Performs points localization using a trained deep-learning model.
|inRoi||Region*||Area of interest|
|inMinDetectionScore||Real||0.0 - 1.0||Minimal score of found points|
|inMinDistanceRatio||Real*||0.01 - 1.0||Minimal distance between found points defined as portion of Feature Size|
|inOverlap||Bool||Add tiles overlapping to improve results quality|
|outLocations||LocationArray||Locations of found points|
|outClassIds||IntegerArray||Ids of found point classes|
|outClassNames||StringArray||Names of found point classes|
|outScores||RealArray||Scores of found points|
For input inImage only pixel formats are supported: 1⨯uint8, 3⨯uint8.
Read more about pixel formats in Image documentation.
Loading Deep Learning Model may take longer timer. Consider using DeepLearning_LoadModel_PointLocation for pre-loading model before execution starts.
- Model provided on inDeepModel input will be loaded automatically on first usage of this filter.
- 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 Anomaly Detection or Instances Segmentation) it is advised that this filter should be executed before other Deep Learning filters. Such "warm up" execution does not have to use real image or roi as long as provided image and roi have similar size.
This filter can throw an exception to report error. Read how to deal with errors in Error Handling.
List of possible exceptions:
|DomainError||Not supported inImage pixel format in DeepLearning_LocatePoints.|
This filter is available on Basic Complexity Level.
Disabled in Lite Edition
Models for Deep Learning may be created using Adaptive Vision Deep Learning Editor.