void BR::Database::fixAndAdd(cv::Mat & image, const cv::vector<cv::Point2f> points, std::string title, std::string author, std::string isbn)
{
cv::Mat output(480, 640, image.type());
cv::vector<cv::Point2f> dst_points(4);
dst_points[0] = cv::Point2f(0,0);
dst_points[1] = cv::Point2f(480,0);
dst_points[2] = cv::Point2f(480,640);
dst_points[3] = cv::Point2f(0,640);
cv::Mat transform = cv::getPerspectiveTransform(points, dst_points);
cv::warpPerspective(image, output, transform, cv::Size(480, 640));
addBook(new Book(isbn, title, author, output));
}
/* modified implementation of match from BestOf2NearestMatcher */
void AffineBestOf2NearestMatcher::match(
const cv::detail::ImageFeatures &features1,
const cv::detail::ImageFeatures &features2,
cv::detail::MatchesInfo &matches_info)
{
(*impl_)(features1, features2, matches_info);
ROS_DEBUG("AffineMatcher: have %lu matches", matches_info.matches.size());
// Check if it makes sense to find homography
if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
return;
// Construct point-point correspondences for homography estimation
// Points are centered s.t. image center is (0,0). This is similar to other
// matchers a shows better results in practice (numerical stability?).
cv::Mat src_points(1, static_cast<int>(matches_info.matches.size()),
CV_32FC2);
cv::Mat dst_points(1, static_cast<int>(matches_info.matches.size()),
CV_32FC2);
for (size_t i = 0; i < matches_info.matches.size(); ++i) {
const cv::DMatch &m = matches_info.matches[i];
cv::Point2f p = features1.keypoints[static_cast<size_t>(m.queryIdx)].pt;
p.x -= features1.img_size.width * 0.5f;
p.y -= features1.img_size.height * 0.5f;
src_points.at<cv::Point2f>(0, static_cast<int>(i)) = p;
p = features2.keypoints[static_cast<size_t>(m.trainIdx)].pt;
p.x -= features2.img_size.width * 0.5f;
p.y -= features2.img_size.height * 0.5f;
dst_points.at<cv::Point2f>(0, static_cast<int>(i)) = p;
}
// Find pair-wise motion
// this is my special modified version of estimateRigidTransform
matches_info.H = estimateRigidTransform(src_points, dst_points,
matches_info.inliers_mask, false);
ROS_DEBUG_STREAM("estimate:\n" << matches_info.H);
if (matches_info.H.empty()) {
// could not find trasformation
matches_info.confidence = 0;
matches_info.num_inliers = 0;
return;
}
// extend H to represent linear tranformation in homogeneous coordinates
matches_info.H.push_back(cv::Mat::zeros(1, 3, CV_64F));
matches_info.H.at<double>(2, 2) = 1;
/* TODO: should we handle determinant ~ 0 (can it happen due to agressive
* scaling?) */
// Find number of inliers
matches_info.num_inliers = 0;
for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
if (matches_info.inliers_mask[i])
matches_info.num_inliers++;
ROS_DEBUG("num_inliers %d", matches_info.num_inliers);
// These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic
// Image Stitching using Invariant Features"
matches_info.confidence =
matches_info.num_inliers / (8 + 0.3 * matches_info.matches.size());
/* we don't want any cuttof for merging maps. TODO: allow to set this in
* constructor. */
// Set zero confidence to remove matches between too close images, as they
// don't provide additional information anyway. The threshold was set
// experimentally.
// matches_info.confidence =
// matches_info.confidence > 3. ? 0. : matches_info.confidence;
/* Unlike other matchers it makes no sense to rerun estimation on liers only.
* estimateRigidTransform already did this for us. So we are done here. */
}
void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo &matches_info)
{
(*impl_)(features1, features2, matches_info);
// Check if it makes sense to find homography
if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
return;
// Construct point-point correspondences for homography estimation
Mat src_points(1, static_cast<int>(matches_info.matches.size()), CV_32FC2);
Mat dst_points(1, static_cast<int>(matches_info.matches.size()), CV_32FC2);
for (size_t i = 0; i < matches_info.matches.size(); ++i)
{
const DMatch& m = matches_info.matches[i];
Point2f p = features1.keypoints[m.queryIdx].pt;
p.x -= features1.img_size.width * 0.5f;
p.y -= features1.img_size.height * 0.5f;
src_points.at<Point2f>(0, static_cast<int>(i)) = p;
p = features2.keypoints[m.trainIdx].pt;
p.x -= features2.img_size.width * 0.5f;
p.y -= features2.img_size.height * 0.5f;
dst_points.at<Point2f>(0, static_cast<int>(i)) = p;
}
// Find pair-wise motion
matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
if (std::abs(determinant(matches_info.H)) < numeric_limits<double>::epsilon())
return;
// Find number of inliers
matches_info.num_inliers = 0;
for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
if (matches_info.inliers_mask[i])
matches_info.num_inliers++;
// These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching
// using Invariant Features"
matches_info.confidence = matches_info.num_inliers / (8 + 0.3 * matches_info.matches.size());
// Set zero confidence to remove matches between too close images, as they don't provide
// additional information anyway. The threshold was set experimentally.
matches_info.confidence = matches_info.confidence > 3. ? 0. : matches_info.confidence;
// Check if we should try to refine motion
if (matches_info.num_inliers < num_matches_thresh2_)
return;
// Construct point-point correspondences for inliers only
src_points.create(1, matches_info.num_inliers, CV_32FC2);
dst_points.create(1, matches_info.num_inliers, CV_32FC2);
int inlier_idx = 0;
for (size_t i = 0; i < matches_info.matches.size(); ++i)
{
if (!matches_info.inliers_mask[i])
continue;
const DMatch& m = matches_info.matches[i];
Point2f p = features1.keypoints[m.queryIdx].pt;
p.x -= features1.img_size.width * 0.5f;
p.y -= features1.img_size.height * 0.5f;
src_points.at<Point2f>(0, inlier_idx) = p;
p = features2.keypoints[m.trainIdx].pt;
p.x -= features2.img_size.width * 0.5f;
p.y -= features2.img_size.height * 0.5f;
dst_points.at<Point2f>(0, inlier_idx) = p;
inlier_idx++;
}
// Rerun motion estimation on inliers only
matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
}
