org.bytedeco.javacpp

Class opencv_ximgproc.SuperpixelSEEDS

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_core.Algorithm

org.bytedeco.javacpp.opencv_ximgproc.SuperpixelSEEDS

All Implemented Interfaces:

AutoCloseable

Enclosing class:

opencv_ximgproc

@Namespace(value="cv::ximgproc")
public static class opencv_ximgproc.SuperpixelSEEDS
extends opencv_core.Algorithm

\addtogroup ximgproc_superpixel \{

/** \brief 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.

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
SuperpixelSEEDS(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
void	getLabelContourMask(opencv_core.Mat image)
void	getLabelContourMask(opencv_core.Mat image, boolean thick_line) \brief Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.
void	getLabelContourMask(opencv_core.UMat image)
void	getLabelContourMask(opencv_core.UMat image, boolean thick_line)
void	getLabels(opencv_core.Mat labels_out) \brief Returns the segmentation labeling of the image.
void	getLabels(opencv_core.UMat labels_out)
int	getNumberOfSuperpixels() \brief Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object.
void	iterate(opencv_core.Mat img)
void	iterate(opencv_core.Mat img, int num_iterations) \brief Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelSEEDS object.
void	iterate(opencv_core.UMat img)
void	iterate(opencv_core.UMat img, int num_iterations)

Methods inherited from class org.bytedeco.javacpp.opencv_core.Algorithm

clear, empty, getDefaultName, position, read, save, save, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

SuperpixelSEEDS

public SuperpixelSEEDS(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getNumberOfSuperpixels

public int getNumberOfSuperpixels()

\brief 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

public void iterate(@ByVal
                    opencv_core.Mat img,
                    int num_iterations)

\brief 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

public void iterate(@ByVal
                    opencv_core.Mat img)

iterate

public void iterate(@ByVal
                    opencv_core.UMat img,
                    int num_iterations)

iterate

public void iterate(@ByVal
                    opencv_core.UMat img)

getLabels

public void getLabels(@ByVal
                      opencv_core.Mat labels_out)

\brief 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()].

getLabels

public void getLabels(@ByVal
                      opencv_core.UMat labels_out)

getLabelContourMask

public void getLabelContourMask(@ByVal
                                opencv_core.Mat image,
                                @Cast(value="bool")
                                boolean thick_line)

\brief 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.


getLabelContourMask

public void getLabelContourMask(@ByVal
                                opencv_core.Mat image)

getLabelContourMask

public void getLabelContourMask(@ByVal
                                opencv_core.UMat image,
                                @Cast(value="bool")
                                boolean thick_line)

getLabelContourMask

public void getLabelContourMask(@ByVal
                                opencv_core.UMat image)