- adding ORB
This commit is contained in:
Vincent Rabaud
@@ -398,6 +398,161 @@ public:
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bool useProvidedKeypoints=false) const;
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};
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/*!
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ORB implementation.
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*/
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class CV_EXPORTS ORB
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{
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public:
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enum PatchSize
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{
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PATCH_LEARNED_31 = 31
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};
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/** the size of the signature in bytes */
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static const int kBytes = 32;
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struct CommonParams
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{
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static const unsigned int DEFAULT_N_LEVELS = 3;
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static const float DEFAULT_SCALE_FACTOR = 1.2;
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static const unsigned int DEFAULT_FIRST_LEVEL = 0;
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static const PatchSize DEFAULT_PATCH_SIZE = PATCH_LEARNED_31;
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/** default constructor */
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CommonParams(float scale_factor = DEFAULT_SCALE_FACTOR, unsigned int n_levels = DEFAULT_N_LEVELS,
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unsigned int first_level = DEFAULT_FIRST_LEVEL, PatchSize patch_size = DEFAULT_PATCH_SIZE) :
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scale_factor_(scale_factor), n_levels_(n_levels), first_level_(first_level >= n_levels ? 0 : first_level),
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patch_size_(patch_size)
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{
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}
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void read(const FileNode& fn);
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void write(FileStorage& fs) const;
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/** Coefficient by which we divide the dimensions from one scale pyramid level to the next */
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float scale_factor_;
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/** The number of levels in the scale pyramid */
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unsigned int n_levels_;
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/** The level at which the image is given
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* if 1, that means we will also look at the image scale_factor_ times bigger
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*/
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unsigned int first_level_;
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/** The size of the patch that will be used for orientation and comparisons */
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PatchSize patch_size_;
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};
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/** Default Constructor */
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ORB()
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{
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}
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/** Constructor
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* @param n_features the number of desired features
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* @param detector_params parameters to use
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*/
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ORB(size_t n_features, const CommonParams & detector_params = CommonParams());
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/** returns the descriptor size in bytes */
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int descriptorSize() const;
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/** Compute the ORB features and descriptors on an image
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* @param img the image to compute the features and descriptors on
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* @param mask the mask to apply
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* @param keypoints the resulting keypoints
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*/
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void
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operator()(const cv::Mat &image, const cv::Mat &mask, std::vector<cv::KeyPoint> & keypoints);
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/** Compute the ORB features and descriptors on an image
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* @param img the image to compute the features and descriptors on
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* @param mask the mask to apply
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* @param keypoints the resulting keypoints
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* @param descriptors the resulting descriptors
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* @param useProvidedKeypoints if true, the keypoints are used as an input
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*/
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void
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operator()(const cv::Mat &image, const cv::Mat &mask, std::vector<cv::KeyPoint> & keypoints, cv::Mat & descriptors,
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bool useProvidedKeypoints = false);
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private:
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/** The size of the patch used when comparing regions in the patterns */
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static const int kKernelWidth = 5;
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/** Compute the ORB features and descriptors on an image
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* @param image the image to compute the features and descriptors on
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* @param mask the mask to apply
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* @param keypoints the resulting keypoints
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* @param descriptors the resulting descriptors
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* @param do_keypoints if true, the keypoints are computed, otherwise used as an input
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* @param do_descriptors if true, also computes the descriptors
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*/
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void
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operator()(const cv::Mat &image, const cv::Mat &mask, std::vector<cv::KeyPoint> & keypoints, cv::Mat & descriptors,
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bool do_keypoints, bool do_descriptors);
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/** Compute the ORB keypoints on an image
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* @param image_pyramid the image pyramid to compute the features and descriptors on
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* @param mask_pyramid the masks to apply at every level
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* @param keypoints the resulting keypoints, clustered per level
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*/
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void computeKeyPoints(const std::vector<cv::Mat>& image_pyramid, const std::vector<cv::Mat>& mask_pyramid,
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std::vector<std::vector<cv::KeyPoint> >& keypoints) const;
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/** Compute the ORB keypoint orientations
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* @param image the image to compute the features and descriptors on
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* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
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* @param level the scale at which we compute the orientation
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* @param keypoints the resulting keypoints
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*/
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void
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computeOrientation(const cv::Mat& image, const cv::Mat& integral_image, unsigned int level,
