Renesas / opencv-lib

openCV library for Renesas RZ/A

Dependents:   RZ_A2M_Mbed_samples

include/opencv2/imgproc.hpp@0:0e0631af0305, 2021-01-29 (annotated)

Committer:
RyoheiHagimoto
Date:
Fri Jan 29 04:53:38 2021 +0000
Revision:
0:0e0631af0305
copied from https://github.com/d-kato/opencv-lib.

Who changed what in which revision?

UserRevisionLine numberNew contents of line
RyoheiHagimoto 0:0e0631af0305 1 /*M///////////////////////////////////////////////////////////////////////////////////////
RyoheiHagimoto 0:0e0631af0305 2 //
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RyoheiHagimoto 0:0e0631af0305 6 // If you do not agree to this license, do not download, install,
RyoheiHagimoto 0:0e0631af0305 7 // copy or use the software.
RyoheiHagimoto 0:0e0631af0305 8 //
RyoheiHagimoto 0:0e0631af0305 9 //
RyoheiHagimoto 0:0e0631af0305 10 // License Agreement
RyoheiHagimoto 0:0e0631af0305 11 // For Open Source Computer Vision Library
RyoheiHagimoto 0:0e0631af0305 12 //
RyoheiHagimoto 0:0e0631af0305 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
RyoheiHagimoto 0:0e0631af0305 14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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RyoheiHagimoto 0:0e0631af0305 19 //
RyoheiHagimoto 0:0e0631af0305 20 // * Redistribution's of source code must retain the above copyright notice,
RyoheiHagimoto 0:0e0631af0305 21 // this list of conditions and the following disclaimer.
RyoheiHagimoto 0:0e0631af0305 22 //
RyoheiHagimoto 0:0e0631af0305 23 // * Redistribution's in binary form must reproduce the above copyright notice,
RyoheiHagimoto 0:0e0631af0305 24 // this list of conditions and the following disclaimer in the documentation
RyoheiHagimoto 0:0e0631af0305 25 // and/or other materials provided with the distribution.
RyoheiHagimoto 0:0e0631af0305 26 //
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RyoheiHagimoto 0:0e0631af0305 28 // derived from this software without specific prior written permission.
RyoheiHagimoto 0:0e0631af0305 29 //
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RyoheiHagimoto 0:0e0631af0305 31 // any express or implied warranties, including, but not limited to, the implied
RyoheiHagimoto 0:0e0631af0305 32 // warranties of merchantability and fitness for a particular purpose are disclaimed.
RyoheiHagimoto 0:0e0631af0305 33 // In no event shall the Intel Corporation or contributors be liable for any direct,
RyoheiHagimoto 0:0e0631af0305 34 // indirect, incidental, special, exemplary, or consequential damages
RyoheiHagimoto 0:0e0631af0305 35 // (including, but not limited to, procurement of substitute goods or services;
RyoheiHagimoto 0:0e0631af0305 36 // loss of use, data, or profits; or business interruption) however caused
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RyoheiHagimoto 0:0e0631af0305 38 // or tort (including negligence or otherwise) arising in any way out of
RyoheiHagimoto 0:0e0631af0305 39 // the use of this software, even if advised of the possibility of such damage.
RyoheiHagimoto 0:0e0631af0305 40 //
RyoheiHagimoto 0:0e0631af0305 41 //M*/
RyoheiHagimoto 0:0e0631af0305 42
RyoheiHagimoto 0:0e0631af0305 43 #ifndef OPENCV_IMGPROC_HPP
RyoheiHagimoto 0:0e0631af0305 44 #define OPENCV_IMGPROC_HPP
RyoheiHagimoto 0:0e0631af0305 45
RyoheiHagimoto 0:0e0631af0305 46 #include "opencv2/core.hpp"
RyoheiHagimoto 0:0e0631af0305 47
RyoheiHagimoto 0:0e0631af0305 48 /**
RyoheiHagimoto 0:0e0631af0305 49 @defgroup imgproc Image processing
RyoheiHagimoto 0:0e0631af0305 50 @{
RyoheiHagimoto 0:0e0631af0305 51 @defgroup imgproc_filter Image Filtering
RyoheiHagimoto 0:0e0631af0305 52
RyoheiHagimoto 0:0e0631af0305 53 Functions and classes described in this section are used to perform various linear or non-linear
RyoheiHagimoto 0:0e0631af0305 54 filtering operations on 2D images (represented as Mat's). It means that for each pixel location
RyoheiHagimoto 0:0e0631af0305 55 \f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
RyoheiHagimoto 0:0e0631af0305 56 compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
RyoheiHagimoto 0:0e0631af0305 57 morphological operations, it is the minimum or maximum values, and so on. The computed response is
RyoheiHagimoto 0:0e0631af0305 58 stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
RyoheiHagimoto 0:0e0631af0305 59 will be of the same size as the input image. Normally, the functions support multi-channel arrays,
RyoheiHagimoto 0:0e0631af0305 60 in which case every channel is processed independently. Therefore, the output image will also have
RyoheiHagimoto 0:0e0631af0305 61 the same number of channels as the input one.
RyoheiHagimoto 0:0e0631af0305 62
RyoheiHagimoto 0:0e0631af0305 63 Another common feature of the functions and classes described in this section is that, unlike
RyoheiHagimoto 0:0e0631af0305 64 simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
RyoheiHagimoto 0:0e0631af0305 65 example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
RyoheiHagimoto 0:0e0631af0305 66 processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
RyoheiHagimoto 0:0e0631af0305 67 of the image. You can let these pixels be the same as the left-most image pixels ("replicated
RyoheiHagimoto 0:0e0631af0305 68 border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
RyoheiHagimoto 0:0e0631af0305 69 border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
RyoheiHagimoto 0:0e0631af0305 70 For details, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 71
RyoheiHagimoto 0:0e0631af0305 72 @anchor filter_depths
RyoheiHagimoto 0:0e0631af0305 73 ### Depth combinations
RyoheiHagimoto 0:0e0631af0305 74 Input depth (src.depth()) | Output depth (ddepth)
RyoheiHagimoto 0:0e0631af0305 75 --------------------------|----------------------
RyoheiHagimoto 0:0e0631af0305 76 CV_8U | -1/CV_16S/CV_32F/CV_64F
RyoheiHagimoto 0:0e0631af0305 77 CV_16U/CV_16S | -1/CV_32F/CV_64F
RyoheiHagimoto 0:0e0631af0305 78 CV_32F | -1/CV_32F/CV_64F
RyoheiHagimoto 0:0e0631af0305 79 CV_64F | -1/CV_64F
RyoheiHagimoto 0:0e0631af0305 80
RyoheiHagimoto 0:0e0631af0305 81 @note when ddepth=-1, the output image will have the same depth as the source.
RyoheiHagimoto 0:0e0631af0305 82
RyoheiHagimoto 0:0e0631af0305 83 @defgroup imgproc_transform Geometric Image Transformations
RyoheiHagimoto 0:0e0631af0305 84
RyoheiHagimoto 0:0e0631af0305 85 The functions in this section perform various geometrical transformations of 2D images. They do not
RyoheiHagimoto 0:0e0631af0305 86 change the image content but deform the pixel grid and map this deformed grid to the destination
RyoheiHagimoto 0:0e0631af0305 87 image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
RyoheiHagimoto 0:0e0631af0305 88 destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
RyoheiHagimoto 0:0e0631af0305 89 functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
RyoheiHagimoto 0:0e0631af0305 90 pixel value:
RyoheiHagimoto 0:0e0631af0305 91
RyoheiHagimoto 0:0e0631af0305 92 \f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]
RyoheiHagimoto 0:0e0631af0305 93
RyoheiHagimoto 0:0e0631af0305 94 In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
RyoheiHagimoto 0:0e0631af0305 95 \texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
RyoheiHagimoto 0:0e0631af0305 96 \f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.
RyoheiHagimoto 0:0e0631af0305 97
RyoheiHagimoto 0:0e0631af0305 98 The actual implementations of the geometrical transformations, from the most generic remap and to
RyoheiHagimoto 0:0e0631af0305 99 the simplest and the fastest resize, need to solve two main problems with the above formula:
RyoheiHagimoto 0:0e0631af0305 100
RyoheiHagimoto 0:0e0631af0305 101 - Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
RyoheiHagimoto 0:0e0631af0305 102 previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both
RyoheiHagimoto 0:0e0631af0305 103 of them may fall outside of the image. In this case, an extrapolation method needs to be used.
RyoheiHagimoto 0:0e0631af0305 104 OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
RyoheiHagimoto 0:0e0631af0305 105 addition, it provides the method BORDER_TRANSPARENT. This means that the corresponding pixels in
RyoheiHagimoto 0:0e0631af0305 106 the destination image will not be modified at all.
RyoheiHagimoto 0:0e0631af0305 107
RyoheiHagimoto 0:0e0631af0305 108 - Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
RyoheiHagimoto 0:0e0631af0305 109 numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
RyoheiHagimoto 0:0e0631af0305 110 transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
RyoheiHagimoto 0:0e0631af0305 111 coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
RyoheiHagimoto 0:0e0631af0305 112 nearest integer coordinates and the corresponding pixel can be used. This is called a
RyoheiHagimoto 0:0e0631af0305 113 nearest-neighbor interpolation. However, a better result can be achieved by using more
RyoheiHagimoto 0:0e0631af0305 114 sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
RyoheiHagimoto 0:0e0631af0305 115 where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
RyoheiHagimoto 0:0e0631af0305 116 f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
RyoheiHagimoto 0:0e0631af0305 117 interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
RyoheiHagimoto 0:0e0631af0305 118 resize for details.
RyoheiHagimoto 0:0e0631af0305 119
RyoheiHagimoto 0:0e0631af0305 120 @defgroup imgproc_misc Miscellaneous Image Transformations
RyoheiHagimoto 0:0e0631af0305 121 @defgroup imgproc_draw Drawing Functions
RyoheiHagimoto 0:0e0631af0305 122
RyoheiHagimoto 0:0e0631af0305 123 Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
RyoheiHagimoto 0:0e0631af0305 124 rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
RyoheiHagimoto 0:0e0631af0305 125 the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
RyoheiHagimoto 0:0e0631af0305 126 for color images and brightness for grayscale images. For color images, the channel ordering is
RyoheiHagimoto 0:0e0631af0305 127 normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
RyoheiHagimoto 0:0e0631af0305 128 color using the Scalar constructor, it should look like:
RyoheiHagimoto 0:0e0631af0305 129
RyoheiHagimoto 0:0e0631af0305 130 \f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]
RyoheiHagimoto 0:0e0631af0305 131
RyoheiHagimoto 0:0e0631af0305 132 If you are using your own image rendering and I/O functions, you can use any channel ordering. The
RyoheiHagimoto 0:0e0631af0305 133 drawing functions process each channel independently and do not depend on the channel order or even
RyoheiHagimoto 0:0e0631af0305 134 on the used color space. The whole image can be converted from BGR to RGB or to a different color
RyoheiHagimoto 0:0e0631af0305 135 space using cvtColor .
RyoheiHagimoto 0:0e0631af0305 136
RyoheiHagimoto 0:0e0631af0305 137 If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
RyoheiHagimoto 0:0e0631af0305 138 many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
RyoheiHagimoto 0:0e0631af0305 139 that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
RyoheiHagimoto 0:0e0631af0305 140 fractional bits is specified by the shift parameter and the real point coordinates are calculated as
RyoheiHagimoto 0:0e0631af0305 141 \f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
RyoheiHagimoto 0:0e0631af0305 142 especially effective when rendering antialiased shapes.
RyoheiHagimoto 0:0e0631af0305 143
RyoheiHagimoto 0:0e0631af0305 144 @note The functions do not support alpha-transparency when the target image is 4-channel. In this
RyoheiHagimoto 0:0e0631af0305 145 case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
RyoheiHagimoto 0:0e0631af0305 146 semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
RyoheiHagimoto 0:0e0631af0305 147 image.
RyoheiHagimoto 0:0e0631af0305 148
RyoheiHagimoto 0:0e0631af0305 149 @defgroup imgproc_colormap ColorMaps in OpenCV
RyoheiHagimoto 0:0e0631af0305 150
RyoheiHagimoto 0:0e0631af0305 151 The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
RyoheiHagimoto 0:0e0631af0305 152 sensitive to observing changes between colors, so you often need to recolor your grayscale images to
RyoheiHagimoto 0:0e0631af0305 153 get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
RyoheiHagimoto 0:0e0631af0305 154 computer vision application.
RyoheiHagimoto 0:0e0631af0305 155
RyoheiHagimoto 0:0e0631af0305 156 In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
RyoheiHagimoto 0:0e0631af0305 157 code reads the path to an image from command line, applies a Jet colormap on it and shows the
RyoheiHagimoto 0:0e0631af0305 158 result:
RyoheiHagimoto 0:0e0631af0305 159
RyoheiHagimoto 0:0e0631af0305 160 @code
RyoheiHagimoto 0:0e0631af0305 161 #include <opencv2/core.hpp>
RyoheiHagimoto 0:0e0631af0305 162 #include <opencv2/imgproc.hpp>
RyoheiHagimoto 0:0e0631af0305 163 #include <opencv2/imgcodecs.hpp>
RyoheiHagimoto 0:0e0631af0305 164 #include <opencv2/highgui.hpp>
RyoheiHagimoto 0:0e0631af0305 165 using namespace cv;
RyoheiHagimoto 0:0e0631af0305 166
RyoheiHagimoto 0:0e0631af0305 167 #include <iostream>
RyoheiHagimoto 0:0e0631af0305 168 using namespace std;
RyoheiHagimoto 0:0e0631af0305 169
RyoheiHagimoto 0:0e0631af0305 170 int main(int argc, const char *argv[])
RyoheiHagimoto 0:0e0631af0305 171 {
RyoheiHagimoto 0:0e0631af0305 172 // We need an input image. (can be grayscale or color)
RyoheiHagimoto 0:0e0631af0305 173 if (argc < 2)
RyoheiHagimoto 0:0e0631af0305 174 {
RyoheiHagimoto 0:0e0631af0305 175 cerr << "We need an image to process here. Please run: colorMap [path_to_image]" << endl;
RyoheiHagimoto 0:0e0631af0305 176 return -1;
RyoheiHagimoto 0:0e0631af0305 177 }
RyoheiHagimoto 0:0e0631af0305 178 Mat img_in = imread(argv[1]);
RyoheiHagimoto 0:0e0631af0305 179 if(img_in.empty())
RyoheiHagimoto 0:0e0631af0305 180 {
RyoheiHagimoto 0:0e0631af0305 181 cerr << "Sample image (" << argv[1] << ") is empty. Please adjust your path, so it points to a valid input image!" << endl;
RyoheiHagimoto 0:0e0631af0305 182 return -1;
RyoheiHagimoto 0:0e0631af0305 183 }
RyoheiHagimoto 0:0e0631af0305 184 // Holds the colormap version of the image:
RyoheiHagimoto 0:0e0631af0305 185 Mat img_color;
RyoheiHagimoto 0:0e0631af0305 186 // Apply the colormap:
RyoheiHagimoto 0:0e0631af0305 187 applyColorMap(img_in, img_color, COLORMAP_JET);
RyoheiHagimoto 0:0e0631af0305 188 // Show the result:
RyoheiHagimoto 0:0e0631af0305 189 imshow("colorMap", img_color);
RyoheiHagimoto 0:0e0631af0305 190 waitKey(0);
RyoheiHagimoto 0:0e0631af0305 191 return 0;
RyoheiHagimoto 0:0e0631af0305 192 }
RyoheiHagimoto 0:0e0631af0305 193 @endcode
RyoheiHagimoto 0:0e0631af0305 194
RyoheiHagimoto 0:0e0631af0305 195 @see cv::ColormapTypes
RyoheiHagimoto 0:0e0631af0305 196
RyoheiHagimoto 0:0e0631af0305 197 @defgroup imgproc_subdiv2d Planar Subdivision
RyoheiHagimoto 0:0e0631af0305 198
RyoheiHagimoto 0:0e0631af0305 199 The Subdiv2D class described in this section is used to perform various planar subdivision on
RyoheiHagimoto 0:0e0631af0305 200 a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
RyoheiHagimoto 0:0e0631af0305 201 using the Delaunay’s algorithm, which corresponds to the dual graph of the Voronoi diagram.
RyoheiHagimoto 0:0e0631af0305 202 In the figure below, the Delaunay’s triangulation is marked with black lines and the Voronoi
RyoheiHagimoto 0:0e0631af0305 203 diagram with red lines.
RyoheiHagimoto 0:0e0631af0305 204
RyoheiHagimoto 0:0e0631af0305 205 ![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png)
RyoheiHagimoto 0:0e0631af0305 206
RyoheiHagimoto 0:0e0631af0305 207 The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
RyoheiHagimoto 0:0e0631af0305 208 location of points on the plane, building special graphs (such as NNG,RNG), and so forth.
RyoheiHagimoto 0:0e0631af0305 209
RyoheiHagimoto 0:0e0631af0305 210 @defgroup imgproc_hist Histograms
RyoheiHagimoto 0:0e0631af0305 211 @defgroup imgproc_shape Structural Analysis and Shape Descriptors
RyoheiHagimoto 0:0e0631af0305 212 @defgroup imgproc_motion Motion Analysis and Object Tracking
RyoheiHagimoto 0:0e0631af0305 213 @defgroup imgproc_feature Feature Detection
RyoheiHagimoto 0:0e0631af0305 214 @defgroup imgproc_object Object Detection
RyoheiHagimoto 0:0e0631af0305 215 @defgroup imgproc_c C API
RyoheiHagimoto 0:0e0631af0305 216 @defgroup imgproc_hal Hardware Acceleration Layer
RyoheiHagimoto 0:0e0631af0305 217 @{
RyoheiHagimoto 0:0e0631af0305 218 @defgroup imgproc_hal_functions Functions
RyoheiHagimoto 0:0e0631af0305 219 @defgroup imgproc_hal_interface Interface
RyoheiHagimoto 0:0e0631af0305 220 @}
RyoheiHagimoto 0:0e0631af0305 221 @}
RyoheiHagimoto 0:0e0631af0305 222 */
RyoheiHagimoto 0:0e0631af0305 223
RyoheiHagimoto 0:0e0631af0305 224 namespace cv
RyoheiHagimoto 0:0e0631af0305 225 {
RyoheiHagimoto 0:0e0631af0305 226
RyoheiHagimoto 0:0e0631af0305 227 /** @addtogroup imgproc
RyoheiHagimoto 0:0e0631af0305 228 @{
RyoheiHagimoto 0:0e0631af0305 229 */
RyoheiHagimoto 0:0e0631af0305 230
RyoheiHagimoto 0:0e0631af0305 231 //! @addtogroup imgproc_filter
RyoheiHagimoto 0:0e0631af0305 232 //! @{
RyoheiHagimoto 0:0e0631af0305 233
RyoheiHagimoto 0:0e0631af0305 234 //! type of morphological operation
RyoheiHagimoto 0:0e0631af0305 235 enum MorphTypes{
RyoheiHagimoto 0:0e0631af0305 236 MORPH_ERODE = 0, //!< see cv::erode
RyoheiHagimoto 0:0e0631af0305 237 MORPH_DILATE = 1, //!< see cv::dilate
RyoheiHagimoto 0:0e0631af0305 238 MORPH_OPEN = 2, //!< an opening operation
RyoheiHagimoto 0:0e0631af0305 239 //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f]
RyoheiHagimoto 0:0e0631af0305 240 MORPH_CLOSE = 3, //!< a closing operation
RyoheiHagimoto 0:0e0631af0305 241 //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f]
RyoheiHagimoto 0:0e0631af0305 242 MORPH_GRADIENT = 4, //!< a morphological gradient
RyoheiHagimoto 0:0e0631af0305 243 //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f]
RyoheiHagimoto 0:0e0631af0305 244 MORPH_TOPHAT = 5, //!< "top hat"
RyoheiHagimoto 0:0e0631af0305 245 //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f]
RyoheiHagimoto 0:0e0631af0305 246 MORPH_BLACKHAT = 6, //!< "black hat"
RyoheiHagimoto 0:0e0631af0305 247 //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f]
RyoheiHagimoto 0:0e0631af0305 248 MORPH_HITMISS = 7 //!< "hit and miss"
RyoheiHagimoto 0:0e0631af0305 249 //!< .- Only supported for CV_8UC1 binary images. Tutorial can be found in [this page](https://web.archive.org/web/20160316070407/http://opencv-code.com/tutorials/hit-or-miss-transform-in-opencv/)
RyoheiHagimoto 0:0e0631af0305 250 };
RyoheiHagimoto 0:0e0631af0305 251
RyoheiHagimoto 0:0e0631af0305 252 //! shape of the structuring element
RyoheiHagimoto 0:0e0631af0305 253 enum MorphShapes {
RyoheiHagimoto 0:0e0631af0305 254 MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f]
RyoheiHagimoto 0:0e0631af0305 255 MORPH_CROSS = 1, //!< a cross-shaped structuring element:
RyoheiHagimoto 0:0e0631af0305 256 //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 257 MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed
RyoheiHagimoto 0:0e0631af0305 258 //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height)
RyoheiHagimoto 0:0e0631af0305 259 };
RyoheiHagimoto 0:0e0631af0305 260
RyoheiHagimoto 0:0e0631af0305 261 //! @} imgproc_filter
RyoheiHagimoto 0:0e0631af0305 262
RyoheiHagimoto 0:0e0631af0305 263 //! @addtogroup imgproc_transform
RyoheiHagimoto 0:0e0631af0305 264 //! @{
RyoheiHagimoto 0:0e0631af0305 265
RyoheiHagimoto 0:0e0631af0305 266 //! interpolation algorithm
RyoheiHagimoto 0:0e0631af0305 267 enum InterpolationFlags{
RyoheiHagimoto 0:0e0631af0305 268 /** nearest neighbor interpolation */
RyoheiHagimoto 0:0e0631af0305 269 INTER_NEAREST = 0,
RyoheiHagimoto 0:0e0631af0305 270 /** bilinear interpolation */
RyoheiHagimoto 0:0e0631af0305 271 INTER_LINEAR = 1,
RyoheiHagimoto 0:0e0631af0305 272 /** bicubic interpolation */
RyoheiHagimoto 0:0e0631af0305 273 INTER_CUBIC = 2,
RyoheiHagimoto 0:0e0631af0305 274 /** resampling using pixel area relation. It may be a preferred method for image decimation, as
RyoheiHagimoto 0:0e0631af0305 275 it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
RyoheiHagimoto 0:0e0631af0305 276 method. */
RyoheiHagimoto 0:0e0631af0305 277 INTER_AREA = 3,
RyoheiHagimoto 0:0e0631af0305 278 /** Lanczos interpolation over 8x8 neighborhood */
RyoheiHagimoto 0:0e0631af0305 279 INTER_LANCZOS4 = 4,
RyoheiHagimoto 0:0e0631af0305 280 /** mask for interpolation codes */
RyoheiHagimoto 0:0e0631af0305 281 INTER_MAX = 7,
RyoheiHagimoto 0:0e0631af0305 282 /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the
RyoheiHagimoto 0:0e0631af0305 283 source image, they are set to zero */
RyoheiHagimoto 0:0e0631af0305 284 WARP_FILL_OUTLIERS = 8,
RyoheiHagimoto 0:0e0631af0305 285 /** flag, inverse transformation
RyoheiHagimoto 0:0e0631af0305 286
RyoheiHagimoto 0:0e0631af0305 287 For example, @ref cv::linearPolar or @ref cv::logPolar transforms:
RyoheiHagimoto 0:0e0631af0305 288 - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$
RyoheiHagimoto 0:0e0631af0305 289 - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$
RyoheiHagimoto 0:0e0631af0305 290 */
RyoheiHagimoto 0:0e0631af0305 291 WARP_INVERSE_MAP = 16
RyoheiHagimoto 0:0e0631af0305 292 };
RyoheiHagimoto 0:0e0631af0305 293
RyoheiHagimoto 0:0e0631af0305 294 enum InterpolationMasks {
RyoheiHagimoto 0:0e0631af0305 295 INTER_BITS = 5,
RyoheiHagimoto 0:0e0631af0305 296 INTER_BITS2 = INTER_BITS * 2,
RyoheiHagimoto 0:0e0631af0305 297 INTER_TAB_SIZE = 1 << INTER_BITS,
RyoheiHagimoto 0:0e0631af0305 298 INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE
RyoheiHagimoto 0:0e0631af0305 299 };
RyoheiHagimoto 0:0e0631af0305 300
RyoheiHagimoto 0:0e0631af0305 301 //! @} imgproc_transform
RyoheiHagimoto 0:0e0631af0305 302
RyoheiHagimoto 0:0e0631af0305 303 //! @addtogroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 304 //! @{
RyoheiHagimoto 0:0e0631af0305 305
RyoheiHagimoto 0:0e0631af0305 306 //! Distance types for Distance Transform and M-estimators
RyoheiHagimoto 0:0e0631af0305 307 //! @see cv::distanceTransform, cv::fitLine
RyoheiHagimoto 0:0e0631af0305 308 enum DistanceTypes {
RyoheiHagimoto 0:0e0631af0305 309 DIST_USER = -1, //!< User defined distance
RyoheiHagimoto 0:0e0631af0305 310 DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2|
RyoheiHagimoto 0:0e0631af0305 311 DIST_L2 = 2, //!< the simple euclidean distance
RyoheiHagimoto 0:0e0631af0305 312 DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|)
RyoheiHagimoto 0:0e0631af0305 313 DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
RyoheiHagimoto 0:0e0631af0305 314 DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
RyoheiHagimoto 0:0e0631af0305 315 DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
RyoheiHagimoto 0:0e0631af0305 316 DIST_HUBER = 7 //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
RyoheiHagimoto 0:0e0631af0305 317 };
RyoheiHagimoto 0:0e0631af0305 318
RyoheiHagimoto 0:0e0631af0305 319 //! Mask size for distance transform
RyoheiHagimoto 0:0e0631af0305 320 enum DistanceTransformMasks {
RyoheiHagimoto 0:0e0631af0305 321 DIST_MASK_3 = 3, //!< mask=3
RyoheiHagimoto 0:0e0631af0305 322 DIST_MASK_5 = 5, //!< mask=5
RyoheiHagimoto 0:0e0631af0305 323 DIST_MASK_PRECISE = 0 //!<
RyoheiHagimoto 0:0e0631af0305 324 };
RyoheiHagimoto 0:0e0631af0305 325
RyoheiHagimoto 0:0e0631af0305 326 //! type of the threshold operation
RyoheiHagimoto 0:0e0631af0305 327 //! ![threshold types](pics/threshold.png)
RyoheiHagimoto 0:0e0631af0305 328 enum ThresholdTypes {
RyoheiHagimoto 0:0e0631af0305 329 THRESH_BINARY = 0, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 330 THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 331 THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 332 THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 333 THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 334 THRESH_MASK = 7,
RyoheiHagimoto 0:0e0631af0305 335 THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
RyoheiHagimoto 0:0e0631af0305 336 THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
RyoheiHagimoto 0:0e0631af0305 337 };
RyoheiHagimoto 0:0e0631af0305 338
RyoheiHagimoto 0:0e0631af0305 339 //! adaptive threshold algorithm
RyoheiHagimoto 0:0e0631af0305 340 //! see cv::adaptiveThreshold
RyoheiHagimoto 0:0e0631af0305 341 enum AdaptiveThresholdTypes {
RyoheiHagimoto 0:0e0631af0305 342 /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times
RyoheiHagimoto 0:0e0631af0305 343 \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */
RyoheiHagimoto 0:0e0631af0305 344 ADAPTIVE_THRESH_MEAN_C = 0,
RyoheiHagimoto 0:0e0631af0305 345 /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian
RyoheiHagimoto 0:0e0631af0305 346 window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$
RyoheiHagimoto 0:0e0631af0305 347 minus C . The default sigma (standard deviation) is used for the specified blockSize . See
RyoheiHagimoto 0:0e0631af0305 348 cv::getGaussianKernel*/
RyoheiHagimoto 0:0e0631af0305 349 ADAPTIVE_THRESH_GAUSSIAN_C = 1
RyoheiHagimoto 0:0e0631af0305 350 };
RyoheiHagimoto 0:0e0631af0305 351
RyoheiHagimoto 0:0e0631af0305 352 //! cv::undistort mode
RyoheiHagimoto 0:0e0631af0305 353 enum UndistortTypes {
RyoheiHagimoto 0:0e0631af0305 354 PROJ_SPHERICAL_ORTHO = 0,
RyoheiHagimoto 0:0e0631af0305 355 PROJ_SPHERICAL_EQRECT = 1
RyoheiHagimoto 0:0e0631af0305 356 };
RyoheiHagimoto 0:0e0631af0305 357
RyoheiHagimoto 0:0e0631af0305 358 //! class of the pixel in GrabCut algorithm
RyoheiHagimoto 0:0e0631af0305 359 enum GrabCutClasses {
RyoheiHagimoto 0:0e0631af0305 360 GC_BGD = 0, //!< an obvious background pixels
RyoheiHagimoto 0:0e0631af0305 361 GC_FGD = 1, //!< an obvious foreground (object) pixel
RyoheiHagimoto 0:0e0631af0305 362 GC_PR_BGD = 2, //!< a possible background pixel
RyoheiHagimoto 0:0e0631af0305 363 GC_PR_FGD = 3 //!< a possible foreground pixel
RyoheiHagimoto 0:0e0631af0305 364 };
RyoheiHagimoto 0:0e0631af0305 365
RyoheiHagimoto 0:0e0631af0305 366 //! GrabCut algorithm flags
RyoheiHagimoto 0:0e0631af0305 367 enum GrabCutModes {
RyoheiHagimoto 0:0e0631af0305 368 /** The function initializes the state and the mask using the provided rectangle. After that it
RyoheiHagimoto 0:0e0631af0305 369 runs iterCount iterations of the algorithm. */
RyoheiHagimoto 0:0e0631af0305 370 GC_INIT_WITH_RECT = 0,
RyoheiHagimoto 0:0e0631af0305 371 /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT
RyoheiHagimoto 0:0e0631af0305 372 and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are
RyoheiHagimoto 0:0e0631af0305 373 automatically initialized with GC_BGD .*/
RyoheiHagimoto 0:0e0631af0305 374 GC_INIT_WITH_MASK = 1,
RyoheiHagimoto 0:0e0631af0305 375 /** The value means that the algorithm should just resume. */
RyoheiHagimoto 0:0e0631af0305 376 GC_EVAL = 2
RyoheiHagimoto 0:0e0631af0305 377 };
RyoheiHagimoto 0:0e0631af0305 378
RyoheiHagimoto 0:0e0631af0305 379 //! distanceTransform algorithm flags
RyoheiHagimoto 0:0e0631af0305 380 enum DistanceTransformLabelTypes {
RyoheiHagimoto 0:0e0631af0305 381 /** each connected component of zeros in src (as well as all the non-zero pixels closest to the
RyoheiHagimoto 0:0e0631af0305 382 connected component) will be assigned the same label */
RyoheiHagimoto 0:0e0631af0305 383 DIST_LABEL_CCOMP = 0,
RyoheiHagimoto 0:0e0631af0305 384 /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */
RyoheiHagimoto 0:0e0631af0305 385 DIST_LABEL_PIXEL = 1
RyoheiHagimoto 0:0e0631af0305 386 };
RyoheiHagimoto 0:0e0631af0305 387
RyoheiHagimoto 0:0e0631af0305 388 //! floodfill algorithm flags
RyoheiHagimoto 0:0e0631af0305 389 enum FloodFillFlags {
RyoheiHagimoto 0:0e0631af0305 390 /** If set, the difference between the current pixel and seed pixel is considered. Otherwise,
RyoheiHagimoto 0:0e0631af0305 391 the difference between neighbor pixels is considered (that is, the range is floating). */
RyoheiHagimoto 0:0e0631af0305 392 FLOODFILL_FIXED_RANGE = 1 << 16,
RyoheiHagimoto 0:0e0631af0305 393 /** If set, the function does not change the image ( newVal is ignored), and only fills the
RyoheiHagimoto 0:0e0631af0305 394 mask with the value specified in bits 8-16 of flags as described above. This option only make
RyoheiHagimoto 0:0e0631af0305 395 sense in function variants that have the mask parameter. */
RyoheiHagimoto 0:0e0631af0305 396 FLOODFILL_MASK_ONLY = 1 << 17
RyoheiHagimoto 0:0e0631af0305 397 };
RyoheiHagimoto 0:0e0631af0305 398
RyoheiHagimoto 0:0e0631af0305 399 //! @} imgproc_misc
RyoheiHagimoto 0:0e0631af0305 400
RyoheiHagimoto 0:0e0631af0305 401 //! @addtogroup imgproc_shape
RyoheiHagimoto 0:0e0631af0305 402 //! @{
RyoheiHagimoto 0:0e0631af0305 403
RyoheiHagimoto 0:0e0631af0305 404 //! connected components algorithm output formats
RyoheiHagimoto 0:0e0631af0305 405 enum ConnectedComponentsTypes {
RyoheiHagimoto 0:0e0631af0305 406 CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding
RyoheiHagimoto 0:0e0631af0305 407 //!< box in the horizontal direction.
RyoheiHagimoto 0:0e0631af0305 408 CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding
RyoheiHagimoto 0:0e0631af0305 409 //!< box in the vertical direction.
RyoheiHagimoto 0:0e0631af0305 410 CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box
RyoheiHagimoto 0:0e0631af0305 411 CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box
RyoheiHagimoto 0:0e0631af0305 412 CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component
RyoheiHagimoto 0:0e0631af0305 413 CC_STAT_MAX = 5
RyoheiHagimoto 0:0e0631af0305 414 };
RyoheiHagimoto 0:0e0631af0305 415
RyoheiHagimoto 0:0e0631af0305 416 //! connected components algorithm
RyoheiHagimoto 0:0e0631af0305 417 enum ConnectedComponentsAlgorithmsTypes {
RyoheiHagimoto 0:0e0631af0305 418 CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
RyoheiHagimoto 0:0e0631af0305 419 CCL_DEFAULT = -1, //!< BBDT algortihm for 8-way connectivity, SAUF algorithm for 4-way connectivity
RyoheiHagimoto 0:0e0631af0305 420 CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
RyoheiHagimoto 0:0e0631af0305 421 };
RyoheiHagimoto 0:0e0631af0305 422
RyoheiHagimoto 0:0e0631af0305 423 //! mode of the contour retrieval algorithm
RyoheiHagimoto 0:0e0631af0305 424 enum RetrievalModes {
RyoheiHagimoto 0:0e0631af0305 425 /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
RyoheiHagimoto 0:0e0631af0305 426 all the contours. */
RyoheiHagimoto 0:0e0631af0305 427 RETR_EXTERNAL = 0,
RyoheiHagimoto 0:0e0631af0305 428 /** retrieves all of the contours without establishing any hierarchical relationships. */
RyoheiHagimoto 0:0e0631af0305 429 RETR_LIST = 1,
RyoheiHagimoto 0:0e0631af0305 430 /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top
RyoheiHagimoto 0:0e0631af0305 431 level, there are external boundaries of the components. At the second level, there are
RyoheiHagimoto 0:0e0631af0305 432 boundaries of the holes. If there is another contour inside a hole of a connected component, it
RyoheiHagimoto 0:0e0631af0305 433 is still put at the top level. */
RyoheiHagimoto 0:0e0631af0305 434 RETR_CCOMP = 2,
RyoheiHagimoto 0:0e0631af0305 435 /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/
RyoheiHagimoto 0:0e0631af0305 436 RETR_TREE = 3,
RyoheiHagimoto 0:0e0631af0305 437 RETR_FLOODFILL = 4 //!<
RyoheiHagimoto 0:0e0631af0305 438 };
RyoheiHagimoto 0:0e0631af0305 439
RyoheiHagimoto 0:0e0631af0305 440 //! the contour approximation algorithm
RyoheiHagimoto 0:0e0631af0305 441 enum ContourApproximationModes {
RyoheiHagimoto 0:0e0631af0305 442 /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and
RyoheiHagimoto 0:0e0631af0305 443 (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is,
RyoheiHagimoto 0:0e0631af0305 444 max(abs(x1-x2),abs(y2-y1))==1. */
RyoheiHagimoto 0:0e0631af0305 445 CHAIN_APPROX_NONE = 1,
RyoheiHagimoto 0:0e0631af0305 446 /** compresses horizontal, vertical, and diagonal segments and leaves only their end points.
