randpattern.hpp

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#ifndef __OPENCV_RANDOMPATTERN_HPP__




#define __OPENCV_RANDOMPATTERN_HPP__








#include "opencv2/features2d.hpp"




#include "opencv2/highgui.hpp"








namespace

cv
{
namespace
randpattern {















class
CV_EXPORTS
RandomPatternCornerFinder



{



public:







/* @brief Construct RandomPatternCornerFinder object










@param patternWidth the real width of "random" pattern in a user defined unit.





@param patternHeight the real height of "random" pattern in a user defined unit.





@param nMiniMatch number of minimal matches, otherwise that image is abandoned





@depth depth of output objectPoints and imagePoints, set it to be CV_32F or CV_64F.





@showExtraction whether show feature extraction, 0 for no and 1 for yes.





@detector feature detector to detect feature points in pattern and images.





@descriptor feature descriptor.





@matcher feature matcher.





*/




RandomPatternCornerFinder(float
patternWidth,
float
patternHeight,



int
nminiMatch = 20,
int
depth = CV_32F,
int
verbose = 0,
int
showExtraction = 0,



Ptr<FeatureDetector>
detector =
AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.005f),



Ptr<DescriptorExtractor>
descriptor =
AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.005f),



Ptr<DescriptorMatcher>
matcher =
DescriptorMatcher::create("BruteForce-L1"));







/* @brief Load pattern image and compute features for pattern





@param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first.





*/




void
loadPattern(const
cv::Mat
&patternImage);







/* @brief Load pattern and features





@param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first.





@param patternKeyPoints keyPoints created from a FeatureDetector.





@param patternDescriptors descriptors created from a DescriptorExtractor.





*/




void
loadPattern(const
cv::Mat
&patternImage,
const
std::vector<cv::KeyPoint> &patternKeyPoints,
const
cv::Mat
&patternDescriptors);







/* @brief Compute matched object points and image points which are used for calibration





The objectPoints (3D) and imagePoints (2D) are stored inside the class. Run getObjectPoints()





and getImagePoints() to get them.










@param inputImages vector of 8-bit grayscale images containing "random" pattern





that are used for calibration.





*/




void
computeObjectImagePoints(std::vector<cv::Mat> inputImages);







//void computeObjectImagePoints2(std::vector<cv::Mat> inputImages);








/* @brief Compute object and image points for a single image. It returns a vector<Mat> that





the first element stores the imagePoints and the second one stores the objectPoints.










@param inputImage single input image for calibration





*/




std::vector<cv::Mat> computeObjectImagePointsForSingle(cv::Mat
inputImage);







/* @brief Get object(3D) points





*/




const
std::vector<cv::Mat> &getObjectPoints();







/* @brief and image(2D) points





*/




const
std::vector<cv::Mat> &getImagePoints();







private:







std::vector<cv::Mat> _objectPonits, _imagePoints;



float
_patternWidth, _patternHeight;



cv::Size
_patternImageSize;



int
_nminiMatch;



int
_depth;



int
_verbose;







Ptr<FeatureDetector>
_detector;



Ptr<DescriptorExtractor>
_descriptor;



Ptr<DescriptorMatcher>
_matcher;



Mat
_descriptorPattern;



std::vector<cv::KeyPoint> _keypointsPattern;



Mat
_patternImage;



int
_showExtraction;







void
keyPoints2MatchedLocation(const
std::vector<cv::KeyPoint>& imageKeypoints,



const
std::vector<cv::KeyPoint>& patternKeypoints,
const
std::vector<cv::DMatch> matchces,



cv::Mat& matchedImagelocation,
cv::Mat& matchedPatternLocation);



void
getFilteredLocation(cv::Mat& imageKeypoints,
cv::Mat& patternKeypoints,
const
cv::Mat
mask);



void
getObjectImagePoints(const
cv::Mat& imageKeypoints,
const
cv::Mat& patternKeypoints);



void
crossCheckMatching(
cv::Ptr<DescriptorMatcher>& descriptorMatcher,



const
Mat& descriptors1,
const
Mat& descriptors2,



std::vector<DMatch>& filteredMatches12,
int
knn=1 );



void
drawCorrespondence(const
Mat& image1,
const
std::vector<cv::KeyPoint> keypoint1,



const
Mat& image2,
const
std::vector<cv::KeyPoint> keypoint2,
const
std::vector<cv::DMatch> matchces,



const
Mat& mask1,
const
Mat& mask2,
const
int
step);


};







/* @brief Class to generate "random" pattern image that are used for RandomPatternCornerFinder




Please refer to paper




B. Li, L. Heng, K. Kevin  and M. Pollefeys, "A Multiple-Camera System




Calibration Toolbox Using A Feature Descriptor-Based Calibration




Pattern", in IROS 2013.




*/




class
CV_EXPORTS
RandomPatternGenerator



{



public:



/* @brief Construct RandomPatternGenerator










@param imageWidth image width of the generated pattern image





@param imageHeight image height of the generated pattern image





*/




RandomPatternGenerator(int
imageWidth,
int
imageHeight);







/* @brief Generate pattern





*/




void
generatePattern();



/* @brief Get pattern





*/




cv::Mat
getPattern();



private:



cv::Mat
_pattern;



int
_imageWidth, _imageHeight;


};










}}
//namespace randpattern, cv




#endif