ImageDev

- Getting Started
- ImageDev Python
- ImageDev C#
- ImageDev C++
- Licensing Information
- Redistributable Files
- Processing
- Image Manipulation
- - Synthetic Images
  - - RandomImage2d
    - RandomImage3d
    - RandomGaussianImage2d
    - RandomGaussianImage3d
    - RadialFunctionImage2d
    - RadialFunctionImage3d
    - OrientedLinesImage2d
    - OrientedLinesImage3d
    - RandomSphereImage3d
    - BoxGridImage2d
    - BoxGridImage3d
    - WedgeMaskImage3d
    - PointSpreadFunctionImage3d
  - Image Editing
  - - ConvertImage
    - ResetImage
    - CopyImage
    - PasteImage2d
    - PasteImage3d
    - CropImage2d
    - CropImage3d
    - SampleImage2d
    - SampleImage3d
    - InterlaceFrames2d
    - DeinterlaceFrames2d
  - Stack Operations
  - - SequenceConcatenation
    - SequenceToMultispectral
    - SequenceToVolume
    - VolumeToSequence
    - MultispectralToSequence
    - GetSliceFromVolume3d
    - SetSliceToVolume3d
    - GetObliqueSliceFromVolume3d
    - ImageStackProjection3d
    - VolumeProjection3d
- Geometry And Matching
- - Geometric Transforms
  - - Shearing3d
    - TranslateImage2d
    - TranslateImage3d
    - Flip2d
    - Flip3d
    - FlipImage2d
    - FlipImage3d
    - TransposeImage2d
    - TransposeImage3d
    - SwapQuadrants
    - Rotate2d
    - Rotate3d
    - RotationGenerator3d
    - Resize2d
    - Resize3d
    - RescaleByFactor
    - RescaleImageByFactor
    - ResampleAffine2d
    - ResampleAffine3d
    - ResampleElastic2d
    - SurfaceUnfolding3d
  - Pattern Recognition
  - - BinaryCorrelation2d
    - CrossCorrelation2d
    - CorrelationByDifference2d
    - CorrelationBySignChange2d
  - Registration
  - - ImagePrealignment3d
    - AffineRegistration
    - ElasticRegistration2d
- Arithmetic And Logic
- - Arithmetical Operations
  - - AbsoluteValue
    - AbsoluteMaximumWithImage
    - ArithmeticOperationWithImage
    - ArithmeticOperationWithValue
    - BlendWithImage
    - BlendWithValue
    - DirectionalBlending2d
    - CombineByMask
    - ImageFormula
    - EigenDecomposition2d
    - EigenDecomposition3d
  - Logical Operations
  - - MaskImage
    - LogicalOperationWithImage
    - LogicalOperationWithValue
    - LogicalNot
    - InvertImage
    - BitShift
- Image Filtering
- - Grayscale Transforms
  - - RescaleIntensity
    - StackRescaleIntensity
    - MatchContrast
    - HistogramEqualization
    - Clahe2d
    - Clahe3d
    - AdaptiveHistogramEqualization
    - ApplyGrayscaleLut
    - BackgroundImage2d
    - RadialBackgroundImage2d
    - ShadingCorrection
    - Exponential
    - Logarithm
    - Square
    - SquareRoot
    - RingArtifactRemoval3d
    - BeamHardeningCorrection3d
  - Color Transforms
  - - ColorToLightness
    - GetColorChannel
    - ColorToGrayscale
    - GrayscaleToColor
    - ColorSpaceConversion
    - ColorAntialiasing2d
    - BayerToRgb2d
    - DecorrelationStretch2d
    - BlendAlphaChannelWithImage
    - BlendAlphaChannelWithValue
  - Smoothing And Denoising
  - - BoxFilter2d
    - BoxFilter3d
    - MedianFilter2d
    - MedianFilter3d
    - GaussianFilter2d
    - GaussianFilter3d
    - ExponentialFilter2d
    - RecursiveExponentialFilter2d
    - RecursiveExponentialFilter3d
    - NonLocalMeansFilter2d
    - NonLocalMeansFilter3d
    - CurvatureDrivenDiffusion
    - BilateralFilter2d
    - BilateralFilter3d
    - MajorityFilter2d
    - MajorityFilter3d
    - SigmaFilter2d
    - SigmaFilter3d
    - Despeckle2d
    - Despeckle3d
    - NagaoFilter2d
    - NagaoFilter3d
    - SnnFilter2d
    - SnnFilter3d
    - FlowInpainting
  - Sharpening
  - - Deblurring2d
    - Delineate2d
    - Delineate3d
    - UnsharpMasking2d
    - UnsharpMasking3d
    - Shade2d
    - RichardsonLucyDeconvolution
  - Texture Filters
  - - MeanFilter2d
    - MeanFilter3d
    - VarianceFilter2d
    - VarianceFilter3d
    - SkewnessFilter2d
    - SkewnessFilter3d
