Python Fast-Quadric-Mesh-Simplification Wrapper
===============================================
This is a python wrapping of the `Fast-Quadric-Mesh-Simplification Library
<https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification/>`_. Having
arrived at the same problem as the original author, but needing a Python
library, this project seeks to extend the work of the original library while
adding integration to Python and the `PyVista
<https://github.com/pyvista/pyvista>`_ project.
For the full documentation visit: https://pyvista.github.io/fast-simplification/
.. image:: https://github.com/pyvista/fast-simplification/raw/main/doc/images/simplify_demo.png
Installation
------------
Fast Simplification can be installed from PyPI using pip on Python >= 3.7::
pip install fast-simplification
See the `Contributing <https://github.com/pyvista/fast-simplification#contributing>`_ for more details regarding development or if the installation through pip doesn't work out.
Basic Usage
-----------
The basic interface is quite straightforward and can work directly
with arrays of points and triangles:
.. code:: python
points = [[ 0.5, -0.5, 0.0],
[ 0.0, -0.5, 0.0],
[-0.5, -0.5, 0.0],
[ 0.5, 0.0, 0.0],
[ 0.0, 0.0, 0.0],
[-0.5, 0.0, 0.0],
[ 0.5, 0.5, 0.0],
[ 0.0, 0.5, 0.0],
[-0.5, 0.5, 0.0]]
faces = [[0, 1, 3],
[4, 3, 1],
[1, 2, 4],
[5, 4, 2],
[3, 4, 6],
[7, 6, 4],
[4, 5, 7],
[8, 7, 5]]
points_out, faces_out = fast_simplification.simplify(points, faces, 0.5)
Advanced Usage
--------------
This library supports direct integration with VTK through PyVista to
provide a simplistic interface to the library. As this library
provides a 4-5x improvement to the VTK decimation algorithms.
.. code:: python
>>> from pyvista import examples
>>> mesh = examples.download_nefertiti()
>>> out = fast_simplification.simplify_mesh(mesh, target_reduction=0.9)
Compare with built-in VTK/PyVista methods:
>>> fas_sim = fast_simplification.simplify_mesh(mesh, target_reduction=0.9)
>>> dec_std = mesh.decimate(0.9) # vtkQuadricDecimation
>>> dec_pro = mesh.decimate_pro(0.9) # vtkDecimatePro
>>> pv.set_plot_theme('document')
>>> pl = pv.Plotter(shape=(2, 2), window_size=(1000, 1000))
>>> pl.add_text('Original', 'upper_right', color='w')
>>> pl.add_mesh(mesh, show_edges=True)
>>> pl.camera_position = cpos
>>> pl.subplot(0, 1)
>>> pl.add_text(
... 'Fast-Quadric-Mesh-Simplification\n~2.2 seconds', 'upper_right', color='w'
... )
>>> pl.add_mesh(fas_sim, show_edges=True)
>>> pl.camera_position = cpos
>>> pl.subplot(1, 0)
>>> pl.add_mesh(dec_std, show_edges=True)
>>> pl.add_text(
... 'vtkQuadricDecimation\n~9.5 seconds', 'upper_right', color='w'
... )
>>> pl.camera_position = cpos
>>> pl.subplot(1, 1)
>>> pl.add_mesh(dec_pro, show_edges=True)
>>> pl.add_text(
... 'vtkDecimatePro\n11.4~ seconds', 'upper_right', color='w'
... )
>>> pl.camera_position = cpos
>>> pl.show()
Comparison to other libraries
-----------------------------
The `pyfqmr <https://github.com/Kramer84/pyfqmr-Fast-Quadric-Mesh-Reduction>`_
library wraps the same header file as this library and has similar capabilities.
In this library, the decision was made to write the Cython layer on top of an
additional C++ layer rather than directly interfacing with wrapper from Cython.
This results in a mild performance improvement.
Reusing the example above:
.. code:: python
Set up a timing function.
