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Merge pull request #451 from engrosamaali91/DevOp
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DevOp For Sphinx Documentation
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@m-reuter
m-reuter committed Feb 8, 2024
2 parents 04be337 + bb0ea35 commit 8c20cca
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171 changes: 86 additions & 85 deletions FastSurferCNN/data_loader/augmentation.py
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148 changes: 75 additions & 73 deletions FastSurferCNN/data_loader/conform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,9 @@
)

HELPTEXT = """
Script to conform an MRI brain image to UCHAR, RAS orientation, and 1mm or minimal isotropic voxels
Script to conform an MRI brain image to UCHAR, RAS orientation,
and 1mm or minimal isotropic voxels
USAGE:
conform.py -i <input> -o <output> <options>
OR
Expand All @@ -51,13 +53,13 @@


def options_parse():
"""Command line option parser.
"""
Command line option parser.
Returns
-------
options
object holding options
Object holding options.
"""
parser = argparse.ArgumentParser(usage=HELPTEXT)
parser.add_argument(
Expand Down Expand Up @@ -145,28 +147,28 @@ def map_image(
order: int = 1,
dtype: Optional[Type] = None
) -> np.ndarray:
"""Map image to new voxel space (RAS orientation).
"""
Map image to new voxel space (RAS orientation).
Parameters
----------
img : nib.analyze.SpatialImage
the src 3D image with data and affine set
The src 3D image with data and affine set.
out_affine : np.ndarray
trg image affine
Trg image affine.
out_shape : tuple[int, ...], np.ndarray
the trg shape information
The trg shape information.
ras2ras : Optional[np.ndarray]
an additional mapping that should be applied (default=id to just reslice)
An additional mapping that should be applied (default=id to just reslice).
order : int
order of interpolation (0=nearest,1=linear(default),2=quadratic,3=cubic)
Order of interpolation (0=nearest,1=linear(default),2=quadratic,3=cubic).
dtype : Optional[Type]
target dtype of the resulting image (relevant for reorientation, default=same as img)
Target dtype of the resulting image (relevant for reorientation, default=same as img).
Returns
-------
np.ndarray
mapped image data array
Mapped image data array.
"""
from scipy.ndimage import affine_transform
from numpy.linalg import inv
Expand Down Expand Up @@ -219,30 +221,30 @@ def getscale(
f_low: float = 0.0,
f_high: float = 0.999
) -> Tuple[float, float]:
"""Get offset and scale of image intensities to robustly rescale to range dst_min..dst_max.
"""
Get offset and scale of image intensities to robustly rescale to range dst_min..dst_max.
Equivalent to how mri_convert conforms images.
Parameters
----------
data : np.ndarray
image data (intensity values)
Image data (intensity values).
dst_min : float
future minimal intensity value
Future minimal intensity value.
dst_max : float
future maximal intensity value
Future maximal intensity value.
f_low : float
robust cropping at low end (0.0 no cropping, default)
Robust cropping at low end (0.0 no cropping, default).
f_high : float
robust cropping at higher end (0.999 crop one thousandth of high intensity voxels, default)
Robust cropping at higher end (0.999 crop one thousandth of high intensity voxels, default).
Returns
-------
float src_min
(adjusted) offset
(adjusted) offset.
float
scale factor
Scale factor.
"""
# get min and max from source
src_min = np.min(data)
Expand Down Expand Up @@ -318,26 +320,26 @@ def scalecrop(
src_min: float,
scale: float
) -> np.ndarray:
"""Crop the intensity ranges to specific min and max values.
"""
Crop the intensity ranges to specific min and max values.
Parameters
----------
data : np.ndarray
Image data (intensity values)
Image data (intensity values).
dst_min : float
future minimal intensity value
Future minimal intensity value.
dst_max : float
future maximal intensity value
Future maximal intensity value.
src_min : float
minimal value to consider from source (crops below)
Minimal value to consider from source (crops below).
scale : float
scale value by which source will be shifted
Scale value by which source will be shifted.
Returns
-------
np.ndarray
scaled image data
Scaled image data.
"""
data_new = dst_min + scale * (data - src_min)

