SuperpixelSEEDS

Objective-C

@interface SuperpixelSEEDS : Algorithm

Swift

class SuperpixelSEEDS : Algorithm

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.

Member of Ximgproc

Methods

  • 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().

    Declaration

    Objective-C

    - (int)getNumberOfSuperpixels;

    Swift

    func getNumberOfSuperpixels() -> Int32

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

    Declaration

    Objective-C

    - (void)getLabelContourMask:(nonnull Mat *)image thick_line:(BOOL)thick_line;

    Swift

    func getLabelContourMask(image: Mat, thick_line: Bool)

    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

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

    Declaration

    Objective-C

    - (void)getLabelContourMask:(nonnull Mat *)image;

    Swift

    func getLabelContourMask(image: Mat)

    Parameters

    image

    Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.

    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

  • Returns the segmentation labeling of the image.

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

    Declaration

    Objective-C

    - (void)getLabels:(nonnull Mat *)labels_out;

    Swift

    func getLabels(labels_out: Mat)

    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()].

  • 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.

    Declaration

    Objective-C

    - (void)iterate:(nonnull Mat *)img num_iterations:(int)num_iterations;

    Swift

    func iterate(img: Mat, num_iterations: Int32)

    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.

    image

  • 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.

    Declaration

    Objective-C

    - (void)iterate:(nonnull Mat *)img;

    Swift

    func iterate(img: Mat)

    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.

    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.

    image