Module: | DL_CO |
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Executes a Classify Object model on a single input image.
Name | Type | Description | |
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inImage | Image | Input image | |
inRoi | Rectangle2D* | Limits the area where a classified object is located | |
inRoiAlignment | CoordinateSystem2D* | ||
inModelId | ClassifyObjectModelId | Identifier of a Classify Object model | |
inCreateHeatmap | Bool | Enables creating a relevance heatmap at the expense of extended execution time | |
outConfidences | ClassConfidenceArray | Returns confidences for all classes | |
outClassName | String | Returns the name of the class with the highest confidence | |
outClassIndex | Integer | Returns the index of the class with the highest confidence | |
outScore | Real | Returns the value of the highest confidence | |
outRelevanceHeatmap | Heatmap | Returns the heatmap indicating how strong specific parts of image influenced the classification result | |
outAlignedRoi | Rectangle2D | 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.
Hints
- It is recommended that the deep learning model is deployed with DL_ClassifyObject_Deploy first and connected through the inModelId input.
- If one decides not to use DL_ClassifyObject_Deploy, then the model will be loaded in the first iteration. It will take up to several seconds.
Remarks
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
This filter can throw an exception to report error. Read how to deal with errors in Error Handling.
List of possible exceptions:
Error type | Description |
---|---|
DomainError | Not supported inImage pixel format in AvsFilter_DL_ClassifyObject. Supported formats: 1xUInt8, 3xUInt8. |
Complexity Level
This filter is available on Basic Complexity Level.
Disabled in Lite Edition
This filter is disabled in Lite Edition. It is available only in full, Aurora Vision Studio Professional version.
See Also
Models for Deep Learning may be created using Aurora Vision Deep Learning Editor.
For more information, see Machine Vision Guide.