GitHub - mfxox/python-pcl: Python bindings to the pointcloud library (pcl)

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Introduction

This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

I/O and integration; saving and loading PCD files
segmentation
SAC
smoothing
filtering
registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

importpclimportnumpyasnpp=pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg=p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model=seg.segment()

or, for smoothing

importpclp=pcl.load("C/table_scene_lms400.pcd")
fil=p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

importnumpyasnpimportpclp=pcl.PointCloud(10) # "empty" point clouda=np.asarray(p) # NumPy view on the clouda[:] =0# fill with zerosprint(p[3]) # prints (0.0, 0.0, 0.0)a[:, 0] =1# set x coordinates to 1print(p[3]) # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Mint 17 with

Python 2.7.6
pcl 1.7.2
Cython 0.21.2

and CentOS 6.5 with

Python 2.6.6
pcl 1.6.0
Cython 0.21

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

.. autosummary::
pcl.PointCloud
pcl.Segmentation
pcl.SegmentationNormal
pcl.StatisticalOutlierRemovalFilter
pcl.MovingLeastSquares
pcl.PassThroughFilter
pcl.VoxelGridFilter

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.

.. automodule:: pcl
:members:
:undoc-members:

Name		Name	Last commit message	Last commit date
examples		examples
pcl		pcl
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
conf.py		conf.py
pcl_logo.png		pcl_logo.png
readme.rst		readme.rst
setup.py		setup.py

Name		Name	Last commit message	Last commit date
examples		examples
pcl		pcl
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
conf.py		conf.py
pcl_logo.png		pcl_logo.png
readme.rst		readme.rst
setup.py		setup.py

Name		Name	Last commit message	Last commit date
examples		examples
pcl		pcl
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
conf.py		conf.py
pcl_logo.png		pcl_logo.png
readme.rst		readme.rst
setup.py		setup.py

Name		Name	Last commit message	Last commit date
examples		examples
pcl		pcl
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
conf.py		conf.py
pcl_logo.png		pcl_logo.png
readme.rst		readme.rst
setup.py		setup.py

Name		Name	Last commit message	Last commit date
examples		examples
pcl		pcl
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
conf.py		conf.py
pcl_logo.png		pcl_logo.png
readme.rst		readme.rst
setup.py		setup.py

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