356 lines
12 KiB
C++
356 lines
12 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_MATCHERS_HPP
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#define OPENCV_STITCHING_MATCHERS_HPP
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#include "opencv2/core.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_XFEATURES2D
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# include "opencv2/xfeatures2d/cuda.hpp"
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#endif
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namespace cv {
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namespace detail {
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//! @addtogroup stitching_match
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//! @{
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/** @brief Structure containing image keypoints and descriptors. */
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struct CV_EXPORTS ImageFeatures
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{
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int img_idx;
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Size img_size;
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std::vector<KeyPoint> keypoints;
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UMat descriptors;
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};
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/** @brief Feature finders base class */
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class CV_EXPORTS FeaturesFinder
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{
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public:
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virtual ~FeaturesFinder() {}
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/** @overload */
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void operator ()(InputArray image, ImageFeatures &features);
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/** @brief Finds features in the given image.
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@param image Source image
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@param features Found features
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@param rois Regions of interest
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@sa detail::ImageFeatures, Rect_
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*/
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void operator ()(InputArray image, ImageFeatures &features, const std::vector<cv::Rect> &rois);
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/** @brief Finds features in the given images in parallel.
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@param images Source images
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@param features Found features for each image
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@param rois Regions of interest for each image
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@sa detail::ImageFeatures, Rect_
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*/
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void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features,
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const std::vector<std::vector<cv::Rect> > &rois);
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/** @overload */
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void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features);
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/** @brief Frees unused memory allocated before if there is any. */
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virtual void collectGarbage() {}
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/* TODO OpenCV ABI 4.x
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reimplement this as public method similar to FeaturesMatcher and remove private function hack
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@return True, if it's possible to use the same finder instance in parallel, false otherwise
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bool isThreadSafe() const { return is_thread_safe_; }
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*/
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protected:
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/** @brief This method must implement features finding logic in order to make the wrappers
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detail::FeaturesFinder::operator()_ work.
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@param image Source image
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@param features Found features
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@sa detail::ImageFeatures */
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virtual void find(InputArray image, ImageFeatures &features) = 0;
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/** @brief uses dynamic_cast to determine thread-safety
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@return True, if it's possible to use the same finder instance in parallel, false otherwise
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*/
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bool isThreadSafe() const;
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};
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/** @brief SURF features finder.
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@sa detail::FeaturesFinder, SURF
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*/
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class CV_EXPORTS SurfFeaturesFinder : public FeaturesFinder
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{
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public:
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SurfFeaturesFinder(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
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int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4);
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private:
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void find(InputArray image, ImageFeatures &features);
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Ptr<FeatureDetector> detector_;
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Ptr<DescriptorExtractor> extractor_;
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Ptr<Feature2D> surf;
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};
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/** @brief ORB features finder. :
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@sa detail::FeaturesFinder, ORB
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*/
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class CV_EXPORTS OrbFeaturesFinder : public FeaturesFinder
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{
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public:
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OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5);
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private:
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void find(InputArray image, ImageFeatures &features);
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Ptr<ORB> orb;
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Size grid_size;
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};
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/** @brief AKAZE features finder. :
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@sa detail::FeaturesFinder, AKAZE
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*/
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class CV_EXPORTS AKAZEFeaturesFinder : public detail::FeaturesFinder
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{
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public:
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AKAZEFeaturesFinder(int descriptor_type = AKAZE::DESCRIPTOR_MLDB,
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int descriptor_size = 0,
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int descriptor_channels = 3,
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float threshold = 0.001f,
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int nOctaves = 4,
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int nOctaveLayers = 4,
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int diffusivity = KAZE::DIFF_PM_G2);
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private:
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void find(InputArray image, detail::ImageFeatures &features);
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Ptr<AKAZE> akaze;
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};
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#ifdef HAVE_OPENCV_XFEATURES2D
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class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder
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{
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public:
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SurfFeaturesFinderGpu(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
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int num_octaves_descr = 4, int num_layers_descr = 2);
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void collectGarbage();
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private:
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void find(InputArray image, ImageFeatures &features);
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cuda::GpuMat image_;
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cuda::GpuMat gray_image_;
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cuda::SURF_CUDA surf_;
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cuda::GpuMat keypoints_;
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cuda::GpuMat descriptors_;
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int num_octaves_, num_layers_;
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int num_octaves_descr_, num_layers_descr_;
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};
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#endif
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/** @brief Structure containing information about matches between two images.