    - KurtosisFilter2d
    - KurtosisFilter3d
    - ContrastFilter2d
    - ContrastFilter3d
    - VariationFilter2d
    - VariationFilter3d
    - EnergyFilter2d
    - EnergyFilter3d
    - EntropyFilter2d
    - EntropyFilter3d
    - AutocorrelationFilter
    - LocalAutocorrelationFilter2d
    - ImageCurvature2d
    - ImageCurvature3d
    - StructureEnhancementFilter2d
    - StructureEnhancementFilter3d
    - ScaleSpaceLogFilter2d
    - ScaleSpaceLogFilter3d
  - Frequency Domain
  - - ComplexFft
    - ComplexFftInverse
    - ComplexCenteredFft
    - ComplexCenteredFftInverse
    - CartesianToPolar2d
    - PolarToCartesian2d
    - ConvolutionWithImage
    - ConvolutionWithImage2d
    - RadialFrequencyFilter2d
    - GaborFiltering2d
- Edge Detection
- - Gradient
  - - GradientOperator2d
    - GradientOperator3d
    - VectorToMagnitude2d
    - VectorToMagnitude3d
    - GradientVector2d
    - GradientVector3d
    - GradientMagnitude2d
    - GradientMagnitude3d
    - MorphologicalGradient2d
    - MorphologicalGradient3d
    - RadialGradientCentered2d
    - RadialGradientCentered3d
    - RadialGradientContour2d
    - RadialGradientContour3d
    - RadialGradientLabel2d
    - RadialGradientLabel3d
  - Laplacian
  - - DobFilter2d
    - DobFilter3d
    - MorphologicalLaplacian
    - RecursiveLaplacian2d
    - RecursiveLaplacian3d
  - Other Derivatives
  - - GaussianDerivative2d
    - GaussianDerivative3d
    - GaussianHessianMatrix2d
    - GaussianHessianMatrix3d
    - GaussianGradientTensor2d
    - GaussianGradientTensor3d
  - Edge Marking
  - - ImageLocalMaxima2d
    - ImageLocalMaxima3d
    - RidgeDetection
    - ZeroCrossings2d
    - DijkstraShortestPath2d
    - EigenvaluesToStructureness2d
    - EigenvaluesToStructureness3d
    - TensorVoting2d
    - CannyEdgeDetector2d
    - CannyEdgeDetector3d
- Image Segmentation
- - Binarization
  - - Thresholding
    - ThresholdingByCriterion
    - AutoThresholdingBright
    - AutoThresholdingDark
    - ColorThresholding
    - TopHat
    - FeatureAdaptiveThresholding
    - AdaptiveThresholding2d
    - AdaptiveThresholding3d
    - HysteresisThresholding
  - Labeling
  - - Labeling2d
    - Labeling3d
    - ReorderLabels
    - AssignLabel
    - AddObjectToLabel
    - GroupCloseLabelsl2d
  - Feature Selection
  - - KillBorder2d
    - KillBorder3d
    - RemoveSmallSpots
    - ObjectBoundaries
    - BinarySmoothing2d
    - BinarySmoothing3d
    - LabelInterfaces
  - Separating And Filling
  - - FillHoles2d
    - FillHoles3d
    - RemoveSmallHoles
    - FillImageBorder3d
    - ExpandLabelBoundaries
    - SeparateObjects
    - HExtremaWatershed
    - SeparateObjectsByBlock
    - HExtremaWatershedByBlock
    - ConvexHull2d
  - Region Growing
  - - MarkerBasedWatershed2d
    - MarkerBasedWatershed3d
    - MarkerBasedWatershedByBlock
    - MarkerBasedWatershedBasinsByBlock
    - ExpandLabelsOnBackground
    - FloodFillThreshold2d
    - FloodFillThreshold3d
    - Waterpixel
  - Computational Geometry
  - - PolylineResampler2d
    - PolylineResampler3d
    - PolylineExtrusion3d
    - SurfaceFitting3d
- Mathematical Morphology
- - Erosion And Dilation
  - - Erosion2d
    - Erosion3d
    - Dilation2d
    - Dilation3d
    - ErosionDisk2d
    - ErosionDisk3d
    - ErosionBall3d
    - DilationDisk2d
    - DilationDisk3d
    - DilationBall3d
    - ErosionLine2d
    - ErosionLine3d
    - DilationLine2d
    - DilationLine3d
    - ErosionColor2d
    - DilationColor2d
    - SelectiveErosion2d
    - SelectiveErosion3d
    - SelectiveDilation2d
    - SelectiveDilation3d
  - Opening And Closing
  - - Opening2d
    - Opening3d
    - Closing2d
    - Closing3d
    - OpeningDisk2d
    - OpeningDisk3d
    - OpeningBall3d
    - ClosingDisk2d
    - ClosingDisk3d
    - ClosingBall3d
    - OpeningLine2d
    - OpeningLine3d
    - ClosingLine2d
    - ClosingLine3d
    - OpeningLineMaximum
