feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
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3rdparty/opencv-4.5.4/modules/stitching/include/opencv2/stitching/detail/matchers.hpp
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3rdparty/opencv-4.5.4/modules/stitching/include/opencv2/stitching/detail/matchers.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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// copy or use the software.
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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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// the use of this software, even if advised of the possibility of such damage.
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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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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_W_SIMPLE ImageFeatures
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{
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CV_PROP_RW int img_idx;
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CV_PROP_RW Size img_size;
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std::vector<KeyPoint> keypoints;
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CV_PROP_RW UMat descriptors;
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CV_WRAP std::vector<KeyPoint> getKeypoints() { return keypoints; };
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};
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/** @brief
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@param featuresFinder
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@param images
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@param features
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@param masks
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*/
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CV_EXPORTS_W void computeImageFeatures(
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const Ptr<Feature2D> &featuresFinder,
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InputArrayOfArrays images,
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CV_OUT std::vector<ImageFeatures> &features,
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InputArrayOfArrays masks = noArray());
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/** @brief
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@param featuresFinder
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@param image
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@param features
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@param mask
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*/
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CV_EXPORTS_AS(computeImageFeatures2) void computeImageFeatures(
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const Ptr<Feature2D> &featuresFinder,
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InputArray image,
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CV_OUT ImageFeatures &features,
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InputArray mask = noArray());
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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_W_SIMPLE 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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CV_PROP_RW int src_img_idx;
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CV_PROP_RW int 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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CV_PROP_RW int num_inliers; //!< Number of geometrically consistent matches
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CV_PROP_RW Mat H; //!< Estimated transformation
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CV_PROP_RW double confidence; //!< Confidence two images are from the same panorama
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CV_WRAP std::vector<DMatch> getMatches() { return matches; };
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CV_WRAP std::vector<uchar> getInliers() { return inliers_mask; };
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};
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/** @brief Feature matchers base class. */
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class CV_EXPORTS_W FeaturesMatcher
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{
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public:
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CV_WRAP 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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CV_WRAP_AS(apply) void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
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CV_OUT 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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CV_WRAP_AS(apply2) void operator ()(const std::vector<ImageFeatures> &features, CV_OUT 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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CV_WRAP 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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CV_WRAP 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_W 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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CV_WRAP 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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CV_WRAP void collectGarbage() CV_OVERRIDE;
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CV_WRAP static Ptr<BestOf2NearestMatcher> create(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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protected:
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void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
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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_W BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
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{
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public:
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CV_WRAP 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 transformation 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_W 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 transformation
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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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CV_WRAP 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) CV_OVERRIDE;
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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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