Executes a Locate Points model on a single input image.
|inRoi||Region*||Limits the area where points may be located|
|inModelId||LocatePointsModelId||Identifier of a Locate Points model|
|inMinDetectionScore||Real||0.0 - 1.0||Sets a minimum required score for a point to be returned|
|inMinDistanceRatio||Real*||0.01 - 1.0||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||Bool||Cuts the image into more overlapping tiles, which improves results quality at the expense of extended execution time|
|outLocations||LocationArray||Returns location of the found points|
|outClassIds||IntegerArray||Returns ids of the found point classes|
|outClassNames||StringArray||Returns names of the found point classes|
|outScores||RealArray||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.
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 FisFilter_DL_LocatePoints. Supported formats: 1xUInt8, 3xUInt8.|
This filter is available on Basic Complexity Level.
Disabled in Lite Edition
This filter is disabled in Lite Edition. It is available only in full, FabImage Studio Professional version.
Models for Deep Learning may be created using FabImage Deep Learning Editor.
For more information, see Machine Vision Guide.