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add some documents of CvMat functions

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ser1zw 2012-12-24 20:24:52 +09:00
parent 5e6e5cad63
commit 392886401b
1 changed files with 77 additions and 51 deletions
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128
ext/opencv/cvmat.cpp
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@ -3403,11 +3403,13 @@ rb_pre_corner_detect(int argc, VALUE *argv, VALUE self)
} }
/* /*
* call-seq: * Calculates eigenvalues and eigenvectors of image blocks for corner detection.
* corner_eigenvv(<i>block_size[,aperture_size]</i>) -> cvmat
* *
* For every pixel considers <i>block_size x block_size</i> neighborhood S(p). * @overload corner_eigenvv(block_size, aperture_size = 3)
* It calculates convariation matrix of derivatives over the neighborhood. * @param block_size [Integer] Neighborhood size.
* @param aperture_size [Integer] Aperture parameter for the sobel operator.
* @return [CvMat] Result array.
* @opencv_func cvCornerEigenValsAndVecs
*/ */
VALUE VALUE
rb_corner_eigenvv(int argc, VALUE *argv, VALUE self) rb_corner_eigenvv(int argc, VALUE *argv, VALUE self)
@ -3427,10 +3429,13 @@ rb_corner_eigenvv(int argc, VALUE *argv, VALUE self)
} }
/* /*
* call-seq: * Calculates the minimal eigenvalue of gradient matrices for corner detection.
* corner_min_eigen_val(<i>block_size[,aperture_size = 3]</i>) -> cvmat
* *
* Calculates minimal eigenvalue of gradient matrices for corner detection. * @overload corner_min_eigen_val(block_size, aperture_size = 3)
* @param block_size [Integer] Neighborhood size.
* @param aperture_size [Integer] Aperture parameter for the sobel operator.
* @return [CvMat] Result array.
* @opencv_func cvCornerMinEigenVal
*/ */
VALUE VALUE
rb_corner_min_eigen_val(int argc, VALUE *argv, VALUE self) rb_corner_min_eigen_val(int argc, VALUE *argv, VALUE self)
@ -3450,10 +3455,14 @@ rb_corner_min_eigen_val(int argc, VALUE *argv, VALUE self)
} }
/* /*
* call-seq: * Harris edge detector.
* corner_harris(<i>block_size[,aperture_size = 3][,k = 0.04]</i>) -> cvmat
* *
* Return image Applied Harris edge detector. * @overload corner_harris(block_size, aperture_size = 3, k = 0.04)
* @param block_size [Integer] Neighborhood size.
* @param aperture_size [Integer] Aperture parameter for the sobel operator.
* @param k [Number] Harris detector free parameter.
* @return [CvMat] The Harris detector responses.
* @opencv_func cvCornerHarris
*/ */
VALUE VALUE
rb_corner_harris(int argc, VALUE *argv, VALUE self) rb_corner_harris(int argc, VALUE *argv, VALUE self)
@ -3472,23 +3481,40 @@ rb_corner_harris(int argc, VALUE *argv, VALUE self)
} }
/* /*
* call-seq: * Finds the positions of internal corners of the chessboard.
* find_chessboard_corners(pattern_size, flag = CV_CALIB_CB_ADAPTIVE_THRESH) -> Array<Array<CvPoint2D32f>, Boolean>
* *
* Returns an array which includes the positions of internal corners of the chessboard, and * @overload find_chessboard_corners(pattern_size, flag = CV_CALIB_CB_ADAPTIVE_THRESH)
* a parameter indicating whether the complete board was found or not. * @param pattern_size [CvSize] Number of inner corners per a chessboard row and column.
* @param flags [Integer] Various operation flags that can be zero or a combination of the following values.
* * CV_CALIB_CB_ADAPTIVE_THRESH
* * Use adaptive thresholding to convert the image to black and white, rather than
* a fixed threshold level (computed from the average image brightness).
* * CV_CALIB_CB_NORMALIZE_IMAGE
* * Normalize the image gamma with CvMat#equalize_hist() before applying fixed or adaptive thresholding.
* * CV_CALIB_CB_FILTER_QUADS
* * Use additional criteria (like contour area, perimeter, square-like shape) to
* filter out false quads extracted at the contour retrieval stage.
* * CALIB_CB_FAST_CHECK
* * Run a fast check on the image that looks for chessboard corners, and shortcut the call
* if none is found. This can drastically speed up the call in the degenerate condition
* when no chessboard is observed.
* @return [Array<Array<CvPoint2D32f>, Boolean>] An array which includes the positions of internal corners
* of the chessboard, and a parameter indicating whether the complete board was found or not.