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It's assumed that there is a transformation between those images. Transformation may be
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homography or affine transformation based on selected matcher.
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@sa detail::FeaturesMatcher
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*/
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struct CV_EXPORTS MatchesInfo
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{
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MatchesInfo();
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MatchesInfo(const MatchesInfo &other);
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MatchesInfo& operator =(const MatchesInfo &other);
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int src_img_idx, dst_img_idx; //!< Images indices (optional)
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std::vector<DMatch> matches;
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std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask
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int num_inliers; //!< Number of geometrically consistent matches
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Mat H; //!< Estimated transformation
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double confidence; //!< Confidence two images are from the same panorama
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};
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/** @brief Feature matchers base class. */
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class CV_EXPORTS FeaturesMatcher
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{
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public:
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virtual ~FeaturesMatcher() {}
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/** @overload
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@param features1 First image features
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@param features2 Second image features
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@param matches_info Found matches
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*/
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void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
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MatchesInfo& matches_info) { match(features1, features2, matches_info); }
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/** @brief Performs images matching.
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@param features Features of the source images
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@param pairwise_matches Found pairwise matches
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@param mask Mask indicating which image pairs must be matched
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The function is parallelized with the TBB library.
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@sa detail::MatchesInfo
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*/
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void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
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const cv::UMat &mask = cv::UMat());
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/** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
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*/
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bool isThreadSafe() const { return is_thread_safe_; }
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/** @brief Frees unused memory allocated before if there is any.
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*/
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virtual void collectGarbage() {}
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protected:
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FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
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/** @brief This method must implement matching logic in order to make the wrappers
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detail::FeaturesMatcher::operator()_ work.
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@param features1 first image features
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@param features2 second image features
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@param matches_info found matches
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*/
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virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
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MatchesInfo& matches_info) = 0;
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bool is_thread_safe_;
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};
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/** @brief Features matcher which finds two best matches for each feature and leaves the best one only if the
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ratio between descriptor distances is greater than the threshold match_conf
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@sa detail::FeaturesMatcher
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*/
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class CV_EXPORTS BestOf2NearestMatcher : public FeaturesMatcher
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{
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public:
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/** @brief Constructs a "best of 2 nearest" matcher.
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@param try_use_gpu Should try to use GPU or not
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@param match_conf Match distances ration threshold
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@param num_matches_thresh1 Minimum number of matches required for the 2D projective transform
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estimation used in the inliers classification step
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@param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
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re-estimation on inliers
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*/
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BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
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int num_matches_thresh2 = 6);
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void collectGarbage();
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protected:
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void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
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int num_matches_thresh1_;
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int num_matches_thresh2_;
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Ptr<FeaturesMatcher> impl_;
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};
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class CV_EXPORTS BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
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{
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public:
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BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
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int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
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void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
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const cv::UMat &mask = cv::UMat());
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protected:
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int range_width_;
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};
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/** @brief Features matcher similar to cv::detail::BestOf2NearestMatcher which
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finds two best matches for each feature and leaves the best one only if the
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ratio between descriptor distances is greater than the threshold match_conf.
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Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
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transformation (affine trasformation estimate will be placed in matches_info).
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@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
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*/
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class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
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{
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public:
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/** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
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between images
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@param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
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transformation with 4 degrees of freedom using only rotation, translation and uniform scaling
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@param try_use_gpu Should try to use GPU or not
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@param match_conf Match distances ration threshold
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@param num_matches_thresh1 Minimum number of matches required for the 2D affine transform
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estimation used in the inliers classification step
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@sa cv::estimateAffine2D cv::estimateAffinePartial2D
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*/
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AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
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float match_conf = 0.3f, int num_matches_thresh1 = 6) :
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BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
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full_affine_(full_affine) {}
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protected:
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void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
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bool full_affine_;
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};
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//! @} stitching_match
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} // namespace detail
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} // namespace cv
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#endif // OPENCV_STITCHING_MATCHERS_HPP
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