Expand All @@ -357,51 +359,51 @@ def rescale(
f_low: float = 0.0,
f_high: float = 0.999
) -> np.ndarray:
"""Rescale image intensity values (0-255).
"""
Rescale image intensity values (0-255).
Parameters
----------
data : np.ndarray
image data (intensity values)
Image data (intensity values).
dst_min : float
future minimal intensity value
Future minimal intensity value.
dst_max : float
future maximal intensity value
Future maximal intensity value.
f_low : float
robust cropping at low end (0.0 no cropping, default)
Robust cropping at low end (0.0 no cropping, default).
f_high : float
robust cropping at higher end (0.999 crop one thousandth of high intensity voxels, default)
Robust cropping at higher end (0.999 crop one thousandth of high intensity voxels, default).
Returns
-------
np.ndarray
scaled image data
Scaled image data.
"""
src_min, scale = getscale(data, dst_min, dst_max, f_low, f_high)
data_new = scalecrop(data, dst_min, dst_max, src_min, scale)
return data_new


def find_min_size(img: nib.analyze.SpatialImage, max_size: float = 1) -> float:
"""Find minimal voxel size <= 1mm.
"""
Find minimal voxel size <= 1mm.
Parameters
----------
img : nib.analyze.SpatialImage
loaded source image
Loaded source image.
max_size : float
maximal voxel size in mm (default: 1.0)
Maximal voxel size in mm (default: 1.0).
Returns
-------
float
Rounded minimal voxel size
Rounded minimal voxel size.
Notes
-----
This function only needs the header (not the data).
"""
# find minimal voxel side length
sizes = np.array(img.header.get_zooms()[:3])
Expand All @@ -415,18 +417,19 @@ def find_img_size_by_fov(
vox_size: float,
min_dim: int = 256
) -> int:
"""Find the cube dimension (>= 256) to cover the field of view of img.
"""
Find the cube dimension (>= 256) to cover the field of view of img.
If vox_size is one, the img_size MUST always be min_dim (the FreeSurfer standard).
Parameters
----------
img : nib.analyze.SpatialImage
loaded source image
Loaded source image.
vox_size : float
the target voxel size in mm
The target voxel size in mm.
min_dim : int
minimal image dimension in voxels (default 256)
Minimal image dimension in voxels (default 256).
Returns
-------
Expand All @@ -436,7 +439,6 @@ def find_img_size_by_fov(
Notes
-----
This function only needs the header (not the data).
"""
if vox_size == 1.0:
return min_dim
Expand Down Expand Up @@ -467,31 +469,30 @@ def conform(
Parameters
----------
img : nib.analyze.SpatialImage
loaded source image
Loaded source image.
order : int
interpolation order (0=nearest,1=linear(default),2=quadratic,3=cubic)
Interpolation order (0=nearest,1=linear(default),2=quadratic,3=cubic).
conform_vox_size : VoxSizeOption
conform image the image to voxel size 1. (default), a
Conform image the image to voxel size 1. (default), a
specific smaller voxel size (0-1, for high-res), or automatically
determine the 'minimum voxel size' from the image (value 'min').
This assumes the smallest of the three voxel sizes.
dtype : Optional[Type]
the dtype to enforce in the image (default: UCHAR, as mri_convert -c)
The dtype to enforce in the image (default: UCHAR, as mri_convert -c).
conform_to_1mm_threshold : Optional[float]
the threshold above which the image is conformed to 1mm
The threshold above which the image is conformed to 1mm
(default: ignore).
Returns
-------
nib.MGHImage
conformed image
Conformed image.
Notes
-----
Unlike mri_convert -c, we first interpolate (float image), and then rescale
to uchar. mri_convert is doing it the other way around. However, we compute
the scale factor from the input to increase similarity.
"""
from nibabel.freesurfer.mghformat import MGHHeader

