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DeepLearning_DetectFeatures

Performs feature classification using trained deep-learning model.

Header:	AVL.h

Syntax

void avl::DeepLearning_DetectFeatures
(
	DeepLearningConnectionState& ioState,
	const avl::Image& inImage,
	const atl::Optional<avl::Region>& inRoi,
	const avl::DeepModel_FeatureDetection& inDeepModel,
	const bool& inOverlap,
	atl::Array<avl::Image>& outHeatmaps,
	atl::Optional<avl::Image&> outFeature1 = atl::NIL,
	atl::Optional<avl::Image&> outFeature2 = atl::NIL,
	atl::Optional<avl::Image&> outFeature3 = atl::NIL,
	atl::Optional<avl::Image&> outFeature4 = atl::NIL
)

void DeepLearning_DetectFeatures
(
	ref DeepLearningConnectionState ioState,
	Image inImage,
	Region? inRoi,
	DeepModel_FeatureDetection inDeepModel,
	bool inOverlap,
	out Image[] outHeatmaps,
	out Image? outFeature1,
	out Image? outFeature2,
	out Image? outFeature3,
	out Image? outFeature4
)

Parameters

Name	Type	Default	Description
ioState	DeepLearningConnectionState&		Object used to maintain state of the function.
inImage	const Image&		Input image
inRoi	const Optional<Region>&	NIL	Area of interest
inDeepModel	const DeepModel_FeatureDetection&		Trained model
inOverlap	const bool&	True	Add tiles overlapping to improve results quality
outHeatmaps	Array<Image>&		Returns heatmaps for all classes
outFeature1	Optional<Image&>	NIL	Returns heatmap for first feature class
outFeature2	Optional<Image&>	NIL	Returns heatmap for second feature class or empty image if class is not specified
outFeature3	Optional<Image&>	NIL	Returns heatmap for third feature class or empty image if class is not specified
outFeature4	Optional<Image&>	NIL	Returns heatmap for fourth feature class or empty image if class is not specified

Requirements

For input inImage only pixel formats are supported: 1xuint8, 3xuint8.

Read more about pixel formats in Image documentation.

Hints

Loading Deep Learning Model may take longer timer. Consider using DeepLearning_LoadModel for pre-loading model before execution starts.

Remarks

Model provided on inDeepModel input will be loaded to service automatically on first usage of Deep Learning filters.
Service automatically releases and loads model into its memory. It may result in releasing model previously loaded using DeepLearning_LoadModel filter.
These filters only communicates with service and cannot be use for parallel computation.

Errors

Error type	Description
DomainError	Empty ROI region in DeepLearning_DetectFeatures
DomainError	Size of ROI differs from size of image in DeepLearning_DetectFeatures
DomainError	Invalid DeepModel in DeepLearning_DetectFeatures.
DomainError	Empty image in DeepLearning_DetectFeatures.