Executes a Detect Anomalies 1 model on a single input image.
void avl::DL_DetectAnomalies1 ( const avl::Image& inImage, const avl::DetectAnomalies1ModelId& inModelId, const bool inComputeReconstruction, avl::Heatmap& outHeatmap, bool& outIsValid, float& outScore, bool& outIsConfident, atl::Optional<avl::Image&> outReconstructedImage = atl::NIL )
|inImage||const Image&||Input image|
|inModelId||const DetectAnomalies1ModelId&||Identifier of a Detect Anomalies 1 model|
|inComputeReconstruction||const bool||True||Enables computing a reconstructed image, which may extend execution time|
|outHeatmap||Heatmap&||Returns a heatmap indicating found anomalies|
|outIsValid||bool&||Returns true if no anomalies were found|
|outScore||float&||Returns score of the image|
|outIsConfident||bool&||Returns false if the score is between T1 and T2|
|outReconstructedImage||Optional<Image&>||NIL||Returns the reconstructed image|
For input inImage only pixel formats are supported: 1⨯uint8, 3⨯uint8.
Read more about pixel formats in Image documentation.
The computation of following outputs can be switched off by passing value
atl::NIL to these parameters: outReconstructedImage.
Read more about Optional Outputs.
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. However, if stopping Deep Learning Service is not an option (e.g. program uses other Deep Learning filters related to Instances Segmentation) it is advised that this filter should be executed at least once before performing operations utilizing Deep Learning Service (e.g. executing filters related to Instances Segmentation). Such "warm up" execution does not have to use real image or roi as long as provided image and roi have similar size.
List of possible exceptions:
|DomainError||Not supported inImage pixel format in DL_DetectAnomalies1.|
Models for Deep Learning may be created using Adaptive Vision Deep Learning Editor or using Training Api.