net.algart.matrices.morphology.AbstractRankMorphology

All Implemented Interfaces:: Cloneable, ArrayProcessor, ArrayProcessorWithContextSwitching, Morphology, RankMorphology

Direct Known Subclasses:: BasicRankMorphology

public abstract class AbstractRankMorphology extends AbstractMorphology implements RankMorphology

A skeletal implementation of the RankMorphology interface to minimize the effort required to implement this interface.

This class extends AbstractMorphology and inherits from it all implementations of Morphology methods, excepting dilationOrErosion, which is declared here as abstract.

Among methods of RankMorphology interface, this class implements the 2nd method from each triplet: 1) asOperation, 2) operation, creating new matrix and 3) operation, storing result in the 1st dest argument. Every 2nd method from such a triplet is implemented via the 3rd one: it creates the necessary resulting updatable matrix and pass it as dest argument to the 3rd method. See more details in comments to the methods of this class.

In addition, this class fully implements all 3 methods asMean, mean (creating new matrix) and mean (storing result in dest argument) via the corresponding asFunctionOfSum and functionOfSum methods. Also this class fully implements the methods which get the percentile indexes in a form of double parameters: these methods are implemented via the analogous methods getting the percentile indexes in Matrix parameters.

Besides implementing methods of RankMorphology, this class declares constantPercentileMatrix(Matrix, double) method, convenient for implementing methods which get the percentile indexes in a form of double parameters.

Author:: Daniel Alievsky

Nested Class Summary

Nested classes/interfaces inherited from interface net.algart.matrices.morphology.Morphology
Morphology.SubtractionMode
Field Summary

Fields inherited from interface net.algart.matrices.morphology.RankMorphology
FILL_MAX_VALUE, FILL_MIN_VALUE, FILL_NEAREST_VALUE
Constructor Summary

Constructors

Modifier

Constructor

Description

protected

AbstractRankMorphology(ArrayContext context)

Creates an instance of this class with the given context.
Method Summary

Modifier and Type

Method

Description

protected abstract Matrix<? extends PArray>

asDilationOrErosion(Matrix<? extends PArray> src, Pattern pattern, boolean isDilation)

This method must be equivalent to AbstractMorphology.asDilation(Matrix src, Pattern pattern) if isDilation argument is true or to AbstractMorphology.asErosion(Matrix src, Pattern pattern) if isDilation argument is false.

Matrix<? extends PArray>

asFunctionOfPercentilePair(Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)

This implementation just calls asFunctionOfPercentilePair(src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).

abstract Matrix<? extends PArray>

asFunctionOfPercentilePair(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)

Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the result of some given function f(v, v₁,v₂) of the source matrix v and two percentiles v₁,v₂ of the source matrix by the specified pattern.

abstract Matrix<? extends PArray>

asFunctionOfSum(Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)

Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the result of some given function f(S) of the aperture sum S of the source matrix by the specified pattern.

Matrix<? extends PArray>

asMean(Matrix<? extends PArray> src, Pattern pattern)

This method is fully implemented in this class via the equivalent call of asFunctionOfSum(Matrix, Pattern, Func) method, as described in comments to this method in RankMorphology interface.

Matrix<? extends PArray>

asMeanBetweenPercentiles(Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)

This implementation just calls asMeanBetweenPercentiles(src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).

abstract Matrix<? extends PArray>

asMeanBetweenPercentiles(Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)

Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the mean between 2 percentiles of the source matrix by the specified pattern.

abstract Matrix<? extends PArray>

asMeanBetweenValues(Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)

Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the mean between 2 values of the source matrix by the specified pattern.

Matrix<? extends PArray>

asPercentile(Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)

This implementation just calls asPercentile(src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).

abstract Matrix<? extends PArray>

asPercentile(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)

Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the percentile of the source matrix by the specified pattern.

abstract <T extends PArray> Matrix<T>

asRank(Class<? extends T> requiredType, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)

Returns an immutable view of the passed baseMatrix matrix, such that any reading data from it calculates and returns the ranks of some given values v regarding the source matrix baseMatrix with the specified pattern.

static Matrix<? extends PArray>

constantPercentileMatrix(Matrix<? extends PArray> src, double percentileIndex)

Returns the matrix with the same dimensions as the given src matrix, backed by a constant array with the given value.

RankMorphology

context(ArrayContext newContext)

This method is implemented here via cloning this object (by standard clone() call) and replacing the value of the field, where a reference to the current context is stored, with newContext value.

protected abstract Matrix<? extends UpdatablePArray>

dilationOrErosion(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern, boolean isDilation, boolean disableMemoryAllocation)

This method must be equivalent to AbstractMorphology.dilation(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation) if isDilation argument is true or to AbstractMorphology.erosion(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation) if isDilation argument is false.

Matrix<? extends UpdatablePArray>

functionOfPercentilePair(Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)

This implementation just calls functionOfPercentilePair(src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).

Matrix<? extends UpdatablePArray>

functionOfPercentilePair(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the function of the source matrix and 2 percentiles by the call functionOfPercentilePair(dest,src,percentileIndexes1,percentileIndexes2,pattern,processingFunc) and returns dest as the result.

void

functionOfPercentilePair(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)

This implementation just calls functionOfPercentilePair(dest,src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).

abstract void

functionOfPercentilePair(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)

Equivalent to RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func) method, but the result matrix will be placed in the dest argument.

Matrix<? extends UpdatablePArray>

functionOfSum(Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the function of aperture sum by the call functionOfSum(dest,src,pattern,processingFunc) and returns dest as the result.

abstract void

functionOfSum(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)

Equivalent to RankMorphology.functionOfSum(Matrix, Pattern, Func) method, but the result matrix will be placed in the dest argument.

