torchio.data.io._to_itk_convention() - Code Metrics - Inspection of "Bump version: 0.17.2 → 0.17.3" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 9ae791...b80c39 )

by Fernando

created 2020-06-30 09:25 UTC

torchio.data.io._to_itk_convention() A

↳ Parent: torchio.data.io

Complexity

Conditions

Size

Total Lines	6
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	5
nop	1
dl	0
loc	6
rs	10
c	0
b	0
f	0

from pathlib import Path
from typing import Tuple
import torch
import numpy as np
import nibabel as nib
import SimpleITK as sitk
from .. import TypePath, TypeData
from ..utils import nib_to_sitk, sitk_to_nib


FLIPXY = np.diag([-1, -1, 1, 1])


def read_image(
        path: TypePath,
        itk_first: bool = False,
        ) -> Tuple[torch.Tensor, np.ndarray]:
    if itk_first:
        try:
            result = _read_sitk(path)
        except RuntimeError:  # try with NiBabel
            result = _read_nibabel(path)
    else:
        try:
            result = _read_nibabel(path)
        except nib.loadsave.ImageFileError:  # try with ITK
            result = _read_sitk(path)
    return result


def _read_nibabel(path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
    nii = nib.load(str(path), mmap=False)
    data = nii.get_fdata(dtype=np.float32)
    tensor = torch.from_numpy(data)
    affine = nii.affine
    return tensor, affine


def _read_sitk(path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
    if Path(path).is_dir():  # assume DICOM
        image = _read_dicom(path)
    else:
        image = sitk.ReadImage(str(path))
    data, affine = sitk_to_nib(image)
    if data.dtype != np.float32:
        data = data.astype(np.float32)
    tensor = torch.from_numpy(data)
    return tensor, affine


def _read_dicom(directory: TypePath):
    directory = Path(directory)
    if not directory.is_dir():  # unreachable if called from _read_sitk
        raise FileNotFoundError(f'Directory "{directory}" not found')
    reader = sitk.ImageSeriesReader()
    dicom_names = reader.GetGDCMSeriesFileNames(str(directory))
    if not dicom_names:
        message = (
            f'The directory "{directory}"'
            ' does not seem to contain DICOM files'
        )
        raise FileNotFoundError(message)
    reader.SetFileNames(dicom_names)
    image = reader.Execute()
    return image


def write_image(
        tensor: torch.Tensor,
        affine: TypeData,
        path: TypePath,
        itk_first: bool = False,
        ) -> None:
    if itk_first:
        try:
            _write_sitk(tensor, affine, path)
        except RuntimeError:  # try with NiBabel
            _write_nibabel(tensor, affine, path)
    else:
        try:
            _write_nibabel(tensor, affine, path)
        except nib.loadsave.ImageFileError:  # try with ITK
            _write_sitk(tensor, affine, path)


def _write_nibabel(
        tensor: torch.Tensor,
        affine: TypeData,
        path: TypePath,
        ) -> None:
    """
    Expects a path with an extension that can be used by nibabel.save
    to write a NIfTI-1 image, such as '.nii.gz' or '.img'
    """
    nii = nib.Nifti1Image(tensor.numpy(), affine)
    nii.header['qform_code'] = 1
    nii.header['sform_code'] = 0
    nii.to_filename(str(path))


def _write_sitk(
        tensor: torch.Tensor,
        affine: TypeData,
        path: TypePath,
        ) -> None:
    image = nib_to_sitk(tensor, affine)
    sitk.WriteImage(image, str(path))


def read_matrix(path: TypePath):
    """Read an affine transform and convert to tensor."""
    path = Path(path)
    suffix = path.suffix
    if suffix in ('.tfm', '.h5'):  # ITK
        tensor = _read_itk_matrix(path)
    elif suffix in ('.txt', '.trsf'):  # NiftyReg, blockmatching
        tensor = _read_niftyreg_matrix(path)
    return tensor



def write_matrix(matrix: torch.Tensor, path: TypePath):
    """Write an affine transform."""
    path = Path(path)
    suffix = path.suffix
    if suffix in ('.tfm', '.h5'):  # ITK
        _write_itk_matrix(matrix, path)
    elif suffix in ('.txt', '.trsf'):  # NiftyReg, blockmatching
        _write_niftyreg_matrix(matrix, path)


def _to_itk_convention(matrix):
    """RAS to LPS"""
    matrix = np.dot(FLIPXY, matrix)
    matrix = np.dot(matrix, FLIPXY)
    matrix = np.linalg.inv(matrix)
    return matrix


