mirror of
https://github.com/ruby-opencv/ruby-opencv
synced 2023-03-27 23:22:12 -04:00
small fix
This commit is contained in:
ser1zw
parent
7f5dd34d23
commit
bd362275e7
11 changed files with 57 additions and 57 deletions
|
|
@ -3982,7 +3982,7 @@ rb_log_polar(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* erode(<i>[element = nil, iteration = 1]</i>) -> cvmat
|
||||
* erode([element = nil, iteration = 1]) -> cvmat
|
||||
*
|
||||
* Create erodes image by using arbitrary structuring element.
|
||||
* <i>element</i> is structuring element used for erosion.
|
||||
|
|
@ -3997,7 +3997,7 @@ rb_erode(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* erode!(<i>[element = nil][,iteration = 1]</i>) -> self
|
||||
* erode!([element = nil][,iteration = 1]) -> self
|
||||
*
|
||||
* Erodes image by using arbitrary structuring element.
|
||||
* see also #erode.
|
||||
|
|
@ -4019,7 +4019,7 @@ rb_erode_bang(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* dilate(<i>[element = nil][,iteration = 1]</i>) -> cvmat
|
||||
* dilate([element = nil][,iteration = 1]) -> cvmat
|
||||
*
|
||||
* Create dilates image by using arbitrary structuring element.
|
||||
* <i>element</i> is structuring element used for erosion.
|
||||
|
|
@ -4034,7 +4034,7 @@ rb_dilate(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* dilate!(<i>[element = nil][,iteration = 1]</i>) -> self
|
||||
* dilate!([element = nil][,iteration = 1]) -> self
|
||||
*
|
||||
* Dilate image by using arbitrary structuring element.
|
||||
* see also #dilate.
|
||||
|
|
@ -4056,7 +4056,7 @@ rb_dilate_bang(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* morpholohy(<i>operation[,element = nil][,iteration = 1]</i>) -> cvmat
|
||||
* morpholohy(operation[,element = nil][,iteration = 1]) -> cvmat
|
||||
*
|
||||
* Performs advanced morphological transformations.
|
||||
* <i>operation</i>
|
||||
|
|
@ -4097,7 +4097,7 @@ rb_morphology(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* smooth_blur_no_scale(<i>[p1 = 3, p2 = 3]</i>) -> cvmat
|
||||
* smooth_blur_no_scale([p1 = 3, p2 = 3]) -> cvmat
|
||||
*
|
||||
* Smooths the image by simple blur with no scaling.
|
||||
* * 8bit unsigned -> return 16bit unsigned
|
||||
|
|
@ -4128,7 +4128,7 @@ rb_smooth_blur_no_scale(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* smooth_blur(<i>[p1 = 3, p2 = 3]</i>) -> cvmat
|
||||
* smooth_blur([p1 = 3, p2 = 3]) -> cvmat
|
||||
*
|
||||
* Smooths the image by simple blur.
|
||||
* Summation over a pixel <i>p1</i> x <i>p2</i> neighborhood with subsequent scaling by 1 / (p1*p2).
|
||||
|
|
@ -4146,7 +4146,7 @@ rb_smooth_blur(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* smooth_gaussian(<i>[p1 = 3, p2 = 3, p3 = 0.0, p4 = 0.0]</i>) -> cvmat
|
||||
* smooth_gaussian([p1 = 3, p2 = 3, p3 = 0.0, p4 = 0.0]) -> cvmat
|
||||
*
|
||||
* Smooths the image by gaussian blur.
|
||||
* Convolving image with <i>p1</i> x <i>p2</i> Gaussian kernel.
|
||||
|
|
@ -4172,7 +4172,7 @@ rb_smooth_gaussian(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* smooth_median(<i>[p1 = 3]</i>) -> cvmat
|
||||
* smooth_median([p1 = 3]) -> cvmat
|
||||
*
|
||||
* Smooths the image by median blur.
|
||||
* Finding median of <i>p1</i> x <i>p1</i> neighborhood (i.e. the neighborhood is square).
