DL_DetectFeatures


Header: AVLDL.h
Namespace: avl
Module: DeepLearning

Executes a Detect Features model on a single input image.

Syntax

void avl::DL_DetectFeatures
(
	const avl::Image& inImage,
	atl::Optional<const avl::Region&> inRoi,
	const avl::DetectFeaturesModelId& inModelId,
	const bool inOverlap,
	atl::Array<avl::Heatmap>& outHeatmaps,
	atl::Optional<avl::Heatmap&> outFeature1 = atl::NIL,
	atl::Optional<avl::Heatmap&> outFeature2 = atl::NIL,
	atl::Optional<avl::Heatmap&> outFeature3 = atl::NIL,
	atl::Optional<avl::Heatmap&> outFeature4 = atl::NIL
)

Parameters

Name Type Default Description
Input value
inImage const Image& Input image
Input value
inRoi Optional<const Region&> NIL Limits an area where features may be detected
Input value
inModelId const DetectFeaturesModelId& Identifier of a Detect Features model
Input value
inOverlap const bool True Cuts the image into more overlapping tiles, which improves results quality at the expense of extended execution time
Output value
outHeatmaps Array<Heatmap>& Returns heatmaps for all classes defined in the model
Output value
outFeature1 Optional<Heatmap&> NIL Returns the heatmap for the first feature class
Output value
outFeature2 Optional<Heatmap&> NIL Returns the heatmap for the second feature class or an empty image if the model does not define more than one class
Output value
outFeature3 Optional<Heatmap&> NIL Returns the heatmap for the third feature class or an empty image if the model does not define more than two classes
Output value
outFeature4 Optional<Heatmap&> NIL Returns the heatmap for the fourth feature class or an empty image if the model does not define more than three classes

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: outFeature1, outFeature2, outFeature3, outFeature4.

Read more about Optional Outputs.

Hints

  • It is recommended that the deep learning model is deployed with DL_DetectFeatures_Deploy first and connected through the inModelId input.
  • If one decides not to use DL_DetectFeatures_Deploy, then the model will be loaded in the first iteration. It will take up to several seconds.
  • Use inOverlap=False to increase execution speed at a cost of lower precision of results.

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

List of possible exceptions:

Error type Description
DomainError Not supported inImage pixel format in DL_DetectFeatures. Supported formats: 1xUInt8, 3xUInt8.

See Also

  • Models for Deep Learning may be created using Aurora Vision Deep Learning Editor or using Training Api.