Stitcher::Status Stitcher::estimateCameraParams()
{
detail::HomographyBasedEstimator estimator;
if (!estimator(features_, pairwise_matches_, cameras_))
return ERR_HOMOGRAPHY_EST_FAIL;
for (size_t i = 0; i < cameras_.size(); ++i)
{
Mat R;
cameras_[i].R.convertTo(R, CV_32F);
cameras_[i].R = R;
LOGLN("Initial intrinsic parameters #" << indices_[i] + 1 << ":\n " << cameras_[i].K());
}
bundle_adjuster_->setConfThresh(conf_thresh_);
if (!(*bundle_adjuster_)(features_, pairwise_matches_, cameras_))
return ERR_CAMERA_PARAMS_ADJUST_FAIL;
// Find median focal length and use it as final image scale
std::vector<double> focals;
for (size_t i = 0; i < cameras_.size(); ++i)
{
LOGLN("Camera #" << indices_[i] + 1 << ":\n" << cameras_[i].K());
focals.push_back(cameras_[i].focal);
}
std::sort(focals.begin(), focals.end());
if (focals.size() % 2 == 1)
warped_image_scale_ = static_cast<float>(focals[focals.size() / 2]);
else
warped_image_scale_ = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
if (do_wave_correct_)
{
std::vector<Mat> rmats;
for (size_t i = 0; i < cameras_.size(); ++i)
rmats.push_back(cameras_[i].R);
detail::waveCorrect(rmats, wave_correct_kind_);
for (size_t i = 0; i < cameras_.size(); ++i)
cameras_[i].R = rmats[i];
}
return OK;
}
void estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
vector<double> &focals)
{
const int num_images = static_cast<int>(features.size());
focals.resize(num_images);
vector<double> all_focals;
for (int i = 0; i < num_images; ++i)
{
for (int j = 0; j < num_images; ++j)
{
const MatchesInfo &m = pairwise_matches[i*num_images + j];
if (m.H.empty())
continue;
double f0, f1;
bool f0ok, f1ok;
focalsFromHomography(m.H, f0, f1, f0ok, f1ok);
if (f0ok && f1ok)
all_focals.push_back(sqrt(f0 * f1));
}
}
if (static_cast<int>(all_focals.size()) >= num_images - 1)
{
double median;
sort(all_focals.begin(), all_focals.end());
if (all_focals.size() % 2 == 1)
median = all_focals[all_focals.size() / 2];
else
median = (all_focals[all_focals.size() / 2 - 1] + all_focals[all_focals.size() / 2]) * 0.5;
for (int i = 0; i < num_images; ++i)
focals[i] = median;
}
else
{
LOGLN("Can't estimate focal length, will use naive approach");
double focals_sum = 0;
for (int i = 0; i < num_images; ++i)
focals_sum += features[i].img_size.width + features[i].img_size.height;
for (int i = 0; i < num_images; ++i)
focals[i] = focals_sum / num_images;
}
}
void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
{
#if ENABLE_LOG
int64 t = getTickCount();
#endif
UMat img = _img.getUMat();
CV_Assert(img.type() == CV_16SC3 || img.type() == CV_8UC3);
CV_Assert(mask.type() == CV_8U);
// Keep source image in memory with small border
int gap = 3 * (1 << num_bands_);
Point tl_new(std::max(dst_roi_.x, tl.x - gap),
std::max(dst_roi_.y, tl.y - gap));
Point br_new(std::min(dst_roi_.br().x, tl.x + img.cols + gap),
std::min(dst_roi_.br().y, tl.y + img.rows + gap));
// Ensure coordinates of top-left, bottom-right corners are divided by (1 << num_bands_).
// After that scale between layers is exactly 2.
//
// We do it to avoid interpolation problems when keeping sub-images only. There is no such problem when
// image is bordered to have size equal to the final image size, but this is too memory hungry approach.
