###########
pyminiply
###########
|pypi| |MIT|
.. |pypi| image:: https://img.shields.io/pypi/v/pyminiply.svg?logo=python&logoColor=white
:target: https://pypi.org/project/pyminiply/
.. |MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg
:target: https://opensource.org/licenses/MIT
``pyminiply`` is a Python library for rapidly reading PLY files. It is a
Python wrapper around the fast C++ PLY reading library provided by
`miniply <ehttps://github.com/vilya/miniply>`_. Thanks @vilya!
The main advantage of ``pyminiply`` over other PLY reading libraries is
its performance. See the benchmarks below for more details.
**************
Installation
**************
The recommended way to install ``pyminiply`` is via PyPI:
.. code:: sh
pip install pyminiply
You can also clone the repository and install it from source:
.. code:: sh
git clone https://github.com/pyvista/pyminiply.git
cd pyminiply
git submodule update --init --recursive
pip install .
*******
Usage
*******
Load in the vertices, indices, normals, UV, and color information from a
PLY file:
.. code:: pycon
>>> import pyminiply
>>> vertices, indices, normals, uv, color = pyminiply.read("example.ply")
>>> vertices
array([[ 5.0000000e-01, -5.0000000e-01, -5.5511151e-17],
[ 4.0000001e-01, -5.0000000e-01, -4.4408922e-17],
[ 3.0000001e-01, -5.0000000e-01, -3.3306692e-17],
...,
[-4.2500001e-01, 5.0000000e-01, 4.7184480e-17],
[-4.7499999e-01, 4.4999999e-01, 5.2735593e-17],
[-5.0000000e-01, 4.2500001e-01, 5.5511151e-17]], dtype=float32)
>>> indices
array([[ 0, 442, 441],
[ 442, 122, 443],
[ 443, 121, 441],
...,
[1677, 438, 1679],
[1679, 439, 1676],
[1677, 1679, 1676]], dtype=int32)
>>> normals
array([[-1.110223e-16, 0.000000e+00, -1.000000e+00],
[-1.110223e-16, 0.000000e+00, -1.000000e+00],
[-1.110223e-16, 0.000000e+00, -1.000000e+00],
...,
[-1.110223e-16, 0.000000e+00, -1.000000e+00],
[-1.110223e-16, 0.000000e+00, -1.000000e+00],
[-1.110223e-16, 0.000000e+00, -1.000000e+00]], dtype=float32)
>>> uv
array([[0. , 0. ],
[0.1 , 0. ],
[0.2 , 0. ],
...,
[0.92499995, 1. ],
[0.975 , 0.95 ],
[1. , 0.92499995]], dtype=float32)
>>> color
array([[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0],
...,
[254, 254, 254],
[254, 254, 254],
[255, 255, 255]], dtype=uint8)
You can also read in the PLY file as a `PyVista
<https://github.com/pyvista>`_ PolyData and immediately plot it.
.. code:: pycon
>>> import pyminiply
>>> mesh = pyminiply.read_as_mesh("example.ply")
>>> mesh
PolyData (0x7f0653579c00)
N Cells: 200
N Points: 121
N Strips: 0
X Bounds: -5.000e-01, 5.000e-01
Y Bounds: -5.000e-01, 5.000e-01
Z Bounds: -5.551e-17, 5.551e-17
N Arrays: 2
>>> mesh.plot()
.. image:: https://github.com/pyvista/pyminiply/raw/main/demo.png
***********
Benchmark
***********
The main reason behind writing yet another PLY file reader for Python is
to leverage the highly performant ``miniply`` library.
There is already an benchmark demonstrating how ``miniply`` outperforms
in comparison to competing C and C++ libraries at `ply_io_benchmark
<https://github.com/mhalber/ply_io_benchmark>`_ when reading PLY files.
The benchmark here shows how ``pyminiply`` performs relative to other
Python PLY file readers.
