SuperpixelSEEDS Methods

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

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.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

Determines whether the specified object is equal to the current object.

Destructor

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object. The function return the boundaries of the superpixel segmentation.

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()].

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().

Gets the Type of the current instance.

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.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Stores algorithm parameters in a file storage