Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Fixed warper selection bug in stitching_detailed. Removed estimation of aspect ratio in BA in stitching to avoid stretching of input images. Did minor refactoring.

Browse Source
pull/13383/head
Alexey Spizhevoy 14 years ago
parent fb2c288627
commit 07efb17d12
4 changed files with 55 additions and 84 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 11
      modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp
  2. 92
      modules/stitching/src/motion_estimators.cpp
  3. 2
      modules/stitching/src/stitcher.cpp
  4. 34
      samples/cpp/stitching_detailed.cpp

11
modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp
Unescape View File

@ -80,15 +80,11 @@ private:
}; };
class CV_EXPORTS BundleAdjuster : public Estimator // Minimizes reprojection error
class CV_EXPORTS BundleAdjusterReproj : public Estimator
{ {
public: public:
enum { NO, RAY_SPACE, FOCAL_RAY_SPACE }; BundleAdjusterReproj(float conf_thresh = 1.f) : conf_thresh_(conf_thresh) {}
BundleAdjuster(int cost_space = FOCAL_RAY_SPACE, float conf_thresh = 1.f)
: cost_space_(cost_space), conf_thresh_(conf_thresh) {}
Mat K;
private: private:
void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches, void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
@ -104,7 +100,6 @@ private:
Mat cameras_; Mat cameras_;
std::vector<std::pair<int,int> > edges_; std::vector<std::pair<int,int> > edges_;
int cost_space_;
float conf_thresh_; float conf_thresh_;
Mat err_, err1_, err2_; Mat err_, err1_, err2_;
Mat J_; Mat J_;

