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|
%global _empty_manifest_terminate_build 0
Name: python-zmesh
Version: 1.6.2
Release: 1
Summary: Multilabel marching cubes and simplification of volumetric data.
License: GPLv3+
URL: https://github.com/seung-lab/zmesh/
Source0: https://mirrors.aliyun.com/pypi/web/packages/f8/4d/3be291deda3cadf04fc346b52cc1ff12e7f5b8d1d9716ffbb0edf91a4ced/zmesh-1.6.2.tar.gz
Requires: python3-numpy
%description
## zmesh: Multi-Label Marching Cubes & Mesh Simplification
[](https://github.com/seung-lab/zmesh/actions/workflows/test.yml) [](https://badge.fury.io/py/zmesh)
```python
from zmesh import Mesher
labels = ... # some dense volumetric labeled image
mesher = Mesher( (4,4,40) ) # anisotropy of image
# initial marching cubes pass
# close controls whether meshes touching
# the image boundary are left open or closed
mesher.mesh(labels, close=False)
meshes = []
for obj_id in mesher.ids():
meshes.append(
mesher.get_mesh(
obj_id,
normals=False, # whether to calculate normals or not
# tries to reduce triangles by this factor
# 0 disables simplification
simplification_factor=100,
# Max tolerable error in physical distance
max_simplification_error=8,
# whether meshes should be centered in the voxel
# on (0,0,0) [False] or (0.5,0.5,0.5) [True]
voxel_centered=False,
)
)
mesher.erase(obj_id) # delete high res mesh
mesher.clear() # clear memory retained by mesher
mesh = meshes[0]
mesh = mesher.simplify(
mesh,
# same as simplification_factor in get_mesh
reduction_factor=100,
# same as max_simplification_error in get_mesh
max_error=40,
compute_normals=False, # whether to also compute face normals
) # apply simplifier to a pre-existing mesh
# compute normals without simplifying
mesh = mesher.compute_normals(mesh)
mesh.vertices
mesh.faces
mesh.normals
mesh.triangles() # compute triangles from vertices and faces
# Extremely common obj format
with open('iconic_doge.obj', 'wb') as f:
f.write(mesh.to_obj())
# Common binary format
with open('iconic_doge.ply', 'wb') as f:
f.write(mesh.to_ply())
# Neuroglancer Precomputed format
with open('10001001:0', 'wb') as f:
f.write(mesh.to_precomputed())
```
## Installation
If binaries are not available for your system, ensure you have a C++ compiler installed.
```bash
pip install zmesh
```
## Performance Tuning & Notes
- The mesher will consume about double memory in 64 bit mode if the size of the
object exceeds <1023, 1023, 511> on the x, y, or z axes. This is due to a limitation
of the 32-bit format.
- The mesher is ambidextrous, it can handle C or Fortran order arrays.
- The maximum vertex range supported `.simplify` after converting to voxel space is 2<sup>20</sup> (appx. 1M) due to the packed 64-bit vertex format.
- There is a longstanding design flaw in `cMesher.hpp` that transposes the returned mesh and resolution. We're working on a backwards compatible solution. That's why you need to do `mesher.mesh(data.T)`.
## Related Projects
- [zi_lib](https://github.com/zlateski/zi_lib) - zmesh makes heavy use of Aleks' C++ library.
- [Igneous](https://github.com/seung-lab/igneous) - Visualization of connectomics data using cloud computing.
## Credits
Thanks to Aleks Zlateski for creating and sharing this beautiful mesher.
Later changes by Will Silversmith, Nico Kemnitz, and Jingpeng Wu.
## References
1. W. Lorensen and H. Cline. "Marching Cubes: A High Resolution 3D Surface Construction Algorithm". pp 163-169. Computer Graphics, Volume 21, Number 4, July 1987. ([link](https://people.eecs.berkeley.edu/~jrs/meshpapers/LorensenCline.pdf))
2. M. Garland and P. Heckbert. "Surface simplification using quadric error metrics". SIGGRAPH '97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. Pages 209–216. August 1997. doi: 10.1145/258734.258849 ([link](https://mgarland.org/files/papers/quadrics.pdf))
3. H. Hoppe. "New Quadric Metric for Simplifying Meshes with Appearance Attributes". IEEE Visualization 1999 Conference. pp. 59-66. doi: 10.1109/VISUAL.1999.809869 ([link](http://hhoppe.com/newqem.pdf))
%package -n python3-zmesh
Summary: Multilabel marching cubes and simplification of volumetric data.
