dtype=<class
               'torch.Tensor'>, only_tensor:bool=True, **kwargs)

Loads and preprocesses a medical image.

Args: file_path: Path to the image. Can be a string, Path object or a list. dtype: Datatype for the return value. Defaults to torchTensor. reorder: Whether to reorder the data to be closest to canonical (RAS+) orientation. Defaults to False. resample: Whether to resample image to different voxel sizes and image dimensions. Defaults to None. only_tensor: To return only an image tensor. Defaults to True.

Returns: The preprocessed image. Returns only the image tensor if only_tensor is True, otherwise returns original image, preprocessed image, and original size.

Type Default Details
file_path (<class ‘str’>, <class ‘pathlib.Path’>, <class ‘fastcore.foundation.L’>, <class ‘list’>) Path to the image
dtype _TensorMeta Tensor Datatype for the return value. Defaults to torchTensor
only_tensor bool True To return only an image tensor
kwargs 
test_eq(image_reader(f)[0].shape, im.shape)