92
modules/stitching/src/motion_estimators.cpp
Unescape View File

@ -155,12 +155,10 @@ void HomographyBasedEstimator::estimate(const vector<ImageFeatures> &features, c
////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////
void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches, void BundleAdjusterReproj::estimate(const vector<ImageFeatures> &features,
vector<CameraParams> &cameras) const vector<MatchesInfo> &pairwise_matches,
vector<CameraParams> &cameras)
{ {
if (cost_space_ == NO)
return;
LOG("Bundle adjustment"); LOG("Bundle adjustment");
int64 t = getTickCount(); int64 t = getTickCount();
@ -169,14 +167,13 @@ void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vecto
pairwise_matches_ = &pairwise_matches[0]; pairwise_matches_ = &pairwise_matches[0];
// Prepare focals and rotations // Prepare focals and rotations
cameras_.create(num_images_ * 7, 1, CV_64F); cameras_.create(num_images_ * 6, 1, CV_64F);
SVD svd; SVD svd;
for (int i = 0; i < num_images_; ++i) for (int i = 0; i < num_images_; ++i)
{ {
cameras_.at<double>(i * 7, 0) = cameras[i].focal; cameras_.at<double>(i * 6, 0) = cameras[i].focal;
cameras_.at<double>(i * 7 + 1, 0) = cameras[i].ppx; cameras_.at<double>(i * 6 + 1, 0) = cameras[i].ppx;
cameras_.at<double>(i * 7 + 2, 0) = cameras[i].ppy; cameras_.at<double>(i * 6 + 2, 0) = cameras[i].ppy;
cameras_.at<double>(i * 7 + 3, 0) = cameras[i].aspect;
svd(cameras[i].R, SVD::FULL_UV); svd(cameras[i].R, SVD::FULL_UV);
Mat R = svd.u * svd.vt; Mat R = svd.u * svd.vt;
@ -185,9 +182,9 @@ void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vecto
Mat rvec; Mat rvec;
Rodrigues(R, rvec); CV_Assert(rvec.type() == CV_32F); Rodrigues(R, rvec); CV_Assert(rvec.type() == CV_32F);
cameras_.at<double>(i * 7 + 4, 0) = rvec.at<float>(0, 0); cameras_.at<double>(i * 6 + 3, 0) = rvec.at<float>(0, 0);
cameras_.at<double>(i * 7 + 5, 0) = rvec.at<float>(1, 0); cameras_.at<double>(i * 6 + 4, 0) = rvec.at<float>(1, 0);
cameras_.at<double>(i * 7 + 6, 0) = rvec.at<float>(2, 0); cameras_.at<double>(i * 6 + 5, 0) = rvec.at<float>(2, 0);
} }
// Select only consistent image pairs for futher adjustment // Select only consistent image pairs for futher adjustment
@ -207,7 +204,7 @@ void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vecto
for (size_t i = 0; i < edges_.size(); ++i) 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); total_num_matches_ += static_cast<int>(pairwise_matches[edges_[i].first * num_images_ + edges_[i].second].num_inliers);
CvLevMarq solver(num_images_ * 7, total_num_matches_ * 2, CvLevMarq solver(num_images_ * 6, total_num_matches_ * 2,
cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 1000, DBL_EPSILON)); cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 1000, DBL_EPSILON));
CvMat matParams = cameras_; CvMat matParams = cameras_;
@ -250,14 +247,13 @@ void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vecto
// Obtain global motion // Obtain global motion
for (int i = 0; i < num_images_; ++i) for (int i = 0; i < num_images_; ++i)
{ {
cameras[i].focal = cameras_.at<double>(i * 7, 0); cameras[i].focal = cameras_.at<double>(i * 6, 0);
cameras[i].ppx = cameras_.at<double>(i * 7 + 1, 0); cameras[i].ppx = cameras_.at<double>(i * 6 + 1, 0);
cameras[i].ppy = cameras_.at<double>(i * 7 + 2, 0); cameras[i].ppy = cameras_.at<double>(i * 6 + 2, 0);
cameras[i].aspect = cameras_.at<double>(i * 7 + 3, 0);
Mat rvec(3, 1, CV_64F); Mat rvec(3, 1, CV_64F);
rvec.at<double>(0, 0) = cameras_.at<double>(i * 7 + 4, 0); rvec.at<double>(0, 0) = cameras_.at<double>(i * 6 + 3, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(i * 7 + 5, 0); rvec.at<double>(1, 0) = cameras_.at<double>(i * 6 + 4, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(i * 7 + 6, 0); rvec.at<double>(2, 0) = cameras_.at<double>(i * 6 + 5, 0);
Rodrigues(rvec, cameras[i].R); Rodrigues(rvec, cameras[i].R);
Mat Mf; Mat Mf;
cameras[i].R.convertTo(Mf, CV_32F); cameras[i].R.convertTo(Mf, CV_32F);
@ -276,7 +272,7 @@ void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vecto
} }
void BundleAdjuster::calcError(Mat &err) void BundleAdjusterReproj::calcError(Mat &err)
{ {
err.create(total_num_matches_ * 2, 1, CV_64F); err.create(total_num_matches_ * 2, 1, CV_64F);
