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https://github.com/opencv/opencv.git
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435 lines
14 KiB
C++
435 lines
14 KiB
C++
#ifndef __OPENCV_FEATURES_2D_MANUAL_HPP__
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#define __OPENCV_FEATURES_2D_MANUAL_HPP__
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_FEATURES2D
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#include "opencv2/features2d.hpp"
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#include "features2d_converters.hpp"
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#undef SIMPLEBLOB // to solve conflict with wincrypt.h on windows
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namespace cv
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{
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class CV_EXPORTS_AS(FeatureDetector) javaFeatureDetector
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{
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public:
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CV_WRAP void detect( const Mat& image, CV_OUT std::vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const
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{ return wrapped->detect(image, keypoints, mask); }
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CV_WRAP void detect( const std::vector<Mat>& images, CV_OUT std::vector<std::vector<KeyPoint> >& keypoints, const std::vector<Mat>& masks=std::vector<Mat>() ) const
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{ return wrapped->detect(images, keypoints, masks); }
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CV_WRAP bool empty() const
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{ return wrapped->empty(); }
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enum
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{
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FAST = 1,
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STAR = 2,
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SIFT = 3,
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SURF = 4,
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ORB = 5,
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MSER = 6,
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GFTT = 7,
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HARRIS = 8,
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SIMPLEBLOB = 9,
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DENSE = 10,
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BRISK = 11,
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AKAZE = 12,
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GRIDDETECTOR = 1000,
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GRID_FAST = GRIDDETECTOR + FAST,
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GRID_STAR = GRIDDETECTOR + STAR,
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GRID_SIFT = GRIDDETECTOR + SIFT,
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GRID_SURF = GRIDDETECTOR + SURF,
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GRID_ORB = GRIDDETECTOR + ORB,
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GRID_MSER = GRIDDETECTOR + MSER,
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GRID_GFTT = GRIDDETECTOR + GFTT,
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GRID_HARRIS = GRIDDETECTOR + HARRIS,
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GRID_SIMPLEBLOB = GRIDDETECTOR + SIMPLEBLOB,
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GRID_DENSE = GRIDDETECTOR + DENSE,
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GRID_BRISK = GRIDDETECTOR + BRISK,
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GRID_AKAZE = GRIDDETECTOR + AKAZE,
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PYRAMIDDETECTOR = 2000,
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PYRAMID_FAST = PYRAMIDDETECTOR + FAST,
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PYRAMID_STAR = PYRAMIDDETECTOR + STAR,
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PYRAMID_SIFT = PYRAMIDDETECTOR + SIFT,
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PYRAMID_SURF = PYRAMIDDETECTOR + SURF,
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PYRAMID_ORB = PYRAMIDDETECTOR + ORB,
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PYRAMID_MSER = PYRAMIDDETECTOR + MSER,
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PYRAMID_GFTT = PYRAMIDDETECTOR + GFTT,
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PYRAMID_HARRIS = PYRAMIDDETECTOR + HARRIS,
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PYRAMID_SIMPLEBLOB = PYRAMIDDETECTOR + SIMPLEBLOB,
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PYRAMID_DENSE = PYRAMIDDETECTOR + DENSE,
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PYRAMID_BRISK = PYRAMIDDETECTOR + BRISK,
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PYRAMID_AKAZE = PYRAMIDDETECTOR + AKAZE,
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DYNAMICDETECTOR = 3000,
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DYNAMIC_FAST = DYNAMICDETECTOR + FAST,
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DYNAMIC_STAR = DYNAMICDETECTOR + STAR,
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DYNAMIC_SIFT = DYNAMICDETECTOR + SIFT,
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DYNAMIC_SURF = DYNAMICDETECTOR + SURF,
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DYNAMIC_ORB = DYNAMICDETECTOR + ORB,
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DYNAMIC_MSER = DYNAMICDETECTOR + MSER,
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DYNAMIC_GFTT = DYNAMICDETECTOR + GFTT,
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DYNAMIC_HARRIS = DYNAMICDETECTOR + HARRIS,
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DYNAMIC_SIMPLEBLOB = DYNAMICDETECTOR + SIMPLEBLOB,
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DYNAMIC_DENSE = DYNAMICDETECTOR + DENSE,
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DYNAMIC_BRISK = DYNAMICDETECTOR + BRISK,
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DYNAMIC_AKAZE = DYNAMICDETECTOR + AKAZE
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};
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//supported: FAST STAR SIFT SURF ORB MSER GFTT HARRIS BRISK AKAZE Grid(XXXX) Pyramid(XXXX) Dynamic(XXXX)
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//not supported: SimpleBlob, Dense
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CV_WRAP static javaFeatureDetector* create( int detectorType )
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{
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//String name;
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if (detectorType > DYNAMICDETECTOR)
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{
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//name = "Dynamic";
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detectorType -= DYNAMICDETECTOR;
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}
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if (detectorType > PYRAMIDDETECTOR)
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{
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//name = "Pyramid";
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detectorType -= PYRAMIDDETECTOR;
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}
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if (detectorType > GRIDDETECTOR)
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{
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//name = "Grid";
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detectorType -= GRIDDETECTOR;
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}
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Ptr<FeatureDetector> fd;
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switch(detectorType)
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{
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case FAST:
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fd = FastFeatureDetector::create();
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break;
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//case STAR:
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// fd = xfeatures2d::StarDetector::create();
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// break;
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//case SIFT:
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// name = name + "SIFT";
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// break;
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//case SURF:
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// name = name + "SURF";
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// break;
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case ORB:
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fd = ORB::create();
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break;
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case MSER:
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fd = MSER::create();
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break;
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case GFTT:
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fd = GFTTDetector::create();
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break;
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case HARRIS:
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{
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Ptr<GFTTDetector> gftt = GFTTDetector::create();
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gftt->setHarrisDetector(true);
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fd = gftt;
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}
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break;
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case SIMPLEBLOB:
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fd = SimpleBlobDetector::create();