RyoheiHagimoto 0:0e0631af0305 447 For example, an up-right rectangular contour is encoded with 4 points. */
RyoheiHagimoto 0:0e0631af0305 448 CHAIN_APPROX_SIMPLE = 2,
RyoheiHagimoto 0:0e0631af0305 449 /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
RyoheiHagimoto 0:0e0631af0305 450 CHAIN_APPROX_TC89_L1 = 3,
RyoheiHagimoto 0:0e0631af0305 451 /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
RyoheiHagimoto 0:0e0631af0305 452 CHAIN_APPROX_TC89_KCOS = 4
RyoheiHagimoto 0:0e0631af0305 453 };
RyoheiHagimoto 0:0e0631af0305 454
RyoheiHagimoto 0:0e0631af0305 455 //! @} imgproc_shape
RyoheiHagimoto 0:0e0631af0305 456
RyoheiHagimoto 0:0e0631af0305 457 //! Variants of a Hough transform
RyoheiHagimoto 0:0e0631af0305 458 enum HoughModes {
RyoheiHagimoto 0:0e0631af0305 459
RyoheiHagimoto 0:0e0631af0305 460 /** classical or standard Hough transform. Every line is represented by two floating-point
RyoheiHagimoto 0:0e0631af0305 461 numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line,
RyoheiHagimoto 0:0e0631af0305 462 and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must
RyoheiHagimoto 0:0e0631af0305 463 be (the created sequence will be) of CV_32FC2 type */
RyoheiHagimoto 0:0e0631af0305 464 HOUGH_STANDARD = 0,
RyoheiHagimoto 0:0e0631af0305 465 /** probabilistic Hough transform (more efficient in case if the picture contains a few long
RyoheiHagimoto 0:0e0631af0305 466 linear segments). It returns line segments rather than the whole line. Each segment is
RyoheiHagimoto 0:0e0631af0305 467 represented by starting and ending points, and the matrix must be (the created sequence will
RyoheiHagimoto 0:0e0631af0305 468 be) of the CV_32SC4 type. */
RyoheiHagimoto 0:0e0631af0305 469 HOUGH_PROBABILISTIC = 1,
RyoheiHagimoto 0:0e0631af0305 470 /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as
RyoheiHagimoto 0:0e0631af0305 471 HOUGH_STANDARD. */
RyoheiHagimoto 0:0e0631af0305 472 HOUGH_MULTI_SCALE = 2,
RyoheiHagimoto 0:0e0631af0305 473 HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90
RyoheiHagimoto 0:0e0631af0305 474 };
RyoheiHagimoto 0:0e0631af0305 475
RyoheiHagimoto 0:0e0631af0305 476 //! Variants of Line Segment %Detector
RyoheiHagimoto 0:0e0631af0305 477 //! @ingroup imgproc_feature
RyoheiHagimoto 0:0e0631af0305 478 enum LineSegmentDetectorModes {
RyoheiHagimoto 0:0e0631af0305 479 LSD_REFINE_NONE = 0, //!< No refinement applied
RyoheiHagimoto 0:0e0631af0305 480 LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
RyoheiHagimoto 0:0e0631af0305 481 LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are
RyoheiHagimoto 0:0e0631af0305 482 //!< refined through increase of precision, decrement in size, etc.
RyoheiHagimoto 0:0e0631af0305 483 };
RyoheiHagimoto 0:0e0631af0305 484
RyoheiHagimoto 0:0e0631af0305 485 /** Histogram comparison methods
RyoheiHagimoto 0:0e0631af0305 486 @ingroup imgproc_hist
RyoheiHagimoto 0:0e0631af0305 487 */
RyoheiHagimoto 0:0e0631af0305 488 enum HistCompMethods {
RyoheiHagimoto 0:0e0631af0305 489 /** Correlation
RyoheiHagimoto 0:0e0631af0305 490 \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
RyoheiHagimoto 0:0e0631af0305 491 where
RyoheiHagimoto 0:0e0631af0305 492 \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f]
RyoheiHagimoto 0:0e0631af0305 493 and \f$N\f$ is a total number of histogram bins. */
RyoheiHagimoto 0:0e0631af0305 494 HISTCMP_CORREL = 0,
RyoheiHagimoto 0:0e0631af0305 495 /** Chi-Square
RyoheiHagimoto 0:0e0631af0305 496 \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */
RyoheiHagimoto 0:0e0631af0305 497 HISTCMP_CHISQR = 1,
RyoheiHagimoto 0:0e0631af0305 498 /** Intersection
RyoheiHagimoto 0:0e0631af0305 499 \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */
RyoheiHagimoto 0:0e0631af0305 500 HISTCMP_INTERSECT = 2,
RyoheiHagimoto 0:0e0631af0305 501 /** Bhattacharyya distance
RyoheiHagimoto 0:0e0631af0305 502 (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.)
RyoheiHagimoto 0:0e0631af0305 503 \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */
RyoheiHagimoto 0:0e0631af0305 504 HISTCMP_BHATTACHARYYA = 3,
RyoheiHagimoto 0:0e0631af0305 505 HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA
RyoheiHagimoto 0:0e0631af0305 506 /** Alternative Chi-Square
RyoheiHagimoto 0:0e0631af0305 507 \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f]
RyoheiHagimoto 0:0e0631af0305 508 This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */
RyoheiHagimoto 0:0e0631af0305 509 HISTCMP_CHISQR_ALT = 4,
RyoheiHagimoto 0:0e0631af0305 510 /** Kullback-Leibler divergence
RyoheiHagimoto 0:0e0631af0305 511 \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */
RyoheiHagimoto 0:0e0631af0305 512 HISTCMP_KL_DIV = 5
RyoheiHagimoto 0:0e0631af0305 513 };
RyoheiHagimoto 0:0e0631af0305 514
RyoheiHagimoto 0:0e0631af0305 515 /** the color conversion code
RyoheiHagimoto 0:0e0631af0305 516 @see @ref imgproc_color_conversions
RyoheiHagimoto 0:0e0631af0305 517 @ingroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 518 */
RyoheiHagimoto 0:0e0631af0305 519 enum ColorConversionCodes {
RyoheiHagimoto 0:0e0631af0305 520 COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image
RyoheiHagimoto 0:0e0631af0305 521 COLOR_RGB2RGBA = COLOR_BGR2BGRA,
RyoheiHagimoto 0:0e0631af0305 522
RyoheiHagimoto 0:0e0631af0305 523 COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image
RyoheiHagimoto 0:0e0631af0305 524 COLOR_RGBA2RGB = COLOR_BGRA2BGR,
RyoheiHagimoto 0:0e0631af0305 525
RyoheiHagimoto 0:0e0631af0305 526 COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
RyoheiHagimoto 0:0e0631af0305 527 COLOR_RGB2BGRA = COLOR_BGR2RGBA,
RyoheiHagimoto 0:0e0631af0305 528
RyoheiHagimoto 0:0e0631af0305 529 COLOR_RGBA2BGR = 3,
RyoheiHagimoto 0:0e0631af0305 530 COLOR_BGRA2RGB = COLOR_RGBA2BGR,
RyoheiHagimoto 0:0e0631af0305 531
RyoheiHagimoto 0:0e0631af0305 532 COLOR_BGR2RGB = 4,
RyoheiHagimoto 0:0e0631af0305 533 COLOR_RGB2BGR = COLOR_BGR2RGB,
RyoheiHagimoto 0:0e0631af0305 534
RyoheiHagimoto 0:0e0631af0305 535 COLOR_BGRA2RGBA = 5,
RyoheiHagimoto 0:0e0631af0305 536 COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
RyoheiHagimoto 0:0e0631af0305 537
RyoheiHagimoto 0:0e0631af0305 538 COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
RyoheiHagimoto 0:0e0631af0305 539 COLOR_RGB2GRAY = 7,
RyoheiHagimoto 0:0e0631af0305 540 COLOR_GRAY2BGR = 8,
RyoheiHagimoto 0:0e0631af0305 541 COLOR_GRAY2RGB = COLOR_GRAY2BGR,
RyoheiHagimoto 0:0e0631af0305 542 COLOR_GRAY2BGRA = 9,
RyoheiHagimoto 0:0e0631af0305 543 COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
RyoheiHagimoto 0:0e0631af0305 544 COLOR_BGRA2GRAY = 10,
RyoheiHagimoto 0:0e0631af0305 545 COLOR_RGBA2GRAY = 11,
RyoheiHagimoto 0:0e0631af0305 546
RyoheiHagimoto 0:0e0631af0305 547 COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
RyoheiHagimoto 0:0e0631af0305 548 COLOR_RGB2BGR565 = 13,
RyoheiHagimoto 0:0e0631af0305 549 COLOR_BGR5652BGR = 14,
RyoheiHagimoto 0:0e0631af0305 550 COLOR_BGR5652RGB = 15,
RyoheiHagimoto 0:0e0631af0305 551 COLOR_BGRA2BGR565 = 16,
RyoheiHagimoto 0:0e0631af0305 552 COLOR_RGBA2BGR565 = 17,
RyoheiHagimoto 0:0e0631af0305 553 COLOR_BGR5652BGRA = 18,
RyoheiHagimoto 0:0e0631af0305 554 COLOR_BGR5652RGBA = 19,
RyoheiHagimoto 0:0e0631af0305 555
RyoheiHagimoto 0:0e0631af0305 556 COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images)
RyoheiHagimoto 0:0e0631af0305 557 COLOR_BGR5652GRAY = 21,
RyoheiHagimoto 0:0e0631af0305 558
RyoheiHagimoto 0:0e0631af0305 559 COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images)
RyoheiHagimoto 0:0e0631af0305 560 COLOR_RGB2BGR555 = 23,
RyoheiHagimoto 0:0e0631af0305 561 COLOR_BGR5552BGR = 24,
RyoheiHagimoto 0:0e0631af0305 562 COLOR_BGR5552RGB = 25,
RyoheiHagimoto 0:0e0631af0305 563 COLOR_BGRA2BGR555 = 26,
RyoheiHagimoto 0:0e0631af0305 564 COLOR_RGBA2BGR555 = 27,
RyoheiHagimoto 0:0e0631af0305 565 COLOR_BGR5552BGRA = 28,
RyoheiHagimoto 0:0e0631af0305 566 COLOR_BGR5552RGBA = 29,
RyoheiHagimoto 0:0e0631af0305 567
RyoheiHagimoto 0:0e0631af0305 568 COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images)
RyoheiHagimoto 0:0e0631af0305 569 COLOR_BGR5552GRAY = 31,
RyoheiHagimoto 0:0e0631af0305 570
RyoheiHagimoto 0:0e0631af0305 571 COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
RyoheiHagimoto 0:0e0631af0305 572 COLOR_RGB2XYZ = 33,
RyoheiHagimoto 0:0e0631af0305 573 COLOR_XYZ2BGR = 34,
RyoheiHagimoto 0:0e0631af0305 574 COLOR_XYZ2RGB = 35,
RyoheiHagimoto 0:0e0631af0305 575
RyoheiHagimoto 0:0e0631af0305 576 COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
RyoheiHagimoto 0:0e0631af0305 577 COLOR_RGB2YCrCb = 37,
RyoheiHagimoto 0:0e0631af0305 578 COLOR_YCrCb2BGR = 38,
RyoheiHagimoto 0:0e0631af0305 579 COLOR_YCrCb2RGB = 39,
RyoheiHagimoto 0:0e0631af0305 580
RyoheiHagimoto 0:0e0631af0305 581 COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
RyoheiHagimoto 0:0e0631af0305 582 COLOR_RGB2HSV = 41,
RyoheiHagimoto 0:0e0631af0305 583
RyoheiHagimoto 0:0e0631af0305 584 COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
RyoheiHagimoto 0:0e0631af0305 585 COLOR_RGB2Lab = 45,
RyoheiHagimoto 0:0e0631af0305 586
RyoheiHagimoto 0:0e0631af0305 587 COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
RyoheiHagimoto 0:0e0631af0305 588 COLOR_RGB2Luv = 51,
RyoheiHagimoto 0:0e0631af0305 589 COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
RyoheiHagimoto 0:0e0631af0305 590 COLOR_RGB2HLS = 53,
RyoheiHagimoto 0:0e0631af0305 591
RyoheiHagimoto 0:0e0631af0305 592 COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
RyoheiHagimoto 0:0e0631af0305 593 COLOR_HSV2RGB = 55,
RyoheiHagimoto 0:0e0631af0305 594
RyoheiHagimoto 0:0e0631af0305 595 COLOR_Lab2BGR = 56,
RyoheiHagimoto 0:0e0631af0305 596 COLOR_Lab2RGB = 57,
RyoheiHagimoto 0:0e0631af0305 597 COLOR_Luv2BGR = 58,
RyoheiHagimoto 0:0e0631af0305 598 COLOR_Luv2RGB = 59,
RyoheiHagimoto 0:0e0631af0305 599 COLOR_HLS2BGR = 60,
RyoheiHagimoto 0:0e0631af0305 600 COLOR_HLS2RGB = 61,
RyoheiHagimoto 0:0e0631af0305 601
RyoheiHagimoto 0:0e0631af0305 602 COLOR_BGR2HSV_FULL = 66, //!<
RyoheiHagimoto 0:0e0631af0305 603 COLOR_RGB2HSV_FULL = 67,
RyoheiHagimoto 0:0e0631af0305 604 COLOR_BGR2HLS_FULL = 68,
RyoheiHagimoto 0:0e0631af0305 605 COLOR_RGB2HLS_FULL = 69,
RyoheiHagimoto 0:0e0631af0305 606
RyoheiHagimoto 0:0e0631af0305 607 COLOR_HSV2BGR_FULL = 70,
RyoheiHagimoto 0:0e0631af0305 608 COLOR_HSV2RGB_FULL = 71,
RyoheiHagimoto 0:0e0631af0305 609 COLOR_HLS2BGR_FULL = 72,
RyoheiHagimoto 0:0e0631af0305 610 COLOR_HLS2RGB_FULL = 73,
RyoheiHagimoto 0:0e0631af0305 611
RyoheiHagimoto 0:0e0631af0305 612 COLOR_LBGR2Lab = 74,
RyoheiHagimoto 0:0e0631af0305 613 COLOR_LRGB2Lab = 75,
RyoheiHagimoto 0:0e0631af0305 614 COLOR_LBGR2Luv = 76,
RyoheiHagimoto 0:0e0631af0305 615 COLOR_LRGB2Luv = 77,
RyoheiHagimoto 0:0e0631af0305 616
RyoheiHagimoto 0:0e0631af0305 617 COLOR_Lab2LBGR = 78,
RyoheiHagimoto 0:0e0631af0305 618 COLOR_Lab2LRGB = 79,
RyoheiHagimoto 0:0e0631af0305 619 COLOR_Luv2LBGR = 80,
RyoheiHagimoto 0:0e0631af0305 620 COLOR_Luv2LRGB = 81,
RyoheiHagimoto 0:0e0631af0305 621
RyoheiHagimoto 0:0e0631af0305 622 COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV
RyoheiHagimoto 0:0e0631af0305 623 COLOR_RGB2YUV = 83,
RyoheiHagimoto 0:0e0631af0305 624 COLOR_YUV2BGR = 84,
RyoheiHagimoto 0:0e0631af0305 625 COLOR_YUV2RGB = 85,
RyoheiHagimoto 0:0e0631af0305 626
RyoheiHagimoto 0:0e0631af0305 627 //! YUV 4:2:0 family to RGB
RyoheiHagimoto 0:0e0631af0305 628 COLOR_YUV2RGB_NV12 = 90,
RyoheiHagimoto 0:0e0631af0305 629 COLOR_YUV2BGR_NV12 = 91,
RyoheiHagimoto 0:0e0631af0305 630 COLOR_YUV2RGB_NV21 = 92,
RyoheiHagimoto 0:0e0631af0305 631 COLOR_YUV2BGR_NV21 = 93,
RyoheiHagimoto 0:0e0631af0305 632 COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
RyoheiHagimoto 0:0e0631af0305 633 COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
RyoheiHagimoto 0:0e0631af0305 634
RyoheiHagimoto 0:0e0631af0305 635 COLOR_YUV2RGBA_NV12 = 94,
RyoheiHagimoto 0:0e0631af0305 636 COLOR_YUV2BGRA_NV12 = 95,
RyoheiHagimoto 0:0e0631af0305 637 COLOR_YUV2RGBA_NV21 = 96,
RyoheiHagimoto 0:0e0631af0305 638 COLOR_YUV2BGRA_NV21 = 97,
RyoheiHagimoto 0:0e0631af0305 639 COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
RyoheiHagimoto 0:0e0631af0305 640 COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
RyoheiHagimoto 0:0e0631af0305 641
RyoheiHagimoto 0:0e0631af0305 642 COLOR_YUV2RGB_YV12 = 98,
RyoheiHagimoto 0:0e0631af0305 643 COLOR_YUV2BGR_YV12 = 99,
RyoheiHagimoto 0:0e0631af0305 644 COLOR_YUV2RGB_IYUV = 100,
RyoheiHagimoto 0:0e0631af0305 645 COLOR_YUV2BGR_IYUV = 101,
RyoheiHagimoto 0:0e0631af0305 646 COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
RyoheiHagimoto 0:0e0631af0305 647 COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
RyoheiHagimoto 0:0e0631af0305 648 COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
RyoheiHagimoto 0:0e0631af0305 649 COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
RyoheiHagimoto 0:0e0631af0305 650
RyoheiHagimoto 0:0e0631af0305 651 COLOR_YUV2RGBA_YV12 = 102,
RyoheiHagimoto 0:0e0631af0305 652 COLOR_YUV2BGRA_YV12 = 103,
RyoheiHagimoto 0:0e0631af0305 653 COLOR_YUV2RGBA_IYUV = 104,
RyoheiHagimoto 0:0e0631af0305 654 COLOR_YUV2BGRA_IYUV = 105,
RyoheiHagimoto 0:0e0631af0305 655 COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
RyoheiHagimoto 0:0e0631af0305 656 COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
RyoheiHagimoto 0:0e0631af0305 657 COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
RyoheiHagimoto 0:0e0631af0305 658 COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
RyoheiHagimoto 0:0e0631af0305 659
RyoheiHagimoto 0:0e0631af0305 660 COLOR_YUV2GRAY_420 = 106,
RyoheiHagimoto 0:0e0631af0305 661 COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 662 COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 663 COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 664 COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 665 COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 666 COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 667 COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
RyoheiHagimoto 0:0e0631af0305 668
RyoheiHagimoto 0:0e0631af0305 669 //! YUV 4:2:2 family to RGB
RyoheiHagimoto 0:0e0631af0305 670 COLOR_YUV2RGB_UYVY = 107,
RyoheiHagimoto 0:0e0631af0305 671 COLOR_YUV2BGR_UYVY = 108,
RyoheiHagimoto 0:0e0631af0305 672 //COLOR_YUV2RGB_VYUY = 109,
RyoheiHagimoto 0:0e0631af0305 673 //COLOR_YUV2BGR_VYUY = 110,
RyoheiHagimoto 0:0e0631af0305 674 COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
RyoheiHagimoto 0:0e0631af0305 675 COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
RyoheiHagimoto 0:0e0631af0305 676 COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
RyoheiHagimoto 0:0e0631af0305 677 COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
RyoheiHagimoto 0:0e0631af0305 678
RyoheiHagimoto 0:0e0631af0305 679 COLOR_YUV2RGBA_UYVY = 111,
RyoheiHagimoto 0:0e0631af0305 680 COLOR_YUV2BGRA_UYVY = 112,
RyoheiHagimoto 0:0e0631af0305 681 //COLOR_YUV2RGBA_VYUY = 113,
RyoheiHagimoto 0:0e0631af0305 682 //COLOR_YUV2BGRA_VYUY = 114,
RyoheiHagimoto 0:0e0631af0305 683 COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
RyoheiHagimoto 0:0e0631af0305 684 COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
RyoheiHagimoto 0:0e0631af0305 685 COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
RyoheiHagimoto 0:0e0631af0305 686 COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
RyoheiHagimoto 0:0e0631af0305 687
RyoheiHagimoto 0:0e0631af0305 688 COLOR_YUV2RGB_YUY2 = 115,
RyoheiHagimoto 0:0e0631af0305 689 COLOR_YUV2BGR_YUY2 = 116,
RyoheiHagimoto 0:0e0631af0305 690 COLOR_YUV2RGB_YVYU = 117,
RyoheiHagimoto 0:0e0631af0305 691 COLOR_YUV2BGR_YVYU = 118,
RyoheiHagimoto 0:0e0631af0305 692 COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
RyoheiHagimoto 0:0e0631af0305 693 COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
RyoheiHagimoto 0:0e0631af0305 694 COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
RyoheiHagimoto 0:0e0631af0305 695 COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
RyoheiHagimoto 0:0e0631af0305 696
RyoheiHagimoto 0:0e0631af0305 697 COLOR_YUV2RGBA_YUY2 = 119,
RyoheiHagimoto 0:0e0631af0305 698 COLOR_YUV2BGRA_YUY2 = 120,
RyoheiHagimoto 0:0e0631af0305 699 COLOR_YUV2RGBA_YVYU = 121,
RyoheiHagimoto 0:0e0631af0305 700 COLOR_YUV2BGRA_YVYU = 122,
RyoheiHagimoto 0:0e0631af0305 701 COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
RyoheiHagimoto 0:0e0631af0305 702 COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
RyoheiHagimoto 0:0e0631af0305 703 COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
RyoheiHagimoto 0:0e0631af0305 704 COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
RyoheiHagimoto 0:0e0631af0305 705
RyoheiHagimoto 0:0e0631af0305 706 COLOR_YUV2GRAY_UYVY = 123,
RyoheiHagimoto 0:0e0631af0305 707 COLOR_YUV2GRAY_YUY2 = 124,
RyoheiHagimoto 0:0e0631af0305 708 //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
RyoheiHagimoto 0:0e0631af0305 709 COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
RyoheiHagimoto 0:0e0631af0305 710 COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
RyoheiHagimoto 0:0e0631af0305 711 COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
RyoheiHagimoto 0:0e0631af0305 712 COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
RyoheiHagimoto 0:0e0631af0305 713 COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
RyoheiHagimoto 0:0e0631af0305 714
RyoheiHagimoto 0:0e0631af0305 715 //! alpha premultiplication
RyoheiHagimoto 0:0e0631af0305 716 COLOR_RGBA2mRGBA = 125,
RyoheiHagimoto 0:0e0631af0305 717 COLOR_mRGBA2RGBA = 126,
RyoheiHagimoto 0:0e0631af0305 718
RyoheiHagimoto 0:0e0631af0305 719 //! RGB to YUV 4:2:0 family
RyoheiHagimoto 0:0e0631af0305 720 COLOR_RGB2YUV_I420 = 127,
RyoheiHagimoto 0:0e0631af0305 721 COLOR_BGR2YUV_I420 = 128,
RyoheiHagimoto 0:0e0631af0305 722 COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
RyoheiHagimoto 0:0e0631af0305 723 COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
RyoheiHagimoto 0:0e0631af0305 724
RyoheiHagimoto 0:0e0631af0305 725 COLOR_RGBA2YUV_I420 = 129,
RyoheiHagimoto 0:0e0631af0305 726 COLOR_BGRA2YUV_I420 = 130,
RyoheiHagimoto 0:0e0631af0305 727 COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
RyoheiHagimoto 0:0e0631af0305 728 COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
RyoheiHagimoto 0:0e0631af0305 729 COLOR_RGB2YUV_YV12 = 131,
RyoheiHagimoto 0:0e0631af0305 730 COLOR_BGR2YUV_YV12 = 132,
RyoheiHagimoto 0:0e0631af0305 731 COLOR_RGBA2YUV_YV12 = 133,
RyoheiHagimoto 0:0e0631af0305 732 COLOR_BGRA2YUV_YV12 = 134,
RyoheiHagimoto 0:0e0631af0305 733
RyoheiHagimoto 0:0e0631af0305 734 //! Demosaicing
RyoheiHagimoto 0:0e0631af0305 735 COLOR_BayerBG2BGR = 46,
RyoheiHagimoto 0:0e0631af0305 736 COLOR_BayerGB2BGR = 47,
RyoheiHagimoto 0:0e0631af0305 737 COLOR_BayerRG2BGR = 48,
RyoheiHagimoto 0:0e0631af0305 738 COLOR_BayerGR2BGR = 49,
RyoheiHagimoto 0:0e0631af0305 739
RyoheiHagimoto 0:0e0631af0305 740 COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
RyoheiHagimoto 0:0e0631af0305 741 COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
RyoheiHagimoto 0:0e0631af0305 742 COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
RyoheiHagimoto 0:0e0631af0305 743 COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
RyoheiHagimoto 0:0e0631af0305 744
RyoheiHagimoto 0:0e0631af0305 745 COLOR_BayerBG2GRAY = 86,
RyoheiHagimoto 0:0e0631af0305 746 COLOR_BayerGB2GRAY = 87,
RyoheiHagimoto 0:0e0631af0305 747 COLOR_BayerRG2GRAY = 88,
RyoheiHagimoto 0:0e0631af0305 748 COLOR_BayerGR2GRAY = 89,
RyoheiHagimoto 0:0e0631af0305 749
RyoheiHagimoto 0:0e0631af0305 750 //! Demosaicing using Variable Number of Gradients
RyoheiHagimoto 0:0e0631af0305 751 COLOR_BayerBG2BGR_VNG = 62,
RyoheiHagimoto 0:0e0631af0305 752 COLOR_BayerGB2BGR_VNG = 63,
RyoheiHagimoto 0:0e0631af0305 753 COLOR_BayerRG2BGR_VNG = 64,
RyoheiHagimoto 0:0e0631af0305 754 COLOR_BayerGR2BGR_VNG = 65,
RyoheiHagimoto 0:0e0631af0305 755
RyoheiHagimoto 0:0e0631af0305 756 COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
RyoheiHagimoto 0:0e0631af0305 757 COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
RyoheiHagimoto 0:0e0631af0305 758 COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
RyoheiHagimoto 0:0e0631af0305 759 COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
RyoheiHagimoto 0:0e0631af0305 760
RyoheiHagimoto 0:0e0631af0305 761 //! Edge-Aware Demosaicing
RyoheiHagimoto 0:0e0631af0305 762 COLOR_BayerBG2BGR_EA = 135,
RyoheiHagimoto 0:0e0631af0305 763 COLOR_BayerGB2BGR_EA = 136,
RyoheiHagimoto 0:0e0631af0305 764 COLOR_BayerRG2BGR_EA = 137,
RyoheiHagimoto 0:0e0631af0305 765 COLOR_BayerGR2BGR_EA = 138,
RyoheiHagimoto 0:0e0631af0305 766
RyoheiHagimoto 0:0e0631af0305 767 COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
RyoheiHagimoto 0:0e0631af0305 768 COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
RyoheiHagimoto 0:0e0631af0305 769 COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
RyoheiHagimoto 0:0e0631af0305 770 COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
RyoheiHagimoto 0:0e0631af0305 771
RyoheiHagimoto 0:0e0631af0305 772
RyoheiHagimoto 0:0e0631af0305 773 COLOR_COLORCVT_MAX = 139
RyoheiHagimoto 0:0e0631af0305 774 };
RyoheiHagimoto 0:0e0631af0305 775
RyoheiHagimoto 0:0e0631af0305 776 /** types of intersection between rectangles
RyoheiHagimoto 0:0e0631af0305 777 @ingroup imgproc_shape
RyoheiHagimoto 0:0e0631af0305 778 */
RyoheiHagimoto 0:0e0631af0305 779 enum RectanglesIntersectTypes {
RyoheiHagimoto 0:0e0631af0305 780 INTERSECT_NONE = 0, //!< No intersection
RyoheiHagimoto 0:0e0631af0305 781 INTERSECT_PARTIAL = 1, //!< There is a partial intersection
RyoheiHagimoto 0:0e0631af0305 782 INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other
RyoheiHagimoto 0:0e0631af0305 783 };
RyoheiHagimoto 0:0e0631af0305 784
RyoheiHagimoto 0:0e0631af0305 785 //! finds arbitrary template in the grayscale image using Generalized Hough Transform
RyoheiHagimoto 0:0e0631af0305 786 class CV_EXPORTS GeneralizedHough : public Algorithm
RyoheiHagimoto 0:0e0631af0305 787 {
RyoheiHagimoto 0:0e0631af0305 788 public:
RyoheiHagimoto 0:0e0631af0305 789 //! set template to search
RyoheiHagimoto 0:0e0631af0305 790 virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0;
RyoheiHagimoto 0:0e0631af0305 791 virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
RyoheiHagimoto 0:0e0631af0305 792
RyoheiHagimoto 0:0e0631af0305 793 //! find template on image
RyoheiHagimoto 0:0e0631af0305 794 virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0;
RyoheiHagimoto 0:0e0631af0305 795 virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0;
RyoheiHagimoto 0:0e0631af0305 796
RyoheiHagimoto 0:0e0631af0305 797 //! Canny low threshold.
RyoheiHagimoto 0:0e0631af0305 798 virtual void setCannyLowThresh(int cannyLowThresh) = 0;
RyoheiHagimoto 0:0e0631af0305 799 virtual int getCannyLowThresh() const = 0;
RyoheiHagimoto 0:0e0631af0305 800
RyoheiHagimoto 0:0e0631af0305 801 //! Canny high threshold.
RyoheiHagimoto 0:0e0631af0305 802 virtual void setCannyHighThresh(int cannyHighThresh) = 0;
RyoheiHagimoto 0:0e0631af0305 803 virtual int getCannyHighThresh() const = 0;
RyoheiHagimoto 0:0e0631af0305 804
RyoheiHagimoto 0:0e0631af0305 805 //! Minimum distance between the centers of the detected objects.
RyoheiHagimoto 0:0e0631af0305 806 virtual void setMinDist(double minDist) = 0;
RyoheiHagimoto 0:0e0631af0305 807 virtual double getMinDist() const = 0;
RyoheiHagimoto 0:0e0631af0305 808
RyoheiHagimoto 0:0e0631af0305 809 //! Inverse ratio of the accumulator resolution to the image resolution.
RyoheiHagimoto 0:0e0631af0305 810 virtual void setDp(double dp) = 0;
RyoheiHagimoto 0:0e0631af0305 811 virtual double getDp() const = 0;
RyoheiHagimoto 0:0e0631af0305 812
RyoheiHagimoto 0:0e0631af0305 813 //! Maximal size of inner buffers.
RyoheiHagimoto 0:0e0631af0305 814 virtual void setMaxBufferSize(int maxBufferSize) = 0;
RyoheiHagimoto 0:0e0631af0305 815 virtual int getMaxBufferSize() const = 0;
RyoheiHagimoto 0:0e0631af0305 816 };
RyoheiHagimoto 0:0e0631af0305 817
RyoheiHagimoto 0:0e0631af0305 818 //! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
RyoheiHagimoto 0:0e0631af0305 819 //! Detects position only without traslation and rotation
RyoheiHagimoto 0:0e0631af0305 820 class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough
RyoheiHagimoto 0:0e0631af0305 821 {
RyoheiHagimoto 0:0e0631af0305 822 public:
RyoheiHagimoto 0:0e0631af0305 823 //! R-Table levels.
RyoheiHagimoto 0:0e0631af0305 824 virtual void setLevels(int levels) = 0;
RyoheiHagimoto 0:0e0631af0305 825 virtual int getLevels() const = 0;
RyoheiHagimoto 0:0e0631af0305 826
RyoheiHagimoto 0:0e0631af0305 827 //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
RyoheiHagimoto 0:0e0631af0305 828 virtual void setVotesThreshold(int votesThreshold) = 0;
RyoheiHagimoto 0:0e0631af0305 829 virtual int getVotesThreshold() const = 0;
RyoheiHagimoto 0:0e0631af0305 830 };
RyoheiHagimoto 0:0e0631af0305 831
RyoheiHagimoto 0:0e0631af0305 832 //! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
RyoheiHagimoto 0:0e0631af0305 833 //! Detects position, traslation and rotation
RyoheiHagimoto 0:0e0631af0305 834 class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough
RyoheiHagimoto 0:0e0631af0305 835 {
RyoheiHagimoto 0:0e0631af0305 836 public:
RyoheiHagimoto 0:0e0631af0305 837 //! Angle difference in degrees between two points in feature.
RyoheiHagimoto 0:0e0631af0305 838 virtual void setXi(double xi) = 0;
RyoheiHagimoto 0:0e0631af0305 839 virtual double getXi() const = 0;
RyoheiHagimoto 0:0e0631af0305 840
RyoheiHagimoto 0:0e0631af0305 841 //! Feature table levels.
RyoheiHagimoto 0:0e0631af0305 842 virtual void setLevels(int levels) = 0;
RyoheiHagimoto 0:0e0631af0305 843 virtual int getLevels() const = 0;
RyoheiHagimoto 0:0e0631af0305 844
RyoheiHagimoto 0:0e0631af0305 845 //! Maximal difference between angles that treated as equal.
RyoheiHagimoto 0:0e0631af0305 846 virtual void setAngleEpsilon(double angleEpsilon) = 0;
RyoheiHagimoto 0:0e0631af0305 847 virtual double getAngleEpsilon() const = 0;
RyoheiHagimoto 0:0e0631af0305 848
RyoheiHagimoto 0:0e0631af0305 849 //! Minimal rotation angle to detect in degrees.
RyoheiHagimoto 0:0e0631af0305 850 virtual void setMinAngle(double minAngle) = 0;
RyoheiHagimoto 0:0e0631af0305 851 virtual double getMinAngle() const = 0;
RyoheiHagimoto 0:0e0631af0305 852
RyoheiHagimoto 0:0e0631af0305 853 //! Maximal rotation angle to detect in degrees.
RyoheiHagimoto 0:0e0631af0305 854 virtual void setMaxAngle(double maxAngle) = 0;
RyoheiHagimoto 0:0e0631af0305 855 virtual double getMaxAngle() const = 0;
RyoheiHagimoto 0:0e0631af0305 856
RyoheiHagimoto 0:0e0631af0305 857 //! Angle step in degrees.
RyoheiHagimoto 0:0e0631af0305 858 virtual void setAngleStep(double angleStep) = 0;
RyoheiHagimoto 0:0e0631af0305 859 virtual double getAngleStep() const = 0;
RyoheiHagimoto 0:0e0631af0305 860
RyoheiHagimoto 0:0e0631af0305 861 //! Angle votes threshold.
RyoheiHagimoto 0:0e0631af0305 862 virtual void setAngleThresh(int angleThresh) = 0;
RyoheiHagimoto 0:0e0631af0305 863 virtual int getAngleThresh() const = 0;
RyoheiHagimoto 0:0e0631af0305 864
RyoheiHagimoto 0:0e0631af0305 865 //! Minimal scale to detect.
RyoheiHagimoto 0:0e0631af0305 866 virtual void setMinScale(double minScale) = 0;
RyoheiHagimoto 0:0e0631af0305 867 virtual double getMinScale() const = 0;
RyoheiHagimoto 0:0e0631af0305 868
RyoheiHagimoto 0:0e0631af0305 869 //! Maximal scale to detect.