Matrix<? extends UpdatablePArray>

mean(Matrix<? extends PArray> src, Pattern pattern)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean by the call mean(dest,src,pattern) and returns dest as the result.

void

mean(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern)

This method is fully implemented in this class via the equivalent call of functionOfSum(Matrix, Matrix, Pattern, Func) method, as described in comments to this method in RankMorphology interface.

Matrix<? extends UpdatablePArray>

meanBetweenPercentiles(Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)

This implementation just calls meanBetweenPercentiles(src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).

Matrix<? extends UpdatablePArray>

meanBetweenPercentiles(Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean between 2 percentiles by the call meanBetweenPercentiles(dest,src,fromPercentileIndexes,toPercentileIndexes,pattern,filler) and returns dest as the result.

void

meanBetweenPercentiles(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)

This implementation just calls meanBetweenPercentiles(dest,src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).

abstract void

meanBetweenPercentiles(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)

Equivalent to RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double) method, but the result matrix will be placed in the dest argument.

Matrix<? extends UpdatablePArray>

meanBetweenValues(Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean between 2 values by the call meanBetweenValues(dest,src,minValues,maxValues,pattern,filler) and returns dest as the result.

abstract void

meanBetweenValues(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)

Equivalent to RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Pattern, double) method, but the result matrix will be placed in the dest argument.

Matrix<? extends UpdatablePArray>

percentile(Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)

This implementation just calls percentile(src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).

Matrix<? extends UpdatablePArray>

percentile(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the percentile by the call percentile(dest,src,percentileIndexes,pattern) and returns dest as the result.

void

percentile(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)

This implementation just calls percentile(dest,src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).

abstract void

percentile(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)

Equivalent to RankMorphology.percentile(Matrix, Matrix, Pattern) method, but the result matrix will be placed in the dest argument.

<T extends PArray> Matrix<? extends T>

rank(Class<? extends T> requiredType, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)

This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,Arrays.elementType(requiredType),baseMatrix.dimensions()), calculates the rank by the call rank(dest,baseMatrix,rankedMatrix,pattern) and returns dest as the result.

abstract void

rank(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)

Equivalent to RankMorphology.rank(Class, Matrix, Matrix, Pattern) method, but the result matrix will be placed in the dest argument and the required type will be chosen automatically as dest.type(PArray.class).

Methods inherited from class net.algart.matrices.morphology.AbstractMorphology
asDilation, asErosion, beucherGradient, closing, dilation, dilation, dilation, dilation, dilationErosion, dimensionsAllowed, erosion, erosion, erosion, erosion, erosionDilation, isPseudoCyclic, maskedDilationErosion, maskedErosionDilation, opening, weakDilation, weakErosion

Methods inherited from class net.algart.arrays.AbstractArrayProcessorWithContextSwitching
context, contextPart, memoryModel

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface net.algart.arrays.ArrayProcessor
context

Methods inherited from interface net.algart.matrices.morphology.Morphology
asDilation, asErosion, beucherGradient, closing, dilation, dilation, dilation, dilation, dilationErosion, erosion, erosion, erosion, erosion, erosionDilation, isPseudoCyclic, maskedDilationErosion, maskedErosionDilation, opening, weakDilation, weakErosion

Constructor Details
- AbstractRankMorphology
  
  protected AbstractRankMorphology(ArrayContext context)
  
  Creates an instance of this class with the given context.
  
  Parameters:
  
  context - the context used by this instance for all operations.
Method Details
- context
  
  public RankMorphology context(ArrayContext newContext)
  
  Description copied from class: AbstractArrayProcessorWithContextSwitching
  
  This method is implemented here via cloning this object (by standard clone() call) and replacing the value of the field, where a reference to the current context is stored, with newContext value. This technique is suitable for most implementation. However, if you need, you can override this method; maybe, it is enough to override clone() instead.
  
  Specified by:
  
  context in interface ArrayProcessorWithContextSwitching
  
  Specified by:
  
  context in interface Morphology
  
  Specified by:
  
  context in interface RankMorphology
  
  Overrides:
  
  context in class AbstractMorphology
  
  Parameters:
  
  newContext - another context, used by the returned instance; may be null.
  
  Returns:
  
  new instance with another context.
- asDilationOrErosion
  
  protected abstract Matrix<? extends PArray> asDilationOrErosion(Matrix<? extends PArray> src, Pattern pattern, boolean isDilation)
  
  Description copied from class: AbstractMorphology
  
  This method must be equivalent to AbstractMorphology.asDilation(Matrix src, Pattern pattern) if isDilation argument is true or to AbstractMorphology.asErosion(Matrix src, Pattern pattern) if isDilation argument is false.
  The implementations of those methods, provided by this class, just call this method with corresponding isDilation argument.
  
  Specified by:
  
  asDilationOrErosion in class AbstractMorphology
  
  Parameters:
  
  src - the source matrix.
  
  pattern - the pattern.
  
  isDilation - what should return this method: dilation or erosion.
  
