Trait opencv::prelude::ScanSegment

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pub trait ScanSegment: AlgorithmTrait + ScanSegmentConst {
    // Required method
    fn as_raw_mut_ScanSegment(&mut self) -> *mut c_void;

    // Provided methods
    fn get_number_of_superpixels(&mut self) -> Result<i32> { ... }
    fn iterate(&mut self, img: &dyn ToInputArray) -> Result<()> { ... }
    fn get_labels(&mut self, labels_out: &mut dyn ToOutputArray) -> Result<()> { ... }
    fn get_label_contour_mask(
        &mut self,
        image: &mut dyn ToOutputArray,
        thick_line: bool
    ) -> Result<()> { ... }
}
Expand description

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.

Required Methods§

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fn as_raw_mut_ScanSegment(&mut self) -> *mut c_void

Provided Methods§

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fn get_number_of_superpixels(&mut self) -> Result<i32>

Returns the actual superpixel segmentation from the last image processed using iterate.

Returns zero if no image has been processed.

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fn iterate(&mut self, img: &dyn ToInputArray) -> Result<()>

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.

Parameters
  • img: Input 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.
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fn get_labels(&mut self, labels_out: &mut dyn ToOutputArray) -> Result<()>

Returns the segmentation labeling of the image.

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

Parameters
  • labels_out: Return: A CV_32UC1 integer array containing the labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
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fn get_label_contour_mask( &mut self, image: &mut dyn ToOutputArray, thick_line: bool ) -> Result<()>

Returns the mask of the superpixel segmentation stored in the ScanSegment object.

The function return the boundaries of the superpixel segmentation.

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.
C++ default parameters
  • thick_line: false

Implementors§