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/*M///////////////////////////////////////////////////////////////////////////////////////
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#ifndef OPENCV_STITCHING_STITCHER_HPP
#define OPENCV_STITCHING_STITCHER_HPP
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/stitching/warpers.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#if defined(Status)
# warning Detected X11 'Status' macro definition, it can cause build conflicts. Please, include this header before any X11 headers.
#endif
/**
@defgroup stitching Images stitching
This figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that
class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to
the particular needs. All building blocks from the pipeline are available in the detail namespace,
one can combine and use them separately.
The implemented stitching pipeline is very similar to the one proposed in @cite BL07 .
![stitching pipeline](StitchingPipeline.jpg)
Camera models
-------------
There are currently 2 camera models implemented in stitching pipeline.
- _Homography model_ expecting perspective transformations between images
implemented in @ref cv::detail::BestOf2NearestMatcher cv::detail::HomographyBasedEstimator
cv::detail::BundleAdjusterReproj cv::detail::BundleAdjusterRay
- _Affine model_ expecting affine transformation with 6 DOF or 4 DOF implemented in
@ref cv::detail::AffineBestOf2NearestMatcher cv::detail::AffineBasedEstimator
cv::detail::BundleAdjusterAffine cv::detail::BundleAdjusterAffinePartial cv::AffineWarper
Homography model is useful for creating photo panoramas captured by camera,
while affine-based model can be used to stitch scans and object captured by
specialized devices. Use @ref cv::Stitcher::create to get preconfigured pipeline for one
of those models.
@note
Certain detailed settings of @ref cv::Stitcher might not make sense. Especially
you should not mix classes implementing affine model and classes implementing
Homography model, as they work with different transformations.
@{
@defgroup stitching_match Features Finding and Images Matching
@defgroup stitching_rotation Rotation Estimation
@defgroup stitching_autocalib Autocalibration
@defgroup stitching_warp Images Warping
@defgroup stitching_seam Seam Estimation
@defgroup stitching_exposure Exposure Compensation
@defgroup stitching_blend Image Blenders
@}
*/
namespace cv {
//! @addtogroup stitching
//! @{
/** @example samples/cpp/stitching.cpp
A basic example on image stitching
*/
/** @example samples/python/stitching.py
A basic example on image stitching in Python.
*/
/** @example samples/cpp/stitching_detailed.cpp
A detailed example on image stitching
*/
/** @brief High level image stitcher.
It's possible to use this class without being aware of the entire stitching pipeline. However, to
be able to achieve higher stitching stability and quality of the final images at least being
familiar with the theory is recommended.
@note
- A basic example on image stitching can be found at
opencv_source_code/samples/cpp/stitching.cpp
- A basic example on image stitching in Python can be found at
opencv_source_code/samples/python/stitching.py
- A detailed example on image stitching can be found at
opencv_source_code/samples/cpp/stitching_detailed.cpp
*/
class CV_EXPORTS_W Stitcher
{
public:
/**
* When setting a resolution for stitching, this values is a placeholder
* for preserving the original resolution.
*/
#if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
static constexpr double ORIG_RESOL = -1.0;
#else
// support MSVS 2013
static const double ORIG_RESOL; // Initialized in stitcher.cpp
#endif
enum Status
{
OK = 0,
ERR_NEED_MORE_IMGS = 1,
ERR_HOMOGRAPHY_EST_FAIL = 2,
ERR_CAMERA_PARAMS_ADJUST_FAIL = 3
};
enum Mode
{
/** Mode for creating photo panoramas. Expects images under perspective
transformation and projects resulting pano to sphere.
@sa detail::BestOf2NearestMatcher SphericalWarper
*/
PANORAMA = 0,
/** Mode for composing scans. Expects images under affine transformation does
not compensate exposure by default.
@sa detail::AffineBestOf2NearestMatcher AffineWarper
*/
SCANS = 1,
};
/** @brief Creates a Stitcher configured in one of the stitching modes.
@param mode Scenario for stitcher operation. This is usually determined by source of images
to stitch and their transformation. Default parameters will be chosen for operation in given
scenario.
@return Stitcher class instance.