  Returns:
  
  the "lazy" matrix containing the dilation or erosion of the source matrix.
- dilationOrErosion
  
  protected abstract Matrix<? extends UpdatablePArray> dilationOrErosion(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern, boolean isDilation, boolean disableMemoryAllocation)
  
  Description copied from class: AbstractMorphology
  This method must be equivalent to AbstractMorphology.dilation(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation) if isDilation argument is true or to AbstractMorphology.erosion(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation) if isDilation argument is false. There is the only little difference: if dest argument is null, this methods does not throw NullPointerException, but allocates new matrix with the same dimensions and element type as src and use it for storing the result. The result (newly created matrix or non-null dest argument) is returned as the result of this method.
  The implementations of AbstractMorphology.dilation(Matrix dest, Pattern pattern), AbstractMorphology.erosion(Matrix dest, Pattern pattern), AbstractMorphology.dilation(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation), AbstractMorphology.erosion(Matrix dest, Matrix src, Pattern pattern, boolean disableMemoryAllocation) methods, provided by this class, just call this method with corresponding isDilation argument and with dest==null in a case of first two methods.
  The implementation of this method, provided by AbstractMorphology class, just copies the result of AbstractMorphology.asDilationOrErosion(Matrix, Pattern, boolean) asDilationOrErosion} method to dest matrix:
  Matrices.copy(context, castDest, AbstractMorphology.asDilationOrErosion(Matrix, Pattern, boolean) asDilationOrErosion}(src, pattern, isDilation));
  
  where castDest is dest if dest.elementType()==src.elementType(), the newly created matrix if dest==null or the dest matrix, cast to the necessary element type, if the source and destination element types are different:
  Matrices.asUpdatableFuncMatrix(true, Func.UPDATABLE_IDENTITY, src.updatableType(UpdatablePArray.class), dest)
  
  The implementations of this method in the inheritors usually provide better algorithms, especially if disableMemoryAllocation argument is false.
  Overrides:
  
  dilationOrErosion in class AbstractMorphology
  
  Parameters:
  
  dest - the target matrix (or null for creating a new matrix).
  
  src - the source matrix.
  
  pattern - the pattern.
  
  isDilation - what should perform this method: dilation or erosion.
  
  disableMemoryAllocation - if false, this method may allocate additional temporary matrices for optimizing the algorithm speed; if true, no any work memory will be allocated.
  
  Returns:
  
  the reference to dest argument if it is not null, newly allocated resulting matrix in other case.
- asPercentile
  
  public abstract Matrix<? extends PArray> asPercentile(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)
  
  Description copied from interface: RankMorphology
  Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the percentile of the source matrix by the specified pattern. See the comments to this class, section 4 about the "percentile" term. The real index r of the percentile for every element of the result is equal to the corresponding element of percentileIndexes matrix. The element type of the created matrix is the same as the element type of the source one.
  If the element type of the source matrix (and, thus, of the result) is fixed-point — boolean, char, byte, short, int or long — then we need to round the real percentile v(r)/σ, defined in the comments to this class. In this case, this method, instead of the real value v(r)/σ, returns an integer w/σ, where w is:
  
  either the integer result of Histogram.iValue (b, (long)Math.floor(r)), if this object works in the simple histogram model (where b is the histogram, corresponding to every aperture position),
  
  or the integer result of Histogram.iPreciseValue (b, r), if it works in the precise histogram model.
  
  It is necessary to remind: in a case of fixed-point elements there is a guarantee that 1/σ is a positive integer number, and w/σ=w*2^β−μ, β = src.array().bitsPerElement(), μ is the binary logarithm of the histogram length M — see the comments to this class, section 3. So, if μ is chosen less than the precision of this matrix β (8 for byte, 16 for short, etc.), then β−μ lowest bits in the result will be always zero. For BasicRankMorphology object, μ is chosen while instantiating this object as min(bitLevels[bitLevels.length-1], β).
  Specified by:
  
  asPercentile in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndexes - the matrix containing r argument: the indexes of the percentile for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the "lazy" matrix containing the percentile of the source matrix.
  
  See Also:
  
  RankMorphology.asPercentile(Matrix, double, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, Matrix, Pattern)
- asPercentile
  
  public Matrix<? extends PArray> asPercentile(Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)
  
  This implementation just calls asPercentile(src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).
  Specified by:
  
  asPercentile in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndex - r argument of the percentile.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the "lazy" matrix containing the percentile of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asPercentile(Matrix, Matrix, Pattern)
  
  RankMorphology.percentile(Matrix, double, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, double, Pattern)
- percentile
  
  public Matrix<? extends UpdatablePArray> percentile(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the percentile by the call percentile(dest,src,percentileIndexes,pattern) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  percentile in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndexes - the matrix containing r argument: the indexes of the percentile for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the percentile of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.percentile(Matrix, double, Pattern)
  
  RankMorphology.asPercentile(Matrix, Matrix, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, Matrix, Pattern)
- percentile
  
  public Matrix<? extends UpdatablePArray> percentile(Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)
  
  This implementation just calls percentile(src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).
  Specified by:
  
  percentile in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndex - r argument of the percentile.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the percentile of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.percentile(Matrix, Matrix, Pattern)
  
  RankMorphology.asPercentile(Matrix, double, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, double, Pattern)
- percentile
  
  public abstract void percentile(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes, Pattern pattern)
  
  Description copied from interface: RankMorphology
  Equivalent to RankMorphology.percentile(Matrix, Matrix, Pattern) method, but the result matrix will be placed in the dest argument.
  If the element type of the passed dest matrix is the same as the element type of the source one, the result, saved in dest, will be identically equal to the result of RankMorphology.percentile(Matrix, Matrix, Pattern) method with the same src, percentileIndexes and pattern arguments. In other case, the result, saved in dest, will be equal to
  Matrices.asFuncMatrix(true, Func.IDENTITY, dest.array().type(), percentile(src, percentileIndexes, pattern))
  
