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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2014, Beat Kueng (beat-kueng@gmx.net), Lukas Vogel, Morten Lysgaard
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_SEEDS_HPP__
+#define __OPENCV_SEEDS_HPP__
+#ifdef __cplusplus
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace ximgproc
+{
+
+//! @addtogroup ximgproc_superpixel
+//! @{
+
+/** @brief 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.
+ */
+class CV_EXPORTS_W SuperpixelSEEDS : public Algorithm
+{
+public:
+
+    /** @brief 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().
+     */
+    CV_WRAP virtual int getNumberOfSuperpixels() = 0;
+
+    /** @brief 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)
+     */
+    CV_WRAP virtual void iterate(InputArray img, int num_iterations=4) = 0;
+
+    /** @brief 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()].
+     */
+    CV_WRAP virtual void getLabels(OutputArray labels_out) = 0;
+
+    /** @brief 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.
+
+    @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](pics/superpixels_demo.png)
+     */
+    CV_WRAP virtual void getLabelContourMask(OutputArray image, bool thick_line = false) = 0;
+
+    virtual ~SuperpixelSEEDS() {}
+};
+
+/** @brief Initializes a SuperpixelSEEDS object.
+
+@param image_width Image width.
+@param image_height Image height.
+@param image_channels Number of channels of the image.
+@param num_superpixels Desired number of superpixels. Note that the actual number may be smaller
+due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to
+get the actual number.
+@param num_levels Number of block levels. The more levels, the more accurate is the segmentation,
+but needs more memory and CPU time.
+@param prior enable 3x3 shape smoothing term if \>0. A larger value leads to smoother shapes. prior
+must be in the range [0, 5].
+@param histogram_bins Number of histogram bins.
+@param double_step If true, iterate each block level twice for higher accuracy.
+
+The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of
+the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS
+superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and
+double_step.
+
+The number of levels in num_levels defines the amount of block levels that the algorithm use in the
+optimization. The initialization is a grid, in which the superpixels are equally distributed through
+the width and the height of the image. The larger blocks correspond to the superpixel size, and the
+levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels,
+recursively until the smaller block level. An example of initialization of 4 block levels is
+illustrated in the following figure.
+
+![image](pics/superpixels_blocks.png)
+ */
+CV_EXPORTS_W Ptr<SuperpixelSEEDS> createSuperpixelSEEDS(
+    int image_width, int image_height, int image_channels,
+    int num_superpixels, int num_levels, int prior = 2,
+    int histogram_bins=5, bool double_step = false);
+
+//! @}
+
+}
+}
+#endif
+#endif