atlannot.utils module¶
Various utilities. (Refactoring in the near future).
- class atlannot.utils.Remapper(*volumes: Sequence[ndarray])[source]¶
Bases:
objectUtility class for remapping of labels.
- Parameters:
volumes – Different volumes to consider.
- atlannot.utils.add_middle_line(image, axis=0, thickness=1, value=1)[source]¶
Add middle line on a given image.
- Parameters:
image (np.ndarray) – Input image which needs a middle line along one axis.
axis (int) – Axis along which to create the middle line.
thickness (int) – Thickness of the middle line.
value (float) – Pixel intensity of the middle line.
- Returns:
new_image – Image as the input image with the middle line as specified.
- Return type:
np.ndarray
- atlannot.utils.atlas_symmetry_score(atlas, axis=1)[source]¶
Compute the symmetric score (in misalignment) of label-based image.
- Parameters:
atlas (np.ndarray) – Image for which symmetric score is computed.
axis (int) – Axis along which the symmetric has to be analyzed.
- Returns:
alignment – Alignment between two halfs of the input atlas.
- Return type:
float
- atlannot.utils.compose(*transforms)[source]¶
Compose several transformations into one.
- Parameters:
transforms (Iterable of np.ndarray) – List of transformations to compose. All the transformations should have the same shape (w, h, 1, 1, 2) for 2D and (w, h, p, 1, 1, 3) for 3D.
- Returns:
transform_composed – Resulting composed transformations of the same shape as input ones.
- Return type:
np.ndarray
- atlannot.utils.create_description(name, args=None)[source]¶
Create description from name and args.
- Parameters:
name (str) – Name of the experiment.
args (None or argparse.Namespace) – Information of the experiment.
- Returns:
description – Entire description of the experiment.
- Return type:
str
- atlannot.utils.edge_laplacian_thick(img, binary=True)[source]¶
Apply Laplacian to an image (results in thick border).
- Parameters:
img (np.ndarray) – Input image for which it is needed to compute a convolution.
binary (bool) – If True, the return image has only binary values, otherwise the return image pixel values are results of the convolution.
- Returns:
result – Resulting Laplacian convolution.
- Return type:
np.ndarray
- atlannot.utils.edge_laplacian_thin(img, binary=True)[source]¶
Apply Laplacian to an image (results in thin border).
- Parameters:
img (np.ndarray) – Input image for which it is needed to compute a convolution.
binary (bool) – If True, the return image has only binary values, otherwise the return image pixel values are results of the convolution.
- Returns:
result – Resulting Laplacian convolution.
- Return type:
np.ndarray
- atlannot.utils.edge_sobel(img, binary=True)[source]¶
Apply Sobel to an image.
- Parameters:
img (np.ndarray) – Input image for which it is needed to compute a convolution.
binary (bool) – If True, the return image has only binary values, otherwise the return image pixel values are results of the convolution.
- Returns:
result – Resulting Sobel convolution.
- Return type:
np.ndarray
- atlannot.utils.image_convolution(img, kernel, kernel_trans=None, *, binary=True)[source]¶
Compute convolution of an image.
- Parameters:
img (np.ndarray) – Input image for which it is needed to compute a convolution.
kernel (np.ndarray) – Kernel to apply to the input image.
kernel_trans (np.ndarray or None) – If specified, a second kernel is applied to the input image.
binary (bool) – If True, the return image has only binary values, otherwise the return image pixel values are results of the convolution.
- Returns:
grad – Resulting image after convolution.
- Return type:
np.ndarray
- atlannot.utils.load_nrrd(path, norm=True)[source]¶
Load nrrd file.
- Parameters:
path (pathlib.Path or str) – Path of the file to read (with nrrd format).
norm (bool) – If True, intensities of the numpy are normalized between 0 and 1.
- Returns:
data – Loaded file.
- Return type:
np.ndarray
- atlannot.utils.load_volume(volume_path, normalize=True)[source]¶
Load volume from the given path.
- Parameters:
volume_path (str or pathlib.Path) – Path of file to load
normalize (bool) – If True, the numpy is normalized between 0 and 1.
- Returns:
img – Loaded path
- Return type:
np.ndarray or None
- atlannot.utils.merge(img1, img2, scale1=1.0, scale2=1.0)[source]¶
Merge two images by taking the maximum intensity.
- Parameters:
img1 (np.ndarray) – First input image.
img2 (np.ndarray) – Second input image. Should have the same shape as img1.
scale1 (float) – Value used to multiply all pixels intensities of img1.
scale2 (float) – Value used to multiply all pixels intensities of img1.
- Returns:
img_res – Resulting image of the same shape as img1 and img2.
- Return type:
np.ndarray
- atlannot.utils.saving_results(output_dir, img_ref=None, img_mov=None, img_reg=None, df=None, description=None)[source]¶
Save results under output_dir.
- Parameters:
output_dir (pathlib.Path) – Output directory where to save all the results.
img_ref (np.ndarray) – Image used as reference image during the registration.
img_mov (np.ndarray) – Image used as moving image during the registration.
img_reg (np.ndarray) – Image resulting after the registration
df (np.ndarray) – Displacement computed with the registration.
description (str) – Description of the experiment.
- atlannot.utils.split_halfs(img, axis)[source]¶
Split image into two half of it. The right part is flipped.
- Parameters:
img (np.ndarray) – Input image.
axis (int) – Axis along which to cut the image into two.
- Returns:
half_1 (np.ndarray) – First resulting half of the input image.
half_2 (np.ndarray) – Second resulting half of the input image, flipped.
- atlannot.utils.stain_symmetry_score(img, axis=1)[source]¶
Compute the symmetric score (SSIM) of intensity-based image.
- Parameters:
img (np.ndarray) – Image for which symmetric score is computed.
axis (int) – Axis along which the symmetric has to be analyzed.
- Returns:
ssmi_value – Structural similarity measure between two halfs of the input image.
- Return type:
float