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322 lines
10 KiB
C++
322 lines
10 KiB
C++
/*
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* A Demo to OpenCV Implementation of SURF
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* Further Information Refer to "SURF: Speed-Up Robust Feature"
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* Author: Liu Liu
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* liuliu.1987+opencv@gmail.com
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*/
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#include "opencv2/objdetect/objdetect.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/calib3d/calib3d.hpp"
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#include "opencv2/imgproc/imgproc_c.h"
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#include <iostream>
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#include <vector>
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#include <stdio.h>
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using namespace std;
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void help()
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{
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printf(
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"This program demonstrated the use of the SURF Detector and Descriptor using\n"
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"either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
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"on planar objects.\n"
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"Usage:\n"
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"./find_obj <object_filename> <scene_filename>, default is box.png and box_in_scene.png\n\n");
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return;
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}
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// define whether to use approximate nearest-neighbor search
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#define USE_FLANN
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IplImage* image = 0;
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double
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compareSURFDescriptors( const float* d1, const float* d2, double best, int length )
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{
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double total_cost = 0;
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assert( length % 4 == 0 );
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for( int i = 0; i < length; i += 4 )
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{
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double t0 = d1[i ] - d2[i ];
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double t1 = d1[i+1] - d2[i+1];
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double t2 = d1[i+2] - d2[i+2];
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double t3 = d1[i+3] - d2[i+3];
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total_cost += t0*t0 + t1*t1 + t2*t2 + t3*t3;
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if( total_cost > best )
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break;
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}
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return total_cost;
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}
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int
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naiveNearestNeighbor( const float* vec, int laplacian,
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const CvSeq* model_keypoints,
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const CvSeq* model_descriptors )
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{
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int length = (int)(model_descriptors->elem_size/sizeof(float));
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int i, neighbor = -1;
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double d, dist1 = 1e6, dist2 = 1e6;
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CvSeqReader reader, kreader;
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cvStartReadSeq( model_keypoints, &kreader, 0 );
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cvStartReadSeq( model_descriptors, &reader, 0 );
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for( i = 0; i < model_descriptors->total; i++ )
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{
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const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
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const float* mvec = (const float*)reader.ptr;
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CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
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CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
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if( laplacian != kp->laplacian )
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continue;
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d = compareSURFDescriptors( vec, mvec, dist2, length );
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if( d < dist1 )
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{
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dist2 = dist1;
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dist1 = d;
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neighbor = i;
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}
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else if ( d < dist2 )
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dist2 = d;
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}
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if ( dist1 < 0.6*dist2 )
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return neighbor;
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return -1;
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}
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void
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findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
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const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector<int>& ptpairs )
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{
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int i;
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CvSeqReader reader, kreader;
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cvStartReadSeq( objectKeypoints, &kreader );
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cvStartReadSeq( objectDescriptors, &reader );
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ptpairs.clear();
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for( i = 0; i < objectDescriptors->total; i++ )
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{
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const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
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const float* descriptor = (const float*)reader.ptr;
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CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
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CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
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int nearest_neighbor = naiveNearestNeighbor( descriptor, kp->laplacian, imageKeypoints, imageDescriptors );
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if( nearest_neighbor >= 0 )
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{
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ptpairs.push_back(i);
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ptpairs.push_back(nearest_neighbor);
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}
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}
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}
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void
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flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
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const CvSeq*, const CvSeq* imageDescriptors, vector<int>& ptpairs )
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{
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int length = (int)(objectDescriptors->elem_size/sizeof(float));
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cv::Mat m_object(objectDescriptors->total, length, CV_32F);
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cv::Mat m_image(imageDescriptors->total, length, CV_32F);
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// copy descriptors
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CvSeqReader obj_reader;
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float* obj_ptr = m_object.ptr<float>(0);
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cvStartReadSeq( objectDescriptors, &obj_reader );
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for(int i = 0; i < objectDescriptors->total; i++ )
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{
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const float* descriptor = (const float*)obj_reader.ptr;
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CV_NEXT_SEQ_ELEM( obj_reader.seq->elem_size, obj_reader );
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memcpy(obj_ptr, descriptor, length*sizeof(float));
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obj_ptr += length;
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}
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CvSeqReader img_reader;
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float* img_ptr = m_image.ptr<float>(0);
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cvStartReadSeq( imageDescriptors, &img_reader );
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for(int i = 0; i < imageDescriptors->total; i++ )
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{
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const float* descriptor = (const float*)img_reader.ptr;
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CV_NEXT_SEQ_ELEM( img_reader.seq->elem_size, img_reader );
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memcpy(img_ptr, descriptor, length*sizeof(float));
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img_ptr += length;
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}
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// find nearest neighbors using FLANN
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cv::Mat m_indices(objectDescriptors->total, 2, CV_32S);
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cv::Mat m_dists(objectDescriptors->total, 2, CV_32F);
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cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4)); // using 4 randomized kdtrees
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flann_index.knnSearch(m_object, m_indices, m_dists, 2, cv::flann::SearchParams(64) ); // maximum number of leafs checked
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int* indices_ptr = m_indices.ptr<int>(0);
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float* dists_ptr = m_dists.ptr<float>(0);
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for (int i=0;i<m_indices.rows;++i) {
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if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
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ptpairs.push_back(i);
