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opencv/modules/stitching/src/stitcher.cpp
Amir Tulegenov 04d907fb97
Merge pull request #19619 from amirtu:OCV-221_get_and_set_cameras_on_stitcher 
* Get and set cameras for sticher.

* Code review fixes.

Co-authored-by: amir.tulegenov <amir.tulegenov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
2021-03-09 17:51:40 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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#include "precomp.hpp"
namespace cv {
#if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
// Stitcher::ORIG_RESOL is initialized in stitching.hpp.
#else
const double Stitcher::ORIG_RESOL = -1.0;
#endif
Ptr<Stitcher> Stitcher::create(Mode mode)
{
Ptr<Stitcher> stitcher = makePtr<Stitcher>();
stitcher->setRegistrationResol(0.6);
stitcher->setSeamEstimationResol(0.1);
stitcher->setCompositingResol(ORIG_RESOL);
stitcher->setPanoConfidenceThresh(1);
stitcher->setSeamFinder(makePtr<detail::GraphCutSeamFinder>(detail::GraphCutSeamFinderBase::COST_COLOR));
stitcher->setBlender(makePtr<detail::MultiBandBlender>(false));
stitcher->setFeaturesFinder(ORB::create());
stitcher->setInterpolationFlags(INTER_LINEAR);
stitcher->work_scale_ = 1;
stitcher->seam_scale_ = 1;
stitcher->seam_work_aspect_ = 1;
stitcher->warped_image_scale_ = 1;
switch (mode)
{
case PANORAMA: // PANORAMA is the default
// mostly already setup
stitcher->setEstimator(makePtr<detail::HomographyBasedEstimator>());
stitcher->setWaveCorrection(true);
stitcher->setWaveCorrectKind(detail::WAVE_CORRECT_HORIZ);
stitcher->setFeaturesMatcher(makePtr<detail::BestOf2NearestMatcher>(false));
stitcher->setBundleAdjuster(makePtr<detail::BundleAdjusterRay>());
stitcher->setWarper(makePtr<SphericalWarper>());
stitcher->setExposureCompensator(makePtr<detail::BlocksGainCompensator>());
break;
case SCANS:
stitcher->setEstimator(makePtr<detail::AffineBasedEstimator>());
stitcher->setWaveCorrection(false);
stitcher->setFeaturesMatcher(makePtr<detail::AffineBestOf2NearestMatcher>(false, false));
stitcher->setBundleAdjuster(makePtr<detail::BundleAdjusterAffinePartial>());
stitcher->setWarper(makePtr<AffineWarper>());
stitcher->setExposureCompensator(makePtr<detail::NoExposureCompensator>());
break;
default:
CV_Error(Error::StsBadArg, "Invalid stitching mode. Must be one of Stitcher::Mode");
break;
}
return stitcher;
}
Stitcher::Status Stitcher::estimateTransform(InputArrayOfArrays images, InputArrayOfArrays masks)
{
CV_INSTRUMENT_REGION();
images.getUMatVector(imgs_);
masks.getUMatVector(masks_);
Status status;
if ((status = matchImages()) != OK)
return status;
if ((status = estimateCameraParams()) != OK)
return status;
return OK;
}
Stitcher::Status Stitcher::composePanorama(OutputArray pano)
{
CV_INSTRUMENT_REGION();
return composePanorama(std::vector<UMat>(), pano);
}
Stitcher::Status Stitcher::composePanorama(InputArrayOfArrays images, OutputArray pano)
{
CV_INSTRUMENT_REGION();
LOGLN("Warping images (auxiliary)... ");
std::vector<UMat> imgs;
images.getUMatVector(imgs);
if (!imgs.empty())
{
CV_Assert(imgs.size() == imgs_.size());
UMat 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_, INTER_LINEAR_EXACT);
seam_est_imgs_[i] = img.clone();
}
std::vector<UMat> seam_est_imgs_subset;
std::vector<UMat> 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;
}
UMat pano_;
#if ENABLE_LOG
int64 t = getTickCount();
#endif
std::vector<Point> corners(imgs_.size());
std::vector<UMat> masks_warped(imgs_.size());
std::vector<UMat> images_warped(imgs_.size());
std::vector<Size> sizes(imgs_.size());
std::vector<UMat> 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, interp_flags_, 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]);
}
LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Compensate exposure before finding seams
exposure_comp_->feed(corners, images_warped, masks_warped);
for (size_t i = 0; i < imgs_.size(); ++i)
exposure_comp_->apply(int(i), corners[i], images_warped[i], masks_warped[i]);
// Find seams
std::vector<UMat> images_warped_f(imgs_.size());
for (size_t i = 0; i < imgs_.size(); ++i)
images_warped[i].convertTo(images_warped_f[i], CV_32F);
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...");
#if ENABLE_LOG
t = getTickCount();
#endif
UMat img_warped, img_warped_s;
UMat 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;
std::vector<detail::CameraParams> cameras_scaled(cameras_);
UMat full_img, img;
for (size_t img_idx = 0; img_idx < imgs_.size(); ++img_idx)
{
LOGLN("Compositing image #" << indices_[img_idx] + 1);
#if ENABLE_LOG
int64 compositing_t = getTickCount();
#endif
// Read image and resize it if necessary
full_img = imgs_[img_idx];
if (!is_compose_scale_set)
{
if (compose_resol_ > 0)
compose_scale = std::min(1.0, std::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
float warp_scale = static_cast<float>(warped_image_scale_ * compose_work_aspect);
w = warper_->create(warp_scale);
