277 lines
14 KiB
C++
277 lines
14 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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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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_STEREO_HPP__
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#define __OPENCV_STEREO_HPP__
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#include "opencv2/core.hpp"
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#include "opencv2/stereo/descriptor.hpp"
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#include <opencv2/stereo/quasi_dense_stereo.hpp>
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/**
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@defgroup stereo Stereo Correspondance Algorithms
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*/
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namespace cv
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{
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namespace stereo
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{
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//! @addtogroup stereo
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//! @{
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/** @brief Filters off small noise blobs (speckles) in the disparity map
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@param img The input 16-bit signed disparity image
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@param newVal The disparity value used to paint-off the speckles
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@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
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affected by the algorithm
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@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
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blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
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disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
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account when specifying this parameter value.
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@param buf The optional temporary buffer to avoid memory allocation within the function.
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*/
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/** @brief The base class for stereo correspondence algorithms.
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*/
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class StereoMatcher : public Algorithm
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{
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public:
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enum { DISP_SHIFT = 4,
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DISP_SCALE = (1 << DISP_SHIFT)
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};
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/** @brief Computes disparity map for the specified stereo pair
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@param left Left 8-bit single-channel image.
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@param right Right image of the same size and the same type as the left one.
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@param disparity Output disparity map. It has the same size as the input images. Some algorithms,
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like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
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has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
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*/
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virtual void compute( InputArray left, InputArray right,
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OutputArray disparity ) = 0;
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virtual int getMinDisparity() const = 0;
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virtual void setMinDisparity(int minDisparity) = 0;
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virtual int getNumDisparities() const = 0;
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virtual void setNumDisparities(int numDisparities) = 0;
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virtual int getBlockSize() const = 0;
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virtual void setBlockSize(int blockSize) = 0;
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virtual int getSpeckleWindowSize() const = 0;
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virtual void setSpeckleWindowSize(int speckleWindowSize) = 0;
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virtual int getSpeckleRange() const = 0;
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virtual void setSpeckleRange(int speckleRange) = 0;
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virtual int getDisp12MaxDiff() const = 0;
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virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0;
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};
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//!speckle removal algorithms. These algorithms have the purpose of removing small regions
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enum {
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CV_SPECKLE_REMOVAL_ALGORITHM, CV_SPECKLE_REMOVAL_AVG_ALGORITHM
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};
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//!subpixel interpolationm methods for disparities.
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enum{
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CV_QUADRATIC_INTERPOLATION, CV_SIMETRICV_INTERPOLATION
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};
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/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and
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contributed to OpenCV by K. Konolige.
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*/
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class StereoBinaryBM : public StereoMatcher
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{
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public:
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enum { PREFILTER_NORMALIZED_RESPONSE = 0,
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PREFILTER_XSOBEL = 1
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};
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virtual int getPreFilterType() const = 0;
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virtual void setPreFilterType(int preFilterType) = 0;
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virtual int getPreFilterSize() const = 0;
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virtual void setPreFilterSize(int preFilterSize) = 0;
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virtual int getPreFilterCap() const = 0;
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virtual void setPreFilterCap(int preFilterCap) = 0;
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virtual int getTextureThreshold() const = 0;
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virtual void setTextureThreshold(int textureThreshold) = 0;
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virtual int getUniquenessRatio() const = 0;
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virtual void setUniquenessRatio(int uniquenessRatio) = 0;
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virtual int getSmallerBlockSize() const = 0;
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virtual void setSmallerBlockSize(int blockSize) = 0;
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virtual int getScalleFactor() const = 0 ;
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virtual void setScalleFactor(int factor) = 0;
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virtual int getSpekleRemovalTechnique() const = 0 ;
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virtual void setSpekleRemovalTechnique(int factor) = 0;
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virtual bool getUsePrefilter() const = 0 ;
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virtual void setUsePrefilter(bool factor) = 0;
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virtual int getBinaryKernelType() const = 0;
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virtual void setBinaryKernelType(int value) = 0;
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virtual int getAgregationWindowSize() const = 0;
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virtual void setAgregationWindowSize(int value) = 0;
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/** @brief Creates StereoBM object
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@param numDisparities the disparity search range. For each pixel algorithm will find the best
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disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
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shifted by changing the minimum disparity.
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@param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
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(as the block is centered at the current pixel). Larger block size implies smoother, though less
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accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
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chance for algorithm to find a wrong correspondence.
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The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
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a specific stereo pair.
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*/
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CV_EXPORTS static Ptr< cv::stereo::StereoBinaryBM > create(int numDisparities = 0, int blockSize = 9);
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};
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/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original
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one as follows:
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- By default, the algorithm is single-pass, which means that you consider only 5 directions
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instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
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algorithm but beware that it may consume a lot of memory.
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- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
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blocks to single pixels.
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- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
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sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
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- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
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example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
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check, quadratic interpolation and speckle filtering).
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@note
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- (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
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at opencv_source_code/samples/python2/stereo_match.py
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*/
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class StereoBinarySGBM : public StereoMatcher
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{
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public:
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enum
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{
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MODE_SGBM = 0,
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MODE_HH = 1
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};
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virtual int getPreFilterCap() const = 0;
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virtual void setPreFilterCap(int preFilterCap) = 0;
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virtual int getUniquenessRatio() const = 0;
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virtual void setUniquenessRatio(int uniquenessRatio) = 0;
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virtual int getP1() const = 0;
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virtual void setP1(int P1) = 0;
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virtual int getP2() const = 0;
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virtual void setP2(int P2) = 0;
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virtual int getMode() const = 0;
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virtual void setMode(int mode) = 0;
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virtual int getSpekleRemovalTechnique() const = 0 ;
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virtual void setSpekleRemovalTechnique(int factor) = 0;
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virtual int getBinaryKernelType() const = 0;
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virtual void setBinaryKernelType(int value) = 0;
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virtual int getSubPixelInterpolationMethod() const = 0;
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virtual void setSubPixelInterpolationMethod(int value) = 0;
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/** @brief Creates StereoSGBM object
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@param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
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rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
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@param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
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zero. In the current implementation, this parameter must be divisible by 16.
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@param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
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somewhere in the 3..11 range.
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@param P1 The first parameter controlling the disparity smoothness.This parameter is used for the case of slanted surfaces (not fronto parallel).
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@param P2 The second parameter controlling the disparity smoothness.This parameter is used for "solving" the depth discontinuities problem.
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The larger the values are, the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
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between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
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pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
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P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
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32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
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@param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
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disparity check. Set it to a non-positive value to disable the check.
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@param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
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computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
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The result values are passed to the Birchfield-Tomasi pixel cost function.
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@param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
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value should "win" the second best value to consider the found match correct. Normally, a value
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within the 5-15 range is good enough.
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@param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
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and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
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50-200 range.
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@param speckleRange Maximum disparity variation within each connected component. If you do speckle
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filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
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Normally, 1 or 2 is good enough.
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@param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
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algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
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huge for HD-size pictures. By default, it is set to false .
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The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
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set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
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to a custom value.
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*/
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CV_EXPORTS static Ptr<cv::stereo::StereoBinarySGBM> create(int minDisparity, int numDisparities, int blockSize,
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int P1 = 100, int P2 = 1000, int disp12MaxDiff = 1,
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int preFilterCap = 0, int uniquenessRatio = 5,
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int speckleWindowSize = 400, int speckleRange = 200,
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int mode = StereoBinarySGBM::MODE_SGBM);
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};
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//! @}
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}//stereo
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} // cv
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#endif
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