cluster_model_componentsT_cluster_model_componentsClusterModelComponentsClusterModelComponentscluster_model_components聚类模型组件（算子）

名称

cluster_model_componentsT_cluster_model_componentsClusterModelComponentsClusterModelComponentscluster_model_components — 在训练结果中采用用于创建模型组件的新参数。

签名

描述

使用 cluster_model_componentscluster_model_componentsClusterModelComponentsClusterModelComponentsClusterModelComponentscluster_model_components ，您可以在使用 train_model_componentstrain_model_componentsTrainModelComponentsTrainModelComponentsTrainModelComponentstrain_model_components 完成首次训练后修改参数。cluster_model_componentscluster_model_componentsClusterModelComponentsClusterModelComponentsClusterModelComponentscluster_model_components 将用于解决歧义的标准 AmbiguityCriterionAmbiguityCriterionAmbiguityCriterionAmbiguityCriterionambiguityCriterionambiguity_criterion、最大轮廓重叠度 MaxContourOverlapMaxContourOverlapMaxContourOverlapMaxContourOverlapmaxContourOverlapmax_contour_overlap 以及训练结果 ComponentTrainingIDComponentTrainingIDComponentTrainingIDComponentTrainingIDcomponentTrainingIDcomponent_training_id 的聚类阈值设置为指定值。这些参数的详细说明可在 train_model_componentstrain_model_componentsTrainModelComponentsTrainModelComponentsTrainModelComponentstrain_model_components 的文档中找到。通过修改这些参数，初始组件合并为刚性模型组件的方式随之改变。例如，聚类阈值选得越大，合并的初始组件就越少。您可以通过反复调用 inspect_clustered_componentsinspect_clustered_componentsInspectClusteredComponentsInspectClusteredComponentsInspectClusteredComponentsinspect_clustered_components 并设置不同参数值，然后使用 get_training_componentsget_training_componentsGetTrainingComponentsGetTrainingComponentsGetTrainingComponentsget_training_components 将选定的值设置为最终参数，从而交互式地选择合适的参数值。

刚性模型组件将通过 ModelComponentsModelComponentsModelComponentsModelComponentsmodelComponentsmodel_components 返回。为获得合理结果，必须将用于 train_model_componentstrain_model_componentsTrainModelComponentsTrainModelComponentsTrainModelComponentstrain_model_components 训练的相同训练图像传递至 TrainingImagesTrainingImagesTrainingImagesTrainingImagestrainingImagestraining_images 参数。训练图像中新建聚类组件的姿态通过形状匹配确定。与 train_model_componentstrain_model_componentsTrainModelComponentsTrainModelComponentsTrainModelComponentstrain_model_components 相同，可通过 set_system('pregenerate_shape_models',...)set_system("pregenerate_shape_models",...)SetSystem("pregenerate_shape_models",...)SetSystem("pregenerate_shape_models",...)SetSystem("pregenerate_shape_models",...)set_system("pregenerate_shape_models",...) 决定是否预生成形状模型。请注意，如果在某个金字塔层级上模型触及图像边界，即使该模型完全位于原始图像内部，也可能无法被检测到。set_system('border_shape_models',...)set_system("border_shape_models",...)SetSystem("border_shape_models",...)SetSystem("border_shape_models",...)SetSystem("border_shape_models",...)set_system("border_shape_models",...) 可用于确定模型是否必须完全位于训练图像内，或是否可部分延伸至图像边界之外。

执行信息

• 多线程类型：可重入（与非独占算子并行运行）。
• 多线程作用域：全局（可从任何线程调用）。
• 未采用并行化处理。

参数

示例（HDevelop）

* Get the model image.
read_image (ModelImage, 'model_image.tif')
* Define the regions for the initial components.
gen_rectangle2 (InitialComponentRegions, 212, 233, 0.62, 167, 29)
gen_rectangle2 (Rectangle2, 298, 363, 1.17, 162, 34)
gen_rectangle2 (Rectangle3, 63, 444, -0.26, 50, 27)
gen_rectangle2 (Rectangle4, 120, 473, 0, 33, 20)
concat_obj (InitialComponentRegions, Rectangle2, InitialComponentRegions)
concat_obj (InitialComponentRegions, Rectangle3, InitialComponentRegions)
concat_obj (InitialComponentRegions, Rectangle4, InitialComponentRegions)
* Get the training images
gen_empty_obj (TrainingImages)
for i := 1 to 4 by 1
    read_image (TrainingImage, 'training_image-'+i$'02'+'.tif')
    concat_obj (TrainingImages, TrainingImage, TrainingImages)
endfor
* Extract the model components and train the relations.
train_model_components (ModelImage, InitialComponentRegions, \
                        TrainingImages, ModelComponents, 22, 60, 30, 0.65, \
                        0, 0, rad(60), 'speed', 'rigidity', 0.2, 0.5, \
                        ComponentTrainingID)
* Find the best value for the parameter ClusterThreshold.
inspect_clustered_components (ModelComponents, ComponentTrainingID, \
                              'rigidity', 0.2, 0.4)
* Adopt the ClusterThreshold into the training result.
cluster_model_components (TrainingImages, ModelComponents, \
                          ComponentTrainingID, 'rigidity', 0.2, 0.4)
* Create the component model based on the training result.
create_trained_component_model (ComponentTrainingID, -rad(30), rad(60), \
                                10, 0.5, 'auto', 'auto', 'none', \
                                'use_polarity', 'false', ComponentModelID, \
                                RootRanking)

结果

如果参数值正确，算子 cluster_model_componentscluster_model_componentsClusterModelComponentsClusterModelComponentsClusterModelComponentscluster_model_components 返回值 2 (H_MSG_TRUE)。当输入为空（没有可用输入图像）时，可设置行为通过算子 set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>)。如有必要，则抛出异常。

可能的前趋

train_model_componentstrain_model_componentsTrainModelComponentsTrainModelComponentsTrainModelComponentstrain_model_components, inspect_clustered_componentsinspect_clustered_componentsInspectClusteredComponentsInspectClusteredComponentsInspectClusteredComponentsinspect_clustered_components

可能的后继

get_training_componentsget_training_componentsGetTrainingComponentsGetTrainingComponentsGetTrainingComponentsget_training_components, create_trained_component_modelcreate_trained_component_modelCreateTrainedComponentModelCreateTrainedComponentModelCreateTrainedComponentModelcreate_trained_component_model, modify_component_relationsmodify_component_relationsModifyComponentRelationsModifyComponentRelationsModifyComponentRelationsmodify_component_relations, write_training_componentswrite_training_componentsWriteTrainingComponentsWriteTrainingComponentsWriteTrainingComponentswrite_training_components, get_component_relationsget_component_relationsGetComponentRelationsGetComponentRelationsGetComponentRelationsget_component_relations, clear_training_componentsclear_training_componentsClearTrainingComponentsClearTrainingComponentsClearTrainingComponentsclear_training_components

模块

匹配