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374 lines
13 KiB
C++
374 lines
13 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
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#define OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
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#include "opencv2/core.hpp"
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#include "matchers.hpp"
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#include "util.hpp"
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#include "camera.hpp"
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namespace cv {
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namespace detail {
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//! @addtogroup stitching_rotation
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//! @{
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/** @brief Rotation estimator base class.
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It takes features of all images, pairwise matches between all images and estimates rotations of all
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cameras.
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@note The coordinate system origin is implementation-dependent, but you can always normalize the
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rotations in respect to the first camera, for instance. :
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*/
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class CV_EXPORTS_W Estimator
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{
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public:
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virtual ~Estimator() {}
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/** @brief Estimates camera parameters.
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@param features Features of images
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@param pairwise_matches Pairwise matches of images
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@param cameras Estimated camera parameters
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@return True in case of success, false otherwise
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*/
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CV_WRAP_AS(apply) bool operator ()(const std::vector<ImageFeatures> &features,
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const std::vector<MatchesInfo> &pairwise_matches,
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CV_OUT CV_IN_OUT std::vector<CameraParams> &cameras)
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{
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return estimate(features, pairwise_matches, cameras);
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}
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protected:
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/** @brief This method must implement camera parameters estimation logic in order to make the wrapper
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detail::Estimator::operator()_ work.
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@param features Features of images
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@param pairwise_matches Pairwise matches of images
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@param cameras Estimated camera parameters
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@return True in case of success, false otherwise
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*/
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virtual bool estimate(const std::vector<ImageFeatures> &features,
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const std::vector<MatchesInfo> &pairwise_matches,
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CV_OUT std::vector<CameraParams> &cameras) = 0;
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};
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/** @brief Homography based rotation estimator.
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*/
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class CV_EXPORTS_W HomographyBasedEstimator : public Estimator
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{
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public:
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CV_WRAP HomographyBasedEstimator(bool is_focals_estimated = false)
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: is_focals_estimated_(is_focals_estimated) {}
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private:
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virtual bool estimate(const std::vector<ImageFeatures> &features,
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const std::vector<MatchesInfo> &pairwise_matches,
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std::vector<CameraParams> &cameras) CV_OVERRIDE;
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bool is_focals_estimated_;
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};
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/** @brief Affine transformation based estimator.
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This estimator uses pairwise transformations estimated by matcher to estimate
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final transformation for each camera.
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@sa cv::detail::HomographyBasedEstimator
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*/
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class CV_EXPORTS_W AffineBasedEstimator : public Estimator
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{
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public:
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CV_WRAP AffineBasedEstimator(){}
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private:
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virtual bool estimate(const std::vector<ImageFeatures> &features,
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const std::vector<MatchesInfo> &pairwise_matches,
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std::vector<CameraParams> &cameras) CV_OVERRIDE;
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};
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/** @brief Base class for all camera parameters refinement methods.
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*/
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class CV_EXPORTS_W BundleAdjusterBase : public Estimator
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{
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public:
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CV_WRAP const Mat refinementMask() const { return refinement_mask_.clone(); }
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CV_WRAP void setRefinementMask(const Mat &mask)
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{
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CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3));
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refinement_mask_ = mask.clone();
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}
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CV_WRAP double confThresh() const { return conf_thresh_; }
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CV_WRAP void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
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CV_WRAP TermCriteria termCriteria() { return term_criteria_; }
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CV_WRAP void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; }
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protected:
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/** @brief Construct a bundle adjuster base instance.
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@param num_params_per_cam Number of parameters per camera
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@param num_errs_per_measurement Number of error terms (components) per match
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*/
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BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement)
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: num_images_(0), total_num_matches_(0),
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num_params_per_cam_(num_params_per_cam),
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num_errs_per_measurement_(num_errs_per_measurement),
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features_(0), pairwise_matches_(0), conf_thresh_(0)
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{
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setRefinementMask(Mat::ones(3, 3, CV_8U));
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setConfThresh(1.);
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setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 1000, DBL_EPSILON));
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}
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// Runs bundle adjustment
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virtual bool estimate(const std::vector<ImageFeatures> &features,
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const std::vector<MatchesInfo> &pairwise_matches,
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std::vector<CameraParams> &cameras) CV_OVERRIDE;
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/** @brief Sets initial camera parameter to refine.
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@param cameras Camera parameters
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*/
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virtual void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) = 0;
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/** @brief Gets the refined camera parameters.
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@param cameras Refined camera parameters
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*/
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virtual void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const = 0;
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/** @brief Calculates error vector.
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@param err Error column-vector of length total_num_matches \* num_errs_per_measurement
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*/
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virtual void calcError(Mat &err) = 0;
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/** @brief Calculates the cost function jacobian.
