268 lines
11 KiB
C++
268 lines
11 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#ifndef OPENCV_STITCHING_MATCHERS_HPP
|
|
#define OPENCV_STITCHING_MATCHERS_HPP
|
|
|
|
#include "opencv2/core.hpp"
|
|
#include "opencv2/features2d.hpp"
|
|
|
|
#include "opencv2/opencv_modules.hpp"
|
|
|
|
namespace cv {
|
|
namespace detail {
|
|
|
|
//! @addtogroup stitching_match
|
|
//! @{
|
|
|
|
/** @brief Structure containing image keypoints and descriptors. */
|
|
struct CV_EXPORTS_W_SIMPLE ImageFeatures
|
|
{
|
|
CV_PROP_RW int img_idx;
|
|
CV_PROP_RW Size img_size;
|
|
CV_PROP_RW std::vector<KeyPoint> keypoints;
|
|
CV_PROP_RW UMat descriptors;
|
|
CV_WRAP std::vector<KeyPoint> getKeypoints() { return keypoints; }
|
|
};
|
|
/** @brief
|
|
|
|
@param featuresFinder
|
|
@param images
|
|
@param features
|
|
@param masks
|
|
*/
|
|
CV_EXPORTS_W void computeImageFeatures(
|
|
const Ptr<Feature2D> &featuresFinder,
|
|
InputArrayOfArrays images,
|
|
CV_OUT std::vector<ImageFeatures> &features,
|
|
InputArrayOfArrays masks = noArray());
|
|
|
|
/** @brief
|
|
|
|
@param featuresFinder
|
|
@param image
|
|
@param features
|
|
@param mask
|
|
*/
|
|
CV_EXPORTS_AS(computeImageFeatures2) void computeImageFeatures(
|
|
const Ptr<Feature2D> &featuresFinder,
|
|
InputArray image,
|
|
CV_OUT ImageFeatures &features,
|
|
InputArray mask = noArray());
|
|
|
|
/** @brief Structure containing information about matches between two images.
|
|
|
|
It's assumed that there is a transformation between those images. Transformation may be
|
|
homography or affine transformation based on selected matcher.
|
|
|
|
@sa detail::FeaturesMatcher
|
|
*/
|
|
struct CV_EXPORTS_W_SIMPLE MatchesInfo
|
|
{
|
|
MatchesInfo();
|
|
MatchesInfo(const MatchesInfo &other);
|
|
MatchesInfo& operator =(const MatchesInfo &other);
|
|
|
|
CV_PROP_RW int src_img_idx;
|
|
CV_PROP_RW int dst_img_idx; //!< Images indices (optional)
|
|
CV_PROP_RW std::vector<DMatch> matches;
|
|
CV_PROP_RW std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask
|
|
CV_PROP_RW int num_inliers; //!< Number of geometrically consistent matches
|
|
CV_PROP_RW Mat H; //!< Estimated transformation
|
|
CV_PROP_RW double confidence; //!< Confidence two images are from the same panorama
|
|
CV_WRAP std::vector<DMatch> getMatches() { return matches; }
|
|
CV_WRAP std::vector<uchar> getInliers() { return inliers_mask; }
|
|
};
|
|
|
|
/** @brief Feature matchers base class. */
|
|
class CV_EXPORTS_W FeaturesMatcher
|
|
{
|
|
public:
|
|
CV_WRAP virtual ~FeaturesMatcher() {}
|
|
|
|
/** @overload
|
|
@param features1 First image features
|
|
@param features2 Second image features
|
|
@param matches_info Found matches
|
|
*/
|
|
CV_WRAP_AS(apply) void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
|
|
CV_OUT MatchesInfo& matches_info) { match(features1, features2, matches_info); }
|
|
|
|
/** @brief Performs images matching.
|
|
|
|
@param features Features of the source images
|
|
@param pairwise_matches Found pairwise matches
|
|
@param mask Mask indicating which image pairs must be matched
|
|
|
|
The function is parallelized with the TBB library.
|
|
|
|
@sa detail::MatchesInfo
|
|
*/
|
|
CV_WRAP_AS(apply2) void operator ()(const std::vector<ImageFeatures> &features, CV_OUT std::vector<MatchesInfo> &pairwise_matches,
|
|
const cv::UMat &mask = cv::UMat()) { match(features, pairwise_matches, mask); }
|
|
|
|
/** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
|
|
*/
|
|
CV_WRAP bool isThreadSafe() const { return is_thread_safe_; }
|
|
|
|
/** @brief Frees unused memory allocated before if there is any.
