Deprecated API

Contents

• Deprecated Interfaces
• Deprecated Classes
• Deprecated Methods
• Deprecated Constructors

Deprecated Interfaces

Interface and Description
net.imglib2.algorithm.gauss3.ConvolverFactory
net.imglib2.algorithm.fill.Filter
net.imglib2.algorithm.fill.Writer

Deprecated Classes

Class and Description
net.imglib2.algorithm.gauss.AbstractLineIterator
net.imglib2.algorithm.convolution.AbstractMultiThreadedConvolution
net.imglib2.algorithm.labeling.AllConnectedComponents
net.imglib2.multithreading.Chunk
net.imglib2.algorithm.gauss3.ConvolverNativeType
net.imglib2.algorithm.gauss3.ConvolverNativeTypeBuffered
net.imglib2.algorithm.gauss3.ConvolverNumericType
net.imglib2.algorithm.gauss3.DoubleConvolverRealType
net.imglib2.algorithm.gauss3.DoubleConvolverRealTypeBuffered
net.imglib2.algorithm.gauss3.FloatConvolverRealType
net.imglib2.algorithm.gauss3.FloatConvolverRealTypeBuffered
net.imglib2.algorithm.gauss.Gauss
net.imglib2.algorithm.gauss.GaussDouble
net.imglib2.algorithm.gauss.GaussFloat
net.imglib2.algorithm.gauss.GaussGeneral
net.imglib2.algorithm.gauss.GaussNativeType
net.imglib2.algorithm.region.localneighborhood.old.LocalNeighborhood
net.imglib2.algorithm.region.localneighborhood.old.LocalNeighborhood2
net.imglib2.algorithm.region.localneighborhood.old.LocalNeighborhoodCursor
net.imglib2.algorithm.region.localneighborhood.old.LocalNeighborhoodCursor2
net.imglib2.algorithm.gauss.SamplingLineIterator
net.imglib2.algorithm.gauss3.SeparableSymmetricConvolution Use SeparableKernelConvolution instead. Convolution with a separable symmetric kernel.
net.imglib2.multithreading.SimpleMultiThreading
net.imglib2.algorithm.fill.TypeWriter
net.imglib2.algorithm.gauss.WritableLineIterator