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std::vector<cv::KeyPoint>& keypoints) const;
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/** Compute the ORB descriptors
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* @param image the image to compute the features and descriptors on
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* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
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* @param level the scale at which we compute the orientation
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* @param keypoints the keypoints to use
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* @param descriptors the resulting descriptors
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*/
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void
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computeDescriptors(const cv::Mat& image, const cv::Mat& integral_image, unsigned int level,
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std::vector<cv::KeyPoint>& keypoints, cv::Mat & descriptors) const;
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/** Compute the integral image and upadte the cached values
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* @param image the image to compute the features and descriptors on
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* @param level the scale at which we compute the orientation
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* @param descriptors the resulting descriptors
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*/
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void computeIntegralImage(const cv::Mat & image, unsigned int level, cv::Mat &integral_image);
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/** Parameters tuning ORB */
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CommonParams params_;
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/** size of the half patch used for orientation computation, see Rosin - 1999 - Measuring Corner Properties */
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int half_patch_size_;
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/** pre-computed offsets used for the Harris verification, one vector per scale */
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std::vector<std::vector<int> > orientation_horizontal_offsets_;
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std::vector<std::vector<int> > orientation_vertical_offsets_;
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/** The steps of the integral images for each scale */
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std::vector<size_t> integral_image_steps_;
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/** The number of desired features per scale */
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std::vector<size_t> n_features_per_level_;
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/** The overall number of desired features */
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size_t n_features_;
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/** the end of a row in a circular patch */
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std::vector<int> u_max_;
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/** The patterns for each level (the patterns are the same, but not their offset */
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class OrbPatterns;
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std::vector<OrbPatterns*> patterns_;
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};
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/*!
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Maximal Stable Extremal Regions class.
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@@ -1365,6 +1520,33 @@ protected:
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SURF surf;
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};
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/** Feature detector for the ORB feature
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* Basically fast followed by a Harris check
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*/
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class CV_EXPORTS OrbFeatureDetector : public cv::FeatureDetector
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{
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public:
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/** Default constructor
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* @param n_features the number of desired features
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* @param params parameters to use
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*/
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OrbFeatureDetector(size_t n_features = 700, ORB::CommonParams params = ORB::CommonParams());
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virtual void read(const cv::FileNode&);
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virtual void write(cv::FileStorage&) const;
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protected:
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virtual void
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detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask = cv::Mat()) const;
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private:
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/** the ORB object we use for the computations */
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mutable ORB orb_;
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/** The parameters used */
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ORB::CommonParams params_;
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/** the number of features that need to be retrieved */
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unsigned int n_features_;
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};
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class CV_EXPORTS SimpleBlobDetector : public cv::FeatureDetector
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{
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public:
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@@ -1720,6 +1902,40 @@ protected:
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SURF surf;
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};
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/** The descriptor extractor for the ORB descriptor
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* There are two ways to speed up its computation:
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* - if you know the step size of the integral image, use setStepSize so that offsets are precomputed and cached
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* - if you know the integral image, use setIntegralImage so that it is not recomputed. This calls
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* setStepSize automatically
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*/
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class OrbDescriptorExtractor : public cv::DescriptorExtractor
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{
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public:
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/** default constructor
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* @param params parameters to use
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*/
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OrbDescriptorExtractor(ORB::CommonParams params = ORB::CommonParams());
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/** destructor */
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~OrbDescriptorExtractor()
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{
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}
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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virtual void read(const cv::FileNode&);
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virtual void write(cv::FileStorage&) const;
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protected:
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void computeImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors) const;
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private:
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/** the ORB object we use for the computations */
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mutable ORB orb_;
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/** The parameters used */
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ORB::CommonParams params_;
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};
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/*
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* CalonderDescriptorExtractor
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*/
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