  So, even if the precision of dest matrix is better than the precision of src — for example, if src.elementType() is byte, but dest.elementType() is double — this method does not try to calculate more precise percentile and rounds results like RankMorphology.asPercentile(Matrix, Matrix, Pattern) and RankMorphology.percentile(Matrix, Matrix, Pattern) methods.
  Specified by:
  
  percentile in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  percentileIndexes - the matrix containing r argument: the indexes of the percentile for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  See Also:
  
  RankMorphology.percentile(Matrix, Matrix, double, Pattern)
  
  RankMorphology.asPercentile(Matrix, Matrix, Pattern)
  
  RankMorphology.percentile(Matrix, Matrix, Pattern)
- percentile
  
  public void percentile(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double percentileIndex, Pattern pattern)
  
  This implementation just calls percentile(dest,src,m,pattern), where m=constantPercentileMatrix(src,percentileIndex).
  Specified by:
  
  percentile in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  percentileIndex - r argument of the percentile.
  
  pattern - the pattern: the shape of the aperture.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.percentile(Matrix, Matrix, Matrix, Pattern)
  
  RankMorphology.asPercentile(Matrix, double, Pattern)
  
  RankMorphology.percentile(Matrix, double, Pattern)
- asRank
  
  public abstract <T extends PArray> Matrix<T> asRank(Class<? extends T> requiredType, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)
  
  Description copied from interface: RankMorphology
  Returns an immutable view of the passed baseMatrix matrix, such that any reading data from it calculates and returns the ranks of some given values v regarding the source matrix baseMatrix with the specified pattern. See the comments to this class, section 4 about the "rank" term. The real value v for every element of the result is equal to the corresponding element of rankedMatrix matrix.
  The matrix, returned by this method, is immutable, and the class of its built-in array implements one of the basic interfaces BitArray, CharArray, ByteArray, ShortArray, IntArray, LongArray, FloatArray or DoubleArray. The class of desired interface (one of 8 possible classes) must be passed as requiredType argument. So, it defines the element type of the returned matrix. The rules of casting the floating-point ranks to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  The element types of baseMatrix and rankedMatrix are usually equal. If they are different (baseMatrix.elementType()!=rankedMatrix.elementType()), this method replaces rankedMatrix with
  Matrices.asFuncMatrix(true, Func.IDENTITY, baseMatrix.array().type(), rankedMatrix)
  
  before all other calculations. In other words, this method always casts the type of the ranked elements to the type of baseMatrix elements. As a result, we can be sure, that if the source baseMatrix matrix is fixed-point (boolean, char, byte, short, int or long elements), then the rank r(v*σ), according to the definition of the "rank" term, will be an integer number. In this case, you can specify requiredType=IntArray.class and get the precise rank without precision loss. Moreover, if you know that the number of points in the pattern (pattern.pointCount()) is less than 2¹⁶=65536 or 2⁸=256, it is enough to specify correspondingly requiredType=ShortArray.class or ByteArray.class.
  Specified by:
  
  asRank in interface RankMorphology
  
  Parameters:
  
  requiredType - the desired type of the built-in array in the returned matrix.
  
  baseMatrix - the source matrix.
  
  rankedMatrix - the matrix containing v argument: the values, the rank of which should be calculated.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the "lazy" matrix containing the rank of the given values.
  
  See Also:
  
  RankMorphology.rank(Class, Matrix, Matrix, Pattern)
  
  RankMorphology.rank(Matrix, Matrix, Matrix, Pattern)
- rank
  
  public <T extends PArray> Matrix<? extends T> rank(Class<? extends T> requiredType, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,Arrays.elementType(requiredType),baseMatrix.dimensions()), calculates the rank by the call rank(dest,baseMatrix,rankedMatrix,pattern) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  rank in interface RankMorphology
  
  Parameters:
  
  requiredType - the desired type of the built-in array in the returned matrix.
  
  baseMatrix - the source matrix.
  
  rankedMatrix - the matrix containing v argument: the values, the rank of which should be calculated.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the rank of the given values.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to baseMatrix.dimCount(), or if requiredType is not one of classes UpdatableBitArray.class / BitArray.class, UpdatableCharArray.class / CharArray.class, UpdatableByteArray.class / ByteArray.class, UpdatableShortArray.class / ShortArray.class, UpdatableIntArray.class / IntArray.class, UpdatableLongArray.class / LongArray.class, UpdatableFloatArray.class / FloatArray.class or UpdatableDoubleArray.class / DoubleArray.class.
  
  See Also:
  
  RankMorphology.asRank(Class, Matrix, Matrix, Pattern)
  
  RankMorphology.rank(Matrix, Matrix, Matrix, Pattern)
- rank
  
  public abstract void rank(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> baseMatrix, Matrix<? extends PArray> rankedMatrix, Pattern pattern)
  
  Description copied from interface: RankMorphology
  Equivalent to RankMorphology.rank(Class, Matrix, Matrix, Pattern) method, but the result matrix will be placed in the dest argument and the required type will be chosen automatically as dest.type(PArray.class). More precisely, the result, saved in dest, will be equal to
  rank(dest.array().type(), baseMatrix, rankedMatrix, pattern)
  
  The element types of baseMatrix and rankedMatrix are usually equal. If they are different (baseMatrix.elementType()!=rankedMatrix.elementType()), this method replaces rankedMatrix with
  Matrices.asFuncMatrix(true, Func.IDENTITY, baseMatrix.array().type(), rankedMatrix)
  
  before all other calculations. In other words, this method always casts the type of the ranked elements to the type of baseMatrix elements. As a result, we can be sure, that if the source baseMatrix matrix is fixed-point (boolean, char, byte, short, int or long elements), then the rank r(v*σ), according to the definition of the "rank" term, will be an integer number. In this case, you can specify requiredType=IntArray.class and get the precise rank without precision loss. Moreover, if you know that the number of points in the pattern (pattern.pointCount()) is less than 2¹⁶=65536 or 2⁸=256, it is enough to specify correspondingly requiredType=ShortArray.class or ByteArray.class. The less result precision allows you to save memory.
  Specified by:
  
  rank in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  baseMatrix - the source matrix.
  
  rankedMatrix - the matrix containing v argument: the values, the rank of which should be calculated.
  
  pattern - the pattern: the shape of the aperture.
  
