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//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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