|
|
*/
|
|
CV_WRAP virtual void collectGarbage() {}
|
|
|
|
protected:
|
|
FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
|
|
|
|
/** @brief This method must implement matching logic in order to make the wrappers
|
|
detail::FeaturesMatcher::operator()_ work.
|
|
|
|
@param features1 first image features
|
|
@param features2 second image features
|
|
@param matches_info found matches
|
|
*/
|
|
virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
|
|
MatchesInfo& matches_info) = 0;
|
|
|
|
/** @brief This method implements logic to match features between arbitrary number of features.
|
|
By default this checks every pair of inputs in the input, but the behaviour can be changed by subclasses.
|
|
|
|
@param features vector of image features
|
|
@param pairwise_matches found matches
|
|
@param mask (optional) mask indicating which image pairs should be matched
|
|
*/
|
|
virtual void match(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
|
|
const cv::UMat &mask = cv::UMat());
|
|
|
|
bool is_thread_safe_;
|
|
};
|
|
|
|
/** @brief Features matcher which finds two best matches for each feature and leaves the best one only if the
|
|
ratio between descriptor distances is greater than the threshold match_conf
|
|
|
|
@sa detail::FeaturesMatcher
|
|
*/
|
|
class CV_EXPORTS_W BestOf2NearestMatcher : public FeaturesMatcher
|
|
{
|
|
public:
|
|
/** @brief Constructs a "best of 2 nearest" matcher.
|
|
|
|
@param try_use_gpu Should try to use GPU or not
|
|
@param match_conf Match distances ration threshold
|
|
@param num_matches_thresh1 Minimum number of matches required for the 2D projective transform
|
|
estimation used in the inliers classification step
|
|
@param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
|
|
re-estimation on inliers
|
|
@param matches_confindece_thresh Matching confidence threshold to take the match into account.
|
|
The threshold was determined experimentally and set to 3 by default.
|
|
*/
|
|
CV_WRAP BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
|
|
int num_matches_thresh2 = 6, double matches_confindece_thresh = 3.);
|
|
|
|
CV_WRAP void collectGarbage() CV_OVERRIDE;
|
|
CV_WRAP static Ptr<BestOf2NearestMatcher> create(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
|
|
int num_matches_thresh2 = 6, double matches_confindece_thresh = 3.);
|
|
|
|
protected:
|
|
|
|
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
|
|
int num_matches_thresh1_;
|
|
int num_matches_thresh2_;
|
|
double matches_confindece_thresh_;
|
|
Ptr<FeaturesMatcher> impl_;
|
|
};
|
|
|
|
class CV_EXPORTS_W BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
|
|
{
|
|
public:
|
|
CV_WRAP BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
|
|
int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
|
|
|
|
protected:
|
|
// indicate that we do not want to hide the base class match method with a different signature
|
|
using BestOf2NearestMatcher::match;
|
|
void match(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
|
|
const cv::UMat &mask = cv::UMat()) CV_OVERRIDE;
|
|
|
|
int range_width_;
|
|
};
|
|
|
|
/** @brief Features matcher similar to cv::detail::BestOf2NearestMatcher which
|
|
finds two best matches for each feature and leaves the best one only if the
|
|
ratio between descriptor distances is greater than the threshold match_conf.
|
|
|
|
Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
|
|
transformation (affine transformation estimate will be placed in matches_info).
|
|
|
|
@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
|
|
*/
|
|
class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
|
|
{
|
|
public:
|
|
/** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
|
|
between images
|
|
|
|
@param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
|
|
transformation with 4 degrees of freedom using only rotation, translation and uniform scaling
|
|
@param try_use_gpu Should try to use GPU or not
|
|
@param match_conf Match distances ration threshold
|
|
@param num_matches_thresh1 Minimum number of matches required for the 2D affine transform
|
|
estimation used in the inliers classification step
|
|
|
|
@sa cv::estimateAffine2D cv::estimateAffinePartial2D
|
|
*/
|
|
CV_WRAP AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
|
|
float match_conf = 0.3f, int num_matches_thresh1 = 6) :
|
|
BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
|
|
full_affine_(full_affine) {}
|
|
|
|
protected:
|
|
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
|
|
|
|
bool full_affine_;
|
|
};
|
|
|
|
//! @} stitching_match
|
|
|
|
} // namespace detail
|
|
} // namespace cv
|
|
|
|
#endif // OPENCV_STITCHING_MATCHERS_HPP
|