  See Also:
  
  RankMorphology.asRank(Class, Matrix, Matrix, Pattern)
  
  RankMorphology.rank(Class, Matrix, Matrix, Pattern)
- asMeanBetweenPercentiles
  
  public abstract Matrix<? extends PArray> asMeanBetweenPercentiles(Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)
  
  Description copied from interface: RankMorphology
  
  Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the mean between 2 percentiles of the source matrix by the specified pattern. See the comments to this class, section 4 about the "mean between 2 percentiles" term. The real indexes r₁ and r₂ of the percentiles for every element of the result are equal to the corresponding elements of fromPercentileIndexes and toPercentileIndexes matrices. The reserved value filler is specified by the last argument of this method. The element type of the created matrix is the same as the element type of the source one.
  If the element type of the source matrix (and, thus, of the result) is fixed-point — boolean, char, byte, short, int or long — then we need to round the real mean, defined in the comments to this class. In this case, the found mean value m is usually truncated to its integer part ⌊m⌋=(long)m (remember that the mean value, according to our definition, is always ≥0). More precisely, the rules of casting the floating-point means to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  Specified by:
  
  asMeanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  fromPercentileIndexes - the matrix containing r₁ argument: the indexes of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndexes - the matrix containing r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  Returns:
  
  the "lazy" matrix containing the mean between 2 given percentiles of the source matrix.
  
  See Also:
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, double, double, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Matrix, Pattern, double)
- asMeanBetweenPercentiles
  
  public Matrix<? extends PArray> asMeanBetweenPercentiles(Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)
  
  This implementation just calls asMeanBetweenPercentiles(src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).
  Specified by:
  
  asMeanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  fromPercentileIndex - r₁ argument: the index of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndex - r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  Returns:
  
  the "lazy" matrix containing the mean between 2 given percentiles of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, double, double, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, double, double, Pattern, double)
- meanBetweenPercentiles
  
  public Matrix<? extends UpdatablePArray> meanBetweenPercentiles(Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean between 2 percentiles by the call meanBetweenPercentiles(dest,src,fromPercentileIndexes,toPercentileIndexes,pattern,filler) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  meanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  fromPercentileIndexes - the matrix containing r₁ argument: the indexes of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndexes - the matrix containing r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  Returns:
  
  the mean between 2 given percentiles of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.meanBetweenPercentiles(Matrix, double, double, Pattern, double)
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Matrix, Pattern, double)
- meanBetweenPercentiles
  
  public Matrix<? extends UpdatablePArray> meanBetweenPercentiles(Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)
  
  This implementation just calls meanBetweenPercentiles(src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).
  Specified by:
  
  meanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  fromPercentileIndex - r₁ argument: the index of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndex - r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  Returns:
  
  the mean between 2 given percentiles of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, double, double, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, double, double, Pattern, double)
- meanBetweenPercentiles
  
  public abstract void meanBetweenPercentiles(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> fromPercentileIndexes, Matrix<? extends PArray> toPercentileIndexes, Pattern pattern, double filler)
  
  Description copied from interface: RankMorphology
  
  Equivalent to RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double) method, but the result matrix will be placed in the dest argument.
  If the element type of dest matrix is fixed-point — boolean, char, byte, short, int or long — then we need to round the real mean, defined in the comments to this class. In this case, the found mean value m is usually truncated to its integer part ⌊m⌋=(long)m (remember that the mean value, according to our definition, is always ≥0). More precisely, the rules of casting the floating-point means to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  Specified by:
  
  meanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  fromPercentileIndexes - the matrix containing r₁ argument: the indexes of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndexes - the matrix containing r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  See Also:
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, double, double, Pattern, double)
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Pattern, double)
- meanBetweenPercentiles
  
  public void meanBetweenPercentiles(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double fromPercentileIndex, double toPercentileIndex, Pattern pattern, double filler)
  
  This implementation just calls meanBetweenPercentiles(dest,src,m1,m2,pattern,filler), where m1=constantPercentileMatrix(src,fromPercentileIndex) and m2=constantPercentileMatrix(src,toPercentileIndex).
  Specified by:
  
  meanBetweenPercentiles in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  fromPercentileIndex - r₁ argument: the index of the less percentile of the averaged range for every element of the result.
  
  toPercentileIndex - r₂ argument: the indexes of the greater percentile of the averaged range for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r₁≥r₂.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.meanBetweenPercentiles(Matrix, Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.asMeanBetweenPercentiles(Matrix, double, double, Pattern, double)
  
  RankMorphology.meanBetweenPercentiles(Matrix, double, double, Pattern, double)
- asMeanBetweenValues
  
  public abstract Matrix<? extends PArray> asMeanBetweenValues(Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)
  
  Description copied from interface: RankMorphology
  Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the mean between 2 values of the source matrix by the specified pattern. See the comments to this class, section 4 about the "mean between 2 values" term. The real numbers v₁ and v₂ for every element of the result are equal to the corresponding elements of minValues and maxValues matrices. The reserved value filler and the mode of calculation (for the case r(v₁*σ)≥r(v₂*σ)) are specified by the last filler argument of this method:
  
  if filler argument is RankMorphology.FILL_MIN_VALUE (Double.NEGATIVE_INFINITY), the mode B is used;
  
  if filler argument is RankMorphology.FILL_MAX_VALUE (Double.POSITIVE_INFINITY), the mode C is used;
  
  if filler argument is RankMorphology.FILL_NEAREST_VALUE (Double.NaN), the mode D is used;
  
  if filler argument contains any other value, the mode A is used and this argument specifies the reserved value filler.
  
