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now BA in opencv_stitching uses only geometrically consistent matches

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This commit is contained in:
Alexey Spizhevoy
2011-05-06 07:14:36 +00:00
parent 15173fc559
commit 29b917a500
3 changed files with 12 additions and 14 deletions
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23
modules/stitching/motion_estimators.cpp
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@@ -309,13 +309,12 @@ void BestOf2NearestMatcher::match(const Mat &img1, const ImageFeatures &features
}
// Find pair-wise motion
vector<uchar> inlier_mask;
matches_info.H = findHomography(src_points, dst_points, inlier_mask, CV_RANSAC);
matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
// Find number of inliers
matches_info.num_inliers = 0;
for (size_t i = 0; i < inlier_mask.size(); ++i)
if (inlier_mask[i])
for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
if (matches_info.inliers_mask[i])
matches_info.num_inliers++;
// Check if we should try to refine motion
@@ -328,8 +327,9 @@ void BestOf2NearestMatcher::match(const Mat &img1, const ImageFeatures &features
int inlier_idx = 0;
for (size_t i = 0; i < matches_info.matches.size(); ++i)
{
if (!inlier_mask[i])
if (!matches_info.inliers_mask[i])
continue;
const DMatch& m = matches_info.matches[i];
Point2f p = features1.keypoints[m.queryIdx].pt;
@@ -346,13 +346,7 @@ void BestOf2NearestMatcher::match(const Mat &img1, const ImageFeatures &features
}
// Rerun motion estimation on inliers only
matches_info.H = findHomography(src_points, dst_points, inlier_mask, CV_RANSAC);
// Find number of inliers
matches_info.num_inliers = 0;
for (size_t i = 0; i < inlier_mask.size(); ++i)
if (inlier_mask[i])
matches_info.num_inliers++;
matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
}
@@ -505,7 +499,7 @@ void BundleAdjuster::estimate(const vector<Mat> &images, const vector<ImageFeatu
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].matches.size());
total_num_matches_ += static_cast<int>(pairwise_matches[edges_[i].first * num_images_ + edges_[i].second].num_inliers);
CvLevMarq solver(num_images_ * 4, total_num_matches_ * 3,
cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 100, DBL_EPSILON));
@@ -599,6 +593,9 @@ void BundleAdjuster::calcError(Mat &err)
for (size_t k = 0; k < matches_info.matches.size(); ++k)
{
if (!matches_info.inliers_mask[k])
continue;
const DMatch& m = matches_info.matches[k];
Point2d kp1 = features1.keypoints[m.queryIdx].pt;