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https://github.com/ruby-opencv/ruby-opencv
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add some documents of CvMat functions
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ser1zw
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1 changed files with 77 additions and 51 deletions
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@ -3403,11 +3403,13 @@ rb_pre_corner_detect(int argc, VALUE *argv, VALUE self)
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}
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/*
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* call-seq:
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* corner_eigenvv(<i>block_size[,aperture_size]</i>) -> cvmat
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* Calculates eigenvalues and eigenvectors of image blocks for corner detection.
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*
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* For every pixel considers <i>block_size x block_size</i> neighborhood S(p).
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* It calculates convariation matrix of derivatives over the neighborhood.
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* @overload corner_eigenvv(block_size, aperture_size = 3)
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* @param block_size [Integer] Neighborhood size.
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* @param aperture_size [Integer] Aperture parameter for the sobel operator.
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* @return [CvMat] Result array.
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* @opencv_func cvCornerEigenValsAndVecs
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*/
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VALUE
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rb_corner_eigenvv(int argc, VALUE *argv, VALUE self)
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@ -3427,10 +3429,13 @@ rb_corner_eigenvv(int argc, VALUE *argv, VALUE self)
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}
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/*
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* call-seq:
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* corner_min_eigen_val(<i>block_size[,aperture_size = 3]</i>) -> cvmat
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* Calculates the minimal eigenvalue of gradient matrices for corner detection.
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*
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* Calculates minimal eigenvalue of gradient matrices for corner detection.
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* @overload corner_min_eigen_val(block_size, aperture_size = 3)
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* @param block_size [Integer] Neighborhood size.
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* @param aperture_size [Integer] Aperture parameter for the sobel operator.
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* @return [CvMat] Result array.
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* @opencv_func cvCornerMinEigenVal
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*/
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VALUE
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rb_corner_min_eigen_val(int argc, VALUE *argv, VALUE self)
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@ -3450,10 +3455,14 @@ rb_corner_min_eigen_val(int argc, VALUE *argv, VALUE self)
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}
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/*
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* call-seq:
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* corner_harris(<i>block_size[,aperture_size = 3][,k = 0.04]</i>) -> cvmat
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* Harris edge detector.
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*
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* Return image Applied Harris edge detector.
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* @overload corner_harris(block_size, aperture_size = 3, k = 0.04)
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* @param block_size [Integer] Neighborhood size.
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* @param aperture_size [Integer] Aperture parameter for the sobel operator.
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* @param k [Number] Harris detector free parameter.
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* @return [CvMat] The Harris detector responses.
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* @opencv_func cvCornerHarris
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*/
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VALUE
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rb_corner_harris(int argc, VALUE *argv, VALUE self)
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@ -3472,23 +3481,40 @@ rb_corner_harris(int argc, VALUE *argv, VALUE self)
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}
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/*
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* call-seq:
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* find_chessboard_corners(pattern_size, flag = CV_CALIB_CB_ADAPTIVE_THRESH) -> Array<Array<CvPoint2D32f>, Boolean>
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* Finds the positions of internal corners of the chessboard.
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*
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* Returns an array which includes the positions of internal corners of the chessboard, and
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* a parameter indicating whether the complete board was found or not.
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* @overload find_chessboard_corners(pattern_size, flag = CV_CALIB_CB_ADAPTIVE_THRESH)
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* @param pattern_size [CvSize] Number of inner corners per a chessboard row and column.
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* @param flags [Integer] Various operation flags that can be zero or a combination of the following values.
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* * CV_CALIB_CB_ADAPTIVE_THRESH
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* * Use adaptive thresholding to convert the image to black and white, rather than
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* a fixed threshold level (computed from the average image brightness).
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* * CV_CALIB_CB_NORMALIZE_IMAGE
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* * Normalize the image gamma with CvMat#equalize_hist() before applying fixed or adaptive thresholding.
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* * CV_CALIB_CB_FILTER_QUADS
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* * Use additional criteria (like contour area, perimeter, square-like shape) to
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* filter out false quads extracted at the contour retrieval stage.
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* * CALIB_CB_FAST_CHECK
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* * Run a fast check on the image that looks for chessboard corners, and shortcut the call
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* if none is found. This can drastically speed up the call in the degenerate condition
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* when no chessboard is observed.
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* @return [Array<Array<CvPoint2D32f>, Boolean>] An array which includes the positions of internal corners
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* of the chessboard, and a parameter indicating whether the complete board was found or not.
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* @opencv_func cvFindChessboardCorners
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* @example
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* mat = CvMat.load('chessboard.jpg', 1)
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* gray = mat.BGR2GRAY
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* pattern_size = CvSize.new(4, 4)
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* corners, found = gray.find_chessboard_corners(pattern_size, CV_CALIB_CB_ADAPTIVE_THRESH)
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*
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* pattern_size (CvSize) - Number of inner corners per a chessboard row and column.
