Operator Reference

HALCON Operator Reference / Classification / Gaussian Mixture Models
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write_samples_class_gmmT_write_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm (Operator)

write_samples_class_gmmT_write_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm — Write the training data of a Gaussian Mixture Model to a file.

Signature

write_samples_class_gmm( : : GMMHandle, FileName : )

Herror T_write_samples_class_gmm(const Htuple GMMHandle, const Htuple FileName)

void WriteSamplesClassGmm(const HTuple& GMMHandle, const HTuple& FileName)

void HClassGmm::WriteSamplesClassGmm(const HString& FileName) const

void HClassGmm::WriteSamplesClassGmm(const char* FileName) const

void HClassGmm::WriteSamplesClassGmm(const wchar_t* FileName) const   ( Windows only)

static void HOperatorSet.WriteSamplesClassGmm(HTuple GMMHandle, HTuple fileName)

void HClassGmm.WriteSamplesClassGmm(string fileName)

def write_samples_class_gmm(gmmhandle: HHandle, file_name: str) -> None

Description

write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm writes the training samples stored in the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle to the file given by FileNameFileNameFileNamefileNamefile_name. write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm can be used to build up a database of training samples, and hence to improve the performance of the GMM by training it with an extended data set (see train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmtrain_class_gmm).

The file FileNameFileNameFileNamefileNamefile_name is overwritten by write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm. Nevertheless, extending the database of training samples is easy because read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm and add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm add the training samples to the training samples that are already stored in memory with the GMM.

The created file can be read with read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp if the classifier of a multilayer perceptron (MLP) should be used. The class of a training sample in the GMM corresponds to a component of the target vector in the MLP being 1.0.

Execution Information

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

Parameters

GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (input_control)  class_gmm → HClassGmm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

GMM handle.

FileNameFileNameFileNamefileNamefile_name (input_control)  filename.write → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

File name.

Result

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

Possible Predecessors

add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm

Possible Successors

clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm

See also

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmcreate_class_gmm, read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp, write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp

Module

Foundation

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