@ -285,28 +281,26 @@ void BundleAdjuster::calcError(Mat &err)
{ {
int i = edges_[edge_idx].first; int i = edges_[edge_idx].first;
int j = edges_[edge_idx].second; int j = edges_[edge_idx].second;
double f1 = cameras_.at<double>(i * 7, 0); double f1 = cameras_.at<double>(i * 6, 0);
double f2 = cameras_.at<double>(j * 7, 0); double f2 = cameras_.at<double>(j * 6, 0);
double ppx1 = cameras_.at<double>(i * 7 + 1, 0); double ppx1 = cameras_.at<double>(i * 6 + 1, 0);
double ppx2 = cameras_.at<double>(j * 7 + 1, 0); double ppx2 = cameras_.at<double>(j * 6 + 1, 0);
double ppy1 = cameras_.at<double>(i * 7 + 2, 0); double ppy1 = cameras_.at<double>(i * 6 + 2, 0);
double ppy2 = cameras_.at<double>(j * 7 + 2, 0); double ppy2 = cameras_.at<double>(j * 6 + 2, 0);
double a1 = cameras_.at<double>(i * 7 + 3, 0);
double a2 = cameras_.at<double>(j * 7 + 3, 0);
double R1[9]; double R1[9];
Mat R1_(3, 3, CV_64F, R1); Mat R1_(3, 3, CV_64F, R1);
Mat rvec(3, 1, CV_64F); Mat rvec(3, 1, CV_64F);
rvec.at<double>(0, 0) = cameras_.at<double>(i * 7 + 4, 0); rvec.at<double>(0, 0) = cameras_.at<double>(i * 6 + 3, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(i * 7 + 5, 0); rvec.at<double>(1, 0) = cameras_.at<double>(i * 6 + 4, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(i * 7 + 6, 0); rvec.at<double>(2, 0) = cameras_.at<double>(i * 6 + 5, 0);
Rodrigues(rvec, R1_); Rodrigues(rvec, R1_);
double R2[9]; double R2[9];
Mat R2_(3, 3, CV_64F, R2); Mat R2_(3, 3, CV_64F, R2);
rvec.at<double>(0, 0) = cameras_.at<double>(j * 7 + 4, 0); rvec.at<double>(0, 0) = cameras_.at<double>(j * 6 + 3, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(j * 7 + 5, 0); rvec.at<double>(1, 0) = cameras_.at<double>(j * 6 + 4, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(j * 7 + 6, 0); rvec.at<double>(2, 0) = cameras_.at<double>(j * 6 + 5, 0);
Rodrigues(rvec, R2_); Rodrigues(rvec, R2_);
const ImageFeatures& features1 = features_[i]; const ImageFeatures& features1 = features_[i];
@ -315,11 +309,11 @@ void BundleAdjuster::calcError(Mat &err)
Mat_<double> K1 = Mat::eye(3, 3, CV_64F); Mat_<double> K1 = Mat::eye(3, 3, CV_64F);
K1(0,0) = f1; K1(0,2) = ppx1; K1(0,0) = f1; K1(0,2) = ppx1;
K1(1,1) = f1*a1; K1(1,2) = ppy1; K1(1,1) = f1; K1(1,2) = ppy1;
Mat_<double> K2 = Mat::eye(3, 3, CV_64F); Mat_<double> K2 = Mat::eye(3, 3, CV_64F);
K2(0,0) = f2; K2(0,2) = ppx2; K2(0,0) = f2; K2(0,2) = ppx2;
K2(1,1) = f2*a2; K2(1,2) = ppy2; K2(1,1) = f2; K2(1,2) = ppy2;
Mat_<double> H = K2 * R2_.inv() * R1_ * K1.inv(); Mat_<double> H = K2 * R2_.inv() * R1_ * K1.inv();
@ -329,8 +323,8 @@ void BundleAdjuster::calcError(Mat &err)
continue; continue;
const DMatch& m = matches_info.matches[k]; const DMatch& m = matches_info.matches[k];
Point2d p1 = features1.keypoints[m.queryIdx].pt; Point2f p1 = features1.keypoints[m.queryIdx].pt;
Point2d p2 = features2.keypoints[m.trainIdx].pt; Point2f p2 = features2.keypoints[m.trainIdx].pt;
double x = H(0,0)*p1.x + H(0,1)*p1.y + H(0,2); double x = H(0,0)*p1.x + H(0,1)*p1.y + H(0,2);
double y = H(1,0)*p1.x + H(1,1)*p1.y + H(1,2); double y = H(1,0)*p1.x + H(1,1)*p1.y + H(1,2);
double z = H(2,0)*p1.x + H(2,1)*p1.y + H(2,2); double z = H(2,0)*p1.x + H(2,1)*p1.y + H(2,2);
@ -343,24 +337,24 @@ void BundleAdjuster::calcError(Mat &err)
} }
void BundleAdjuster::calcJacobian() void BundleAdjusterReproj::calcJacobian()
{ {
J_.create(total_num_matches_ * 2, num_images_ * 7, CV_64F); J_.create(total_num_matches_ * 2, num_images_ * 6, CV_64F);
double val; double val;
const double step = 1e-3; const double step = 1e-4;
for (int i = 0; i < num_images_; ++i) for (int i = 0; i < num_images_; ++i)
{ {
for (int j = 0; j < 7; ++j) for (int j = 0; j < 6; ++j)
{ {
val = cameras_.at<double>(i * 7 + j, 0); val = cameras_.at<double>(i * 6 + j, 0);
cameras_.at<double>(i * 7+ j, 0) = val - step; cameras_.at<double>(i * 6 + j, 0) = val - step;
calcError(err1_); calcError(err1_);
cameras_.at<double>(i * 7 + j, 0) = val + step; cameras_.at<double>(i * 6 + j, 0) = val + step;
calcError(err2_); calcError(err2_);
calcDeriv(err1_, err2_, 2 * step, J_.col(i * 7 + j)); calcDeriv(err1_, err2_, 2 * step, J_.col(i * 6 + j));
cameras_.at<double>(i * 7 + j, 0) = val; cameras_.at<double>(i * 6 + j, 0) = val;
} }
} }
} }