RyoheiHagimoto 0:0e0631af0305 870 virtual void setMaxScale(double maxScale) = 0;
RyoheiHagimoto 0:0e0631af0305 871 virtual double getMaxScale() const = 0;
RyoheiHagimoto 0:0e0631af0305 872
RyoheiHagimoto 0:0e0631af0305 873 //! Scale step.
RyoheiHagimoto 0:0e0631af0305 874 virtual void setScaleStep(double scaleStep) = 0;
RyoheiHagimoto 0:0e0631af0305 875 virtual double getScaleStep() const = 0;
RyoheiHagimoto 0:0e0631af0305 876
RyoheiHagimoto 0:0e0631af0305 877 //! Scale votes threshold.
RyoheiHagimoto 0:0e0631af0305 878 virtual void setScaleThresh(int scaleThresh) = 0;
RyoheiHagimoto 0:0e0631af0305 879 virtual int getScaleThresh() const = 0;
RyoheiHagimoto 0:0e0631af0305 880
RyoheiHagimoto 0:0e0631af0305 881 //! Position votes threshold.
RyoheiHagimoto 0:0e0631af0305 882 virtual void setPosThresh(int posThresh) = 0;
RyoheiHagimoto 0:0e0631af0305 883 virtual int getPosThresh() const = 0;
RyoheiHagimoto 0:0e0631af0305 884 };
RyoheiHagimoto 0:0e0631af0305 885
RyoheiHagimoto 0:0e0631af0305 886
RyoheiHagimoto 0:0e0631af0305 887 class CV_EXPORTS_W CLAHE : public Algorithm
RyoheiHagimoto 0:0e0631af0305 888 {
RyoheiHagimoto 0:0e0631af0305 889 public:
RyoheiHagimoto 0:0e0631af0305 890 CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
RyoheiHagimoto 0:0e0631af0305 891
RyoheiHagimoto 0:0e0631af0305 892 CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
RyoheiHagimoto 0:0e0631af0305 893 CV_WRAP virtual double getClipLimit() const = 0;
RyoheiHagimoto 0:0e0631af0305 894
RyoheiHagimoto 0:0e0631af0305 895 CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0;
RyoheiHagimoto 0:0e0631af0305 896 CV_WRAP virtual Size getTilesGridSize() const = 0;
RyoheiHagimoto 0:0e0631af0305 897
RyoheiHagimoto 0:0e0631af0305 898 CV_WRAP virtual void collectGarbage() = 0;
RyoheiHagimoto 0:0e0631af0305 899 };
RyoheiHagimoto 0:0e0631af0305 900
RyoheiHagimoto 0:0e0631af0305 901
RyoheiHagimoto 0:0e0631af0305 902 //! @addtogroup imgproc_subdiv2d
RyoheiHagimoto 0:0e0631af0305 903 //! @{
RyoheiHagimoto 0:0e0631af0305 904
RyoheiHagimoto 0:0e0631af0305 905 class CV_EXPORTS_W Subdiv2D
RyoheiHagimoto 0:0e0631af0305 906 {
RyoheiHagimoto 0:0e0631af0305 907 public:
RyoheiHagimoto 0:0e0631af0305 908 /** Subdiv2D point location cases */
RyoheiHagimoto 0:0e0631af0305 909 enum { PTLOC_ERROR = -2, //!< Point location error
RyoheiHagimoto 0:0e0631af0305 910 PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect
RyoheiHagimoto 0:0e0631af0305 911 PTLOC_INSIDE = 0, //!< Point inside some facet
RyoheiHagimoto 0:0e0631af0305 912 PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices
RyoheiHagimoto 0:0e0631af0305 913 PTLOC_ON_EDGE = 2 //!< Point on some edge
RyoheiHagimoto 0:0e0631af0305 914 };
RyoheiHagimoto 0:0e0631af0305 915
RyoheiHagimoto 0:0e0631af0305 916 /** Subdiv2D edge type navigation (see: getEdge()) */
RyoheiHagimoto 0:0e0631af0305 917 enum { NEXT_AROUND_ORG = 0x00,
RyoheiHagimoto 0:0e0631af0305 918 NEXT_AROUND_DST = 0x22,
RyoheiHagimoto 0:0e0631af0305 919 PREV_AROUND_ORG = 0x11,
RyoheiHagimoto 0:0e0631af0305 920 PREV_AROUND_DST = 0x33,
RyoheiHagimoto 0:0e0631af0305 921 NEXT_AROUND_LEFT = 0x13,
RyoheiHagimoto 0:0e0631af0305 922 NEXT_AROUND_RIGHT = 0x31,
RyoheiHagimoto 0:0e0631af0305 923 PREV_AROUND_LEFT = 0x20,
RyoheiHagimoto 0:0e0631af0305 924 PREV_AROUND_RIGHT = 0x02
RyoheiHagimoto 0:0e0631af0305 925 };
RyoheiHagimoto 0:0e0631af0305 926
RyoheiHagimoto 0:0e0631af0305 927 /** creates an empty Subdiv2D object.
RyoheiHagimoto 0:0e0631af0305 928 To create a new empty Delaunay subdivision you need to use the initDelaunay() function.
RyoheiHagimoto 0:0e0631af0305 929 */
RyoheiHagimoto 0:0e0631af0305 930 CV_WRAP Subdiv2D();
RyoheiHagimoto 0:0e0631af0305 931
RyoheiHagimoto 0:0e0631af0305 932 /** @overload
RyoheiHagimoto 0:0e0631af0305 933
RyoheiHagimoto 0:0e0631af0305 934 @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision.
RyoheiHagimoto 0:0e0631af0305 935
RyoheiHagimoto 0:0e0631af0305 936 The function creates an empty Delaunay subdivision where 2D points can be added using the function
RyoheiHagimoto 0:0e0631af0305 937 insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime
RyoheiHagimoto 0:0e0631af0305 938 error is raised.
RyoheiHagimoto 0:0e0631af0305 939 */
RyoheiHagimoto 0:0e0631af0305 940 CV_WRAP Subdiv2D(Rect rect);
RyoheiHagimoto 0:0e0631af0305 941
RyoheiHagimoto 0:0e0631af0305 942 /** @brief Creates a new empty Delaunay subdivision
RyoheiHagimoto 0:0e0631af0305 943
RyoheiHagimoto 0:0e0631af0305 944 @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision.
RyoheiHagimoto 0:0e0631af0305 945
RyoheiHagimoto 0:0e0631af0305 946 */
RyoheiHagimoto 0:0e0631af0305 947 CV_WRAP void initDelaunay(Rect rect);
RyoheiHagimoto 0:0e0631af0305 948
RyoheiHagimoto 0:0e0631af0305 949 /** @brief Insert a single point into a Delaunay triangulation.
RyoheiHagimoto 0:0e0631af0305 950
RyoheiHagimoto 0:0e0631af0305 951 @param pt – Point to insert.
RyoheiHagimoto 0:0e0631af0305 952
RyoheiHagimoto 0:0e0631af0305 953 The function inserts a single point into a subdivision and modifies the subdivision topology
RyoheiHagimoto 0:0e0631af0305 954 appropriately. If a point with the same coordinates exists already, no new point is added.
RyoheiHagimoto 0:0e0631af0305 955 @returns the ID of the point.
RyoheiHagimoto 0:0e0631af0305 956
RyoheiHagimoto 0:0e0631af0305 957 @note If the point is outside of the triangulation specified rect a runtime error is raised.
RyoheiHagimoto 0:0e0631af0305 958 */
RyoheiHagimoto 0:0e0631af0305 959 CV_WRAP int insert(Point2f pt);
RyoheiHagimoto 0:0e0631af0305 960
RyoheiHagimoto 0:0e0631af0305 961 /** @brief Insert multiple points into a Delaunay triangulation.
RyoheiHagimoto 0:0e0631af0305 962
RyoheiHagimoto 0:0e0631af0305 963 @param ptvec – Points to insert.
RyoheiHagimoto 0:0e0631af0305 964
RyoheiHagimoto 0:0e0631af0305 965 The function inserts a vector of points into a subdivision and modifies the subdivision topology
RyoheiHagimoto 0:0e0631af0305 966 appropriately.
RyoheiHagimoto 0:0e0631af0305 967 */
RyoheiHagimoto 0:0e0631af0305 968 CV_WRAP void insert(const std::vector<Point2f>& ptvec);
RyoheiHagimoto 0:0e0631af0305 969
RyoheiHagimoto 0:0e0631af0305 970 /** @brief Returns the location of a point within a Delaunay triangulation.
RyoheiHagimoto 0:0e0631af0305 971
RyoheiHagimoto 0:0e0631af0305 972 @param pt – Point to locate.
RyoheiHagimoto 0:0e0631af0305 973 @param edge – Output edge that the point belongs to or is located to the right of it.
RyoheiHagimoto 0:0e0631af0305 974 @param vertex – Optional output vertex the input point coincides with.
RyoheiHagimoto 0:0e0631af0305 975
RyoheiHagimoto 0:0e0631af0305 976 The function locates the input point within the subdivision and gives one of the triangle edges
RyoheiHagimoto 0:0e0631af0305 977 or vertices.
RyoheiHagimoto 0:0e0631af0305 978
RyoheiHagimoto 0:0e0631af0305 979 @returns an integer which specify one of the following five cases for point location:
RyoheiHagimoto 0:0e0631af0305 980 - The point falls into some facet. The function returns PTLOC_INSIDE and edge will contain one of
RyoheiHagimoto 0:0e0631af0305 981 edges of the facet.
RyoheiHagimoto 0:0e0631af0305 982 - The point falls onto the edge. The function returns PTLOC_ON_EDGE and edge will contain this edge.
RyoheiHagimoto 0:0e0631af0305 983 - The point coincides with one of the subdivision vertices. The function returns PTLOC_VERTEX and
RyoheiHagimoto 0:0e0631af0305 984 vertex will contain a pointer to the vertex.
RyoheiHagimoto 0:0e0631af0305 985 - The point is outside the subdivision reference rectangle. The function returns PTLOC_OUTSIDE_RECT
RyoheiHagimoto 0:0e0631af0305 986 and no pointers are filled.
RyoheiHagimoto 0:0e0631af0305 987 - One of input arguments is invalid. A runtime error is raised or, if silent or “parent” error
RyoheiHagimoto 0:0e0631af0305 988 processing mode is selected, CV_PTLOC_ERROR is returnd.
RyoheiHagimoto 0:0e0631af0305 989 */
RyoheiHagimoto 0:0e0631af0305 990 CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
RyoheiHagimoto 0:0e0631af0305 991
RyoheiHagimoto 0:0e0631af0305 992 /** @brief Finds the subdivision vertex closest to the given point.
RyoheiHagimoto 0:0e0631af0305 993
RyoheiHagimoto 0:0e0631af0305 994 @param pt – Input point.
RyoheiHagimoto 0:0e0631af0305 995 @param nearestPt – Output subdivision vertex point.
RyoheiHagimoto 0:0e0631af0305 996
RyoheiHagimoto 0:0e0631af0305 997 The function is another function that locates the input point within the subdivision. It finds the
RyoheiHagimoto 0:0e0631af0305 998 subdivision vertex that is the closest to the input point. It is not necessarily one of vertices
RyoheiHagimoto 0:0e0631af0305 999 of the facet containing the input point, though the facet (located using locate() ) is used as a
RyoheiHagimoto 0:0e0631af0305 1000 starting point.
RyoheiHagimoto 0:0e0631af0305 1001
RyoheiHagimoto 0:0e0631af0305 1002 @returns vertex ID.
RyoheiHagimoto 0:0e0631af0305 1003 */
RyoheiHagimoto 0:0e0631af0305 1004 CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0);
RyoheiHagimoto 0:0e0631af0305 1005
RyoheiHagimoto 0:0e0631af0305 1006 /** @brief Returns a list of all edges.
RyoheiHagimoto 0:0e0631af0305 1007
RyoheiHagimoto 0:0e0631af0305 1008 @param edgeList – Output vector.
RyoheiHagimoto 0:0e0631af0305 1009
RyoheiHagimoto 0:0e0631af0305 1010 The function gives each edge as a 4 numbers vector, where each two are one of the edge
RyoheiHagimoto 0:0e0631af0305 1011 vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3].
RyoheiHagimoto 0:0e0631af0305 1012 */
RyoheiHagimoto 0:0e0631af0305 1013 CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const;
RyoheiHagimoto 0:0e0631af0305 1014
RyoheiHagimoto 0:0e0631af0305 1015 /** @brief Returns a list of the leading edge ID connected to each triangle.
RyoheiHagimoto 0:0e0631af0305 1016
RyoheiHagimoto 0:0e0631af0305 1017 @param leadingEdgeList – Output vector.
RyoheiHagimoto 0:0e0631af0305 1018
RyoheiHagimoto 0:0e0631af0305 1019 The function gives one edge ID for each triangle.
RyoheiHagimoto 0:0e0631af0305 1020 */
RyoheiHagimoto 0:0e0631af0305 1021 CV_WRAP void getLeadingEdgeList(CV_OUT std::vector<int>& leadingEdgeList) const;
RyoheiHagimoto 0:0e0631af0305 1022
RyoheiHagimoto 0:0e0631af0305 1023 /** @brief Returns a list of all triangles.
RyoheiHagimoto 0:0e0631af0305 1024
RyoheiHagimoto 0:0e0631af0305 1025 @param triangleList – Output vector.
RyoheiHagimoto 0:0e0631af0305 1026
RyoheiHagimoto 0:0e0631af0305 1027 The function gives each triangle as a 6 numbers vector, where each two are one of the triangle
RyoheiHagimoto 0:0e0631af0305 1028 vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5].
RyoheiHagimoto 0:0e0631af0305 1029 */
RyoheiHagimoto 0:0e0631af0305 1030 CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const;
RyoheiHagimoto 0:0e0631af0305 1031
RyoheiHagimoto 0:0e0631af0305 1032 /** @brief Returns a list of all Voroni facets.
RyoheiHagimoto 0:0e0631af0305 1033
RyoheiHagimoto 0:0e0631af0305 1034 @param idx – Vector of vertices IDs to consider. For all vertices you can pass empty vector.
RyoheiHagimoto 0:0e0631af0305 1035 @param facetList – Output vector of the Voroni facets.
RyoheiHagimoto 0:0e0631af0305 1036 @param facetCenters – Output vector of the Voroni facets center points.
RyoheiHagimoto 0:0e0631af0305 1037
RyoheiHagimoto 0:0e0631af0305 1038 */
RyoheiHagimoto 0:0e0631af0305 1039 CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList,
RyoheiHagimoto 0:0e0631af0305 1040 CV_OUT std::vector<Point2f>& facetCenters);
RyoheiHagimoto 0:0e0631af0305 1041
RyoheiHagimoto 0:0e0631af0305 1042 /** @brief Returns vertex location from vertex ID.
RyoheiHagimoto 0:0e0631af0305 1043
RyoheiHagimoto 0:0e0631af0305 1044 @param vertex – vertex ID.
RyoheiHagimoto 0:0e0631af0305 1045 @param firstEdge – Optional. The first edge ID which is connected to the vertex.
RyoheiHagimoto 0:0e0631af0305 1046 @returns vertex (x,y)
RyoheiHagimoto 0:0e0631af0305 1047
RyoheiHagimoto 0:0e0631af0305 1048 */
RyoheiHagimoto 0:0e0631af0305 1049 CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const;
RyoheiHagimoto 0:0e0631af0305 1050
RyoheiHagimoto 0:0e0631af0305 1051 /** @brief Returns one of the edges related to the given edge.
RyoheiHagimoto 0:0e0631af0305 1052
RyoheiHagimoto 0:0e0631af0305 1053 @param edge – Subdivision edge ID.
RyoheiHagimoto 0:0e0631af0305 1054 @param nextEdgeType - Parameter specifying which of the related edges to return.
RyoheiHagimoto 0:0e0631af0305 1055 The following values are possible:
RyoheiHagimoto 0:0e0631af0305 1056 - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge)
RyoheiHagimoto 0:0e0631af0305 1057 - NEXT_AROUND_DST next around the edge vertex ( eDnext )
RyoheiHagimoto 0:0e0631af0305 1058 - PREV_AROUND_ORG previous around the edge origin (reversed eRnext )
RyoheiHagimoto 0:0e0631af0305 1059 - PREV_AROUND_DST previous around the edge destination (reversed eLnext )
RyoheiHagimoto 0:0e0631af0305 1060 - NEXT_AROUND_LEFT next around the left facet ( eLnext )
RyoheiHagimoto 0:0e0631af0305 1061 - NEXT_AROUND_RIGHT next around the right facet ( eRnext )
RyoheiHagimoto 0:0e0631af0305 1062 - PREV_AROUND_LEFT previous around the left facet (reversed eOnext )
RyoheiHagimoto 0:0e0631af0305 1063 - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext )
RyoheiHagimoto 0:0e0631af0305 1064
RyoheiHagimoto 0:0e0631af0305 1065 ![sample output](pics/quadedge.png)
RyoheiHagimoto 0:0e0631af0305 1066
RyoheiHagimoto 0:0e0631af0305 1067 @returns edge ID related to the input edge.
RyoheiHagimoto 0:0e0631af0305 1068 */
RyoheiHagimoto 0:0e0631af0305 1069 CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
RyoheiHagimoto 0:0e0631af0305 1070
RyoheiHagimoto 0:0e0631af0305 1071 /** @brief Returns next edge around the edge origin.
RyoheiHagimoto 0:0e0631af0305 1072
RyoheiHagimoto 0:0e0631af0305 1073 @param edge – Subdivision edge ID.
RyoheiHagimoto 0:0e0631af0305 1074
RyoheiHagimoto 0:0e0631af0305 1075 @returns an integer which is next edge ID around the edge origin: eOnext on the
RyoheiHagimoto 0:0e0631af0305 1076 picture above if e is the input edge).
RyoheiHagimoto 0:0e0631af0305 1077 */
RyoheiHagimoto 0:0e0631af0305 1078 CV_WRAP int nextEdge(int edge) const;
RyoheiHagimoto 0:0e0631af0305 1079
RyoheiHagimoto 0:0e0631af0305 1080 /** @brief Returns another edge of the same quad-edge.
RyoheiHagimoto 0:0e0631af0305 1081
RyoheiHagimoto 0:0e0631af0305 1082 @param edge – Subdivision edge ID.
RyoheiHagimoto 0:0e0631af0305 1083 @param rotate - Parameter specifying which of the edges of the same quad-edge as the input
RyoheiHagimoto 0:0e0631af0305 1084 one to return. The following values are possible:
RyoheiHagimoto 0:0e0631af0305 1085 - 0 - the input edge ( e on the picture below if e is the input edge)
RyoheiHagimoto 0:0e0631af0305 1086 - 1 - the rotated edge ( eRot )
RyoheiHagimoto 0:0e0631af0305 1087 - 2 - the reversed edge (reversed e (in green))
RyoheiHagimoto 0:0e0631af0305 1088 - 3 - the reversed rotated edge (reversed eRot (in green))
RyoheiHagimoto 0:0e0631af0305 1089
RyoheiHagimoto 0:0e0631af0305 1090 @returns one of the edges ID of the same quad-edge as the input edge.
RyoheiHagimoto 0:0e0631af0305 1091 */
RyoheiHagimoto 0:0e0631af0305 1092 CV_WRAP int rotateEdge(int edge, int rotate) const;
RyoheiHagimoto 0:0e0631af0305 1093 CV_WRAP int symEdge(int edge) const;
RyoheiHagimoto 0:0e0631af0305 1094
RyoheiHagimoto 0:0e0631af0305 1095 /** @brief Returns the edge origin.
RyoheiHagimoto 0:0e0631af0305 1096
RyoheiHagimoto 0:0e0631af0305 1097 @param edge – Subdivision edge ID.
RyoheiHagimoto 0:0e0631af0305 1098 @param orgpt – Output vertex location.
RyoheiHagimoto 0:0e0631af0305 1099
RyoheiHagimoto 0:0e0631af0305 1100 @returns vertex ID.
RyoheiHagimoto 0:0e0631af0305 1101 */
RyoheiHagimoto 0:0e0631af0305 1102 CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const;
RyoheiHagimoto 0:0e0631af0305 1103
RyoheiHagimoto 0:0e0631af0305 1104 /** @brief Returns the edge destination.
RyoheiHagimoto 0:0e0631af0305 1105
RyoheiHagimoto 0:0e0631af0305 1106 @param edge – Subdivision edge ID.
RyoheiHagimoto 0:0e0631af0305 1107 @param dstpt – Output vertex location.
RyoheiHagimoto 0:0e0631af0305 1108
RyoheiHagimoto 0:0e0631af0305 1109 @returns vertex ID.
RyoheiHagimoto 0:0e0631af0305 1110 */
RyoheiHagimoto 0:0e0631af0305 1111 CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const;
RyoheiHagimoto 0:0e0631af0305 1112
RyoheiHagimoto 0:0e0631af0305 1113 protected:
RyoheiHagimoto 0:0e0631af0305 1114 int newEdge();
RyoheiHagimoto 0:0e0631af0305 1115 void deleteEdge(int edge);
RyoheiHagimoto 0:0e0631af0305 1116 int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0);
RyoheiHagimoto 0:0e0631af0305 1117 void deletePoint(int vtx);
RyoheiHagimoto 0:0e0631af0305 1118 void setEdgePoints( int edge, int orgPt, int dstPt );
RyoheiHagimoto 0:0e0631af0305 1119 void splice( int edgeA, int edgeB );
RyoheiHagimoto 0:0e0631af0305 1120 int connectEdges( int edgeA, int edgeB );
RyoheiHagimoto 0:0e0631af0305 1121 void swapEdges( int edge );
RyoheiHagimoto 0:0e0631af0305 1122 int isRightOf(Point2f pt, int edge) const;
RyoheiHagimoto 0:0e0631af0305 1123 void calcVoronoi();
RyoheiHagimoto 0:0e0631af0305 1124 void clearVoronoi();
RyoheiHagimoto 0:0e0631af0305 1125 void checkSubdiv() const;
RyoheiHagimoto 0:0e0631af0305 1126
RyoheiHagimoto 0:0e0631af0305 1127 struct CV_EXPORTS Vertex
RyoheiHagimoto 0:0e0631af0305 1128 {
RyoheiHagimoto 0:0e0631af0305 1129 Vertex();
RyoheiHagimoto 0:0e0631af0305 1130 Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0);
RyoheiHagimoto 0:0e0631af0305 1131 bool isvirtual() const;
RyoheiHagimoto 0:0e0631af0305 1132 bool isfree() const;
RyoheiHagimoto 0:0e0631af0305 1133
RyoheiHagimoto 0:0e0631af0305 1134 int firstEdge;
RyoheiHagimoto 0:0e0631af0305 1135 int type;
RyoheiHagimoto 0:0e0631af0305 1136 Point2f pt;
RyoheiHagimoto 0:0e0631af0305 1137 };
RyoheiHagimoto 0:0e0631af0305 1138
RyoheiHagimoto 0:0e0631af0305 1139 struct CV_EXPORTS QuadEdge
RyoheiHagimoto 0:0e0631af0305 1140 {
RyoheiHagimoto 0:0e0631af0305 1141 QuadEdge();
RyoheiHagimoto 0:0e0631af0305 1142 QuadEdge(int edgeidx);
RyoheiHagimoto 0:0e0631af0305 1143 bool isfree() const;
RyoheiHagimoto 0:0e0631af0305 1144
RyoheiHagimoto 0:0e0631af0305 1145 int next[4];
RyoheiHagimoto 0:0e0631af0305 1146 int pt[4];
RyoheiHagimoto 0:0e0631af0305 1147 };
RyoheiHagimoto 0:0e0631af0305 1148
RyoheiHagimoto 0:0e0631af0305 1149 //! All of the vertices
RyoheiHagimoto 0:0e0631af0305 1150 std::vector<Vertex> vtx;
RyoheiHagimoto 0:0e0631af0305 1151 //! All of the edges
RyoheiHagimoto 0:0e0631af0305 1152 std::vector<QuadEdge> qedges;
RyoheiHagimoto 0:0e0631af0305 1153 int freeQEdge;
RyoheiHagimoto 0:0e0631af0305 1154 int freePoint;
RyoheiHagimoto 0:0e0631af0305 1155 bool validGeometry;
RyoheiHagimoto 0:0e0631af0305 1156
RyoheiHagimoto 0:0e0631af0305 1157 int recentEdge;
RyoheiHagimoto 0:0e0631af0305 1158 //! Top left corner of the bounding rect
RyoheiHagimoto 0:0e0631af0305 1159 Point2f topLeft;
RyoheiHagimoto 0:0e0631af0305 1160 //! Bottom right corner of the bounding rect
RyoheiHagimoto 0:0e0631af0305 1161 Point2f bottomRight;
RyoheiHagimoto 0:0e0631af0305 1162 };
RyoheiHagimoto 0:0e0631af0305 1163
RyoheiHagimoto 0:0e0631af0305 1164 //! @} imgproc_subdiv2d
RyoheiHagimoto 0:0e0631af0305 1165
RyoheiHagimoto 0:0e0631af0305 1166 //! @addtogroup imgproc_feature
RyoheiHagimoto 0:0e0631af0305 1167 //! @{
RyoheiHagimoto 0:0e0631af0305 1168
RyoheiHagimoto 0:0e0631af0305 1169 /** @example lsd_lines.cpp
RyoheiHagimoto 0:0e0631af0305 1170 An example using the LineSegmentDetector
RyoheiHagimoto 0:0e0631af0305 1171 */
RyoheiHagimoto 0:0e0631af0305 1172
RyoheiHagimoto 0:0e0631af0305 1173 /** @brief Line segment detector class
RyoheiHagimoto 0:0e0631af0305 1174
RyoheiHagimoto 0:0e0631af0305 1175 following the algorithm described at @cite Rafael12 .
RyoheiHagimoto 0:0e0631af0305 1176 */
RyoheiHagimoto 0:0e0631af0305 1177 class CV_EXPORTS_W LineSegmentDetector : public Algorithm
RyoheiHagimoto 0:0e0631af0305 1178 {
RyoheiHagimoto 0:0e0631af0305 1179 public:
RyoheiHagimoto 0:0e0631af0305 1180
RyoheiHagimoto 0:0e0631af0305 1181 /** @brief Finds lines in the input image.
RyoheiHagimoto 0:0e0631af0305 1182
RyoheiHagimoto 0:0e0631af0305 1183 This is the output of the default parameters of the algorithm on the above shown image.
RyoheiHagimoto 0:0e0631af0305 1184
RyoheiHagimoto 0:0e0631af0305 1185 ![image](pics/building_lsd.png)
RyoheiHagimoto 0:0e0631af0305 1186
RyoheiHagimoto 0:0e0631af0305 1187 @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
RyoheiHagimoto 0:0e0631af0305 1188 `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
RyoheiHagimoto 0:0e0631af0305 1189 @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where
RyoheiHagimoto 0:0e0631af0305 1190 Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
RyoheiHagimoto 0:0e0631af0305 1191 oriented depending on the gradient.
RyoheiHagimoto 0:0e0631af0305 1192 @param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
RyoheiHagimoto 0:0e0631af0305 1193 @param prec Vector of precisions with which the lines are found.
RyoheiHagimoto 0:0e0631af0305 1194 @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
RyoheiHagimoto 0:0e0631af0305 1195 bigger the value, logarithmically better the detection.
RyoheiHagimoto 0:0e0631af0305 1196 - -1 corresponds to 10 mean false alarms
RyoheiHagimoto 0:0e0631af0305 1197 - 0 corresponds to 1 mean false alarm
RyoheiHagimoto 0:0e0631af0305 1198 - 1 corresponds to 0.1 mean false alarms
RyoheiHagimoto 0:0e0631af0305 1199 This vector will be calculated only when the objects type is LSD_REFINE_ADV.
RyoheiHagimoto 0:0e0631af0305 1200 */
RyoheiHagimoto 0:0e0631af0305 1201 CV_WRAP virtual void detect(InputArray _image, OutputArray _lines,
RyoheiHagimoto 0:0e0631af0305 1202 OutputArray width = noArray(), OutputArray prec = noArray(),
RyoheiHagimoto 0:0e0631af0305 1203 OutputArray nfa = noArray()) = 0;
RyoheiHagimoto 0:0e0631af0305 1204
RyoheiHagimoto 0:0e0631af0305 1205 /** @brief Draws the line segments on a given image.
RyoheiHagimoto 0:0e0631af0305 1206 @param _image The image, where the liens will be drawn. Should be bigger or equal to the image,
RyoheiHagimoto 0:0e0631af0305 1207 where the lines were found.
RyoheiHagimoto 0:0e0631af0305 1208 @param lines A vector of the lines that needed to be drawn.
RyoheiHagimoto 0:0e0631af0305 1209 */
RyoheiHagimoto 0:0e0631af0305 1210 CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0;
RyoheiHagimoto 0:0e0631af0305 1211
RyoheiHagimoto 0:0e0631af0305 1212 /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
RyoheiHagimoto 0:0e0631af0305 1213
RyoheiHagimoto 0:0e0631af0305 1214 @param size The size of the image, where lines1 and lines2 were found.
RyoheiHagimoto 0:0e0631af0305 1215 @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
RyoheiHagimoto 0:0e0631af0305 1216 @param lines2 The second group of lines. They visualized in red color.
RyoheiHagimoto 0:0e0631af0305 1217 @param _image Optional image, where the lines will be drawn. The image should be color(3-channel)
RyoheiHagimoto 0:0e0631af0305 1218 in order for lines1 and lines2 to be drawn in the above mentioned colors.
RyoheiHagimoto 0:0e0631af0305 1219 */
RyoheiHagimoto 0:0e0631af0305 1220 CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0;
RyoheiHagimoto 0:0e0631af0305 1221
RyoheiHagimoto 0:0e0631af0305 1222 virtual ~LineSegmentDetector() { }
RyoheiHagimoto 0:0e0631af0305 1223 };
RyoheiHagimoto 0:0e0631af0305 1224
RyoheiHagimoto 0:0e0631af0305 1225 /** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
RyoheiHagimoto 0:0e0631af0305 1226
RyoheiHagimoto 0:0e0631af0305 1227 The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
RyoheiHagimoto 0:0e0631af0305 1228 to edit those, as to tailor it for their own application.
RyoheiHagimoto 0:0e0631af0305 1229
RyoheiHagimoto 0:0e0631af0305 1230 @param _refine The way found lines will be refined, see cv::LineSegmentDetectorModes
RyoheiHagimoto 0:0e0631af0305 1231 @param _scale The scale of the image that will be used to find the lines. Range (0..1].
RyoheiHagimoto 0:0e0631af0305 1232 @param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
RyoheiHagimoto 0:0e0631af0305 1233 @param _quant Bound to the quantization error on the gradient norm.
RyoheiHagimoto 0:0e0631af0305 1234 @param _ang_th Gradient angle tolerance in degrees.
RyoheiHagimoto 0:0e0631af0305 1235 @param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advancent refinement
RyoheiHagimoto 0:0e0631af0305 1236 is chosen.
RyoheiHagimoto 0:0e0631af0305 1237 @param _density_th Minimal density of aligned region points in the enclosing rectangle.
RyoheiHagimoto 0:0e0631af0305 1238 @param _n_bins Number of bins in pseudo-ordering of gradient modulus.
RyoheiHagimoto 0:0e0631af0305 1239 */
RyoheiHagimoto 0:0e0631af0305 1240 CV_EXPORTS_W Ptr<LineSegmentDetector> createLineSegmentDetector(
RyoheiHagimoto 0:0e0631af0305 1241 int _refine = LSD_REFINE_STD, double _scale = 0.8,
RyoheiHagimoto 0:0e0631af0305 1242 double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5,
RyoheiHagimoto 0:0e0631af0305 1243 double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024);
RyoheiHagimoto 0:0e0631af0305 1244
RyoheiHagimoto 0:0e0631af0305 1245 //! @} imgproc_feature
RyoheiHagimoto 0:0e0631af0305 1246
RyoheiHagimoto 0:0e0631af0305 1247 //! @addtogroup imgproc_filter
RyoheiHagimoto 0:0e0631af0305 1248 //! @{
RyoheiHagimoto 0:0e0631af0305 1249
RyoheiHagimoto 0:0e0631af0305 1250 /** @brief Returns Gaussian filter coefficients.
RyoheiHagimoto 0:0e0631af0305 1251
RyoheiHagimoto 0:0e0631af0305 1252 The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
RyoheiHagimoto 0:0e0631af0305 1253 coefficients:
RyoheiHagimoto 0:0e0631af0305 1254
RyoheiHagimoto 0:0e0631af0305 1255 \f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
RyoheiHagimoto 0:0e0631af0305 1256
RyoheiHagimoto 0:0e0631af0305 1257 where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
RyoheiHagimoto 0:0e0631af0305 1258
RyoheiHagimoto 0:0e0631af0305 1259 Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
RyoheiHagimoto 0:0e0631af0305 1260 smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
RyoheiHagimoto 0:0e0631af0305 1261 You may also use the higher-level GaussianBlur.
RyoheiHagimoto 0:0e0631af0305 1262 @param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
RyoheiHagimoto 0:0e0631af0305 1263 @param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
RyoheiHagimoto 0:0e0631af0305 1264 `sigma = 0.3\*((ksize-1)\*0.5 - 1) + 0.8`.
RyoheiHagimoto 0:0e0631af0305 1265 @param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
RyoheiHagimoto 0:0e0631af0305 1266 @sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
RyoheiHagimoto 0:0e0631af0305 1267 */
RyoheiHagimoto 0:0e0631af0305 1268 CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F );
RyoheiHagimoto 0:0e0631af0305 1269
RyoheiHagimoto 0:0e0631af0305 1270 /** @brief Returns filter coefficients for computing spatial image derivatives.
RyoheiHagimoto 0:0e0631af0305 1271
RyoheiHagimoto 0:0e0631af0305 1272 The function computes and returns the filter coefficients for spatial image derivatives. When
RyoheiHagimoto 0:0e0631af0305 1273 `ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see cv::Scharr). Otherwise, Sobel
RyoheiHagimoto 0:0e0631af0305 1274 kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to
RyoheiHagimoto 0:0e0631af0305 1275
RyoheiHagimoto 0:0e0631af0305 1276 @param kx Output matrix of row filter coefficients. It has the type ktype .
RyoheiHagimoto 0:0e0631af0305 1277 @param ky Output matrix of column filter coefficients. It has the type ktype .
RyoheiHagimoto 0:0e0631af0305 1278 @param dx Derivative order in respect of x.
RyoheiHagimoto 0:0e0631af0305 1279 @param dy Derivative order in respect of y.
RyoheiHagimoto 0:0e0631af0305 1280 @param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
RyoheiHagimoto 0:0e0631af0305 1281 @param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
RyoheiHagimoto 0:0e0631af0305 1282 Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
RyoheiHagimoto 0:0e0631af0305 1283 going to filter floating-point images, you are likely to use the normalized kernels. But if you
RyoheiHagimoto 0:0e0631af0305 1284 compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
RyoheiHagimoto 0:0e0631af0305 1285 all the fractional bits, you may want to set normalize=false .
RyoheiHagimoto 0:0e0631af0305 1286 @param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
RyoheiHagimoto 0:0e0631af0305 1287 */
RyoheiHagimoto 0:0e0631af0305 1288 CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
RyoheiHagimoto 0:0e0631af0305 1289 int dx, int dy, int ksize,
RyoheiHagimoto 0:0e0631af0305 1290 bool normalize = false, int ktype = CV_32F );
RyoheiHagimoto 0:0e0631af0305 1291
RyoheiHagimoto 0:0e0631af0305 1292 /** @brief Returns Gabor filter coefficients.