tl_new.x = dst_roi_.x + (((tl_new.x - dst_roi_.x) >> num_bands_) << num_bands_);
tl_new.y = dst_roi_.y + (((tl_new.y - dst_roi_.y) >> num_bands_) << num_bands_);
int width = br_new.x - tl_new.x;
int height = br_new.y - tl_new.y;
width += ((1 << num_bands_) - width % (1 << num_bands_)) % (1 << num_bands_);
height += ((1 << num_bands_) - height % (1 << num_bands_)) % (1 << num_bands_);
br_new.x = tl_new.x + width;
br_new.y = tl_new.y + height;
int dy = std::max(br_new.y - dst_roi_.br().y, 0);
int dx = std::max(br_new.x - dst_roi_.br().x, 0);
tl_new.x -= dx; br_new.x -= dx;
tl_new.y -= dy; br_new.y -= dy;
int top = tl.y - tl_new.y;
int left = tl.x - tl_new.x;
int bottom = br_new.y - tl.y - img.rows;
int right = br_new.x - tl.x - img.cols;
// Create the source image Laplacian pyramid
UMat img_with_border;
copyMakeBorder(_img, img_with_border, top, bottom, left, right,
BORDER_REFLECT);
LOGLN(" Add border to the source image, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
std::vector<UMat> src_pyr_laplace;
if (can_use_gpu_ && img_with_border.depth() == CV_16S)
createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace);
else
createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
LOGLN(" Create the source image Laplacian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
// Create the weight map Gaussian pyramid
UMat weight_map;
std::vector<UMat> weight_pyr_gauss(num_bands_ + 1);
if(weight_type_ == CV_32F)
{
mask.getUMat().convertTo(weight_map, CV_32F, 1./255.);
}
else // weight_type_ == CV_16S
{
mask.getUMat().convertTo(weight_map, CV_16S);
UMat add_mask;
compare(mask, 0, add_mask, CMP_NE);
add(weight_map, Scalar::all(1), weight_map, add_mask);
}
copyMakeBorder(weight_map, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
for (int i = 0; i < num_bands_; ++i)
pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]);
LOGLN(" Create the weight map Gaussian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
int y_tl = tl_new.y - dst_roi_.y;
int y_br = br_new.y - dst_roi_.y;
int x_tl = tl_new.x - dst_roi_.x;
int x_br = br_new.x - dst_roi_.x;
// Add weighted layer of the source image to the final Laplacian pyramid layer
for (int i = 0; i <= num_bands_; ++i)
{
Rect rc(x_tl, y_tl, x_br - x_tl, y_br - y_tl);
#ifdef HAVE_OPENCL
if ( !cv::ocl::useOpenCL() ||
!ocl_MultiBandBlender_feed(src_pyr_laplace[i], weight_pyr_gauss[i],
dst_pyr_laplace_[i](rc), dst_band_weights_[i](rc)) )
#endif
{
Mat _src_pyr_laplace = src_pyr_laplace[i].getMat(ACCESS_READ);
Mat _dst_pyr_laplace = dst_pyr_laplace_[i](rc).getMat(ACCESS_RW);
Mat _weight_pyr_gauss = weight_pyr_gauss[i].getMat(ACCESS_READ);
Mat _dst_band_weights = dst_band_weights_[i](rc).getMat(ACCESS_RW);
if(weight_type_ == CV_32F)
{
for (int y = 0; y < rc.height; ++y)
{
const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
const float* weight_row = _weight_pyr_gauss.ptr<float>(y);
float* dst_weight_row = _dst_band_weights.ptr<float>(y);
for (int x = 0; x < rc.width; ++x)
{
dst_row[x].x += static_cast<short>(src_row[x].x * weight_row[x]);
dst_row[x].y += static_cast<short>(src_row[x].y * weight_row[x]);
dst_row[x].z += static_cast<short>(src_row[x].z * weight_row[x]);
dst_weight_row[x] += weight_row[x];
}
}
}
else // weight_type_ == CV_16S
{
for (int y = 0; y < y_br - y_tl; ++y)
{
const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
const short* weight_row = _weight_pyr_gauss.ptr<short>(y);
short* dst_weight_row = _dst_band_weights.ptr<short>(y);
for (int x = 0; x < x_br - x_tl; ++x)
{
dst_row[x].x += short((src_row[x].x * weight_row[x]) >> 8);
dst_row[x].y += short((src_row[x].y * weight_row[x]) >> 8);
