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Akaze descriptor with DESCRIPTOR_KAZE_UPRIGHT added
This commit is contained in:
laurentBerger 2015-05-07 22:48:13 +02:00
parent cfcef3ec98
commit 966d50762d
1 changed files with 53 additions and 36 deletions
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89
samples/cpp/matchmethod_orb_akaze_brisk.cpp
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@ -21,8 +21,8 @@ int main(int argc, char *argv[])
vector<String> typeAlgoMatch;
vector<String> fileName;
help();
system("cd");
// This descriptor are going to be detect and compute
typeDesc.push_back("AKAZE-DESCRIPTOR_KAZE_UPRIGHT"); // see http://docs.opencv.org/trunk/d8/d30/classcv_1_1AKAZE.html
typeDesc.push_back("AKAZE"); // see http://docs.opencv.org/trunk/d8/d30/classcv_1_1AKAZE.html
typeDesc.push_back("ORB"); // see http://docs.opencv.org/trunk/de/dbf/classcv_1_1BRISK.html
typeDesc.push_back("BRISK"); // see http://docs.opencv.org/trunk/db/d95/classcv_1_1ORB.html
@ -74,9 +74,12 @@ int main(int argc, char *argv[])
// Descriptor for img1 and img2
Mat descImg1, descImg2;
vector<String>::iterator itMatcher = typeAlgoMatch.end();
if (*itDesc == "AKAZE-DESCRIPTOR_KAZE_UPRIGHT"){
b = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
}
if (*itDesc == "AKAZE"){
b = AKAZE::create();
}
}
if (*itDesc == "ORB"){
b = ORB::create();
}
@ -93,44 +96,57 @@ int main(int argc, char *argv[])
// Match method loop
for (itMatcher = typeAlgoMatch.begin(); itMatcher != typeAlgoMatch.end(); itMatcher++){
descriptorMatcher = DescriptorMatcher::create(*itMatcher);
descriptorMatcher->match(descImg1, descImg2, matches, Mat());
// Keep best matches only to have a nice drawing.
// We sort distance between descriptor matches
Mat index;
int nbMatch=int(matches.size());
Mat tab(nbMatch, 1, CV_32F);
for (int i = 0; i<nbMatch; i++)
if ((*itMatcher == "BruteForce-Hamming" || *itMatcher == "BruteForce-Hamming(2)") && (b->descriptorType() == CV_32F || b->defaultNorm() <= NORM_L2SQR) )
{
tab.at<float>(i, 0) = matches[i].distance;
cout << "**************************************************************************\n";
cout << "It's strange. You should use Hamming distance only for a binary descriptor\n";
cout << "**************************************************************************\n";
}
sortIdx(tab, index, SORT_EVERY_COLUMN + SORT_ASCENDING);
vector<DMatch> bestMatches;
for (int i = 0; i<30; i++)
try
{
bestMatches.push_back(matches[index.at<int>(i, 0)]);
descriptorMatcher->match(descImg1, descImg2, matches, Mat());
// Keep best matches only to have a nice drawing.
// We sort distance between descriptor matches
Mat index;
int nbMatch=int(matches.size());
Mat tab(nbMatch, 1, CV_32F);
for (int i = 0; i<nbMatch; i++)
{
tab.at<float>(i, 0) = matches[i].distance;
}
sortIdx(tab, index, SORT_EVERY_COLUMN + SORT_ASCENDING);
vector<DMatch> bestMatches;
for (int i = 0; i<30; i++)
{
bestMatches.push_back(matches[index.at<int>(i, 0)]);
}
Mat result;
drawMatches(img1, keyImg1, img2, keyImg2, bestMatches, result);
namedWindow(*itDesc+": "+*itMatcher, WINDOW_AUTOSIZE);
imshow(*itDesc + ": " + *itMatcher, result);
// Saved result could be wrong due to bug 4308
FileStorage fs(*itDesc + "_" + *itMatcher + ".yml", FileStorage::WRITE);
fs<<"Matches"<<matches;
vector<DMatch>::iterator it;
cout<<"**********Match results**********\n";
cout << "Index \tIndex \tdistance\n";
cout << "in img1\tin img2\n";
// Use to compute distance between keyPoint matches and to evaluate match algorithm
double cumSumDist2=0;
for (it = bestMatches.begin(); it != bestMatches.end(); it++)
{
cout << it->queryIdx << "\t" << it->trainIdx << "\t" << it->distance << "\n";
Point2d p=keyImg1[it->queryIdx].pt-keyImg2[it->trainIdx].pt;
cumSumDist2=p.x*p.x+p.y*p.y;
}
desMethCmp.push_back(cumSumDist2);
waitKey();
}
Mat result;
drawMatches(img1, keyImg1, img2, keyImg2, bestMatches, result);
namedWindow(*itDesc+": "+*itMatcher, WINDOW_AUTOSIZE);
imshow(*itDesc + ": " + *itMatcher, result);
// Saved result could be wrong due to bug 4308
FileStorage fs(*itDesc + "_" + *itMatcher + ".yml", FileStorage::WRITE);
fs<<"Matches"<<matches;
vector<DMatch>::iterator it;
cout<<"**********Match results**********\n";
cout << "Index \tIndex \tdistance\n";
cout << "in img1\tin img2\n";
// Use to compute distance between keyPoint matches and to evaluate match algorithm
double cumSumDist2=0;
for (it = bestMatches.begin(); it != bestMatches.end(); it++)
{
cout << it->queryIdx << "\t" << it->trainIdx << "\t" << it->distance << "\n";
Point2d p=keyImg1[it->queryIdx].pt-keyImg2[it->trainIdx].pt;
cumSumDist2=p.x*p.x+p.y*p.y;
catch (Exception& e)
{
desMethCmp.push_back(-1);
}
}
desMethCmp.push_back(cumSumDist2);
waitKey();
}
}
catch (Exception& e)
{
@ -139,11 +155,12 @@ int main(int argc, char *argv[])
{
cout << "Matcher : " << *itMatcher << "\n";
}
cout<<e.msg<<endl;
cout << e.msg << endl;
}
}
int i=0;
cout << "Cumulative distance between keypoint match for different algorithm and feature detector \n\t";
cout << "We cannot say which is the best but we can say results are differents! \n\t";
for (vector<String>::iterator itMatcher = typeAlgoMatch.begin(); itMatcher != typeAlgoMatch.end(); itMatcher++)
{
cout<<*itMatcher<<"\t";