RyoheiHagimoto 0:0e0631af0305 1293
RyoheiHagimoto 0:0e0631af0305 1294 For more details about gabor filter equations and parameters, see: [Gabor
RyoheiHagimoto 0:0e0631af0305 1295 Filter](http://en.wikipedia.org/wiki/Gabor_filter).
RyoheiHagimoto 0:0e0631af0305 1296
RyoheiHagimoto 0:0e0631af0305 1297 @param ksize Size of the filter returned.
RyoheiHagimoto 0:0e0631af0305 1298 @param sigma Standard deviation of the gaussian envelope.
RyoheiHagimoto 0:0e0631af0305 1299 @param theta Orientation of the normal to the parallel stripes of a Gabor function.
RyoheiHagimoto 0:0e0631af0305 1300 @param lambd Wavelength of the sinusoidal factor.
RyoheiHagimoto 0:0e0631af0305 1301 @param gamma Spatial aspect ratio.
RyoheiHagimoto 0:0e0631af0305 1302 @param psi Phase offset.
RyoheiHagimoto 0:0e0631af0305 1303 @param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
RyoheiHagimoto 0:0e0631af0305 1304 */
RyoheiHagimoto 0:0e0631af0305 1305 CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
RyoheiHagimoto 0:0e0631af0305 1306 double gamma, double psi = CV_PI*0.5, int ktype = CV_64F );
RyoheiHagimoto 0:0e0631af0305 1307
RyoheiHagimoto 0:0e0631af0305 1308 //! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
RyoheiHagimoto 0:0e0631af0305 1309 static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
RyoheiHagimoto 0:0e0631af0305 1310
RyoheiHagimoto 0:0e0631af0305 1311 /** @brief Returns a structuring element of the specified size and shape for morphological operations.
RyoheiHagimoto 0:0e0631af0305 1312
RyoheiHagimoto 0:0e0631af0305 1313 The function constructs and returns the structuring element that can be further passed to cv::erode,
RyoheiHagimoto 0:0e0631af0305 1314 cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
RyoheiHagimoto 0:0e0631af0305 1315 the structuring element.
RyoheiHagimoto 0:0e0631af0305 1316
RyoheiHagimoto 0:0e0631af0305 1317 @param shape Element shape that could be one of cv::MorphShapes
RyoheiHagimoto 0:0e0631af0305 1318 @param ksize Size of the structuring element.
RyoheiHagimoto 0:0e0631af0305 1319 @param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
RyoheiHagimoto 0:0e0631af0305 1320 anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
RyoheiHagimoto 0:0e0631af0305 1321 position. In other cases the anchor just regulates how much the result of the morphological
RyoheiHagimoto 0:0e0631af0305 1322 operation is shifted.
RyoheiHagimoto 0:0e0631af0305 1323 */
RyoheiHagimoto 0:0e0631af0305 1324 CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
RyoheiHagimoto 0:0e0631af0305 1325
RyoheiHagimoto 0:0e0631af0305 1326 /** @brief Blurs an image using the median filter.
RyoheiHagimoto 0:0e0631af0305 1327
RyoheiHagimoto 0:0e0631af0305 1328 The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
RyoheiHagimoto 0:0e0631af0305 1329 \texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
RyoheiHagimoto 0:0e0631af0305 1330 In-place operation is supported.
RyoheiHagimoto 0:0e0631af0305 1331
RyoheiHagimoto 0:0e0631af0305 1332 @note The median filter uses BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1333
RyoheiHagimoto 0:0e0631af0305 1334 @param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
RyoheiHagimoto 0:0e0631af0305 1335 CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
RyoheiHagimoto 0:0e0631af0305 1336 @param dst destination array of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 1337 @param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
RyoheiHagimoto 0:0e0631af0305 1338 @sa bilateralFilter, blur, boxFilter, GaussianBlur
RyoheiHagimoto 0:0e0631af0305 1339 */
RyoheiHagimoto 0:0e0631af0305 1340 CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
RyoheiHagimoto 0:0e0631af0305 1341
RyoheiHagimoto 0:0e0631af0305 1342 /** @brief Blurs an image using a Gaussian filter.
RyoheiHagimoto 0:0e0631af0305 1343
RyoheiHagimoto 0:0e0631af0305 1344 The function convolves the source image with the specified Gaussian kernel. In-place filtering is
RyoheiHagimoto 0:0e0631af0305 1345 supported.
RyoheiHagimoto 0:0e0631af0305 1346
RyoheiHagimoto 0:0e0631af0305 1347 @param src input image; the image can have any number of channels, which are processed
RyoheiHagimoto 0:0e0631af0305 1348 independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 1349 @param dst output image of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 1350 @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
RyoheiHagimoto 0:0e0631af0305 1351 positive and odd. Or, they can be zero's and then they are computed from sigma.
RyoheiHagimoto 0:0e0631af0305 1352 @param sigmaX Gaussian kernel standard deviation in X direction.
RyoheiHagimoto 0:0e0631af0305 1353 @param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
RyoheiHagimoto 0:0e0631af0305 1354 equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
RyoheiHagimoto 0:0e0631af0305 1355 respectively (see cv::getGaussianKernel for details); to fully control the result regardless of
RyoheiHagimoto 0:0e0631af0305 1356 possible future modifications of all this semantics, it is recommended to specify all of ksize,
RyoheiHagimoto 0:0e0631af0305 1357 sigmaX, and sigmaY.
RyoheiHagimoto 0:0e0631af0305 1358 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1359
RyoheiHagimoto 0:0e0631af0305 1360 @sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
RyoheiHagimoto 0:0e0631af0305 1361 */
RyoheiHagimoto 0:0e0631af0305 1362 CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize,
RyoheiHagimoto 0:0e0631af0305 1363 double sigmaX, double sigmaY = 0,
RyoheiHagimoto 0:0e0631af0305 1364 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1365
RyoheiHagimoto 0:0e0631af0305 1366 /** @brief Applies the bilateral filter to an image.
RyoheiHagimoto 0:0e0631af0305 1367
RyoheiHagimoto 0:0e0631af0305 1368 The function applies bilateral filtering to the input image, as described in
RyoheiHagimoto 0:0e0631af0305 1369 http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
RyoheiHagimoto 0:0e0631af0305 1370 bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
RyoheiHagimoto 0:0e0631af0305 1371 very slow compared to most filters.
RyoheiHagimoto 0:0e0631af0305 1372
RyoheiHagimoto 0:0e0631af0305 1373 _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
RyoheiHagimoto 0:0e0631af0305 1374 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
RyoheiHagimoto 0:0e0631af0305 1375 strong effect, making the image look "cartoonish".
RyoheiHagimoto 0:0e0631af0305 1376
RyoheiHagimoto 0:0e0631af0305 1377 _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
RyoheiHagimoto 0:0e0631af0305 1378 applications, and perhaps d=9 for offline applications that need heavy noise filtering.
RyoheiHagimoto 0:0e0631af0305 1379
RyoheiHagimoto 0:0e0631af0305 1380 This filter does not work inplace.
RyoheiHagimoto 0:0e0631af0305 1381 @param src Source 8-bit or floating-point, 1-channel or 3-channel image.
RyoheiHagimoto 0:0e0631af0305 1382 @param dst Destination image of the same size and type as src .
RyoheiHagimoto 0:0e0631af0305 1383 @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
RyoheiHagimoto 0:0e0631af0305 1384 it is computed from sigmaSpace.
RyoheiHagimoto 0:0e0631af0305 1385 @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
RyoheiHagimoto 0:0e0631af0305 1386 farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
RyoheiHagimoto 0:0e0631af0305 1387 in larger areas of semi-equal color.
RyoheiHagimoto 0:0e0631af0305 1388 @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
RyoheiHagimoto 0:0e0631af0305 1389 farther pixels will influence each other as long as their colors are close enough (see sigmaColor
RyoheiHagimoto 0:0e0631af0305 1390 ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
RyoheiHagimoto 0:0e0631af0305 1391 proportional to sigmaSpace.
RyoheiHagimoto 0:0e0631af0305 1392 @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1393 */
RyoheiHagimoto 0:0e0631af0305 1394 CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
RyoheiHagimoto 0:0e0631af0305 1395 double sigmaColor, double sigmaSpace,
RyoheiHagimoto 0:0e0631af0305 1396 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1397
RyoheiHagimoto 0:0e0631af0305 1398 /** @brief Blurs an image using the box filter.
RyoheiHagimoto 0:0e0631af0305 1399
RyoheiHagimoto 0:0e0631af0305 1400 The function smoothes an image using the kernel:
RyoheiHagimoto 0:0e0631af0305 1401
RyoheiHagimoto 0:0e0631af0305 1402 \f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 1403
RyoheiHagimoto 0:0e0631af0305 1404 where
RyoheiHagimoto 0:0e0631af0305 1405
RyoheiHagimoto 0:0e0631af0305 1406 \f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 1407
RyoheiHagimoto 0:0e0631af0305 1408 Unnormalized box filter is useful for computing various integral characteristics over each pixel
RyoheiHagimoto 0:0e0631af0305 1409 neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
RyoheiHagimoto 0:0e0631af0305 1410 algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral.
RyoheiHagimoto 0:0e0631af0305 1411
RyoheiHagimoto 0:0e0631af0305 1412 @param src input image.
RyoheiHagimoto 0:0e0631af0305 1413 @param dst output image of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 1414 @param ddepth the output image depth (-1 to use src.depth()).
RyoheiHagimoto 0:0e0631af0305 1415 @param ksize blurring kernel size.
RyoheiHagimoto 0:0e0631af0305 1416 @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
RyoheiHagimoto 0:0e0631af0305 1417 center.
RyoheiHagimoto 0:0e0631af0305 1418 @param normalize flag, specifying whether the kernel is normalized by its area or not.
RyoheiHagimoto 0:0e0631af0305 1419 @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1420 @sa blur, bilateralFilter, GaussianBlur, medianBlur, integral
RyoheiHagimoto 0:0e0631af0305 1421 */
RyoheiHagimoto 0:0e0631af0305 1422 CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1423 Size ksize, Point anchor = Point(-1,-1),
RyoheiHagimoto 0:0e0631af0305 1424 bool normalize = true,
RyoheiHagimoto 0:0e0631af0305 1425 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1426
RyoheiHagimoto 0:0e0631af0305 1427 /** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
RyoheiHagimoto 0:0e0631af0305 1428
RyoheiHagimoto 0:0e0631af0305 1429 For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
RyoheiHagimoto 0:0e0631af0305 1430 pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
RyoheiHagimoto 0:0e0631af0305 1431
RyoheiHagimoto 0:0e0631af0305 1432 The unnormalized square box filter can be useful in computing local image statistics such as the the local
RyoheiHagimoto 0:0e0631af0305 1433 variance and standard deviation around the neighborhood of a pixel.
RyoheiHagimoto 0:0e0631af0305 1434
RyoheiHagimoto 0:0e0631af0305 1435 @param _src input image
RyoheiHagimoto 0:0e0631af0305 1436 @param _dst output image of the same size and type as _src
RyoheiHagimoto 0:0e0631af0305 1437 @param ddepth the output image depth (-1 to use src.depth())
RyoheiHagimoto 0:0e0631af0305 1438 @param ksize kernel size
RyoheiHagimoto 0:0e0631af0305 1439 @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
RyoheiHagimoto 0:0e0631af0305 1440 center.
RyoheiHagimoto 0:0e0631af0305 1441 @param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
RyoheiHagimoto 0:0e0631af0305 1442 @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1443 @sa boxFilter
RyoheiHagimoto 0:0e0631af0305 1444 */
RyoheiHagimoto 0:0e0631af0305 1445 CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1446 Size ksize, Point anchor = Point(-1, -1),
RyoheiHagimoto 0:0e0631af0305 1447 bool normalize = true,
RyoheiHagimoto 0:0e0631af0305 1448 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1449
RyoheiHagimoto 0:0e0631af0305 1450 /** @brief Blurs an image using the normalized box filter.
RyoheiHagimoto 0:0e0631af0305 1451
RyoheiHagimoto 0:0e0631af0305 1452 The function smoothes an image using the kernel:
RyoheiHagimoto 0:0e0631af0305 1453
RyoheiHagimoto 0:0e0631af0305 1454 \f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 1455
RyoheiHagimoto 0:0e0631af0305 1456 The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(),
RyoheiHagimoto 0:0e0631af0305 1457 anchor, true, borderType)`.
RyoheiHagimoto 0:0e0631af0305 1458
RyoheiHagimoto 0:0e0631af0305 1459 @param src input image; it can have any number of channels, which are processed independently, but
RyoheiHagimoto 0:0e0631af0305 1460 the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 1461 @param dst output image of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 1462 @param ksize blurring kernel size.
RyoheiHagimoto 0:0e0631af0305 1463 @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
RyoheiHagimoto 0:0e0631af0305 1464 center.
RyoheiHagimoto 0:0e0631af0305 1465 @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1466 @sa boxFilter, bilateralFilter, GaussianBlur, medianBlur
RyoheiHagimoto 0:0e0631af0305 1467 */
RyoheiHagimoto 0:0e0631af0305 1468 CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 1469 Size ksize, Point anchor = Point(-1,-1),
RyoheiHagimoto 0:0e0631af0305 1470 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1471
RyoheiHagimoto 0:0e0631af0305 1472 /** @brief Convolves an image with the kernel.
RyoheiHagimoto 0:0e0631af0305 1473
RyoheiHagimoto 0:0e0631af0305 1474 The function applies an arbitrary linear filter to an image. In-place operation is supported. When
RyoheiHagimoto 0:0e0631af0305 1475 the aperture is partially outside the image, the function interpolates outlier pixel values
RyoheiHagimoto 0:0e0631af0305 1476 according to the specified border mode.
RyoheiHagimoto 0:0e0631af0305 1477
RyoheiHagimoto 0:0e0631af0305 1478 The function does actually compute correlation, not the convolution:
RyoheiHagimoto 0:0e0631af0305 1479
RyoheiHagimoto 0:0e0631af0305 1480 \f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
RyoheiHagimoto 0:0e0631af0305 1481
RyoheiHagimoto 0:0e0631af0305 1482 That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
RyoheiHagimoto 0:0e0631af0305 1483 the kernel using cv::flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
RyoheiHagimoto 0:0e0631af0305 1484 anchor.y - 1)`.
RyoheiHagimoto 0:0e0631af0305 1485
RyoheiHagimoto 0:0e0631af0305 1486 The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or
RyoheiHagimoto 0:0e0631af0305 1487 larger) and the direct algorithm for small kernels.
RyoheiHagimoto 0:0e0631af0305 1488
RyoheiHagimoto 0:0e0631af0305 1489 @param src input image.
RyoheiHagimoto 0:0e0631af0305 1490 @param dst output image of the same size and the same number of channels as src.
RyoheiHagimoto 0:0e0631af0305 1491 @param ddepth desired depth of the destination image, see @ref filter_depths "combinations"
RyoheiHagimoto 0:0e0631af0305 1492 @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
RyoheiHagimoto 0:0e0631af0305 1493 matrix; if you want to apply different kernels to different channels, split the image into
RyoheiHagimoto 0:0e0631af0305 1494 separate color planes using split and process them individually.
RyoheiHagimoto 0:0e0631af0305 1495 @param anchor anchor of the kernel that indicates the relative position of a filtered point within
RyoheiHagimoto 0:0e0631af0305 1496 the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
RyoheiHagimoto 0:0e0631af0305 1497 is at the kernel center.
RyoheiHagimoto 0:0e0631af0305 1498 @param delta optional value added to the filtered pixels before storing them in dst.
RyoheiHagimoto 0:0e0631af0305 1499 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1500 @sa sepFilter2D, dft, matchTemplate
RyoheiHagimoto 0:0e0631af0305 1501 */
RyoheiHagimoto 0:0e0631af0305 1502 CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1503 InputArray kernel, Point anchor = Point(-1,-1),
RyoheiHagimoto 0:0e0631af0305 1504 double delta = 0, int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1505
RyoheiHagimoto 0:0e0631af0305 1506 /** @brief Applies a separable linear filter to an image.
RyoheiHagimoto 0:0e0631af0305 1507
RyoheiHagimoto 0:0e0631af0305 1508 The function applies a separable linear filter to the image. That is, first, every row of src is
RyoheiHagimoto 0:0e0631af0305 1509 filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
RyoheiHagimoto 0:0e0631af0305 1510 kernel kernelY. The final result shifted by delta is stored in dst .
RyoheiHagimoto 0:0e0631af0305 1511
RyoheiHagimoto 0:0e0631af0305 1512 @param src Source image.
RyoheiHagimoto 0:0e0631af0305 1513 @param dst Destination image of the same size and the same number of channels as src .
RyoheiHagimoto 0:0e0631af0305 1514 @param ddepth Destination image depth, see @ref filter_depths "combinations"
RyoheiHagimoto 0:0e0631af0305 1515 @param kernelX Coefficients for filtering each row.
RyoheiHagimoto 0:0e0631af0305 1516 @param kernelY Coefficients for filtering each column.
RyoheiHagimoto 0:0e0631af0305 1517 @param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
RyoheiHagimoto 0:0e0631af0305 1518 is at the kernel center.
RyoheiHagimoto 0:0e0631af0305 1519 @param delta Value added to the filtered results before storing them.
RyoheiHagimoto 0:0e0631af0305 1520 @param borderType Pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1521 @sa filter2D, Sobel, GaussianBlur, boxFilter, blur
RyoheiHagimoto 0:0e0631af0305 1522 */
RyoheiHagimoto 0:0e0631af0305 1523 CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1524 InputArray kernelX, InputArray kernelY,
RyoheiHagimoto 0:0e0631af0305 1525 Point anchor = Point(-1,-1),
RyoheiHagimoto 0:0e0631af0305 1526 double delta = 0, int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1527
RyoheiHagimoto 0:0e0631af0305 1528 /** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1529
RyoheiHagimoto 0:0e0631af0305 1530 In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
RyoheiHagimoto 0:0e0631af0305 1531 calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
RyoheiHagimoto 0:0e0631af0305 1532 kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
RyoheiHagimoto 0:0e0631af0305 1533 or the second x- or y- derivatives.
RyoheiHagimoto 0:0e0631af0305 1534
RyoheiHagimoto 0:0e0631af0305 1535 There is also the special value `ksize = CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
RyoheiHagimoto 0:0e0631af0305 1536 filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
RyoheiHagimoto 0:0e0631af0305 1537
RyoheiHagimoto 0:0e0631af0305 1538 \f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
RyoheiHagimoto 0:0e0631af0305 1539
RyoheiHagimoto 0:0e0631af0305 1540 for the x-derivative, or transposed for the y-derivative.
RyoheiHagimoto 0:0e0631af0305 1541
RyoheiHagimoto 0:0e0631af0305 1542 The function calculates an image derivative by convolving the image with the appropriate kernel:
RyoheiHagimoto 0:0e0631af0305 1543
RyoheiHagimoto 0:0e0631af0305 1544 \f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
RyoheiHagimoto 0:0e0631af0305 1545
RyoheiHagimoto 0:0e0631af0305 1546 The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
RyoheiHagimoto 0:0e0631af0305 1547 resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
RyoheiHagimoto 0:0e0631af0305 1548 or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
RyoheiHagimoto 0:0e0631af0305 1549 case corresponds to a kernel of:
RyoheiHagimoto 0:0e0631af0305 1550
RyoheiHagimoto 0:0e0631af0305 1551 \f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
RyoheiHagimoto 0:0e0631af0305 1552
RyoheiHagimoto 0:0e0631af0305 1553 The second case corresponds to a kernel of:
RyoheiHagimoto 0:0e0631af0305 1554
RyoheiHagimoto 0:0e0631af0305 1555 \f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
RyoheiHagimoto 0:0e0631af0305 1556
RyoheiHagimoto 0:0e0631af0305 1557 @param src input image.
RyoheiHagimoto 0:0e0631af0305 1558 @param dst output image of the same size and the same number of channels as src .
RyoheiHagimoto 0:0e0631af0305 1559 @param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
RyoheiHagimoto 0:0e0631af0305 1560 8-bit input images it will result in truncated derivatives.
RyoheiHagimoto 0:0e0631af0305 1561 @param dx order of the derivative x.
RyoheiHagimoto 0:0e0631af0305 1562 @param dy order of the derivative y.
RyoheiHagimoto 0:0e0631af0305 1563 @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
RyoheiHagimoto 0:0e0631af0305 1564 @param scale optional scale factor for the computed derivative values; by default, no scaling is
RyoheiHagimoto 0:0e0631af0305 1565 applied (see cv::getDerivKernels for details).
RyoheiHagimoto 0:0e0631af0305 1566 @param delta optional delta value that is added to the results prior to storing them in dst.
RyoheiHagimoto 0:0e0631af0305 1567 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1568 @sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
RyoheiHagimoto 0:0e0631af0305 1569 */
RyoheiHagimoto 0:0e0631af0305 1570 CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1571 int dx, int dy, int ksize = 3,
RyoheiHagimoto 0:0e0631af0305 1572 double scale = 1, double delta = 0,
RyoheiHagimoto 0:0e0631af0305 1573 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1574
RyoheiHagimoto 0:0e0631af0305 1575 /** @brief Calculates the first order image derivative in both x and y using a Sobel operator
RyoheiHagimoto 0:0e0631af0305 1576
RyoheiHagimoto 0:0e0631af0305 1577 Equivalent to calling:
RyoheiHagimoto 0:0e0631af0305 1578
RyoheiHagimoto 0:0e0631af0305 1579 @code
RyoheiHagimoto 0:0e0631af0305 1580 Sobel( src, dx, CV_16SC1, 1, 0, 3 );
RyoheiHagimoto 0:0e0631af0305 1581 Sobel( src, dy, CV_16SC1, 0, 1, 3 );
RyoheiHagimoto 0:0e0631af0305 1582 @endcode
RyoheiHagimoto 0:0e0631af0305 1583
RyoheiHagimoto 0:0e0631af0305 1584 @param src input image.
RyoheiHagimoto 0:0e0631af0305 1585 @param dx output image with first-order derivative in x.
RyoheiHagimoto 0:0e0631af0305 1586 @param dy output image with first-order derivative in y.
RyoheiHagimoto 0:0e0631af0305 1587 @param ksize size of Sobel kernel. It must be 3.
RyoheiHagimoto 0:0e0631af0305 1588 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1589
RyoheiHagimoto 0:0e0631af0305 1590 @sa Sobel
RyoheiHagimoto 0:0e0631af0305 1591 */
RyoheiHagimoto 0:0e0631af0305 1592
RyoheiHagimoto 0:0e0631af0305 1593 CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx,
RyoheiHagimoto 0:0e0631af0305 1594 OutputArray dy, int ksize = 3,
RyoheiHagimoto 0:0e0631af0305 1595 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1596
RyoheiHagimoto 0:0e0631af0305 1597 /** @brief Calculates the first x- or y- image derivative using Scharr operator.
RyoheiHagimoto 0:0e0631af0305 1598
RyoheiHagimoto 0:0e0631af0305 1599 The function computes the first x- or y- spatial image derivative using the Scharr operator. The
RyoheiHagimoto 0:0e0631af0305 1600 call
RyoheiHagimoto 0:0e0631af0305 1601
RyoheiHagimoto 0:0e0631af0305 1602 \f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
RyoheiHagimoto 0:0e0631af0305 1603
RyoheiHagimoto 0:0e0631af0305 1604 is equivalent to
RyoheiHagimoto 0:0e0631af0305 1605
RyoheiHagimoto 0:0e0631af0305 1606 \f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\f]
RyoheiHagimoto 0:0e0631af0305 1607
RyoheiHagimoto 0:0e0631af0305 1608 @param src input image.
RyoheiHagimoto 0:0e0631af0305 1609 @param dst output image of the same size and the same number of channels as src.
RyoheiHagimoto 0:0e0631af0305 1610 @param ddepth output image depth, see @ref filter_depths "combinations"
RyoheiHagimoto 0:0e0631af0305 1611 @param dx order of the derivative x.
RyoheiHagimoto 0:0e0631af0305 1612 @param dy order of the derivative y.
RyoheiHagimoto 0:0e0631af0305 1613 @param scale optional scale factor for the computed derivative values; by default, no scaling is
RyoheiHagimoto 0:0e0631af0305 1614 applied (see getDerivKernels for details).
RyoheiHagimoto 0:0e0631af0305 1615 @param delta optional delta value that is added to the results prior to storing them in dst.
RyoheiHagimoto 0:0e0631af0305 1616 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1617 @sa cartToPolar
RyoheiHagimoto 0:0e0631af0305 1618 */
RyoheiHagimoto 0:0e0631af0305 1619 CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1620 int dx, int dy, double scale = 1, double delta = 0,
RyoheiHagimoto 0:0e0631af0305 1621 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1622
RyoheiHagimoto 0:0e0631af0305 1623 /** @example laplace.cpp
RyoheiHagimoto 0:0e0631af0305 1624 An example using Laplace transformations for edge detection
RyoheiHagimoto 0:0e0631af0305 1625 */
RyoheiHagimoto 0:0e0631af0305 1626
RyoheiHagimoto 0:0e0631af0305 1627 /** @brief Calculates the Laplacian of an image.
RyoheiHagimoto 0:0e0631af0305 1628
RyoheiHagimoto 0:0e0631af0305 1629 The function calculates the Laplacian of the source image by adding up the second x and y
RyoheiHagimoto 0:0e0631af0305 1630 derivatives calculated using the Sobel operator:
RyoheiHagimoto 0:0e0631af0305 1631
RyoheiHagimoto 0:0e0631af0305 1632 \f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
RyoheiHagimoto 0:0e0631af0305 1633
RyoheiHagimoto 0:0e0631af0305 1634 This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
RyoheiHagimoto 0:0e0631af0305 1635 with the following \f$3 \times 3\f$ aperture:
RyoheiHagimoto 0:0e0631af0305 1636
RyoheiHagimoto 0:0e0631af0305 1637 \f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
RyoheiHagimoto 0:0e0631af0305 1638
RyoheiHagimoto 0:0e0631af0305 1639 @param src Source image.
RyoheiHagimoto 0:0e0631af0305 1640 @param dst Destination image of the same size and the same number of channels as src .
RyoheiHagimoto 0:0e0631af0305 1641 @param ddepth Desired depth of the destination image.
RyoheiHagimoto 0:0e0631af0305 1642 @param ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for
RyoheiHagimoto 0:0e0631af0305 1643 details. The size must be positive and odd.
RyoheiHagimoto 0:0e0631af0305 1644 @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
RyoheiHagimoto 0:0e0631af0305 1645 applied. See getDerivKernels for details.
RyoheiHagimoto 0:0e0631af0305 1646 @param delta Optional delta value that is added to the results prior to storing them in dst .
RyoheiHagimoto 0:0e0631af0305 1647 @param borderType Pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 1648 @sa Sobel, Scharr
RyoheiHagimoto 0:0e0631af0305 1649 */
RyoheiHagimoto 0:0e0631af0305 1650 CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
RyoheiHagimoto 0:0e0631af0305 1651 int ksize = 1, double scale = 1, double delta = 0,
RyoheiHagimoto 0:0e0631af0305 1652 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1653
RyoheiHagimoto 0:0e0631af0305 1654 //! @} imgproc_filter
RyoheiHagimoto 0:0e0631af0305 1655
RyoheiHagimoto 0:0e0631af0305 1656 //! @addtogroup imgproc_feature
RyoheiHagimoto 0:0e0631af0305 1657 //! @{
RyoheiHagimoto 0:0e0631af0305 1658
RyoheiHagimoto 0:0e0631af0305 1659 /** @example edge.cpp
RyoheiHagimoto 0:0e0631af0305 1660 An example on using the canny edge detector
RyoheiHagimoto 0:0e0631af0305 1661 */
RyoheiHagimoto 0:0e0631af0305 1662
RyoheiHagimoto 0:0e0631af0305 1663 /** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
RyoheiHagimoto 0:0e0631af0305 1664
RyoheiHagimoto 0:0e0631af0305 1665 The function finds edges in the input image image and marks them in the output map edges using the
RyoheiHagimoto 0:0e0631af0305 1666 Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
RyoheiHagimoto 0:0e0631af0305 1667 largest value is used to find initial segments of strong edges. See
RyoheiHagimoto 0:0e0631af0305 1668 <http://en.wikipedia.org/wiki/Canny_edge_detector>
RyoheiHagimoto 0:0e0631af0305 1669
RyoheiHagimoto 0:0e0631af0305 1670 @param image 8-bit input image.
RyoheiHagimoto 0:0e0631af0305 1671 @param edges output edge map; single channels 8-bit image, which has the same size as image .
RyoheiHagimoto 0:0e0631af0305 1672 @param threshold1 first threshold for the hysteresis procedure.
RyoheiHagimoto 0:0e0631af0305 1673 @param threshold2 second threshold for the hysteresis procedure.
RyoheiHagimoto 0:0e0631af0305 1674 @param apertureSize aperture size for the Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1675 @param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
RyoheiHagimoto 0:0e0631af0305 1676 \f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
RyoheiHagimoto 0:0e0631af0305 1677 L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
RyoheiHagimoto 0:0e0631af0305 1678 L2gradient=false ).
RyoheiHagimoto 0:0e0631af0305 1679 */
RyoheiHagimoto 0:0e0631af0305 1680 CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
RyoheiHagimoto 0:0e0631af0305 1681 double threshold1, double threshold2,
RyoheiHagimoto 0:0e0631af0305 1682 int apertureSize = 3, bool L2gradient = false );
RyoheiHagimoto 0:0e0631af0305 1683
RyoheiHagimoto 0:0e0631af0305 1684 /** \overload
RyoheiHagimoto 0:0e0631af0305 1685
RyoheiHagimoto 0:0e0631af0305 1686 Finds edges in an image using the Canny algorithm with custom image gradient.
RyoheiHagimoto 0:0e0631af0305 1687
RyoheiHagimoto 0:0e0631af0305 1688 @param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
RyoheiHagimoto 0:0e0631af0305 1689 @param dy 16-bit y derivative of input image (same type as dx).
RyoheiHagimoto 0:0e0631af0305 1690 @param edges,threshold1,threshold2,L2gradient See cv::Canny
RyoheiHagimoto 0:0e0631af0305 1691 */
RyoheiHagimoto 0:0e0631af0305 1692 CV_EXPORTS_W void Canny( InputArray dx, InputArray dy,
RyoheiHagimoto 0:0e0631af0305 1693 OutputArray edges,
RyoheiHagimoto 0:0e0631af0305 1694 double threshold1, double threshold2,
RyoheiHagimoto 0:0e0631af0305 1695 bool L2gradient = false );
RyoheiHagimoto 0:0e0631af0305 1696
RyoheiHagimoto 0:0e0631af0305 1697 /** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
RyoheiHagimoto 0:0e0631af0305 1698
RyoheiHagimoto 0:0e0631af0305 1699 The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
RyoheiHagimoto 0:0e0631af0305 1700 eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
RyoheiHagimoto 0:0e0631af0305 1701 of the formulae in the cornerEigenValsAndVecs description.
RyoheiHagimoto 0:0e0631af0305 1702
RyoheiHagimoto 0:0e0631af0305 1703 @param src Input single-channel 8-bit or floating-point image.
RyoheiHagimoto 0:0e0631af0305 1704 @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
RyoheiHagimoto 0:0e0631af0305 1705 src .
RyoheiHagimoto 0:0e0631af0305 1706 @param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
RyoheiHagimoto 0:0e0631af0305 1707 @param ksize Aperture parameter for the Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1708 @param borderType Pixel extrapolation method. See cv::BorderTypes.
RyoheiHagimoto 0:0e0631af0305 1709 */
RyoheiHagimoto 0:0e0631af0305 1710 CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 1711 int blockSize, int ksize = 3,
RyoheiHagimoto 0:0e0631af0305 1712 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1713
RyoheiHagimoto 0:0e0631af0305 1714 /** @brief Harris corner detector.
RyoheiHagimoto 0:0e0631af0305 1715
RyoheiHagimoto 0:0e0631af0305 1716 The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
RyoheiHagimoto 0:0e0631af0305 1717 cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
RyoheiHagimoto 0:0e0631af0305 1718 matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
RyoheiHagimoto 0:0e0631af0305 1719 computes the following characteristic:
RyoheiHagimoto 0:0e0631af0305 1720
RyoheiHagimoto 0:0e0631af0305 1721 \f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
RyoheiHagimoto 0:0e0631af0305 1722
RyoheiHagimoto 0:0e0631af0305 1723 Corners in the image can be found as the local maxima of this response map.
RyoheiHagimoto 0:0e0631af0305 1724
RyoheiHagimoto 0:0e0631af0305 1725 @param src Input single-channel 8-bit or floating-point image.
RyoheiHagimoto 0:0e0631af0305 1726 @param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
RyoheiHagimoto 0:0e0631af0305 1727 size as src .
RyoheiHagimoto 0:0e0631af0305 1728 @param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
RyoheiHagimoto 0:0e0631af0305 1729 @param ksize Aperture parameter for the Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1730 @param k Harris detector free parameter. See the formula below.
RyoheiHagimoto 0:0e0631af0305 1731 @param borderType Pixel extrapolation method. See cv::BorderTypes.
RyoheiHagimoto 0:0e0631af0305 1732 */
RyoheiHagimoto 0:0e0631af0305 1733 CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
RyoheiHagimoto 0:0e0631af0305 1734 int ksize, double k,
RyoheiHagimoto 0:0e0631af0305 1735 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1736
RyoheiHagimoto 0:0e0631af0305 1737 /** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection.
RyoheiHagimoto 0:0e0631af0305 1738
RyoheiHagimoto 0:0e0631af0305 1739 For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize
RyoheiHagimoto 0:0e0631af0305 1740 neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as:
RyoheiHagimoto 0:0e0631af0305 1741
RyoheiHagimoto 0:0e0631af0305 1742 \f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 1743
RyoheiHagimoto 0:0e0631af0305 1744 where the derivatives are computed using the Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1745
RyoheiHagimoto 0:0e0631af0305 1746 After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as
RyoheiHagimoto 0:0e0631af0305 1747 \f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where
RyoheiHagimoto 0:0e0631af0305 1748
RyoheiHagimoto 0:0e0631af0305 1749 - \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$
RyoheiHagimoto 0:0e0631af0305 1750 - \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$
RyoheiHagimoto 0:0e0631af0305 1751 - \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$
RyoheiHagimoto 0:0e0631af0305 1752
RyoheiHagimoto 0:0e0631af0305 1753 The output of the function can be used for robust edge or corner detection.
RyoheiHagimoto 0:0e0631af0305 1754
RyoheiHagimoto 0:0e0631af0305 1755 @param src Input single-channel 8-bit or floating-point image.
RyoheiHagimoto 0:0e0631af0305 1756 @param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
RyoheiHagimoto 0:0e0631af0305 1757 @param blockSize Neighborhood size (see details below).
RyoheiHagimoto 0:0e0631af0305 1758 @param ksize Aperture parameter for the Sobel operator.
RyoheiHagimoto 0:0e0631af0305 1759 @param borderType Pixel extrapolation method. See cv::BorderTypes.
RyoheiHagimoto 0:0e0631af0305 1760
RyoheiHagimoto 0:0e0631af0305 1761 @sa cornerMinEigenVal, cornerHarris, preCornerDetect
RyoheiHagimoto 0:0e0631af0305 1762 */
RyoheiHagimoto 0:0e0631af0305 1763 CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 1764 int blockSize, int ksize,
RyoheiHagimoto 0:0e0631af0305 1765 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1766
RyoheiHagimoto 0:0e0631af0305 1767 /** @brief Calculates a feature map for corner detection.
RyoheiHagimoto 0:0e0631af0305 1768
RyoheiHagimoto 0:0e0631af0305 1769 The function calculates the complex spatial derivative-based function of the source image
RyoheiHagimoto 0:0e0631af0305 1770
RyoheiHagimoto 0:0e0631af0305 1771 \f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f]
RyoheiHagimoto 0:0e0631af0305 1772
RyoheiHagimoto 0:0e0631af0305 1773 where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image
RyoheiHagimoto 0:0e0631af0305 1774 derivatives, and \f$D_{xy}\f$ is the mixed derivative.
