modified FernClassifier::train(); remove old RTreeClassifier and added new implementation CalonderClassifier; removed old find_obj_calonder and added new one
This commit is contained in:
Maria Dimashova
@@ -1,309 +1,154 @@
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//Calonder descriptor sample
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#include <stdio.h>
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#if 0
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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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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/features2d/features2d.hpp>
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#include <iostream>
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#include <fstream>
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using namespace std;
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using namespace cv;
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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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void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
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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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H.create(3, 3, CV_32FC1);
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H.at<float>(0,0) = rng.uniform( 0.8f, 1.2f);
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H.at<float>(0,1) = rng.uniform(-0.1f, 0.1f);
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H.at<float>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
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H.at<float>(1,0) = rng.uniform(-0.1f, 0.1f);
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H.at<float>(1,1) = rng.uniform( 0.8f, 1.2f);
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H.at<float>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
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H.at<float>(2,0) = rng.uniform( -1e-4f, 1e-4f);
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H.at<float>(2,1) = rng.uniform( -1e-4f, 1e-4f);
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H.at<float>(2,2) = rng.uniform( 0.8f, 1.2f);
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warpPerspective( src, dst, H, src.size() );
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}
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void FindAffineTransform(const vector<CvPoint>& p1, const vector<CvPoint>& p2, CvMat* affine)
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int main( int argc, char **argv )
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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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if( argc != 4 && argc != 3 )
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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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cout << "Format:" << endl <<
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" classifier(xml to write) test_image file_with_train_images_filenames(txt)" <<
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" or" << endl <<
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" classifier(xml to read) test_image" << endl;
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return -1;
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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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CalonderClassifier classifier;
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if( argc == 4 ) // Train
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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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// Read train images and test image
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ifstream fst( argv[3], ifstream::in );
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vector<Mat> trainImgs;
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while( !fst.eof() )
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{
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string str;
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getline( fst, str );
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if (str.empty()) break;
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Mat img = imread( str, CV_LOAD_IMAGE_GRAYSCALE );
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if( !img.empty() )
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trainImgs.push_back( img );
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}
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if( trainImgs.empty() )
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{
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cout << "All train images can not be read." << endl;
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return -1;
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}
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cout << trainImgs.size() << " train images were read." << endl;
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// Extract keypoints from train images
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SurfFeatureDetector detector;
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vector<vector<Point2f> > trainPoints( trainImgs.size() );
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for( size_t i = 0; i < trainImgs.size(); i++ )
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{
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vector<KeyPoint> kps;
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detector.detect( trainImgs[i], kps );
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KeyPoint::convert( kps, trainPoints[i] );
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}
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// Train Calonder classifier on extracted points
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classifier.setVerbose( true);
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classifier.train( trainPoints, trainImgs );
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// Write Calonder classifier
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FileStorage fs( argv[1], FileStorage::WRITE );
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classifier.write( fs );
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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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else
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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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// Read Calonder classifier
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FileStorage fs( argv[1], FileStorage::READ );
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classifier.read( fs.root() );
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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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#endif
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int main( int, char** )
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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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#if 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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#endif
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if( classifier.empty() )
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{
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cout << "Calonder classifier is empty" << endl;
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return -1;
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}
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// Test Calonder classifier on test image and warped one
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Mat testImg1 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE ), testImg2, H12;
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if( testImg1.empty() )
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{
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cout << "Test image can not be read." << endl;
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return -1;
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}
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warpPerspectiveRand( testImg1, testImg2, H12, theRNG() );
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// Exstract keypoints from test images
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SurfFeatureDetector detector;
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vector<KeyPoint> testKeypoints1; detector.detect( testImg1, testKeypoints1 );
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vector<KeyPoint> testKeypoints2; detector.detect( testImg2, testKeypoints2 );
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vector<Point2f> testPoints1; KeyPoint::convert( testKeypoints1, testPoints1 );
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vector<Point2f> testPoints2; KeyPoint::convert( testKeypoints2, testPoints2 );
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// Calculate Calonder descriptors
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int signatureSize = classifier.getSignatureSize();
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vector<float> r1(testPoints1.size()*signatureSize), r2(testPoints2.size()*signatureSize);
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vector<float>::iterator rit = r1.begin();
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for( size_t i = 0; i < testPoints1.size(); i++ )
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{
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vector<float> s;
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classifier( testImg1, testPoints1[i], s );
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copy( s.begin(), s.end(), rit );
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rit += s.size();
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}
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rit = r2.begin();
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for( size_t i = 0; i < testPoints2.size(); i++ )
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{
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vector<float> s;
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classifier( testImg2, testPoints2[i], s );
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copy( s.begin(), s.end(), rit );
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rit += s.size();
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}
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Mat descriptors1(testPoints1.size(), classifier.getSignatureSize(), CV_32FC1, &r1[0] ),
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descriptors2(testPoints2.size(), classifier.getSignatureSize(), CV_32FC1, &r2[0] );
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// Match descriptors
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BruteForceMatcher<L1<float> > matcher;
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matcher.add( descriptors2 );
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vector<int> matches;
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matcher.match( descriptors1, matches );
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// Draw results
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// Prepare inlier mask
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vector<char> matchesMask( matches.size(), 0 );
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Mat points1t; perspectiveTransform(Mat(testPoints1), points1t, H12);
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vector<int>::const_iterator mit = matches.begin();
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for( size_t mi = 0; mi < matches.size(); mi++ )
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||||
{
|
||||
if( norm(testPoints2[matches[mi]] - points1t.at<Point2f>(mi,0)) < 4 ) // inlier
|
||||
matchesMask[mi] = 1;
|
||||
}
|
||||
// Draw
|
||||
Mat drawImg;
|
||||
drawMatches( testImg1, testKeypoints1, testImg2, testKeypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask );
|
||||
string winName = "Matches";
|
||||
namedWindow( winName, WINDOW_AUTOSIZE );
|
||||
imshow( winName, drawImg );
|
||||
waitKey();
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user