Expand Down Expand Up @@ -577,33 +578,34 @@ def is_conform(
verbose: bool = True,
conform_to_1mm_threshold: Optional[float] = None
) -> bool:
"""Check if an image is already conformed or not.
"""
Check if an image is already conformed or not.
Dimensions: 256x256x256, Voxel size: 1x1x1, LIA orientation, and data type UCHAR.
Parameters
----------
img : nib.analyze.SpatialImage
Loaded source image
Loaded source image.
conform_vox_size : VoxSizeOption
which voxel size to conform to. Can either be a float between 0.0 and
Which voxel size to conform to. Can either be a float between 0.0 and
1.0 or 'min' check, whether the image is conformed to the minimal voxels size, i.e.
conforming to smaller, but isotropic voxel sizes for high-res (default: 1.0).
eps : float
allowed deviation from zero for LIA orientation check (default: 1e-06).
Allowed deviation from zero for LIA orientation check (default: 1e-06).
Small inaccuracies can occur through the inversion operation. Already conformed
images are thus sometimes not correctly recognized. The epsilon accounts for
these small shifts.
check_dtype : bool
specifies whether the UCHAR dtype condition is checked for;
Specifies whether the UCHAR dtype condition is checked for;
this is not done when the input is a segmentation (default: True).
dtype : Optional[Type]
specifies the intended target dtype (default: uint8 = UCHAR)
Specifies the intended target dtype (default: uint8 = UCHAR).
verbose : bool
if True, details of which conformance conditions are violated (if any)
If True, details of which conformance conditions are violated (if any)
are displayed (default: True).
conform_to_1mm_threshold : Optional[float]
the threshold above which the image is conformed to 1mm
The threshold above which the image is conformed to 1mm
(default: ignore).
Returns
Expand All @@ -614,7 +616,6 @@ def is_conform(
Notes
-----
This function only needs the header (not the data).
"""
conformed_vox_size, conformed_img_size = get_conformed_vox_img_size(
img, conform_vox_size, conform_to_1mm_threshold=conform_to_1mm_threshold
Expand Down Expand Up @@ -683,7 +684,8 @@ def get_conformed_vox_img_size(
conform_vox_size: VoxSizeOption,
conform_to_1mm_threshold: Optional[float] = None
) -> Tuple[float, int]:
"""Extract the voxel size and the image size.
"""
Extract the voxel size and the image size.
This function only needs the header (not the data).
Expand All @@ -702,9 +704,9 @@ def get_conformed_vox_img_size(
Returns
-------
conformed_vox_size : float
The conformed voxel size of the image.
The determined voxel size to conform the image to.
conformed_img_size : int
The conformed image size of the image.
The size of the image adjusted to the conformed voxel size.
"""
# this is similar to mri_convert --conform_min
if isinstance(conform_vox_size, str) and conform_vox_size.lower() in [
Expand All @@ -730,7 +732,8 @@ def check_affine_in_nifti(
img: Union[nib.Nifti1Image, nib.Nifti2Image],
logger: Optional[logging.Logger] = None
) -> bool:
"""Check the affine in nifti Image.
"""
Check the affine in nifti Image.
Sets affine with qform, if it exists and differs from sform.
If qform does not exist, voxel sizes between header information and information
Expand All @@ -740,16 +743,15 @@ def check_affine_in_nifti(
Parameters
----------
img : Union[nib.Nifti1Image, nib.Nifti2Image]
loaded nifti-image
Loaded nifti-image.
logger : Optional[logging.Logger]
Logger object or None (default) to log or print an info message to
stdout (for None)
stdout (for None).
Returns
-------
False, if
voxel sizes in affine and header differ
bool
False, if voxel sizes in affine and header differ.
"""
check = True
message = ""
Expand Down
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