RyoheiHagimoto 0:0e0631af0305 1775
RyoheiHagimoto 0:0e0631af0305 1776 The corners can be found as local maximums of the functions, as shown below:
RyoheiHagimoto 0:0e0631af0305 1777 @code
RyoheiHagimoto 0:0e0631af0305 1778 Mat corners, dilated_corners;
RyoheiHagimoto 0:0e0631af0305 1779 preCornerDetect(image, corners, 3);
RyoheiHagimoto 0:0e0631af0305 1780 // dilation with 3x3 rectangular structuring element
RyoheiHagimoto 0:0e0631af0305 1781 dilate(corners, dilated_corners, Mat(), 1);
RyoheiHagimoto 0:0e0631af0305 1782 Mat corner_mask = corners == dilated_corners;
RyoheiHagimoto 0:0e0631af0305 1783 @endcode
RyoheiHagimoto 0:0e0631af0305 1784
RyoheiHagimoto 0:0e0631af0305 1785 @param src Source single-channel 8-bit of floating-point image.
RyoheiHagimoto 0:0e0631af0305 1786 @param dst Output image that has the type CV_32F and the same size as src .
RyoheiHagimoto 0:0e0631af0305 1787 @param ksize %Aperture size of the Sobel .
RyoheiHagimoto 0:0e0631af0305 1788 @param borderType Pixel extrapolation method. See cv::BorderTypes.
RyoheiHagimoto 0:0e0631af0305 1789 */
RyoheiHagimoto 0:0e0631af0305 1790 CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
RyoheiHagimoto 0:0e0631af0305 1791 int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 1792
RyoheiHagimoto 0:0e0631af0305 1793 /** @brief Refines the corner locations.
RyoheiHagimoto 0:0e0631af0305 1794
RyoheiHagimoto 0:0e0631af0305 1795 The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as
RyoheiHagimoto 0:0e0631af0305 1796 shown on the figure below.
RyoheiHagimoto 0:0e0631af0305 1797
RyoheiHagimoto 0:0e0631af0305 1798 ![image](pics/cornersubpix.png)
RyoheiHagimoto 0:0e0631af0305 1799
RyoheiHagimoto 0:0e0631af0305 1800 Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
RyoheiHagimoto 0:0e0631af0305 1801 to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
RyoheiHagimoto 0:0e0631af0305 1802 subject to image and measurement noise. Consider the expression:
RyoheiHagimoto 0:0e0631af0305 1803
RyoheiHagimoto 0:0e0631af0305 1804 \f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f]
RyoheiHagimoto 0:0e0631af0305 1805
RyoheiHagimoto 0:0e0631af0305 1806 where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The
RyoheiHagimoto 0:0e0631af0305 1807 value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up
RyoheiHagimoto 0:0e0631af0305 1808 with \f$\epsilon_i\f$ set to zero:
RyoheiHagimoto 0:0e0631af0305 1809
RyoheiHagimoto 0:0e0631af0305 1810 \f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f]
RyoheiHagimoto 0:0e0631af0305 1811
RyoheiHagimoto 0:0e0631af0305 1812 where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
RyoheiHagimoto 0:0e0631af0305 1813 gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
RyoheiHagimoto 0:0e0631af0305 1814
RyoheiHagimoto 0:0e0631af0305 1815 \f[q = G^{-1} \cdot b\f]
RyoheiHagimoto 0:0e0631af0305 1816
RyoheiHagimoto 0:0e0631af0305 1817 The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
RyoheiHagimoto 0:0e0631af0305 1818 until the center stays within a set threshold.
RyoheiHagimoto 0:0e0631af0305 1819
RyoheiHagimoto 0:0e0631af0305 1820 @param image Input image.
RyoheiHagimoto 0:0e0631af0305 1821 @param corners Initial coordinates of the input corners and refined coordinates provided for
RyoheiHagimoto 0:0e0631af0305 1822 output.
RyoheiHagimoto 0:0e0631af0305 1823 @param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
RyoheiHagimoto 0:0e0631af0305 1824 then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used.
RyoheiHagimoto 0:0e0631af0305 1825 @param zeroZone Half of the size of the dead region in the middle of the search zone over which
RyoheiHagimoto 0:0e0631af0305 1826 the summation in the formula below is not done. It is used sometimes to avoid possible
RyoheiHagimoto 0:0e0631af0305 1827 singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
RyoheiHagimoto 0:0e0631af0305 1828 a size.
RyoheiHagimoto 0:0e0631af0305 1829 @param criteria Criteria for termination of the iterative process of corner refinement. That is,
RyoheiHagimoto 0:0e0631af0305 1830 the process of corner position refinement stops either after criteria.maxCount iterations or when
RyoheiHagimoto 0:0e0631af0305 1831 the corner position moves by less than criteria.epsilon on some iteration.
RyoheiHagimoto 0:0e0631af0305 1832 */
RyoheiHagimoto 0:0e0631af0305 1833 CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
RyoheiHagimoto 0:0e0631af0305 1834 Size winSize, Size zeroZone,
RyoheiHagimoto 0:0e0631af0305 1835 TermCriteria criteria );
RyoheiHagimoto 0:0e0631af0305 1836
RyoheiHagimoto 0:0e0631af0305 1837 /** @brief Determines strong corners on an image.
RyoheiHagimoto 0:0e0631af0305 1838
RyoheiHagimoto 0:0e0631af0305 1839 The function finds the most prominent corners in the image or in the specified image region, as
RyoheiHagimoto 0:0e0631af0305 1840 described in @cite Shi94
RyoheiHagimoto 0:0e0631af0305 1841
RyoheiHagimoto 0:0e0631af0305 1842 - Function calculates the corner quality measure at every source image pixel using the
RyoheiHagimoto 0:0e0631af0305 1843 cornerMinEigenVal or cornerHarris .
RyoheiHagimoto 0:0e0631af0305 1844 - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
RyoheiHagimoto 0:0e0631af0305 1845 retained).
RyoheiHagimoto 0:0e0631af0305 1846 - The corners with the minimal eigenvalue less than
RyoheiHagimoto 0:0e0631af0305 1847 \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
RyoheiHagimoto 0:0e0631af0305 1848 - The remaining corners are sorted by the quality measure in the descending order.
RyoheiHagimoto 0:0e0631af0305 1849 - Function throws away each corner for which there is a stronger corner at a distance less than
RyoheiHagimoto 0:0e0631af0305 1850 maxDistance.
RyoheiHagimoto 0:0e0631af0305 1851
RyoheiHagimoto 0:0e0631af0305 1852 The function can be used to initialize a point-based tracker of an object.
RyoheiHagimoto 0:0e0631af0305 1853
RyoheiHagimoto 0:0e0631af0305 1854 @note If the function is called with different values A and B of the parameter qualityLevel , and
RyoheiHagimoto 0:0e0631af0305 1855 A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
RyoheiHagimoto 0:0e0631af0305 1856 with qualityLevel=B .
RyoheiHagimoto 0:0e0631af0305 1857
RyoheiHagimoto 0:0e0631af0305 1858 @param image Input 8-bit or floating-point 32-bit, single-channel image.
RyoheiHagimoto 0:0e0631af0305 1859 @param corners Output vector of detected corners.
RyoheiHagimoto 0:0e0631af0305 1860 @param maxCorners Maximum number of corners to return. If there are more corners than are found,
RyoheiHagimoto 0:0e0631af0305 1861 the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
RyoheiHagimoto 0:0e0631af0305 1862 and all detected corners are returned.
RyoheiHagimoto 0:0e0631af0305 1863 @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
RyoheiHagimoto 0:0e0631af0305 1864 parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
RyoheiHagimoto 0:0e0631af0305 1865 (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the
RyoheiHagimoto 0:0e0631af0305 1866 quality measure less than the product are rejected. For example, if the best corner has the
RyoheiHagimoto 0:0e0631af0305 1867 quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
RyoheiHagimoto 0:0e0631af0305 1868 less than 15 are rejected.
RyoheiHagimoto 0:0e0631af0305 1869 @param minDistance Minimum possible Euclidean distance between the returned corners.
RyoheiHagimoto 0:0e0631af0305 1870 @param mask Optional region of interest. If the image is not empty (it needs to have the type
RyoheiHagimoto 0:0e0631af0305 1871 CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
RyoheiHagimoto 0:0e0631af0305 1872 @param blockSize Size of an average block for computing a derivative covariation matrix over each
RyoheiHagimoto 0:0e0631af0305 1873 pixel neighborhood. See cornerEigenValsAndVecs .
RyoheiHagimoto 0:0e0631af0305 1874 @param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris)
RyoheiHagimoto 0:0e0631af0305 1875 or cornerMinEigenVal.
RyoheiHagimoto 0:0e0631af0305 1876 @param k Free parameter of the Harris detector.
RyoheiHagimoto 0:0e0631af0305 1877
RyoheiHagimoto 0:0e0631af0305 1878 @sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
RyoheiHagimoto 0:0e0631af0305 1879 */
RyoheiHagimoto 0:0e0631af0305 1880 CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
RyoheiHagimoto 0:0e0631af0305 1881 int maxCorners, double qualityLevel, double minDistance,
RyoheiHagimoto 0:0e0631af0305 1882 InputArray mask = noArray(), int blockSize = 3,
RyoheiHagimoto 0:0e0631af0305 1883 bool useHarrisDetector = false, double k = 0.04 );
RyoheiHagimoto 0:0e0631af0305 1884
RyoheiHagimoto 0:0e0631af0305 1885 /** @example houghlines.cpp
RyoheiHagimoto 0:0e0631af0305 1886 An example using the Hough line detector
RyoheiHagimoto 0:0e0631af0305 1887 */
RyoheiHagimoto 0:0e0631af0305 1888
RyoheiHagimoto 0:0e0631af0305 1889 /** @brief Finds lines in a binary image using the standard Hough transform.
RyoheiHagimoto 0:0e0631af0305 1890
RyoheiHagimoto 0:0e0631af0305 1891 The function implements the standard or standard multi-scale Hough transform algorithm for line
RyoheiHagimoto 0:0e0631af0305 1892 detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
RyoheiHagimoto 0:0e0631af0305 1893 transform.
RyoheiHagimoto 0:0e0631af0305 1894
RyoheiHagimoto 0:0e0631af0305 1895 @param image 8-bit, single-channel binary source image. The image may be modified by the function.
RyoheiHagimoto 0:0e0631af0305 1896 @param lines Output vector of lines. Each line is represented by a two-element vector
RyoheiHagimoto 0:0e0631af0305 1897 \f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
RyoheiHagimoto 0:0e0631af0305 1898 the image). \f$\theta\f$ is the line rotation angle in radians (
RyoheiHagimoto 0:0e0631af0305 1899 \f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
RyoheiHagimoto 0:0e0631af0305 1900 @param rho Distance resolution of the accumulator in pixels.
RyoheiHagimoto 0:0e0631af0305 1901 @param theta Angle resolution of the accumulator in radians.
RyoheiHagimoto 0:0e0631af0305 1902 @param threshold Accumulator threshold parameter. Only those lines are returned that get enough
RyoheiHagimoto 0:0e0631af0305 1903 votes ( \f$>\texttt{threshold}\f$ ).
RyoheiHagimoto 0:0e0631af0305 1904 @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho .
RyoheiHagimoto 0:0e0631af0305 1905 The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
RyoheiHagimoto 0:0e0631af0305 1906 rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these
RyoheiHagimoto 0:0e0631af0305 1907 parameters should be positive.
RyoheiHagimoto 0:0e0631af0305 1908 @param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
RyoheiHagimoto 0:0e0631af0305 1909 @param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
RyoheiHagimoto 0:0e0631af0305 1910 Must fall between 0 and max_theta.
RyoheiHagimoto 0:0e0631af0305 1911 @param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines.
RyoheiHagimoto 0:0e0631af0305 1912 Must fall between min_theta and CV_PI.
RyoheiHagimoto 0:0e0631af0305 1913 */
RyoheiHagimoto 0:0e0631af0305 1914 CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
RyoheiHagimoto 0:0e0631af0305 1915 double rho, double theta, int threshold,
RyoheiHagimoto 0:0e0631af0305 1916 double srn = 0, double stn = 0,
RyoheiHagimoto 0:0e0631af0305 1917 double min_theta = 0, double max_theta = CV_PI );
RyoheiHagimoto 0:0e0631af0305 1918
RyoheiHagimoto 0:0e0631af0305 1919 /** @brief Finds line segments in a binary image using the probabilistic Hough transform.
RyoheiHagimoto 0:0e0631af0305 1920
RyoheiHagimoto 0:0e0631af0305 1921 The function implements the probabilistic Hough transform algorithm for line detection, described
RyoheiHagimoto 0:0e0631af0305 1922 in @cite Matas00
RyoheiHagimoto 0:0e0631af0305 1923
RyoheiHagimoto 0:0e0631af0305 1924 See the line detection example below:
RyoheiHagimoto 0:0e0631af0305 1925
RyoheiHagimoto 0:0e0631af0305 1926 @code
RyoheiHagimoto 0:0e0631af0305 1927 #include <opencv2/imgproc.hpp>
RyoheiHagimoto 0:0e0631af0305 1928 #include <opencv2/highgui.hpp>
RyoheiHagimoto 0:0e0631af0305 1929
RyoheiHagimoto 0:0e0631af0305 1930 using namespace cv;
RyoheiHagimoto 0:0e0631af0305 1931 using namespace std;
RyoheiHagimoto 0:0e0631af0305 1932
RyoheiHagimoto 0:0e0631af0305 1933 int main(int argc, char** argv)
RyoheiHagimoto 0:0e0631af0305 1934 {
RyoheiHagimoto 0:0e0631af0305 1935 Mat src, dst, color_dst;
RyoheiHagimoto 0:0e0631af0305 1936 if( argc != 2 || !(src=imread(argv[1], 0)).data)
RyoheiHagimoto 0:0e0631af0305 1937 return -1;
RyoheiHagimoto 0:0e0631af0305 1938
RyoheiHagimoto 0:0e0631af0305 1939 Canny( src, dst, 50, 200, 3 );
RyoheiHagimoto 0:0e0631af0305 1940 cvtColor( dst, color_dst, COLOR_GRAY2BGR );
RyoheiHagimoto 0:0e0631af0305 1941
RyoheiHagimoto 0:0e0631af0305 1942 #if 0
RyoheiHagimoto 0:0e0631af0305 1943 vector<Vec2f> lines;
RyoheiHagimoto 0:0e0631af0305 1944 HoughLines( dst, lines, 1, CV_PI/180, 100 );
RyoheiHagimoto 0:0e0631af0305 1945
RyoheiHagimoto 0:0e0631af0305 1946 for( size_t i = 0; i < lines.size(); i++ )
RyoheiHagimoto 0:0e0631af0305 1947 {
RyoheiHagimoto 0:0e0631af0305 1948 float rho = lines[i][0];
RyoheiHagimoto 0:0e0631af0305 1949 float theta = lines[i][1];
RyoheiHagimoto 0:0e0631af0305 1950 double a = cos(theta), b = sin(theta);
RyoheiHagimoto 0:0e0631af0305 1951 double x0 = a*rho, y0 = b*rho;
RyoheiHagimoto 0:0e0631af0305 1952 Point pt1(cvRound(x0 + 1000*(-b)),
RyoheiHagimoto 0:0e0631af0305 1953 cvRound(y0 + 1000*(a)));
RyoheiHagimoto 0:0e0631af0305 1954 Point pt2(cvRound(x0 - 1000*(-b)),
RyoheiHagimoto 0:0e0631af0305 1955 cvRound(y0 - 1000*(a)));
RyoheiHagimoto 0:0e0631af0305 1956 line( color_dst, pt1, pt2, Scalar(0,0,255), 3, 8 );
RyoheiHagimoto 0:0e0631af0305 1957 }
RyoheiHagimoto 0:0e0631af0305 1958 #else
RyoheiHagimoto 0:0e0631af0305 1959 vector<Vec4i> lines;
RyoheiHagimoto 0:0e0631af0305 1960 HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 );
RyoheiHagimoto 0:0e0631af0305 1961 for( size_t i = 0; i < lines.size(); i++ )
RyoheiHagimoto 0:0e0631af0305 1962 {
RyoheiHagimoto 0:0e0631af0305 1963 line( color_dst, Point(lines[i][0], lines[i][1]),
RyoheiHagimoto 0:0e0631af0305 1964 Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 );
RyoheiHagimoto 0:0e0631af0305 1965 }
RyoheiHagimoto 0:0e0631af0305 1966 #endif
RyoheiHagimoto 0:0e0631af0305 1967 namedWindow( "Source", 1 );
RyoheiHagimoto 0:0e0631af0305 1968 imshow( "Source", src );
RyoheiHagimoto 0:0e0631af0305 1969
RyoheiHagimoto 0:0e0631af0305 1970 namedWindow( "Detected Lines", 1 );
RyoheiHagimoto 0:0e0631af0305 1971 imshow( "Detected Lines", color_dst );
RyoheiHagimoto 0:0e0631af0305 1972
RyoheiHagimoto 0:0e0631af0305 1973 waitKey(0);
RyoheiHagimoto 0:0e0631af0305 1974 return 0;
RyoheiHagimoto 0:0e0631af0305 1975 }
RyoheiHagimoto 0:0e0631af0305 1976 @endcode
RyoheiHagimoto 0:0e0631af0305 1977 This is a sample picture the function parameters have been tuned for:
RyoheiHagimoto 0:0e0631af0305 1978
RyoheiHagimoto 0:0e0631af0305 1979 ![image](pics/building.jpg)
RyoheiHagimoto 0:0e0631af0305 1980
RyoheiHagimoto 0:0e0631af0305 1981 And this is the output of the above program in case of the probabilistic Hough transform:
RyoheiHagimoto 0:0e0631af0305 1982
RyoheiHagimoto 0:0e0631af0305 1983 ![image](pics/houghp.png)
RyoheiHagimoto 0:0e0631af0305 1984
RyoheiHagimoto 0:0e0631af0305 1985 @param image 8-bit, single-channel binary source image. The image may be modified by the function.
RyoheiHagimoto 0:0e0631af0305 1986 @param lines Output vector of lines. Each line is represented by a 4-element vector
RyoheiHagimoto 0:0e0631af0305 1987 \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
RyoheiHagimoto 0:0e0631af0305 1988 line segment.
RyoheiHagimoto 0:0e0631af0305 1989 @param rho Distance resolution of the accumulator in pixels.
RyoheiHagimoto 0:0e0631af0305 1990 @param theta Angle resolution of the accumulator in radians.
RyoheiHagimoto 0:0e0631af0305 1991 @param threshold Accumulator threshold parameter. Only those lines are returned that get enough
RyoheiHagimoto 0:0e0631af0305 1992 votes ( \f$>\texttt{threshold}\f$ ).
RyoheiHagimoto 0:0e0631af0305 1993 @param minLineLength Minimum line length. Line segments shorter than that are rejected.
RyoheiHagimoto 0:0e0631af0305 1994 @param maxLineGap Maximum allowed gap between points on the same line to link them.
RyoheiHagimoto 0:0e0631af0305 1995
RyoheiHagimoto 0:0e0631af0305 1996 @sa LineSegmentDetector
RyoheiHagimoto 0:0e0631af0305 1997 */
RyoheiHagimoto 0:0e0631af0305 1998 CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
RyoheiHagimoto 0:0e0631af0305 1999 double rho, double theta, int threshold,
RyoheiHagimoto 0:0e0631af0305 2000 double minLineLength = 0, double maxLineGap = 0 );
RyoheiHagimoto 0:0e0631af0305 2001
RyoheiHagimoto 0:0e0631af0305 2002 /** @example houghcircles.cpp
RyoheiHagimoto 0:0e0631af0305 2003 An example using the Hough circle detector
RyoheiHagimoto 0:0e0631af0305 2004 */
RyoheiHagimoto 0:0e0631af0305 2005
RyoheiHagimoto 0:0e0631af0305 2006 /** @brief Finds circles in a grayscale image using the Hough transform.
RyoheiHagimoto 0:0e0631af0305 2007
RyoheiHagimoto 0:0e0631af0305 2008 The function finds circles in a grayscale image using a modification of the Hough transform.
RyoheiHagimoto 0:0e0631af0305 2009
RyoheiHagimoto 0:0e0631af0305 2010 Example: :
RyoheiHagimoto 0:0e0631af0305 2011 @code
RyoheiHagimoto 0:0e0631af0305 2012 #include <opencv2/imgproc.hpp>
RyoheiHagimoto 0:0e0631af0305 2013 #include <opencv2/highgui.hpp>
RyoheiHagimoto 0:0e0631af0305 2014 #include <math.h>
RyoheiHagimoto 0:0e0631af0305 2015
RyoheiHagimoto 0:0e0631af0305 2016 using namespace cv;
RyoheiHagimoto 0:0e0631af0305 2017 using namespace std;
RyoheiHagimoto 0:0e0631af0305 2018
RyoheiHagimoto 0:0e0631af0305 2019 int main(int argc, char** argv)
RyoheiHagimoto 0:0e0631af0305 2020 {
RyoheiHagimoto 0:0e0631af0305 2021 Mat img, gray;
RyoheiHagimoto 0:0e0631af0305 2022 if( argc != 2 || !(img=imread(argv[1], 1)).data)
RyoheiHagimoto 0:0e0631af0305 2023 return -1;
RyoheiHagimoto 0:0e0631af0305 2024 cvtColor(img, gray, COLOR_BGR2GRAY);
RyoheiHagimoto 0:0e0631af0305 2025 // smooth it, otherwise a lot of false circles may be detected
RyoheiHagimoto 0:0e0631af0305 2026 GaussianBlur( gray, gray, Size(9, 9), 2, 2 );
RyoheiHagimoto 0:0e0631af0305 2027 vector<Vec3f> circles;
RyoheiHagimoto 0:0e0631af0305 2028 HoughCircles(gray, circles, HOUGH_GRADIENT,
RyoheiHagimoto 0:0e0631af0305 2029 2, gray.rows/4, 200, 100 );
RyoheiHagimoto 0:0e0631af0305 2030 for( size_t i = 0; i < circles.size(); i++ )
RyoheiHagimoto 0:0e0631af0305 2031 {
RyoheiHagimoto 0:0e0631af0305 2032 Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
RyoheiHagimoto 0:0e0631af0305 2033 int radius = cvRound(circles[i][2]);
RyoheiHagimoto 0:0e0631af0305 2034 // draw the circle center
RyoheiHagimoto 0:0e0631af0305 2035 circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 );
RyoheiHagimoto 0:0e0631af0305 2036 // draw the circle outline
RyoheiHagimoto 0:0e0631af0305 2037 circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 );
RyoheiHagimoto 0:0e0631af0305 2038 }
RyoheiHagimoto 0:0e0631af0305 2039 namedWindow( "circles", 1 );
RyoheiHagimoto 0:0e0631af0305 2040 imshow( "circles", img );
RyoheiHagimoto 0:0e0631af0305 2041
RyoheiHagimoto 0:0e0631af0305 2042 waitKey(0);
RyoheiHagimoto 0:0e0631af0305 2043 return 0;
RyoheiHagimoto 0:0e0631af0305 2044 }
RyoheiHagimoto 0:0e0631af0305 2045 @endcode
RyoheiHagimoto 0:0e0631af0305 2046
RyoheiHagimoto 0:0e0631af0305 2047 @note Usually the function detects the centers of circles well. However, it may fail to find correct
RyoheiHagimoto 0:0e0631af0305 2048 radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if
RyoheiHagimoto 0:0e0631af0305 2049 you know it. Or, you may ignore the returned radius, use only the center, and find the correct
RyoheiHagimoto 0:0e0631af0305 2050 radius using an additional procedure.
RyoheiHagimoto 0:0e0631af0305 2051
RyoheiHagimoto 0:0e0631af0305 2052 @param image 8-bit, single-channel, grayscale input image.
RyoheiHagimoto 0:0e0631af0305 2053 @param circles Output vector of found circles. Each vector is encoded as a 3-element
RyoheiHagimoto 0:0e0631af0305 2054 floating-point vector \f$(x, y, radius)\f$ .
RyoheiHagimoto 0:0e0631af0305 2055 @param method Detection method, see cv::HoughModes. Currently, the only implemented method is HOUGH_GRADIENT
RyoheiHagimoto 0:0e0631af0305 2056 @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
RyoheiHagimoto 0:0e0631af0305 2057 dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
RyoheiHagimoto 0:0e0631af0305 2058 half as big width and height.
RyoheiHagimoto 0:0e0631af0305 2059 @param minDist Minimum distance between the centers of the detected circles. If the parameter is
RyoheiHagimoto 0:0e0631af0305 2060 too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
RyoheiHagimoto 0:0e0631af0305 2061 too large, some circles may be missed.
RyoheiHagimoto 0:0e0631af0305 2062 @param param1 First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher
RyoheiHagimoto 0:0e0631af0305 2063 threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
RyoheiHagimoto 0:0e0631af0305 2064 @param param2 Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the
RyoheiHagimoto 0:0e0631af0305 2065 accumulator threshold for the circle centers at the detection stage. The smaller it is, the more
RyoheiHagimoto 0:0e0631af0305 2066 false circles may be detected. Circles, corresponding to the larger accumulator values, will be
RyoheiHagimoto 0:0e0631af0305 2067 returned first.
RyoheiHagimoto 0:0e0631af0305 2068 @param minRadius Minimum circle radius.
RyoheiHagimoto 0:0e0631af0305 2069 @param maxRadius Maximum circle radius.
RyoheiHagimoto 0:0e0631af0305 2070
RyoheiHagimoto 0:0e0631af0305 2071 @sa fitEllipse, minEnclosingCircle
RyoheiHagimoto 0:0e0631af0305 2072 */
RyoheiHagimoto 0:0e0631af0305 2073 CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
RyoheiHagimoto 0:0e0631af0305 2074 int method, double dp, double minDist,
RyoheiHagimoto 0:0e0631af0305 2075 double param1 = 100, double param2 = 100,
RyoheiHagimoto 0:0e0631af0305 2076 int minRadius = 0, int maxRadius = 0 );
RyoheiHagimoto 0:0e0631af0305 2077
RyoheiHagimoto 0:0e0631af0305 2078 //! @} imgproc_feature
RyoheiHagimoto 0:0e0631af0305 2079
RyoheiHagimoto 0:0e0631af0305 2080 //! @addtogroup imgproc_filter
RyoheiHagimoto 0:0e0631af0305 2081 //! @{
RyoheiHagimoto 0:0e0631af0305 2082
RyoheiHagimoto 0:0e0631af0305 2083 /** @example morphology2.cpp
RyoheiHagimoto 0:0e0631af0305 2084 An example using the morphological operations
RyoheiHagimoto 0:0e0631af0305 2085 */
RyoheiHagimoto 0:0e0631af0305 2086
RyoheiHagimoto 0:0e0631af0305 2087 /** @brief Erodes an image by using a specific structuring element.
RyoheiHagimoto 0:0e0631af0305 2088
RyoheiHagimoto 0:0e0631af0305 2089 The function erodes the source image using the specified structuring element that determines the
RyoheiHagimoto 0:0e0631af0305 2090 shape of a pixel neighborhood over which the minimum is taken:
RyoheiHagimoto 0:0e0631af0305 2091
RyoheiHagimoto 0:0e0631af0305 2092 \f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
RyoheiHagimoto 0:0e0631af0305 2093
RyoheiHagimoto 0:0e0631af0305 2094 The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
RyoheiHagimoto 0:0e0631af0305 2095 case of multi-channel images, each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2096
RyoheiHagimoto 0:0e0631af0305 2097 @param src input image; the number of channels can be arbitrary, but the depth should be one of
RyoheiHagimoto 0:0e0631af0305 2098 CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 2099 @param dst output image of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 2100 @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
RyoheiHagimoto 0:0e0631af0305 2101 structuring element is used. Kernel can be created using getStructuringElement.
RyoheiHagimoto 0:0e0631af0305 2102 @param anchor position of the anchor within the element; default value (-1, -1) means that the
RyoheiHagimoto 0:0e0631af0305 2103 anchor is at the element center.
RyoheiHagimoto 0:0e0631af0305 2104 @param iterations number of times erosion is applied.
RyoheiHagimoto 0:0e0631af0305 2105 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 2106 @param borderValue border value in case of a constant border
RyoheiHagimoto 0:0e0631af0305 2107 @sa dilate, morphologyEx, getStructuringElement
RyoheiHagimoto 0:0e0631af0305 2108 */
RyoheiHagimoto 0:0e0631af0305 2109 CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
RyoheiHagimoto 0:0e0631af0305 2110 Point anchor = Point(-1,-1), int iterations = 1,
RyoheiHagimoto 0:0e0631af0305 2111 int borderType = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2112 const Scalar& borderValue = morphologyDefaultBorderValue() );
RyoheiHagimoto 0:0e0631af0305 2113
RyoheiHagimoto 0:0e0631af0305 2114 /** @brief Dilates an image by using a specific structuring element.
RyoheiHagimoto 0:0e0631af0305 2115
RyoheiHagimoto 0:0e0631af0305 2116 The function dilates the source image using the specified structuring element that determines the
RyoheiHagimoto 0:0e0631af0305 2117 shape of a pixel neighborhood over which the maximum is taken:
RyoheiHagimoto 0:0e0631af0305 2118 \f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
RyoheiHagimoto 0:0e0631af0305 2119
RyoheiHagimoto 0:0e0631af0305 2120 The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
RyoheiHagimoto 0:0e0631af0305 2121 case of multi-channel images, each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2122
RyoheiHagimoto 0:0e0631af0305 2123 @param src input image; the number of channels can be arbitrary, but the depth should be one of
RyoheiHagimoto 0:0e0631af0305 2124 CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 2125 @param dst output image of the same size and type as src\`.
RyoheiHagimoto 0:0e0631af0305 2126 @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
RyoheiHagimoto 0:0e0631af0305 2127 structuring element is used. Kernel can be created using getStructuringElement
RyoheiHagimoto 0:0e0631af0305 2128 @param anchor position of the anchor within the element; default value (-1, -1) means that the
RyoheiHagimoto 0:0e0631af0305 2129 anchor is at the element center.
RyoheiHagimoto 0:0e0631af0305 2130 @param iterations number of times dilation is applied.
RyoheiHagimoto 0:0e0631af0305 2131 @param borderType pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 2132 @param borderValue border value in case of a constant border
RyoheiHagimoto 0:0e0631af0305 2133 @sa erode, morphologyEx, getStructuringElement
RyoheiHagimoto 0:0e0631af0305 2134 */
RyoheiHagimoto 0:0e0631af0305 2135 CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
RyoheiHagimoto 0:0e0631af0305 2136 Point anchor = Point(-1,-1), int iterations = 1,
RyoheiHagimoto 0:0e0631af0305 2137 int borderType = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2138 const Scalar& borderValue = morphologyDefaultBorderValue() );
RyoheiHagimoto 0:0e0631af0305 2139
RyoheiHagimoto 0:0e0631af0305 2140 /** @brief Performs advanced morphological transformations.
RyoheiHagimoto 0:0e0631af0305 2141
RyoheiHagimoto 0:0e0631af0305 2142 The function morphologyEx can perform advanced morphological transformations using an erosion and dilation as
RyoheiHagimoto 0:0e0631af0305 2143 basic operations.
RyoheiHagimoto 0:0e0631af0305 2144
RyoheiHagimoto 0:0e0631af0305 2145 Any of the operations can be done in-place. In case of multi-channel images, each channel is
RyoheiHagimoto 0:0e0631af0305 2146 processed independently.
RyoheiHagimoto 0:0e0631af0305 2147
RyoheiHagimoto 0:0e0631af0305 2148 @param src Source image. The number of channels can be arbitrary. The depth should be one of
RyoheiHagimoto 0:0e0631af0305 2149 CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 2150 @param dst Destination image of the same size and type as source image.
RyoheiHagimoto 0:0e0631af0305 2151 @param op Type of a morphological operation, see cv::MorphTypes
RyoheiHagimoto 0:0e0631af0305 2152 @param kernel Structuring element. It can be created using cv::getStructuringElement.
RyoheiHagimoto 0:0e0631af0305 2153 @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
RyoheiHagimoto 0:0e0631af0305 2154 kernel center.
RyoheiHagimoto 0:0e0631af0305 2155 @param iterations Number of times erosion and dilation are applied.
RyoheiHagimoto 0:0e0631af0305 2156 @param borderType Pixel extrapolation method, see cv::BorderTypes
RyoheiHagimoto 0:0e0631af0305 2157 @param borderValue Border value in case of a constant border. The default value has a special
RyoheiHagimoto 0:0e0631af0305 2158 meaning.
RyoheiHagimoto 0:0e0631af0305 2159 @sa dilate, erode, getStructuringElement
RyoheiHagimoto 0:0e0631af0305 2160 */
RyoheiHagimoto 0:0e0631af0305 2161 CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2162 int op, InputArray kernel,
RyoheiHagimoto 0:0e0631af0305 2163 Point anchor = Point(-1,-1), int iterations = 1,
RyoheiHagimoto 0:0e0631af0305 2164 int borderType = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2165 const Scalar& borderValue = morphologyDefaultBorderValue() );
RyoheiHagimoto 0:0e0631af0305 2166
RyoheiHagimoto 0:0e0631af0305 2167 //! @} imgproc_filter
RyoheiHagimoto 0:0e0631af0305 2168
RyoheiHagimoto 0:0e0631af0305 2169 //! @addtogroup imgproc_transform
RyoheiHagimoto 0:0e0631af0305 2170 //! @{
RyoheiHagimoto 0:0e0631af0305 2171
RyoheiHagimoto 0:0e0631af0305 2172 /** @brief Resizes an image.
RyoheiHagimoto 0:0e0631af0305 2173
RyoheiHagimoto 0:0e0631af0305 2174 The function resize resizes the image src down to or up to the specified size. Note that the
RyoheiHagimoto 0:0e0631af0305 2175 initial dst type or size are not taken into account. Instead, the size and type are derived from
RyoheiHagimoto 0:0e0631af0305 2176 the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
RyoheiHagimoto 0:0e0631af0305 2177 you may call the function as follows:
RyoheiHagimoto 0:0e0631af0305 2178 @code
RyoheiHagimoto 0:0e0631af0305 2179 // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
RyoheiHagimoto 0:0e0631af0305 2180 resize(src, dst, dst.size(), 0, 0, interpolation);
RyoheiHagimoto 0:0e0631af0305 2181 @endcode
RyoheiHagimoto 0:0e0631af0305 2182 If you want to decimate the image by factor of 2 in each direction, you can call the function this
RyoheiHagimoto 0:0e0631af0305 2183 way:
RyoheiHagimoto 0:0e0631af0305 2184 @code
RyoheiHagimoto 0:0e0631af0305 2185 // specify fx and fy and let the function compute the destination image size.
RyoheiHagimoto 0:0e0631af0305 2186 resize(src, dst, Size(), 0.5, 0.5, interpolation);
RyoheiHagimoto 0:0e0631af0305 2187 @endcode
RyoheiHagimoto 0:0e0631af0305 2188 To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to
RyoheiHagimoto 0:0e0631af0305 2189 enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR
RyoheiHagimoto 0:0e0631af0305 2190 (faster but still looks OK).
RyoheiHagimoto 0:0e0631af0305 2191
RyoheiHagimoto 0:0e0631af0305 2192 @param src input image.
RyoheiHagimoto 0:0e0631af0305 2193 @param dst output image; it has the size dsize (when it is non-zero) or the size computed from
RyoheiHagimoto 0:0e0631af0305 2194 src.size(), fx, and fy; the type of dst is the same as of src.
