tomopy.misc.corr

Module for data correction and masking functions.

Functions:

adjust_range(arr[, dmin, dmax])

Change dynamic range of values in an array.

circ_mask(arr, axis[, ratio, val, ncore])

Apply circular mask to a 3D array.

gaussian_filter(arr[, sigma, order, axis, ncore])

Apply Gaussian filter to 3D array along specified axis.

median_filter(arr[, size, axis, ncore])

Apply median filter to 3D array along specified axis.

median_filter_cuda(arr[, size, axis])

Apply median filter to 3D array along 0 axis with GPU support.

median_filter_nonfinite(arr[, size, callback])

Remove nonfinite values from a 3D array using an in-place 2D median filter.

sobel_filter(arr[, axis, ncore])

Apply Sobel filter to 3D array along specified axis.

remove_nan(arr[, val, ncore])

Replace NaN values in array with a given value.

remove_neg(arr[, val, ncore])

Replace negative values in array with a given value.

remove_outlier(arr, dif[, size, axis, ...])

Remove high intensity bright spots from a N-dimensional array by chunking along the specified dimension, and performing (N-1)-dimensional median filtering along the other dimensions.

remove_outlier_cuda(arr, dif[, size, axis])

Remove high intensity bright spots from a 3D array along axis 0 dimension using GPU.

remove_ring(rec[, center_x, center_y, ...])

Remove ring artifacts from images in the reconstructed domain.
tomopy.misc.corr.adjust_range(arr, dmin=None, dmax=None)[source]

Change dynamic range of values in an array.

Parameters

  • arr (ndarray) – Input array.

  • dmin, dmax (float, optional) – Mininum and maximum values to rescale data.

Returns

ndarray – Output array.

tomopy.misc.corr.circ_mask(arr, axis, ratio=1, val=0.0, ncore=None)[source]

Apply circular mask to a 3D array.

Parameters

  • arr (ndarray) – Arbitrary 3D array.

  • axis (int) – Axis along which mask will be performed.

  • ratio (int, optional) – Ratio of the mask’s diameter in pixels to the smallest edge size along given axis.

  • val (int, optional) – Value for the masked region.

Returns

ndarray – Masked array.

tomopy.misc.corr.enhance_projs_aps_1id(imgstack, median_ks=5, ncore=None)[source]

Enhance the projection images with weak contrast collected at APS 1ID

This filter uses a median fileter (will be switched to enhanced recursive median fileter, ERMF, in the future) for denoising, and a histogram equalization for dynamic range adjustment to bring out the details.

Parameters

  • imgstack (np.ndarray) – tomopy images stacks (axis_0 is the oemga direction)

  • median_ks (int, optional) – 2D median filter kernel size for local noise suppresion

  • ncore (int, optional) – number of cores used for speed up

Returns

ndarray – 3D enhanced image stacks.

tomopy.misc.corr.gaussian_filter(arr, sigma=3, order=0, axis=0, ncore=None)[source]

Apply Gaussian filter to 3D array along specified axis.

Parameters

  • arr (ndarray) – Input array.

  • sigma (scalar or sequence of scalars) – Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

  • order ({0, 1, 2, 3} or sequence from same set, optional) – Order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented

  • axis (int, optional) – Axis along which median filtering is performed.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

ndarray – 3D array of same shape as input.

tomopy.misc.corr.inpainter_morph(arr, mask, size=5, iterations=2, inpainting_type='random', axis=None, ncore=None)[source]

Apply 3D or 2D morphological inpainter (extrapolator) to a given missing data array using provided mask (boolean). The algorithm is presented in the paper [B10].

If applied to 3D tomographic data, one can use the 2D module applied to a sinogram by specifing the axis in the list of parameters bellow. Alternatively, one can use the 3D module with symmetric 3D kernels (axis=None). The latter is more recommended for 3D data as it ensures the smoother intensity transition in every direction.

New in version 1.15.

Parameters

  • arr (ndarray) – Input 3D or 2D array of float32 data type with the missing data.

  • mask (ndarray, bool) – Boolean mask array of the same size as arr. True values in the mask indicate the region that needs to be inpainted.

  • size (int, optional) – The size of the searching window (a kernel).

  • iterations (int, optional) – Iterations of the algorithm after the inpainted region was processed fully. The larger numbers usually lead to oversmoothing of the area, we recommend 2.

  • inpainting_type (str, optional) – The type of the inpainting technique. Choose between ‘mean’, ‘median’ or ‘random’ neighbour selection. We suggest using ‘random’ to minimise the “spilling” of intensities inside the inpainted areas.

  • axis (int, optional) – Split the data along this axis, and perform inpainting in the remaining dimensions. If set to None then the 3D inpainting is enabled, which is recommended to use for 3D data. If the 2D inpainting used for 3D data, it is best to apply it in sinogram space.

  • ncore (int, optional) – Number of cores that will be assigned to jobs. All cores will be used if unspecified.

Returns

ndarray – Inpainted array of float32 data type.

Raises

ValueError – If the input array is not float32. If the input mask array is not bool. If inpainting_type is not ‘mean’, ‘median’ or ‘random’. If axis is an integer for 2D input data. If axis is outside of the accepted range.

tomopy.misc.corr.median_filter(arr, size=3, axis=0, ncore=None)[source]

Apply median filter to 3D array along specified axis.

Parameters

  • arr (ndarray) – Input array.

  • size (int, optional) – The size of the filter.

  • axis (int, optional) – Axis along which median filtering is performed.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

ndarray – Median filtered 3D array.

tomopy.misc.corr.median_filter3d(arr, size=3, ncore=None)[source]

Apply 3D median filter to a 3D array.

New in version 1.13.

Parameters

  • arr (ndarray) – Input 3D array either float32 or uint16 data type.

