Copyright (C) 2011-2018 HERE Europe B.V.
The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
At present, pptk consists of the following features.
The screenshots above show various point datasets visualized using pptk.
The bildstein1
Lidar point cloud from Semantic3D (left),
Beijing GPS trajectories from Geolife (middle left),
DistrictofColumbia.geojson
2-d polygons from US building footprints (middle right),
and a Mobius strip (right).
For details, see the tutorials.
Unless otherwise noted in LICENSE
files for specific files or directories,
the LICENSE in the root applies to all content in this repository.
One can either install pptk directly from PyPI
>> pip install pptk
or from the .whl file that results from building pptk from source.
>> pip install <.whl file>
In Python, generate 100 random 3-d points.
>> import numpy as np
>> x = np.random.rand(100, 3)
Visualize.
>> import pptk
>> v = pptk.viewer(x)
Set point size to 0.01.
>> v.set(point_size=0.01)
For more advanced examples, see tutorials.
We provide CMake scripts for automating most of the build process, but ask the user to manually prepare dependencies and record their paths in the following CMake cache variables.
Numpy_INCLUDE_DIR
PYTHON_INCLUDE_DIR
PYTHON_LIBRARY
Eigen_INCLUDE_DIR
TBB_INCLUDE_DIR
TBB_tbb_LIBRARY
TBB_tbb_RUNTIME
TBB_tbbmalloc_LIBRARY
TBB_tbbmalloc_RUNTIME
Qt5_DIR
To set these variables, either use one of CMake's GUIs (ccmake or cmake-gui),
or provide an initial CMakeCache.txt in the target build folder
(for examples of initial cache files, see the CMakeCache.
Listed are versions of libraries used to develop pptk, though earlier versions of these libraries may also work.
>> mkdir <build_folder>
Create an initial CMakeCache.txt under
Type the following...
>> cd <build_folder>
>> cmake -G "NMake Makefiles" <source_folder>
>> nmake
>> python setup.py bdist_wheel
>> pip install dist\<.whl file>
Similar to building on Windows.
Similar to building on Windows.