Executes a Locate Points model on a single input image.
void avl::DL_LocatePoints ( const avl::Image& inImage, atl::Optional<const avl::Region&> inRoi, const avl::LocatePointsModelId& inModelId, const float inMinDetectionScore, const atl::Optional<float>& inMinDistanceRatio, const bool inOverlap, atl::Array<avl::Location>& outLocations, atl::Array<int>& outClassIds, atl::Array<atl::String>& outClassNames, atl::Array<float>& outScores )
|inImage||const Image&||Input image|
|inRoi||Optional<const Region&>||NIL||Limits the area where points may be located|
|inModelId||const LocatePointsModelId&||Identifier of a Locate Points model|
|inMinDetectionScore||const float||0.0 - 1.0||0.5f||Sets a minimum required score for a point to be returned|
|inMinDistanceRatio||const Optional<float>&||0.01 - 1.0||NIL||Sets a minimum distance between the returned points defined as a portion of the Feature Size. If not set, a value determined during the training is used|
|inOverlap||const bool||True||Cuts the image into more overlapping tiles, which improves results quality at the expense of extended execution time|
|outLocations||Array<Location>&||Returns location of the found points|
|outClassIds||Array<int>&||Returns ids of the found point classes|
|outClassNames||Array<String>&||Returns names of the found point classes|
|outScores||Array<float>&||Returns scores of the found points|
For input inImage only pixel formats are supported: 1⨯uint8, 3⨯uint8.
Read more about pixel formats in Image documentation.
- It is recommended that the deep learning model is deployed with DL_LocatePoints_Deploy first and connected through the inModelId input.
- If one decides not to use DL_LocatePoints_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.
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_LocatePoints.|
Models for Deep Learning may be created using Adaptive Vision Deep Learning Editor or using Training Api.