/* Compute likely matches between two sets of keypoints */
std::vector<KeypointMatch> MatchKeys(const std::vector<KeypointWithDesc> &k1,
const std::vector<KeypointWithDesc> &k2,
bool registered, double ratio)
{
int num_pts = 0;
std::vector<KeypointMatch> matches;
std::vector<KeypointWithDesc> pts;
int *registered_idxs = NULL;
//register为0时，pts保存k2的描述子，每一行是一个描述子
if (!registered) {
pts = k2;
num_pts = (int)k2.size();
}
//registered为1时，registered_idxs保存k2中那些m_extra>=0的点的索引号
else {
registered_idxs = new int[(int)k2.size()];
for (int i = 0; i < (int)k2.size(); i++) {
if (k2[i].m_extra >= 0) {
registered_idxs[num_pts] = i;
pts.push_back(k2[i]);
num_pts++;
}
}
}
for (int i = 0; i < (int)k1.size(); i++)
{
int dist[2];
int index = BruteForceMatch(k1[i], pts, dist);
if (sqrt(((double)dist[0]) / ((double)dist[1])) <= ratio) {
//registered为0时，matches保存k1 k2匹配的描述子序号对
if (!registered) {
matches.push_back(KeypointMatch(i, index));
}
//为1时，rnn_idx[0]为当前pts中与k1 i匹配的，registered_idxs[nn_idx[0]]k2的真正序号
//这里实际上也是让matches保存k1 k2匹配的描述子序号对
else {
KeypointMatch match = KeypointMatch(i, registered_idxs[index]);
matches.push_back(match);
}
}
}
int num_matches = (int)matches.size();
printf("[MatchKeys] Found %d matches\n", num_matches);
if (registered_idxs != NULL) delete registered_idxs;
return matches;
}
void BaseApp::SetMatchesFromTracks(int img1, int img2)
{
std::vector<int> &tracks1 = m_image_data[img1].m_visible_points;
std::vector<int> &tracks2 = m_image_data[img2].m_visible_points;
std::vector<int> isect = GetVectorIntersection(tracks1, tracks2);
int num_isect = (int) isect.size();
if (num_isect == 0)
return;
MatchIndex idx = GetMatchIndex(img1, img2);
std::vector<KeypointMatch> &matches = m_matches.GetMatchList(idx);
// m_match_lists[idx];
matches.clear();
matches.resize(num_isect);
for (int i = 0; i < num_isect; i++) {
int tr = isect[i];
#if 0
int num_views = (int) m_track_data[tr].m_views.size();
int k1 = -1, k2 = -1;
for (int j = 0; j < num_views; j++) {
if (m_track_data[tr].m_views[j].first == img1) {
k1 = m_track_data[tr].m_views[j].second;
}
if (m_track_data[tr].m_views[j].first == img2) {
k2 = m_track_data[tr].m_views[j].second;
}
}
assert(k1 != -1 && k2 != -1);
#endif
std::pair<std::vector<int>::const_iterator,
std::vector<int>::const_iterator> p;
const std::vector<int> &pt1 = m_image_data[img1].m_visible_points;
p = equal_range(pt1.begin(), pt1.end(), tr);
assert(p.first != p.second);
int offset = p.first - pt1.begin();
int k1 = m_image_data[img1].m_visible_keys[offset];
const std::vector<int> &pt2 = m_image_data[img2].m_visible_points;
p = equal_range(pt2.begin(), pt2.end(), tr);
assert(p.first != p.second);
offset = p.first - pt2.begin();
int k2 = m_image_data[img2].m_visible_keys[offset];
matches[i] = KeypointMatch(k1, k2);
}
}
void BaseApp::SetMatchesFromTracks()
{
/* Clear all matches */
// ClearMatches();
RemoveAllMatches();
int num_tracks = (int) m_track_data.size();
int num_tracks_used = 0;
for (int i = 0; i < num_tracks; i++) {
TrackData &t = m_track_data[i];
int num_views = (int) t.m_views.size();
if (num_views < m_min_track_views)
continue; /* Not enough observations */
if (num_views > m_max_track_views)
continue; /* Too many observations */
for (int j = 0; j < num_views; j++) {
for (int k = 0; k < num_views; k++) {
if (j == k) continue;
