99 lines
3.4 KiB
C++
99 lines
3.4 KiB
C++
#include "opencv2/calib3d/calib3d.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/imgproc/imgproc_c.h"
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#include <cstdio>
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using namespace cv;
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void help()
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{
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printf("Use the SURF descriptor for matching keypoints between 2 images\n");
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printf("Format: \n./generic_descriptor_match [image1] [image2] [algorithm] [XML params]\n");
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printf("For example: ./generic_descriptor_match scene_l.bmp scene_r.bmp FERN fern_params.xml\n");
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}
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IplImage* DrawCorrespondences(IplImage* img1, const vector<KeyPoint>& features1, IplImage* img2,
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const vector<KeyPoint>& features2, const vector<DMatch>& desc_idx);
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int main(int argc, char** argv)
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{
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if (argc != 5)
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{
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help();
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return 0;
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}
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std::string img1_name = std::string(argv[1]);
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std::string img2_name = std::string(argv[2]);
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std::string alg_name = std::string(argv[3]);
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std::string params_filename = std::string(argv[4]);
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Ptr<GenericDescriptorMatcher> descriptorMatcher = GenericDescriptorMatcher::create(alg_name, params_filename);
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if( descriptorMatcher == 0 )
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{
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printf ("Cannot create descriptor\n");
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return 0;
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}
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//printf("Reading the images...\n");
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IplImage* img1 = cvLoadImage(img1_name.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
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IplImage* img2 = cvLoadImage(img2_name.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
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// extract keypoints from the first image
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SURF surf_extractor(5.0e3);
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vector<KeyPoint> keypoints1;
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// printf("Extracting keypoints\n");
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surf_extractor(img1, Mat(), keypoints1);
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printf("Extracted %d keypoints from the first image\n", (int)keypoints1.size());
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vector<KeyPoint> keypoints2;
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surf_extractor(img2, Mat(), keypoints2);
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printf("Extracted %d keypoints from the second image\n", (int)keypoints2.size());
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printf("Finding nearest neighbors... \n");
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// find NN for each of keypoints2 in keypoints1
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vector<DMatch> matches2to1;
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descriptorMatcher->match( img2, keypoints2, img1, keypoints1, matches2to1 );
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printf("Done\n");
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IplImage* img_corr = DrawCorrespondences(img1, keypoints1, img2, keypoints2, matches2to1);
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cvNamedWindow("correspondences", 1);
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cvShowImage("correspondences", img_corr);
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cvWaitKey(0);
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cvReleaseImage(&img1);
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cvReleaseImage(&img2);
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cvReleaseImage(&img_corr);
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}
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IplImage* DrawCorrespondences(IplImage* img1, const vector<KeyPoint>& features1, IplImage* img2,
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const vector<KeyPoint>& features2, const vector<DMatch>& desc_idx)
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{
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IplImage* img_corr = cvCreateImage(cvSize(img1->width + img2->width, MAX(img1->height, img2->height)),
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IPL_DEPTH_8U, 3);
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cvSetImageROI(img_corr, cvRect(0, 0, img1->width, img1->height));
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cvCvtColor(img1, img_corr, CV_GRAY2RGB);
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cvSetImageROI(img_corr, cvRect(img1->width, 0, img2->width, img2->height));
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cvCvtColor(img2, img_corr, CV_GRAY2RGB);
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cvResetImageROI(img_corr);
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for (size_t i = 0; i < features1.size(); i++)
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{
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cvCircle(img_corr, features1[i].pt, 3, CV_RGB(255, 0, 0));
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}
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for (size_t i = 0; i < features2.size(); i++)
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{
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CvPoint pt = cvPoint(cvRound(features2[i].pt.x + img1->width), cvRound(features2[i].pt.y));
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cvCircle(img_corr, pt, 3, CV_RGB(255, 0, 0));
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cvLine(img_corr, features1[desc_idx[i].trainIdx].pt, pt, CV_RGB(0, 255, 0));
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}
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return img_corr;
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}
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