Here are the timings from reading in a 1,000,000 point binary PLY file:
+-------------+-----------------+
| Library | Time (seconds) |
+=============+=================+
| pyminiply | 0.046 |
+-------------+-----------------+
| open3d | 0.149 |
+-------------+-----------------+
| PyVista | 0.409 |
| (VTK) | |
+-------------+-----------------+
| meshio | 0.512 |
+-------------+-----------------+
| plyfile | 8.939 |
+-------------+-----------------+
**Benchmark source:**
.. code:: python
import time
import numpy as np
import pyvista as pv
import pyminiply
import plyfile
import meshio
import open3d
filename = 'tmp.ply'
mesh = pv.Plane(i_resolution=999, j_resolution=999).triangulate()
mesh.clear_data()
mesh.save(filename)
# pyminiply
tstart = time.time()
pyminiply.read(filename)
tend = time.time() - tstart; print(f'pyminiply: {tend:.3f}')
# open3d
tstart = time.time()
open3d.io.read_point_cloud(filename)
tend = time.time() - tstart; print(f'open3d: {tend:.3f}')
# VTK/PyVista
tstart = time.time()
pv.read(filename)
tend = time.time() - tstart; print(f'VTK/PyVista: {tend:.3f}')
tstart = time.time()
meshio.read(filename)
tend = time.time() - tstart; print(f'meshio: {tend:.3f}')
# plyfile
tstart = time.time()
plyfile.PlyData.read(filename)
tend = time.time() - tstart; print(f'plyfile: {tend:.3f}')
Comparison with VTK and PyVista
===============================
Here's an additional benchmark comparing VTK/PyVista with ``pyminiply``:
.. code:: python
import numpy as np
import time
import pyvista as pv
import matplotlib.pyplot as plt
import pyminiply
times = []
filename = 'tmp.ply'
for res in range(50, 800, 50):
mesh = pv.Plane(i_resolution=res, j_resolution=res).triangulate().subdivide(2)
mesh.clear_data()
mesh.save(filename)
tstart = time.time()
pv_mesh = pv.read(filename)
vtk_time = time.time() - tstart
tstart = time.time()
ply_mesh = pyminiply.read_as_mesh(filename)
ply_reader_time = time.time() - tstart
assert np.allclose(pv_mesh['Normals'], ply_mesh['Normals'])
assert np.allclose(pv_mesh.points, ply_mesh.points)
assert np.allclose(pv_mesh._connectivity_array, ply_mesh._connectivity_array)
times.append([mesh.n_points, vtk_time, ply_reader_time])
print(times[-1])
times = np.array(times)
plt.figure(1)
plt.title('PLY load time')
plt.plot(times[:, 0], times[:, 1], label='VTK')
plt.plot(times[:, 0], times[:, 2], label='pyminiply')
plt.xlabel('Number of Points')
plt.ylabel('Time to Load (seconds)')
plt.legend()
plt.figure(2)
plt.title('PLY load time (Log-Log)')
plt.loglog(times[:, 0], times[:, 1], label='VTK')
plt.loglog(times[:, 0], times[:, 2], label='pyminiply')
plt.xlabel('Number of Points')
plt.ylabel('Time to Load (seconds)')
plt.legend()
plt.show()
.. image:: https://github.com/pyvista/pyminiply/raw/main/bench0.png
.. image:: https://github.com/pyvista/pyminiply/raw/main/bench1.png
*****************************
License and Acknowledgments
*****************************
This project relies on ``miniply`` and credit goes to the original
author for the excellent C++ library. That work is licensed under the
MIT License.
The work in this repository is also licensed under the MIT License.
*********
Support
*********
If you are having issues, please feel free to raise an `Issue
<https://github.com/pyvista/pyminiply/issues>`_.
