Class SuperpixelSEEDS

Namespace

OpenCvSharp.XImgProc

Assembly

OpenCvSharp.dll

Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in @cite VBRV14.

The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy function that is based on color histograms and a boundary term, which is optional.The energy function encourages superpixels to be of the same color, and if the boundary term is activated, the superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the solution.The algorithm runs in real-time using a single CPU.

public class SuperpixelSEEDS : Algorithm, IDisposable, ICvPtrHolder

Inheritance

object

DisposableObject

DisposableCvObject

Algorithm

SuperpixelSEEDS

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

SuperpixelSEEDS(nint)

Creates instance by raw pointer

protected SuperpixelSEEDS(nint p)

Parameters

p nint

Methods

Create(int, int, int, int, int, int, int, bool)

Initializes a SuperpixelSEEDS object.

The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels.It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step.

The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization.The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image.The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.

public static SuperpixelSEEDS Create(int imageWidth, int imageHeight, int imageChannels, int numSuperpixels, int numLevels, int prior = 2, int histogramBins = 5, bool doubleStep = false)

Parameters

imageWidth int

Image width.

imageHeight int

Image height.

imageChannels int

Number of channels of the image.

numSuperpixels int

Desired number of superpixels. Note that the actual number may be smaller due to restrictions(depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number.

numLevels int

Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time.

prior int

enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range[0, 5].

histogramBins int

Number of histogram bins.

doubleStep bool

If true, iterate each block level twice for higher accuracy.

Returns

SuperpixelSEEDS

DisposeManaged()

Releases managed resources

protected override void DisposeManaged()

GetLabelContourMask(OutputArray, bool)

Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS 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 ssthe 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_32UC1 integer array containing the labels of the superpixel segmentation.The labels are in the range[0, getNumberOfSuperpixels()].

GetNumberOfSuperpixels()

Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object.

The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS().

public virtual int GetNumberOfSuperpixels()

Returns

int

Iterate(InputArray, int)

Input image. Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of channels must match with the initialized image size & channels with the function createSuperpixelSEEDS(). It should be in HSV or Lab color space.Lab is a bit better, but also slower.

public virtual void Iterate(InputArray img, int numIterations = 10)

Parameters

img InputArray

Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of channels must match with the initialized image size & channels with the function createSuperpixelSEEDS(). It should be in HSV or Lab color space.Lab is a bit better, but also slower.

numIterations int

Number of pixel level iterations. Higher number improves the result.