RyoheiHagimoto 0:0e0631af0305 2195 @param dsize output image size; if it equals zero, it is computed as:
RyoheiHagimoto 0:0e0631af0305 2196 \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
RyoheiHagimoto 0:0e0631af0305 2197 Either dsize or both fx and fy must be non-zero.
RyoheiHagimoto 0:0e0631af0305 2198 @param fx scale factor along the horizontal axis; when it equals 0, it is computed as
RyoheiHagimoto 0:0e0631af0305 2199 \f[\texttt{(double)dsize.width/src.cols}\f]
RyoheiHagimoto 0:0e0631af0305 2200 @param fy scale factor along the vertical axis; when it equals 0, it is computed as
RyoheiHagimoto 0:0e0631af0305 2201 \f[\texttt{(double)dsize.height/src.rows}\f]
RyoheiHagimoto 0:0e0631af0305 2202 @param interpolation interpolation method, see cv::InterpolationFlags
RyoheiHagimoto 0:0e0631af0305 2203
RyoheiHagimoto 0:0e0631af0305 2204 @sa warpAffine, warpPerspective, remap
RyoheiHagimoto 0:0e0631af0305 2205 */
RyoheiHagimoto 0:0e0631af0305 2206 CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2207 Size dsize, double fx = 0, double fy = 0,
RyoheiHagimoto 0:0e0631af0305 2208 int interpolation = INTER_LINEAR );
RyoheiHagimoto 0:0e0631af0305 2209
RyoheiHagimoto 0:0e0631af0305 2210 /** @brief Applies an affine transformation to an image.
RyoheiHagimoto 0:0e0631af0305 2211
RyoheiHagimoto 0:0e0631af0305 2212 The function warpAffine transforms the source image using the specified matrix:
RyoheiHagimoto 0:0e0631af0305 2213
RyoheiHagimoto 0:0e0631af0305 2214 \f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f]
RyoheiHagimoto 0:0e0631af0305 2215
RyoheiHagimoto 0:0e0631af0305 2216 when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
RyoheiHagimoto 0:0e0631af0305 2217 with cv::invertAffineTransform and then put in the formula above instead of M. The function cannot
RyoheiHagimoto 0:0e0631af0305 2218 operate in-place.
RyoheiHagimoto 0:0e0631af0305 2219
RyoheiHagimoto 0:0e0631af0305 2220 @param src input image.
RyoheiHagimoto 0:0e0631af0305 2221 @param dst output image that has the size dsize and the same type as src .
RyoheiHagimoto 0:0e0631af0305 2222 @param M \f$2\times 3\f$ transformation matrix.
RyoheiHagimoto 0:0e0631af0305 2223 @param dsize size of the output image.
RyoheiHagimoto 0:0e0631af0305 2224 @param flags combination of interpolation methods (see cv::InterpolationFlags) and the optional
RyoheiHagimoto 0:0e0631af0305 2225 flag WARP_INVERSE_MAP that means that M is the inverse transformation (
RyoheiHagimoto 0:0e0631af0305 2226 \f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
RyoheiHagimoto 0:0e0631af0305 2227 @param borderMode pixel extrapolation method (see cv::BorderTypes); when
RyoheiHagimoto 0:0e0631af0305 2228 borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
RyoheiHagimoto 0:0e0631af0305 2229 the "outliers" in the source image are not modified by the function.
RyoheiHagimoto 0:0e0631af0305 2230 @param borderValue value used in case of a constant border; by default, it is 0.
RyoheiHagimoto 0:0e0631af0305 2231
RyoheiHagimoto 0:0e0631af0305 2232 @sa warpPerspective, resize, remap, getRectSubPix, transform
RyoheiHagimoto 0:0e0631af0305 2233 */
RyoheiHagimoto 0:0e0631af0305 2234 CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2235 InputArray M, Size dsize,
RyoheiHagimoto 0:0e0631af0305 2236 int flags = INTER_LINEAR,
RyoheiHagimoto 0:0e0631af0305 2237 int borderMode = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2238 const Scalar& borderValue = Scalar());
RyoheiHagimoto 0:0e0631af0305 2239
RyoheiHagimoto 0:0e0631af0305 2240 /** @brief Applies a perspective transformation to an image.
RyoheiHagimoto 0:0e0631af0305 2241
RyoheiHagimoto 0:0e0631af0305 2242 The function warpPerspective transforms the source image using the specified matrix:
RyoheiHagimoto 0:0e0631af0305 2243
RyoheiHagimoto 0:0e0631af0305 2244 \f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
RyoheiHagimoto 0:0e0631af0305 2245 \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
RyoheiHagimoto 0:0e0631af0305 2246
RyoheiHagimoto 0:0e0631af0305 2247 when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
RyoheiHagimoto 0:0e0631af0305 2248 and then put in the formula above instead of M. The function cannot operate in-place.
RyoheiHagimoto 0:0e0631af0305 2249
RyoheiHagimoto 0:0e0631af0305 2250 @param src input image.
RyoheiHagimoto 0:0e0631af0305 2251 @param dst output image that has the size dsize and the same type as src .
RyoheiHagimoto 0:0e0631af0305 2252 @param M \f$3\times 3\f$ transformation matrix.
RyoheiHagimoto 0:0e0631af0305 2253 @param dsize size of the output image.
RyoheiHagimoto 0:0e0631af0305 2254 @param flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the
RyoheiHagimoto 0:0e0631af0305 2255 optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation (
RyoheiHagimoto 0:0e0631af0305 2256 \f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
RyoheiHagimoto 0:0e0631af0305 2257 @param borderMode pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE).
RyoheiHagimoto 0:0e0631af0305 2258 @param borderValue value used in case of a constant border; by default, it equals 0.
RyoheiHagimoto 0:0e0631af0305 2259
RyoheiHagimoto 0:0e0631af0305 2260 @sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform
RyoheiHagimoto 0:0e0631af0305 2261 */
RyoheiHagimoto 0:0e0631af0305 2262 CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2263 InputArray M, Size dsize,
RyoheiHagimoto 0:0e0631af0305 2264 int flags = INTER_LINEAR,
RyoheiHagimoto 0:0e0631af0305 2265 int borderMode = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2266 const Scalar& borderValue = Scalar());
RyoheiHagimoto 0:0e0631af0305 2267
RyoheiHagimoto 0:0e0631af0305 2268 /** @brief Applies a generic geometrical transformation to an image.
RyoheiHagimoto 0:0e0631af0305 2269
RyoheiHagimoto 0:0e0631af0305 2270 The function remap transforms the source image using the specified map:
RyoheiHagimoto 0:0e0631af0305 2271
RyoheiHagimoto 0:0e0631af0305 2272 \f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f]
RyoheiHagimoto 0:0e0631af0305 2273
RyoheiHagimoto 0:0e0631af0305 2274 where values of pixels with non-integer coordinates are computed using one of available
RyoheiHagimoto 0:0e0631af0305 2275 interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
RyoheiHagimoto 0:0e0631af0305 2276 in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
RyoheiHagimoto 0:0e0631af0305 2277 \f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
RyoheiHagimoto 0:0e0631af0305 2278 convert from floating to fixed-point representations of a map is that they can yield much faster
RyoheiHagimoto 0:0e0631af0305 2279 (\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
RyoheiHagimoto 0:0e0631af0305 2280 cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
RyoheiHagimoto 0:0e0631af0305 2281
RyoheiHagimoto 0:0e0631af0305 2282 This function cannot operate in-place.
RyoheiHagimoto 0:0e0631af0305 2283
RyoheiHagimoto 0:0e0631af0305 2284 @param src Source image.
RyoheiHagimoto 0:0e0631af0305 2285 @param dst Destination image. It has the same size as map1 and the same type as src .
RyoheiHagimoto 0:0e0631af0305 2286 @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
RyoheiHagimoto 0:0e0631af0305 2287 CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
RyoheiHagimoto 0:0e0631af0305 2288 representation to fixed-point for speed.
RyoheiHagimoto 0:0e0631af0305 2289 @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
RyoheiHagimoto 0:0e0631af0305 2290 if map1 is (x,y) points), respectively.
RyoheiHagimoto 0:0e0631af0305 2291 @param interpolation Interpolation method (see cv::InterpolationFlags). The method INTER_AREA is
RyoheiHagimoto 0:0e0631af0305 2292 not supported by this function.
RyoheiHagimoto 0:0e0631af0305 2293 @param borderMode Pixel extrapolation method (see cv::BorderTypes). When
RyoheiHagimoto 0:0e0631af0305 2294 borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that
RyoheiHagimoto 0:0e0631af0305 2295 corresponds to the "outliers" in the source image are not modified by the function.
RyoheiHagimoto 0:0e0631af0305 2296 @param borderValue Value used in case of a constant border. By default, it is 0.
RyoheiHagimoto 0:0e0631af0305 2297 @note
RyoheiHagimoto 0:0e0631af0305 2298 Due to current implementaion limitations the size of an input and output images should be less than 32767x32767.
RyoheiHagimoto 0:0e0631af0305 2299 */
RyoheiHagimoto 0:0e0631af0305 2300 CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2301 InputArray map1, InputArray map2,
RyoheiHagimoto 0:0e0631af0305 2302 int interpolation, int borderMode = BORDER_CONSTANT,
RyoheiHagimoto 0:0e0631af0305 2303 const Scalar& borderValue = Scalar());
RyoheiHagimoto 0:0e0631af0305 2304
RyoheiHagimoto 0:0e0631af0305 2305 /** @brief Converts image transformation maps from one representation to another.
RyoheiHagimoto 0:0e0631af0305 2306
RyoheiHagimoto 0:0e0631af0305 2307 The function converts a pair of maps for remap from one representation to another. The following
RyoheiHagimoto 0:0e0631af0305 2308 options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
RyoheiHagimoto 0:0e0631af0305 2309 supported:
RyoheiHagimoto 0:0e0631af0305 2310
RyoheiHagimoto 0:0e0631af0305 2311 - \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the
RyoheiHagimoto 0:0e0631af0305 2312 most frequently used conversion operation, in which the original floating-point maps (see remap )
RyoheiHagimoto 0:0e0631af0305 2313 are converted to a more compact and much faster fixed-point representation. The first output array
RyoheiHagimoto 0:0e0631af0305 2314 contains the rounded coordinates and the second array (created only when nninterpolation=false )
RyoheiHagimoto 0:0e0631af0305 2315 contains indices in the interpolation tables.
RyoheiHagimoto 0:0e0631af0305 2316
RyoheiHagimoto 0:0e0631af0305 2317 - \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but
RyoheiHagimoto 0:0e0631af0305 2318 the original maps are stored in one 2-channel matrix.
RyoheiHagimoto 0:0e0631af0305 2319
RyoheiHagimoto 0:0e0631af0305 2320 - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
RyoheiHagimoto 0:0e0631af0305 2321 as the originals.
RyoheiHagimoto 0:0e0631af0305 2322
RyoheiHagimoto 0:0e0631af0305 2323 @param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
RyoheiHagimoto 0:0e0631af0305 2324 @param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
RyoheiHagimoto 0:0e0631af0305 2325 respectively.
RyoheiHagimoto 0:0e0631af0305 2326 @param dstmap1 The first output map that has the type dstmap1type and the same size as src .
RyoheiHagimoto 0:0e0631af0305 2327 @param dstmap2 The second output map.
RyoheiHagimoto 0:0e0631af0305 2328 @param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
RyoheiHagimoto 0:0e0631af0305 2329 CV_32FC2 .
RyoheiHagimoto 0:0e0631af0305 2330 @param nninterpolation Flag indicating whether the fixed-point maps are used for the
RyoheiHagimoto 0:0e0631af0305 2331 nearest-neighbor or for a more complex interpolation.
RyoheiHagimoto 0:0e0631af0305 2332
RyoheiHagimoto 0:0e0631af0305 2333 @sa remap, undistort, initUndistortRectifyMap
RyoheiHagimoto 0:0e0631af0305 2334 */
RyoheiHagimoto 0:0e0631af0305 2335 CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
RyoheiHagimoto 0:0e0631af0305 2336 OutputArray dstmap1, OutputArray dstmap2,
RyoheiHagimoto 0:0e0631af0305 2337 int dstmap1type, bool nninterpolation = false );
RyoheiHagimoto 0:0e0631af0305 2338
RyoheiHagimoto 0:0e0631af0305 2339 /** @brief Calculates an affine matrix of 2D rotation.
RyoheiHagimoto 0:0e0631af0305 2340
RyoheiHagimoto 0:0e0631af0305 2341 The function calculates the following matrix:
RyoheiHagimoto 0:0e0631af0305 2342
RyoheiHagimoto 0:0e0631af0305 2343 \f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2344
RyoheiHagimoto 0:0e0631af0305 2345 where
RyoheiHagimoto 0:0e0631af0305 2346
RyoheiHagimoto 0:0e0631af0305 2347 \f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2348
RyoheiHagimoto 0:0e0631af0305 2349 The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
RyoheiHagimoto 0:0e0631af0305 2350
RyoheiHagimoto 0:0e0631af0305 2351 @param center Center of the rotation in the source image.
RyoheiHagimoto 0:0e0631af0305 2352 @param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
RyoheiHagimoto 0:0e0631af0305 2353 coordinate origin is assumed to be the top-left corner).
RyoheiHagimoto 0:0e0631af0305 2354 @param scale Isotropic scale factor.
RyoheiHagimoto 0:0e0631af0305 2355
RyoheiHagimoto 0:0e0631af0305 2356 @sa getAffineTransform, warpAffine, transform
RyoheiHagimoto 0:0e0631af0305 2357 */
RyoheiHagimoto 0:0e0631af0305 2358 CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale );
RyoheiHagimoto 0:0e0631af0305 2359
RyoheiHagimoto 0:0e0631af0305 2360 //! returns 3x3 perspective transformation for the corresponding 4 point pairs.
RyoheiHagimoto 0:0e0631af0305 2361 CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] );
RyoheiHagimoto 0:0e0631af0305 2362
RyoheiHagimoto 0:0e0631af0305 2363 /** @brief Calculates an affine transform from three pairs of the corresponding points.
RyoheiHagimoto 0:0e0631af0305 2364
RyoheiHagimoto 0:0e0631af0305 2365 The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
RyoheiHagimoto 0:0e0631af0305 2366
RyoheiHagimoto 0:0e0631af0305 2367 \f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2368
RyoheiHagimoto 0:0e0631af0305 2369 where
RyoheiHagimoto 0:0e0631af0305 2370
RyoheiHagimoto 0:0e0631af0305 2371 \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
RyoheiHagimoto 0:0e0631af0305 2372
RyoheiHagimoto 0:0e0631af0305 2373 @param src Coordinates of triangle vertices in the source image.
RyoheiHagimoto 0:0e0631af0305 2374 @param dst Coordinates of the corresponding triangle vertices in the destination image.
RyoheiHagimoto 0:0e0631af0305 2375
RyoheiHagimoto 0:0e0631af0305 2376 @sa warpAffine, transform
RyoheiHagimoto 0:0e0631af0305 2377 */
RyoheiHagimoto 0:0e0631af0305 2378 CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
RyoheiHagimoto 0:0e0631af0305 2379
RyoheiHagimoto 0:0e0631af0305 2380 /** @brief Inverts an affine transformation.
RyoheiHagimoto 0:0e0631af0305 2381
RyoheiHagimoto 0:0e0631af0305 2382 The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
RyoheiHagimoto 0:0e0631af0305 2383
RyoheiHagimoto 0:0e0631af0305 2384 \f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2385
RyoheiHagimoto 0:0e0631af0305 2386 The result is also a \f$2 \times 3\f$ matrix of the same type as M.
RyoheiHagimoto 0:0e0631af0305 2387
RyoheiHagimoto 0:0e0631af0305 2388 @param M Original affine transformation.
RyoheiHagimoto 0:0e0631af0305 2389 @param iM Output reverse affine transformation.
RyoheiHagimoto 0:0e0631af0305 2390 */
RyoheiHagimoto 0:0e0631af0305 2391 CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
RyoheiHagimoto 0:0e0631af0305 2392
RyoheiHagimoto 0:0e0631af0305 2393 /** @brief Calculates a perspective transform from four pairs of the corresponding points.
RyoheiHagimoto 0:0e0631af0305 2394
RyoheiHagimoto 0:0e0631af0305 2395 The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
RyoheiHagimoto 0:0e0631af0305 2396
RyoheiHagimoto 0:0e0631af0305 2397 \f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2398
RyoheiHagimoto 0:0e0631af0305 2399 where
RyoheiHagimoto 0:0e0631af0305 2400
RyoheiHagimoto 0:0e0631af0305 2401 \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
RyoheiHagimoto 0:0e0631af0305 2402
RyoheiHagimoto 0:0e0631af0305 2403 @param src Coordinates of quadrangle vertices in the source image.
RyoheiHagimoto 0:0e0631af0305 2404 @param dst Coordinates of the corresponding quadrangle vertices in the destination image.
RyoheiHagimoto 0:0e0631af0305 2405
RyoheiHagimoto 0:0e0631af0305 2406 @sa findHomography, warpPerspective, perspectiveTransform
RyoheiHagimoto 0:0e0631af0305 2407 */
RyoheiHagimoto 0:0e0631af0305 2408 CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst );
RyoheiHagimoto 0:0e0631af0305 2409
RyoheiHagimoto 0:0e0631af0305 2410 CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
RyoheiHagimoto 0:0e0631af0305 2411
RyoheiHagimoto 0:0e0631af0305 2412 /** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
RyoheiHagimoto 0:0e0631af0305 2413
RyoheiHagimoto 0:0e0631af0305 2414 The function getRectSubPix extracts pixels from src:
RyoheiHagimoto 0:0e0631af0305 2415
RyoheiHagimoto 0:0e0631af0305 2416 \f[dst(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
RyoheiHagimoto 0:0e0631af0305 2417
RyoheiHagimoto 0:0e0631af0305 2418 where the values of the pixels at non-integer coordinates are retrieved using bilinear
RyoheiHagimoto 0:0e0631af0305 2419 interpolation. Every channel of multi-channel images is processed independently. While the center of
RyoheiHagimoto 0:0e0631af0305 2420 the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the
RyoheiHagimoto 0:0e0631af0305 2421 replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of
RyoheiHagimoto 0:0e0631af0305 2422 the image.
RyoheiHagimoto 0:0e0631af0305 2423
RyoheiHagimoto 0:0e0631af0305 2424 @param image Source image.
RyoheiHagimoto 0:0e0631af0305 2425 @param patchSize Size of the extracted patch.
RyoheiHagimoto 0:0e0631af0305 2426 @param center Floating point coordinates of the center of the extracted rectangle within the
RyoheiHagimoto 0:0e0631af0305 2427 source image. The center must be inside the image.
RyoheiHagimoto 0:0e0631af0305 2428 @param patch Extracted patch that has the size patchSize and the same number of channels as src .
RyoheiHagimoto 0:0e0631af0305 2429 @param patchType Depth of the extracted pixels. By default, they have the same depth as src .
RyoheiHagimoto 0:0e0631af0305 2430
RyoheiHagimoto 0:0e0631af0305 2431 @sa warpAffine, warpPerspective
RyoheiHagimoto 0:0e0631af0305 2432 */
RyoheiHagimoto 0:0e0631af0305 2433 CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
RyoheiHagimoto 0:0e0631af0305 2434 Point2f center, OutputArray patch, int patchType = -1 );
RyoheiHagimoto 0:0e0631af0305 2435
RyoheiHagimoto 0:0e0631af0305 2436 /** @example polar_transforms.cpp
RyoheiHagimoto 0:0e0631af0305 2437 An example using the cv::linearPolar and cv::logPolar operations
RyoheiHagimoto 0:0e0631af0305 2438 */
RyoheiHagimoto 0:0e0631af0305 2439
RyoheiHagimoto 0:0e0631af0305 2440 /** @brief Remaps an image to semilog-polar coordinates space.
RyoheiHagimoto 0:0e0631af0305 2441
RyoheiHagimoto 0:0e0631af0305 2442 Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image"):
RyoheiHagimoto 0:0e0631af0305 2443 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2444 dst( \rho , \phi ) = src(x,y) \\
RyoheiHagimoto 0:0e0631af0305 2445 dst.size() \leftarrow src.size()
RyoheiHagimoto 0:0e0631af0305 2446 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2447
RyoheiHagimoto 0:0e0631af0305 2448 where
RyoheiHagimoto 0:0e0631af0305 2449 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2450 I = (dx,dy) = (x - center.x,y - center.y) \\
RyoheiHagimoto 0:0e0631af0305 2451 \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
RyoheiHagimoto 0:0e0631af0305 2452 \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg} \\
RyoheiHagimoto 0:0e0631af0305 2453 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2454
RyoheiHagimoto 0:0e0631af0305 2455 and
RyoheiHagimoto 0:0e0631af0305 2456 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2457 M = src.cols / log_e(maxRadius) \\
RyoheiHagimoto 0:0e0631af0305 2458 Ky = src.rows / 360 \\
RyoheiHagimoto 0:0e0631af0305 2459 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2460
RyoheiHagimoto 0:0e0631af0305 2461 The function emulates the human "foveal" vision and can be used for fast scale and
RyoheiHagimoto 0:0e0631af0305 2462 rotation-invariant template matching, for object tracking and so forth.
RyoheiHagimoto 0:0e0631af0305 2463 @param src Source image
RyoheiHagimoto 0:0e0631af0305 2464 @param dst Destination image. It will have same size and type as src.
RyoheiHagimoto 0:0e0631af0305 2465 @param center The transformation center; where the output precision is maximal
RyoheiHagimoto 0:0e0631af0305 2466 @param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
RyoheiHagimoto 0:0e0631af0305 2467 @param flags A combination of interpolation methods, see cv::InterpolationFlags
RyoheiHagimoto 0:0e0631af0305 2468
RyoheiHagimoto 0:0e0631af0305 2469 @note
RyoheiHagimoto 0:0e0631af0305 2470 - The function can not operate in-place.
RyoheiHagimoto 0:0e0631af0305 2471 - To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
RyoheiHagimoto 0:0e0631af0305 2472 */
RyoheiHagimoto 0:0e0631af0305 2473 CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2474 Point2f center, double M, int flags );
RyoheiHagimoto 0:0e0631af0305 2475
RyoheiHagimoto 0:0e0631af0305 2476 /** @brief Remaps an image to polar coordinates space.
RyoheiHagimoto 0:0e0631af0305 2477
RyoheiHagimoto 0:0e0631af0305 2478 @anchor polar_remaps_reference_image
RyoheiHagimoto 0:0e0631af0305 2479 ![Polar remaps reference](pics/polar_remap_doc.png)
RyoheiHagimoto 0:0e0631af0305 2480
RyoheiHagimoto 0:0e0631af0305 2481 Transform the source image using the following transformation:
RyoheiHagimoto 0:0e0631af0305 2482 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2483 dst( \rho , \phi ) = src(x,y) \\
RyoheiHagimoto 0:0e0631af0305 2484 dst.size() \leftarrow src.size()
RyoheiHagimoto 0:0e0631af0305 2485 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2486
RyoheiHagimoto 0:0e0631af0305 2487 where
RyoheiHagimoto 0:0e0631af0305 2488 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2489 I = (dx,dy) = (x - center.x,y - center.y) \\
RyoheiHagimoto 0:0e0631af0305 2490 \rho = Kx \cdot \texttt{magnitude} (I) ,\\
RyoheiHagimoto 0:0e0631af0305 2491 \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg}
RyoheiHagimoto 0:0e0631af0305 2492 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2493
RyoheiHagimoto 0:0e0631af0305 2494 and
RyoheiHagimoto 0:0e0631af0305 2495 \f[\begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2496 Kx = src.cols / maxRadius \\
RyoheiHagimoto 0:0e0631af0305 2497 Ky = src.rows / 360
RyoheiHagimoto 0:0e0631af0305 2498 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2499
RyoheiHagimoto 0:0e0631af0305 2500
RyoheiHagimoto 0:0e0631af0305 2501 @param src Source image
RyoheiHagimoto 0:0e0631af0305 2502 @param dst Destination image. It will have same size and type as src.
RyoheiHagimoto 0:0e0631af0305 2503 @param center The transformation center;
RyoheiHagimoto 0:0e0631af0305 2504 @param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
RyoheiHagimoto 0:0e0631af0305 2505 @param flags A combination of interpolation methods, see cv::InterpolationFlags
RyoheiHagimoto 0:0e0631af0305 2506
RyoheiHagimoto 0:0e0631af0305 2507 @note
RyoheiHagimoto 0:0e0631af0305 2508 - The function can not operate in-place.
RyoheiHagimoto 0:0e0631af0305 2509 - To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
RyoheiHagimoto 0:0e0631af0305 2510
RyoheiHagimoto 0:0e0631af0305 2511 */
RyoheiHagimoto 0:0e0631af0305 2512 CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2513 Point2f center, double maxRadius, int flags );
RyoheiHagimoto 0:0e0631af0305 2514
RyoheiHagimoto 0:0e0631af0305 2515 //! @} imgproc_transform
RyoheiHagimoto 0:0e0631af0305 2516
RyoheiHagimoto 0:0e0631af0305 2517 //! @addtogroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 2518 //! @{
RyoheiHagimoto 0:0e0631af0305 2519
RyoheiHagimoto 0:0e0631af0305 2520 /** @overload */
RyoheiHagimoto 0:0e0631af0305 2521 CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
RyoheiHagimoto 0:0e0631af0305 2522
RyoheiHagimoto 0:0e0631af0305 2523 /** @overload */
RyoheiHagimoto 0:0e0631af0305 2524 CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
RyoheiHagimoto 0:0e0631af0305 2525 OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
RyoheiHagimoto 0:0e0631af0305 2526
RyoheiHagimoto 0:0e0631af0305 2527 /** @brief Calculates the integral of an image.
RyoheiHagimoto 0:0e0631af0305 2528
RyoheiHagimoto 0:0e0631af0305 2529 The function calculates one or more integral images for the source image as follows:
RyoheiHagimoto 0:0e0631af0305 2530
RyoheiHagimoto 0:0e0631af0305 2531 \f[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\f]
RyoheiHagimoto 0:0e0631af0305 2532
RyoheiHagimoto 0:0e0631af0305 2533 \f[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\f]
RyoheiHagimoto 0:0e0631af0305 2534
RyoheiHagimoto 0:0e0631af0305 2535 \f[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\f]
RyoheiHagimoto 0:0e0631af0305 2536
RyoheiHagimoto 0:0e0631af0305 2537 Using these integral images, you can calculate sum, mean, and standard deviation over a specific
RyoheiHagimoto 0:0e0631af0305 2538 up-right or rotated rectangular region of the image in a constant time, for example:
RyoheiHagimoto 0:0e0631af0305 2539
RyoheiHagimoto 0:0e0631af0305 2540 \f[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f]
RyoheiHagimoto 0:0e0631af0305 2541
RyoheiHagimoto 0:0e0631af0305 2542 It makes possible to do a fast blurring or fast block correlation with a variable window size, for
RyoheiHagimoto 0:0e0631af0305 2543 example. In case of multi-channel images, sums for each channel are accumulated independently.
RyoheiHagimoto 0:0e0631af0305 2544
RyoheiHagimoto 0:0e0631af0305 2545 As a practical example, the next figure shows the calculation of the integral of a straight
RyoheiHagimoto 0:0e0631af0305 2546 rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the
RyoheiHagimoto 0:0e0631af0305 2547 original image are shown, as well as the relative pixels in the integral images sum and tilted .
RyoheiHagimoto 0:0e0631af0305 2548
RyoheiHagimoto 0:0e0631af0305 2549 ![integral calculation example](pics/integral.png)
RyoheiHagimoto 0:0e0631af0305 2550
RyoheiHagimoto 0:0e0631af0305 2551 @param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f).
RyoheiHagimoto 0:0e0631af0305 2552 @param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f).
RyoheiHagimoto 0:0e0631af0305 2553 @param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision
RyoheiHagimoto 0:0e0631af0305 2554 floating-point (64f) array.
RyoheiHagimoto 0:0e0631af0305 2555 @param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with
RyoheiHagimoto 0:0e0631af0305 2556 the same data type as sum.
RyoheiHagimoto 0:0e0631af0305 2557 @param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
RyoheiHagimoto 0:0e0631af0305 2558 CV_64F.
RyoheiHagimoto 0:0e0631af0305 2559 @param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
RyoheiHagimoto 0:0e0631af0305 2560 */
RyoheiHagimoto 0:0e0631af0305 2561 CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
RyoheiHagimoto 0:0e0631af0305 2562 OutputArray sqsum, OutputArray tilted,
RyoheiHagimoto 0:0e0631af0305 2563 int sdepth = -1, int sqdepth = -1 );
RyoheiHagimoto 0:0e0631af0305 2564
RyoheiHagimoto 0:0e0631af0305 2565 //! @} imgproc_misc
RyoheiHagimoto 0:0e0631af0305 2566
RyoheiHagimoto 0:0e0631af0305 2567 //! @addtogroup imgproc_motion
RyoheiHagimoto 0:0e0631af0305 2568 //! @{
RyoheiHagimoto 0:0e0631af0305 2569
RyoheiHagimoto 0:0e0631af0305 2570 /** @brief Adds an image to the accumulator.
RyoheiHagimoto 0:0e0631af0305 2571
RyoheiHagimoto 0:0e0631af0305 2572 The function adds src or some of its elements to dst :
RyoheiHagimoto 0:0e0631af0305 2573
RyoheiHagimoto 0:0e0631af0305 2574 \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
RyoheiHagimoto 0:0e0631af0305 2575
RyoheiHagimoto 0:0e0631af0305 2576 The function supports multi-channel images. Each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2577
RyoheiHagimoto 0:0e0631af0305 2578 The functions accumulate\* can be used, for example, to collect statistics of a scene background
RyoheiHagimoto 0:0e0631af0305 2579 viewed by a still camera and for the further foreground-background segmentation.
RyoheiHagimoto 0:0e0631af0305 2580
RyoheiHagimoto 0:0e0631af0305 2581 @param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
RyoheiHagimoto 0:0e0631af0305 2582 @param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
RyoheiHagimoto 0:0e0631af0305 2583 floating-point.
RyoheiHagimoto 0:0e0631af0305 2584 @param mask Optional operation mask.
RyoheiHagimoto 0:0e0631af0305 2585
RyoheiHagimoto 0:0e0631af0305 2586 @sa accumulateSquare, accumulateProduct, accumulateWeighted
RyoheiHagimoto 0:0e0631af0305 2587 */
RyoheiHagimoto 0:0e0631af0305 2588 CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2589 InputArray mask = noArray() );
RyoheiHagimoto 0:0e0631af0305 2590
RyoheiHagimoto 0:0e0631af0305 2591 /** @brief Adds the square of a source image to the accumulator.
RyoheiHagimoto 0:0e0631af0305 2592
RyoheiHagimoto 0:0e0631af0305 2593 The function adds the input image src or its selected region, raised to a power of 2, to the
RyoheiHagimoto 0:0e0631af0305 2594 accumulator dst :
RyoheiHagimoto 0:0e0631af0305 2595
RyoheiHagimoto 0:0e0631af0305 2596 \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
RyoheiHagimoto 0:0e0631af0305 2597
RyoheiHagimoto 0:0e0631af0305 2598 The function supports multi-channel images. Each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2599
RyoheiHagimoto 0:0e0631af0305 2600 @param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
RyoheiHagimoto 0:0e0631af0305 2601 @param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
RyoheiHagimoto 0:0e0631af0305 2602 floating-point.
RyoheiHagimoto 0:0e0631af0305 2603 @param mask Optional operation mask.
RyoheiHagimoto 0:0e0631af0305 2604
RyoheiHagimoto 0:0e0631af0305 2605 @sa accumulateSquare, accumulateProduct, accumulateWeighted
RyoheiHagimoto 0:0e0631af0305 2606 */
RyoheiHagimoto 0:0e0631af0305 2607 CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2608 InputArray mask = noArray() );
RyoheiHagimoto 0:0e0631af0305 2609
RyoheiHagimoto 0:0e0631af0305 2610 /** @brief Adds the per-element product of two input images to the accumulator.
RyoheiHagimoto 0:0e0631af0305 2611
RyoheiHagimoto 0:0e0631af0305 2612 The function adds the product of two images or their selected regions to the accumulator dst :
RyoheiHagimoto 0:0e0631af0305 2613
RyoheiHagimoto 0:0e0631af0305 2614 \f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
RyoheiHagimoto 0:0e0631af0305 2615
RyoheiHagimoto 0:0e0631af0305 2616 The function supports multi-channel images. Each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2617
RyoheiHagimoto 0:0e0631af0305 2618 @param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
RyoheiHagimoto 0:0e0631af0305 2619 @param src2 Second input image of the same type and the same size as src1 .
RyoheiHagimoto 0:0e0631af0305 2620 @param dst %Accumulator with the same number of channels as input images, 32-bit or 64-bit
RyoheiHagimoto 0:0e0631af0305 2621 floating-point.
RyoheiHagimoto 0:0e0631af0305 2622 @param mask Optional operation mask.
RyoheiHagimoto 0:0e0631af0305 2623
RyoheiHagimoto 0:0e0631af0305 2624 @sa accumulate, accumulateSquare, accumulateWeighted
RyoheiHagimoto 0:0e0631af0305 2625 */
RyoheiHagimoto 0:0e0631af0305 2626 CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
RyoheiHagimoto 0:0e0631af0305 2627 InputOutputArray dst, InputArray mask=noArray() );
RyoheiHagimoto 0:0e0631af0305 2628
RyoheiHagimoto 0:0e0631af0305 2629 /** @brief Updates a running average.
RyoheiHagimoto 0:0e0631af0305 2630
RyoheiHagimoto 0:0e0631af0305 2631 The function calculates the weighted sum of the input image src and the accumulator dst so that dst
RyoheiHagimoto 0:0e0631af0305 2632 becomes a running average of a frame sequence:
RyoheiHagimoto 0:0e0631af0305 2633
RyoheiHagimoto 0:0e0631af0305 2634 \f[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
RyoheiHagimoto 0:0e0631af0305 2635
RyoheiHagimoto 0:0e0631af0305 2636 That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
RyoheiHagimoto 0:0e0631af0305 2637 The function supports multi-channel images. Each channel is processed independently.
RyoheiHagimoto 0:0e0631af0305 2638
RyoheiHagimoto 0:0e0631af0305 2639 @param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
RyoheiHagimoto 0:0e0631af0305 2640 @param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
RyoheiHagimoto 0:0e0631af0305 2641 floating-point.
RyoheiHagimoto 0:0e0631af0305 2642 @param alpha Weight of the input image.
RyoheiHagimoto 0:0e0631af0305 2643 @param mask Optional operation mask.
RyoheiHagimoto 0:0e0631af0305 2644
RyoheiHagimoto 0:0e0631af0305 2645 @sa accumulate, accumulateSquare, accumulateProduct
RyoheiHagimoto 0:0e0631af0305 2646 */
RyoheiHagimoto 0:0e0631af0305 2647 CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2648 double alpha, InputArray mask = noArray() );
RyoheiHagimoto 0:0e0631af0305 2649
RyoheiHagimoto 0:0e0631af0305 2650 /** @brief The function is used to detect translational shifts that occur between two images.
RyoheiHagimoto 0:0e0631af0305 2651
RyoheiHagimoto 0:0e0631af0305 2652 The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
RyoheiHagimoto 0:0e0631af0305 2653 the frequency domain. It can be used for fast image registration as well as motion estimation. For
RyoheiHagimoto 0:0e0631af0305 2654 more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
RyoheiHagimoto 0:0e0631af0305 2655
RyoheiHagimoto 0:0e0631af0305 2656 Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
RyoheiHagimoto 0:0e0631af0305 2657 with getOptimalDFTSize.
