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AvsFilter_DL_ClassifyObject
Header: | AVL.h |
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Namespace: | avl |
Module: | DL_CO |
Executes a Classify Object model on a single input image.
Syntax
void avl::AvsFilter_DL_ClassifyObject ( const avl::Image& inImage, atl::Optional<const avl::Rectangle2D&> inRoi, atl::Optional<const avl::CoordinateSystem2D&> inRoiAlignment, const avl::ClassifyObjectModelId& inModelId, const bool inCreateHeatmap, atl::Array<avl::ClassConfidence>& outConfidences, atl::String& outClassName, int& outClassIndex, float& outScore, avl::Heatmap& outRelevanceHeatmap, atl::Optional<avl::Rectangle2D&> outAlignedRoi = atl::NIL )
Parameters
Name | Type | Default | Description | |
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inImage | const Image& | Input image | |
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inRoi | Optional<const Rectangle2D&> | NIL | Limits the area where a classified object is located |
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inRoiAlignment | Optional<const CoordinateSystem2D&> | NIL | |
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inModelId | const ClassifyObjectModelId& | Identifier of a Classify Object model | |
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inCreateHeatmap | const bool | False | Enables creating a relevance heatmap at the expense of extended execution time |
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outConfidences | Array<ClassConfidence>& | Returns confidences for all classes | |
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outClassName | String& | Returns the name of the class with the highest confidence | |
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outClassIndex | int& | Returns the index of the class with the highest confidence | |
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outScore | float& | Returns the value of the highest confidence | |
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outRelevanceHeatmap | Heatmap& | Returns the heatmap indicating how strong specific parts of image influenced the classification result | |
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outAlignedRoi | Optional<Rectangle2D&> | NIL | Input roi after the transformation |
Requirements
For input inImage only pixel formats are supported: 1⨯uint8, 3⨯uint8.
Read more about pixel formats in Image documentation.
Optional Outputs
The computation of following outputs can be switched off by passing value atl::NIL
to these parameters: outAlignedRoi.
Read more about Optional Outputs.
Hints
- It is recommended that the deep learning model is deployed with AvsFilter_DL_ClassifyObject_Deploy first and connected through the inModelId input.
- If one decides not to use AvsFilter_DL_ClassifyObject_Deploy, then the model will be loaded in the first iteration. It will take up to several seconds.
Remarks
![](../../img/weaver/weaver_logo_slogan.png)
This filter should not be executed along with running Deep Learning Service as it may result in degraded performance or even out-of-memory errors.
Errors
List of possible exceptions:
Error type | Description |
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DomainError | Not supported inImage pixel format in AvsFilter_DL_ClassifyObject. Supported formats: 1xUInt8, 3xUInt8. |
See Also
Models for Deep Learning may be created using Aurora Vision Deep Learning Editor or using Training Api.
For more information, see Machine Vision Guide.