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added leaveBiggestComponent function into opencv_stitching

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This commit is contained in:
Alexey Spizhevoy
2011-05-18 07:11:45 +00:00
parent f3b45af876
commit 21d89cc420
5 changed files with 109 additions and 45 deletions
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94
modules/stitching/motion_estimators.cpp
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@@ -13,12 +13,7 @@ using namespace cv;
CameraParams::CameraParams() : focal(1), R(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
CameraParams::CameraParams(const CameraParams &other)
{
*this = other;
}
CameraParams::CameraParams(const CameraParams &other) { *this = other; }
const CameraParams& CameraParams::operator =(const CameraParams &other)
{
@@ -84,13 +79,14 @@ void HomographyBasedEstimator::estimate(const vector<Mat> &images, const vector<
int pair_idx = i * num_images + j;
if (pairwise_matches[pair_idx].H.empty())
continue;
double f_to, f_from;
bool f_to_ok, f_from_ok;
focalsFromHomography(pairwise_matches[pair_idx].H.inv(), f_to, f_from, f_to_ok, f_from_ok);
if (f_from_ok)
focals.push_back(f_from);
if (f_to_ok)
focals.push_back(f_to);
if (f_from_ok) focals.push_back(f_from);
if (f_to_ok) focals.push_back(f_to);
if (f_from_ok && f_to_ok)
{
is_focal_estimated[i] = true;
@@ -98,6 +94,7 @@ void HomographyBasedEstimator::estimate(const vector<Mat> &images, const vector<
}
}
}
is_focals_estimated_ = true;
for (int i = 0; i < num_images; ++i)
is_focals_estimated_ = is_focals_estimated_ && is_focal_estimated[i];
@@ -132,9 +129,12 @@ void BundleAdjuster::estimate(const vector<Mat> &images, const vector<ImageFeatu
for (int i = 0; i < num_images_; ++i)
{
cameras_.at<double>(i * 4, 0) = cameras[i].focal;
svd(cameras[i].R, SVD::FULL_UV);
Mat R = svd.u * svd.vt;
if (determinant(R) < 0) R *= -1;
if (determinant(R) < 0)
R *= -1;
Mat rvec;
Rodrigues(R, rvec); CV_Assert(rvec.type() == CV_32F);
cameras_.at<double>(i * 4 + 1, 0) = rvec.at<float>(0, 0);
@@ -142,20 +142,19 @@ void BundleAdjuster::estimate(const vector<Mat> &images, const vector<ImageFeatu
cameras_.at<double>(i * 4 + 3, 0) = rvec.at<float>(2, 0);
}
// Select only consistent image pairs for futher adjustment
edges_.clear();
for (int i = 0; i < num_images_ - 1; ++i)
{
for (int j = i + 1; j < num_images_; ++j)
{
int pair_idx = i * num_images_ + j;
const MatchesInfo& mi = pairwise_matches_[pair_idx];
float ni = static_cast<float>(mi.num_inliers);
float nf = static_cast<float>(mi.matches.size());
if (ni / (8.f + 0.3f * nf) > dist_thresh_)
{
const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];
if (matches_info.confidence > conf_thresh_)
edges_.push_back(make_pair(i, j));
}
}
// Compute number of correspondences
total_num_matches_ = 0;
for (size_t i = 0; i < edges_.size(); ++i)
total_num_matches_ += static_cast<int>(pairwise_matches[edges_[i].first * num_images_ + edges_[i].second].num_inliers);
@@ -369,7 +368,6 @@ void waveCorrect(vector<Mat> &rmats)
normalize(r0, r0);
r1.cross(r0).copyTo(r2);
if (determinant(R) < 0)
R *= -1;
@@ -380,6 +378,64 @@ void waveCorrect(vector<Mat> &rmats)
//////////////////////////////////////////////////////////////////////////////
void leaveBiggestComponent(vector<Mat> &images, vector<ImageFeatures> &features,
vector<MatchesInfo> &pairwise_matches, float conf_threshold)
{
const int num_images = static_cast<int>(images.size());
DjSets comps(num_images);
for (int i = 0; i < num_images; ++i)
{
for (int j = 0; j < num_images; ++j)
{
if (pairwise_matches[i*num_images + j].confidence < conf_threshold)
continue;
int comp1 = comps.find(i);
int comp2 = comps.find(j);
if (comp1 != comp2)
comps.merge(comp1, comp2);
}
}
int max_comp = max_element(comps.size.begin(), comps.size.end()) - comps.size.begin();
vector<int> indices;
vector<int> indices_removed;
for (int i = 0; i < num_images; ++i)
if (comps.find(i) == max_comp)
indices.push_back(i);
else
indices_removed.push_back(i);
vector<Mat> images_subset;
vector<ImageFeatures> features_subset;
vector<MatchesInfo> pairwise_matches_subset;
for (size_t i = 0; i < indices.size(); ++i)
{
images_subset.push_back(images[indices[i]]);
features_subset.push_back(features[indices[i]]);
for (size_t j = 0; j < indices.size(); ++j)
{
pairwise_matches_subset.push_back(pairwise_matches[indices[i]*num_images + indices[j]]);
pairwise_matches_subset.back().src_img_idx = i;
pairwise_matches_subset.back().dst_img_idx = j;
}
}
if (static_cast<int>(images_subset.size()) == num_images)
return;
LOG("Removed some images, because can't match them: (");
LOG(indices_removed[0]);
for (size_t i = 1; i < indices_removed.size(); ++i) LOG(", " << indices_removed[i]);
LOGLN(")");
images = images_subset;
features = features_subset;
pairwise_matches = pairwise_matches_subset;
}
void findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise_matches,
Graph &span_tree, vector<int> &centers)
{
@@ -391,6 +447,8 @@ void findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise_mat
{
for (int j = 0; j < num_images; ++j)
{
if (pairwise_matches[i * num_images + j].H.empty())
continue;
float conf = static_cast<float>(pairwise_matches[i * num_images + j].num_inliers);
graph.addEdge(i, j, conf);
edges.push_back(GraphEdge(i, j, conf));