Provides: python-zmesh
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-zmesh
## zmesh: Multi-Label Marching Cubes & Mesh Simplification
[](https://github.com/seung-lab/zmesh/actions/workflows/test.yml) [](https://badge.fury.io/py/zmesh)
```python
from zmesh import Mesher
labels = ... # some dense volumetric labeled image
mesher = Mesher( (4,4,40) ) # anisotropy of image
# initial marching cubes pass
# close controls whether meshes touching
# the image boundary are left open or closed
mesher.mesh(labels, close=False)
meshes = []
for obj_id in mesher.ids():
meshes.append(
mesher.get_mesh(
obj_id,
normals=False, # whether to calculate normals or not
# tries to reduce triangles by this factor
# 0 disables simplification
simplification_factor=100,
# Max tolerable error in physical distance
max_simplification_error=8,
# whether meshes should be centered in the voxel
# on (0,0,0) [False] or (0.5,0.5,0.5) [True]
voxel_centered=False,
)
)
mesher.erase(obj_id) # delete high res mesh
mesher.clear() # clear memory retained by mesher
mesh = meshes[0]
mesh = mesher.simplify(
mesh,
# same as simplification_factor in get_mesh
reduction_factor=100,
# same as max_simplification_error in get_mesh
max_error=40,
compute_normals=False, # whether to also compute face normals
) # apply simplifier to a pre-existing mesh
# compute normals without simplifying
mesh = mesher.compute_normals(mesh)
mesh.vertices
mesh.faces
mesh.normals
mesh.triangles() # compute triangles from vertices and faces
# Extremely common obj format
with open('iconic_doge.obj', 'wb') as f:
f.write(mesh.to_obj())
# Common binary format
with open('iconic_doge.ply', 'wb') as f:
f.write(mesh.to_ply())
# Neuroglancer Precomputed format
with open('10001001:0', 'wb') as f:
f.write(mesh.to_precomputed())
```
## Installation
If binaries are not available for your system, ensure you have a C++ compiler installed.
```bash
pip install zmesh
```
## Performance Tuning & Notes
- The mesher will consume about double memory in 64 bit mode if the size of the
object exceeds <1023, 1023, 511> on the x, y, or z axes. This is due to a limitation
of the 32-bit format.
- The mesher is ambidextrous, it can handle C or Fortran order arrays.
- The maximum vertex range supported `.simplify` after converting to voxel space is 2<sup>20</sup> (appx. 1M) due to the packed 64-bit vertex format.
- There is a longstanding design flaw in `cMesher.hpp` that transposes the returned mesh and resolution. We're working on a backwards compatible solution. That's why you need to do `mesher.mesh(data.T)`.
## Related Projects
- [zi_lib](https://github.com/zlateski/zi_lib) - zmesh makes heavy use of Aleks' C++ library.
- [Igneous](https://github.com/seung-lab/igneous) - Visualization of connectomics data using cloud computing.
## Credits
Thanks to Aleks Zlateski for creating and sharing this beautiful mesher.
Later changes by Will Silversmith, Nico Kemnitz, and Jingpeng Wu.