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break;
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//case DENSE:
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// name = name + "Dense";
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// break;
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case BRISK:
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fd = BRISK::create();
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break;
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case AKAZE:
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fd = AKAZE::create();
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break;
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default:
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CV_Error( Error::StsBadArg, "Specified feature detector type is not supported." );
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break;
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}
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return new javaFeatureDetector(fd);
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}
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CV_WRAP void write( const String& fileName ) const
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{
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FileStorage fs(fileName, FileStorage::WRITE);
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wrapped->write(fs);
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}
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CV_WRAP void read( const String& fileName )
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{
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FileStorage fs(fileName, FileStorage::READ);
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wrapped->read(fs.root());
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}
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private:
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javaFeatureDetector(Ptr<FeatureDetector> _wrapped) : wrapped(_wrapped)
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{}
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Ptr<FeatureDetector> wrapped;
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};
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class CV_EXPORTS_AS(DescriptorMatcher) javaDescriptorMatcher
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{
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public:
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CV_WRAP bool isMaskSupported() const
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{ return wrapped->isMaskSupported(); }
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CV_WRAP void add( const std::vector<Mat>& descriptors )
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{ return wrapped->add(descriptors); }
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CV_WRAP const std::vector<Mat>& getTrainDescriptors() const
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{ return wrapped->getTrainDescriptors(); }
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CV_WRAP void clear()
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{ return wrapped->clear(); }
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CV_WRAP bool empty() const
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{ return wrapped->empty(); }
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CV_WRAP void train()
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{ return wrapped->train(); }
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CV_WRAP void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
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CV_OUT std::vector<DMatch>& matches, const Mat& mask=Mat() ) const
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{ return wrapped->match(queryDescriptors, trainDescriptors, matches, mask); }
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CV_WRAP void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
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CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
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const Mat& mask=Mat(), bool compactResult=false ) const
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{ return wrapped->knnMatch(queryDescriptors, trainDescriptors, matches, k, mask, compactResult); }
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CV_WRAP void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
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CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
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const Mat& mask=Mat(), bool compactResult=false ) const
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{ return wrapped->radiusMatch(queryDescriptors, trainDescriptors, matches, maxDistance, mask, compactResult); }
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CV_WRAP void match( const Mat& queryDescriptors, CV_OUT std::vector<DMatch>& matches,
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const std::vector<Mat>& masks=std::vector<Mat>() )
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{ return wrapped->match(queryDescriptors, matches, masks); }
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CV_WRAP void knnMatch( const Mat& queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
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const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false )
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{ return wrapped->knnMatch(queryDescriptors, matches, k, masks, compactResult); }
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CV_WRAP void radiusMatch( const Mat& queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
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const std::vector<Mat>& masks=std::vector<Mat>(), bool compactResult=false )
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{ return wrapped->radiusMatch(queryDescriptors, matches, maxDistance, masks, compactResult); }
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enum
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{
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FLANNBASED = 1,
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BRUTEFORCE = 2,
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BRUTEFORCE_L1 = 3,
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BRUTEFORCE_HAMMING = 4,
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BRUTEFORCE_HAMMINGLUT = 5,
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BRUTEFORCE_SL2 = 6
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};
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CV_WRAP_AS(clone) javaDescriptorMatcher* jclone( bool emptyTrainData=false ) const
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{
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return new javaDescriptorMatcher(wrapped->clone(emptyTrainData));
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}
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//supported: FlannBased, BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT
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CV_WRAP static javaDescriptorMatcher* create( int matcherType )
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{
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String name;
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switch(matcherType)
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{
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case FLANNBASED:
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name = "FlannBased";
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break;
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case BRUTEFORCE:
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name = "BruteForce";
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break;
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case BRUTEFORCE_L1:
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name = "BruteForce-L1";
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break;
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case BRUTEFORCE_HAMMING:
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name = "BruteForce-Hamming";
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break;
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case BRUTEFORCE_HAMMINGLUT:
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name = "BruteForce-HammingLUT";
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break;
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case BRUTEFORCE_SL2:
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name = "BruteForce-SL2";
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break;
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default:
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CV_Error( Error::StsBadArg, "Specified descriptor matcher type is not supported." );
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break;
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}
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return new javaDescriptorMatcher(DescriptorMatcher::create(name));
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}
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CV_WRAP void write( const String& fileName ) const
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{
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FileStorage fs(fileName, FileStorage::WRITE);
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wrapped->write(fs);
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}
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CV_WRAP void read( const String& fileName )
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{
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FileStorage fs(fileName, FileStorage::READ);
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wrapped->read(fs.root());
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}
