//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ximgproc;

import org.opencv.core.Algorithm;
import org.opencv.core.Mat;

// C++: class SuperpixelSEEDS
/**
 * Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels
 * algorithm described in CITE: VBRV14 .
 *
 * The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy
 * function that is based on color histograms and a boundary term, which is optional. The energy
 * function encourages superpixels to be of the same color, and if the boundary term is activated, the
 * superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular
 * grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the
 * solution. The algorithm runs in real-time using a single CPU.
 */
public class SuperpixelSEEDS extends Algorithm {

    protected SuperpixelSEEDS(long addr) { super(addr); }

    // internal usage only
    public static SuperpixelSEEDS __fromPtr__(long addr) { return new SuperpixelSEEDS(addr); }

    //
    // C++:  int cv::ximgproc::SuperpixelSEEDS::getNumberOfSuperpixels()
    //

    /**
     * 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().
     * @return automatically generated
     */
    public int getNumberOfSuperpixels() {
        return getNumberOfSuperpixels_0(nativeObj);
    }


    //
    // C++:  void cv::ximgproc::SuperpixelSEEDS::iterate(Mat img, int num_iterations = 4)
    //

    /**
     * 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.
     *
     *     @param 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.
     *
     *     @param 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](pics/superpixels_blocks2.png)
     */
    public void iterate(Mat img, int num_iterations) {
        iterate_0(nativeObj, img.nativeObj, num_iterations);
    }

    /**
     * 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.
     *
     *     @param 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.
     *
     *
     *     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](pics/superpixels_blocks2.png)
     */
    public void iterate(Mat img) {
        iterate_1(nativeObj, img.nativeObj);
    }


    //
    // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabels(Mat& labels_out)
    //

    /**
     * Returns the segmentation labeling of the image.
     *
     *     Each label represents a superpixel, and each pixel is assigned to one superpixel label.
     *
     *     @param 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()].
     */
    public void getLabels(Mat labels_out) {
        getLabels_0(nativeObj, labels_out.nativeObj);
    }


    //
    // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabelContourMask(Mat& image, bool thick_line = false)
    //

    /**
     * Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.
     *
     *     @param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border,
     *     and 0 otherwise.
     *
     *     @param 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.
     *
     *     <b>Note:</b>
     * <ul>
     *   <li>
     *           (Python) A demo on how to generate superpixels in images from the webcam can be found at
     *             opencv_source_code/samples/python2/seeds.py
     *   <ul>
     *     <li>
     *            (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.
     *     </li>
     *     <li>
     *            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.
     *     </li>
     *   </ul>
     *
     *     ![image](pics/superpixels_demo.png)
     *   </li>
     * </ul>
     */
    public void getLabelContourMask(Mat image, boolean thick_line) {
        getLabelContourMask_0(nativeObj, image.nativeObj, thick_line);
    }

    /**
     * Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.
     *
     *     @param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border,
     *     and 0 otherwise.
     *
     *     are masked.
     *
     *     The function return the boundaries of the superpixel segmentation.
     *
     *     <b>Note:</b>
     * <ul>
     *   <li>
     *           (Python) A demo on how to generate superpixels in images from the webcam can be found at
     *             opencv_source_code/samples/python2/seeds.py
     *   <ul>
     *     <li>
     *            (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.
     *     </li>
     *     <li>
     *            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.
     *     </li>
     *   </ul>
     *
     *     ![image](pics/superpixels_demo.png)
     *   </li>
     * </ul>
     */
    public void getLabelContourMask(Mat image) {
        getLabelContourMask_1(nativeObj, image.nativeObj);
    }


    @Override
    protected void finalize() throws Throwable {
        delete(nativeObj);
    }



    // C++:  int cv::ximgproc::SuperpixelSEEDS::getNumberOfSuperpixels()
    private static native int getNumberOfSuperpixels_0(long nativeObj);

    // C++:  void cv::ximgproc::SuperpixelSEEDS::iterate(Mat img, int num_iterations = 4)
    private static native void iterate_0(long nativeObj, long img_nativeObj, int num_iterations);
    private static native void iterate_1(long nativeObj, long img_nativeObj);

    // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabels(Mat& labels_out)
    private static native void getLabels_0(long nativeObj, long labels_out_nativeObj);

    // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabelContourMask(Mat& image, bool thick_line = false)
    private static native void getLabelContourMask_0(long nativeObj, long image_nativeObj, boolean thick_line);
    private static native void getLabelContourMask_1(long nativeObj, long image_nativeObj);

    // native support for java finalize()
    private static native void delete(long nativeObj);

}