def _from_itk_convention(matrix):
    """LPS to RAS"""
    matrix = np.dot(matrix, FLIPXY)
    matrix = np.dot(FLIPXY, matrix)
    matrix = np.linalg.inv(matrix)
    return matrix


def _read_itk_matrix(path):
    """Read an affine transform in ITK's .tfm format"""
    transform = sitk.ReadTransform(str(path))
    parameters = transform.GetParameters()
    rotation_parameters = parameters[:9]
    rotation_matrix = np.array(rotation_parameters).reshape(3, 3)
    translation_parameters = parameters[9:]
    translation_vector = np.array(translation_parameters).reshape(3, 1)
    matrix = np.hstack([rotation_matrix, translation_vector])
    homogeneous_matrix_lps = np.vstack([matrix, [0, 0, 0, 1]])
    homogeneous_matrix_ras = _from_itk_convention(homogeneous_matrix_lps)
    return torch.from_numpy(homogeneous_matrix_ras)


def _write_itk_matrix(matrix, tfm_path):
    """The tfm file contains the matrix from floating to reference."""
    transform = _matrix_to_itk_transform(matrix)
    transform.WriteTransform(str(tfm_path))


def _matrix_to_itk_transform(matrix, dimensions=3):
    matrix = _to_itk_convention(matrix)
    rotation = matrix[:dimensions, :dimensions].ravel().tolist()
    translation = matrix[:dimensions, 3].tolist()
    transform = sitk.AffineTransform(rotation, translation)
    return transform


def _read_niftyreg_matrix(trsf_path):
    """Read a NiftyReg matrix and return it as a NumPy array"""
    matrix = np.loadtxt(trsf_path)
    matrix = np.linalg.inv(matrix)
    return torch.from_numpy(matrix)


def _write_niftyreg_matrix(matrix, txt_path):
    """Write an affine transform in NiftyReg's .txt format (ref -> flo)"""
    matrix = np.linalg.inv(matrix)
    np.savetxt(txt_path, matrix, fmt='%.8f')