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private:
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javaDescriptorMatcher(Ptr<DescriptorMatcher> _wrapped) : wrapped(_wrapped)
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{}
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Ptr<DescriptorMatcher> wrapped;
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};
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class CV_EXPORTS_AS(DescriptorExtractor) javaDescriptorExtractor
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{
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public:
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CV_WRAP void compute( const Mat& image, CV_IN_OUT std::vector<KeyPoint>& keypoints, Mat& descriptors ) const
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{ return wrapped->compute(image, keypoints, descriptors); }
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CV_WRAP void compute( const std::vector<Mat>& images, CV_IN_OUT std::vector<std::vector<KeyPoint> >& keypoints, CV_OUT std::vector<Mat>& descriptors ) const
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{ return wrapped->compute(images, keypoints, descriptors); }
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CV_WRAP int descriptorSize() const
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{ return wrapped->descriptorSize(); }
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CV_WRAP int descriptorType() const
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{ return wrapped->descriptorType(); }
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CV_WRAP bool empty() const
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{ return wrapped->empty(); }
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enum
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{
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SIFT = 1,
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SURF = 2,
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ORB = 3,
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BRIEF = 4,
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BRISK = 5,
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FREAK = 6,
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AKAZE = 7,
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OPPONENTEXTRACTOR = 1000,
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OPPONENT_SIFT = OPPONENTEXTRACTOR + SIFT,
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OPPONENT_SURF = OPPONENTEXTRACTOR + SURF,
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OPPONENT_ORB = OPPONENTEXTRACTOR + ORB,
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OPPONENT_BRIEF = OPPONENTEXTRACTOR + BRIEF,
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OPPONENT_BRISK = OPPONENTEXTRACTOR + BRISK,
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OPPONENT_FREAK = OPPONENTEXTRACTOR + FREAK,
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OPPONENT_AKAZE = OPPONENTEXTRACTOR + AKAZE
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};
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//supported SIFT, SURF, ORB, BRIEF, BRISK, FREAK, AKAZE, Opponent(XXXX)
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//not supported: Calonder
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CV_WRAP static javaDescriptorExtractor* create( int extractorType )
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{
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//String name;
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if (extractorType > OPPONENTEXTRACTOR)
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{
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//name = "Opponent";
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extractorType -= OPPONENTEXTRACTOR;
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}
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Ptr<DescriptorExtractor> de;
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switch(extractorType)
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{
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//case SIFT:
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// name = name + "SIFT";
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// break;
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//case SURF:
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// name = name + "SURF";
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// break;
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case ORB:
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de = ORB::create();
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break;
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//case BRIEF:
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// name = name + "BRIEF";
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// break;
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case BRISK:
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de = BRISK::create();
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break;
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//case FREAK:
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// name = name + "FREAK";
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// break;
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case AKAZE:
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de = AKAZE::create();
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break;
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default:
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CV_Error( Error::StsBadArg, "Specified descriptor extractor type is not supported." );
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break;
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}
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return new javaDescriptorExtractor(de);
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}
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CV_WRAP void write( const String& fileName ) const
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{
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FileStorage fs(fileName, FileStorage::WRITE);
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wrapped->write(fs);
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}
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CV_WRAP void read( const String& fileName )
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{
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FileStorage fs(fileName, FileStorage::READ);
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wrapped->read(fs.root());
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}
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private:
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javaDescriptorExtractor(Ptr<DescriptorExtractor> _wrapped) : wrapped(_wrapped)
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{}
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Ptr<DescriptorExtractor> wrapped;
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};
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#if 0
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//DO NOT REMOVE! The block is required for sources parser
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enum
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{
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DRAW_OVER_OUTIMG = 1, // Output image matrix will not be created (Mat::create).
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// Matches will be drawn on existing content of output image.
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NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
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DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around keypoint with keypoint size and
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// orientation will be drawn.
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};
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// Draw keypoints.
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CV_EXPORTS_W void drawKeypoints( const Mat& image, const std::vector<KeyPoint>& keypoints, Mat& outImage,
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const Scalar& color=Scalar::all(-1), int flags=0 );
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// Draws matches of keypints from two images on output image.
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CV_EXPORTS_W void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
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const Mat& img2, const std::vector<KeyPoint>& keypoints2,
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const std::vector<DMatch>& matches1to2, Mat& outImg,
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const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
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const std::vector<char>& matchesMask=std::vector<char>(), int flags=0 );
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CV_EXPORTS_AS(drawMatches2) void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
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const Mat& img2, const std::vector<KeyPoint>& keypoints2,
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const std::vector<std::vector<DMatch> >& matches1to2, Mat& outImg,
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const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
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const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=0);
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#endif
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} //cv
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#endif // HAVE_OPENCV_FEATURES2D
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#endif // __OPENCV_FEATURES_2D_MANUAL_HPP__
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