  The element type of the created matrix is the same as the element type of the source one.
  If the element type of the source matrix (and, thus, of the result) is fixed-point — boolean, char, byte, short, int or long — then we need to round the real mean, defined in the comments to this class. In this case, the found mean value m is usually truncated to its integer part ⌊m⌋=(long)m (remember that the mean value, according to our definition, is always ≥0). More precisely, the rules of casting the floating-point means to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  Specified by:
  
  asMeanBetweenValues in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  minValues - the matrix containing v₁ argument: the low bound of the averaged range of values for every element of the result.
  
  maxValues - the matrix containing v₂ argument: the high bound of the averaged range of values for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r(v₁*σ)≥r(v₂*σ), or one of the special keys RankMorphology.FILL_MIN_VALUE, RankMorphology.FILL_MAX_VALUE, RankMorphology.FILL_NEAREST_VALUE, which mean using of special calculation modes B, C, D.
  
  Returns:
  
  the "lazy" matrix containing the mean between 2 given values of the source matrix.
  
  See Also:
  
  RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Matrix, Pattern, double)
- meanBetweenValues
  
  public Matrix<? extends UpdatablePArray> meanBetweenValues(Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean between 2 values by the call meanBetweenValues(dest,src,minValues,maxValues,pattern,filler) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  meanBetweenValues in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  minValues - the matrix containing v₁ argument: the low bound of the averaged range of values for every element of the result.
  
  maxValues - the matrix containing v₂ argument: the high bound of the averaged range of values for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r(v₁*σ)≥r(v₂*σ), or one of the special keys RankMorphology.FILL_MIN_VALUE, RankMorphology.FILL_MAX_VALUE, RankMorphology.FILL_NEAREST_VALUE, which mean using of special calculation modes B, C, D.
  
  Returns:
  
  the mean between 2 given values of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asMeanBetweenValues(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Matrix, Pattern, double)
- meanBetweenValues
  
  public abstract void meanBetweenValues(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> minValues, Matrix<? extends PArray> maxValues, Pattern pattern, double filler)
  
  Description copied from interface: RankMorphology
  
  Equivalent to RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Pattern, double) method, but the result matrix will be placed in the dest argument.
  If the element type of dest matrix is fixed-point — boolean, char, byte, short, int or long — then we need to round the real mean, defined in the comments to this class. In this case, the found mean value m is usually truncated to its integer part ⌊m⌋=(long)m (remember that the mean value, according to our definition, is always ≥0). More precisely, the rules of casting the floating-point means to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  Specified by:
  
  meanBetweenValues in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  minValues - the matrix containing v₁ argument: the low bound of the averaged range of values for every element of the result.
  
  maxValues - the matrix containing v₂ argument: the high bound of the averaged range of values for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  filler - the reserved value, returned when r(v₁*σ)≥r(v₂*σ), or one of the special keys RankMorphology.FILL_MIN_VALUE, RankMorphology.FILL_MAX_VALUE, RankMorphology.FILL_NEAREST_VALUE, which mean using of special calculation modes B, C, D.
  
  See Also:
  
  RankMorphology.asMeanBetweenValues(Matrix, Matrix, Matrix, Pattern, double)
  
  RankMorphology.meanBetweenValues(Matrix, Matrix, Matrix, Pattern, double)
- asMean
  
  public Matrix<? extends PArray> asMean(Matrix<? extends PArray> src, Pattern pattern)
  
  This method is fully implemented in this class via the equivalent call of asFunctionOfSum(Matrix, Pattern, Func) method, as described in comments to this method in RankMorphology interface.
  Specified by:
  
  asMean in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the "lazy" matrix containing the mean of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.mean(Matrix, Pattern)
  
  RankMorphology.mean(Matrix, Matrix, Pattern)
- mean
  
  public Matrix<? extends UpdatablePArray> mean(Matrix<? extends PArray> src, Pattern pattern)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the mean by the call mean(dest,src,pattern) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  mean in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  Returns:
  
  the mean of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asMean(Matrix, Pattern)
  
  RankMorphology.mean(Matrix, Matrix, Pattern)
- mean
  
  public void mean(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern)
  
  This method is fully implemented in this class via the equivalent call of functionOfSum(Matrix, Matrix, Pattern, Func) method, as described in comments to this method in RankMorphology interface.
  Specified by:
  
  mean in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asMean(Matrix, Pattern)
  
  RankMorphology.mean(Matrix, Pattern)
- asFunctionOfSum
  
  public abstract Matrix<? extends PArray> asFunctionOfSum(Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)
  
  Description copied from interface: RankMorphology
  
  Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the result of some given function f(S) of the aperture sum S of the source matrix by the specified pattern. See the comments to this class, section 4 about the "aperture sum" term. The element type of the created matrix is the same as the element type of the source one.
  The function, applied to each calculated aperture sum S, is specified via processingFunc argument. Namely, for each aperture position this method calculates the aperture sum S of the source matrix and returns processingFunc.get(S) in the corresponding element of the resulting matrix.
  If the element type of the source matrix (and, thus, of the result) is fixed-point — boolean, char, byte, short, int or long — then we need to round the real function result. In this case, the found function result f is usually truncated to its integer part (long)f. More precisely, the rules of casting the floating-point function results to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  This method can be considered as a generalization of RankMorphology.asMean(Matrix, Pattern).
  Specified by:
  
  asFunctionOfSum in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated aperture sum.
  