    - ClosingLineMinimum
    - OpeningColor2d
    - ClosingColor2d
    - OpeningByReconstruction2d
    - OpeningByReconstruction3d
    - ClosingByReconstruction2d
    - ClosingByReconstruction3d
    - OpeningDiskByReconstruction2d
    - OpeningBallByReconstruction3d
    - ClosingDiskByReconstruction2d
    - ClosingBallByReconstruction3d
    - SelectiveOpening2d
    - SelectiveOpening3d
    - SelectiveClosing2d
    - SelectiveClosing3d
  - Hit Or Miss And Skeleton
  - - Skeleton
    - Centerline3d
    - InfluenceZones
    - Centroid2d
    - EndPoints2d
    - EndPoints3d
    - Pruning2d
    - Pruning3d
    - IsolatedPoints2d
    - IsolatedPoints3d
    - TriplePoints2d
    - InteriorPoints2d
    - ApplyMorphologicalLut2d
    - ApplyMorphologicalLut3d
  - Geodesic Transformations
  - - ReconstructionFromMarkers2d
    - ReconstructionFromMarkers3d
    - GrayscaleReconstruction2d
    - GrayscaleReconstruction3d
    - GrayscaleFillHoles2d
    - GrayscaleFillHoles3d
    - HExtrema2d
    - HExtrema3d
    - RegionalExtrema2d
    - RegionalExtrema3d
    - UltimateErosion
  - Distance Maps
  - - DistanceMap2d
    - DistanceMap3d
    - EuclideanDistanceMap3d
    - EuclideanDistanceMap2d
    - GeodesicDistanceMap
    - ClosestBoundaryPoints2d
    - ClosestBoundaryPoints3d
    - TimeMap
    - LocalThicknessMap3d
    - GeodesicPropagation2d
    - GeodesicMaxima
    - GeodesicMinima
- Image Analysis
- - Image Measurements
  - - BinaryAnalysis
    - Area2d
    - Area3d
    - IntensityIntegral2d
    - IntensityIntegral3d
    - ObjectCount
    - DistanceMean2d
    - EulerNumber2d
    - EulerNumber3d
    - FractalDimension
    - CurvatureIntegrals3d
  - Image Statistics
  - - IntensityExtrema
    - IntensityStatistics
    - IntensityMaskedStatistics
    - IntensityHistogram
    - IntensityBinHistogram
    - AutoThresholdingValue
    - CompareImage
    - CompareValue
    - SimilarityMetricValue
    - SsimValue2d
    - SsimValue3d
    - RegionSimilarity
    - RadialIntensityProfile2d
    - RadialIntensityProfile3d
    - CylindricalIntensityProfile3d
    - RadialAutocorrelationProfile
    - Cooccurrence2d
    - MeasureGaussianNoise
    - LocalMaxima2d
    - LocalMaxima3d
    - PeakExtractor
    - LocalDensityMap2d
    - LocalDensityMap3d
    - SegmentationMetrics
  - Individual Measurements
  - - VolumeFractionByLabel
    - LabelCountByRegion
    - VolumeFractionByRegion
    - LabelAnalysis
    - LabelFiltering
    - LabelFilteringAnalysis
    - FilterAnalysis
    - MeasurementToImage
    - FilterByMeasurement
    - SieveLabeling
  - Moments And Orientations
  - - MomentsOfInertia2d
    - MomentsOfInertia3d
    - InertiaMoment2d
    - Eccentricity2d
    - OrientationMapFourier3d
    - OrientationMatchingFourier3d
  - Morphometry
  - - ObjectVolume
    - TotalImageVolume
    - ObjectBoundaryArea
    - TotalImageBoundaryArea
    - AverageObjectAreaPerSlice3d
    - AverageObjectCountPerSlice3d
    - ObjectImageRatio
    - ObjectSpecificSurface
    - ObjectSurfaceDensity
    - PorosityPercentage3d
    - FragmentationIndex
    - StructureModelIndex3d
    - AverageObjectThickness3d
    - AverageSpaceThickness3d
    - ObjectLinearDensity3d
    - DegreeOfAnisotropy
  - Propagation
  - - AxisPropagationBinary3d
    - AxisPropagationBinaryWithSeeds3d
    - AxisPropagationLabel3d
    - AxisPropagationLabelWithSeeds3d
    - AxisPropagationDistance3d
    - EstimateShortestPath3d
    - PercolationThreshold3d
    - AxisConnectivity3d
    - CentroidPathTortuosity3d
- Machine Learning
- - Classification
  - - AutoSegmentation3Phases
    - AutoIntensityClassification
    - SupervisedTextureClassification2d
    - SupervisedTextureClassification3d
    - TextureClassificationCreate
    - TextureClassificationTrain
    - TextureClassificationApply
  - Deep learning
  - - OnnxPredictionFiltering2d