* @opencv_func cvFindChessboardCorners
* @example
* mat = CvMat.load('chessboard.jpg', 1)
* gray = mat.BGR2GRAY
* pattern_size = CvSize.new(4, 4)
* corners, found = gray.find_chessboard_corners(pattern_size, CV_CALIB_CB_ADAPTIVE_THRESH)
* *
* pattern_size (CvSize) - Number of inner corners per a chessboard row and column. * if found
* flags (Integer) - Various operation flags that can be zero or a combination of the following values * corners = gray.find_corner_sub_pix(corners, CvSize.new(3, 3), CvSize.new(-1, -1), CvTermCriteria.new(20, 0.03));
* * CV_CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, * end
* rather than a fixed threshold level (computed from the average image brightness). *
* * CV_CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with CvMat#equalize_hist() before applying fixed * result = mat.draw_chessboard_corners(pattern_size, corners, found)
* or adaptive thresholding. * w = GUI::Window.new('Result')
* * CV_CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) * w.show result
* to filter out false quads extracted at the contour retrieval stage. * GUI::wait_key
* * CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut
* the call if none is found. This can drastically speed up the call in the degenerate condition
* when no chessboard is observed.
*/ */
VALUE VALUE
rb_find_chessboard_corners(int argc, VALUE *argv, VALUE self) rb_find_chessboard_corners(int argc, VALUE *argv, VALUE self)
@ -3518,16 +3544,16 @@ rb_find_chessboard_corners(int argc, VALUE *argv, VALUE self)
} }
/* /*
* call-seq: * Refines the corner locations.
* find_corner_sub_pix(corners, win_size, zero_zone, criteria) -> Array<CvPoint2D32f> *
* * @overload find_corner_sub_pix(corners, win_size, zero_zone, criteria)
* Returns refined corner locations. * @param corners [Array<CvPoint>] Initial coordinates of the input corners.
* * @param win_size [CvSize] Half of the side length of the search window.
* corners - Initial coordinates of the input corners and refined coordinates provided for output. * @param zero_zone [CvSize] Half of the size of the dead region in the middle of the search zone over
* win_size - Half of the side length of the search window. * which the summation in the formula below is not done.
* zero_zone - Half of the size of the dead region in the middle of the search zone over * @param criteria [CvTermCriteria] Criteria for termination of the iterative process of corner refinement.
* which the summation in the formula below is not done. * @return [Array<CvPoint2D32f>] Refined corner coordinates.
* criteria - Criteria for termination of the iterative process of corner refinement. * @opencv_func cvFindCornerSubPix
*/ */
VALUE VALUE
rb_find_corner_sub_pix(VALUE self, VALUE corners, VALUE win_size, VALUE zero_zone, VALUE criteria) rb_find_corner_sub_pix(VALUE self, VALUE corners, VALUE win_size, VALUE zero_zone, VALUE criteria)
@ -3558,24 +3584,24 @@ rb_find_corner_sub_pix(VALUE self, VALUE corners, VALUE win_size, VALUE zero_zon
} }
/* /*
* call-seq:
* good_features_to_track(<i>quality_level, min_distance[, good_features_to_track_option]</i>)
* -> array (include CvPoint2D32f)
* Determines strong corners on an image. * Determines strong corners on an image.
* *
* quality_level – Multiplier for the max/min eigenvalue; specifies the minimal accepted quality of image corners * @overload good_features_to_track(quality_level, min_distance, good_features_to_track_option = {})
* min_distance – Limit, specifying the minimum possible distance between the returned corners; Euclidian distance is used * @param quality_level [Number] Parameter characterizing the minimal accepted quality of image corners.
* <i>good_features_to_track_option</i> should be Hash include these keys. * The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
* :mask * or the Harris function response.
* Region of interest. The function selects points either in the specified region or in the whole image * @param min_distance [Number] Minimum possible Euclidean distance between the returned corners.
* if the mask is nil. * @param good_features_to_track_option [Hash] Options.
* :block_size * @option good_features_to_track_option [CvMat] :mask (nil) Optional region of interest.
* Size of the averaging block, passed to the underlying CornerMinEigenVal or CornerHarris used by the function. * If the image is not empty (it needs to have the type CV_8UC1 and the same size as image),
* :use_harris * it specifies the region in which the corners are detected.
* If true, Harris operator ( CornerHarris ) is used instead of default CornerMinEigenVal * @option good_features_to_track_option [Integer] :block_size (3) Size of an average block for computing
* :k * a derivative covariation matrix over each pixel neighborhood.
* Free parameter of Harris detector; used only if ( :use_harris => true ) * @option good_features_to_track_option [Boolean] :use_harris (false) Parameter indicating whether
* note: <i>good_features_to_track_option</i>'s default value is CvMat::GOOD_FEATURES_TO_TRACK_OPTION * to use a Harris detector.
* @option good_features_to_track_option [Number] :k (0.04) Free parameter of the Harris detector.
* @return [Array<CvPoint2D32f>] Output vector of detected corners.
* @opencv_func cvGoodFeaturesToTrack
*/ */
VALUE VALUE
rb_good_features_to_track(int argc, VALUE *argv, VALUE self) rb_good_features_to_track(int argc, VALUE *argv, VALUE self)