mindboggle.shapes.zernike package

Submodules

mindboggle.shapes.zernike.helpers module

mindboggle.shapes.zernike.pipelines module

mindboggle.shapes.zernike.zernike module

Compute the Zernike moments of a collection of points.

Authors:

Copyright 2013, Mindboggle team (http://mindboggle.info), Apache v2.0 License

zernike_moments(points, faces, order=10, scale_input=True, decimate_fraction=0, decimate_smooth=0, verbose=False)

Compute the Zernike moments of a surface patch of points and faces.

Optionally decimate the input mesh.

Note::
Decimation sometimes leads to an error of “Segmentation fault: 11” (Twins-2-1 left label 14 gives such an error only when decimated.)
Parameters:
  • points (list of lists of 3 floats) – x,y,z coordinates for each vertex
  • faces (list of lists of 3 integers) – each list contains indices to vertices that form a triangle on a mesh
  • order (integer) – order of the moments being calculated
  • scale_input (bool) – translate and scale each object so it is bounded by a unit sphere? (this is the expected input to zernike_moments())
  • decimate_fraction (float) – fraction of mesh faces to remove for decimation (0 for no decimation)
  • decimate_smooth (integer) – number of smoothing steps for decimation
  • verbose (bool) – print statements?
Returns:

descriptors – Zernike descriptors
Return type:

list of floats

Examples

>>> # Example 1: simple cube (decimation results in a Segmentation Fault):
>>> import numpy as np
>>> from mindboggle.shapes.zernike.zernike import zernike_moments
>>> points = [[0,0,0], [1,0,0], [0,0,1], [0,1,1],
...           [1,0,1], [0,1,0], [1,1,1], [1,1,0]]
>>> faces = [[0,2,4], [0,1,4], [2,3,4], [3,4,5], [3,5,6], [0,1,7]]
>>> order = 3
>>> scale_input = True
>>> decimate_fraction = 0
>>> decimate_smooth = 0
>>> verbose = False
>>> descriptors = zernike_moments(points, faces, order, scale_input,
...     decimate_fraction, decimate_smooth, verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors]
[0.09189, 0.09357, 0.04309, 0.06466, 0.0382, 0.04138]
Example 2: Twins-2-1 left postcentral pial surface – NO decimation:
(zernike_moments took 142 seconds for order = 3 with no decimation)
>>> from mindboggle.shapes.zernike.zernike import zernike_moments
>>> from mindboggle.mio.vtks import read_vtk
>>> from mindboggle.guts.mesh import keep_faces
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> points, f1,f2, faces, labels, f3,f4,f5 = read_vtk(label_file)
>>> I22 = [i for i,x in enumerate(labels) if x==1022] # postcentral
>>> faces = keep_faces(faces, I22)
>>> order = 3
>>> scale_input = True
>>> decimate_fraction = 0
>>> decimate_smooth = 0
>>> verbose = False
>>> descriptors = zernike_moments(points, faces, order, scale_input,
...     decimate_fraction, decimate_smooth, verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors]
[0.00471, 0.0084, 0.00295, 0.00762, 0.0014, 0.00076]

Example 3: left postcentral + pars triangularis pial surfaces:

>>> from mindboggle.mio.vtks import read_vtk, write_vtk
>>> points, f1,f2, faces, labels, f3,f4,f5 = read_vtk(label_file)
>>> I20 = [i for i,x in enumerate(labels) if x==1020] # pars triangularis
>>> I22 = [i for i,x in enumerate(labels) if x==1022] # postcentral
>>> I22.extend(I20)
>>> faces = keep_faces(faces, I22)
>>> order = 3
>>> scale_input = True
>>> decimate_fraction = 0
>>> decimate_smooth = 0
>>> verbose = False
>>> descriptors = zernike_moments(points, faces, order, scale_input,
...     decimate_fraction, decimate_smooth, verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors]
[0.00586, 0.00973, 0.00322, 0.00818, 0.0013, 0.00131]

View both segments (skip test):

>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> scalars = -1 * np.ones(np.shape(labels)) # doctest: +SKIP
>>> scalars[I22] = 1 # doctest: +SKIP
>>> rewrite_scalars(label_file, 'test_two_labels.vtk', scalars,
...                 'two_labels', scalars) # doctest: +SKIP
>>> plot_surfaces(vtk_file) # doctest: +SKIP
zernike_moments_per_label(vtk_file, order=10, exclude_labels=[-1], scale_input=True, decimate_fraction=0, decimate_smooth=25, verbose=False)

Compute the Zernike moments per labeled region in a file.

Optionally decimate the input mesh.

Parameters:
  • vtk_file (string) – name of VTK surface mesh file containing index scalars (labels)
  • order (integer) – number of moments to compute
  • exclude_labels (list of integers) – labels to be excluded
  • scale_input (bool) – translate and scale each object so it is bounded by a unit sphere? (this is the expected input to zernike_moments())
  • decimate_fraction (float) – fraction of mesh faces to remove for decimation (1 for no decimation)
  • decimate_smooth (integer) – number of smoothing steps for decimation
  • verbose (bool) – print statements?
Returns:

  • descriptors_lists (list of lists of floats) – Zernike descriptors per label
  • label_list (list of integers) – list of unique labels for which moments are computed

Examples

>>> # Zernike moments per label of a FreeSurfer-labeled left cortex.
>>> # Uncomment "if label==22:" below to run example
>>> # for left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> order = 3
>>> exclude_labels = [-1]
>>> scale_input = True
>>> verbose = False
>>> descriptors_lists, label_list = zernike_moments_per_label(vtk_file,
...     order, exclude_labels, scale_input, verbose)
>>> label_list[0:10]
[999, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors_lists[0]]
[0.00587, 0.01143, 0.0031, 0.00881, 0.00107, 0.00041]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors_lists[1]]
[4e-05, 9e-05, 3e-05, 9e-05, 2e-05, 1e-05]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors_lists[2]]
[0.00144, 0.00232, 0.00128, 0.00304, 0.00084, 0.00051]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors_lists[3]]
[0.00393, 0.006, 0.00371, 0.00852, 0.00251, 0.00153]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in descriptors_lists[4]]
[0.00043, 0.0003, 0.00095, 0.00051, 0.00115, 0.00116]

Module contents