306 lines
8.2 KiB
C++
306 lines
8.2 KiB
C++
//Calonder descriptor sample
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#include <cxcore.h>
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#include <cv.h>
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#include <cvaux.h>
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#include <highgui.h>
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#include <vector>
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using namespace std;
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// Number of training points (set to -1 to use all points)
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const int n_points = -1;
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//Draw the border of projection of train image calculed by averaging detected correspondences
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const bool draw_border = true;
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void cvmSet6(CvMat* m, int row, int col, float val1, float val2, float val3, float val4, float val5, float val6)
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{
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cvmSet(m, row, col, val1);
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cvmSet(m, row, col + 1, val2);
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cvmSet(m, row, col + 2, val3);
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cvmSet(m, row, col + 3, val4);
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cvmSet(m, row, col + 4, val5);
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cvmSet(m, row, col + 5, val6);
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}
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void FindAffineTransform(const vector<CvPoint>& p1, const vector<CvPoint>& p2, CvMat* affine)
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{
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int eq_num = 2*(int)p1.size();
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CvMat* A = cvCreateMat(eq_num, 6, CV_32FC1);
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CvMat* B = cvCreateMat(eq_num, 1, CV_32FC1);
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CvMat* X = cvCreateMat(6, 1, CV_32FC1);
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for(int i = 0; i < (int)p1.size(); i++)
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{
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cvmSet6(A, 2*i, 0, p1[i].x, p1[i].y, 1, 0, 0, 0);
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cvmSet6(A, 2*i + 1, 0, 0, 0, 0, p1[i].x, p1[i].y, 1);
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cvmSet(B, 2*i, 0, p2[i].x);
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cvmSet(B, 2*i + 1, 0, p2[i].y);
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}
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cvSolve(A, B, X, CV_SVD);
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cvmSet(affine, 0, 0, cvmGet(X, 0, 0));
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cvmSet(affine, 0, 1, cvmGet(X, 1, 0));
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cvmSet(affine, 0, 2, cvmGet(X, 2, 0));
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cvmSet(affine, 1, 0, cvmGet(X, 3, 0));
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cvmSet(affine, 1, 1, cvmGet(X, 4, 0));
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cvmSet(affine, 1, 2, cvmGet(X, 5, 0));
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cvReleaseMat(&A);
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cvReleaseMat(&B);
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cvReleaseMat(&X);
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}
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void MapVectorAffine(const vector<CvPoint>& p1, vector<CvPoint>& p2, CvMat* transform)
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{
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float a = cvmGet(transform, 0, 0);
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float b = cvmGet(transform, 0, 1);
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float c = cvmGet(transform, 0, 2);
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float d = cvmGet(transform, 1, 0);
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float e = cvmGet(transform, 1, 1);
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float f = cvmGet(transform, 1, 2);
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for(int i = 0; i < (int)p1.size(); i++)
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{
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float x = a*p1[i].x + b*p1[i].y + c;
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float y = d*p1[i].x + e*p1[i].y + f;
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p2.push_back(cvPoint(x, y));
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}
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}
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float CalcAffineReprojectionError(const vector<CvPoint>& p1, const vector<CvPoint>& p2, CvMat* transform)
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{
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vector<CvPoint> mapped_p1;
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MapVectorAffine(p1, mapped_p1, transform);
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float error = 0;
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for(int i = 0; i < (int)p2.size(); i++)
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{
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error += ((p2[i].x - mapped_p1[i].x)*(p2[i].x - mapped_p1[i].x)+(p2[i].y - mapped_p1[i].y)*(p2[i].y - mapped_p1[i].y));
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}
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error /= p2.size();
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return error;
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}
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int main( int argc, char** argv )
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{
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printf("calonder_sample is under construction\n");
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return 0;
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IplImage* test_image;
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IplImage* train_image;
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if (argc < 3)
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{
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test_image = cvLoadImage("box_in_scene.png",0);
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train_image = cvLoadImage("box.png ",0);
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if (!test_image || !train_image)
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{
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printf("Usage: calonder_sample <train_image> <test_image>");
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return 0;
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}
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}
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else
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{
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test_image = cvLoadImage(argv[2],0);
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train_image = cvLoadImage(argv[1],0);
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}
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if (!train_image)
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{
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printf("Unable to load train image\n");
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return 0;
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}
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if (!test_image)
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{
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printf("Unable to load test image\n");
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return 0;
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}
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CvMemStorage* storage = cvCreateMemStorage(0);
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CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
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CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
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CvSURFParams params = cvSURFParams(500, 1);
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cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors, storage, params );
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cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors, storage, params );
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cv::RTreeClassifier detector;
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int patch_width = cv::PATCH_SIZE;
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int patch_height = cv::PATCH_SIZE;
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vector<cv::BaseKeypoint> base_set;
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int i=0;
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CvSURFPoint* point;
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for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
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{
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point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
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base_set.push_back(cv::BaseKeypoint(point->pt.x,point->pt.y,train_image));
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}
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//Detector training
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cv::RNG rng( cvGetTickCount() );
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cv::PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,-CV_PI/3,CV_PI/3);
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printf("RTree Classifier training...\n");
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detector.train(base_set,rng,gen,24,cv::DEFAULT_DEPTH,2000,(int)base_set.size(),detector.DEFAULT_NUM_QUANT_BITS);
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printf("Done\n");