  • size (int, optional) – The size of the filter’s kernel.

  • ncore (int, optional) – Number of cores that will be assigned to jobs. All cores will be used if unspecified.

Returns

ndarray – Median filtered 3D array either float32 or uint16 data type.

Raises

ValueError – If the input array is not three dimensional.

tomopy.misc.corr.median_filter_cuda(arr, size=3, axis=0)[source]

Apply median filter to 3D array along 0 axis with GPU support. The winAllow is for A6000, Tian X support 3 to 8

Parameters

  • arr (ndarray) – Input array.

  • size (int, optional) – The size of the filter.

  • axis (int, optional) – Axis along which median filtering is performed.

Returns

ndarray – Median filtered 3D array.

Example

import tomocuda tomocuda.remove_outlier_cuda(arr, dif, 5)

For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information

tomopy.misc.corr.median_filter_nonfinite(arr, size=3, callback=None)[source]

Remove nonfinite values from a 3D array using an in-place 2D median filter.

The 2D selective median filter is applied along the last two axes of the array.

New in version 1.11.

Parameters

  • arr (ndarray) – The 3D array with nonfinite values in it.

  • size (int, optional) – The size of the filter.

  • callback (func(total, description, unit)) – A function called after every internal loop iteration. total is number of loop iterations. description is ‘Nonfinite median filter’. unit is ‘ prjs’.

Returns

ndarray – The corrected 3D array with all nonfinite values removed based upon the local median value defined by the kernel size.

Raises

ValueError – If the filter comes across a kernel only containing non-finite values a ValueError is raised for the user to increase their kernel size.

tomopy.misc.corr.remove_nan(arr, val=0.0, ncore=None)[source]

Replace NaN values in array with a given value.

Parameters

  • arr (ndarray) – Input array.

  • val (float, optional) – Values to be replaced with NaN values in array.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

ndarray – Corrected array.

tomopy.misc.corr.remove_neg(arr, val=0.0, ncore=None)[source]

Replace negative values in array with a given value.

Parameters

  • arr (ndarray) – Input array.

  • val (float, optional) – Values to be replaced with negative values in array.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier(arr, dif, size=3, axis=0, ncore=None, out=None)[source]

Remove high intensity bright spots from a N-dimensional array by chunking along the specified dimension, and performing (N-1)-dimensional median filtering along the other dimensions.

Parameters

  • arr (ndarray) – Input array.

  • dif (float) – Expected difference value between outlier value and the median value of the array.

  • size (int) – Size of the median filter.

  • axis (int, optional) – Axis along which to chunk.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.

Returns

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier1d(arr, dif, size=3, axis=0, ncore=None, out=None)[source]

Remove high intensity bright spots from an array, using a one-dimensional median filter along the specified axis.

Parameters

  • arr (ndarray) – Input array.

  • dif (float) – Expected difference value between outlier value and the median value of the array.

  • size (int) – Size of the median filter.

  • axis (int, optional) – Axis along which median filtering is performed.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.

Returns

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier3d(arr, dif, size=3, ncore=None)[source]

Selectively applies 3D median filter to a 3D array to remove outliers. Also called a dezinger.

New in version 1.13.

Parameters

  • arr (ndarray) – Input 3D array either float32 or uint16 data type.

  • dif (float) – Expected difference value between outlier value and the median value of the array.

  • size (int, optional) – The size of the filter’s kernel.

  • ncore (int, optional) – Number of cores that will be assigned to jobs. All cores will be used if unspecified.

Returns

ndarray – Dezingered 3D array either float32 or uint16 data type.

Raises

ValueError – If the input array is not three dimensional.

tomopy.misc.corr.remove_outlier_cuda(arr, dif, size=3, axis=0)[source]

Remove high intensity bright spots from a 3D array along axis 0 dimension using GPU.

Parameters

  • arr (ndarray) – Input array.

  • dif (float) – Expected difference value between outlier value and the median value of the array.

  • size (int) – Size of the median filter.

  • axis (int, optional) – Axis along which outlier removal is performed.

Returns

ndarray – Corrected array.

Example

>>> import tomocuda
>>> tomocuda.remove_outlier_cuda(arr, dif, 5)

For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information

tomopy.misc.corr.remove_ring(rec, center_x=None, center_y=None, thresh=300.0, thresh_max=300.0, thresh_min=-100.0, theta_min=30, rwidth=30, int_mode='WRAP', ncore=None, nchunk=None, out=None)[source]

Remove ring artifacts from images in the reconstructed domain. Descriptions of parameters need to be more clear for sure.

Parameters

  • arr (ndarray) – Array of reconstruction data

  • center_x (float, optional) – abscissa location of center of rotation

  • center_y (float, optional) – ordinate location of center of rotation

  • thresh (float, optional) – maximum value of an offset due to a ring artifact

  • thresh_max (float, optional) – max value for portion of image to filter

  • thresh_min (float, optional) – min value for portion of image to filer

  • theta_min (int, optional) – Features larger than twice this angle (degrees) will be considered a ring artifact. Must be less than 180 degrees.

  • rwidth (int, optional) – Maximum width of the rings to be filtered in pixels

  • int_mode (str, optional) – ‘WRAP’ for wrapping at 0 and 360 degrees, ‘REFLECT’ for reflective boundaries at 0 and 180 degrees.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

  • nchunk (int, optional) – Chunk size for each core.

  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.

Returns

ndarray – Corrected reconstruction data

tomopy.misc.corr.sobel_filter(arr, axis=0, ncore=None)[source]

Apply Sobel filter to 3D array along specified axis.

Parameters

  • arr (ndarray) – Input array.

  • axis (int, optional) – Axis along which sobel filtering is performed.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

ndarray – 3D array of same shape as input.