2
modules/stitching/src/stitcher.cpp
Unescape View File

@ -189,7 +189,7 @@ Stitcher::Status Stitcher::stitch(InputArray imgs_, OutputArray pano_)
LOGLN("Initial intrinsic parameters #" << indices[i]+1 << ":\n " << cameras[i].K()); LOGLN("Initial intrinsic parameters #" << indices[i]+1 << ":\n " << cameras[i].K());
} }
detail::BundleAdjuster adjuster(detail::BundleAdjuster::FOCAL_RAY_SPACE, conf_thresh_); detail::BundleAdjusterReproj adjuster(conf_thresh_);
adjuster(features, pairwise_matches, cameras); adjuster(features, pairwise_matches, cameras);
// Find median focal length // Find median focal length

34
samples/cpp/stitching_detailed.cpp
Unescape View File

@ -79,8 +79,6 @@ void printUsage()
" --conf_thresh <float>\n" " --conf_thresh <float>\n"
" Threshold for two images are from the same panorama confidence.\n" " Threshold for two images are from the same panorama confidence.\n"
" The default is 1.0.\n" " The default is 1.0.\n"
" --ba (no|ray|focal_ray)\n"
" Bundle adjustment cost function. The default is 'focal_ray'.\n"
" --wave_correct (no|yes)\n" " --wave_correct (no|yes)\n"
" Perform wave effect correction. The default is 'yes'.\n" " Perform wave effect correction. The default is 'yes'.\n"
" --save_graph <file_name>\n" " --save_graph <file_name>\n"
@ -115,7 +113,6 @@ bool try_gpu = false;
double work_megapix = 0.6; double work_megapix = 0.6;
double seam_megapix = 0.1; double seam_megapix = 0.1;
double compose_megapix = -1; double compose_megapix = -1;
int ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
float conf_thresh = 1.f; float conf_thresh = 1.f;
bool wave_correct = true; bool wave_correct = true;
bool save_graph = false; bool save_graph = false;
@ -184,21 +181,6 @@ int parseCmdArgs(int argc, char** argv)
match_conf = static_cast<float>(atof(argv[i + 1])); match_conf = static_cast<float>(atof(argv[i + 1]));
i++; i++;
} }
else if (string(argv[i]) == "--ba")
{
if (string(argv[i + 1]) == "no")
ba_space = BundleAdjuster::NO;
else if (string(argv[i + 1]) == "ray")
ba_space = BundleAdjuster::RAY_SPACE;
else if (string(argv[i + 1]) == "focal_ray")
ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
else
{
cout << "Bad bundle adjustment space\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--conf_thresh") else if (string(argv[i]) == "--conf_thresh")
{ {
conf_thresh = static_cast<float>(atof(argv[i + 1])); conf_thresh = static_cast<float>(atof(argv[i + 1]));
@ -431,14 +413,14 @@ int main(int argc, char* argv[])
LOGLN("Initial focal length #" << indices[i]+1 << ": " << cameras[i].focal); LOGLN("Initial focal length #" << indices[i]+1 << ": " << cameras[i].focal);
} }
BundleAdjuster adjuster(ba_space, conf_thresh); BundleAdjusterReproj adjuster(conf_thresh);
adjuster(features, pairwise_matches, cameras); adjuster(features, pairwise_matches, cameras);
// Find median focal length // Find median focal length
vector<double> focals; vector<double> focals;
for (size_t i = 0; i < cameras.size(); ++i) for (size_t i = 0; i < cameras.size(); ++i)
{ {
LOGLN("Camera #" << indices[i]+1 << " focal length: " << cameras[i].focal); LOGLN("Camera #" << indices[i]+1 << ":\n" << cameras[i].K());
focals.push_back(cameras[i].focal); focals.push_back(cameras[i].focal);
} }
nth_element(focals.begin(), focals.begin() + focals.size()/2, focals.end()); nth_element(focals.begin(), focals.begin() + focals.size()/2, focals.end());
@ -476,16 +458,16 @@ int main(int argc, char* argv[])
#ifndef ANDROID #ifndef ANDROID
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0) if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
{ {
if (warp_type == "plane") warper_creator = new cv::PlaneWarper(); if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu();
else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper(); else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu();
else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper(); else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu();
} }
else else
#endif #endif
{ {
if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu(); if (warp_type == "plane") warper_creator = new cv::PlaneWarper();
else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu(); else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper();
else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu(); else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper();
} }
if (warper_creator.empty()) if (warper_creator.empty())

Write Preview
Loading…
Cancel
Save