RyoheiHagimoto 0:0e0631af0305 2658
RyoheiHagimoto 0:0e0631af0305 2659 The function performs the following equations:
RyoheiHagimoto 0:0e0631af0305 2660 - First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
RyoheiHagimoto 0:0e0631af0305 2661 image to remove possible edge effects. This window is cached until the array size changes to speed
RyoheiHagimoto 0:0e0631af0305 2662 up processing time.
RyoheiHagimoto 0:0e0631af0305 2663 - Next it computes the forward DFTs of each source array:
RyoheiHagimoto 0:0e0631af0305 2664 \f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f]
RyoheiHagimoto 0:0e0631af0305 2665 where \f$\mathcal{F}\f$ is the forward DFT.
RyoheiHagimoto 0:0e0631af0305 2666 - It then computes the cross-power spectrum of each frequency domain array:
RyoheiHagimoto 0:0e0631af0305 2667 \f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f]
RyoheiHagimoto 0:0e0631af0305 2668 - Next the cross-correlation is converted back into the time domain via the inverse DFT:
RyoheiHagimoto 0:0e0631af0305 2669 \f[r = \mathcal{F}^{-1}\{R\}\f]
RyoheiHagimoto 0:0e0631af0305 2670 - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
RyoheiHagimoto 0:0e0631af0305 2671 achieve sub-pixel accuracy.
RyoheiHagimoto 0:0e0631af0305 2672 \f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f]
RyoheiHagimoto 0:0e0631af0305 2673 - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5
RyoheiHagimoto 0:0e0631af0305 2674 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single
RyoheiHagimoto 0:0e0631af0305 2675 peak) and will be smaller when there are multiple peaks.
RyoheiHagimoto 0:0e0631af0305 2676
RyoheiHagimoto 0:0e0631af0305 2677 @param src1 Source floating point array (CV_32FC1 or CV_64FC1)
RyoheiHagimoto 0:0e0631af0305 2678 @param src2 Source floating point array (CV_32FC1 or CV_64FC1)
RyoheiHagimoto 0:0e0631af0305 2679 @param window Floating point array with windowing coefficients to reduce edge effects (optional).
RyoheiHagimoto 0:0e0631af0305 2680 @param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
RyoheiHagimoto 0:0e0631af0305 2681 @returns detected phase shift (sub-pixel) between the two arrays.
RyoheiHagimoto 0:0e0631af0305 2682
RyoheiHagimoto 0:0e0631af0305 2683 @sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
RyoheiHagimoto 0:0e0631af0305 2684 */
RyoheiHagimoto 0:0e0631af0305 2685 CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2,
RyoheiHagimoto 0:0e0631af0305 2686 InputArray window = noArray(), CV_OUT double* response = 0);
RyoheiHagimoto 0:0e0631af0305 2687
RyoheiHagimoto 0:0e0631af0305 2688 /** @brief This function computes a Hanning window coefficients in two dimensions.
RyoheiHagimoto 0:0e0631af0305 2689
RyoheiHagimoto 0:0e0631af0305 2690 See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
RyoheiHagimoto 0:0e0631af0305 2691 for more information.
RyoheiHagimoto 0:0e0631af0305 2692
RyoheiHagimoto 0:0e0631af0305 2693 An example is shown below:
RyoheiHagimoto 0:0e0631af0305 2694 @code
RyoheiHagimoto 0:0e0631af0305 2695 // create hanning window of size 100x100 and type CV_32F
RyoheiHagimoto 0:0e0631af0305 2696 Mat hann;
RyoheiHagimoto 0:0e0631af0305 2697 createHanningWindow(hann, Size(100, 100), CV_32F);
RyoheiHagimoto 0:0e0631af0305 2698 @endcode
RyoheiHagimoto 0:0e0631af0305 2699 @param dst Destination array to place Hann coefficients in
RyoheiHagimoto 0:0e0631af0305 2700 @param winSize The window size specifications
RyoheiHagimoto 0:0e0631af0305 2701 @param type Created array type
RyoheiHagimoto 0:0e0631af0305 2702 */
RyoheiHagimoto 0:0e0631af0305 2703 CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
RyoheiHagimoto 0:0e0631af0305 2704
RyoheiHagimoto 0:0e0631af0305 2705 //! @} imgproc_motion
RyoheiHagimoto 0:0e0631af0305 2706
RyoheiHagimoto 0:0e0631af0305 2707 //! @addtogroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 2708 //! @{
RyoheiHagimoto 0:0e0631af0305 2709
RyoheiHagimoto 0:0e0631af0305 2710 /** @brief Applies a fixed-level threshold to each array element.
RyoheiHagimoto 0:0e0631af0305 2711
RyoheiHagimoto 0:0e0631af0305 2712 The function applies fixed-level thresholding to a single-channel array. The function is typically
RyoheiHagimoto 0:0e0631af0305 2713 used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for
RyoheiHagimoto 0:0e0631af0305 2714 this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
RyoheiHagimoto 0:0e0631af0305 2715 values. There are several types of thresholding supported by the function. They are determined by
RyoheiHagimoto 0:0e0631af0305 2716 type parameter.
RyoheiHagimoto 0:0e0631af0305 2717
RyoheiHagimoto 0:0e0631af0305 2718 Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the
RyoheiHagimoto 0:0e0631af0305 2719 above values. In these cases, the function determines the optimal threshold value using the Otsu's
RyoheiHagimoto 0:0e0631af0305 2720 or Triangle algorithm and uses it instead of the specified thresh . The function returns the
RyoheiHagimoto 0:0e0631af0305 2721 computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit
RyoheiHagimoto 0:0e0631af0305 2722 images.
RyoheiHagimoto 0:0e0631af0305 2723
RyoheiHagimoto 0:0e0631af0305 2724 @param src input array (single-channel, 8-bit or 32-bit floating point).
RyoheiHagimoto 0:0e0631af0305 2725 @param dst output array of the same size and type as src.
RyoheiHagimoto 0:0e0631af0305 2726 @param thresh threshold value.
RyoheiHagimoto 0:0e0631af0305 2727 @param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding
RyoheiHagimoto 0:0e0631af0305 2728 types.
RyoheiHagimoto 0:0e0631af0305 2729 @param type thresholding type (see the cv::ThresholdTypes).
RyoheiHagimoto 0:0e0631af0305 2730
RyoheiHagimoto 0:0e0631af0305 2731 @sa adaptiveThreshold, findContours, compare, min, max
RyoheiHagimoto 0:0e0631af0305 2732 */
RyoheiHagimoto 0:0e0631af0305 2733 CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2734 double thresh, double maxval, int type );
RyoheiHagimoto 0:0e0631af0305 2735
RyoheiHagimoto 0:0e0631af0305 2736
RyoheiHagimoto 0:0e0631af0305 2737 /** @brief Applies an adaptive threshold to an array.
RyoheiHagimoto 0:0e0631af0305 2738
RyoheiHagimoto 0:0e0631af0305 2739 The function transforms a grayscale image to a binary image according to the formulae:
RyoheiHagimoto 0:0e0631af0305 2740 - **THRESH_BINARY**
RyoheiHagimoto 0:0e0631af0305 2741 \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 2742 - **THRESH_BINARY_INV**
RyoheiHagimoto 0:0e0631af0305 2743 \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
RyoheiHagimoto 0:0e0631af0305 2744 where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
RyoheiHagimoto 0:0e0631af0305 2745
RyoheiHagimoto 0:0e0631af0305 2746 The function can process the image in-place.
RyoheiHagimoto 0:0e0631af0305 2747
RyoheiHagimoto 0:0e0631af0305 2748 @param src Source 8-bit single-channel image.
RyoheiHagimoto 0:0e0631af0305 2749 @param dst Destination image of the same size and the same type as src.
RyoheiHagimoto 0:0e0631af0305 2750 @param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
RyoheiHagimoto 0:0e0631af0305 2751 @param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes
RyoheiHagimoto 0:0e0631af0305 2752 @param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV,
RyoheiHagimoto 0:0e0631af0305 2753 see cv::ThresholdTypes.
RyoheiHagimoto 0:0e0631af0305 2754 @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
RyoheiHagimoto 0:0e0631af0305 2755 pixel: 3, 5, 7, and so on.
RyoheiHagimoto 0:0e0631af0305 2756 @param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
RyoheiHagimoto 0:0e0631af0305 2757 is positive but may be zero or negative as well.
RyoheiHagimoto 0:0e0631af0305 2758
RyoheiHagimoto 0:0e0631af0305 2759 @sa threshold, blur, GaussianBlur
RyoheiHagimoto 0:0e0631af0305 2760 */
RyoheiHagimoto 0:0e0631af0305 2761 CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2762 double maxValue, int adaptiveMethod,
RyoheiHagimoto 0:0e0631af0305 2763 int thresholdType, int blockSize, double C );
RyoheiHagimoto 0:0e0631af0305 2764
RyoheiHagimoto 0:0e0631af0305 2765 //! @} imgproc_misc
RyoheiHagimoto 0:0e0631af0305 2766
RyoheiHagimoto 0:0e0631af0305 2767 //! @addtogroup imgproc_filter
RyoheiHagimoto 0:0e0631af0305 2768 //! @{
RyoheiHagimoto 0:0e0631af0305 2769
RyoheiHagimoto 0:0e0631af0305 2770 /** @brief Blurs an image and downsamples it.
RyoheiHagimoto 0:0e0631af0305 2771
RyoheiHagimoto 0:0e0631af0305 2772 By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
RyoheiHagimoto 0:0e0631af0305 2773 any case, the following conditions should be satisfied:
RyoheiHagimoto 0:0e0631af0305 2774
RyoheiHagimoto 0:0e0631af0305 2775 \f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2776
RyoheiHagimoto 0:0e0631af0305 2777 The function performs the downsampling step of the Gaussian pyramid construction. First, it
RyoheiHagimoto 0:0e0631af0305 2778 convolves the source image with the kernel:
RyoheiHagimoto 0:0e0631af0305 2779
RyoheiHagimoto 0:0e0631af0305 2780 \f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2781
RyoheiHagimoto 0:0e0631af0305 2782 Then, it downsamples the image by rejecting even rows and columns.
RyoheiHagimoto 0:0e0631af0305 2783
RyoheiHagimoto 0:0e0631af0305 2784 @param src input image.
RyoheiHagimoto 0:0e0631af0305 2785 @param dst output image; it has the specified size and the same type as src.
RyoheiHagimoto 0:0e0631af0305 2786 @param dstsize size of the output image.
RyoheiHagimoto 0:0e0631af0305 2787 @param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)
RyoheiHagimoto 0:0e0631af0305 2788 */
RyoheiHagimoto 0:0e0631af0305 2789 CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2790 const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 2791
RyoheiHagimoto 0:0e0631af0305 2792 /** @brief Upsamples an image and then blurs it.
RyoheiHagimoto 0:0e0631af0305 2793
RyoheiHagimoto 0:0e0631af0305 2794 By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
RyoheiHagimoto 0:0e0631af0305 2795 case, the following conditions should be satisfied:
RyoheiHagimoto 0:0e0631af0305 2796
RyoheiHagimoto 0:0e0631af0305 2797 \f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 2798
RyoheiHagimoto 0:0e0631af0305 2799 The function performs the upsampling step of the Gaussian pyramid construction, though it can
RyoheiHagimoto 0:0e0631af0305 2800 actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
RyoheiHagimoto 0:0e0631af0305 2801 injecting even zero rows and columns and then convolves the result with the same kernel as in
RyoheiHagimoto 0:0e0631af0305 2802 pyrDown multiplied by 4.
RyoheiHagimoto 0:0e0631af0305 2803
RyoheiHagimoto 0:0e0631af0305 2804 @param src input image.
RyoheiHagimoto 0:0e0631af0305 2805 @param dst output image. It has the specified size and the same type as src .
RyoheiHagimoto 0:0e0631af0305 2806 @param dstsize size of the output image.
RyoheiHagimoto 0:0e0631af0305 2807 @param borderType Pixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported)
RyoheiHagimoto 0:0e0631af0305 2808 */
RyoheiHagimoto 0:0e0631af0305 2809 CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2810 const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 2811
RyoheiHagimoto 0:0e0631af0305 2812 /** @brief Constructs the Gaussian pyramid for an image.
RyoheiHagimoto 0:0e0631af0305 2813
RyoheiHagimoto 0:0e0631af0305 2814 The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
RyoheiHagimoto 0:0e0631af0305 2815 pyrDown to the previously built pyramid layers, starting from `dst[0]==src`.
RyoheiHagimoto 0:0e0631af0305 2816
RyoheiHagimoto 0:0e0631af0305 2817 @param src Source image. Check pyrDown for the list of supported types.
RyoheiHagimoto 0:0e0631af0305 2818 @param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the
RyoheiHagimoto 0:0e0631af0305 2819 same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
RyoheiHagimoto 0:0e0631af0305 2820 @param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
RyoheiHagimoto 0:0e0631af0305 2821 @param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)
RyoheiHagimoto 0:0e0631af0305 2822 */
RyoheiHagimoto 0:0e0631af0305 2823 CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
RyoheiHagimoto 0:0e0631af0305 2824 int maxlevel, int borderType = BORDER_DEFAULT );
RyoheiHagimoto 0:0e0631af0305 2825
RyoheiHagimoto 0:0e0631af0305 2826 //! @} imgproc_filter
RyoheiHagimoto 0:0e0631af0305 2827
RyoheiHagimoto 0:0e0631af0305 2828 //! @addtogroup imgproc_transform
RyoheiHagimoto 0:0e0631af0305 2829 //! @{
RyoheiHagimoto 0:0e0631af0305 2830
RyoheiHagimoto 0:0e0631af0305 2831 /** @brief Transforms an image to compensate for lens distortion.
RyoheiHagimoto 0:0e0631af0305 2832
RyoheiHagimoto 0:0e0631af0305 2833 The function transforms an image to compensate radial and tangential lens distortion.
RyoheiHagimoto 0:0e0631af0305 2834
RyoheiHagimoto 0:0e0631af0305 2835 The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap
RyoheiHagimoto 0:0e0631af0305 2836 (with bilinear interpolation). See the former function for details of the transformation being
RyoheiHagimoto 0:0e0631af0305 2837 performed.
RyoheiHagimoto 0:0e0631af0305 2838
RyoheiHagimoto 0:0e0631af0305 2839 Those pixels in the destination image, for which there is no correspondent pixels in the source
RyoheiHagimoto 0:0e0631af0305 2840 image, are filled with zeros (black color).
RyoheiHagimoto 0:0e0631af0305 2841
RyoheiHagimoto 0:0e0631af0305 2842 A particular subset of the source image that will be visible in the corrected image can be regulated
RyoheiHagimoto 0:0e0631af0305 2843 by newCameraMatrix. You can use cv::getOptimalNewCameraMatrix to compute the appropriate
RyoheiHagimoto 0:0e0631af0305 2844 newCameraMatrix depending on your requirements.
RyoheiHagimoto 0:0e0631af0305 2845
RyoheiHagimoto 0:0e0631af0305 2846 The camera matrix and the distortion parameters can be determined using cv::calibrateCamera. If
RyoheiHagimoto 0:0e0631af0305 2847 the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
RyoheiHagimoto 0:0e0631af0305 2848 f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
RyoheiHagimoto 0:0e0631af0305 2849 the same.
RyoheiHagimoto 0:0e0631af0305 2850
RyoheiHagimoto 0:0e0631af0305 2851 @param src Input (distorted) image.
RyoheiHagimoto 0:0e0631af0305 2852 @param dst Output (corrected) image that has the same size and type as src .
RyoheiHagimoto 0:0e0631af0305 2853 @param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
RyoheiHagimoto 0:0e0631af0305 2854 @param distCoeffs Input vector of distortion coefficients
RyoheiHagimoto 0:0e0631af0305 2855 \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
RyoheiHagimoto 0:0e0631af0305 2856 of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
RyoheiHagimoto 0:0e0631af0305 2857 @param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
RyoheiHagimoto 0:0e0631af0305 2858 cameraMatrix but you may additionally scale and shift the result by using a different matrix.
RyoheiHagimoto 0:0e0631af0305 2859 */
RyoheiHagimoto 0:0e0631af0305 2860 CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 2861 InputArray cameraMatrix,
RyoheiHagimoto 0:0e0631af0305 2862 InputArray distCoeffs,
RyoheiHagimoto 0:0e0631af0305 2863 InputArray newCameraMatrix = noArray() );
RyoheiHagimoto 0:0e0631af0305 2864
RyoheiHagimoto 0:0e0631af0305 2865 /** @brief Computes the undistortion and rectification transformation map.
RyoheiHagimoto 0:0e0631af0305 2866
RyoheiHagimoto 0:0e0631af0305 2867 The function computes the joint undistortion and rectification transformation and represents the
RyoheiHagimoto 0:0e0631af0305 2868 result in the form of maps for remap. The undistorted image looks like original, as if it is
RyoheiHagimoto 0:0e0631af0305 2869 captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
RyoheiHagimoto 0:0e0631af0305 2870 monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
RyoheiHagimoto 0:0e0631af0305 2871 cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
RyoheiHagimoto 0:0e0631af0305 2872 newCameraMatrix is normally set to P1 or P2 computed by cv::stereoRectify .
RyoheiHagimoto 0:0e0631af0305 2873
RyoheiHagimoto 0:0e0631af0305 2874 Also, this new camera is oriented differently in the coordinate space, according to R. That, for
RyoheiHagimoto 0:0e0631af0305 2875 example, helps to align two heads of a stereo camera so that the epipolar lines on both images
RyoheiHagimoto 0:0e0631af0305 2876 become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
RyoheiHagimoto 0:0e0631af0305 2877
RyoheiHagimoto 0:0e0631af0305 2878 The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
RyoheiHagimoto 0:0e0631af0305 2879 is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
RyoheiHagimoto 0:0e0631af0305 2880 computes the corresponding coordinates in the source image (that is, in the original image from
RyoheiHagimoto 0:0e0631af0305 2881 camera). The following process is applied:
RyoheiHagimoto 0:0e0631af0305 2882 \f[
RyoheiHagimoto 0:0e0631af0305 2883 \begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2884 x \leftarrow (u - {c'}_x)/{f'}_x \\
RyoheiHagimoto 0:0e0631af0305 2885 y \leftarrow (v - {c'}_y)/{f'}_y \\
RyoheiHagimoto 0:0e0631af0305 2886 {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\
RyoheiHagimoto 0:0e0631af0305 2887 x' \leftarrow X/W \\
RyoheiHagimoto 0:0e0631af0305 2888 y' \leftarrow Y/W \\
RyoheiHagimoto 0:0e0631af0305 2889 r^2 \leftarrow x'^2 + y'^2 \\
RyoheiHagimoto 0:0e0631af0305 2890 x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
RyoheiHagimoto 0:0e0631af0305 2891 + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\
RyoheiHagimoto 0:0e0631af0305 2892 y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
RyoheiHagimoto 0:0e0631af0305 2893 + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
RyoheiHagimoto 0:0e0631af0305 2894 s\vecthree{x'''}{y'''}{1} =
RyoheiHagimoto 0:0e0631af0305 2895 \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
RyoheiHagimoto 0:0e0631af0305 2896 {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
RyoheiHagimoto 0:0e0631af0305 2897 {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
RyoheiHagimoto 0:0e0631af0305 2898 map_x(u,v) \leftarrow x''' f_x + c_x \\
RyoheiHagimoto 0:0e0631af0305 2899 map_y(u,v) \leftarrow y''' f_y + c_y
RyoheiHagimoto 0:0e0631af0305 2900 \end{array}
RyoheiHagimoto 0:0e0631af0305 2901 \f]
RyoheiHagimoto 0:0e0631af0305 2902 where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
RyoheiHagimoto 0:0e0631af0305 2903 are the distortion coefficients.
RyoheiHagimoto 0:0e0631af0305 2904
RyoheiHagimoto 0:0e0631af0305 2905 In case of a stereo camera, this function is called twice: once for each camera head, after
RyoheiHagimoto 0:0e0631af0305 2906 stereoRectify, which in its turn is called after cv::stereoCalibrate. But if the stereo camera
RyoheiHagimoto 0:0e0631af0305 2907 was not calibrated, it is still possible to compute the rectification transformations directly from
RyoheiHagimoto 0:0e0631af0305 2908 the fundamental matrix using cv::stereoRectifyUncalibrated. For each camera, the function computes
RyoheiHagimoto 0:0e0631af0305 2909 homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
RyoheiHagimoto 0:0e0631af0305 2910 space. R can be computed from H as
RyoheiHagimoto 0:0e0631af0305 2911 \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
RyoheiHagimoto 0:0e0631af0305 2912 where cameraMatrix can be chosen arbitrarily.
RyoheiHagimoto 0:0e0631af0305 2913
RyoheiHagimoto 0:0e0631af0305 2914 @param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
RyoheiHagimoto 0:0e0631af0305 2915 @param distCoeffs Input vector of distortion coefficients
RyoheiHagimoto 0:0e0631af0305 2916 \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
RyoheiHagimoto 0:0e0631af0305 2917 of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
RyoheiHagimoto 0:0e0631af0305 2918 @param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
RyoheiHagimoto 0:0e0631af0305 2919 computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation
RyoheiHagimoto 0:0e0631af0305 2920 is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
RyoheiHagimoto 0:0e0631af0305 2921 @param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
RyoheiHagimoto 0:0e0631af0305 2922 @param size Undistorted image size.
RyoheiHagimoto 0:0e0631af0305 2923 @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2, see cv::convertMaps
RyoheiHagimoto 0:0e0631af0305 2924 @param map1 The first output map.
RyoheiHagimoto 0:0e0631af0305 2925 @param map2 The second output map.
RyoheiHagimoto 0:0e0631af0305 2926 */
RyoheiHagimoto 0:0e0631af0305 2927 CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
RyoheiHagimoto 0:0e0631af0305 2928 InputArray R, InputArray newCameraMatrix,
RyoheiHagimoto 0:0e0631af0305 2929 Size size, int m1type, OutputArray map1, OutputArray map2 );
RyoheiHagimoto 0:0e0631af0305 2930
RyoheiHagimoto 0:0e0631af0305 2931 //! initializes maps for cv::remap() for wide-angle
RyoheiHagimoto 0:0e0631af0305 2932 CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
RyoheiHagimoto 0:0e0631af0305 2933 Size imageSize, int destImageWidth,
RyoheiHagimoto 0:0e0631af0305 2934 int m1type, OutputArray map1, OutputArray map2,
RyoheiHagimoto 0:0e0631af0305 2935 int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
RyoheiHagimoto 0:0e0631af0305 2936
RyoheiHagimoto 0:0e0631af0305 2937 /** @brief Returns the default new camera matrix.
RyoheiHagimoto 0:0e0631af0305 2938
RyoheiHagimoto 0:0e0631af0305 2939 The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
RyoheiHagimoto 0:0e0631af0305 2940 centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
RyoheiHagimoto 0:0e0631af0305 2941
RyoheiHagimoto 0:0e0631af0305 2942 In the latter case, the new camera matrix will be:
RyoheiHagimoto 0:0e0631af0305 2943
RyoheiHagimoto 0:0e0631af0305 2944 \f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f]
RyoheiHagimoto 0:0e0631af0305 2945
RyoheiHagimoto 0:0e0631af0305 2946 where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
RyoheiHagimoto 0:0e0631af0305 2947
RyoheiHagimoto 0:0e0631af0305 2948 By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not
RyoheiHagimoto 0:0e0631af0305 2949 move the principal point. However, when you work with stereo, it is important to move the principal
RyoheiHagimoto 0:0e0631af0305 2950 points in both views to the same y-coordinate (which is required by most of stereo correspondence
RyoheiHagimoto 0:0e0631af0305 2951 algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
RyoheiHagimoto 0:0e0631af0305 2952 each view where the principal points are located at the center.
RyoheiHagimoto 0:0e0631af0305 2953
RyoheiHagimoto 0:0e0631af0305 2954 @param cameraMatrix Input camera matrix.
RyoheiHagimoto 0:0e0631af0305 2955 @param imgsize Camera view image size in pixels.
RyoheiHagimoto 0:0e0631af0305 2956 @param centerPrincipalPoint Location of the principal point in the new camera matrix. The
RyoheiHagimoto 0:0e0631af0305 2957 parameter indicates whether this location should be at the image center or not.
RyoheiHagimoto 0:0e0631af0305 2958 */
RyoheiHagimoto 0:0e0631af0305 2959 CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(),
RyoheiHagimoto 0:0e0631af0305 2960 bool centerPrincipalPoint = false );
RyoheiHagimoto 0:0e0631af0305 2961
RyoheiHagimoto 0:0e0631af0305 2962 /** @brief Computes the ideal point coordinates from the observed point coordinates.
RyoheiHagimoto 0:0e0631af0305 2963
RyoheiHagimoto 0:0e0631af0305 2964 The function is similar to cv::undistort and cv::initUndistortRectifyMap but it operates on a
RyoheiHagimoto 0:0e0631af0305 2965 sparse set of points instead of a raster image. Also the function performs a reverse transformation
RyoheiHagimoto 0:0e0631af0305 2966 to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
RyoheiHagimoto 0:0e0631af0305 2967 planar object, it does, up to a translation vector, if the proper R is specified.
RyoheiHagimoto 0:0e0631af0305 2968
RyoheiHagimoto 0:0e0631af0305 2969 For each observed point coordinate \f$(u, v)\f$ the function computes:
RyoheiHagimoto 0:0e0631af0305 2970 \f[
RyoheiHagimoto 0:0e0631af0305 2971 \begin{array}{l}
RyoheiHagimoto 0:0e0631af0305 2972 x^{"} \leftarrow (u - c_x)/f_x \\
RyoheiHagimoto 0:0e0631af0305 2973 y^{"} \leftarrow (v - c_y)/f_y \\
RyoheiHagimoto 0:0e0631af0305 2974 (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
RyoheiHagimoto 0:0e0631af0305 2975 {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
RyoheiHagimoto 0:0e0631af0305 2976 x \leftarrow X/W \\
RyoheiHagimoto 0:0e0631af0305 2977 y \leftarrow Y/W \\
RyoheiHagimoto 0:0e0631af0305 2978 \text{only performed if P is specified:} \\
RyoheiHagimoto 0:0e0631af0305 2979 u' \leftarrow x {f'}_x + {c'}_x \\
RyoheiHagimoto 0:0e0631af0305 2980 v' \leftarrow y {f'}_y + {c'}_y
RyoheiHagimoto 0:0e0631af0305 2981 \end{array}
RyoheiHagimoto 0:0e0631af0305 2982 \f]
RyoheiHagimoto 0:0e0631af0305 2983
RyoheiHagimoto 0:0e0631af0305 2984 where *undistort* is an approximate iterative algorithm that estimates the normalized original
RyoheiHagimoto 0:0e0631af0305 2985 point coordinates out of the normalized distorted point coordinates ("normalized" means that the
RyoheiHagimoto 0:0e0631af0305 2986 coordinates do not depend on the camera matrix).
RyoheiHagimoto 0:0e0631af0305 2987
RyoheiHagimoto 0:0e0631af0305 2988 The function can be used for both a stereo camera head or a monocular camera (when R is empty).
RyoheiHagimoto 0:0e0631af0305 2989
RyoheiHagimoto 0:0e0631af0305 2990 @param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
RyoheiHagimoto 0:0e0631af0305 2991 @param dst Output ideal point coordinates after undistortion and reverse perspective
RyoheiHagimoto 0:0e0631af0305 2992 transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
RyoheiHagimoto 0:0e0631af0305 2993 @param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
RyoheiHagimoto 0:0e0631af0305 2994 @param distCoeffs Input vector of distortion coefficients
RyoheiHagimoto 0:0e0631af0305 2995 \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
RyoheiHagimoto 0:0e0631af0305 2996 of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
RyoheiHagimoto 0:0e0631af0305 2997 @param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
RyoheiHagimoto 0:0e0631af0305 2998 cv::stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
RyoheiHagimoto 0:0e0631af0305 2999 @param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
RyoheiHagimoto 0:0e0631af0305 3000 cv::stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
RyoheiHagimoto 0:0e0631af0305 3001 */
RyoheiHagimoto 0:0e0631af0305 3002 CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 3003 InputArray cameraMatrix, InputArray distCoeffs,
RyoheiHagimoto 0:0e0631af0305 3004 InputArray R = noArray(), InputArray P = noArray());
RyoheiHagimoto 0:0e0631af0305 3005
RyoheiHagimoto 0:0e0631af0305 3006 //! @} imgproc_transform
RyoheiHagimoto 0:0e0631af0305 3007
RyoheiHagimoto 0:0e0631af0305 3008 //! @addtogroup imgproc_hist
RyoheiHagimoto 0:0e0631af0305 3009 //! @{
RyoheiHagimoto 0:0e0631af0305 3010
RyoheiHagimoto 0:0e0631af0305 3011 /** @example demhist.cpp
RyoheiHagimoto 0:0e0631af0305 3012 An example for creating histograms of an image
RyoheiHagimoto 0:0e0631af0305 3013 */
RyoheiHagimoto 0:0e0631af0305 3014
RyoheiHagimoto 0:0e0631af0305 3015 /** @brief Calculates a histogram of a set of arrays.
RyoheiHagimoto 0:0e0631af0305 3016
RyoheiHagimoto 0:0e0631af0305 3017 The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used
RyoheiHagimoto 0:0e0631af0305 3018 to increment a histogram bin are taken from the corresponding input arrays at the same location. The
RyoheiHagimoto 0:0e0631af0305 3019 sample below shows how to compute a 2D Hue-Saturation histogram for a color image. :
RyoheiHagimoto 0:0e0631af0305 3020 @code
RyoheiHagimoto 0:0e0631af0305 3021 #include <opencv2/imgproc.hpp>
RyoheiHagimoto 0:0e0631af0305 3022 #include <opencv2/highgui.hpp>
RyoheiHagimoto 0:0e0631af0305 3023
RyoheiHagimoto 0:0e0631af0305 3024 using namespace cv;
RyoheiHagimoto 0:0e0631af0305 3025
RyoheiHagimoto 0:0e0631af0305 3026 int main( int argc, char** argv )
RyoheiHagimoto 0:0e0631af0305 3027 {
RyoheiHagimoto 0:0e0631af0305 3028 Mat src, hsv;
RyoheiHagimoto 0:0e0631af0305 3029 if( argc != 2 || !(src=imread(argv[1], 1)).data )
RyoheiHagimoto 0:0e0631af0305 3030 return -1;
RyoheiHagimoto 0:0e0631af0305 3031
RyoheiHagimoto 0:0e0631af0305 3032 cvtColor(src, hsv, COLOR_BGR2HSV);
RyoheiHagimoto 0:0e0631af0305 3033
RyoheiHagimoto 0:0e0631af0305 3034 // Quantize the hue to 30 levels
RyoheiHagimoto 0:0e0631af0305 3035 // and the saturation to 32 levels
RyoheiHagimoto 0:0e0631af0305 3036 int hbins = 30, sbins = 32;
RyoheiHagimoto 0:0e0631af0305 3037 int histSize[] = {hbins, sbins};
RyoheiHagimoto 0:0e0631af0305 3038 // hue varies from 0 to 179, see cvtColor
RyoheiHagimoto 0:0e0631af0305 3039 float hranges[] = { 0, 180 };
RyoheiHagimoto 0:0e0631af0305 3040 // saturation varies from 0 (black-gray-white) to
RyoheiHagimoto 0:0e0631af0305 3041 // 255 (pure spectrum color)
RyoheiHagimoto 0:0e0631af0305 3042 float sranges[] = { 0, 256 };
RyoheiHagimoto 0:0e0631af0305 3043 const float* ranges[] = { hranges, sranges };
RyoheiHagimoto 0:0e0631af0305 3044 MatND hist;
RyoheiHagimoto 0:0e0631af0305 3045 // we compute the histogram from the 0-th and 1-st channels
RyoheiHagimoto 0:0e0631af0305 3046 int channels[] = {0, 1};
RyoheiHagimoto 0:0e0631af0305 3047
RyoheiHagimoto 0:0e0631af0305 3048 calcHist( &hsv, 1, channels, Mat(), // do not use mask
RyoheiHagimoto 0:0e0631af0305 3049 hist, 2, histSize, ranges,
RyoheiHagimoto 0:0e0631af0305 3050 true, // the histogram is uniform
RyoheiHagimoto 0:0e0631af0305 3051 false );
RyoheiHagimoto 0:0e0631af0305 3052 double maxVal=0;
RyoheiHagimoto 0:0e0631af0305 3053 minMaxLoc(hist, 0, &maxVal, 0, 0);
RyoheiHagimoto 0:0e0631af0305 3054
RyoheiHagimoto 0:0e0631af0305 3055 int scale = 10;
RyoheiHagimoto 0:0e0631af0305 3056 Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
RyoheiHagimoto 0:0e0631af0305 3057
RyoheiHagimoto 0:0e0631af0305 3058 for( int h = 0; h < hbins; h++ )
RyoheiHagimoto 0:0e0631af0305 3059 for( int s = 0; s < sbins; s++ )
RyoheiHagimoto 0:0e0631af0305 3060 {
RyoheiHagimoto 0:0e0631af0305 3061 float binVal = hist.at<float>(h, s);
RyoheiHagimoto 0:0e0631af0305 3062 int intensity = cvRound(binVal*255/maxVal);
RyoheiHagimoto 0:0e0631af0305 3063 rectangle( histImg, Point(h*scale, s*scale),
RyoheiHagimoto 0:0e0631af0305 3064 Point( (h+1)*scale - 1, (s+1)*scale - 1),
RyoheiHagimoto 0:0e0631af0305 3065 Scalar::all(intensity),
RyoheiHagimoto 0:0e0631af0305 3066 CV_FILLED );
RyoheiHagimoto 0:0e0631af0305 3067 }
RyoheiHagimoto 0:0e0631af0305 3068
RyoheiHagimoto 0:0e0631af0305 3069 namedWindow( "Source", 1 );
RyoheiHagimoto 0:0e0631af0305 3070 imshow( "Source", src );
RyoheiHagimoto 0:0e0631af0305 3071
RyoheiHagimoto 0:0e0631af0305 3072 namedWindow( "H-S Histogram", 1 );
RyoheiHagimoto 0:0e0631af0305 3073 imshow( "H-S Histogram", histImg );
RyoheiHagimoto 0:0e0631af0305 3074 waitKey();
RyoheiHagimoto 0:0e0631af0305 3075 }
RyoheiHagimoto 0:0e0631af0305 3076 @endcode
RyoheiHagimoto 0:0e0631af0305 3077
RyoheiHagimoto 0:0e0631af0305 3078 @param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
RyoheiHagimoto 0:0e0631af0305 3079 size. Each of them can have an arbitrary number of channels.
RyoheiHagimoto 0:0e0631af0305 3080 @param nimages Number of source images.
RyoheiHagimoto 0:0e0631af0305 3081 @param channels List of the dims channels used to compute the histogram. The first array channels
RyoheiHagimoto 0:0e0631af0305 3082 are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
RyoheiHagimoto 0:0e0631af0305 3083 images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
RyoheiHagimoto 0:0e0631af0305 3084 @param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
RyoheiHagimoto 0:0e0631af0305 3085 as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
RyoheiHagimoto 0:0e0631af0305 3086 @param hist Output histogram, which is a dense or sparse dims -dimensional array.
RyoheiHagimoto 0:0e0631af0305 3087 @param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
RyoheiHagimoto 0:0e0631af0305 3088 (equal to 32 in the current OpenCV version).
RyoheiHagimoto 0:0e0631af0305 3089 @param histSize Array of histogram sizes in each dimension.
RyoheiHagimoto 0:0e0631af0305 3090 @param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the
RyoheiHagimoto 0:0e0631af0305 3091 histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower
RyoheiHagimoto 0:0e0631af0305 3092 (inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary
RyoheiHagimoto 0:0e0631af0305 3093 \f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a
RyoheiHagimoto 0:0e0631af0305 3094 uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (
RyoheiHagimoto 0:0e0631af0305 3095 uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
RyoheiHagimoto 0:0e0631af0305 3096 \f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$
RyoheiHagimoto 0:0e0631af0305 3097 . The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not
RyoheiHagimoto 0:0e0631af0305 3098 counted in the histogram.