int v1 = t.m_views[j].first;
int v2 = t.m_views[k].first;
int k1 = t.m_views[j].second;
int k2 = t.m_views[k].second;
MatchIndex idx = GetMatchIndex(v1, v2);
// m_matches[idx] = true;
SetMatch(v1, v2);
// m_match_lists[idx].push_back(KeypointMatch(k1,k2));
m_matches.GetMatchList(idx).push_back(KeypointMatch(k1, k2));
}
}
num_tracks_used++;
}
LOGI("[BaseApp::SetMatchesFromTracks] Used %d tracks\n",
num_tracks_used);
}
/* Compute likely matches between two sets of keypoints */
std::vector<KeypointMatch>
MatchKeysExhaustive(const std::vector<KeypointWithDesc> &k1,
const std::vector<KeypointWithDesc> &k2,
bool registered, double ratio)
{
int num_pts = 0;
std::vector<KeypointMatch> matches;
int *registered_idxs = NULL;
if (!registered) {
num_pts = (int) k2.size();
} else {
registered_idxs = new int[(int) k2.size()];
for (int i = 0; i < (int) k2.size(); i++) {
if (k2[i].m_extra >= 0) {
registered_idxs[num_pts] = i;
num_pts++;
}
}
}
/* Create a new array of points */
ann_1_1_char::ANNpointArray pts = ann_1_1_char::annAllocPts(num_pts, 128);
if (!registered) {
for (int i = 0; i < num_pts; i++) {
int j;
for (j = 0; j < 128; j++) {
pts[i][j] = k2[i].m_d[j];
}
}
} else {
for (int i = 0; i < num_pts; i++) {
int j;
int idx = registered_idxs[i];
for (j = 0; j < 128; j++) {
pts[i][j] = k2[idx].m_d[j];
}
}
}
// clock_t start = clock();
/* Create a search tree for k2 */
ann_1_1_char::ANNkd_tree *tree =
new ann_1_1_char::ANNkd_tree(pts, num_pts, 128, 4);
// clock_t end = clock();
// printf("Building tree took %0.3fs\n",
// (end - start) / ((double) CLOCKS_PER_SEC));
/* Now do the search */
ann_1_1_char::ANNpoint query = ann_1_1_char::annAllocPt(128);
// start = clock();
for (int i = 0; i < (int) k1.size(); i++) {
int j;
for (j = 0; j < 128; j++) {
query[j] = k1[i].m_d[j];
}
ann_1_1_char::ANNidx nn_idx[2];
ann_1_1_char::ANNdist dist[2];
tree->annkSearch(query, 2, nn_idx, dist, 0.0);
if (sqrt(((double) dist[0]) / ((double) dist[1])) <= ratio) {
if (!registered) {
matches.push_back(KeypointMatch(i, nn_idx[0]));
} else {
KeypointMatch match =
KeypointMatch(i, registered_idxs[nn_idx[0]]);
matches.push_back(match);
}
}
}
// end = clock();
// printf("Searching tree took %0.3fs\n",
// (end - start) / ((double) CLOCKS_PER_SEC));
int num_matches = (int) matches.size();
printf("[MatchKeys] Found %d matches\n", num_matches);
/* Cleanup */
ann_1_1_char::annDeallocPts(pts);
ann_1_1_char::annDeallocPt(query);
delete tree;
return matches;
}
/* Compute likely matches between two sets of keypoints */
std::vector<KeypointMatch> MatchKeys(const std::vector<KeypointWithDesc> &k1,
const std::vector<KeypointWithDesc> &k2,
bool registered, double ratio)
{
ann_1_1_char::annMaxPtsVisit(200);
int num_pts = 0;
std::vector<KeypointMatch> matches;
int *registered_idxs = NULL;
if (!registered) {
num_pts = (int) k2.size();
}
//registered为1时，registered_idxs保存k2中那些m_extra>=0的点的索引号
else {
registered_idxs = new int[(int) k2.size()];
for (int i = 0; i < (int) k2.size(); i++) {
if (k2[i].m_extra >= 0) {
registered_idxs[num_pts] = i;
num_pts++;
}
}
}
/* Create a new array of points */
ann_1_1_char::ANNpointArray pts = ann_1_1_char::annAllocPts(num_pts, DESCRIPTOR_LENGTH);
//register为0时，pts保存k2的描述子，每一行是一个描述子
if (!registered) {
for (int i = 0; i < num_pts; i++) {
int j;
for (j = 0; j < DESCRIPTOR_LENGTH; j++) {