    - OnnxPredictionFiltering3d
    - OnnxPredictionSegmentation2d
    - OnnxPredictionSegmentation3d
    - OnnxPredictionTwoOutputs2d
    - OnnxPredictionTwoOutputs3d
- Business Application
- - Dental
  - - DentalWallSegmentation3d
    - DentalMeshExtraction3d
    - DentalUnfolding3d
- IO
- - Image Data
  - - ReadVipImage
    - WriteVipImage
  - Algebraic Data
  - - ReadArrayXd
    - ReadArrayXi64
    - WriteArrayXd
    - WriteArrayXi64
  - Model
  - - ReadOnnxModel
- CUDA
- - Geometry And Matching
  - - CudaCrossCorrelation2d
    - CudaTemplateMatching3d
  - Image Filtering
  - - CudaBoxFilter2d
    - CudaMedianFilter2d
    - CudaGaussianFilter3d
    - CudaAdaptiveManifoldNlmFilter
  - Mathematical Morphology
  - - CudaErosion2d
    - CudaDilation2d
    - CudaOpening2d
    - CudaClosing2d
- Utilities
- Initialization
- Processing
- Syntax Check
- Visualization
- Data Object
- Image Data
- Kernels
- - MorphologicalKernel2d
  - MorphologicalKernel3d
  - MorphologicalLut2d
  - MorphologicalLut3d
- Model
- - TextureClassificationModel
  - OnnxModel
- Global Measurements
- - Image Measurements
  - - Area2dMsr
    - Area3dMsr
    - IntensityIntegral2dMsr
    - IntensityIntegral3dMsr
    - ObjectCountMsr
    - DistanceMeanMsr
    - Euler2dMsr
    - Euler3dMsr
    - FractalMsr
    - CurvatureIntegralsMsr
  - Image Statistics
  - - IntensityExtremaMsr
    - StatisticsMsr
    - IntensityHistogramMsr
    - IntensityBinHistogramMsr
    - CompareMsr
    - SimilarityMetricValueMsr
    - SsimValueMsr
    - RegionSimilarityMsr
    - SegmentationMetricsObjectMsr
    - SegmentationMetricsImageMsr
    - CylindricalIntensityProfileMsr
    - RadialIntensityProfileMsr
    - CorrelationMsr
    - CoocurrrenceMsr
    - GaussianNoiseMsr
    - LocalMaximaMsr
    - PeakExtractorMsr
  - Individual Measurements
  - - VolumeFractionByLabelMsr
    - LabelCountByRegionMsr
    - VolumeFractionByRegionMsr
  - Moments And Orientations
  - - MomentsOfInertia2dMsr
    - MomentsOfInertia3dMsr
    - MeanInertiaMomentMsr
    - Eccentricity2dMsr
    - LocalOrientation3dMsr
  - Morphometry
  - - ObjectVolumeMsr
    - ImageVolumeMsr
    - ObjectBoundaryAreaMsr
    - ImageBoundaryAreaMsr
    - AverageObjectAreaPerSliceMsr
    - AverageObjectCountPerSliceMsr
    - ObjectImageRatioMsr
    - ObjectSpecificSurfaceMsr
    - ObjectSurfaceDensityMsr
    - PorosityPercentageMsr
    - FragmentationMsr
    - StructureModelIndexMsr
    - AverageObjectThicknessMsr
    - AverageSpaceThicknessMsr
    - ObjectLinearDensityMsr
    - AnisotropyDegreeMsr
  - Propagation
  - - AxisPropagationBinary3dMsr
    - AxisPropagationLabel3dMsr
    - AxisPropagationDistance3dMsr
    - PercolationThreshold3dMsr
    - CentroidTortuosity3dMsr
  - Geometry And Matching
  - - CrossCorrelation2dMsr
  - Edge Detection
  - - CannyEdgeDetectorMsr
  - Image Segmentation
  - - AutoThresholdingMsr
    - AutoSegmentation3PhasesMsr
  - Mathematical Morphology
  - - ApplyMorphologicalLutMsr
- Analysis Measurements
- - AnalysisMsr
  - Measurement
  - Native Measurements
  - - Geometry
    - Feret
    - Intensity
    - Histogram
    - Inertia
    - Locate
    - Segment
    - Cooccurrence
  - CustomMeasurement
  - MeasurementAttributes
  - - Quantile
    - Histogram
    - Feret2d
    - Feret3d
    - NeighborCount
    - Cooccurrence
    - Breadth3d
  - MeasurementGroup
- Measurement Information
- - FieldInformation
  - CustomMeasurementInfo
- CUDA
- - CudaContext
- User Guide
- First Steps
- - Hello ImageDev
  - Initialize ImageDev
- Image Management
- - Create 2D Image
  - Create 3D Image
  - Data Transfer
- Standard Processing
- - Denoising
  - Thresholding
- Image Analysis
- - Global Analysis
  - Label Analysis
  - Measurement Browsing
  - Measurement Group
  - Custom Analysis
- Advanced Processing
- - Algorithm Tracking
- Typical Use Cases
- - Particle Analysis
  - Pore Analysis