dst_row[x].z += short((src_row[x].z * weight_row[x]) >> 8);
dst_weight_row[x] += weight_row[x];
}
}
}
}
#ifdef HAVE_OPENCL
else
{
Stitcher::Status Stitcher::matchImages()
{
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS;
}
work_scale_ = 1;
seam_work_aspect_ = 1;
seam_scale_ = 1;
bool is_work_scale_set = false;
bool is_seam_scale_set = false;
Mat full_img, img;
features_.resize(imgs_.size());
seam_est_imgs_.resize(imgs_.size());
full_img_sizes_.resize(imgs_.size());
LOGLN("Finding features...");
int64 t = getTickCount();
for (size_t i = 0; i < imgs_.size(); ++i)
{
full_img = imgs_[i];
full_img_sizes_[i] = full_img.size();
if (registr_resol_ < 0)
{
img = full_img;
work_scale_ = 1;
is_work_scale_set = true;
}
else
{
if (!is_work_scale_set)
{
work_scale_ = min(1.0, sqrt(registr_resol_ * 1e6 / full_img.size().area()));
is_work_scale_set = true;
}
resize(full_img, img, Size(), work_scale_, work_scale_);
}
if (!is_seam_scale_set)
{
seam_scale_ = min(1.0, sqrt(seam_est_resol_ * 1e6 / full_img.size().area()));
seam_work_aspect_ = seam_scale_ / work_scale_;
is_seam_scale_set = true;
}
if (rois_.empty())
(*features_finder_)(img, features_[i]);
else
(*features_finder_)(img, features_[i], rois_[i]);
features_[i].img_idx = (int)i;
LOGLN("Features in image #" << i+1 << ": " << features_[i].keypoints.size());
resize(full_img, img, Size(), seam_scale_, seam_scale_);
seam_est_imgs_[i] = img.clone();
}
// Do it to save memory
features_finder_->collectGarbage();
full_img.release();
img.release();
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOG("Pairwise matching");
t = getTickCount();
(*features_matcher_)(features_, pairwise_matches_, matching_mask_);
features_matcher_->collectGarbage();
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Leave only images we are sure are from the same panorama
indices_ = detail::leaveBiggestComponent(features_, pairwise_matches_, (float)conf_thresh_);
vector<Mat> seam_est_imgs_subset;
vector<Mat> imgs_subset;
vector<Size> full_img_sizes_subset;
for (size_t i = 0; i < indices_.size(); ++i)
{
imgs_subset.push_back(imgs_[indices_[i]]);
seam_est_imgs_subset.push_back(seam_est_imgs_[indices_[i]]);
full_img_sizes_subset.push_back(full_img_sizes_[indices_[i]]);
}
seam_est_imgs_ = seam_est_imgs_subset;
imgs_ = imgs_subset;
full_img_sizes_ = full_img_sizes_subset;
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS;
}
return OK;
}
Stitcher::Status Stitcher::composePanorama(InputArray images, OutputArray pano)
{
LOGLN("Warping images (auxiliary)... ");
vector<Mat> imgs;
images.getMatVector(imgs);
if (!imgs.empty())
{
CV_Assert(imgs.size() == imgs_.size());
Mat img;
seam_est_imgs_.resize(imgs.size());
for (size_t i = 0; i < imgs.size(); ++i)
{
imgs_[i] = imgs[i];
resize(imgs[i], img, Size(), seam_scale_, seam_scale_);
seam_est_imgs_[i] = img.clone();
}
vector<Mat> seam_est_imgs_subset;
vector<Mat> imgs_subset;
for (size_t i = 0; i < indices_.size(); ++i)
{
imgs_subset.push_back(imgs_[indices_[i]]);
seam_est_imgs_subset.push_back(seam_est_imgs_[indices_[i]]);
}
seam_est_imgs_ = seam_est_imgs_subset;
imgs_ = imgs_subset;
}
Mat &pano_ = pano.getMatRef();
int64 t = getTickCount();
vector<Point> corners(imgs_.size());
vector<Mat> masks_warped(imgs_.size());
vector<Mat> images_warped(imgs_.size());
vector<Size> sizes(imgs_.size());
vector<Mat> masks(imgs_.size());
// Prepare image masks
for (size_t i = 0; i < imgs_.size(); ++i)
{
masks[i].create(seam_est_imgs_[i].size(), CV_8U);
masks[i].setTo(Scalar::all(255));
}
// Warp images and their masks
Ptr<detail::RotationWarper> w = warper_->create(float(warped_image_scale_ * seam_work_aspect_));
