GHZMRSpaceXR/Assets/OpenCVForUnity/org/opencv_contrib/ximgproc/SuperpixelSEEDS.cs

using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;

namespace OpenCVForUnity.XimgprocModule
{

    // 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 : Algorithm
    {

        protected override void Dispose(bool disposing)
        {

            try
            {
                if (disposing)
                {
                }
                if (IsEnabledDispose)
                {
                    if (nativeObj != IntPtr.Zero)
                        ximgproc_SuperpixelSEEDS_delete(nativeObj);
                    nativeObj = IntPtr.Zero;
                }
            }
            finally
            {
                base.Dispose(disposing);
            }

        }

        protected internal SuperpixelSEEDS(IntPtr addr) : base(addr) { }

        // internal usage only
        public static new SuperpixelSEEDS __fromPtr__(IntPtr 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()
        {
            ThrowIfDisposed();

            return ximgproc_SuperpixelSEEDS_getNumberOfSuperpixels_10(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)
        {
            ThrowIfDisposed();
            if (img != null) img.ThrowIfDisposed();

            ximgproc_SuperpixelSEEDS_iterate_10(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)
        {
            ThrowIfDisposed();
            if (img != null) img.ThrowIfDisposed();

            ximgproc_SuperpixelSEEDS_iterate_11(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)
        {
            ThrowIfDisposed();
            if (labels_out != null) labels_out.ThrowIfDisposed();

            ximgproc_SuperpixelSEEDS_getLabels_10(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, bool thick_line)
        {
            ThrowIfDisposed();
            if (image != null) image.ThrowIfDisposed();

            ximgproc_SuperpixelSEEDS_getLabelContourMask_10(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)
        {
            ThrowIfDisposed();
            if (image != null) image.ThrowIfDisposed();

            ximgproc_SuperpixelSEEDS_getLabelContourMask_11(nativeObj, image.nativeObj);


        }


#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
        const string LIBNAME = "__Internal";
#else
        const string LIBNAME = "opencvforunity";
#endif



        // C++:  int cv::ximgproc::SuperpixelSEEDS::getNumberOfSuperpixels()
        [DllImport(LIBNAME)]
        private static extern int ximgproc_SuperpixelSEEDS_getNumberOfSuperpixels_10(IntPtr nativeObj);

        // C++:  void cv::ximgproc::SuperpixelSEEDS::iterate(Mat img, int num_iterations = 4)
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_iterate_10(IntPtr nativeObj, IntPtr img_nativeObj, int num_iterations);
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_iterate_11(IntPtr nativeObj, IntPtr img_nativeObj);

        // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabels(Mat& labels_out)
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_getLabels_10(IntPtr nativeObj, IntPtr labels_out_nativeObj);

        // C++:  void cv::ximgproc::SuperpixelSEEDS::getLabelContourMask(Mat& image, bool thick_line = false)
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_getLabelContourMask_10(IntPtr nativeObj, IntPtr image_nativeObj, [MarshalAs(UnmanagedType.U1)] bool thick_line);
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_getLabelContourMask_11(IntPtr nativeObj, IntPtr image_nativeObj);

        // native support for java finalize()
        [DllImport(LIBNAME)]
        private static extern void ximgproc_SuperpixelSEEDS_delete(IntPtr nativeObj);

    }
}