# SeparateObjects

Separates connected objects in a binary image.
Access to parameter description
For an introduction:

- section Introduction To Watershed
- section Geodesic Transformations

This algorithm is based on the watershed algorithm to extract the lines separating objects of a binary image. These lines are used as boundary lines between overlapping particles.
This algorithm is a high-level combination of distance transform, numerical reconstruction, and watershed algorithms.
A contrast level parameter is used to reduce the number of markers for the watershed process and thus of false positive separation lines.
There is a limitation to the separation ability: if some particles overlap too much, they are not separated by the algorithm. A sensible criteria is that the distance between particle centroids should be larger than their greatest radius.
Note: This algorithm requires up to 10 times the size of the input image as free memory. If the system does not have enough free memory, this algorithm will fail during its computation. In this case, the SeparateObjectsByBlock should be used instead.
See also

- SeparateObjectsByBlock
- HExtremaWatershed
- DistanceMap2d
- HExtrema2d
- MarkerBasedWatershed2d

See related example

- ParticleAnalysis

##
Function Syntax

This function returns outputImage.

    // Function prototype
    std::shared_ptr< iolink::ImageView >
    separateObjects( std::shared_ptr< iolink::ImageView > inputBinaryImage,
                     int32_t contrastValue,
                     SeparateObjects::OutputType outputType,
                     SeparateObjects::AlgorithmMode algorithmMode,
                     SeparateObjects::Neighborhood neighborhood,
                     std::shared_ptr< iolink::ImageView > outputImage = nullptr );

This function returns outputImage.