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ptpairs.push_back(indices_ptr[2*i]);
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}
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}
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}
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/* a rough implementation for object location */
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int
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locatePlanarObject( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
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const CvSeq* imageKeypoints, const CvSeq* imageDescriptors,
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const CvPoint src_corners[4], CvPoint dst_corners[4] )
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{
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double h[9];
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CvMat _h = cvMat(3, 3, CV_64F, h);
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vector<int> ptpairs;
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vector<CvPoint2D32f> pt1, pt2;
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CvMat _pt1, _pt2;
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int i, n;
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#ifdef USE_FLANN
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flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
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#else
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findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
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#endif
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n = (int)(ptpairs.size()/2);
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if( n < 4 )
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return 0;
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pt1.resize(n);
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pt2.resize(n);
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for( i = 0; i < n; i++ )
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{
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pt1[i] = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))->pt;
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pt2[i] = ((CvSURFPoint*)cvGetSeqElem(imageKeypoints,ptpairs[i*2+1]))->pt;
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}
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_pt1 = cvMat(1, n, CV_32FC2, &pt1[0] );
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_pt2 = cvMat(1, n, CV_32FC2, &pt2[0] );
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if( !cvFindHomography( &_pt1, &_pt2, &_h, CV_RANSAC, 5 ))
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return 0;
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for( i = 0; i < 4; i++ )
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{
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double x = src_corners[i].x, y = src_corners[i].y;
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double Z = 1./(h[6]*x + h[7]*y + h[8]);
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double X = (h[0]*x + h[1]*y + h[2])*Z;
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double Y = (h[3]*x + h[4]*y + h[5])*Z;
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dst_corners[i] = cvPoint(cvRound(X), cvRound(Y));
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}
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return 1;
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}
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int main(int argc, char** argv)
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{
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const char* object_filename = argc == 3 ? argv[1] : "box.png";
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const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
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help();
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IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
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IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
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if( !object || !image )
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{
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fprintf( stderr, "Can not load %s and/or %s\n",
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object_filename, scene_filename );
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exit(-1);
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}
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CvMemStorage* storage = cvCreateMemStorage(0);
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cvNamedWindow("Object", 1);
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cvNamedWindow("Object Correspond", 1);
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static CvScalar colors[] =
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{
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{{0,0,255}},
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{{0,128,255}},
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{{0,255,255}},
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{{0,255,0}},
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{{255,128,0}},
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{{255,255,0}},
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{{255,0,0}},
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{{255,0,255}},
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{{255,255,255}}
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};
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IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
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cvCvtColor( object, object_color, CV_GRAY2BGR );
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CvSeq* objectKeypoints = 0, *objectDescriptors = 0;
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CvSeq* imageKeypoints = 0, *imageDescriptors = 0;
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int i;
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CvSURFParams params = cvSURFParams(500, 1);
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double tt = (double)cvGetTickCount();
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cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
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printf("Object Descriptors: %d\n", objectDescriptors->total);
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cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
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printf("Image Descriptors: %d\n", imageDescriptors->total);
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tt = (double)cvGetTickCount() - tt;
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printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));
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CvPoint src_corners[4] = {{0,0}, {object->width,0}, {object->width, object->height}, {0, object->height}};
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CvPoint dst_corners[4];
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IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 );
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cvSetImageROI( correspond, cvRect( 0, 0, object->width, object->height ) );
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cvCopy( object, correspond );
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cvSetImageROI( correspond, cvRect( 0, object->height, correspond->width, correspond->height ) );
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cvCopy( image, correspond );
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cvResetImageROI( correspond );
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#ifdef USE_FLANN
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printf("Using approximate nearest neighbor search\n");
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#endif
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if( locatePlanarObject( objectKeypoints, objectDescriptors, imageKeypoints,
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imageDescriptors, src_corners, dst_corners ))
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{
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for( i = 0; i < 4; i++ )
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{
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CvPoint r1 = dst_corners[i%4];
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CvPoint r2 = dst_corners[(i+1)%4];
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cvLine( correspond, cvPoint(r1.x, r1.y+object->height ),
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cvPoint(r2.x, r2.y+object->height ), colors[8] );
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}
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}
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vector<int> ptpairs;
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#ifdef USE_FLANN
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flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
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#else
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findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
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#endif
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for( i = 0; i < (int)ptpairs.size(); i += 2 )
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{
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CvSURFPoint* r1 = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, ptpairs[i] );
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CvSURFPoint* r2 = (CvSURFPoint*)cvGetSeqElem( imageKeypoints, ptpairs[i+1] );
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cvLine( correspond, cvPointFrom32f(r1->pt),
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cvPoint(cvRound(r2->pt.x), cvRound(r2->pt.y+object->height)), colors[8] );
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}
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cvShowImage( "Object Correspond", correspond );
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for( i = 0; i < objectKeypoints->total; i++ )
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{
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CvSURFPoint* r = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, i );
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CvPoint center;
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int radius;
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center.x = cvRound(r->pt.x);
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center.y = cvRound(r->pt.y);
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radius = cvRound(r->size*1.2/9.*2);
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cvCircle( object_color, center, radius, colors[0], 1, 8, 0 );
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}
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cvShowImage( "Object", object_color );
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cvWaitKey(0);
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cvDestroyWindow("Object");
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cvDestroyWindow("Object Correspond");
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return 0;
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}
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