Class SuperpixelSLIC

Namespace

OpenCvSharp.XImgProc

Assembly

OpenCvSharp.dll

Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in @cite Achanta2012.

public class SuperpixelSLIC : Algorithm, IDisposable, ICvPtrHolder

Inheritance

object

DisposableObject

DisposableCvObject

Algorithm

SuperpixelSLIC

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

SuperpixelSLIC(nint)

Creates instance by raw pointer

protected SuperpixelSLIC(nint p)

Parameters

p nint

Methods

Create(InputArray, SLICType, int, float)

Initialize a SuperpixelSLIC object.

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

public static SuperpixelSLIC Create(InputArray image, SLICType algorithm = SLICType.SLICO, int regionSize = 10, float ruler = 10)

Parameters

image InputArray

Image to segment

algorithm SLICType

Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels.

regionSize int

Chooses an average superpixel size measured in pixels

ruler float

Chooses the enforcement of superpixel smoothness factor of superpixel

Returns

SuperpixelSLIC

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 SuperpixelSLIC 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

GetNumberOfSuperpixels()

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

public virtual int GetNumberOfSuperpixels()

Returns

int

Iterate(int)

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

This function can be called again without the need of initializing the algorithm with createSuperpixelSLIC(). 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 createSuperpixelSLIC(). 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.