void _hammingmatch_lowetest_slow(const cv::Mat &_des1, const cv::Mat &_des2, MatchList &_good, const float ratio){
// compute and copmare norms row by row
int _n1 = _des1.rows;
int _n2 = _des2.rows;
int _idx, _mindist1, _mindist2;
cv::DMatch _mtch;
for ( int _i = 0; _i < _n1; ++_i ){
_idx=-1; _mindist1 = 10000; _mindist2 = 1000000; // some arbirtrary large value for initializing
// compute best idx for row _i of _des1
for ( int _j = 0; _j < _n2; ++_j ){
// calculate hamming distance
int _val = cv::normHamming(_des1.row(_i).data, _des2.row(_j).data, 4);
if ( _val > 8) {continue;} // skip to make it fast
_val = cv::normHamming(_des1.row(_i).data, _des2.row(_j).data, _des2.cols);
if ( _val < _mindist2 ){
if ( _val < _mindist1 ){
_mindist2 = _mindist1;
_mindist1 = _val;
_idx = _j;
} else _mindist2 = _val;
}
}
// ratio test
if ( (_idx != -1) && (_mindist1 < (_mindist2 * ratio ))){
// all is okay
printf("distance %d: %d\n", _i, _mindist1);
_mtch.distance = _mindist1;
_mtch.queryIdx = _i; // the first
_mtch.trainIdx = _idx; // the second
_good.push_back(_mtch);
}
}
}
void HammingMatchBatch::getResult(MatchList &good) const{
good.clear();
cv::DMatch _mtch;
for ( int _i = 0; _i < this->result.rows; ++_i){
_mtch.queryIdx = this->result.at<int>(_i, 0);
_mtch.trainIdx = this->result.at<int>(_i, 1);
_mtch.distance = this->result.at<int>(_i, 2);
if ( _mtch.queryIdx != -1 ) good.push_back(_mtch);
}
}
MatchList filterPlatformMatches(const Game *g, std::pair<ExtractMap::const_iterator, ExtractMap::const_iterator> range) {
bool hasPlatformMatch = false;
for (ExtractMap::const_iterator i = range.first; i != range.second; ++i) {
if (i->second.desc.platform == g->platform) {
hasPlatformMatch = true;
break;
}
}
MatchList result;
if (hasPlatformMatch) {
for (ExtractMap::const_iterator i = range.first; i != range.second; ++i) {
if (i->second.desc.platform == g->platform)
result.push_back(i);
}
} else {
for (ExtractMap::const_iterator i = range.first; i != range.second; ++i)
result.push_back(i);
}
return result;
}
void _hammingmatchserial(const cv::Mat &des1, const cv::Mat &des2, MatchList &good, const float ratio){
int _n1 = des1.rows;
int dist, idx;
cv::DMatch _mtch;
int step = des1.step / sizeof(des1.ptr()[0]);
for (int _i = 0; _i < _n1; ++_i){
if ( des2.cols == 64 ){
idx = _bestmatch64(des1.data+step*_i, des2, dist, ratio, FASTHAMMING_FREAK_PARSCORE);
} else {
idx = _bestmatch64(des1.data+step*_i, des2, dist, ratio);
}
if ( idx != -1){
_mtch.distance = dist;
_mtch.queryIdx = _i;
_mtch.trainIdx = idx;
good.push_back(_mtch);
}
}
}