  Returns:
  
  the "lazy" matrix containing the result of the given function for the aperture sum of the source matrix.
  
  See Also:
  
  RankMorphology.functionOfSum(Matrix, Pattern, Func)
  
  RankMorphology.functionOfSum(Matrix, Matrix, Pattern, Func)
- functionOfSum
  
  public Matrix<? extends UpdatablePArray> functionOfSum(Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the function of aperture sum by the call functionOfSum(dest,src,pattern,processingFunc) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  functionOfSum in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated aperture sum.
  
  Returns:
  
  the result of the given function for the aperture sum of the source matrix.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asFunctionOfSum(Matrix, Pattern, Func)
  
  RankMorphology.functionOfSum(Matrix, Matrix, Pattern, Func)
- functionOfSum
  
  public abstract void functionOfSum(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Pattern pattern, Func processingFunc)
  
  Description copied from interface: RankMorphology
  
  Equivalent to RankMorphology.functionOfSum(Matrix, Pattern, Func) method, but the result matrix will be placed in the dest argument.
  If the element type of dest matrix is fixed-point — boolean, char, byte, short, int or long — then we need to round the real function result. In this case, the found function result f is usually truncated to its integer part (long)f. More precisely, the rules of casting the floating-point function results to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  This method can be considered as a generalization of RankMorphology.mean(Matrix, Matrix, Pattern).
  Specified by:
  
  functionOfSum in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated aperture sum.
  
  See Also:
  
  RankMorphology.asFunctionOfSum(Matrix, Pattern, Func)
  
  RankMorphology.functionOfSum(Matrix, Pattern, Func)
- asFunctionOfPercentilePair
  
  public abstract Matrix<? extends PArray> asFunctionOfPercentilePair(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)
  
  Description copied from interface: RankMorphology
  
  Returns an immutable view of the passed source matrix, such that any reading data from it calculates and returns the result of some given function f(v, v₁,v₂) of the source matrix v and two percentiles v₁,v₂ of the source matrix by the specified pattern. See the comments to this class, section 4 about the "percentile" term. The real indexes r of two percentiles for every element of the result are equal to the corresponding elements of percentileIndexes1 (for v₁) or percentileIndexes2 matrix (for v₂). The element type of the created matrix is the same as the element type of the source one.
  The function, applied to each calculated three (v,v₁,v₂), is specified via processingFunc argument. Namely, for each aperture position x this method takes the value v — the element of the source matrix src at this aperture position x, calculates two percentiles v₁ and v₂ of the source matrix and returns processingFunc.get(v,v₁,v₂) in the corresponding element of the resulting matrix.
  If the element type of the source matrix (and, thus, of the result) is fixed-point — boolean, char, byte, short, int or long — then we need to round the real function result. In this case, the found function result f is usually truncated to its integer part (long)f. More precisely, the rules of casting the floating-point function results to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  You can get the same results by 2 calls of RankMorphology.asPercentile(Matrix, Matrix, Pattern) method for both matrices of percentile indexes and applying the function to them and to the source matrix via Matrices.asFuncMatrix(Func, Class, Matrix, Matrix, Matrix) method. But such a way works slower and is less convenient, than this method. A typical application of this method in image processing area is the contrasting image — in this case, we recommend using ContrastingFunc object as processingFunc argument.
  Specified by:
  
  asFunctionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndexes1 - the 1st matrix containing r argument: the indexes of the 1st percentile v₁ for every element of the result.
  
  percentileIndexes2 - the 2nd matrix containing r argument: the indexes of the 2nd percentile v₂ for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  Returns:
  
  the "lazy" matrix containing the result of the given function.
  
  See Also:
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, double, double, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Matrix, Pattern, Func)
- asFunctionOfPercentilePair
  
  public Matrix<? extends PArray> asFunctionOfPercentilePair(Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)
  
  This implementation just calls asFunctionOfPercentilePair(src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).
  Specified by:
  
  asFunctionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndex1 - the 1st r argument: the index of the 1st percentile v₁.
  
  percentileIndex2 - the 2nd r argument: the index of the 2nd percentile v₂.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  Returns:
  
  the "lazy" matrix containing the result of the given function.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, double, double, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, double, double, Pattern, Func)
- functionOfPercentilePair
  
  public Matrix<? extends UpdatablePArray> functionOfPercentilePair(Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)
  
  This implementation creates a new updatable matrix dest by the call dest=memoryModel.newMatrix(UpdatablePArray.class,src), calculates the function of the source matrix and 2 percentiles by the call functionOfPercentilePair(dest,src,percentileIndexes1,percentileIndexes2,pattern,processingFunc) and returns dest as the result. All necessary checks of correctness of the arguments are performed before allocating new matrix.
  Specified by:
  
  functionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndexes1 - the 1st matrix containing r argument: the indexes of the 1st percentile v₁ for every element of the result.
  