    // Function prototype.
    separate_objects(input_binary_image: idt.ImageType,
                     contrast_value: int = 2,
                     output_type: SeparateObjects.OutputType = SeparateObjects.OutputType.SEPARATED_OBJECTS,
                     algorithm_mode: SeparateObjects.AlgorithmMode = SeparateObjects.AlgorithmMode.REPEATABLE,
                     neighborhood: SeparateObjects.Neighborhood = SeparateObjects.Neighborhood.CONNECTIVITY_26,
                     output_image: idt.ImageType = None) -> idt.ImageType

This function returns outputImage.

    // Function prototype.
    public static IOLink.ImageView
    SeparateObjects( IOLink.ImageView inputBinaryImage,
                     Int32 contrastValue = 2,
                     SeparateObjects.OutputType outputType = ImageDev.SeparateObjects.OutputType.SEPARATED_OBJECTS,
                     SeparateObjects.AlgorithmMode algorithmMode = ImageDev.SeparateObjects.AlgorithmMode.REPEATABLE,
                     SeparateObjects.Neighborhood neighborhood = ImageDev.SeparateObjects.Neighborhood.CONNECTIVITY_26,
                     IOLink.ImageView outputImage = null );

##
Class Syntax

    // Command constructor.
    SeparateObjects();

    /// Gets the inputBinaryImage parameter.
    /// The input binary image.
    std::shared_ptr< iolink::ImageView > inputBinaryImage() const;
    /// Sets the inputBinaryImage parameter.
    /// The input binary image.
    void setInputBinaryImage( std::shared_ptr< iolink::ImageView > inputBinaryImage );

    /// Gets the contrastValue parameter.
    /// The depth of the valley used to select the markers of the watershed.
    int32_t contrastValue() const;
    /// Sets the contrastValue parameter.
    /// The depth of the valley used to select the markers of the watershed.
    void setContrastValue( const int32_t& contrastValue );

    /// Gets the outputType parameter.
    /// The type of result image.
    SeparateObjects::OutputType outputType() const;
    /// Sets the outputType parameter.
    /// The type of result image.
    void setOutputType( const SeparateObjects::OutputType& outputType );

    /// Gets the algorithmMode parameter.
    /// The mode for applying the watershed algorithm.
    SeparateObjects::AlgorithmMode algorithmMode() const;
    /// Sets the algorithmMode parameter.
    /// The mode for applying the watershed algorithm.
    void setAlgorithmMode( const SeparateObjects::AlgorithmMode& algorithmMode );

    /// Gets the neighborhood parameter.
    /// The 3D neighborhood configuration. This parameter is ignored with a 2D input image.
    SeparateObjects::Neighborhood neighborhood() const;
    /// Sets the neighborhood parameter.
    /// The 3D neighborhood configuration. This parameter is ignored with a 2D input image.
    void setNeighborhood( const SeparateObjects::Neighborhood& neighborhood );

    /// Gets the outputImage parameter.
    /// The output binary or label image. Its dimensions are forced to the same values as the input image. Its type depends on the outputType parameter.
    std::shared_ptr< iolink::ImageView > outputImage() const;
    /// Sets the outputImage parameter.
    /// The output binary or label image. Its dimensions are forced to the same values as the input image. Its type depends on the outputType parameter.
    void setOutputImage( std::shared_ptr< iolink::ImageView > outputImage );

    // Method to launch the command.
    void execute();

    # Property of the inputBinaryImage parameter.
    SeparateObjects.input_binary_image
    # Property of the contrastValue parameter.
    SeparateObjects.contrast_value
    # Property of the outputType parameter.
    SeparateObjects.output_type
    # Property of the algorithmMode parameter.
    SeparateObjects.algorithm_mode
    # Property of the neighborhood parameter.
    SeparateObjects.neighborhood
    # Property of the outputImage parameter.
    SeparateObjects.output_image

    // Method to launch the command.
    execute()

    // Command constructor.
    SeparateObjects()