|
||||
|
|
@ -4190,7 +4190,7 @@ rb_smooth_median(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* smooth_bilateral(<i>[p1 = 3][p2 = 3]</i>) -> cvmat
|
||||
* smooth_bilateral([p1 = 3][p2 = 3]) -> cvmat
|
||||
*
|
||||
* Smooths the image by bilateral filter.
|
||||
* Applying bilateral 3x3 filtering with color sigma=<i>p1</i> and space sigma=<i>p2</i>.
|
||||
|
|
@ -4277,7 +4277,7 @@ rb_smooth(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* filter2d(<i>kernel[,anchor]</i>) -> cvmat
|
||||
* filter2d(kernel[,anchor]) -> cvmat
|
||||
*
|
||||
* Convolves image with the kernel.
|
||||
* Convolution kernel, single-channel floating point matrix (or same depth of self's).
|
||||
|
|
@ -4304,7 +4304,7 @@ rb_filter2d(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* copy_make_border(<i>border_type, size, offset[,value = CvScalar.new(0)]</i>)
|
||||
* copy_make_border(border_type, size, offset[,value = CvScalar.new(0)])
|
||||
*
|
||||
* Copies image and makes border around it.
|
||||
* <i>border_type</i>:
|
||||
|
|
@ -4348,7 +4348,7 @@ rb_copy_make_border(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* integral(<i>need_sqsum = false, need_tilted_sum = false</i>) -> [cvmat, cvmat or nil, cvmat or nil]
|
||||
* integral(need_sqsum = false, need_tilted_sum = false) -> [cvmat, cvmat or nil, cvmat or nil]
|
||||
*
|
||||
* Calculates integral images.
|
||||
* If <i>need_sqsum</i> = true, calculate the integral image for squared pixel values.
|
||||
|
|
@ -4538,7 +4538,7 @@ rb_adaptive_threshold(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* pyr_down(<i>[filter = :gaussian_5x5]</i>) -> cvmat
|
||||
* pyr_down([filter = :gaussian_5x5]) -> cvmat
|
||||
*
|
||||
* Return downsamples image.
|
||||
*
|
||||
|
|
@ -4578,7 +4578,7 @@ rb_pyr_down(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* pyr_up(<i>[filter = :gaussian_5x5]</i>) -> cvmat
|
||||
* pyr_up([filter = :gaussian_5x5]) -> cvmat
|
||||
*
|
||||
* Return upsamples image.
|
||||
*
|
||||
|
|
@ -4859,7 +4859,7 @@ rb_draw_chessboard_corners_bang(VALUE self, VALUE pattern_size, VALUE corners, V
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* pyr_mean_shift_filtering(<i>sp, sr[,max_level = 1][termcrit = CvTermCriteria.new(5,1)]</i>) -> cvmat
|
||||
* pyr_mean_shift_filtering(sp, sr[,max_level = 1][termcrit = CvTermCriteria.new(5,1)]) -> cvmat
|
||||
*
|
||||
* Does meanshift image segmentation.
|
||||
*
|
||||
|
|
@ -5061,7 +5061,7 @@ rb_hough_circles(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* inpaint(<i>inpaint_method, mask, radius</i>) -> cvmat
|
||||
* inpaint(inpaint_method, mask, radius) -> cvmat
|
||||
*
|
||||
* Inpaints the selected region in the image
|
||||
* The radius of circlular neighborhood of each point inpainted that is considered by the algorithm.
|
||||
|
|
@ -5193,7 +5193,7 @@ rb_match_template(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* match_shapes(<i>object, method</i>) -> float
|
||||
* match_shapes(object, method) -> float
|
||||
*
|
||||
* Compares two shapes(self and object). <i>object</i> should be CvMat or CvContour.
|
||||
*
|
||||
|
|
@ -5228,7 +5228,7 @@ rb_match_shapes(int argc, VALUE *argv, VALUE self)
|
|||
/**
|
||||
* Port from OpenCV sample: matching_to_many_images.cpp
|
||||
* call-seq:
|
||||
* match_descriptors(<i>images[, detector_type="SURF"][, descriptor_type="SURF"][, matcher_type="FlannBased"]</i>) -> Hash
|
||||
* match_descriptors(images[, detector_type="SURF"][, descriptor_type="SURF"][, matcher_type="FlannBased"]) -> Hash
|
||||
*
|
||||
* Matching descriptors detected on one image to descriptors detected in image array.