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* flags (Integer) - Various operation flags that can be zero or a combination of the following values
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* * CV_CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white,
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* rather than a fixed threshold level (computed from the average image brightness).
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* * CV_CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with CvMat#equalize_hist() before applying fixed
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* or adaptive thresholding.
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* * CV_CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape)
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* to filter out false quads extracted at the contour retrieval stage.
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* * CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut
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* the call if none is found. This can drastically speed up the call in the degenerate condition
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* when no chessboard is observed.
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* if found
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* corners = gray.find_corner_sub_pix(corners, CvSize.new(3, 3), CvSize.new(-1, -1), CvTermCriteria.new(20, 0.03));
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* end
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*
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* result = mat.draw_chessboard_corners(pattern_size, corners, found)
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* w = GUI::Window.new('Result')
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* w.show result
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* GUI::wait_key
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*/
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VALUE
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rb_find_chessboard_corners(int argc, VALUE *argv, VALUE self)
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@ -3518,16 +3544,16 @@ rb_find_chessboard_corners(int argc, VALUE *argv, VALUE self)
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}
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/*
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* call-seq:
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* find_corner_sub_pix(corners, win_size, zero_zone, criteria) -> Array<CvPoint2D32f>
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*
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* Returns refined corner locations.
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*
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* corners - Initial coordinates of the input corners and refined coordinates provided for output.
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* win_size - Half of the side length of the search window.
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* zero_zone - Half of the size of the dead region in the middle of the search zone over
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* which the summation in the formula below is not done.
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* criteria - Criteria for termination of the iterative process of corner refinement.
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* Refines the corner locations.
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*
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* @overload find_corner_sub_pix(corners, win_size, zero_zone, criteria)
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* @param corners [Array<CvPoint>] Initial coordinates of the input corners.
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* @param win_size [CvSize] Half of the side length of the search window.
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* @param zero_zone [CvSize] Half of the size of the dead region in the middle of the search zone over
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* which the summation in the formula below is not done.
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* @param criteria [CvTermCriteria] Criteria for termination of the iterative process of corner refinement.
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* @return [Array<CvPoint2D32f>] Refined corner coordinates.
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* @opencv_func cvFindCornerSubPix
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*/
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VALUE
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rb_find_corner_sub_pix(VALUE self, VALUE corners, VALUE win_size, VALUE zero_zone, VALUE criteria)
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@ -3558,24 +3584,24 @@ rb_find_corner_sub_pix(VALUE self, VALUE corners, VALUE win_size, VALUE zero_zon
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}
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/*
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* call-seq:
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* good_features_to_track(<i>quality_level, min_distance[, good_features_to_track_option]</i>)
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* -> array (include CvPoint2D32f)
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* Determines strong corners on an image.
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*
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* quality_level – Multiplier for the max/min eigenvalue; specifies the minimal accepted quality of image corners
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* min_distance – Limit, specifying the minimum possible distance between the returned corners; Euclidian distance is used
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* <i>good_features_to_track_option</i> should be Hash include these keys.
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* :mask
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* Region of interest. The function selects points either in the specified region or in the whole image
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* if the mask is nil.
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* :block_size
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* Size of the averaging block, passed to the underlying CornerMinEigenVal or CornerHarris used by the function.
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* :use_harris
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* If true, Harris operator ( CornerHarris ) is used instead of default CornerMinEigenVal
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* :k
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* Free parameter of Harris detector; used only if ( :use_harris => true )
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* note: <i>good_features_to_track_option</i>'s default value is CvMat::GOOD_FEATURES_TO_TRACK_OPTION
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* @overload good_features_to_track(quality_level, min_distance, good_features_to_track_option = {})
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* @param quality_level [Number] Parameter characterizing the minimal accepted quality of image corners.
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* The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
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* or the Harris function response.
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* @param min_distance [Number] Minimum possible Euclidean distance between the returned corners.
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* @param good_features_to_track_option [Hash] Options.
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* @option good_features_to_track_option [CvMat] :mask (nil) Optional region of interest.
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* If the image is not empty (it needs to have the type CV_8UC1 and the same size as image),
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* it specifies the region in which the corners are detected.
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* @option good_features_to_track_option [Integer] :block_size (3) Size of an average block for computing
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* a derivative covariation matrix over each pixel neighborhood.
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* @option good_features_to_track_option [Boolean] :use_harris (false) Parameter indicating whether
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* to use a Harris detector.
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* @option good_features_to_track_option [Number] :k (0.04) Free parameter of the Harris detector.
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* @return [Array<CvPoint2D32f>] Output vector of detected corners.
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* @opencv_func cvGoodFeaturesToTrack
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*/
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VALUE
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rb_good_features_to_track(int argc, VALUE *argv, VALUE self)
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