    // Property of the inputBinaryImage parameter.
    SeparateObjects.inputBinaryImage
    // Property of the contrastValue parameter.
    SeparateObjects.contrastValue
    // Property of the outputType parameter.
    SeparateObjects.outputType
    // Property of the algorithmMode parameter.
    SeparateObjects.algorithmMode
    // Property of the neighborhood parameter.
    SeparateObjects.neighborhood
    // Property of the outputImage parameter.
    SeparateObjects.outputImage

    // Method to launch the command.
    Execute()

## Parameters

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputBinaryImage | The input binary image. | Image | Binary | nullptr
| outputType | The type of result image.
SEPARATED_OBJECTS | The result image is a binary image representing the original data minus separation lines (split particles).
WATERSHED_RIDGES | The result image is a binary image representing the separation lines (watershed).
SEPARATED_BASINS | The result image is a label image representing the catchment areas (labeled complement of the watershed).
CONTIGUOUS_BASINS | The result image is a label image representing the catchment areas with separation lines filled by a label.
CONTIGUOUS_OBJECTS | The result image is a label image representing the original image with labels taking into account the watershed.
| Enumeration |  | SEPARATED_OBJECTS
| algorithmMode | The mode for applying the watershed algorithm.
REPEATABLE | The result is repeatable but slower to compute.
FAST | The result is faster to compute but not repeatable because of asynchronous parallel computation. Since a watershed problem does not generally have a unique solution, two processings of the same image can lead to two different results (both exact).
| Enumeration |  | REPEATABLE
| contrastValue | The depth of the valley used to select the markers of the watershed. | Int32 | >=0 | 2
| neighborhood | The 3D neighborhood configuration. This parameter is ignored with a 2D input image.
CONNECTIVITY_6 | The structuring element is composed of voxels with a common face with the voxel of interest.
CONNECTIVITY_18 | The structuring element is composed of voxels with at least one common edge.
CONNECTIVITY_26 | The structuring element is a full cube.
| Enumeration |  | CONNECTIVITY_26
| outputImage | The output binary or label image. Its dimensions are forced to the same values as the input image. Its type depends on the outputType parameter. | Image |  | nullptr

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| input_binary_image | The input binary image. | image | Binary | None
| output_type | The type of result image.
SEPARATED_OBJECTS | The result image is a binary image representing the original data minus separation lines (split particles).
WATERSHED_RIDGES | The result image is a binary image representing the separation lines (watershed).
SEPARATED_BASINS | The result image is a label image representing the catchment areas (labeled complement of the watershed).
CONTIGUOUS_BASINS | The result image is a label image representing the catchment areas with separation lines filled by a label.
CONTIGUOUS_OBJECTS | The result image is a label image representing the original image with labels taking into account the watershed.
| enumeration |  | SEPARATED_OBJECTS
| algorithm_mode | The mode for applying the watershed algorithm.
REPEATABLE | The result is repeatable but slower to compute.
FAST | The result is faster to compute but not repeatable because of asynchronous parallel computation. Since a watershed problem does not generally have a unique solution, two processings of the same image can lead to two different results (both exact).
| enumeration |  | REPEATABLE
| contrast_value | The depth of the valley used to select the markers of the watershed. | int32 | >=0 | 2
| neighborhood | The 3D neighborhood configuration. This parameter is ignored with a 2D input image.
CONNECTIVITY_6 | The structuring element is composed of voxels with a common face with the voxel of interest.
CONNECTIVITY_18 | The structuring element is composed of voxels with at least one common edge.
CONNECTIVITY_26 | The structuring element is a full cube.
| enumeration |  | CONNECTIVITY_26
| output_image | The output binary or label image. Its dimensions are forced to the same values as the input image. Its type depends on the outputType parameter. | image |  | None