for (size_t i = 0; i < imgs_.size(); ++i)
{
Mat_<float> K;
cameras_[i].K().convertTo(K, CV_32F);
K(0,0) *= (float)seam_work_aspect_;
K(0,2) *= (float)seam_work_aspect_;
K(1,1) *= (float)seam_work_aspect_;
K(1,2) *= (float)seam_work_aspect_;
corners[i] = w->warp(seam_est_imgs_[i], K, cameras_[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
sizes[i] = images_warped[i].size();
w->warp(masks[i], K, cameras_[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
}
vector<Mat> images_warped_f(imgs_.size());
for (size_t i = 0; i < imgs_.size(); ++i)
images_warped[i].convertTo(images_warped_f[i], CV_32F);
LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Find seams
exposure_comp_->feed(corners, images_warped, masks_warped);
seam_finder_->find(images_warped_f, corners, masks_warped);
// Release unused memory
seam_est_imgs_.clear();
images_warped.clear();
images_warped_f.clear();
masks.clear();
LOGLN("Compositing...");
t = getTickCount();
Mat img_warped, img_warped_s;
Mat dilated_mask, seam_mask, mask, mask_warped;
//double compose_seam_aspect = 1;
double compose_work_aspect = 1;
bool is_blender_prepared = false;
double compose_scale = 1;
bool is_compose_scale_set = false;
Mat full_img, img;
for (size_t img_idx = 0; img_idx < imgs_.size(); ++img_idx)
{
LOGLN("Compositing image #" << indices_[img_idx] + 1);
// Read image and resize it if necessary
full_img = imgs_[img_idx];
if (!is_compose_scale_set)
{
if (compose_resol_ > 0)
compose_scale = min(1.0, sqrt(compose_resol_ * 1e6 / full_img.size().area()));
is_compose_scale_set = true;
// Compute relative scales
//compose_seam_aspect = compose_scale / seam_scale_;
compose_work_aspect = compose_scale / work_scale_;
// Update warped image scale
warped_image_scale_ *= static_cast<float>(compose_work_aspect);
w = warper_->create((float)warped_image_scale_);
// Update corners and sizes
for (size_t i = 0; i < imgs_.size(); ++i)
{
// Update intrinsics
cameras_[i].focal *= compose_work_aspect;
cameras_[i].ppx *= compose_work_aspect;
cameras_[i].ppy *= compose_work_aspect;
// Update corner and size
Size sz = full_img_sizes_[i];
if (std::abs(compose_scale - 1) > 1e-1)
{
sz.width = cvRound(full_img_sizes_[i].width * compose_scale);
sz.height = cvRound(full_img_sizes_[i].height * compose_scale);
}
Mat K;
cameras_[i].K().convertTo(K, CV_32F);
Rect roi = w->warpRoi(sz, K, cameras_[i].R);
corners[i] = roi.tl();
sizes[i] = roi.size();
}
}
if (std::abs(compose_scale - 1) > 1e-1)
resize(full_img, img, Size(), compose_scale, compose_scale);
else
img = full_img;
full_img.release();
Size img_size = img.size();
Mat K;
cameras_[img_idx].K().convertTo(K, CV_32F);
// Warp the current image
w->warp(img, K, cameras_[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
// Warp the current image mask
mask.create(img_size, CV_8U);
mask.setTo(Scalar::all(255));
w->warp(mask, K, cameras_[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);
// Compensate exposure
exposure_comp_->apply((int)img_idx, corners[img_idx], img_warped, mask_warped);
img_warped.convertTo(img_warped_s, CV_16S);
img_warped.release();
img.release();
mask.release();
// Make sure seam mask has proper size
dilate(masks_warped[img_idx], dilated_mask, Mat());
resize(dilated_mask, seam_mask, mask_warped.size());
mask_warped = seam_mask & mask_warped;
if (!is_blender_prepared)
{
blender_->prepare(corners, sizes);
is_blender_prepared = true;
}
// Blend the current image
blender_->feed(img_warped_s, mask_warped, corners[img_idx]);
}
Mat result, result_mask;
blender_->blend(result, result_mask);
LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Preliminary result is in CV_16SC3 format, but all values are in [0,255] range,
