Class WrapperGradientCornerIntensity<I extends ImageGray<I>,D extends ImageGray<D>>

java.lang.Object
boofcv.abst.feature.detect.intensity.BaseGeneralFeatureIntensity<I,D>
boofcv.abst.feature.detect.intensity.WrapperGradientCornerIntensity<I,D>
All Implemented Interfaces:
GeneralFeatureIntensity<I,D>

public class WrapperGradientCornerIntensity<I extends ImageGray<I>,D extends ImageGray<D>> extends BaseGeneralFeatureIntensity<I,D>
Wrapper around children of GradientCornerIntensity.
  • Constructor Details

  • Method Details

    • process

      public void process(I image, @Nullable D derivX, @Nullable D derivY, @Nullable D derivXX, @Nullable D derivYY, @Nullable D derivXY)
      Description copied from interface: GeneralFeatureIntensity
      Computes the corner's intensity. Before computing the various image derivatives call GeneralFeatureIntensity.getRequiresGradient() and GeneralFeatureIntensity.getRequiresHessian() to see if they are needed.
      Parameters:
      image - Original input image
      derivX - First derivative x-axis
      derivY - First derivative x-axis
      derivXX - Second derivative x-axis x-axis
      derivYY - Second derivative x-axis y-axis
      derivXY - Second derivative x-axis y-axis
    • getCandidatesMin

      @Nullable public @Nullable ListIntPoint2D getCandidatesMin()
      Description copied from interface: GeneralFeatureIntensity
      (Optional) Returns a list of candidate for local minimums.
      Returns:
      List of potential features. If not supported then null is returned.
    • getCandidatesMax

      @Nullable public @Nullable ListIntPoint2D getCandidatesMax()
      Description copied from interface: GeneralFeatureIntensity
      (Optional) Returns a list of candidate for local maximums.
      Returns:
      List of potential features. If not supported then null is returned.
    • getRequiresGradient

      public boolean getRequiresGradient()
      Description copied from interface: GeneralFeatureIntensity
      If the image gradient is required for calculations.
      Returns:
      true if the image gradient is required.
    • getRequiresHessian

      public boolean getRequiresHessian()
      Description copied from interface: GeneralFeatureIntensity
      Is the image's second derivative required?
      Returns:
      is the hessian required.
    • hasCandidates

      public boolean hasCandidates()
      Description copied from interface: GeneralFeatureIntensity
      If true there is a list of candidate corners for minimums and/or maximums.
    • getIgnoreBorder

      public int getIgnoreBorder()
      Description copied from interface: GeneralFeatureIntensity
      Pixels within this distance from the image border are not processed.
      Returns:
      Size of unprocessed border around the image.
    • localMaximums

      public boolean localMaximums()
      Description copied from interface: GeneralFeatureIntensity
      Indicates if local maximums are features or not.
      Returns:
      true for local maximum features.
    • getImageType

      public Class<I> getImageType()
      Description copied from interface: GeneralFeatureIntensity
      Input image type. Null if the input image isn't required.
      Specified by:
      getImageType in interface GeneralFeatureIntensity<I extends ImageGray<I>,D extends ImageGray<D>>
      Overrides:
      getImageType in class BaseGeneralFeatureIntensity<I extends ImageGray<I>,D extends ImageGray<D>>
    • getDerivType

      public Class<D> getDerivType()
      Description copied from interface: GeneralFeatureIntensity
      Derivative image type. Null if the derivative image isn't required.
      Specified by:
      getDerivType in interface GeneralFeatureIntensity<I extends ImageGray<I>,D extends ImageGray<D>>
      Overrides:
      getDerivType in class BaseGeneralFeatureIntensity<I extends ImageGray<I>,D extends ImageGray<D>>
    • localMinimums

      public boolean localMinimums()
      Description copied from interface: GeneralFeatureIntensity
      Indicates if local minimums are features or not.
      Returns:
      true for local minimum features.