>>> import pyfqmr
>>> vertices = mesh.points
>>> faces = mesh.faces.reshape(-1, 4)[:, 1:]
>>> def time_pyfqmr():
... mesh_simplifier = pyfqmr.Simplify()
... mesh_simplifier.setMesh(vertices, faces)
... mesh_simplifier.simplify_mesh(
... target_count=out.n_faces, aggressiveness=7, verbose=0
... )
... vertices_out, faces_out, normals_out = mesh_simplifier.getMesh()
... return vertices_out, faces_out, normals_out
Now, time it and compare with the non-VTK API of this library:
.. code:: python
>>> timeit time_pyfqmr()
2.75 s ± 5.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
>>> timeit vout, fout = fast_simplification.simplify(vertices, faces, 0.9)
2.05 s ± 3.18 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Additionally, the ``fast-simplification`` library has direct plugins
to the ``pyvista`` library, making it easy to read and write meshes:
.. code:: python
>>> import pyvista
>>> import fast_simplification
>>> mesh = pyvista.read('my_mesh.stl')
>>> simple = fast_simplification.simplify_mesh(mesh)
>>> simple.save('my_simple_mesh.stl')
Since both libraries are based on the same core C++ code, feel free to
use whichever gives you the best performance and interoperability.
Replay decimation functionality
-------------------------------
This library also provides an interface to keep track of the successive
collapses that occur during the decimation process and to replay the
decimation process. This can be useful for different applications, such
as:
* applying the same decimation to a collection of meshes that share the
same topology
* computing a correspondence map between the vertices of the original
mesh and the vertices of the decimated mesh, to transfer field data from
one to the other for example
* replaying the decimation process with a smaller target reduction than
the original one, faster than decimating the original mesh with the
smaller target reduction
To use this functionality, you need to set the ``return_collapses``
parameter to ``True`` when calling ``simplify``. This will return the
successive collapses of the decimation process in addition to points
and faces.
.. code:: python
>>> import fast_simplification
>>> import pyvista
>>> mesh = pyvista.Sphere()
>>> points, faces = mesh.points, mesh.faces.reshape(-1, 4)[:, 1:]
>>> points_out, faces_out, collapses = fast_simplification.simplify(points, faces, 0.9, return_collapses=True)
Now you can call ``replay_simplification`` to replay the decimation process
and obtain the mapping between the vertices of the original mesh and the
vertices of the decimated mesh.
.. code:: python
>>> points_out, faces_out, indice_mapping = fast_simplification.replay_simplification(points, faces, collapses)
>>> i = 3
>>> print(f'Vertex {i} of the original mesh is mapped to {indice_mapping[i]} of the decimated mesh')
You can also use the ``replay_simplification`` function to replay the
decimation process with a smaller target reduction than the original one.
This is faster than decimating the original mesh with the smaller target
reduction. To do so, you need to pass a subset of the collapses to the
``replay_simplification`` function. For example, to replay the decimation
process with a target reduction of 50% the initial rate, you can run:
.. code:: python
>>> import numpy as np
>>> collapses_half = collapses[:int(0.5 * len(collapses))]
>>> points_out, faces_out, indice_mapping = fast_simplification.replay_simplification(points, faces, collapses_half)
If you have a collection of meshes that share the same topology, you can
apply the same decimation to all of them by calling ``replay_simplification``
with the same collapses for each mesh. This ensure that the decimated meshes
will share the same topology.
.. code:: python
>>> import numpy as np
>>> # Assume that you have a collection of meshes stored in a list meshes
>>> _, _, collapses = fast_simplification.simplify(meshes[0].points, meshes[0].faces,
... 0.9, return_collapses=True)
>>> decimated_meshes = []
>>> for mesh in meshes:
... points_out, faces_out, _ = fast_simplification.replay_simplification(mesh.points, mesh.faces, collapses)
... decimated_meshes.append(pyvista.PolyData(points_out, faces_out))
Contributing
------------
Contribute to this repository by forking this repository and installing in
development mode with::
git clone https://github.com/<USERNAME>/fast-simplification
pip install -e .
pip install -r requirements_test.txt
You can then add your feature or commit your bug fix and then run your unit
testing with::
pytest
Unit testing will automatically enforce minimum code coverage standards.
Next, to ensure your code meets minimum code styling standards, run::
pip install pre-commit
pre-commit run --all-files
Finally, `create a pull request`_ from your fork and I'll be sure to review it.
