Trait opencv::prelude::SuperpixelSEEDSTrait

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pub trait SuperpixelSEEDSTrait: AlgorithmTrait + SuperpixelSEEDSTraitConst {
    // Required method
    fn as_raw_mut_SuperpixelSEEDS(&mut self) -> *mut c_void;

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

Mutable methods for crate::ximgproc::SuperpixelSEEDS

Required Methods§

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

Provided Methods§

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

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().

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fn iterate( &mut self, img: &impl ToInputArray, num_iterations: i32 ) -> Result<()>

Calculates the superpixel segmentation on a given image with the initialized parameters in the SuperpixelSEEDS object.

This function can be called again for other images without the need of initializing the algorithm with createSuperpixelSEEDS(). This save the computational cost of allocating memory for all the structures of the algorithm.

Parameters

  • img: 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.

  • num_iterations: Number of pixel level iterations. Higher number improves the result.

The function computes the superpixels segmentation of an image with the parameters initialized with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries from large to smaller size, finalizing with proposing pixel updates. An illustrative example can be seen below.

image

C++ default parameters
  • num_iterations: 4
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fn get_labels(&mut self, labels_out: &mut impl 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()].

The function returns an image with ssthe 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 impl ToOutputArray, thick_line: bool ) -> Result<()>

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

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.

The function return the boundaries of the superpixel segmentation.

Note:

  • (Python) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/samples/python2/seeds.py
  • (cpp) A demo on how to generate superpixels in images from the webcam can be found at opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command line argument, the static image will be used instead of the webcam.
  • It will show a window with the video from the webcam with the superpixel boundaries marked in red (see below). Use Space to switch between different output modes. At the top of the window there are 4 sliders, from which the user can change on-the-fly the number of superpixels, the number of block levels, the strength of the boundary prior term to modify the shape, and the number of iterations at pixel level. This is useful to play with the parameters and set them to the user convenience. In the console the frame-rate of the algorithm is indicated.

image

C++ default parameters
  • thick_line: false

Implementors§