1. Canupo plugin

Canupo is a standard plugin of cloudComPy.

The availability of the plugin can be tested with the cloudComPy.isPluginCanupo() function:

isCanupo_available = cc.isPluginCanupo()

Canupo is a submodule of cloudCompy:

import cloudComPy as cc
# ...
if cc.isPluginCanupo():
    import cloudComPy.Canupo
cloudComPy.Canupo.Classify(cloud: _cloudComPy.ccPointCloud, classifierFilename: QString, corePointCloud: _cloudComPy.ccPointCloud = None, coreSource: _Canupo.CORE_CLOUD_SOURCES = <CORE_CLOUD_SOURCES.ORIGINAL: 0>, MscFilename: QString = '', confidenceThreshold: float = 0.0, generateAdditionalSF: bool = False, generateRoughnessSF: bool = False, maxThreadCount: int = 0, useActiveSFForConfidence: bool = False, samplingDist: float = 0.0) bool

Classify a point cloud using an existing Canupo classifier.

See Canupo plugin.

  • cloud (ccPointCloud) – the point cloud to classify.

  • classifierFilename (string) – the path of the Canupo classifier file.

  • corePointCloud (ccPointCloud,optional) – core PointCloud to use if coreSource=OTHER, default None

  • coreSource (int,optional) – type of Core source from [ORIGINAL, OTHER, SUBSAMPLED, MSC_FILE], default ORIGINAL

  • MscFilename (string,optional) – path of the Msc file, default “”

  • confidenceThreshold (double,optional) – threshold to use for classification, default 0.

  • generateAdditionalSF (bool,optional) – default False

  • generateRoughnessSF (bool,optional) – default False

  • maxThreadCount (int,optional) – number of threads used for parallel computation, default 0 meaning automatic

  • useActiveSFForConfidence (bool,optional) – use the active scalarField as confidence, default False

  • samplingDist (double,optional) – default 0., to use if coreSource=SUBSAMPLED, must be >0 in that case.


whether the classification is successful or not

Return type


class cloudComPy.Canupo.CORE_CLOUD_SOURCES