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputBinaryImage | The input binary image. | Image | Binary | null
| outputType | The type of result image.
SEPARATED_OBJECTS | The result image is a binary image representing the original data minus separation lines (split particles).
WATERSHED_RIDGES | The result image is a binary image representing the separation lines (watershed).
SEPARATED_BASINS | The result image is a label image representing the catchment areas (labeled complement of the watershed).
CONTIGUOUS_BASINS | The result image is a label image representing the catchment areas with separation lines filled by a label.
CONTIGUOUS_OBJECTS | The result image is a label image representing the original image with labels taking into account the watershed.
| Enumeration |  | SEPARATED_OBJECTS
| algorithmMode | The mode for applying the watershed algorithm.
REPEATABLE | The result is repeatable but slower to compute.
FAST | The result is faster to compute but not repeatable because of asynchronous parallel computation. Since a watershed problem does not generally have a unique solution, two processings of the same image can lead to two different results (both exact).
| Enumeration |  | REPEATABLE
| contrastValue | The depth of the valley used to select the markers of the watershed. | Int32 | >=0 | 2
| neighborhood | The 3D neighborhood configuration. This parameter is ignored with a 2D input image.
CONNECTIVITY_6 | The structuring element is composed of voxels with a common face with the voxel of interest.
CONNECTIVITY_18 | The structuring element is composed of voxels with at least one common edge.
CONNECTIVITY_26 | The structuring element is a full cube.
| Enumeration |  | CONNECTIVITY_26
| outputImage | The output binary or label image. Its dimensions are forced to the same values as the input image. Its type depends on the outputType parameter. | Image |  | null

## Object Examples

    auto foam_sep = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam_sep.vip" );

    SeparateObjects separateObjectsAlgo;
    separateObjectsAlgo.setInputBinaryImage( foam_sep );
    separateObjectsAlgo.setContrastValue( 4 );
    separateObjectsAlgo.setOutputType( SeparateObjects::OutputType::SEPARATED_OBJECTS );
    separateObjectsAlgo.setAlgorithmMode( SeparateObjects::AlgorithmMode::REPEATABLE );
    separateObjectsAlgo.setNeighborhood( SeparateObjects::Neighborhood::CONNECTIVITY_26 );
    separateObjectsAlgo.execute();

    std::cout << "outputImage:" << separateObjectsAlgo.outputImage()->toString();

    foam_sep = imagedev.read_vip_image(imagedev_data.get_image_path("foam_sep.vip"))

    separate_objects_algo = imagedev.SeparateObjects()
    separate_objects_algo.input_binary_image = foam_sep
    separate_objects_algo.contrast_value = 4
    separate_objects_algo.output_type = imagedev.SeparateObjects.SEPARATED_OBJECTS
    separate_objects_algo.algorithm_mode = imagedev.SeparateObjects.REPEATABLE
    separate_objects_algo.neighborhood = imagedev.SeparateObjects.CONNECTIVITY_26
    separate_objects_algo.execute()

    print("output_image:", str(separate_objects_algo.output_image))

    ImageView foam_sep = Data.ReadVipImage( @"Data/images/foam_sep.vip" );

    SeparateObjects separateObjectsAlgo = new SeparateObjects
    {
        inputBinaryImage = foam_sep,
        contrastValue = 4,
        outputType = SeparateObjects.OutputType.SEPARATED_OBJECTS,
        algorithmMode = SeparateObjects.AlgorithmMode.REPEATABLE,
        neighborhood = SeparateObjects.Neighborhood.CONNECTIVITY_26
    };
    separateObjectsAlgo.Execute();

    Console.WriteLine( "outputImage:" + separateObjectsAlgo.outputImage.ToString() );

## Function Examples

    auto foam_sep = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam_sep.vip" );

    auto result = separateObjects( foam_sep, 4, SeparateObjects::OutputType::SEPARATED_OBJECTS, SeparateObjects::AlgorithmMode::REPEATABLE, SeparateObjects::Neighborhood::CONNECTIVITY_26 );

    std::cout << "outputImage:" << result->toString();

    foam_sep = imagedev.read_vip_image(imagedev_data.get_image_path("foam_sep.vip"))

    result = imagedev.separate_objects(foam_sep, 4, imagedev.SeparateObjects.SEPARATED_OBJECTS, imagedev.SeparateObjects.REPEATABLE, imagedev.SeparateObjects.CONNECTIVITY_26)

    print("output_image:", str(result))

    ImageView foam_sep = Data.ReadVipImage( @"Data/images/foam_sep.vip" );

    IOLink.ImageView result = Processing.SeparateObjects( foam_sep, 4, SeparateObjects.OutputType.SEPARATED_OBJECTS, SeparateObjects.AlgorithmMode.REPEATABLE, SeparateObjects.Neighborhood.CONNECTIVITY_26 );

    Console.WriteLine( "outputImage:" + result.ToString() );

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