ximgproc_createSuperpixelSEEDS

Extended image processingfunctionOpenCV 5.0.0

import { ximgproc_createSuperpixelSEEDS } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: SLIC, SEEDS and LSC superpixels →

Group nearby pixels into small coherent regions to reduce the cost of later reasoning.

VISUAL WALKTHROUGHSegmentation

REFERENCE INPUTImage with local structure

The same scene stays here while the working view changes.

→

RESULTRefine image-aware boundaries

Five assignment/mean updates bend regions toward the object boundaries. This colour-spatial clustering example omits native connectivity cleanup.

Computed colour-spatial clustering example. SLIC, SEEDS and LSC use different optimization and connectivity rules.

Initializes a SuperpixelSEEDS object.

ximgproc_createSuperpixelSEEDS(image_width: number, image_height: number, image_channels: number, num_superpixels: number, num_levels: number, prior: number, histogram_bins: number, double_step: boolean): ximgproc_SuperpixelSEEDS | null;

4 available overloads

ximgproc_createSuperpixelSEEDS(image_width: number, image_height: number, image_channels: number, num_superpixels: number, num_levels: number): ximgproc_SuperpixelSEEDS | null;

ximgproc_createSuperpixelSEEDS(image_width: number, image_height: number, image_channels: number, num_superpixels: number, num_levels: number, prior: number): ximgproc_SuperpixelSEEDS | null;

ximgproc_createSuperpixelSEEDS(image_width: number, image_height: number, image_channels: number, num_superpixels: number, num_levels: number, prior: number, histogram_bins: number): ximgproc_SuperpixelSEEDS | null;

ximgproc_createSuperpixelSEEDS(image_width: number, image_height: number, image_channels: number, num_superpixels: number, num_levels: number, prior: number, histogram_bins: number, double_step: boolean): ximgproc_SuperpixelSEEDS | null;

image_width

Image width.

image_height

Image height.

image_channels

Number of channels of the image.

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 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.

prior

enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5].

histogram_bins

Number of histogram bins.

double_step

If true, iterate each block level twice for higher accuracy.

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.

Returns

The ximgproc_SuperpixelSEEDS | null result.

Upstream declaration ↗

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