int main() {
std::vector<int> v(500);
sift0(begin(v), 50);
std::cout << "sift0(begin(v), 50):\n";
print_sieve(begin(v), 50);
sift1(begin(v), 50);
std::cout << "sift1(begin(v), 50):\n";
print_sieve(begin(v), 50);
sift(begin(v), 500);
std::cout << "sift(begin(v), 500):\n";
print_sieve(begin(v), 500);
std::cout << "gcm(15, 9) = " << gcm(15, 9) << std::endl;
}
int main( int argc, char** argv ) {
string filename1 = "image2.jpg";
string filename2 = "image1.jpg";
cv::Mat oriImg1 = cv::imread( filename1, 0 );
cv::Mat oriImg2 = cv::imread( filename2, 0 );
// extract sift features
cv::SIFT sift1( 0, 3, 0.04, 10, 1.0 );
cv::SIFT sift2( 0, 3, 0.04, 10, 1.0 );
vector<cv::KeyPoint> siftFeats1, siftFeats2;
const int width1 = 400;
const int width2 = width1 + rand()%10;
int height1 = (int)(oriImg1.rows*(float)width1/(float)oriImg1.cols);
int height2 = (int)(oriImg2.rows*(float)width2/(float)oriImg2.cols);
cv::Mat img1, img2;
cv::resize( oriImg1, img1, cv::Size(width1, height1));
cv::resize( oriImg2, img2, cv::Size(width2, height2));
cv::Mat siftDescs1, siftDescs2, siftMask1, siftMask2;
sift1( img1, siftMask1, siftFeats1, siftDescs1, false );
sift2( img2, siftMask2, siftFeats2, siftDescs2, false );
cout << siftFeats1.size() << ", " << siftFeats2.size() << endl;
// click seed area in oriImg1
vector<cv::Point> corners;
corners = SelectCorners( img1 );
int cornernum = (int)corners.size();
int corxmin, corymin, corxmax, corymax;
corxmin = corymin = INT_MAX; corxmax = corymax = INT_MIN;
for ( int i = 0; i < cornernum; i ++ ) {
if ( corners[i].x < corxmin )
corxmin = corners[i].x;
if ( corners[i].x > corxmax )
corxmax = corners[i].x;
if ( corners[i].y < corymin )
corymin = corners[i].y;
if ( corners[i].y > corymax )
corymax = corners[i].y;
}
cout << corxmin << " "
<< corxmax << " "
<< corymin << " "
<< corymax << endl;
cv::Point lupt(corxmin, corymin), rdpt(corxmax, corymax);
// matched feature search using knn
const int k = 1;
vector<int> idxs;
vector<distindex> dists;
vector<distindex> alldists;
vector<double> distdiffs, xydiffs;
for ( int i = 0; i < (int)siftFeats1.size(); i ++ ) {
/* if ( siftFeats1[i].pt.x < corxmax &&
siftFeats1[i].pt.x > corxmin &&
siftFeats1[i].pt.y < corymax &&
siftFeats1[i].pt.y > corymin ) {*/
if ( i%50 == 0 )
cout << "process feature " << i << "...\n";
cv::Mat cimg1 = img1.clone();
cv::Mat cimg2 = img2.clone();
cv::circle( cimg1, siftFeats1[i].pt, 5, cv::Scalar::all(255), 2 );
double xydiff = 0, distdiff = 0;
distindex alldist;
KNNSearch( i, siftDescs1, siftDescs2, idxs, dists, k );
for ( int j = 0; j < k; j ++ ) {
xydiff += sqrt( (siftFeats2[idxs[j]].pt.x-siftFeats1[i].pt.x)*(siftFeats2[idxs[j]].pt.x-siftFeats1[i].pt.x) +
(siftFeats2[idxs[j]].pt.y-siftFeats1[i].pt.y)*(siftFeats2[idxs[j]].pt.y-siftFeats1[i].pt.y) );
distdiff += dists[j].dist;
cv::circle( cimg2, siftFeats2[idxs[j]].pt, 5, cv::Scalar::all(255), 2 );
}
alldist.ori = i;
alldist.index = idxs[0];
alldist.dist = distdiff;
alldists.push_back( alldist );
distdiffs.push_back( distdiff );
xydiffs.push_back( xydiff );
/* cout << endl;
cv::imshow( "cimg1", cimg1 );
cv::imshow( "cimg2", cimg2 );
cv::waitKey(0);*/
idxs.clear();
dists.clear();
/* }*/
}
cv::Mat diffMat( img1.rows, img1.cols, CV_8UC1, cv::Scalar::all(0) );
// normalize descriptor euclidean distance
int mindist = INT_MAX, maxdist = INT_MIN;
for ( int i = 0; i < (int)distdiffs.size(); i ++ ) {
if ( distdiffs[i] < mindist )
mindist = distdiffs[i];
if ( distdiffs[i] > maxdist )
maxdist = distdiffs[i];
}
for ( int i = 0; i < (int)distdiffs.size(); i ++ ) {
distdiffs[i] = (distdiffs[i]-mindist)/(maxdist-mindist)*255;
}
// normalize pixel distance
int minxy = INT_MAX, maxxy = INT_MIN;
for ( int i = 0; i < (int)distdiffs.size(); i++ ) {
if ( xydiffs[i] < minxy )
minxy = xydiffs[i];
if ( xydiffs[i] > maxxy )
maxxy = xydiffs[i];
}
for ( int i = 0; i < (int)xydiffs.size(); i ++ ) {
xydiffs[i] = (xydiffs[i]-minxy)/(maxxy-minxy)*255;
}
// sort the descriptor difference using euclidean between knns
const float perc = .1;
int uniNum = (int)siftFeats1.size()*perc;
sort( alldists.begin(), alldists.end() );
cv::Mat cimg1 = img1.clone();
for ( int i = 0; i < (int)alldists.size(); i ++ ) {
if ( i > (int)alldists.size()-uniNum )
cv::circle( cimg1, siftFeats1[alldists[i].ori].pt, 4, cv::Scalar::all(distdiffs[alldists[i].ori]), 2 );
else
cv::circle( cimg1, siftFeats1[alldists[i].ori].pt, 4, cv::Scalar::all(50), 2 );
}
/* if ( (int)distdiffs.size() == (int)siftFeats1.size() ) {
for ( int i = 0; i < (int)siftFeats1.size(); i++ ) {
if ( distdiffs[i] > 220 ) {
cv::circle( diffMat, siftFeats1[i].pt, 4, cv::Scalar::all(distdiffs[i]), 2 );
}
}
}*/
cv::imshow( "diffMat", cimg1 );
cv::imwrite( "inv_knn_1_perc_.1_com.jpg", cimg1 );
cv::waitKey(0);
}