// so convert it to avoid user confusing
result.convertTo(pano_, CV_8U);
return OK;
}
void BlockBase::print(LogId logId, bool recursive)
{
LOGLN(logId, "(", pos.x, ",", pos.y, "), size=", m_size);
}
void RhoanaGainCompensator::feed(const vector<Point> &corners, const vector<UMat> &images,
const vector<pair<UMat,uchar> > &masks)
{
LOGLN("Exposure compensation...");
#if ENABLE_LOG
int64 t = getTickCount();
#endif
CV_Assert(corners.size() == images.size() && images.size() == masks.size());
const int num_images = static_cast<int>(images.size());
Mat_<int> N(num_images, num_images); N.setTo(0);
Mat_<double> I(num_images, num_images); I.setTo(0);
//Rect dst_roi = resultRoi(corners, images);
Mat subimg1, subimg2;
Mat_<uchar> submask1, submask2, intersect;
for (int i = 0; i < num_images; ++i)
{
//std::cout << "Here1.1" << std::endl;
for (int j = i; j < num_images; ++j)
{
Rect roi;
if (overlapRoi(corners[i], corners[j], images[i].size(), images[j].size(), roi))
{
subimg1 = images[i](Rect(roi.tl() - corners[i], roi.br() - corners[i])).getMat(ACCESS_READ);
subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j])).getMat(ACCESS_READ);
submask1 = masks[i].first(Rect(roi.tl() - corners[i], roi.br() - corners[i])).getMat(ACCESS_READ);
submask2 = masks[j].first(Rect(roi.tl() - corners[j], roi.br() - corners[j])).getMat(ACCESS_READ);
intersect = (submask1 == masks[i].second) & (submask2 == masks[j].second);
N(i, j) = N(j, i) = std::max(1, countNonZero(intersect));
double Isum1 = 0, Isum2 = 0;
for (int y = 0; y < roi.height; ++y)
{
///const Point3_<uchar>* r1 = subimg1.ptr<Point3_<uchar> >(y);
///const Point3_<uchar>* r2 = subimg2.ptr<Point3_<uchar> >(y);
const uchar* r1 = subimg1.ptr<uchar>(y);
const uchar* r2 = subimg2.ptr<uchar>(y);
for (int x = 0; x < roi.width; ++x)
{
if (intersect(y, x))
{
///Isum1 += sqrt(static_cast<double>(sqr(r1[x].x) + sqr(r1[x].y) + sqr(r1[x].z)));
///Isum2 += sqrt(static_cast<double>(sqr(r2[x].x) + sqr(r2[x].y) + sqr(r2[x].z)));
Isum1 += sqrt(static_cast<double>(sqr(r1[x])));
Isum2 += sqrt(static_cast<double>(sqr(r2[x])));
}
}
}
I(i, j) = Isum1 / N(i, j);
I(j, i) = Isum2 / N(i, j);
}
}
//std::cout << "Here1.2" << std::endl;
}
double alpha = 0.01;
double beta = 100;
Mat_<double> A(num_images, num_images); A.setTo(0);
Mat_<double> b(num_images, 1); b.setTo(0);
for (int i = 0; i < num_images; ++i)
{
for (int j = 0; j < num_images; ++j)
{
b(i, 0) += beta * N(i, j);
A(i, i) += beta * N(i, j);
if (j == i) continue;
A(i, i) += 2 * alpha * I(i, j) * I(i, j) * N(i, j);
A(i, j) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j);
}
}
solve(A, b, gains_);
LOGLN("Exposure compensation, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
}
void GuiSystem::handleInput(InputEvent &ev) {
if (ev.isConsumed())
return;
switch (ev.type) {
case InputEvent::EV_KEY_DOWN:
if (pFocusedElement_) {
pFocusedElement_->keyDown(ev.key);
ev.consume();
}
break;
case InputEvent::EV_KEY_UP:
if (pFocusedElement_) {
pFocusedElement_->keyUp(ev.key);
ev.consume();
}
break;
case InputEvent::EV_KEY_CHAR:
if (pFocusedElement_) {
pFocusedElement_->keyChar(ev.ch);
ev.consume();
}
break;
case InputEvent::EV_MOUSE_DOWN:
if (pCaptured) {
pCaptured->mouseDown((MouseButtons)ev.mouseButton);
ev.consume();
} else {
if (lastUnderMouse) {
lastUnderMouse->mouseDown((MouseButtons)ev.mouseButton);
int lastZ = 1;
if (pFocusedElement_ != lastUnderMouse) {
if (pFocusedElement_) {
pFocusedElement_->focusLost();
lastZ += pFocusedElement_->getZValue();
}
pFocusedElement_ = lastUnderMouse;
pFocusedElement_->focusGot();
