Python wrapper for CloudCompare
This site in development already contains reference material, Linux and Windows 10 binaries for a Conda environment (see below).
This project proposes a Python module to interface to CloudCompare, of equivalent level to the command mode of CloudCompare.
CloudComPy does not yet cover all the features of CloudCompare. Features are added according to the needs reported by users, feel free to post issues, preferably on GitHub, but it's also possible on GitLab (Following a temporary problem of visibility of the CloudComPy developer's contributions on GitHub, the CloudComPy repository has been cloned on GitLab to achieve redundancy).
Here is an example of a Python script:
import cloudComPy as cc # import the CloudComPy module
cc.initCC() # to do once before dealing with plugins
cloud = cc.loadPointCloud("myCloud.xyz") # read a point cloud from a file
print("cloud name: %s"%cloud.getName())
res=cc.computeCurvature(cc.CurvatureType.GAUSSIAN_CURV, 0.05, [cloud]) # compute curvature as a scalar field
nsf = cloud.getNumberOfScalarFields()
sfCurv=cloud.getScalarField(nsf-1)
cloud.setCurrentOutScalarField(nsf-1)
filteredCloud=cc.filterBySFValue(0.01, sfCurv.getMax(), cloud) # keep only the points above a given curvature
ok = filteredCloud.exportCoordToSF(False, False, True) # Z coordinate as a scalar Field
nsf = cloud.getNumberOfScalarFields()
sf1=filteredCloud.getScalarField(nsf-1)
mean, var = sf1.computeMeanAndVariance()
# using Numpy...
coordinates = filteredCloud.toNpArrayCopy() # coordinates as a numpy array
x=coordinates[:,0] # x column
y=coordinates[:,1]
z=coordinates[:,2]
f=(2*x-y)*(x+3*y) # elementwise operation on arrays
asf1=sf1.toNpArray() # scalar field as a numpy array
sf1.fromNpArrayCopy(f) # replace scalar field values by a numpy array
res=cc.SavePointCloud(filteredCloud,"myModifiedCloud.bin") #save the point cloud to a file
As you can see, it is possible to read and write point clouds, access scalar fields from Numpy (with or without copy of data), call some CloudCompare functions to transform point clouds.
The reference documentation gives the list of available methods.
From the Python interpreter, Docstrings provide some documentation on the methods and their arguments.
Release notes starts with the October 18, 2021 CloudComPy release as reference. Have a look here.
CloudComPy is built on Windows and Linux in a Conda environment, which facilitates building and binary portability.
The binary provision includes CloudCompare and its Python interface CloudComPy, the Conda environment provides the IDE Spyder and Jupyter.
The binary are available here. See the instructions below.
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To install and use a Windows binary, consult the Windows installation documentation in the doc directory.
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To install and use a Linux binary, consult the Linux installation documentation in the doc directory.
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Experimental: To install and use a macOS binary, consult the macOS installation documentation in the doc directory.
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Experimental: to test and run a Linux Conda binary in a docker container, see the docker configuration documentation in the doc directory.
Prerequisites for CloudComPy are Python3, PyBind11 and Numpy plus, of course, everything needed to build CloudCompare and its plugins.
With CloudComPy you build CloudCompare and the associated Python module.
Compilation is done with CMake, minimum version 3.13 or newer.
The minimum required version of each prerequisite is not always precisely identified. Examples of constructions that work are given here.
To build CloudComPy on Windows 10 or 11 with conda packages, consult the Windows build documentation in the doc directory.
To build CloudComPy on Linux64 with conda packages, consult the Linux Conda build documentation in the doc directory.
To build CloudComPy using Docker, for Linux64 and conda3, see building with Docker in the doc directory).
To build CloudComPy on Ubuntu 20.04, consult the Ubuntu build documentation in the doc directory.
