Initializes a SuperpixelSEEDS object

obj = cv.SuperpixelSEEDS(image_size, num_superpixels, num_levels)
obj = cv.SuperpixelSEEDS(..., 'OptionName',optionValue, ...)

Input

• image_size Image size specified as [height,width] or [height,width,number_of_channels].
• num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use cv.SuperpixelSEEDS.getNumberOfSuperpixels to get the actual number.
• num_levels Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. Minimum is 2.

Options

• Prior enable 3x3 shape smoothing term if > 0. A larger value leads to smoother shapes. Prior must be in the range [0, 5]. default 2
• HistogramBins Number of histogram bins. default 5
• DoubleStep If true, iterate each block level twice for higher accuracy. default false

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, Prior, HistogramBins and DoubleStep.

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 2x2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.