Raw data
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"description": "###########\n pyminiply\n###########\n\n|pypi| |MIT|\n\n.. |pypi| image:: https://img.shields.io/pypi/v/pyminiply.svg?logo=python&logoColor=white\n :target: https://pypi.org/project/pyminiply/\n\n.. |MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n :target: https://opensource.org/licenses/MIT\n\n``pyminiply`` is a Python library for rapidly reading PLY files. It is a\nPython wrapper around the fast C++ PLY reading library provided by\n`miniply <ehttps://github.com/vilya/miniply>`_. Thanks @vilya!\n\nThe main advantage of ``pyminiply`` over other PLY reading libraries is\nits performance. See the benchmarks below for more details.\n\n**************\n Installation\n**************\n\nThe recommended way to install ``pyminiply`` is via PyPI:\n\n.. code:: sh\n\n pip install pyminiply\n\nYou can also clone the repository and install it from source:\n\n.. code:: sh\n\n git clone https://github.com/pyvista/pyminiply.git\n cd pyminiply\n git submodule update --init --recursive\n pip install .\n\n*******\n Usage\n*******\n\nLoad in the vertices, indices, normals, UV, and color information from a\nPLY file:\n\n.. code:: pycon\n\n >>> import pyminiply\n >>> vertices, indices, normals, uv, color = pyminiply.read(\"example.ply\")\n >>> vertices\n array([[ 5.0000000e-01, -5.0000000e-01, -5.5511151e-17],\n [ 4.0000001e-01, -5.0000000e-01, -4.4408922e-17],\n [ 3.0000001e-01, -5.0000000e-01, -3.3306692e-17],\n ...,\n [-4.2500001e-01, 5.0000000e-01, 4.7184480e-17],\n [-4.7499999e-01, 4.4999999e-01, 5.2735593e-17],\n [-5.0000000e-01, 4.2500001e-01, 5.5511151e-17]], dtype=float32)\n >>> indices\n array([[ 0, 442, 441],\n [ 442, 122, 443],\n [ 443, 121, 441],\n ...,\n [1677, 438, 1679],\n [1679, 439, 1676],\n [1677, 1679, 1676]], dtype=int32)\n >>> normals\n array([[-1.110223e-16, 0.000000e+00, -1.000000e+00],\n [-1.110223e-16, 0.000000e+00, -1.000000e+00],\n [-1.110223e-16, 0.000000e+00, -1.000000e+00],\n ...,\n [-1.110223e-16, 0.000000e+00, -1.000000e+00],\n [-1.110223e-16, 0.000000e+00, -1.000000e+00],\n [-1.110223e-16, 0.000000e+00, -1.000000e+00]], dtype=float32)\n >>> uv\n array([[0. , 0. ],\n [0.1 , 0. ],\n [0.2 , 0. ],\n ...,\n [0.92499995, 1. ],\n [0.975 , 0.95 ],\n [1. , 0.92499995]], dtype=float32)\n >>> color\n array([[ 0, 0, 0],\n [ 0, 0, 0],\n [ 0, 0, 0],\n ...,\n [254, 254, 254],\n [254, 254, 254],\n [255, 255, 255]], dtype=uint8)\n\nYou can also read in the PLY file as a `PyVista\n<https://github.com/pyvista>`_ PolyData and immediately plot it.\n\n.. code:: pycon\n\n >>> import pyminiply\n >>> mesh = pyminiply.read_as_mesh(\"example.ply\")\n >>> mesh\n PolyData (0x7f0653579c00)\n N Cells: 200\n N Points: 121\n N Strips: 0\n X Bounds: -5.000e-01, 5.000e-01\n Y Bounds: -5.000e-01, 5.000e-01\n Z Bounds: -5.551e-17, 5.551e-17\n N Arrays: 2\n\n >>> mesh.plot()\n\n.. image:: https://github.com/pyvista/pyminiply/raw/main/demo.png\n\n***********\n Benchmark\n***********\n\nThe main reason behind writing yet another PLY file reader for Python is\nto leverage the highly performant ``miniply`` library.\n\nThere is already an benchmark demonstrating how ``miniply`` outperforms\nin comparison to competing C and C++ libraries at `ply_io_benchmark\n<https://github.com/mhalber/ply_io_benchmark>`_ when reading PLY files.