.. _create a pull request: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request
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"description": "Python Fast-Quadric-Mesh-Simplification Wrapper\n===============================================\nThis is a python wrapping of the `Fast-Quadric-Mesh-Simplification Library\n<https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification/>`_. Having\narrived at the same problem as the original author, but needing a Python\nlibrary, this project seeks to extend the work of the original library while\nadding integration to Python and the `PyVista\n<https://github.com/pyvista/pyvista>`_ project.\n\nFor the full documentation visit: https://pyvista.github.io/fast-simplification/\n\n.. image:: https://github.com/pyvista/fast-simplification/raw/main/doc/images/simplify_demo.png\n\nInstallation\n------------\nFast Simplification can be installed from PyPI using pip on Python >= 3.7::\n\n pip install fast-simplification\n\nSee the `Contributing <https://github.com/pyvista/fast-simplification#contributing>`_ for more details regarding development or if the installation through pip doesn't work out.\n\nBasic Usage\n-----------\nThe basic interface is quite straightforward and can work directly\nwith arrays of points and triangles:\n\n.. code:: python\n\n points = [[ 0.5, -0.5, 0.0],\n [ 0.0, -0.5, 0.0],\n [-0.5, -0.5, 0.0],\n [ 0.5, 0.0, 0.0],\n [ 0.0, 0.0, 0.0],\n [-0.5, 0.0, 0.0],\n [ 0.5, 0.5, 0.0],\n [ 0.0, 0.5, 0.0],\n [-0.5, 0.5, 0.0]]\n\n faces = [[0, 1, 3],\n [4, 3, 1],\n [1, 2, 4],\n [5, 4, 2],\n [3, 4, 6],\n [7, 6, 4],\n [4, 5, 7],\n [8, 7, 5]]\n\n points_out, faces_out = fast_simplification.simplify(points, faces, 0.5)\n\n\nAdvanced Usage\n--------------\nThis library supports direct integration with VTK through PyVista to\nprovide a simplistic interface to the library. As this library\nprovides a 4-5x improvement to the VTK decimation algorithms.\n\n.. code:: python\n\n >>> from pyvista import examples\n >>> mesh = examples.download_nefertiti()\n >>> out = fast_simplification.simplify_mesh(mesh, target_reduction=0.9)\n\n Compare with built-in VTK/PyVista methods:\n\n >>> fas_sim = fast_simplification.simplify_mesh(mesh, target_reduction=0.9)\n >>> dec_std = mesh.decimate(0.9) # vtkQuadricDecimation\n >>> dec_pro = mesh.decimate_pro(0.9) # vtkDecimatePro\n\n >>> pv.set_plot_theme('document')\n >>> pl = pv.Plotter(shape=(2, 2), window_size=(1000, 1000))\n >>> pl.add_text('Original', 'upper_right', color='w')\n >>> pl.add_mesh(mesh, show_edges=True)\n >>> pl.camera_position = cpos\n\n >>> pl.subplot(0, 1)\n >>> pl.add_text(\n ... 'Fast-Quadric-Mesh-Simplification\\n~2.2 seconds', 'upper_right', color='w'\n ... )\n >>> pl.add_mesh(fas_sim, show_edges=True)\n >>> pl.camera_position = cpos\n\n >>> pl.subplot(1, 0)\n >>> pl.add_mesh(dec_std, show_edges=True)\n >>> pl.add_text(\n ... 'vtkQuadricDecimation\\n~9.5 seconds', 'upper_right', color='w'\n ... )\n >>> pl.camera_position = cpos\n\n >>> pl.subplot(1, 1)\n >>> pl.add_mesh(dec_pro, show_edges=True)\n >>> pl.add_text(\n ... 'vtkDecimatePro\\n11.4~ seconds', 'upper_right', color='w'\n ... )\n >>> pl.camera_position = cpos\n >>> pl.show()\n\n\nComparison to other libraries\n-----------------------------\nThe `pyfqmr <https://github.com/Kramer84/pyfqmr-Fast-Quadric-Mesh-Reduction>`_\nlibrary wraps the same header file as this library and has similar capabilities.\nIn this library, the decision was made to write the Cython layer on top of an\nadditional C++ layer rather than directly interfacing with wrapper from Cython.\nThis results in a mild performance improvement.\n\nReusing the example above:\n\n.. code:: python\n\n Set up a timing function.