pFocusedElement_->setZValue(lastZ+1);
normalizeZValuesAndSort(pFocusedElement_);
}
ev.consume();
}
}
break;
case InputEvent::EV_MOUSE_UP:
if (pCaptured) {
pCaptured->mouseUp((MouseButtons)ev.mouseButton);
ev.consume();
} else {
if (lastUnderMouse) {
lastUnderMouse->mouseUp((MouseButtons)ev.mouseButton);
ev.consume();
}
}
break;
case InputEvent::EV_MOUSE_MOVED:
if (pCaptured) {
pCaptured->mouseMoved(glm::vec2(ev.dx, ev.dy), glm::vec2(ev.x, ev.y));
ev.consume();
} else {
IGuiElement *crt = getElementUnderMouse(ev.x, ev.y);
if (crt != lastUnderMouse) {
if (lastUnderMouse)
lastUnderMouse->mouseLeave();
lastUnderMouse = crt;
if (lastUnderMouse) {
lastUnderMouse->mouseEnter();
}
}
if (lastUnderMouse)
lastUnderMouse->mouseMoved(glm::vec2(ev.dx, ev.dy), glm::vec2(ev.x, ev.y));
}
break;
case InputEvent::EV_MOUSE_SCROLL:
if (pCaptured) {
pCaptured->mouseScroll(ev.dz);
ev.consume();
} else {
if (lastUnderMouse) {
lastUnderMouse->mouseScroll(ev.dz);
ev.consume();
}
}
break;
default:
LOGLN("unknown event type: " << ev.type);
}
}
Stitcher::Status Stitcher::matchImages()
{
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS;
}
work_scale_ = 1;
seam_work_aspect_ = 1;
seam_scale_ = 1;
bool is_work_scale_set = false;
bool is_seam_scale_set = false;
features_.resize(imgs_.size());
seam_est_imgs_.resize(imgs_.size());
full_img_sizes_.resize(imgs_.size());
LOGLN("Finding features...");
#if ENABLE_LOG
int64 t = getTickCount();
#endif
std::vector<UMat> feature_find_imgs(imgs_.size());
std::vector<UMat> feature_find_masks(masks_.size());
for (size_t i = 0; i < imgs_.size(); ++i)
{
full_img_sizes_[i] = imgs_[i].size();
if (registr_resol_ < 0)
{
feature_find_imgs[i] = imgs_[i];
work_scale_ = 1;
is_work_scale_set = true;
}
else
{
if (!is_work_scale_set)
{
work_scale_ = std::min(1.0, std::sqrt(registr_resol_ * 1e6 / full_img_sizes_[i].area()));
is_work_scale_set = true;
}
resize(imgs_[i], feature_find_imgs[i], Size(), work_scale_, work_scale_, INTER_LINEAR_EXACT);
}
if (!is_seam_scale_set)
{
seam_scale_ = std::min(1.0, std::sqrt(seam_est_resol_ * 1e6 / full_img_sizes_[i].area()));
seam_work_aspect_ = seam_scale_ / work_scale_;
is_seam_scale_set = true;
}
if (!masks_.empty())
{
resize(masks_[i], feature_find_masks[i], Size(), work_scale_, work_scale_, INTER_NEAREST);
}
features_[i].img_idx = (int)i;
LOGLN("Features in image #" << i+1 << ": " << features_[i].keypoints.size());
resize(imgs_[i], seam_est_imgs_[i], Size(), seam_scale_, seam_scale_, INTER_LINEAR_EXACT);
}
// find features possibly in parallel
detail::computeImageFeatures(features_finder_, feature_find_imgs, features_, feature_find_masks);
// Do it to save memory
feature_find_imgs.clear();
feature_find_masks.clear();
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOG("Pairwise matching");
#if ENABLE_LOG
t = getTickCount();
#endif
(*features_matcher_)(features_, pairwise_matches_, matching_mask_);
features_matcher_->collectGarbage();
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Leave only images we are sure are from the same panorama
indices_ = detail::leaveBiggestComponent(features_, pairwise_matches_, (float)conf_thresh_);
std::vector<UMat> seam_est_imgs_subset;
std::vector<UMat> imgs_subset;
std::vector<Size> full_img_sizes_subset;
for (size_t i = 0; i < indices_.size(); ++i)
{
imgs_subset.push_back(imgs_[indices_[i]]);
seam_est_imgs_subset.push_back(seam_est_imgs_[indices_[i]]);
full_img_sizes_subset.push_back(full_img_sizes_[indices_[i]]);
}
seam_est_imgs_ = seam_est_imgs_subset;
imgs_ = imgs_subset;
full_img_sizes_ = full_img_sizes_subset;
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS;
}
return OK;
}