|
||||
* Returns a Hash contains match count of each image index.
|
||||
|
|
@ -5360,7 +5360,7 @@ rb_cam_shift(VALUE self, VALUE window, VALUE criteria)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* snake_image(<i>points, alpha, beta, gamma, window, criteria[, calc_gradient = true]</i>) -> array(pointset)
|
||||
* snake_image(points, alpha, beta, gamma, window, criteria[, calc_gradient = true]) -> array(pointset)
|
||||
*
|
||||
* Updates snake in order to minimize its total energy that is a sum of internal energy
|
||||
* that depends on contour shape (the smoother contour is, the smaller internal energy is)
|
||||
|
|
@ -5444,7 +5444,7 @@ rb_snake_image(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* optical_flow_hs(<i>prev[,velx = nil][,vely = nil][,options]</i>) -> [cvmat, cvmat]
|
||||
* optical_flow_hs(prev[,velx = nil][,vely = nil][,options]) -> [cvmat, cvmat]
|
||||
*
|
||||
* Calculates optical flow for two images (previous -> self) using Horn & Schunck algorithm.
|
||||
* Return horizontal component of the optical flow and vertical component of the optical flow.
|
||||
|
|
@ -5500,7 +5500,7 @@ rb_optical_flow_hs(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* optical_flow_lk(<i>prev, win_size</i>) -> [cvmat, cvmat]
|
||||
* optical_flow_lk(prev, win_size) -> [cvmat, cvmat]
|
||||
*
|
||||
* Calculates optical flow for two images (previous -> self) using Lucas & Kanade algorithm
|
||||
* Return horizontal component of the optical flow and vertical component of the optical flow.
|
||||
|
|
@ -5529,7 +5529,7 @@ rb_optical_flow_lk(VALUE self, VALUE prev, VALUE win_size)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* optical_flow_bm(<i>prev[,velx = nil][,vely = nil][,option]</i>) -> [cvmat, cvmat]
|
||||
* optical_flow_bm(prev[,velx = nil][,vely = nil][,option]) -> [cvmat, cvmat]
|
||||
*
|
||||
* Calculates optical flow for two images (previous -> self) using block matching method.
|
||||
* Return horizontal component of the optical flow and vertical component of the optical flow.
|
||||
|
|
@ -5592,7 +5592,7 @@ rb_optical_flow_bm(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* CvMat.find_fundamental_mat(<i>points1, points2[,options = {}]</i>) -> fundamental_matrix(cvmat) or nil
|
||||
* CvMat.find_fundamental_mat(points1, points2[,options = {}]) -> fundamental_matrix(cvmat) or nil
|
||||
*
|
||||
* Calculates fundamental matrix from corresponding points.
|
||||
* Size of the output fundamental matrix is 3x3 or 9x3 (7-point method may return up to 3 matrices)
|
||||
|
|
@ -5655,7 +5655,7 @@ rb_find_fundamental_mat(int argc, VALUE *argv, VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* CvMat.compute_correspond_epilines(<i>points, which_image, fundamental_matrix</i>) -> correspondent_lines(cvmat)
|
||||
* CvMat.compute_correspond_epilines(points, which_image, fundamental_matrix) -> correspondent_lines(cvmat)
|
||||
*
|
||||
* For points in one image of stereo pair computes the corresponding epilines in the other image.
|
||||
* Finds equation of a line that contains the corresponding point (i.e. projection of the same 3D point)
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ rb_allocate(VALUE klass)
|
|||
}
|
||||
/*
|
||||
* call-seq:
|
||||
* CvMoments.new(<i>src[,is_binary = nil]</i>)
|
||||
* CvMoments.new(src[,is_binary = nil])
|
||||
*
|
||||
* Calculates all moments up to third order of a polygon or rasterized shape.
|
||||
* <i>src</i> should be CvMat or CvPolygon.
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ rb_allocate(VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* new(<i>[d1][,d2][,d3][,d4]</i>)
|
||||
* new([d1][,d2][,d3][,d4])
|
||||
*
|
||||
* Create new Scalar. Argument should be Fixnum (or nil as 0).