*/
CV_WRAP static Ptr<Stitcher> create(Mode mode = Stitcher::PANORAMA);
CV_WRAP double registrationResol() const { return registr_resol_; }
CV_WRAP void setRegistrationResol(double resol_mpx) { registr_resol_ = resol_mpx; }
CV_WRAP double seamEstimationResol() const { return seam_est_resol_; }
CV_WRAP void setSeamEstimationResol(double resol_mpx) { seam_est_resol_ = resol_mpx; }
CV_WRAP double compositingResol() const { return compose_resol_; }
CV_WRAP void setCompositingResol(double resol_mpx) { compose_resol_ = resol_mpx; }
CV_WRAP double panoConfidenceThresh() const { return conf_thresh_; }
CV_WRAP void setPanoConfidenceThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
CV_WRAP bool waveCorrection() const { return do_wave_correct_; }
CV_WRAP void setWaveCorrection(bool flag) { do_wave_correct_ = flag; }
CV_WRAP InterpolationFlags interpolationFlags() const { return interp_flags_; }
CV_WRAP void setInterpolationFlags(InterpolationFlags interp_flags) { interp_flags_ = interp_flags; }
detail::WaveCorrectKind waveCorrectKind() const { return wave_correct_kind_; }
void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; }
Ptr<Feature2D> featuresFinder() { return features_finder_; }
const Ptr<Feature2D> featuresFinder() const { return features_finder_; }
void setFeaturesFinder(Ptr<Feature2D> features_finder)
{ features_finder_ = features_finder; }
Ptr<detail::FeaturesMatcher> featuresMatcher() { return features_matcher_; }
const Ptr<detail::FeaturesMatcher> featuresMatcher() const { return features_matcher_; }
void setFeaturesMatcher(Ptr<detail::FeaturesMatcher> features_matcher)
{ features_matcher_ = features_matcher; }
const cv::UMat& matchingMask() const { return matching_mask_; }
void setMatchingMask(const cv::UMat &mask)
{
CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows);
matching_mask_ = mask.clone();
}
Ptr<detail::BundleAdjusterBase> bundleAdjuster() { return bundle_adjuster_; }
const Ptr<detail::BundleAdjusterBase> bundleAdjuster() const { return bundle_adjuster_; }
void setBundleAdjuster(Ptr<detail::BundleAdjusterBase> bundle_adjuster)
{ bundle_adjuster_ = bundle_adjuster; }
Ptr<detail::Estimator> estimator() { return estimator_; }
const Ptr<detail::Estimator> estimator() const { return estimator_; }
void setEstimator(Ptr<detail::Estimator> estimator)
{ estimator_ = estimator; }
Ptr<WarperCreator> warper() { return warper_; }
const Ptr<WarperCreator> warper() const { return warper_; }
void setWarper(Ptr<WarperCreator> creator) { warper_ = creator; }
Ptr<detail::ExposureCompensator> exposureCompensator() { return exposure_comp_; }
const Ptr<detail::ExposureCompensator> exposureCompensator() const { return exposure_comp_; }
void setExposureCompensator(Ptr<detail::ExposureCompensator> exposure_comp)
{ exposure_comp_ = exposure_comp; }
Ptr<detail::SeamFinder> seamFinder() { return seam_finder_; }
const Ptr<detail::SeamFinder> seamFinder() const { return seam_finder_; }
void setSeamFinder(Ptr<detail::SeamFinder> seam_finder) { seam_finder_ = seam_finder; }
Ptr<detail::Blender> blender() { return blender_; }
const Ptr<detail::Blender> blender() const { return blender_; }
void setBlender(Ptr<detail::Blender> b) { blender_ = b; }
/** @brief These functions try to match the given images and to estimate rotations of each camera.
@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.
@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@return Status code.
*/
CV_WRAP Status estimateTransform(InputArrayOfArrays images, InputArrayOfArrays masks = noArray());
/** @brief These function restors camera rotation and camera intrinsics of each camera
* that can be got with @ref Stitcher::cameras call
@param images Input images.
@param cameras Estimated rotation of cameras for each of the input images.
@param component Indices (0-based) of images constituting the final panorama (optional).
@return Status code.
*/
Status setTransform(InputArrayOfArrays images,
const std::vector<detail::CameraParams> &cameras,
const std::vector<int> &component);
/** @overload */
Status setTransform(InputArrayOfArrays images, const std::vector<detail::CameraParams> &cameras);
/** @overload */
CV_WRAP Status composePanorama(OutputArray pano);
/** @brief These functions try to compose the given images (or images stored internally from the other function
calls) into the final pano under the assumption that the image transformations were estimated
before.
@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.
@param images Input images.
@param pano Final pano.
@return Status code.
*/
CV_WRAP Status composePanorama(InputArrayOfArrays images, OutputArray pano);
/** @overload */
CV_WRAP Status stitch(InputArrayOfArrays images, OutputArray pano);
/** @brief These functions try to stitch the given images.
@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@param pano Final pano.
@return Status code.
*/
CV_WRAP Status stitch(InputArrayOfArrays images, InputArrayOfArrays masks, OutputArray pano);
std::vector<int> component() const { return indices_; }
std::vector<detail::CameraParams> cameras() const { return cameras_; }
CV_WRAP double workScale() const { return work_scale_; }
UMat resultMask() const { return result_mask_; }
private:
Status matchImages();
Status estimateCameraParams();
double registr_resol_;
double seam_est_resol_;
double compose_resol_;
double conf_thresh_;
InterpolationFlags interp_flags_;
Ptr<Feature2D> features_finder_;
Ptr<detail::FeaturesMatcher> features_matcher_;
cv::UMat matching_mask_;
Ptr<detail::BundleAdjusterBase> bundle_adjuster_;
Ptr<detail::Estimator> estimator_;
bool do_wave_correct_;
detail::WaveCorrectKind wave_correct_kind_;
Ptr<WarperCreator> warper_;
Ptr<detail::ExposureCompensator> exposure_comp_;
Ptr<detail::SeamFinder> seam_finder_;
Ptr<detail::Blender> blender_;
std::vector<cv::UMat> imgs_;
std::vector<cv::UMat> masks_;
std::vector<cv::Size> full_img_sizes_;
std::vector<detail::ImageFeatures> features_;
std::vector<detail::MatchesInfo> pairwise_matches_;
std::vector<cv::UMat> seam_est_imgs_;
std::vector<int> indices_;
std::vector<detail::CameraParams> cameras_;
UMat result_mask_;
double work_scale_;
double seam_scale_;
double seam_work_aspect_;
double warped_image_scale_;
};
/**
* @deprecated use Stitcher::create
*/
CV_DEPRECATED Ptr<Stitcher> createStitcher(bool try_use_gpu = false);
/**
* @deprecated use Stitcher::create
*/
CV_DEPRECATED Ptr<Stitcher> createStitcherScans(bool try_use_gpu = false);
//! @} stitching
} // namespace cv
#endif // OPENCV_STITCHING_STITCHER_HPP