// Update corners and sizes
for (size_t i = 0; i < imgs_.size(); ++i)
{
// Update intrinsics
cameras_scaled[i].ppx *= compose_work_aspect;
cameras_scaled[i].ppy *= compose_work_aspect;
cameras_scaled[i].focal *= 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_scaled[i].K().convertTo(K, CV_32F);
Rect roi = w->warpRoi(sz, K, cameras_scaled[i].R);
corners[i] = roi.tl();
sizes[i] = roi.size();
}
}
if (std::abs(compose_scale - 1) > 1e-1)
{
#if ENABLE_LOG
int64 resize_t = getTickCount();
#endif
resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);
LOGLN(" resize time: " << ((getTickCount() - resize_t) / getTickFrequency()) << " sec");
}
else
img = full_img;
full_img.release();
Size img_size = img.size();
LOGLN(" after resize time: " << ((getTickCount() - compositing_t) / getTickFrequency()) << " sec");
Mat K;
cameras_scaled[img_idx].K().convertTo(K, CV_32F);
#if ENABLE_LOG
int64 pt = getTickCount();
#endif
// Warp the current image
w->warp(img, K, cameras_[img_idx].R, interp_flags_, BORDER_REFLECT, img_warped);
LOGLN(" warp the current image: " << ((getTickCount() - pt) / getTickFrequency()) << " sec");
#if ENABLE_LOG
pt = getTickCount();
#endif
// 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);
LOGLN(" warp the current image mask: " << ((getTickCount() - pt) / getTickFrequency()) << " sec");
#if ENABLE_LOG
pt = getTickCount();
#endif
// Compensate exposure
exposure_comp_->apply((int)img_idx, corners[img_idx], img_warped, mask_warped);
LOGLN(" compensate exposure: " << ((getTickCount() - pt) / getTickFrequency()) << " sec");
#if ENABLE_LOG
pt = getTickCount();
#endif
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(), 0, 0, INTER_LINEAR_EXACT);
bitwise_and(seam_mask, mask_warped, mask_warped);
LOGLN(" other: " << ((getTickCount() - pt) / getTickFrequency()) << " sec");
#if ENABLE_LOG
pt = getTickCount();
#endif
if (!is_blender_prepared)
{
blender_->prepare(corners, sizes);
is_blender_prepared = true;
}
LOGLN(" other2: " << ((getTickCount() - pt) / getTickFrequency()) << " sec");
LOGLN(" feed...");
#if ENABLE_LOG
int64 feed_t = getTickCount();
#endif
// Blend the current image
blender_->feed(img_warped_s, mask_warped, corners[img_idx]);
LOGLN(" feed time: " << ((getTickCount() - feed_t) / getTickFrequency()) << " sec");
LOGLN("Compositing ## time: " << ((getTickCount() - compositing_t) / getTickFrequency()) << " sec");
}
#if ENABLE_LOG
int64 blend_t = getTickCount();
#endif
UMat result;
blender_->blend(result, result_mask_);
LOGLN("blend time: " << ((getTickCount() - blend_t) / getTickFrequency()) << " sec");
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;
}
Stitcher::Status Stitcher::stitch(InputArrayOfArrays images, OutputArray pano)
{
return stitch(images, noArray(), pano);
}
Stitcher::Status Stitcher::stitch(InputArrayOfArrays images, InputArrayOfArrays masks, OutputArray pano)
{
CV_INSTRUMENT_REGION();
Status status = estimateTransform(images, masks);
if (status != OK)
return status;
return composePanorama(pano);
}
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;
}
Stitcher::Status Stitcher::estimateCameraParams()
{
// estimate homography in global frame
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.clone());
detail::waveCorrect(rmats, wave_correct_kind_);
for (size_t i = 0; i < cameras_.size(); ++i)
cameras_[i].R = rmats[i];
}
return OK;
}
Stitcher::Status Stitcher::setTransform(InputArrayOfArrays images, const std::vector<detail::CameraParams> &cameras)
{
std::vector<int> component;
for (int i = 0; i < (int)images.total(); i++)
component.push_back(i);
return setTransform(images, cameras, component);
}
Stitcher::Status Stitcher::setTransform(
InputArrayOfArrays images, const std::vector<detail::CameraParams> &cameras, const std::vector<int> &component)
{
// CV_Assert(images.size() == cameras.size());
images.getUMatVector(imgs_);
masks_.clear();
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;
seam_est_imgs_.resize(imgs_.size());
full_img_sizes_.resize(imgs_.size());
for (size_t i = 0; i < imgs_.size(); ++i)
{
full_img_sizes_[i] = imgs_[i].size();
if (registr_resol_ < 0)
{
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;
}
}
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;
}
resize(imgs_[i], seam_est_imgs_[i], Size(), seam_scale_, seam_scale_, INTER_LINEAR_EXACT);
}
features_.clear();
pairwise_matches_.clear();
indices_ = component;
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;
}
cameras_ = cameras;
std::vector<double> focals;
for (size_t i = 0; i < cameras.size(); ++i)
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;
return Status::OK;
}
CV_DEPRECATED Ptr<Stitcher> createStitcher(bool /*ignored*/)
{
CV_INSTRUMENT_REGION();
return Stitcher::create(Stitcher::PANORAMA);
}
CV_DEPRECATED Ptr<Stitcher> createStitcherScans(bool /*ignored*/)
{
CV_INSTRUMENT_REGION();
return Stitcher::create(Stitcher::SCANS);
}
} // namespace cv
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