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diff --git a/thirdparty/linux/include/opencv2/ximgproc/seeds.hpp b/thirdparty/linux/include/opencv2/ximgproc/seeds.hpp new file mode 100644 index 0000000..4db8b8f --- /dev/null +++ b/thirdparty/linux/include/opencv2/ximgproc/seeds.hpp @@ -0,0 +1,183 @@ +/*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. + +  + */ + 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. + +  + */ + 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. + + + */ +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 |