\nThe benchmark here shows how ``pyminiply`` performs relative to other\nPython PLY file readers.\n\nHere are the timings from reading in a 1,000,000 point binary PLY file:\n\n+-------------+-----------------+\n| Library | Time (seconds) |\n+=============+=================+\n| pyminiply | 0.046 |\n+-------------+-----------------+\n| open3d | 0.149 |\n+-------------+-----------------+\n| PyVista | 0.409 |\n| (VTK) | |\n+-------------+-----------------+\n| meshio | 0.512 |\n+-------------+-----------------+\n| plyfile | 8.939 |\n+-------------+-----------------+\n\n**Benchmark source:**\n\n.. code:: python\n\n import time\n\n import numpy as np\n import pyvista as pv\n import pyminiply\n import plyfile\n import meshio\n import open3d\n\n filename = 'tmp.ply'\n mesh = pv.Plane(i_resolution=999, j_resolution=999).triangulate()\n mesh.clear_data()\n mesh.save(filename)\n\n # pyminiply\n tstart = time.time()\n pyminiply.read(filename)\n tend = time.time() - tstart; print(f'pyminiply: {tend:.3f}')\n\n # open3d\n tstart = time.time()\n open3d.io.read_point_cloud(filename)\n tend = time.time() - tstart; print(f'open3d: {tend:.3f}')\n\n # VTK/PyVista\n tstart = time.time()\n pv.read(filename)\n tend = time.time() - tstart; print(f'VTK/PyVista: {tend:.3f}')\n\n tstart = time.time()\n meshio.read(filename)\n tend = time.time() - tstart; print(f'meshio: {tend:.3f}')\n\n # plyfile\n tstart = time.time()\n plyfile.PlyData.read(filename)\n tend = time.time() - tstart; print(f'plyfile: {tend:.3f}')\n\nComparison with VTK and PyVista\n===============================\n\nHere's an additional benchmark comparing VTK/PyVista with ``pyminiply``:\n\n.. code:: python\n\n import numpy as np\n import time\n import pyvista as pv\n import matplotlib.pyplot as plt\n import pyminiply\n\n times = []\n filename = 'tmp.ply'\n for res in range(50, 800, 50):\n mesh = pv.Plane(i_resolution=res, j_resolution=res).triangulate().subdivide(2)\n mesh.clear_data()\n mesh.save(filename)\n\n tstart = time.time()\n pv_mesh = pv.read(filename)\n vtk_time = time.time() - tstart\n\n tstart = time.time()\n ply_mesh = pyminiply.read_as_mesh(filename)\n ply_reader_time = time.time() - tstart\n\n assert np.allclose(pv_mesh['Normals'], ply_mesh['Normals'])\n assert np.allclose(pv_mesh.points, ply_mesh.points)\n assert np.allclose(pv_mesh._connectivity_array, ply_mesh._connectivity_array)\n\n times.append([mesh.n_points, vtk_time, ply_reader_time])\n print(times[-1])\n\n\n times = np.array(times)\n plt.figure(1)\n plt.title('PLY load time')\n plt.plot(times[:, 0], times[:, 1], label='VTK')\n plt.plot(times[:, 0], times[:, 2], label='pyminiply')\n plt.xlabel('Number of Points')\n plt.ylabel('Time to Load (seconds)')\n plt.legend()\n\n plt.figure(2)\n plt.title('PLY load time (Log-Log)')\n plt.loglog(times[:, 0], times[:, 1], label='VTK')\n plt.loglog(times[:, 0], times[:, 2], label='pyminiply')\n plt.xlabel('Number of Points')\n plt.ylabel('Time to Load (seconds)')\n plt.legend()\n plt.show()\n\n.. image:: https://github.com/pyvista/pyminiply/raw/main/bench0.png\n\n.. image:: https://github.com/pyvista/pyminiply/raw/main/bench1.png\n\n*****************************\n License and Acknowledgments\n*****************************\n\nThis project relies on ``miniply`` and credit goes to the original\nauthor for the excellent C++ library. 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