Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

198 lines
7.7 KiB

#include <highgui.h>
#include "opencv2/core/core.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include <iostream>
using namespace cv;
using namespace std;
#define DRAW_RICH_KEYPOINTS_MODE 0
#define DRAW_OUTLIERS_MODE 0
void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
{
H.create(3, 3, CV_32FC1);
H.at<float>(0,0) = rng.uniform( 0.8f, 1.2f);
H.at<float>(0,1) = rng.uniform(-0.1f, 0.1f);
H.at<float>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
H.at<float>(1,0) = rng.uniform(-0.1f, 0.1f);
H.at<float>(1,1) = rng.uniform( 0.8f, 1.2f);
H.at<float>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
H.at<float>(2,0) = rng.uniform( -1e-4f, 1e-4f);
H.at<float>(2,1) = rng.uniform( -1e-4f, 1e-4f);
H.at<float>(2,2) = rng.uniform( 0.8f, 1.2f);
warpPerspective( src, dst, H, src.size() );
}
const string winName = "correspondences";
void doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
vector<KeyPoint>& keypoints1, const Mat& descriptors1,
Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
Ptr<DescriptorMatcher>& descriptorMatcher,
double ransacReprojThreshold, RNG& rng )
{
assert( !img1.empty() );
Mat H12;
if( isWarpPerspective )
warpPerspectiveRand(img1, img2, H12, rng );
else
assert( !img2.empty()/* && img2.cols==img1.cols && img2.rows==img1.rows*/ );
cout << endl << "< Extracting keypoints from second image..." << endl;
vector<KeyPoint> keypoints2;
detector->detect( img2, keypoints2 );
cout << keypoints2.size() << " points" << endl << ">" << endl;
if( !H12.empty() )
{
cout << "< Evaluate feature detector..." << endl;
float repeatability;
int correspCount;
evaluateFeatureDetector( img1, img2, H12, &keypoints1, &keypoints2, repeatability, correspCount );
cout << "repeatability = " << repeatability << endl;
cout << "correspCount = " << correspCount << endl;
cout << ">" << endl;
}
cout << "< Computing descriptors for keypoints from second image..." << endl;
Mat descriptors2;
descriptorExtractor->compute( img2, keypoints2, descriptors2 );
cout << ">" << endl;
cout << "< Matching descriptors..." << endl;
vector<int> matches;
descriptorMatcher->clear();
descriptorMatcher->add( descriptors2 );
descriptorMatcher->match( descriptors1, matches );
cout << ">" << endl;
if( !H12.empty() )
{
cout << "< Evaluate descriptor match..." << endl;
vector<Point2f> curve;
Ptr<GenericDescriptorMatch> gdm = new VectorDescriptorMatch( descriptorExtractor, descriptorMatcher );
evaluateDescriptorMatch( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
for( float l_p = 0; l_p < 1 - FLT_EPSILON; l_p+=0.1 )
cout << "1-precision = " << l_p << "; recall = " << getRecall( curve, l_p ) << endl;
cout << ">" << endl;
}
if( !isWarpPerspective && ransacReprojThreshold >= 0 )
{
cout << "< Computing homography (RANSAC)..." << endl;
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, matches);
H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
cout << ">" << endl;
}
Mat drawImg;
if( !H12.empty() ) // filter outliers
{
vector<char> matchesMask( matches.size(), 0 );
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, matches);
Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
vector<int>::const_iterator mit = matches.begin();
for( size_t i1 = 0; i1 < points1.size(); i1++ )
{
if( norm(points2[i1] - points1t.at<Point2f>(i1,0)) < 4 ) // inlier
matchesMask[i1] = 1;
}
// draw inliers
drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
, DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
);
#if DRAW_OUTLIERS_MODE
// draw outliers
for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
matchesMask[i1] = !matchesMask[i1];
drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
#endif
}
else
drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg );
imshow( winName, drawImg );
}
int main(int argc, char** argv)
{
if( argc != 4 && argc != 6 )
{
cout << "Format:" << endl;
cout << "case1: second image is obtained from the first (given) image using random generated homography matrix" << endl;
cout << argv[0] << " [detectorType] [descriptorType] [image1]" << endl;
cout << "case2: both images are given. If ransacReprojThreshold>=0 then homography matrix are calculated" << endl;
cout << argv[0] << " [detectorType] [descriptorType] [image1] [image2] [ransacReprojThreshold]" << endl;
cout << endl << "Mathes are filtered using homography matrix in case1 and case2 (if ransacReprojThreshold>=0)" << endl;
return -1;
}
bool isWarpPerspective = argc == 4;
double ransacReprojThreshold = -1;
if( !isWarpPerspective )
ransacReprojThreshold = atof(argv[5]);
cout << "< Creating detector, descriptor extractor and descriptor matcher ..." << endl;
Ptr<FeatureDetector> detector = createDetector( argv[1] );
Ptr<DescriptorExtractor> descriptorExtractor = createDescriptorExtractor( argv[2] );
Ptr<DescriptorMatcher> descriptorMatcher = createDescriptorMatcher( "BruteForce" );
cout << ">" << endl;
if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() )
{
cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;
return -1;
}
cout << "< Reading the images..." << endl;
Mat img1 = imread( argv[3], CV_LOAD_IMAGE_GRAYSCALE), img2;
if( !isWarpPerspective )
img2 = imread( argv[4], CV_LOAD_IMAGE_GRAYSCALE);
cout << ">" << endl;
if( img1.empty() || (!isWarpPerspective && img2.empty()) )
{
cout << "Can not read images" << endl;
return -1;
}
cout << endl << "< Extracting keypoints from first image..." << endl;
vector<KeyPoint> keypoints1;
detector->detect( img1, keypoints1 );
cout << keypoints1.size() << " points" << endl << ">" << endl;
cout << "< Computing descriptors for keypoints from first image..." << endl;
Mat descriptors1;
descriptorExtractor->compute( img1, keypoints1, descriptors1 );
cout << ">" << endl;
namedWindow(winName, 1);
RNG rng = theRNG();
doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
detector, descriptorExtractor, descriptorMatcher,
ransacReprojThreshold, rng );
for(;;)
{
char c = (char)cvWaitKey(0);
if( c == '\x1b' ) // esc
{
cout << "Exiting ..." << endl;
return 0;
}
else if( isWarpPerspective )
{
doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
detector, descriptorExtractor, descriptorMatcher,
ransacReprojThreshold, rng );
}
}
waitKey(0);
return 0;
}