Operator Reference

HALCON Operator Reference / Classification / Gaussian Mixture Models

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clear_samples_class_gmmT_clear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm (Operator)

clear_samples_class_gmmT_clear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm — Clear the training data of a Gaussian Mixture Model.

Signature

clear_samples_class_gmm( : : GMMHandle : )

Herror T_clear_samples_class_gmm(const Htuple GMMHandle)

void ClearSamplesClassGmm(const HTuple& GMMHandle)

static void HClassGmm::ClearSamplesClassGmm(const HClassGmmArray& GMMHandle)

void HClassGmm::ClearSamplesClassGmm() const

static void HOperatorSet.ClearSamplesClassGmm(HTuple GMMHandle)

static void HClassGmm.ClearSamplesClassGmm(HClassGmm[] GMMHandle)

void HClassGmm.ClearSamplesClassGmm()

def clear_samples_class_gmm(gmmhandle: MaybeSequence[HHandle]) -> None

Description

clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm clears all training samples that have been stored in the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle. clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm should only be used if the GMM is trained in the same process that uses the GMM for evaluation with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm or for classification with classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm. In this case, the memory required for the training samples can be freed with clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm, and hence memory can be saved. In the normal usage, in which the GMM is trained offline and written to a file with write_class_gmmwrite_class_gmmWriteClassGmmWriteClassGmmwrite_class_gmm, it is typically unnecessary to call clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm because write_class_gmmwrite_class_gmmWriteClassGmmWriteClassGmmwrite_class_gmm does not save the training samples, and hence the online process, which reads the GMM with read_class_gmmread_class_gmmReadClassGmmReadClassGmmread_class_gmm, requires no memory for the training samples.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

This operator modifies the state of the following input parameter:

• GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters

GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (input_control, state is modified)  class_gmm(-array) → HClassGmm, HTupleMaybeSequence[HHandle]HTupleHtuple (handle) (IntPtr) (HHandle) (handle)

GMM handle.

Result

If the parameters are valid, the operator clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm returns the value 2 ( H_MSG_TRUE) . If necessary an exception is raised.

Possible Predecessors

train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmtrain_class_gmm, write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm

See also

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmcreate_class_gmm, clear_class_gmmclear_class_gmmClearClassGmmClearClassGmmclear_class_gmm, add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm, read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm

Module

Foundation

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ClassesClasses | | Operators