184 lines
8.6 KiB
C++
184 lines
8.6 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2014, Beat Kueng (beat-kueng@gmx.net), Lukas Vogel, Morten Lysgaard
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_SEEDS_HPP__
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#define __OPENCV_SEEDS_HPP__
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#ifdef __cplusplus
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#include <opencv2/core.hpp>
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namespace cv
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{
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namespace ximgproc
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{
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//! @addtogroup ximgproc_superpixel
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//! @{
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/** @brief Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels
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algorithm described in @cite VBRV14 .
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The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy
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function that is based on color histograms and a boundary term, which is optional. The energy
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function encourages superpixels to be of the same color, and if the boundary term is activated, the
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superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular
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grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the
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solution. The algorithm runs in real-time using a single CPU.
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*/
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class CV_EXPORTS_W SuperpixelSEEDS : public Algorithm
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{
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public:
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/** @brief Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object.
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The function computes the superpixels segmentation of an image with the parameters initialized
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with the function createSuperpixelSEEDS().
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*/
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CV_WRAP virtual int getNumberOfSuperpixels() = 0;
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/** @brief Calculates the superpixel segmentation on a given image with the initialized
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parameters in the SuperpixelSEEDS object.
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This function can be called again for other images without the need of initializing the
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algorithm with createSuperpixelSEEDS(). This save the computational cost of allocating memory
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for all the structures of the algorithm.
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@param img Input image. Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of
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channels must match with the initialized image size & channels with the function
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createSuperpixelSEEDS(). It should be in HSV or Lab color space. Lab is a bit better, but also
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slower.
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@param num_iterations Number of pixel level iterations. Higher number improves the result.
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The function computes the superpixels segmentation of an image with the parameters initialized
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with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and
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then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries
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from large to smaller size, finalizing with proposing pixel updates. An illustrative example
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can be seen below.
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*/
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CV_WRAP virtual void iterate(InputArray img, int num_iterations=4) = 0;
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/** @brief Returns the segmentation labeling of the image.
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Each label represents a superpixel, and each pixel is assigned to one superpixel label.
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@param labels_out Return: A CV_32UC1 integer array containing the labels of the superpixel
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segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
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The function returns an image with ssthe labels of the superpixel segmentation. The labels are in
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the range [0, getNumberOfSuperpixels()].
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*/
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CV_WRAP virtual void getLabels(OutputArray labels_out) = 0;
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/** @brief Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.
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@param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border,
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and 0 otherwise.
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@param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border
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are masked.
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The function return the boundaries of the superpixel segmentation.
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@note
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- (Python) A demo on how to generate superpixels in images from the webcam can be found at
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opencv_source_code/samples/python2/seeds.py
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- (cpp) A demo on how to generate superpixels in images from the webcam can be found at
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opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command
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line argument, the static image will be used instead of the webcam.
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- It will show a window with the video from the webcam with the superpixel boundaries marked
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in red (see below). Use Space to switch between different output modes. At the top of the
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window there are 4 sliders, from which the user can change on-the-fly the number of
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superpixels, the number of block levels, the strength of the boundary prior term to modify
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the shape, and the number of iterations at pixel level. This is useful to play with the
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parameters and set them to the user convenience. In the console the frame-rate of the
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algorithm is indicated.
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*/
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CV_WRAP virtual void getLabelContourMask(OutputArray image, bool thick_line = false) = 0;
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virtual ~SuperpixelSEEDS() {}
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};
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/** @brief Initializes a SuperpixelSEEDS object.
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@param image_width Image width.
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@param image_height Image height.
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@param image_channels Number of channels of the image.
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@param num_superpixels Desired number of superpixels. Note that the actual number may be smaller
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due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to
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get the actual number.
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@param num_levels Number of block levels. The more levels, the more accurate is the segmentation,
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but needs more memory and CPU time.
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@param prior enable 3x3 shape smoothing term if \>0. A larger value leads to smoother shapes. prior
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must be in the range [0, 5].
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@param histogram_bins Number of histogram bins.
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@param double_step If true, iterate each block level twice for higher accuracy.
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The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of
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the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS
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superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and
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double_step.
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The number of levels in num_levels defines the amount of block levels that the algorithm use in the
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optimization. The initialization is a grid, in which the superpixels are equally distributed through
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the width and the height of the image. The larger blocks correspond to the superpixel size, and the
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levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels,
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recursively until the smaller block level. An example of initialization of 4 block levels is
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illustrated in the following figure.
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*/
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CV_EXPORTS_W Ptr<SuperpixelSEEDS> createSuperpixelSEEDS(
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int image_width, int image_height, int image_channels,
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int num_superpixels, int num_levels, int prior = 2,
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int histogram_bins=5, bool double_step = false);
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//! @}
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}
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}
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#endif
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#endif
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