pts[i][j] = k2[i].m_d[j];
}
}
}
//registered为1时，pts保存k2中m_extra>=0的点的描述子
else {
for (int i = 0; i < num_pts; i++) {
int j;
int idx = registered_idxs[i];
for (j = 0; j < DESCRIPTOR_LENGTH; j++) {
pts[i][j] = k2[idx].m_d[j];
}
}
}
// clock_t start = clock();
/* Create a search tree for k2 */
ann_1_1_char::ANNkd_tree *tree = new ann_1_1_char::ANNkd_tree(pts, num_pts, DESCRIPTOR_LENGTH, 4);
// clock_t end = clock();
// printf("Building tree took %0.3fs\n",
// (end - start) / ((double) CLOCKS_PER_SEC));
/* Now do the search */
ann_1_1_char::ANNpoint query = ann_1_1_char::annAllocPt(DESCRIPTOR_LENGTH);
// start = clock();
for (int i = 0; i < (int) k1.size(); i++) {
int j;
for (j = 0; j < DESCRIPTOR_LENGTH; j++) {
query[j] = k1[i].m_d[j];
}
ann_1_1_char::ANNidx nn_idx[2];
ann_1_1_char::ANNdist dist[2];
tree->annkPriSearch(query, 2, nn_idx, dist, 0.0);
if (sqrt(((double) dist[0]) / ((double) dist[1])) <= ratio) {
//registered为0时，matches保存k1 k2匹配的描述子序号对
if (!registered) {
matches.push_back(KeypointMatch(i, nn_idx[0]));
}
//为1时，rnn_idx[0]为当前pts中与k1 i匹配的，registered_idxs[nn_idx[0]]k2的真正序号
//这里实际上也是让matches保存k1 k2匹配的描述子序号对
else {
KeypointMatch match = KeypointMatch(i, registered_idxs[nn_idx[0]]);
matches.push_back(match);
}
}
}
// end = clock();
// printf("Searching tree took %0.3fs\n",
// (end - start) / ((double) CLOCKS_PER_SEC));
int num_matches = (int) matches.size();
printf("[MatchKeys] Found %d matches\n", num_matches);
/* Cleanup */
ann_1_1_char::annDeallocPts(pts);
ann_1_1_char::annDeallocPt(query);
delete tree;
return matches;
}
/* Compute likely matches between the reconstructed points and a set
* of keypoints and the reconstructed points */
std::vector<KeypointMatch>
BundlerApp::MatchPointsToKeys(const std::vector<KeypointWithDesc> &keys,
double ratio)
{
printf("MatchKeysToPoints, error\n");
exit(EXIT_FAILURE);
ann_1_1_char::annMaxPtsVisit(200);
int num_points = (int) keys.size();
std::vector<KeypointMatch> matches;
/* Create a new array of points */
ann_1_1_char::ANNpointArray pts = ann_1_1_char::annAllocPts(num_points, DESCRIPTOR_LENGTH);
for (int i = 0; i < num_points; i++)
for (int j = 0; j < DESCRIPTOR_LENGTH; j++)
pts[i][j] = keys[i].m_d[j];
clock_t start = clock();
/* Create a search tree for k2 */
ann_1_1_char::ANNkd_tree *tree = new ann_1_1_char::ANNkd_tree(pts, num_points, DESCRIPTOR_LENGTH, 4);
clock_t end = clock();
printf("[MatchPointsToKeys] Building tree took %0.3fs\n",
(end - start) / ((double) CLOCKS_PER_SEC));
/* Now do the search */
ann_1_1_char::ANNpoint query = ann_1_1_char::annAllocPt(DESCRIPTOR_LENGTH);
start = clock();
for (int i = 0; i < (int) m_point_data.size(); i++) {
int j;
for (j = 0; j < DESCRIPTOR_LENGTH; j++) {
query[j] = m_point_data[i].m_desc[j];
}
ann_1_1_char::ANNidx nn_idx[2];
ann_1_1_char::ANNdist dist[2];
tree->annkPriSearch(query, 2, nn_idx, dist, 0.0);
if (sqrt(((double) dist[0]) / ((double) dist[1])) < ratio) {
matches.push_back(KeypointMatch(i, nn_idx[0]));
}
}
end = clock();
printf("[MatchPointsToKeys] Searching tree took %0.3fs\n",
(end - start) / ((double) CLOCKS_PER_SEC));
int num_matches = (int) matches.size();
printf("[MatchPointsToKeys] Found %d matches\n", num_matches);
fflush(stdout);
/* Cleanup */
ann_1_1_char::annDeallocPts(pts);
ann_1_1_char::annDeallocPt(query);
delete tree;
return matches;
}