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@param jac Jacobian matrix of dimensions
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(total_num_matches \* num_errs_per_measurement) x (num_images \* num_params_per_cam)
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*/
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virtual void calcJacobian(Mat &jac) = 0;
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// 3x3 8U mask, where 0 means don't refine respective parameter, != 0 means refine
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Mat refinement_mask_;
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int num_images_;
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int total_num_matches_;
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int num_params_per_cam_;
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int num_errs_per_measurement_;
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const ImageFeatures *features_;
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const MatchesInfo *pairwise_matches_;
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// Threshold to filter out poorly matched image pairs
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double conf_thresh_;
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//Levenberg-Marquardt algorithm termination criteria
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TermCriteria term_criteria_;
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// Camera parameters matrix (CV_64F)
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Mat cam_params_;
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// Connected images pairs
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std::vector<std::pair<int,int> > edges_;
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};
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/** @brief Stub bundle adjuster that does nothing.
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*/
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class CV_EXPORTS_W NoBundleAdjuster : public BundleAdjusterBase
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{
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public:
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CV_WRAP NoBundleAdjuster() : BundleAdjusterBase(0, 0) {}
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private:
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bool estimate(const std::vector<ImageFeatures> &, const std::vector<MatchesInfo> &,
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std::vector<CameraParams> &) CV_OVERRIDE
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{
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return true;
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}
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void setUpInitialCameraParams(const std::vector<CameraParams> &) CV_OVERRIDE {}
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void obtainRefinedCameraParams(std::vector<CameraParams> &) const CV_OVERRIDE {}
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void calcError(Mat &) CV_OVERRIDE {}
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void calcJacobian(Mat &) CV_OVERRIDE {}
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};
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/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection
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error squares
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It can estimate focal length, aspect ratio, principal point.
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You can affect only on them via the refinement mask.
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*/
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class CV_EXPORTS_W BundleAdjusterReproj : public BundleAdjusterBase
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{
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public:
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CV_WRAP BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {}
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private:
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void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
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void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
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void calcError(Mat &err) CV_OVERRIDE;
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void calcJacobian(Mat &jac) CV_OVERRIDE;
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Mat err1_, err2_;
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};
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/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the distances
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between the rays passing through the camera center and a feature. :
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It can estimate focal length. It ignores the refinement mask for now.
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*/
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class CV_EXPORTS_W BundleAdjusterRay : public BundleAdjusterBase
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{
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public:
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CV_WRAP BundleAdjusterRay() : BundleAdjusterBase(4, 3) {}
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private:
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void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
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void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
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void calcError(Mat &err) CV_OVERRIDE;
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void calcJacobian(Mat &jac) CV_OVERRIDE;
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Mat err1_, err2_;
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};
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/** @brief Bundle adjuster that expects affine transformation
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represented in homogeneous coordinates in R for each camera param. Implements
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camera parameters refinement algorithm which minimizes sum of the reprojection
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error squares
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It estimates all transformation parameters. Refinement mask is ignored.
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@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial
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*/
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class CV_EXPORTS_W BundleAdjusterAffine : public BundleAdjusterBase
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{
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public:
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CV_WRAP BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {}
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private:
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void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
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void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
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void calcError(Mat &err) CV_OVERRIDE;
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void calcJacobian(Mat &jac) CV_OVERRIDE;
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Mat err1_, err2_;
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};
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/** @brief Bundle adjuster that expects affine transformation with 4 DOF
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represented in homogeneous coordinates in R for each camera param. Implements
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camera parameters refinement algorithm which minimizes sum of the reprojection
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error squares
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It estimates all transformation parameters. Refinement mask is ignored.
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@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine
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*/
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class CV_EXPORTS_W BundleAdjusterAffinePartial : public BundleAdjusterBase
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{
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public:
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CV_WRAP BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {}
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private:
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void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
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void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
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void calcError(Mat &err) CV_OVERRIDE;
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void calcJacobian(Mat &jac) CV_OVERRIDE;
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Mat err1_, err2_;
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};
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enum WaveCorrectKind
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{
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WAVE_CORRECT_HORIZ,
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WAVE_CORRECT_VERT,
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WAVE_CORRECT_AUTO
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};
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/** @brief Tries to detect the wave correction kind depending
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on whether a panorama spans horizontally or vertically
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@param rmats Camera rotation matrices.
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@return The correction kind to use for this panorama
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*/
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CV_EXPORTS
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WaveCorrectKind autoDetectWaveCorrectKind(const std::vector<Mat> &rmats);
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/** @brief Tries to make panorama more horizontal (or vertical).
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@param rmats Camera rotation matrices.
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@param kind Correction kind, see detail::WaveCorrectKind.
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*/
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void CV_EXPORTS_W waveCorrect(CV_IN_OUT std::vector<Mat> &rmats, WaveCorrectKind kind);
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//////////////////////////////////////////////////////////////////////////////
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// Auxiliary functions
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// Returns matches graph representation in DOT language
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String CV_EXPORTS_W matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches,
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float conf_threshold);
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CV_EXPORTS_W std::vector<int> leaveBiggestComponent(
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std::vector<ImageFeatures> &features,
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std::vector<MatchesInfo> &pairwise_matches,
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float conf_threshold);
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void CV_EXPORTS findMaxSpanningTree(
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int num_images, const std::vector<MatchesInfo> &pairwise_matches,
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Graph &span_tree, std::vector<int> ¢ers);
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//! @} stitching_rotation
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} // namespace detail
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} // namespace cv
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#endif // OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
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