199 lines
7.7 KiB
C++
199 lines
7.7 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//authors: Danail Stoyanov, Evangelos Mazomenos, Dimitrios Psychogyios
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//__OPENCV_QUASI_DENSE_STEREO_H__
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#ifndef __OPENCV_QUASI_DENSE_STEREO_H__
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#define __OPENCV_QUASI_DENSE_STEREO_H__
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#include <opencv2/core.hpp>
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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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*/
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// A basic match structure
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struct CV_EXPORTS_W_SIMPLE MatchQuasiDense
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{
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CV_PROP_RW cv::Point2i p0;
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CV_PROP_RW cv::Point2i p1;
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CV_PROP_RW float corr;
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CV_WRAP MatchQuasiDense() { corr = 0; }
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CV_WRAP_AS(apply) bool operator < (const MatchQuasiDense & rhs) const//fixme may be used uninitialized in this function
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{
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return this->corr < rhs.corr;
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}
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};
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struct CV_EXPORTS_W_SIMPLE PropagationParameters
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{
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CV_PROP_RW int corrWinSizeX; // similarity window
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CV_PROP_RW int corrWinSizeY;
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CV_PROP_RW int borderX; // border to ignore
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CV_PROP_RW int borderY;
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//matching
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CV_PROP_RW float correlationThreshold; // correlation threshold
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CV_PROP_RW float textrureThreshold; // texture threshold
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CV_PROP_RW int neighborhoodSize; // neighborhood size
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CV_PROP_RW int disparityGradient; // disparity gradient threshold
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// Parameters for LK flow algorithm
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CV_PROP_RW int lkTemplateSize;
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CV_PROP_RW int lkPyrLvl;
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CV_PROP_RW int lkTermParam1;
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CV_PROP_RW float lkTermParam2;
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// Parameters for GFT algorithm.
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CV_PROP_RW float gftQualityThres;
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CV_PROP_RW int gftMinSeperationDist;
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CV_PROP_RW int gftMaxNumFeatures;
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};
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/**
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* @brief Class containing the methods needed for Quasi Dense Stereo computation.
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*
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* This module contains the code to perform quasi dense stereo matching.
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* The method initially starts with a sparse 3D reconstruction based on feature matching across a
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* stereo image pair and subsequently propagates the structure into neighboring image regions.
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* To obtain initial seed correspondences, the algorithm locates Shi and Tomashi features in the
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* left image of the stereo pair and then tracks them using pyramidal Lucas-Kanade in the right image.
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* To densify the sparse correspondences, the algorithm computes the zero-mean normalized
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* cross-correlation (ZNCC) in small patches around every seed pair and uses it as a quality metric
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* for each match. In this code, we introduce a custom structure to store the location and ZNCC value
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* of correspondences called "Match". Seed Matches are stored in a priority queue sorted according to
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* their ZNCC value, allowing for the best quality Match to be readily available. The algorithm pops
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* Matches and uses them to extract new matches around them. This is done by considering a small
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* neighboring area around each Seed and retrieving correspondences above a certain texture threshold
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* that are not previously computed. New matches are stored in the seed priority queue and used as seeds.
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* The propagation process ends when no additional matches can be retrieved.
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*
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*
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* @sa This code represents the work presented in @cite Stoyanov2010.
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* If this code is useful for your work please cite @cite Stoyanov2010.
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*
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* Also the original growing scheme idea is described in @cite Lhuillier2000
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*
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*/
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class CV_EXPORTS_W QuasiDenseStereo
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{
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public:
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/**
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* @brief destructor
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* Method to free all the memory allocated by matrices and vectors in this class.
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*/
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CV_WRAP virtual ~QuasiDenseStereo() = 0;
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/**
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* @brief Load a file containing the configuration parameters of the class.
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* @param[in] filepath The location of the .YAML file containing the configuration parameters.
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* @note default value is an empty string in which case the default parameters will be loaded.
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* @retval 1: If the path is not empty and the program loaded the parameters successfully.
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* @retval 0: If the path is empty and the program loaded default parameters.
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* @retval -1: If the file location is not valid or the program could not open the file and
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* loaded default parameters from defaults.hpp.
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* @note The method is automatically called in the constructor and configures the class.
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* @note Loading different parameters will have an effect on the output. This is useful for tuning
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* in case of video processing.
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* @sa loadParameters
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*/
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CV_WRAP virtual int loadParameters(cv::String filepath) = 0;
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/**
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* @brief Save a file containing all the configuration parameters the class is currently set to.
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* @param[in] filepath The location to store the parameters file.
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* @note Calling this method with no arguments will result in storing class parameters to a file
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* names "qds_parameters.yaml" in the root project folder.
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* @note This method can be used to generate a template file for tuning the class.
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* @sa loadParameters
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*/
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CV_WRAP virtual int saveParameters(cv::String filepath) = 0;
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/**
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* @brief Get The sparse corresponding points.
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* @param[out] sMatches A vector containing all sparse correspondences.
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* @note The method clears the sMatches vector.
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* @note The returned Match elements inside the sMatches vector, do not use corr member.
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*/
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CV_WRAP virtual void getSparseMatches(CV_OUT std::vector<MatchQuasiDense> &sMatches) = 0;
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/**
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* @brief Get The dense corresponding points.
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* @param[out] denseMatches A vector containing all dense matches.
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* @note The method clears the denseMatches vector.
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* @note The returned Match elements inside the sMatches vector, do not use corr member.
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*/
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CV_WRAP virtual void getDenseMatches(CV_OUT std::vector<MatchQuasiDense> &denseMatches) = 0;
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/**
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* @brief Main process of the algorithm. This method computes the sparse seeds and then densifies them.
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*
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* Initially input images are converted to gray-scale and then the sparseMatching method
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* is called to obtain the sparse stereo. Finally quasiDenseMatching is called to densify the corresponding
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* points.
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* @param[in] imgLeft The left Channel of a stereo image pair.
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* @param[in] imgRight The right Channel of a stereo image pair.
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* @note If input images are in color, the method assumes that are BGR and converts them to grayscale.
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* @sa sparseMatching
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* @sa quasiDenseMatching
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*/
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CV_WRAP virtual void process(const cv::Mat &imgLeft ,const cv::Mat &imgRight) = 0;
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/**
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* @brief Specify pixel coordinates in the left image and get its corresponding location in the right image.
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* @param[in] x The x pixel coordinate in the left image channel.
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* @param[in] y The y pixel coordinate in the left image channel.
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* @retval cv::Point(x, y) The location of the corresponding pixel in the right image.
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* @retval cv::Point(0, 0) (NO_MATCH) if no match is found in the right image for the specified pixel location in the left image.
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* @note This method should be always called after process, otherwise the matches will not be correct.
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*/
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CV_WRAP virtual cv::Point2f getMatch(const int x, const int y) = 0;
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/**
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* @brief Compute and return the disparity map based on the correspondences found in the "process" method.
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* @note Default level is 50
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* @return cv::Mat containing a the disparity image in grayscale.
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* @sa computeDisparity
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* @sa quantizeDisparity
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*/
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CV_WRAP virtual cv::Mat getDisparity() = 0;
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CV_WRAP static cv::Ptr<QuasiDenseStereo> create(cv::Size monoImgSize, cv::String paramFilepath = cv::String());
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CV_PROP_RW PropagationParameters Param;
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};
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} //namespace cv
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} //namespace stereo
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/** @}*/
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#endif // __OPENCV_QUASI_DENSE_STEREO_H__
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