1			from pathlib import Path
2			from typing import Tuple
3			import torch
4			import numpy as np
5			import nibabel as nib
6			import SimpleITK as sitk
7			from .. import TypePath, TypeData
8			from ..utils import nib_to_sitk, sitk_to_nib
9
10
11			FLIPXY = np.diag([-1, -1, 1, 1])
12
13
14			def read_image(
15			path: TypePath,
16			itk_first: bool = False,
17			) -> Tuple[torch.Tensor, np.ndarray]:
18			if itk_first:
19			try:
20			result = _read_sitk(path)
21			except RuntimeError: # try with NiBabel
22			result = _read_nibabel(path)
23			else:
24			try:
25			result = _read_nibabel(path)
26			except nib.loadsave.ImageFileError: # try with ITK
27			result = _read_sitk(path)
28			return result
29
30
31			def _read_nibabel(path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
32			nii = nib.load(str(path), mmap=False)
33			data = nii.get_fdata(dtype=np.float32)
34			tensor = torch.from_numpy(data)
35			affine = nii.affine
36			return tensor, affine
37
38
39			def _read_sitk(path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
40			if Path(path).is_dir(): # assume DICOM
41			image = _read_dicom(path)
42			else:
43			image = sitk.ReadImage(str(path))
44			data, affine = sitk_to_nib(image)
45			if data.dtype != np.float32:
46			data = data.astype(np.float32)
47			tensor = torch.from_numpy(data)
48			return tensor, affine
49
50
51			def _read_dicom(directory: TypePath):
52			directory = Path(directory)
53			if not directory.is_dir(): # unreachable if called from _read_sitk
54			raise FileNotFoundError(f'Directory "{directory}" not found')
55			reader = sitk.ImageSeriesReader()
56			dicom_names = reader.GetGDCMSeriesFileNames(str(directory))
57			if not dicom_names:
58			message = (
59			f'The directory "{directory}"'
60			' does not seem to contain DICOM files'
61			)
62			raise FileNotFoundError(message)
63			reader.SetFileNames(dicom_names)
64			image = reader.Execute()
65			return image
66
67
68			def write_image(
69			tensor: torch.Tensor,
70			affine: TypeData,
71			path: TypePath,
72			itk_first: bool = False,
73			) -> None:
74			if itk_first:
75			try:
76			_write_sitk(tensor, affine, path)
77			except RuntimeError: # try with NiBabel
78			_write_nibabel(tensor, affine, path)
79			else:
80			try:
81			_write_nibabel(tensor, affine, path)
82			except nib.loadsave.ImageFileError: # try with ITK
83			_write_sitk(tensor, affine, path)
84
85
86			def _write_nibabel(
87			tensor: torch.Tensor,
88			affine: TypeData,
89			path: TypePath,
90			) -> None:
91			"""
92			Expects a path with an extension that can be used by nibabel.save
93			to write a NIfTI-1 image, such as '.nii.gz' or '.img'
94			"""
95			nii = nib.Nifti1Image(tensor.numpy(), affine)
96			nii.header['qform_code'] = 1
97			nii.header['sform_code'] = 0
98			nii.to_filename(str(path))
99
100
101			def _write_sitk(
102			tensor: torch.Tensor,
103			affine: TypeData,
104			path: TypePath,
105			) -> None:
106			image = nib_to_sitk(tensor, affine)
107			sitk.WriteImage(image, str(path))
108
109
110			def read_matrix(path: TypePath):
111			"""Read an affine transform and convert to tensor."""
112			path = Path(path)
113			suffix = path.suffix
114			if suffix in ('.tfm', '.h5'): # ITK
115			tensor = _read_itk_matrix(path)
116			elif suffix in ('.txt', '.trsf'): # NiftyReg, blockmatching
117			tensor = _read_niftyreg_matrix(path)
118			return tensor
			0 ignored issues – show introduced 2020-06-30 09:27 UTC by Report Bug Copy Issue Report The variable `tensor` does not seem to be defined for all execution paths. Loading history...
119
120
121			def write_matrix(matrix: torch.Tensor, path: TypePath):
122			"""Write an affine transform."""
123			path = Path(path)
124			suffix = path.suffix
125			if suffix in ('.tfm', '.h5'): # ITK
126			_write_itk_matrix(matrix, path)
127			elif suffix in ('.txt', '.trsf'): # NiftyReg, blockmatching
128			_write_niftyreg_matrix(matrix, path)
129
130
131			def _to_itk_convention(matrix):
132			"""RAS to LPS"""
133			matrix = np.dot(FLIPXY, matrix)
134			matrix = np.dot(matrix, FLIPXY)
135			matrix = np.linalg.inv(matrix)
136			return matrix
137
138
139			def _from_itk_convention(matrix):
140			"""LPS to RAS"""
141			matrix = np.dot(matrix, FLIPXY)
142			matrix = np.dot(FLIPXY, matrix)
143			matrix = np.linalg.inv(matrix)
144			return matrix
145
146
147			def _read_itk_matrix(path):
148			"""Read an affine transform in ITK's .tfm format"""
149			transform = sitk.ReadTransform(str(path))
150			parameters = transform.GetParameters()
151			rotation_parameters = parameters[:9]
152			rotation_matrix = np.array(rotation_parameters).reshape(3, 3)
153			translation_parameters = parameters[9:]
154			translation_vector = np.array(translation_parameters).reshape(3, 1)
155			matrix = np.hstack([rotation_matrix, translation_vector])
156			homogeneous_matrix_lps = np.vstack([matrix, [0, 0, 0, 1]])
157			homogeneous_matrix_ras = _from_itk_convention(homogeneous_matrix_lps)
158			return torch.from_numpy(homogeneous_matrix_ras)
159
160
161			def _write_itk_matrix(matrix, tfm_path):
162			"""The tfm file contains the matrix from floating to reference."""
163			transform = _matrix_to_itk_transform(matrix)
164			transform.WriteTransform(str(tfm_path))
165
166
167			def _matrix_to_itk_transform(matrix, dimensions=3):
168			matrix = _to_itk_convention(matrix)
169			rotation = matrix[:dimensions, :dimensions].ravel().tolist()
170			translation = matrix[:dimensions, 3].tolist()
171			transform = sitk.AffineTransform(rotation, translation)
172			return transform
173
174
175			def _read_niftyreg_matrix(trsf_path):
176			"""Read a NiftyReg matrix and return it as a NumPy array"""
177			matrix = np.loadtxt(trsf_path)
178			matrix = np.linalg.inv(matrix)
179			return torch.from_numpy(matrix)
180
181
182			def _write_niftyreg_matrix(matrix, txt_path):
183			"""Write an affine transform in NiftyReg's .txt format (ref -> flo)"""
184			matrix = np.linalg.inv(matrix)
185			np.savetxt(txt_path, matrix, fmt='%.8f')
186

fepegar / torchio

Push — master ( 9ae791...b80c39 )

torchio.data.io._to_itk_convention() A

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