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https://github.com/heremaps/pptk
The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
https://github.com/heremaps/pptk
3-d gps-data graphics-3d kd-tree lidar octree pointclouds visualization
Last synced: 20 days ago
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The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
- Host: GitHub
- URL: https://github.com/heremaps/pptk
- Owner: heremaps
- License: mit
- Created: 2018-07-11T08:33:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-17T19:34:33.000Z (about 4 years ago)
- Last Synced: 2024-11-13T21:03:54.273Z (30 days ago)
- Topics: 3-d, gps-data, graphics-3d, kd-tree, lidar, octree, pointclouds, visualization
- Language: C++
- Homepage: https://heremaps.github.io/pptk
- Size: 8.49 MB
- Stars: 615
- Watchers: 22
- Forks: 114
- Open Issues: 59
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-robotic-tooling - pptk - Point Processing Toolkit from HEREMaps (Sensor Processing / Point Cloud Processing)
- awesome-robotic-tooling - pptk - Point Processing Toolkit from HEREMaps. (Sensor Processing / Lidar and Point Cloud Processing)
README
# pptk - Point Processing Toolkit
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.
* A 3-d point cloud viewer that
- accepts any 3-column numpy array as input,
- renders tens of millions of points interactively using an octree-based level of detail mechanism,
- supports point selection for inspecting and annotating point data.
* A fully parallelized point k-d tree that supports k-nearest neighbor queries and r-near range queries
(both build and queries have been parallelized).
* A normal estimation routine based on principal component analysis of point cloud neighborhoods.
[Homepage](https://heremaps.github.io/pptk/index.html)
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](https://heremaps.github.io/pptk/tutorial.html).
## License
Unless otherwise noted in `LICENSE` files for specific files or directories,
the [LICENSE](LICENSE) in the root applies to all content in this repository.
## Install
One can either install pptk directly from PyPI
```
>> pip install pptk
```
or from the .whl file that results from [building pptk from source](#build).
```
>> pip install <.whl file>
```
## Quickstart
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](https://heremaps.github.io/pptk/tutorial.html).
## Build
We provide CMake scripts for automating most of the build process, but ask the
user to manually prepare [dependencies](#requirements) 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..txt files)
##### Requirements
Listed are versions of libraries used to develop pptk, though earlier versions
of these libraries may also work.
* [QT](https://www.qt.io/) 5.4
* [TBB](https://www.threadingbuildingblocks.org/) 4.3
* [Eigen](http://eigen.tuxfamily.org) 3.2.9
* [Python](https://www.python.org/) 2.7+ or 3.6+
* [Numpy](http://www.numpy.org/) 1.13
##### Windows
1. Create an empty build folder
```
>> mkdir
```
2. Create an initial CMakeCache.txt under and use it to provide
values for the CMake cache variables listed above. (e.g. see CMakeCache.win.txt)
3. Type the following...
```
>> cd
>> cmake -G "NMake Makefiles"
>> nmake
>> python setup.py bdist_wheel
>> pip install dist\<.whl file>
```
##### Linux
Similar to building on Windows.
##### Mac
Similar to building on Windows.