  percentileIndexes2 - the 2nd matrix containing r argument: the indexes of the 2nd percentile v₂ for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  Returns:
  
  the result of the given function.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.functionOfPercentilePair(Matrix, double, double, Pattern, Func)
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Matrix, Pattern, Func)
- functionOfPercentilePair
  
  public Matrix<? extends UpdatablePArray> functionOfPercentilePair(Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)
  
  This implementation just calls functionOfPercentilePair(src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).
  Specified by:
  
  functionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  src - the source matrix.
  
  percentileIndex1 - the 1st r argument: the index of the 1st percentile v₁.
  
  percentileIndex2 - the 2nd r argument: the index of the 2nd percentile v₂.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  Returns:
  
  the result of the given function.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, double, double, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, double, double, Pattern, Func)
- functionOfPercentilePair
  
  public abstract void functionOfPercentilePair(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, Matrix<? extends PArray> percentileIndexes1, Matrix<? extends PArray> percentileIndexes2, Pattern pattern, Func processingFunc)
  
  Description copied from interface: RankMorphology
  
  Equivalent to RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func) method, but the result matrix will be placed in the dest argument.
  If the element type of dest matrix is fixed-point — boolean, char, byte, short, int or long — then we need to round the real function result. In this case, the found function result f is usually truncated to its integer part (long)f. More precisely, the rules of casting the floating-point function results to the desired element type are the same as in Arrays.asFuncArray(boolean, Func, Class, PArray...) method with the argument truncateOverflows=true.
  Specified by:
  
  functionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  percentileIndexes1 - the 1st matrix containing r argument: the indexes of the 1st percentile v₁ for every element of the result.
  
  percentileIndexes2 - the 2nd matrix containing r argument: the indexes of the 2nd percentile v₂ for every element of the result.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  See Also:
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, double, double, Pattern, Func)
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Pattern, Func)
- functionOfPercentilePair
  
  public void functionOfPercentilePair(Matrix<? extends UpdatablePArray> dest, Matrix<? extends PArray> src, double percentileIndex1, double percentileIndex2, Pattern pattern, Func processingFunc)
  
  This implementation just calls functionOfPercentilePair(dest,src,m1,m2,pattern,processingFunc), where m1=constantPercentileMatrix(src,percentileIndex1) and m2=constantPercentileMatrix(src,percentileIndex2).
  Specified by:
  
  functionOfPercentilePair in interface RankMorphology
  
  Parameters:
  
  dest - the target matrix.
  
  src - the source matrix.
  
  percentileIndex1 - the 1st r argument: the index of the 1st percentile v₁.
  
  percentileIndex2 - the 2nd r argument: the index of the 2nd percentile v₂.
  
  pattern - the pattern: the shape of the aperture.
  
  processingFunc - the function, which should be applied to every calculated three (v,v₁,v₂), where v is the element of the source matrix, v₁ and v₂ are the corresponding percentiles.
  
  Throws:
  
  NullPointerException - if one of the arguments is null.
  
  SizeMismatchException - if the passed matrices have different dimensions.
  
  IllegalArgumentException - if the number of the pattern dimensions pattern.dimCount() is not equal to src.dimCount().
  
  See Also:
  
  RankMorphology.functionOfPercentilePair(Matrix, Matrix, Matrix, Matrix, Pattern, Func)
  
  RankMorphology.asFunctionOfPercentilePair(Matrix, double, double, Pattern, Func)
  
  RankMorphology.functionOfPercentilePair(Matrix, double, double, Pattern, Func)
- constantPercentileMatrix
  
  public static Matrix<? extends PArray> constantPercentileMatrix(Matrix<? extends PArray> src, double percentileIndex)
  Returns the matrix with the same dimensions as the given src matrix, backed by a constant array with the given value. More precisely, returns src.matrix(const), where
  
  const = Arrays.nLongCopies(src.size(), (long)percentileIndex), if percentileIndex==(long)percentileIndex,
  
  or const = Arrays.nDoubleCopies(src.size(), percentileIndex) in other case.
  Parameters:
  
  src - some matrix.
  
  percentileIndex - some filler.
  
  Returns:
  
  the constant matrix with the same dimensions, filled by percentileIndex.

Class AbstractRankMorphology

Nested Class Summary

Nested classes/interfaces inherited from interface net.algart.matrices.morphology.Morphology

Field Summary

Fields inherited from interface net.algart.matrices.morphology.RankMorphology

Constructor Summary

Method Summary

Methods inherited from class net.algart.matrices.morphology.AbstractMorphology

Methods inherited from class net.algart.arrays.AbstractArrayProcessorWithContextSwitching

Methods inherited from class java.lang.Object

Methods inherited from interface net.algart.arrays.ArrayProcessor

Methods inherited from interface net.algart.matrices.morphology.Morphology

Constructor Details

AbstractRankMorphology

Method Details

context

asDilationOrErosion

dilationOrErosion

asPercentile

asPercentile

percentile

percentile

percentile

percentile

asRank

rank

rank

asMeanBetweenPercentiles

asMeanBetweenPercentiles

meanBetweenPercentiles

meanBetweenPercentiles

meanBetweenPercentiles

meanBetweenPercentiles

asMeanBetweenValues

meanBetweenValues

meanBetweenValues

asMean

mean

mean

asFunctionOfSum

functionOfSum

functionOfSum

asFunctionOfPercentilePair

asFunctionOfPercentilePair

functionOfPercentilePair

functionOfPercentilePair

functionOfPercentilePair

functionOfPercentilePair

constantPercentileMatrix