norok2 / raster_geometry

Create/manipulate N-dim raster geometric shapes.

Raster Geometry

Raster Geometry - Create/manipulate N-dim raster geometric shapes.

Overview

This software provides a library for generating or otherwise manipulating N-dimensional raster geometric shapes.

Most of the code is used is used in a number of projects where it is tested against real-life scenarios.

As a result of the code maturity, some of the library components may undergo (eventually heavy) refactoring. While this is not expected, this will be documented. Please file a bug report if you detect an undocumented refactoring.

Releases information are available through NEWS.rst.

For a more comprehensive list of changes see CHANGELOG.rst (automatically generated from the version control system).

Features

The 2D geometrical shapes currently available are:

• square
• rectangle
• rhombus
• circle
• ellipse

The 3D geometrical shapes currently available are:

• cube
• cuboid
• rhomboid
• sphere
• ellipsoid
• cylinder

The N-dim geometrical shapes currently available are:

• cuboid: sum[abs(x_n/a_n)^inf] < 1
• superellipsoid: sum[abs(x_n/a_n)^k] < 1
• prism: stack (N-1)-D rendered objects on given axis

etc.

Additional may be added in the future.

Installation

The recommended way of installing the software is through PyPI:

$ pip install raster_geometry

Alternatively, you can clone the source repository from GitHub:

$ git clone [email protected]:norok2/raster_geometry.git
$ cd raster_geometry
$ pip install -e .

For more details see also INSTALL.rst.

License

This work is licensed through the terms and conditions of the GPLv3+ See the accompanying LICENSE.rst for more details.

Acknowledgements

For a complete list of authors please see AUTHORS.rst.

People who have influenced this work are acknowledged in THANKS.rst.

raster_geometry's Issues

incompatible with numpy 1.24.1

It seems that raster-geometry uses numpy.float, which does not exist anymore on numpy 1.24

I guess it's possible to replace it with numpy.single ( https://numpy.org/doc/stable/user/basics.types.html )

Computation time increases rapidly with increasing shape size

Thank you very much for your hard work developing this very useful library!

I was curious if you have any thoughts or suggestions for speeding up computation time for generating large shapes (specifically using nd_superellipsoid()). I have noticed that as the array shape increases, the computation time dramatically increases as well.

%time rg.nd_superellipsoid((5,5,5), 2, rel_sizes=False).astype(np.int_)
%time rg.nd_superellipsoid((50,50,50), 2, rel_sizes=False).astype(np.int_)
%time rg.nd_superellipsoid((500,500,500), 2, rel_sizes=False).astype(np.int_)
%time rg.nd_superellipsoid((1000,1000,1000), 2, rel_sizes=False).astype(np.int_)

CPU times: user 298 µs, sys: 343 µs, total: 641 µs
Wall time: 4.31 ms
CPU times: user 1.24 ms, sys: 1.43 ms, total: 2.67 ms
Wall time: 5.05 ms
CPU times: user 275 ms, sys: 375 ms, total: 650 ms
Wall time: 650 ms
CPU times: user 1.98 s, sys: 2.95 s, total: 4.93 s
Wall time: 4.94 s

I have been struggling for a while to try to come up with a solution, however I have been unsuccessful so far. Do you believe there can be a way to possibly speed up this calculation? Thank you in advance!

raster_geometry.sphere is giving different volumes when only the position changes

Hi All,

I have a set of midpoints (from labelled tumours within a set of scans [512x512x512 voxels]). I'm just trying to create a sphere with a radius of 10 voxels at each midpoint. To do this, I'm using raster_geometry.sphere and defining the position as the midpoint/512 (i.e. position = midpoint/ 512 , raster_geometry.sphere(512, 10, positon) )

When I'm doing this, however, the volume of the sphere changes. Is there something I don't understand about raster_geometry?

from logzero import logger as logging
from lama import common
import numpy as np
import SimpleITK as sitk
import raster_geometry as rg

for i, img_path in enumerate(scan_paths):
    logging.info(img_path)
    logging.info(tumour_paths[i])

    m_loader = common.LoadImage(tumour_paths[i])
    mask = m_loader.img

    m_array = sitk.GetArrayFromImage(mask)

    s = ndimage.find_objects(m_array)[-1]

    midpoint = [(np.mean([s[0].start, s[0].stop]))/512,
                (np.mean([s[1].start, s[1].stop]))/512,
                (np.mean([s[2].start, s[2].stop]))/512]

    print("Original Midpoint", [i*512 for i in midpoint])

    print("Modified midpoint", midpoint)

    arr = rg.sphere(512, 10, midpoint).astype(np.int_)

    print(np.count_nonzero(arr))
    print(np.sum(arr))

Example Output:

[I 220525 17:37:58 radiomics_normaliser:86] E:\220204_BQ_dataset\220521_BQ_norm\imgs\200721_MPTLVo3_GFSeeds_4T1R_4T1R_D7_C1_002.nrrd
[I 220525 17:37:58 radiomics_normaliser:87] E:\220204_BQ_dataset\220521_BQ_norm\tumour_respaced\200721_MPTLVo3_GFSeeds_4T1R_4T1R_D7_C1_002.nrrd
Original Midpoint [260.5, 252.5, 162.0]
Modified midpoint [0.5087890625, 0.4931640625, 0.31640625]
4160
4160
[I 220525 17:38:14 radiomics_normaliser:86] E:\220204_BQ_dataset\220521_BQ_norm\imgs\200721_MPTLVo3_GFseeds_4T1R_4T1R_D7_C1_003.nrrd
[I 220525 17:38:14 radiomics_normaliser:87] E:\220204_BQ_dataset\220521_BQ_norm\tumour_respaced\200721_MPTLVo3_GFseeds_4T1R_4T1R_D7_C1_003.nrrd
Original Midpoint [219.5, 234.5, 165.5]
Modified midpoint [0.4287109375, 0.4580078125, 0.3232421875]
4165
4165