Operator Reference

HALCON Operator Reference / Segmentation / Classification
ClassesClasses | | Operators

add_samples_image_class_gmmT_add_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm (Operator)

add_samples_image_class_gmmT_add_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm — Add training samples from an image to the training data of a Gaussian Mixture Model.

Signature

add_samples_image_class_gmm(Image, ClassRegions : : GMMHandle, Randomize : )

Herror T_add_samples_image_class_gmm(const Hobject Image, const Hobject ClassRegions, const Htuple GMMHandle, const Htuple Randomize)

void AddSamplesImageClassGmm(const HObject& Image, const HObject& ClassRegions, const HTuple& GMMHandle, const HTuple& Randomize)

void HImage::AddSamplesImageClassGmm(const HRegion& ClassRegions, const HClassGmm& GMMHandle, double Randomize) const

void HClassGmm::AddSamplesImageClassGmm(const HImage& Image, const HRegion& ClassRegions, double Randomize) const

static void HOperatorSet.AddSamplesImageClassGmm(HObject image, HObject classRegions, HTuple GMMHandle, HTuple randomize)

void HImage.AddSamplesImageClassGmm(HRegion classRegions, HClassGmm GMMHandle, double randomize)

void HClassGmm.AddSamplesImageClassGmm(HImage image, HRegion classRegions, double randomize)

def add_samples_image_class_gmm(image: HObject, class_regions: HObject, gmmhandle: HHandle, randomize: float) -> None

Description

add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm adds training samples from the ImageImageImageimageimage to the Gaussian Mixture Model (GMM) given by GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle. add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm is used to store the training samples before a classifier to be used for the pixel classification of multichannel images with classify_image_class_gmmclassify_image_class_gmmClassifyImageClassGmmClassifyImageClassGmmclassify_image_class_gmm is trained. add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm works analogously to add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm. The ImageImageImageimageimage must have a number of channels equal to NumDimNumDimNumDimnumDimnum_dim, as specified with create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmcreate_class_gmm. The training regions for the NumClassesNumClassesNumClassesnumClassesnum_classes pixel classes are passed in ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions. Hence, ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions must be a tuple containing NumClassesNumClassesNumClassesnumClassesnum_classes regions. The order of the regions in ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions determines the class of the pixels. If there are no samples for a particular class in ImageImageImageimageimage an empty region must be passed at the position of the class in ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions. With this mechanism it is possible to use multiple images to add training samples for all relevant classes to the GMM by calling add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm multiple times with the different images and suitably chosen regions. The regions in ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions should contain representative training samples for the respective classes. Hence, they need not cover the entire image. The regions in ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions should not overlap each other, because this would lead to the fact that in the training data the samples from the overlapping areas would be assigned to multiple classes, which may lead to a lower classification performance. Image data of integer type can be particularly badly suited for modeling with a GMM. RandomizeRandomizeRandomizerandomizerandomize can be used to overcome this problem, as explained in add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm.

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:

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

Parameters

ImageImageImageimageimage (input_object)  (multichannel-)image → objectHImageHObjectHObjectHobject (byte / cyclic / direction / int1 / int2 / uint2 / int4 / real)

Training image.

ClassRegionsClassRegionsClassRegionsclassRegionsclass_regions (input_object)  region-array → objectHRegionHObjectHObjectHobject

Regions of the classes to be trained.

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

GMM handle.

RandomizeRandomizeRandomizerandomizerandomize (input_control)  real → HTuplefloatHTupleHtuple (real) (double) (double) (double)

Standard deviation of the Gaussian noise added to the training data.

Default: 0.0

Suggested values: 0.0, 1.5, 2.0

Restriction: Randomize >= 0.0

Result

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

Possible Predecessors

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmcreate_class_gmm

Possible Successors

train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmtrain_class_gmm, write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm

Alternatives

read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm

See also

classify_image_class_gmmclassify_image_class_gmmClassifyImageClassGmmClassifyImageClassGmmclassify_image_class_gmm, add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm, clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm, get_sample_num_class_gmmget_sample_num_class_gmmGetSampleNumClassGmmGetSampleNumClassGmmget_sample_num_class_gmm, get_sample_class_gmmget_sample_class_gmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmm

Module

Foundation

ClassesClasses | | Operators