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ rb_allocate(VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* CvSeq.new(<i>type[,storage]</i>)
|
||||
* CvSeq.new(type[,storage])
|
||||
*
|
||||
* Return a new CvSeq. <i>type</i> should be following classes.
|
||||
*
|
||||
|
|
@ -335,7 +335,7 @@ rb_seq_push(VALUE self, VALUE args, int flag)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* push(<i>obj, ...</i>) -> self
|
||||
* push(obj, ...) -> self
|
||||
*
|
||||
* Append - Pushes the given object(s) on the end of this sequence. This expression return the sequence itself,
|
||||
* so several append may be chained together.
|
||||
|
|
@ -499,7 +499,7 @@ rb_each_index(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* insert(<i>index,obj</i>) -> self
|
||||
* insert(index,obj) -> self
|
||||
*
|
||||
* Inserts the given values before element with the given index (which may be negative).
|
||||
*/
|
||||
|
|
@ -527,7 +527,7 @@ rb_insert(VALUE self, VALUE index, VALUE object)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* remove(<i>index</i>) -> obj or nil
|
||||
* remove(index) -> obj or nil
|
||||
*
|
||||
* Deletes the elements at the specified index.
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ rb_allocate(VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* new(<i>start, end</i>)
|
||||
* new(start, end)
|
||||
*
|
||||
* Create new slice object.
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -28,8 +28,8 @@ rb_class()
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* CvVideoWriter.new(<i>filname, fourcc, fps, size[, is_color]</i>) -> cvvideowriter
|
||||
* CvVideoWriter.new(<i>filname, fourcc, fps, size[, is_color]</i>){|vw| ... } -> nil
|
||||
* CvVideoWriter.new(filname, fourcc, fps, size[, is_color]) -> cvvideowriter
|
||||
* CvVideoWriter.new(filname, fourcc, fps, size[, is_color]){|vw| ... } -> nil
|
||||
*
|
||||
* Open new video writer. If block given, writer is closed automatically when end of block.
|
||||
*
|
||||
|
|
@ -81,7 +81,7 @@ rb_initialize(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* write(<i>frame</i>)
|
||||
* write(frame)
|
||||
*
|
||||
* Write image as frame of video stream.
|
||||
* <i>frame</i> should be IplImage
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ rb_module_GUI()
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* wait_key(<i>[delay]</i>) -> int or nil
|
||||
* wait_key([delay]) -> int or nil
|
||||
*
|
||||
* Waits for a pressed key each GUI wedget.
|
||||
* Return the code of the pressed key or nil if no key were pressed until the specified timeout has elapsed.
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ rb_allocate(VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* new(<i>width, height[, depth = CV_8U][, channel = 3]</i>)
|
||||
* new(width, height[, depth = CV_8U][, channel = 3])
|
||||
*
|
||||
* Create width * height image. Each element-value set 0.
|
||||
*
|
||||
|
|
@ -65,7 +65,7 @@ rb_initialize(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* IplImage::load(<i>filename[,iscolor = CV_LOAD_IMAGE_COLOR]</i>)
|
||||
* IplImage::load(filename[,iscolor = CV_LOAD_IMAGE_COLOR])
|
||||
*
|
||||
* Load an image from file.
|
||||
* iscolor = CV_LOAD_IMAGE_COLOR, the loaded image is forced to be a 3-channel color image
|
||||
|
|
@ -149,8 +149,8 @@ rb_get_roi(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* set_roi(<i>rect</i>)
|
||||
* set_roi(<i>rect</i>){|image| ...}
|
||||
* set_roi(rect)
|
||||
* set_roi(rect){|image| ...}
|
||||
*
|
||||
* Set ROI. <i>rect</i> should be CvRect or compatible object.
|
||||
* Return self.
|
||||
|
|
@ -210,8 +210,8 @@ rb_get_coi(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* set_coi(<i>coi</i>)
|
||||
* set_coi(<i>coi</i>){|image| ...}
|
||||
* set_coi(coi)
|
||||
* set_coi(coi){|image| ...}
|
||||
*
|
||||
* Set COI. <i>coi</i> should be Fixnum.
|
||||
* Return self.
