OpenCVForUnity.XimgprocModule.SuperpixelSEEDS Class Reference

Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in [VBRV14] . More...

Inheritance diagram for OpenCVForUnity.XimgprocModule.SuperpixelSEEDS:

Public Member Functions
int	getNumberOfSuperpixels ()
	Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object. More...
void	iterate (Mat img, int num_iterations)
	Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelSEEDS object. More...
void	iterate (Mat img)
	Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelSEEDS object. More...
void	getLabels (Mat labels_out)
	Returns the segmentation labeling of the image. More...
void	getLabelContourMask (Mat image, bool thick_line)
	Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object. More...
void	getLabelContourMask (Mat image)
	Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object. More...
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr	getNativeObjAddr ()
virtual void	clear ()
	Clears the algorithm state. More...
virtual bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
void	save (string filename)
virtual string	getDefaultName ()
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()

Static Public Member Functions
static new SuperpixelSEEDS	__fromPtr__ (IntPtr addr)
Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Protected Member Functions
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (bool isEnabledDispose)
	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed [get, protected set]
bool	IsEnabledDispose [get, set]

Detailed Description

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

Member Function Documentation

__fromPtr__()

static new SuperpixelSEEDS OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.__fromPtr__ ( IntPtr  addr )

static

Dispose()

override void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.Dispose ( bool  disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

getLabelContourMask() [1/2]

void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.getLabelContourMask	(	Mat	image,
		bool	thick_line
	)

Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.

Parameters

image	Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
thick_line	If false, the border is only one pixel wide, otherwise all pixels at the border are masked.

The function return the boundaries of the superpixel segmentation.

Note

• (Python) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/samples/python2/seeds.py
  • (cpp) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command line argument, the static image will be used instead of the webcam.
  • It will show a window with the video from the webcam with the superpixel boundaries marked in red (see below). Use Space to switch between different output modes. At the top of the window there are 4 sliders, from which the user can change on-the-fly the number of superpixels, the number of block levels, the strength of the boundary prior term to modify the shape, and the number of iterations at pixel level. This is useful to play with the parameters and set them to the user convenience. In the console the frame-rate of the algorithm is indicated.

image

getLabelContourMask() [2/2]

void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.getLabelContourMask

(

Mat

image

)

Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.

Parameters

image	Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
thick_line	If false, the border is only one pixel wide, otherwise all pixels at the border are masked.

The function return the boundaries of the superpixel segmentation.

Note

• (Python) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/samples/python2/seeds.py
  • (cpp) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command line argument, the static image will be used instead of the webcam.
  • It will show a window with the video from the webcam with the superpixel boundaries marked in red (see below). Use Space to switch between different output modes. At the top of the window there are 4 sliders, from which the user can change on-the-fly the number of superpixels, the number of block levels, the strength of the boundary prior term to modify the shape, and the number of iterations at pixel level. This is useful to play with the parameters and set them to the user convenience. In the console the frame-rate of the algorithm is indicated.

image

getLabels()

void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.getLabels

(

Mat

labels_out

)

Returns the segmentation labeling of the image.

Each label represents a superpixel, and each pixel is assigned to one superpixel label.

Parameters

labels_out

Return: A CV_32UC1 integer array containing the labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()].

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

getNumberOfSuperpixels()

int OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.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().

iterate() [1/2]

void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.iterate	(	Mat	img,
		int	num_iterations
	)

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

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

Parameters

img

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.

num_iterations

Number of pixel level iterations. Higher number improves the result. The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries from large to smaller size, finalizing with proposing pixel updates. An illustrative example can be seen below. 

iterate() [2/2]

void OpenCVForUnity.XimgprocModule.SuperpixelSEEDS.iterate

(

Mat

img

)

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

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

Parameters

img

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.

num_iterations

Number of pixel level iterations. Higher number improves the result. The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries from large to smaller size, finalizing with proposing pixel updates. An illustrative example can be seen below. 

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The documentation for this class was generated from the following file:

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/ximgproc/SuperpixelSEEDS.cs