## References
1. W. Lorensen and H. Cline. "Marching Cubes: A High Resolution 3D Surface Construction Algorithm". pp 163-169. Computer Graphics, Volume 21, Number 4, July 1987. ([link](https://people.eecs.berkeley.edu/~jrs/meshpapers/LorensenCline.pdf))
2. M. Garland and P. Heckbert. "Surface simplification using quadric error metrics". SIGGRAPH '97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. Pages 209–216. August 1997. doi: 10.1145/258734.258849 ([link](https://mgarland.org/files/papers/quadrics.pdf))
3. H. Hoppe. "New Quadric Metric for Simplifying Meshes with Appearance Attributes". IEEE Visualization 1999 Conference. pp. 59-66. doi: 10.1109/VISUAL.1999.809869 ([link](http://hhoppe.com/newqem.pdf))
%package help
Summary: Development documents and examples for zmesh
Provides: python3-zmesh-doc
%description help
## zmesh: Multi-Label Marching Cubes & Mesh Simplification
[](https://github.com/seung-lab/zmesh/actions/workflows/test.yml) [](https://badge.fury.io/py/zmesh)
```python
from zmesh import Mesher
labels = ... # some dense volumetric labeled image
mesher = Mesher( (4,4,40) ) # anisotropy of image
# initial marching cubes pass
# close controls whether meshes touching
# the image boundary are left open or closed
mesher.mesh(labels, close=False)
meshes = []
for obj_id in mesher.ids():
meshes.append(
mesher.get_mesh(
obj_id,
normals=False, # whether to calculate normals or not
# tries to reduce triangles by this factor
# 0 disables simplification
simplification_factor=100,
# Max tolerable error in physical distance
max_simplification_error=8,
# whether meshes should be centered in the voxel
# on (0,0,0) [False] or (0.5,0.5,0.5) [True]
voxel_centered=False,
)
)
mesher.erase(obj_id) # delete high res mesh
mesher.clear() # clear memory retained by mesher
mesh = meshes[0]
mesh = mesher.simplify(
mesh,
# same as simplification_factor in get_mesh
reduction_factor=100,
# same as max_simplification_error in get_mesh
max_error=40,
compute_normals=False, # whether to also compute face normals
) # apply simplifier to a pre-existing mesh
# compute normals without simplifying
mesh = mesher.compute_normals(mesh)
mesh.vertices
mesh.faces
mesh.normals
mesh.triangles() # compute triangles from vertices and faces
# Extremely common obj format
with open('iconic_doge.obj', 'wb') as f:
f.write(mesh.to_obj())
# Common binary format
with open('iconic_doge.ply', 'wb') as f:
f.write(mesh.to_ply())
# Neuroglancer Precomputed format
with open('10001001:0', 'wb') as f:
f.write(mesh.to_precomputed())
```
## Installation
If binaries are not available for your system, ensure you have a C++ compiler installed.
```bash
pip install zmesh
```
## Performance Tuning & Notes
- The mesher will consume about double memory in 64 bit mode if the size of the
object exceeds <1023, 1023, 511> on the x, y, or z axes. This is due to a limitation
of the 32-bit format.
- The mesher is ambidextrous, it can handle C or Fortran order arrays.
- The maximum vertex range supported `.simplify` after converting to voxel space is 2<sup>20</sup> (appx. 1M) due to the packed 64-bit vertex format.
- There is a longstanding design flaw in `cMesher.hpp` that transposes the returned mesh and resolution. We're working on a backwards compatible solution. That's why you need to do `mesher.mesh(data.T)`.
## Related Projects
- [zi_lib](https://github.com/zlateski/zi_lib) - zmesh makes heavy use of Aleks' C++ library.
- [Igneous](https://github.com/seung-lab/igneous) - Visualization of connectomics data using cloud computing.
## Credits
Thanks to Aleks Zlateski for creating and sharing this beautiful mesher.
Later changes by Will Silversmith, Nico Kemnitz, and Jingpeng Wu.
## References
1. W. Lorensen and H. Cline. "Marching Cubes: A High Resolution 3D Surface Construction Algorithm". pp 163-169. Computer Graphics, Volume 21, Number 4, July 1987. ([link](https://people.eecs.berkeley.edu/~jrs/meshpapers/LorensenCline.pdf))
2. M. Garland and P. Heckbert. "Surface simplification using quadric error metrics". SIGGRAPH '97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. Pages 209–216. August 1997. doi: 10.1145/258734.258849 ([link](https://mgarland.org/files/papers/quadrics.pdf))
3. H. Hoppe. "New Quadric Metric for Simplifying Meshes with Appearance Attributes". IEEE Visualization 1999 Conference. pp. 59-66. doi: 10.1109/VISUAL.1999.809869 ([link](http://hhoppe.com/newqem.pdf))
%prep
%autosetup -n zmesh-1.6.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-zmesh -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.2-1
- Package Spec generated
|
