Class SuperpixelLSC

Namespace

OpenCvSharp.XImgProc

Assembly

OpenCvSharp.dll

Class implementing the LSC (Linear Spectral Clustering) superpixels algorithm described in @cite LiCVPR2015LSC.

LSC(Linear Spectral Clustering) produces compact and uniform superpixels with low computational costs.Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels.LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images.

public class SuperpixelLSC : Algorithm, IDisposable, ICvPtrHolder

Inheritance

object

DisposableObject

DisposableCvObject

Algorithm

SuperpixelLSC

Implements

IDisposable

ICvPtrHolder

Inherited Members

Algorithm.Write(FileStorage)

Algorithm.Read(FileNode)

Algorithm.Empty

Algorithm.Save(string)

Algorithm.GetDefaultName()

DisposableCvObject.ptr

DisposableCvObject.DisposeUnmanaged()

DisposableCvObject.CvPtr

DisposableObject.DataHandle

DisposableObject.IsDisposed

DisposableObject.IsEnabledDispose

DisposableObject.AllocatedMemory

DisposableObject.AllocatedMemorySize

DisposableObject.Dispose()

DisposableObject.Dispose(bool)

DisposableObject.AllocGCHandle(object)

DisposableObject.AllocMemory(int)

DisposableObject.NotifyMemoryPressure(long)

DisposableObject.ThrowIfDisposed()

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

SuperpixelLSC(nint)

Creates instance by raw pointer

protected SuperpixelLSC(nint p)

Parameters

p nint

Methods

Create(InputArray, int, float)

Class implementing the LSC (Linear Spectral Clustering) superpixels.

The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler.It preallocate some buffers for future computing iterations over the given image.An example of LSC is illustrated in the following picture. For enhanced results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space.

public static SuperpixelLSC Create(InputArray image, int regionSize = 10, float ratio = 0.075)

Parameters

image InputArray

image Image to segment

regionSize int

Chooses an average superpixel size measured in pixels

ratio float

Chooses the enforcement of superpixel compactness factor of superpixel

Returns

SuperpixelLSC

DisposeManaged()

Releases managed resources

protected override void DisposeManaged()

EnforceLabelConnectivity(int)

Enforce label connectivity. The function merge component that is too small, assigning the previously found adjacent label to this component.Calling this function may change the final number of superpixels.

public virtual void EnforceLabelConnectivity(int minElementSize = 20)

Parameters

minElementSize int

The minimum element size in percents that should be absorbed into a bigger superpixel.Given resulted average superpixel size valid value should be in 0-100 range, 25 means that less then a quarter sized superpixel should be absorbed, this is default.

GetLabelContourMask(OutputArray, bool)

Returns the mask of the superpixel segmentation stored in SuperpixelLSC object. The function return the boundaries of the superpixel segmentation.

public virtual void GetLabelContourMask(OutputArray image, bool thickLine = true)

Parameters

image OutputArray

Return: CV_8U1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.

thickLine bool

If false, the border is only one pixel wide, otherwise all pixels at the border are masked.

GetLabels(OutputArray)

Returns the segmentation labeling of the image. Each label represents a superpixel, and each pixel is assigned to one superpixel label.

The function returns an image with the labels of the superpixel segmentation.The labels are in the range [0, getNumberOfSuperpixels()].

public virtual void GetLabels(OutputArray labelsOut)

Parameters

labelsOut OutputArray

Return: A CV_32SC1 integer array containing the labels of the superpixel segmentation.The labels are in the range[0, getNumberOfSuperpixels()].

GetNumberOfSuperpixels()

Calculates the actual amount of superpixels on a given segmentation computed and stored in SuperpixelLSC object.

public virtual int GetNumberOfSuperpixels()

Returns

int

Iterate(int)

Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelLSC object.

This function can be called again without the need of initializing the algorithm with createSuperpixelLSC(). This save the computational cost of allocating memory for all the structures of the algorithm.

The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelLSC(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of edges boundaries.

public virtual void Iterate(int numIterations = 10)

Parameters

numIterations int

Number of iterations. Higher number improves the result.