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DeepLearning_DetectAnomalies2


Detects anomalies using trained deep-learning model.

Header:AVL.h

Syntax

C++
C#
 
void avl::DeepLearning_DetectAnomalies2
(
	DeepLearningConnectionState& ioState,
	const avl::Image& inImage,
	const avl::DeepModel_AnomalyDetection2& inDeepModel,
	avl::Image& outHeatmap,
	bool& outIsValid,
	float& outScore,
	bool& outIsConfident
)

Parameters

Name Type Default Description
ioState DeepLearningConnectionState& Object used to maintain state of the function.
inImage const Image& Input image
inDeepModel const DeepModel_AnomalyDetection2& Trained model
outHeatmap Image& Image contains heatmaps for each input image channel
outIsValid bool& Returns true if anomaly was not found
outScore float& Score of classification
outIsConfident bool& Returns "false" if score is between T1 and T2

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.

See Also

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