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float* signature = new float[detector.original_num_classes()];
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float* best_corr;
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int* best_corr_idx;
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if (imageKeypoints->total > 0)
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{
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best_corr = new float[imageKeypoints->total];
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best_corr_idx = new int[imageKeypoints->total];
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}
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for(i=0; i < imageKeypoints->total; i++)
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{
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point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
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int part_idx = -1;
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float prob = 0.0f;
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CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,(int)(point->pt.y) - patch_height/2, patch_width, patch_height);
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cvSetImageROI(test_image, roi);
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roi = cvGetImageROI(test_image);
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if(roi.width != patch_width || roi.height != patch_height)
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{
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best_corr_idx[i] = part_idx;
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best_corr[i] = prob;
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}
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else
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{
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cvSetImageROI(test_image, roi);
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IplImage* roi_image = cvCreateImage(cvSize(roi.width, roi.height), test_image->depth, test_image->nChannels);
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cvCopy(test_image,roi_image);
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detector.getSignature(roi_image, signature);
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for (int j = 0; j< detector.original_num_classes();j++)
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{
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if (prob < signature[j])
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{
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part_idx = j;
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prob = signature[j];
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}
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}
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best_corr_idx[i] = part_idx;
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best_corr[i] = prob;
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if (roi_image)
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cvReleaseImage(&roi_image);
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}
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cvResetImageROI(test_image);
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}
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float min_prob = 0.0f;
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vector<CvPoint> object;
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vector<CvPoint> features;
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for (int j=0;j<objectKeypoints->total;j++)
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{
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float prob = 0.0f;
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int idx = -1;
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for (i = 0; i<imageKeypoints->total;i++)
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{
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if ((best_corr_idx[i]!=j)||(best_corr[i] < min_prob))
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continue;
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if (best_corr[i] > prob)
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{
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prob = best_corr[i];
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idx = i;
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}
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}
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if (idx >=0)
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{
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point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,j);
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object.push_back(cvPoint((int)point->pt.x,(int)point->pt.y));
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point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,idx);
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features.push_back(cvPoint((int)point->pt.x,(int)point->pt.y));
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}
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}
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if ((int)object.size() > 3)
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{
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CvMat* affine = cvCreateMat(2, 3, CV_32FC1);
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FindAffineTransform(object,features,affine);
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vector<CvPoint> corners;
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vector<CvPoint> mapped_corners;
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corners.push_back(cvPoint(0,0));
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corners.push_back(cvPoint(0,train_image->height));
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corners.push_back(cvPoint(train_image->width,0));
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corners.push_back(cvPoint(train_image->width,train_image->height));
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MapVectorAffine(corners,mapped_corners,affine);
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//Drawing the result
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IplImage* result = cvCreateImage(cvSize(test_image->width > train_image->width ? test_image->width : train_image->width,
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train_image->height + test_image->height),
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test_image->depth, test_image->nChannels);
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cvSetImageROI(result,cvRect(0,0,train_image->width, train_image->height));
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cvCopy(train_image,result);
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cvResetImageROI(result);
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cvSetImageROI(result,cvRect(0,train_image->height,test_image->width, test_image->height));
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cvCopy(test_image,result);
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cvResetImageROI(result);
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for (int i=0;i<(int)features.size();i++)
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{
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cvLine(result,object[i],cvPoint(features[i].x,features[i].y+train_image->height),cvScalar(255));
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}
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if (draw_border)
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{
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cvLine(result,cvPoint(mapped_corners[0].x, mapped_corners[0].y+train_image->height),
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cvPoint(mapped_corners[1].x, mapped_corners[1].y+train_image->height),cvScalar(150),3);
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cvLine(result,cvPoint(mapped_corners[0].x, mapped_corners[0].y+train_image->height),
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cvPoint(mapped_corners[2].x, mapped_corners[2].y+train_image->height),cvScalar(150),3);
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cvLine(result,cvPoint(mapped_corners[1].x, mapped_corners[1].y+train_image->height),
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cvPoint(mapped_corners[3].x, mapped_corners[3].y+train_image->height),cvScalar(150),3);
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cvLine(result,cvPoint(mapped_corners[2].x, mapped_corners[2].y+train_image->height),
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cvPoint(mapped_corners[3].x, mapped_corners[3].y+train_image->height),cvScalar(150),3);
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}
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cvSaveImage("Result.jpg",result);
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cvNamedWindow("Result",0);
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cvShowImage("Result",result);
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cvWaitKey();
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cvReleaseMat(&affine);
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cvReleaseImage(&result);
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}
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else
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{
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printf("Unable to find correspondence\n");
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}
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if (signature)
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delete[] signature;
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if (best_corr)
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delete[] best_corr;
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cvReleaseMemStorage(&storage);
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cvReleaseImage(&train_image);
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cvReleaseImage(&test_image);
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return 0;
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}
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