\n\n >>> import pyfqmr\n >>> vertices = mesh.points\n >>> faces = mesh.faces.reshape(-1, 4)[:, 1:]\n >>> def time_pyfqmr():\n ... mesh_simplifier = pyfqmr.Simplify()\n ... mesh_simplifier.setMesh(vertices, faces)\n ... mesh_simplifier.simplify_mesh(\n ... target_count=out.n_faces, aggressiveness=7, verbose=0\n ... )\n ... vertices_out, faces_out, normals_out = mesh_simplifier.getMesh()\n ... return vertices_out, faces_out, normals_out\n\nNow, time it and compare with the non-VTK API of this library:\n\n.. code:: python\n\n >>> timeit time_pyfqmr()\n 2.75 s \u00b1 5.35 ms per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n\n >>> timeit vout, fout = fast_simplification.simplify(vertices, faces, 0.9)\n 2.05 s \u00b1 3.18 ms per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n\nAdditionally, the ``fast-simplification`` library has direct plugins\nto the ``pyvista`` library, making it easy to read and write meshes:\n\n.. code:: python\n\n >>> import pyvista\n >>> import fast_simplification\n >>> mesh = pyvista.read('my_mesh.stl')\n >>> simple = fast_simplification.simplify_mesh(mesh)\n >>> simple.save('my_simple_mesh.stl')\n\nSince both libraries are based on the same core C++ code, feel free to\nuse whichever gives you the best performance and interoperability.\n\nReplay decimation functionality\n-------------------------------\nThis library also provides an interface to keep track of the successive\ncollapses that occur during the decimation process and to replay the\ndecimation process. This can be useful for different applications, such\nas:\n\n* applying the same decimation to a collection of meshes that share the\n same topology\n* computing a correspondence map between the vertices of the original\n mesh and the vertices of the decimated mesh, to transfer field data from\n one to the other for example\n* replaying the decimation process with a smaller target reduction than\n the original one, faster than decimating the original mesh with the\n smaller target reduction\n\nTo use this functionality, you need to set the ``return_collapses``\nparameter to ``True`` when calling ``simplify``. 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To do so, you need to pass a subset of the collapses to the\n``replay_simplification`` function. For example, to replay the decimation\nprocess with a target reduction of 50% the initial rate, you can run:\n\n.. code:: python\n\n >>> import numpy as np\n >>> collapses_half = collapses[:int(0.5 * len(collapses))]\n >>> points_out, faces_out, indice_mapping = fast_simplification.replay_simplification(points, faces, collapses_half)\n\nIf you have a collection of meshes that share the same topology, you can\napply the same decimation to all of them by calling ``replay_simplification``\nwith the same collapses for each mesh. This ensure that the decimated meshes\nwill share the same topology.\n\n.. code:: python\n\n >>> import numpy as np\n >>> #\u00a0Assume that you have a collection of meshes stored in a list meshes\n >>> _, _, collapses = fast_simplification.simplify(meshes[0].points, meshes[0].faces,\n ... 0.9, return_collapses=True)\n >>> decimated_meshes = []\n >>> for mesh in meshes:\n ... points_out, faces_out, _ = fast_simplification.replay_simplification(mesh.points, mesh.faces, collapses)\n ... decimated_meshes.append(pyvista.PolyData(points_out, faces_out))\n\nContributing\n------------\nContribute to this repository by forking this repository and installing in\ndevelopment mode with::\n\n git clone https://github.com/<USERNAME>/fast-simplification\n pip install -e .\n pip install -r requirements_test.txt\n\nYou can then add your feature or commit your bug fix and then run your unit\ntesting with::\n\n pytest\n\nUnit testing will automatically enforce minimum code coverage standards.\n\nNext, to ensure your code meets minimum code styling standards, run::\n\n pip install pre-commit\n pre-commit run --all-files\n\nFinally, `create a pull request`_ from your fork and I'll be sure to review it.\n\n.. _create a pull request: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request\n",
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