Retrieves the latest suppression mask for semantic processing.
This method returns a binary mask indicating areas that should be ignored or suppressed
during semantic processing. Suppression masks are useful for filtering out regions
that are not relevant for semantic understanding, such as areas with poor image quality.
- Returns: A tuple containing the operation status and semantic result if successful
## Example
swift <br />let (status, result) = semanticsSession.getLatestSuppressionMask() <br />if status.isOk(), let semanticResult = result { <br /> print("Suppression mask image size: \\(semanticResult.image?.width ?? 0) x \\(semanticResult.image?.height ?? 0)") <br /> // Apply suppression mask to filter semantic results <br /> if let mask = semanticResult.image { <br /> applySuppressionMask(mask, toSemanticResults: otherResults) <br /> } <br />} <br /> ## Suppression Mask Usage
Suppression masks are binary images where:
- 0: Areas to be suppressed (ignored in semantic processing)
- 1: Areas to be processed normally
Use suppression masks to improve semantic processing quality by excluding
problematic regions from analysis.
Retrieves the latest suppression mask for semantic processing.
This method returns a binary mask indicating areas that should be ignored or suppressed
during semantic processing. Suppression masks are useful for filtering out regions
that are not relevant for semantic understanding, such as areas with poor image quality.
- Returns: A tuple containing the operation status and semantic result if successful
## Example
swift <br />let (status, result) = semanticsSession.getLatestSuppressionMask() <br />if status.isOk(), let semanticResult = result { <br /> print("Suppression mask image size: \\(semanticResult.image?.width ?? 0) x \\(semanticResult.image?.height ?? 0)") <br /> // Apply suppression mask to filter semantic results <br /> if let mask = semanticResult.image { <br /> applySuppressionMask(mask, toSemanticResults: otherResults) <br /> } <br />} <br />## Suppression Mask Usage
Suppression masks are binary images where:
- 0: Areas to be suppressed (ignored in semantic processing)
- 1: Areas to be processed normally
Use suppression masks to improve semantic processing quality by excluding
problematic regions from analysis.