|
||||
|
|
@ -254,7 +254,7 @@ rb_reset_coi(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* IplImage.smoothness(<i>lowFreqRatio, blankDensity, messyDensity, highFreqRatio</i>) -> [ symbol, float, float ]
|
||||
* IplImage.smoothness(lowFreqRatio, blankDensity, messyDensity, highFreqRatio) -> [ symbol, float, float ]
|
||||
*
|
||||
* Determines if the image's smoothness is either, :smooth, :messy, or :blank.
|
||||
*
|
||||
|
|
@ -559,7 +559,7 @@ high_pass_range(const IplImage *pImage, float lostPercentage, int &outLow, int &
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* pyr_segmentation(<i>level, threshold1, threshold2</i>) -> [iplimage, cvseq(include cvconnectedcomp)]
|
||||
* pyr_segmentation(level, threshold1, threshold2) -> [iplimage, cvseq(include cvconnectedcomp)]
|
||||
*
|
||||
* Does image segmentation by pyramids.
|
||||
* The pyramid builds up to the level <i>level<i>.
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ rb_fit_ellipse2(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* convex_hull2(<i>[orientation_clockwise = true]</i>) -> cvcontour
|
||||
* convex_hull2([orientation_clockwise = true]) -> cvcontour
|
||||
*
|
||||
* Finds convex hull of 2D point set using Sklansky's algorithm.
|
||||
*
|
||||
|
|
@ -113,7 +113,7 @@ rb_check_contour_convexity(VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* convexity_defects(<i>hull</i>) -> cvseq(include CvConvexityDefect)
|
||||
* convexity_defects(hull) -> cvseq(include CvConvexityDefect)
|
||||
*
|
||||
* Finds convexity defects of contour.
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -42,8 +42,8 @@ void trackbar_free(void *ptr) {
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* new(<i>name,maxval[,val],&block</i>)
|
||||
* new(<i>name,maxval[,val]</i>){|value| ... }
|
||||
* new(name,maxval[,val],&block)
|
||||
* new(name,maxval[,val]){|value| ... }
|
||||
*
|
||||
* Create new Trackbar.
|
||||
* <i>name</i> should be String.
|
||||
|
|
|
|||
|
|
@ -57,7 +57,7 @@ window_free(void *ptr)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* new(<i>name[, flags]</i>)
|
||||
* new(name[, flags])
|
||||
*
|
||||
* Create new window named <i>name</i>.
|
||||
* If <i>flags</i> is CV_WINDOW_AUTOSIZE (default), window size automatically resize when image given.
|
||||
|
|
@ -140,8 +140,8 @@ rb_destroy_all(VALUE klass)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* resize(<i>size</i>)
|
||||
* resize(<i>width, height</i>)
|
||||
* resize(size)
|
||||
* resize(width, height)
|
||||
*
|
||||
* Set window size.
|
||||
*/
|
||||
|
|
@ -176,8 +176,8 @@ rb_resize(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* move(<i>point</i>)
|
||||
* move(<i>x, y</i>)
|
||||
* move(point)
|
||||
* move(x, y)
|
||||
*
|
||||
* Set window position.
|
||||
*/
|
||||
|
|
@ -212,7 +212,7 @@ rb_move(int argc, VALUE *argv, VALUE self)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* show_image(<i>image</i>)
|
||||
* show_image(image)
|
||||
*
|
||||
* Show the image. If the window was created with <i>flags</i> = CV_WINDOW_AUTOSIZE then the image is shown
|
||||
* with its original size, otherwize the image is scaled to fit the window.
|
||||
|
|
@ -239,9 +239,9 @@ trackbar_callback(int value, void* block)
|
|||
|
||||
/*
|
||||
* call-seq:
|
||||
* set_trackbar(<i>trackbar</i>)
|
||||
* set_trackbar(<i>name,maxval[,val],&block</i>)
|
||||
* set_trackbar(<i>name,maxval[,val]</i>){|value| ... }
|
||||
* set_trackbar(trackbar)
|
||||
* set_trackbar(name,maxval[,val],&block)
|
||||
* set_trackbar(name,maxval[,val]){|value| ... }
|
||||
*
|
||||
* Create Trackbar on this window. Return new Trackbar.
|
||||
* see Trackbar.new
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue