ximgproc_ScanSegment
import { ximgproc_ScanSegment } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits Algorithm.
Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels algorithm by Loke SC, et al. [loke2021accelerated] for original paper.
The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than existing superpixel segmentation methods. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. The computational complexity is quadratic O(n2) and more suited to smaller images, but can still process a 2MP colour image faster than the SEEDS algorithm in OpenCV. The output is deterministic when the number of processing threads is fixed, and requires the source image to be in Lab colour format.
Constructors and members
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
getNumberOfSuperpixels
Returns the actual superpixel segmentation from the last image processed using iterate.
Returns zero if no image has been processed.
getNumberOfSuperpixels(): number;The number result.
iterate
Calculates the superpixel segmentation on a given image with the initialized parameters in the ScanSegment object.
This function can be called again for other images without the need of initializing the algorithm with createScanSegment().
This save the computational cost of allocating memory for all the structures of the algorithm.
iterate(img: Mat): void;imgInput image. Supported format: CV_8UC3. Image size must match with the initialized image size with the function createScanSegment(). It MUST be in Lab color space.
getLabels
Returns the segmentation labeling of the image.
Each label represents a superpixel, and each pixel is assigned to one superpixel label.
getLabels(labels_out: Mat): void;labels_outOutput destination, filled by the native operation. Return: A CV_32UC1 integer array containing the labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
getLabelContourMask
Returns the mask of the superpixel segmentation stored in the ScanSegment object.
The function return the boundaries of the superpixel segmentation.
getLabelContourMask(image: Mat, thick_line: boolean): void;2 available overloads
getLabelContourMask(image: Mat): void;getLabelContourMask(image: Mat, thick_line: boolean): void;imageOutput destination, filled by the native operation. Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
thick_lineIf false, the border is only one pixel wide, otherwise all pixels at the border are masked.
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