cluster_model_components🔗
Short description🔗
cluster_model_components — Adopt new parameters that are used to create the model components into the
training result.
Warning🔗
cluster_model_components is obsolete and is only provided for
reasons of backward compatibility.
The operator will be removed with HALCON 26.11.
Signature🔗
cluster_model_components( image TrainingImages, out region ModelComponents, component_training ComponentTrainingID, string AmbiguityCriterion, real MaxContourOverlap, real ClusterThreshold )
Description🔗
With cluster_model_components you can modify parameters
after a first training has been performed using
train_model_components. cluster_model_components
sets the criterion AmbiguityCriterion that is used to solve
the ambiguities, the maximum contour overlap
MaxContourOverlap, and the cluster threshold of the training
result ComponentTrainingID to the specified values. A
detailed description of these parameters can be found in the
documentation of train_model_components. By modifying these
parameters, the way in which the initial components are merged into
rigid model components changes. For example, the greater the
cluster threshold is chosen, the fewer initial components are
merged. You can select suitable parameter values interactively by
repeatedly calling inspect_clustered_components with
different parameter values and then setting the chosen values by
using get_training_components.
The rigid model components are returned in
ModelComponents. In order to receive reasonable results, it
is essential that the same training images that were used to perform
the training with train_model_components are passed in
TrainingImages. The pose of the newly clustered components
within the training images is determined using the shape-based
matching. As in train_model_components, one can decide
whether the shape models should be pregenerated by using
set_system('pregenerate_shape_models',...).
Note that, if for a certain pyramid level the model touches the
image border, it might not be found even if it lies completely
within the original image.
set_system('border_shape_models',...) can be used to
determine whether the models must lie completely within the training
images or whether they can extend partially beyond the image border.
Execution information🔗
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🔗
TrainingImages (input_object) (multichannel-)image(-array) → object (byte / uint2)
Training images that were used for training the model components.
ModelComponents (output_object) region(-array) → object
Contour regions of rigid model components.
ComponentTrainingID (input_control) component_training → (handle)
Handle of the training result.
AmbiguityCriterion (input_control) string → (string)
Criterion for solving the ambiguities.
Default: 'rigidity'
List of values: 'distance', 'distance_orientation', 'orientation', 'rigidity'
MaxContourOverlap (input_control) real → (real)
Maximum contour overlap of the found initial components.
Default: 0.2
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Minimum increment: 0.01
Recommended increment: 0.05
Restriction: 0 <= MaxContourOverlap && MaxContourOverlap <= 1
ClusterThreshold (input_control) real → (real)
Threshold for clustering the initial components.
Default: 0.5
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: 0 <= ClusterThreshold && ClusterThreshold <= 1
Result🔗
If the parameter values are correct, the operator
cluster_model_components returns the value 2 (H_MSG_TRUE). If the input is
empty (no input images are available) the behavior can be set via
set_system('no_object_result',<Result>).
If necessary, an exception is raised.
Module🔗
Matching