Deprecated Methods

Method and Description
net.imglib2.algorithm.blocks.BlockAlgoUtils.cellImg(PrimitiveBlocks<S>, UnaryBlockOperator<S, T>, T, long[], int[])
net.imglib2.algorithm.blocks.BlockAlgoUtils.cellLoader(PrimitiveBlocks<S>, UnaryBlockOperator<S, T>)
net.imglib2.algorithm.gauss3.SeparableSymmetricConvolution.convolve(double[][], RandomAccessible<S>, RandomAccessibleInterval<T>, ConvolverFactory<S, I>, ConvolverFactory<I, I>, ConvolverFactory<I, T>, ConvolverFactory<S, T>, ImgFactory<I>, ExecutorService) Use SeparableKernelConvolution.convolution(Kernel1D[]). Or LineConvolution and Convolution.concat(net.imglib2.algorithm.convolution.Convolution<T>...). Convolve source with a separable symmetric kernel and write the result to output. In-place operation (source==target) is supported. Calculations are done in the intermediate type determined by the ConvolverFactories.
net.imglib2.algorithm.gauss3.SeparableSymmetricConvolution.convolve(double[][], RandomAccessible<S>, RandomAccessibleInterval<T>, ConvolverFactory<S, I>, ConvolverFactory<I, I>, ConvolverFactory<I, T>, ConvolverFactory<S, T>, ImgFactory<I>, I, ExecutorService) Use SeparableSymmetricConvolution.convolve( double[][], RandomAccessible, RandomAccessibleInterval, ConvolverFactory, ConvolverFactory, ConvolverFactory, ConvolverFactory, ImgFactory, ExecutorService) instead.
net.imglib2.algorithm.gauss3.SeparableSymmetricConvolution.convolve(double[][], RandomAccessible<S>, RandomAccessibleInterval<T>, ExecutorService) Use SeparableKernelConvolution.convolution(Kernel1D[]) Convolve source with a separable symmetric kernel and write the result to output. In-place operation (source==target) is supported. If the target type T is DoubleType, all calculations are done in double precision. For all other target RealTypes float precision is used. General NumericTypes are computed in their own precision. The source type S and target type T are either both RealTypes or both the same type.
net.imglib2.algorithm.gauss3.SeparableSymmetricConvolution.convolve1d(double[], RandomAccessible<S>, RandomAccessibleInterval<T>, ConvolverFactory<S, T>, ExecutorService) Use SeparableKernelConvolution.convolution1d(net.imglib2.algorithm.convolution.kernel.Kernel1D, int)
net.imglib2.algorithm.linalg.eigen.TensorEigenValues.createAppropriateResultImg(RandomAccessibleInterval<T>, ImgFactory<U>, U) Use TensorEigenValues.createAppropriateResultImg(RandomAccessibleInterval, ImgFactory) instead.
net.imglib2.algorithm.fill.FloodFill.fill(RandomAccessible<T>, RandomAccessible<U>, Localizable, T, U, Shape, Filter<Pair<T, U>, Pair<T, U>>)
net.imglib2.algorithm.fill.FloodFill.fill(RandomAccessible<T>, RandomAccessible<U>, Localizable, T, U, Shape, Filter<Pair<T, U>, Pair<T, U>>, Writer<U>)
net.imglib2.algorithm.fill.FloodFill.fill(RandomAccessible<T>, RandomAccessible<U>, Localizable, U, Shape, Filter<Pair<T, U>, Pair<T, U>>)
net.imglib2.algorithm.localextrema.LocalExtrema.findLocalExtrema(RandomAccessibleInterval<T>, LocalExtrema.LocalNeighborhoodCheck<P, T>, ExecutorService)
net.imglib2.algorithm.gauss3.Gauss3.gauss(double[], RandomAccessible<S>, RandomAccessibleInterval<T>, ExecutorService) Deprecated. Please use gauss(sigma, source, target) instead. The ExecutorService used to calculate the Gaussion convolution may by set with the Parallelization context, as show in this example: Parallelization.runWithExecutor( executorService, () -> gauss( sigma, source, target ) ); Apply Gaussian convolution to source and write the result to output. In-place operation (source==target) is supported. If the target type T is DoubleType, all calculations are done in double precision. For all other target RealTypes float precision is used. General NumericTypes are computed in their own precision. The source type S and target type T are either both RealTypes or both the same type.
net.imglib2.algorithm.gauss3.Gauss3.gauss(double[], RandomAccessible<S>, RandomAccessibleInterval<T>, int) Deprecated. Please use gauss(sigma, source, target) instead. The number of threads used to calculate the Gaussion convolution may by set with the Parallelization context, as show in this example: Parallelization.runWithNumThreads( numThreads, () -> gauss( sigma, source, target ) ); Apply Gaussian convolution to source and write the result to output. In-place operation (source==target) is supported. If the target type T is DoubleType, all calculations are done in double precision. For all other target RealTypes float precision is used. General NumericTypes are computed in their own precision. The source type S and target type T are either both RealTypes or both the same type.
net.imglib2.algorithm.convolution.MultiDimensionConvolution.setExecutor(ExecutorService)
net.imglib2.algorithm.convolution.LineConvolution.setExecutor(ExecutorService)
net.imglib2.algorithm.convolution.AbstractMultiThreadedConvolution.setExecutor(ExecutorService)
net.imglib2.algorithm.convolution.Convolution.setExecutor(ExecutorService)

Deprecated Constructors

Constructor and Description
net.imglib2.algorithm.integral.ScaleAreaAveraging2d(Img<T>, R, Converter<T, R>, long[])
net.imglib2.algorithm.integral.ScaleAreaAveraging2d(Img<T>, R, long[])