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init: 64 bits version of the webcam

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This commit is contained in:
Nicolas RACOT
2026-07-13 16:31:52 +02:00
parent c0f3eeb00d
commit 07e526544d
381 changed files with 43996 additions and 9097 deletions
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119
include/opencv2/stitching/detail/matchers.hpp
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@ -40,8 +40,8 @@
//
//M*/
#ifndef __OPENCV_STITCHING_MATCHERS_HPP__
#define __OPENCV_STITCHING_MATCHERS_HPP__
#ifndef OPENCV_STITCHING_MATCHERS_HPP
#define OPENCV_STITCHING_MATCHERS_HPP
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
@ -83,9 +83,27 @@ public:
@sa detail::ImageFeatures, Rect_
*/
void operator ()(InputArray image, ImageFeatures &features, const std::vector<cv::Rect> &rois);
/** @brief Finds features in the given images in parallel.
@param images Source images
@param features Found features for each image
@param rois Regions of interest for each image
@sa detail::ImageFeatures, Rect_
*/
void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features,
const std::vector<std::vector<cv::Rect> > &rois);
/** @overload */
void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features);
/** @brief Frees unused memory allocated before if there is any. */
virtual void collectGarbage() {}
/* TODO OpenCV ABI 4.x
reimplement this as public method similar to FeaturesMatcher and remove private function hack
@return True, if it's possible to use the same finder instance in parallel, false otherwise
bool isThreadSafe() const { return is_thread_safe_; }
*/
protected:
/** @brief This method must implement features finding logic in order to make the wrappers
detail::FeaturesFinder::operator()_ work.
@ -95,6 +113,10 @@ protected:
@sa detail::ImageFeatures */
virtual void find(InputArray image, ImageFeatures &features) = 0;
/** @brief uses dynamic_cast to determine thread-safety
@return True, if it's possible to use the same finder instance in parallel, false otherwise
*/
bool isThreadSafe() const;
};
/** @brief SURF features finder.
@ -108,13 +130,28 @@ public:
int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4);
private:
void find(InputArray image, ImageFeatures &features);
void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
Ptr<FeatureDetector> detector_;
Ptr<DescriptorExtractor> extractor_;
Ptr<Feature2D> surf;
};
/** @brief SIFT features finder.
@sa detail::FeaturesFinder, SIFT
*/
class CV_EXPORTS SiftFeaturesFinder : public FeaturesFinder
{
public:
SiftFeaturesFinder();
private:
void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
Ptr<Feature2D> sift;
};
/** @brief ORB features finder. :
@sa detail::FeaturesFinder, ORB
@ -125,12 +162,32 @@ public:
OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5);
private:
void find(InputArray image, ImageFeatures &features);
void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
Ptr<ORB> orb;
Size grid_size;
};
/** @brief AKAZE features finder. :
@sa detail::FeaturesFinder, AKAZE
*/
class CV_EXPORTS AKAZEFeaturesFinder : public detail::FeaturesFinder
{
public:
AKAZEFeaturesFinder(int descriptor_type = AKAZE::DESCRIPTOR_MLDB,
int descriptor_size = 0,
int descriptor_channels = 3,
float threshold = 0.001f,
int nOctaves = 4,
int nOctaveLayers = 4,
int diffusivity = KAZE::DIFF_PM_G2);
private:
void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
Ptr<AKAZE> akaze;
};
#ifdef HAVE_OPENCV_XFEATURES2D
class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder
@ -139,10 +196,10 @@ public:
SurfFeaturesFinderGpu(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
int num_octaves_descr = 4, int num_layers_descr = 2);
void collectGarbage();
void collectGarbage() CV_OVERRIDE;
private:
void find(InputArray image, ImageFeatures &features);
void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
cuda::GpuMat image_;
cuda::GpuMat gray_image_;
@ -156,19 +213,22 @@ private:
/** @brief Structure containing information about matches between two images.
It's assumed that there is a homography between those 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 MatchesInfo
{
MatchesInfo();
MatchesInfo(const MatchesInfo &other);
const MatchesInfo& operator =(const MatchesInfo &other);
MatchesInfo& operator =(const MatchesInfo &other);
int src_img_idx, dst_img_idx; //!< Images indices (optional)
std::vector<DMatch> matches;
std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask
int num_inliers; //!< Number of geometrically consistent matches
Mat H; //!< Estimated homography
Mat H; //!< Estimated transformation
double confidence; //!< Confidence two images are from the same panorama
};
@ -243,10 +303,10 @@ public:
BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6);
void collectGarbage();
void collectGarbage() CV_OVERRIDE;
protected:
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
int num_matches_thresh1_;
int num_matches_thresh2_;
@ -267,9 +327,44 @@ protected:
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 trasformation estimate will be placed in matches_info).
@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
*/
class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
{
public:
/** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
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
*/
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__
#endif // OPENCV_STITCHING_MATCHERS_HPP