RyoheiHagimoto 0:0e0631af0305 3099 @param uniform Flag indicating whether the histogram is uniform or not (see above).
RyoheiHagimoto 0:0e0631af0305 3100 @param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning
RyoheiHagimoto 0:0e0631af0305 3101 when it is allocated. This feature enables you to compute a single histogram from several sets of
RyoheiHagimoto 0:0e0631af0305 3102 arrays, or to update the histogram in time.
RyoheiHagimoto 0:0e0631af0305 3103 */
RyoheiHagimoto 0:0e0631af0305 3104 CV_EXPORTS void calcHist( const Mat* images, int nimages,
RyoheiHagimoto 0:0e0631af0305 3105 const int* channels, InputArray mask,
RyoheiHagimoto 0:0e0631af0305 3106 OutputArray hist, int dims, const int* histSize,
RyoheiHagimoto 0:0e0631af0305 3107 const float** ranges, bool uniform = true, bool accumulate = false );
RyoheiHagimoto 0:0e0631af0305 3108
RyoheiHagimoto 0:0e0631af0305 3109 /** @overload
RyoheiHagimoto 0:0e0631af0305 3110
RyoheiHagimoto 0:0e0631af0305 3111 this variant uses cv::SparseMat for output
RyoheiHagimoto 0:0e0631af0305 3112 */
RyoheiHagimoto 0:0e0631af0305 3113 CV_EXPORTS void calcHist( const Mat* images, int nimages,
RyoheiHagimoto 0:0e0631af0305 3114 const int* channels, InputArray mask,
RyoheiHagimoto 0:0e0631af0305 3115 SparseMat& hist, int dims,
RyoheiHagimoto 0:0e0631af0305 3116 const int* histSize, const float** ranges,
RyoheiHagimoto 0:0e0631af0305 3117 bool uniform = true, bool accumulate = false );
RyoheiHagimoto 0:0e0631af0305 3118
RyoheiHagimoto 0:0e0631af0305 3119 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3120 CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
RyoheiHagimoto 0:0e0631af0305 3121 const std::vector<int>& channels,
RyoheiHagimoto 0:0e0631af0305 3122 InputArray mask, OutputArray hist,
RyoheiHagimoto 0:0e0631af0305 3123 const std::vector<int>& histSize,
RyoheiHagimoto 0:0e0631af0305 3124 const std::vector<float>& ranges,
RyoheiHagimoto 0:0e0631af0305 3125 bool accumulate = false );
RyoheiHagimoto 0:0e0631af0305 3126
RyoheiHagimoto 0:0e0631af0305 3127 /** @brief Calculates the back projection of a histogram.
RyoheiHagimoto 0:0e0631af0305 3128
RyoheiHagimoto 0:0e0631af0305 3129 The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to
RyoheiHagimoto 0:0e0631af0305 3130 cv::calcHist , at each location (x, y) the function collects the values from the selected channels
RyoheiHagimoto 0:0e0631af0305 3131 in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
RyoheiHagimoto 0:0e0631af0305 3132 function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of
RyoheiHagimoto 0:0e0631af0305 3133 statistics, the function computes probability of each element value in respect with the empirical
RyoheiHagimoto 0:0e0631af0305 3134 probability distribution represented by the histogram. See how, for example, you can find and track
RyoheiHagimoto 0:0e0631af0305 3135 a bright-colored object in a scene:
RyoheiHagimoto 0:0e0631af0305 3136
RyoheiHagimoto 0:0e0631af0305 3137 - Before tracking, show the object to the camera so that it covers almost the whole frame.
RyoheiHagimoto 0:0e0631af0305 3138 Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant
RyoheiHagimoto 0:0e0631af0305 3139 colors in the object.
RyoheiHagimoto 0:0e0631af0305 3140
RyoheiHagimoto 0:0e0631af0305 3141 - When tracking, calculate a back projection of a hue plane of each input video frame using that
RyoheiHagimoto 0:0e0631af0305 3142 pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make
RyoheiHagimoto 0:0e0631af0305 3143 sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
RyoheiHagimoto 0:0e0631af0305 3144
RyoheiHagimoto 0:0e0631af0305 3145 - Find connected components in the resulting picture and choose, for example, the largest
RyoheiHagimoto 0:0e0631af0305 3146 component.
RyoheiHagimoto 0:0e0631af0305 3147
RyoheiHagimoto 0:0e0631af0305 3148 This is an approximate algorithm of the CamShift color object tracker.
RyoheiHagimoto 0:0e0631af0305 3149
RyoheiHagimoto 0:0e0631af0305 3150 @param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
RyoheiHagimoto 0:0e0631af0305 3151 size. Each of them can have an arbitrary number of channels.
RyoheiHagimoto 0:0e0631af0305 3152 @param nimages Number of source images.
RyoheiHagimoto 0:0e0631af0305 3153 @param channels The list of channels used to compute the back projection. The number of channels
RyoheiHagimoto 0:0e0631af0305 3154 must match the histogram dimensionality. The first array channels are numerated from 0 to
RyoheiHagimoto 0:0e0631af0305 3155 images[0].channels()-1 , the second array channels are counted from images[0].channels() to
RyoheiHagimoto 0:0e0631af0305 3156 images[0].channels() + images[1].channels()-1, and so on.
RyoheiHagimoto 0:0e0631af0305 3157 @param hist Input histogram that can be dense or sparse.
RyoheiHagimoto 0:0e0631af0305 3158 @param backProject Destination back projection array that is a single-channel array of the same
RyoheiHagimoto 0:0e0631af0305 3159 size and depth as images[0] .
RyoheiHagimoto 0:0e0631af0305 3160 @param ranges Array of arrays of the histogram bin boundaries in each dimension. See cv::calcHist .
RyoheiHagimoto 0:0e0631af0305 3161 @param scale Optional scale factor for the output back projection.
RyoheiHagimoto 0:0e0631af0305 3162 @param uniform Flag indicating whether the histogram is uniform or not (see above).
RyoheiHagimoto 0:0e0631af0305 3163
RyoheiHagimoto 0:0e0631af0305 3164 @sa cv::calcHist, cv::compareHist
RyoheiHagimoto 0:0e0631af0305 3165 */
RyoheiHagimoto 0:0e0631af0305 3166 CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
RyoheiHagimoto 0:0e0631af0305 3167 const int* channels, InputArray hist,
RyoheiHagimoto 0:0e0631af0305 3168 OutputArray backProject, const float** ranges,
RyoheiHagimoto 0:0e0631af0305 3169 double scale = 1, bool uniform = true );
RyoheiHagimoto 0:0e0631af0305 3170
RyoheiHagimoto 0:0e0631af0305 3171 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3172 CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
RyoheiHagimoto 0:0e0631af0305 3173 const int* channels, const SparseMat& hist,
RyoheiHagimoto 0:0e0631af0305 3174 OutputArray backProject, const float** ranges,
RyoheiHagimoto 0:0e0631af0305 3175 double scale = 1, bool uniform = true );
RyoheiHagimoto 0:0e0631af0305 3176
RyoheiHagimoto 0:0e0631af0305 3177 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3178 CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
RyoheiHagimoto 0:0e0631af0305 3179 InputArray hist, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 3180 const std::vector<float>& ranges,
RyoheiHagimoto 0:0e0631af0305 3181 double scale );
RyoheiHagimoto 0:0e0631af0305 3182
RyoheiHagimoto 0:0e0631af0305 3183 /** @brief Compares two histograms.
RyoheiHagimoto 0:0e0631af0305 3184
RyoheiHagimoto 0:0e0631af0305 3185 The function cv::compareHist compares two dense or two sparse histograms using the specified method.
RyoheiHagimoto 0:0e0631af0305 3186
RyoheiHagimoto 0:0e0631af0305 3187 The function returns \f$d(H_1, H_2)\f$ .
RyoheiHagimoto 0:0e0631af0305 3188
RyoheiHagimoto 0:0e0631af0305 3189 While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
RyoheiHagimoto 0:0e0631af0305 3190 for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
RyoheiHagimoto 0:0e0631af0305 3191 problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
RyoheiHagimoto 0:0e0631af0305 3192 or more general sparse configurations of weighted points, consider using the cv::EMD function.
RyoheiHagimoto 0:0e0631af0305 3193
RyoheiHagimoto 0:0e0631af0305 3194 @param H1 First compared histogram.
RyoheiHagimoto 0:0e0631af0305 3195 @param H2 Second compared histogram of the same size as H1 .
RyoheiHagimoto 0:0e0631af0305 3196 @param method Comparison method, see cv::HistCompMethods
RyoheiHagimoto 0:0e0631af0305 3197 */
RyoheiHagimoto 0:0e0631af0305 3198 CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
RyoheiHagimoto 0:0e0631af0305 3199
RyoheiHagimoto 0:0e0631af0305 3200 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3201 CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
RyoheiHagimoto 0:0e0631af0305 3202
RyoheiHagimoto 0:0e0631af0305 3203 /** @brief Equalizes the histogram of a grayscale image.
RyoheiHagimoto 0:0e0631af0305 3204
RyoheiHagimoto 0:0e0631af0305 3205 The function equalizes the histogram of the input image using the following algorithm:
RyoheiHagimoto 0:0e0631af0305 3206
RyoheiHagimoto 0:0e0631af0305 3207 - Calculate the histogram \f$H\f$ for src .
RyoheiHagimoto 0:0e0631af0305 3208 - Normalize the histogram so that the sum of histogram bins is 255.
RyoheiHagimoto 0:0e0631af0305 3209 - Compute the integral of the histogram:
RyoheiHagimoto 0:0e0631af0305 3210 \f[H'_i = \sum _{0 \le j < i} H(j)\f]
RyoheiHagimoto 0:0e0631af0305 3211 - Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
RyoheiHagimoto 0:0e0631af0305 3212
RyoheiHagimoto 0:0e0631af0305 3213 The algorithm normalizes the brightness and increases the contrast of the image.
RyoheiHagimoto 0:0e0631af0305 3214
RyoheiHagimoto 0:0e0631af0305 3215 @param src Source 8-bit single channel image.
RyoheiHagimoto 0:0e0631af0305 3216 @param dst Destination image of the same size and type as src .
RyoheiHagimoto 0:0e0631af0305 3217 */
RyoheiHagimoto 0:0e0631af0305 3218 CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
RyoheiHagimoto 0:0e0631af0305 3219
RyoheiHagimoto 0:0e0631af0305 3220 /** @brief Computes the "minimal work" distance between two weighted point configurations.
RyoheiHagimoto 0:0e0631af0305 3221
RyoheiHagimoto 0:0e0631af0305 3222 The function computes the earth mover distance and/or a lower boundary of the distance between the
RyoheiHagimoto 0:0e0631af0305 3223 two weighted point configurations. One of the applications described in @cite RubnerSept98,
RyoheiHagimoto 0:0e0631af0305 3224 @cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
RyoheiHagimoto 0:0e0631af0305 3225 problem that is solved using some modification of a simplex algorithm, thus the complexity is
RyoheiHagimoto 0:0e0631af0305 3226 exponential in the worst case, though, on average it is much faster. In the case of a real metric
RyoheiHagimoto 0:0e0631af0305 3227 the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
RyoheiHagimoto 0:0e0631af0305 3228 to determine roughly whether the two signatures are far enough so that they cannot relate to the
RyoheiHagimoto 0:0e0631af0305 3229 same object.
RyoheiHagimoto 0:0e0631af0305 3230
RyoheiHagimoto 0:0e0631af0305 3231 @param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
RyoheiHagimoto 0:0e0631af0305 3232 Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
RyoheiHagimoto 0:0e0631af0305 3233 a single column (weights only) if the user-defined cost matrix is used. The weights must be
RyoheiHagimoto 0:0e0631af0305 3234 non-negative and have at least one non-zero value.
RyoheiHagimoto 0:0e0631af0305 3235 @param signature2 Second signature of the same format as signature1 , though the number of rows
RyoheiHagimoto 0:0e0631af0305 3236 may be different. The total weights may be different. In this case an extra "dummy" point is added
RyoheiHagimoto 0:0e0631af0305 3237 to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
RyoheiHagimoto 0:0e0631af0305 3238 value.
RyoheiHagimoto 0:0e0631af0305 3239 @param distType Used metric. See cv::DistanceTypes.
RyoheiHagimoto 0:0e0631af0305 3240 @param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
RyoheiHagimoto 0:0e0631af0305 3241 is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
RyoheiHagimoto 0:0e0631af0305 3242 @param lowerBound Optional input/output parameter: lower boundary of a distance between the two
RyoheiHagimoto 0:0e0631af0305 3243 signatures that is a distance between mass centers. The lower boundary may not be calculated if
RyoheiHagimoto 0:0e0631af0305 3244 the user-defined cost matrix is used, the total weights of point configurations are not equal, or
RyoheiHagimoto 0:0e0631af0305 3245 if the signatures consist of weights only (the signature matrices have a single column). You
RyoheiHagimoto 0:0e0631af0305 3246 **must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
RyoheiHagimoto 0:0e0631af0305 3247 equal to \*lowerBound (it means that the signatures are far enough), the function does not
RyoheiHagimoto 0:0e0631af0305 3248 calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
RyoheiHagimoto 0:0e0631af0305 3249 return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
RyoheiHagimoto 0:0e0631af0305 3250 should be set to 0.
RyoheiHagimoto 0:0e0631af0305 3251 @param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
RyoheiHagimoto 0:0e0631af0305 3252 a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
RyoheiHagimoto 0:0e0631af0305 3253 */
RyoheiHagimoto 0:0e0631af0305 3254 CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
RyoheiHagimoto 0:0e0631af0305 3255 int distType, InputArray cost=noArray(),
RyoheiHagimoto 0:0e0631af0305 3256 float* lowerBound = 0, OutputArray flow = noArray() );
RyoheiHagimoto 0:0e0631af0305 3257
RyoheiHagimoto 0:0e0631af0305 3258 //! @} imgproc_hist
RyoheiHagimoto 0:0e0631af0305 3259
RyoheiHagimoto 0:0e0631af0305 3260 /** @example watershed.cpp
RyoheiHagimoto 0:0e0631af0305 3261 An example using the watershed algorithm
RyoheiHagimoto 0:0e0631af0305 3262 */
RyoheiHagimoto 0:0e0631af0305 3263
RyoheiHagimoto 0:0e0631af0305 3264 /** @brief Performs a marker-based image segmentation using the watershed algorithm.
RyoheiHagimoto 0:0e0631af0305 3265
RyoheiHagimoto 0:0e0631af0305 3266 The function implements one of the variants of watershed, non-parametric marker-based segmentation
RyoheiHagimoto 0:0e0631af0305 3267 algorithm, described in @cite Meyer92 .
RyoheiHagimoto 0:0e0631af0305 3268
RyoheiHagimoto 0:0e0631af0305 3269 Before passing the image to the function, you have to roughly outline the desired regions in the
RyoheiHagimoto 0:0e0631af0305 3270 image markers with positive (\>0) indices. So, every region is represented as one or more connected
RyoheiHagimoto 0:0e0631af0305 3271 components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
RyoheiHagimoto 0:0e0631af0305 3272 mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of
RyoheiHagimoto 0:0e0631af0305 3273 the future image regions. All the other pixels in markers , whose relation to the outlined regions
RyoheiHagimoto 0:0e0631af0305 3274 is not known and should be defined by the algorithm, should be set to 0's. In the function output,
RyoheiHagimoto 0:0e0631af0305 3275 each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
RyoheiHagimoto 0:0e0631af0305 3276 regions.
RyoheiHagimoto 0:0e0631af0305 3277
RyoheiHagimoto 0:0e0631af0305 3278 @note Any two neighbor connected components are not necessarily separated by a watershed boundary
RyoheiHagimoto 0:0e0631af0305 3279 (-1's pixels); for example, they can touch each other in the initial marker image passed to the
RyoheiHagimoto 0:0e0631af0305 3280 function.
RyoheiHagimoto 0:0e0631af0305 3281
RyoheiHagimoto 0:0e0631af0305 3282 @param image Input 8-bit 3-channel image.
RyoheiHagimoto 0:0e0631af0305 3283 @param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
RyoheiHagimoto 0:0e0631af0305 3284 size as image .
RyoheiHagimoto 0:0e0631af0305 3285
RyoheiHagimoto 0:0e0631af0305 3286 @sa findContours
RyoheiHagimoto 0:0e0631af0305 3287
RyoheiHagimoto 0:0e0631af0305 3288 @ingroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 3289 */
RyoheiHagimoto 0:0e0631af0305 3290 CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
RyoheiHagimoto 0:0e0631af0305 3291
RyoheiHagimoto 0:0e0631af0305 3292 //! @addtogroup imgproc_filter
RyoheiHagimoto 0:0e0631af0305 3293 //! @{
RyoheiHagimoto 0:0e0631af0305 3294
RyoheiHagimoto 0:0e0631af0305 3295 /** @brief Performs initial step of meanshift segmentation of an image.
RyoheiHagimoto 0:0e0631af0305 3296
RyoheiHagimoto 0:0e0631af0305 3297 The function implements the filtering stage of meanshift segmentation, that is, the output of the
RyoheiHagimoto 0:0e0631af0305 3298 function is the filtered "posterized" image with color gradients and fine-grain texture flattened.
RyoheiHagimoto 0:0e0631af0305 3299 At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes
RyoheiHagimoto 0:0e0631af0305 3300 meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is
RyoheiHagimoto 0:0e0631af0305 3301 considered:
RyoheiHagimoto 0:0e0631af0305 3302
RyoheiHagimoto 0:0e0631af0305 3303 \f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f]
RyoheiHagimoto 0:0e0631af0305 3304
RyoheiHagimoto 0:0e0631af0305 3305 where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively
RyoheiHagimoto 0:0e0631af0305 3306 (though, the algorithm does not depend on the color space used, so any 3-component color space can
RyoheiHagimoto 0:0e0631af0305 3307 be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector
RyoheiHagimoto 0:0e0631af0305 3308 (R',G',B') are found and they act as the neighborhood center on the next iteration:
RyoheiHagimoto 0:0e0631af0305 3309
RyoheiHagimoto 0:0e0631af0305 3310 \f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f]
RyoheiHagimoto 0:0e0631af0305 3311
RyoheiHagimoto 0:0e0631af0305 3312 After the iterations over, the color components of the initial pixel (that is, the pixel from where
RyoheiHagimoto 0:0e0631af0305 3313 the iterations started) are set to the final value (average color at the last iteration):
RyoheiHagimoto 0:0e0631af0305 3314
RyoheiHagimoto 0:0e0631af0305 3315 \f[I(X,Y) <- (R*,G*,B*)\f]
RyoheiHagimoto 0:0e0631af0305 3316
RyoheiHagimoto 0:0e0631af0305 3317 When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
RyoheiHagimoto 0:0e0631af0305 3318 run on the smallest layer first. After that, the results are propagated to the larger layer and the
RyoheiHagimoto 0:0e0631af0305 3319 iterations are run again only on those pixels where the layer colors differ by more than sr from the
RyoheiHagimoto 0:0e0631af0305 3320 lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
RyoheiHagimoto 0:0e0631af0305 3321 results will be actually different from the ones obtained by running the meanshift procedure on the
RyoheiHagimoto 0:0e0631af0305 3322 whole original image (i.e. when maxLevel==0).
RyoheiHagimoto 0:0e0631af0305 3323
RyoheiHagimoto 0:0e0631af0305 3324 @param src The source 8-bit, 3-channel image.
RyoheiHagimoto 0:0e0631af0305 3325 @param dst The destination image of the same format and the same size as the source.
RyoheiHagimoto 0:0e0631af0305 3326 @param sp The spatial window radius.
RyoheiHagimoto 0:0e0631af0305 3327 @param sr The color window radius.
RyoheiHagimoto 0:0e0631af0305 3328 @param maxLevel Maximum level of the pyramid for the segmentation.
RyoheiHagimoto 0:0e0631af0305 3329 @param termcrit Termination criteria: when to stop meanshift iterations.
RyoheiHagimoto 0:0e0631af0305 3330 */
RyoheiHagimoto 0:0e0631af0305 3331 CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 3332 double sp, double sr, int maxLevel = 1,
RyoheiHagimoto 0:0e0631af0305 3333 TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
RyoheiHagimoto 0:0e0631af0305 3334
RyoheiHagimoto 0:0e0631af0305 3335 //! @}
RyoheiHagimoto 0:0e0631af0305 3336
RyoheiHagimoto 0:0e0631af0305 3337 //! @addtogroup imgproc_misc
RyoheiHagimoto 0:0e0631af0305 3338 //! @{
RyoheiHagimoto 0:0e0631af0305 3339
RyoheiHagimoto 0:0e0631af0305 3340 /** @example grabcut.cpp
RyoheiHagimoto 0:0e0631af0305 3341 An example using the GrabCut algorithm
RyoheiHagimoto 0:0e0631af0305 3342 */
RyoheiHagimoto 0:0e0631af0305 3343
RyoheiHagimoto 0:0e0631af0305 3344 /** @brief Runs the GrabCut algorithm.
RyoheiHagimoto 0:0e0631af0305 3345
RyoheiHagimoto 0:0e0631af0305 3346 The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
RyoheiHagimoto 0:0e0631af0305 3347
RyoheiHagimoto 0:0e0631af0305 3348 @param img Input 8-bit 3-channel image.
RyoheiHagimoto 0:0e0631af0305 3349 @param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
RyoheiHagimoto 0:0e0631af0305 3350 mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses.
RyoheiHagimoto 0:0e0631af0305 3351 @param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
RyoheiHagimoto 0:0e0631af0305 3352 "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
RyoheiHagimoto 0:0e0631af0305 3353 @param bgdModel Temporary array for the background model. Do not modify it while you are
RyoheiHagimoto 0:0e0631af0305 3354 processing the same image.
RyoheiHagimoto 0:0e0631af0305 3355 @param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
RyoheiHagimoto 0:0e0631af0305 3356 processing the same image.
RyoheiHagimoto 0:0e0631af0305 3357 @param iterCount Number of iterations the algorithm should make before returning the result. Note
RyoheiHagimoto 0:0e0631af0305 3358 that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or
RyoheiHagimoto 0:0e0631af0305 3359 mode==GC_EVAL .
RyoheiHagimoto 0:0e0631af0305 3360 @param mode Operation mode that could be one of the cv::GrabCutModes
RyoheiHagimoto 0:0e0631af0305 3361 */
RyoheiHagimoto 0:0e0631af0305 3362 CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
RyoheiHagimoto 0:0e0631af0305 3363 InputOutputArray bgdModel, InputOutputArray fgdModel,
RyoheiHagimoto 0:0e0631af0305 3364 int iterCount, int mode = GC_EVAL );
RyoheiHagimoto 0:0e0631af0305 3365
RyoheiHagimoto 0:0e0631af0305 3366 /** @example distrans.cpp
RyoheiHagimoto 0:0e0631af0305 3367 An example on using the distance transform\
RyoheiHagimoto 0:0e0631af0305 3368 */
RyoheiHagimoto 0:0e0631af0305 3369
RyoheiHagimoto 0:0e0631af0305 3370
RyoheiHagimoto 0:0e0631af0305 3371 /** @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
RyoheiHagimoto 0:0e0631af0305 3372
RyoheiHagimoto 0:0e0631af0305 3373 The function cv::distanceTransform calculates the approximate or precise distance from every binary
RyoheiHagimoto 0:0e0631af0305 3374 image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
RyoheiHagimoto 0:0e0631af0305 3375
RyoheiHagimoto 0:0e0631af0305 3376 When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the
RyoheiHagimoto 0:0e0631af0305 3377 algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
RyoheiHagimoto 0:0e0631af0305 3378
RyoheiHagimoto 0:0e0631af0305 3379 In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
RyoheiHagimoto 0:0e0631af0305 3380 finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
RyoheiHagimoto 0:0e0631af0305 3381 diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
RyoheiHagimoto 0:0e0631af0305 3382 distance is calculated as a sum of these basic distances. Since the distance function should be
RyoheiHagimoto 0:0e0631af0305 3383 symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
RyoheiHagimoto 0:0e0631af0305 3384 the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
RyoheiHagimoto 0:0e0631af0305 3385 same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated
RyoheiHagimoto 0:0e0631af0305 3386 precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a
RyoheiHagimoto 0:0e0631af0305 3387 relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
RyoheiHagimoto 0:0e0631af0305 3388 uses the values suggested in the original paper:
RyoheiHagimoto 0:0e0631af0305 3389 - DIST_L1: `a = 1, b = 2`
RyoheiHagimoto 0:0e0631af0305 3390 - DIST_L2:
RyoheiHagimoto 0:0e0631af0305 3391 - `3 x 3`: `a=0.955, b=1.3693`
RyoheiHagimoto 0:0e0631af0305 3392 - `5 x 5`: `a=1, b=1.4, c=2.1969`
RyoheiHagimoto 0:0e0631af0305 3393 - DIST_C: `a = 1, b = 1`
RyoheiHagimoto 0:0e0631af0305 3394
RyoheiHagimoto 0:0e0631af0305 3395 Typically, for a fast, coarse distance estimation DIST_L2, a \f$3\times 3\f$ mask is used. For a
RyoheiHagimoto 0:0e0631af0305 3396 more accurate distance estimation DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
RyoheiHagimoto 0:0e0631af0305 3397 Note that both the precise and the approximate algorithms are linear on the number of pixels.
RyoheiHagimoto 0:0e0631af0305 3398
RyoheiHagimoto 0:0e0631af0305 3399 This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
RyoheiHagimoto 0:0e0631af0305 3400 but also identifies the nearest connected component consisting of zero pixels
RyoheiHagimoto 0:0e0631af0305 3401 (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the
RyoheiHagimoto 0:0e0631af0305 3402 component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function
RyoheiHagimoto 0:0e0631af0305 3403 automatically finds connected components of zero pixels in the input image and marks them with
RyoheiHagimoto 0:0e0631af0305 3404 distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and
RyoheiHagimoto 0:0e0631af0305 3405 marks all the zero pixels with distinct labels.
RyoheiHagimoto 0:0e0631af0305 3406
RyoheiHagimoto 0:0e0631af0305 3407 In this mode, the complexity is still linear. That is, the function provides a very fast way to
RyoheiHagimoto 0:0e0631af0305 3408 compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
RyoheiHagimoto 0:0e0631af0305 3409 approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported
RyoheiHagimoto 0:0e0631af0305 3410 yet.
RyoheiHagimoto 0:0e0631af0305 3411
RyoheiHagimoto 0:0e0631af0305 3412 @param src 8-bit, single-channel (binary) source image.
RyoheiHagimoto 0:0e0631af0305 3413 @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
RyoheiHagimoto 0:0e0631af0305 3414 single-channel image of the same size as src.
RyoheiHagimoto 0:0e0631af0305 3415 @param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
RyoheiHagimoto 0:0e0631af0305 3416 CV_32SC1 and the same size as src.
RyoheiHagimoto 0:0e0631af0305 3417 @param distanceType Type of distance, see cv::DistanceTypes
RyoheiHagimoto 0:0e0631af0305 3418 @param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks.
RyoheiHagimoto 0:0e0631af0305 3419 DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type,
RyoheiHagimoto 0:0e0631af0305 3420 the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
RyoheiHagimoto 0:0e0631af0305 3421 5\f$ or any larger aperture.
RyoheiHagimoto 0:0e0631af0305 3422 @param labelType Type of the label array to build, see cv::DistanceTransformLabelTypes.
RyoheiHagimoto 0:0e0631af0305 3423 */
RyoheiHagimoto 0:0e0631af0305 3424 CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 3425 OutputArray labels, int distanceType, int maskSize,
RyoheiHagimoto 0:0e0631af0305 3426 int labelType = DIST_LABEL_CCOMP );
RyoheiHagimoto 0:0e0631af0305 3427
RyoheiHagimoto 0:0e0631af0305 3428 /** @overload
RyoheiHagimoto 0:0e0631af0305 3429 @param src 8-bit, single-channel (binary) source image.
RyoheiHagimoto 0:0e0631af0305 3430 @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
RyoheiHagimoto 0:0e0631af0305 3431 single-channel image of the same size as src .
RyoheiHagimoto 0:0e0631af0305 3432 @param distanceType Type of distance, see cv::DistanceTypes
RyoheiHagimoto 0:0e0631af0305 3433 @param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the
RyoheiHagimoto 0:0e0631af0305 3434 DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
RyoheiHagimoto 0:0e0631af0305 3435 the same result as \f$5\times 5\f$ or any larger aperture.
RyoheiHagimoto 0:0e0631af0305 3436 @param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
RyoheiHagimoto 0:0e0631af0305 3437 the first variant of the function and distanceType == DIST_L1.
RyoheiHagimoto 0:0e0631af0305 3438 */
RyoheiHagimoto 0:0e0631af0305 3439 CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
RyoheiHagimoto 0:0e0631af0305 3440 int distanceType, int maskSize, int dstType=CV_32F);
RyoheiHagimoto 0:0e0631af0305 3441
RyoheiHagimoto 0:0e0631af0305 3442 /** @example ffilldemo.cpp
RyoheiHagimoto 0:0e0631af0305 3443 An example using the FloodFill technique
RyoheiHagimoto 0:0e0631af0305 3444 */
RyoheiHagimoto 0:0e0631af0305 3445
RyoheiHagimoto 0:0e0631af0305 3446 /** @overload
RyoheiHagimoto 0:0e0631af0305 3447
RyoheiHagimoto 0:0e0631af0305 3448 variant without `mask` parameter
RyoheiHagimoto 0:0e0631af0305 3449 */
RyoheiHagimoto 0:0e0631af0305 3450 CV_EXPORTS int floodFill( InputOutputArray image,
RyoheiHagimoto 0:0e0631af0305 3451 Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0,
RyoheiHagimoto 0:0e0631af0305 3452 Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
RyoheiHagimoto 0:0e0631af0305 3453 int flags = 4 );
RyoheiHagimoto 0:0e0631af0305 3454
RyoheiHagimoto 0:0e0631af0305 3455 /** @brief Fills a connected component with the given color.
RyoheiHagimoto 0:0e0631af0305 3456
RyoheiHagimoto 0:0e0631af0305 3457 The function cv::floodFill fills a connected component starting from the seed point with the specified
RyoheiHagimoto 0:0e0631af0305 3458 color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
RyoheiHagimoto 0:0e0631af0305 3459 pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if:
RyoheiHagimoto 0:0e0631af0305 3460
RyoheiHagimoto 0:0e0631af0305 3461 - in case of a grayscale image and floating range
RyoheiHagimoto 0:0e0631af0305 3462 \f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f]
RyoheiHagimoto 0:0e0631af0305 3463
RyoheiHagimoto 0:0e0631af0305 3464
RyoheiHagimoto 0:0e0631af0305 3465 - in case of a grayscale image and fixed range
RyoheiHagimoto 0:0e0631af0305 3466 \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f]
RyoheiHagimoto 0:0e0631af0305 3467
RyoheiHagimoto 0:0e0631af0305 3468
RyoheiHagimoto 0:0e0631af0305 3469 - in case of a color image and floating range
RyoheiHagimoto 0:0e0631af0305 3470 \f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f]
RyoheiHagimoto 0:0e0631af0305 3471 \f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f]
RyoheiHagimoto 0:0e0631af0305 3472 and
RyoheiHagimoto 0:0e0631af0305 3473 \f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f]
RyoheiHagimoto 0:0e0631af0305 3474
RyoheiHagimoto 0:0e0631af0305 3475
RyoheiHagimoto 0:0e0631af0305 3476 - in case of a color image and fixed range
RyoheiHagimoto 0:0e0631af0305 3477 \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f]
RyoheiHagimoto 0:0e0631af0305 3478 \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f]
RyoheiHagimoto 0:0e0631af0305 3479 and
RyoheiHagimoto 0:0e0631af0305 3480 \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f]
RyoheiHagimoto 0:0e0631af0305 3481
RyoheiHagimoto 0:0e0631af0305 3482
RyoheiHagimoto 0:0e0631af0305 3483 where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the
RyoheiHagimoto 0:0e0631af0305 3484 component. That is, to be added to the connected component, a color/brightness of the pixel should
RyoheiHagimoto 0:0e0631af0305 3485 be close enough to:
RyoheiHagimoto 0:0e0631af0305 3486 - Color/brightness of one of its neighbors that already belong to the connected component in case
RyoheiHagimoto 0:0e0631af0305 3487 of a floating range.
RyoheiHagimoto 0:0e0631af0305 3488 - Color/brightness of the seed point in case of a fixed range.
RyoheiHagimoto 0:0e0631af0305 3489
RyoheiHagimoto 0:0e0631af0305 3490 Use these functions to either mark a connected component with the specified color in-place, or build
RyoheiHagimoto 0:0e0631af0305 3491 a mask and then extract the contour, or copy the region to another image, and so on.
RyoheiHagimoto 0:0e0631af0305 3492
RyoheiHagimoto 0:0e0631af0305 3493 @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
RyoheiHagimoto 0:0e0631af0305 3494 function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
RyoheiHagimoto 0:0e0631af0305 3495 the details below.
RyoheiHagimoto 0:0e0631af0305 3496 @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
RyoheiHagimoto 0:0e0631af0305 3497 taller than image. Since this is both an input and output parameter, you must take responsibility
RyoheiHagimoto 0:0e0631af0305 3498 of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
RyoheiHagimoto 0:0e0631af0305 3499 an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
RyoheiHagimoto 0:0e0631af0305 3500 mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
RyoheiHagimoto 0:0e0631af0305 3501 as described below. It is therefore possible to use the same mask in multiple calls to the function
RyoheiHagimoto 0:0e0631af0305 3502 to make sure the filled areas do not overlap.
RyoheiHagimoto 0:0e0631af0305 3503 @param seedPoint Starting point.
RyoheiHagimoto 0:0e0631af0305 3504 @param newVal New value of the repainted domain pixels.
RyoheiHagimoto 0:0e0631af0305 3505 @param loDiff Maximal lower brightness/color difference between the currently observed pixel and
RyoheiHagimoto 0:0e0631af0305 3506 one of its neighbors belonging to the component, or a seed pixel being added to the component.
RyoheiHagimoto 0:0e0631af0305 3507 @param upDiff Maximal upper brightness/color difference between the currently observed pixel and
RyoheiHagimoto 0:0e0631af0305 3508 one of its neighbors belonging to the component, or a seed pixel being added to the component.
RyoheiHagimoto 0:0e0631af0305 3509 @param rect Optional output parameter set by the function to the minimum bounding rectangle of the
RyoheiHagimoto 0:0e0631af0305 3510 repainted domain.
RyoheiHagimoto 0:0e0631af0305 3511 @param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
RyoheiHagimoto 0:0e0631af0305 3512 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
RyoheiHagimoto 0:0e0631af0305 3513 connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
RyoheiHagimoto 0:0e0631af0305 3514 will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
RyoheiHagimoto 0:0e0631af0305 3515 the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest
RyoheiHagimoto 0:0e0631af0305 3516 neighbours and fill the mask with a value of 255. The following additional options occupy higher
RyoheiHagimoto 0:0e0631af0305 3517 bits and therefore may be further combined with the connectivity and mask fill values using
RyoheiHagimoto 0:0e0631af0305 3518 bit-wise or (|), see cv::FloodFillFlags.
RyoheiHagimoto 0:0e0631af0305 3519
RyoheiHagimoto 0:0e0631af0305 3520 @note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the
RyoheiHagimoto 0:0e0631af0305 3521 pixel \f$(x+1, y+1)\f$ in the mask .
RyoheiHagimoto 0:0e0631af0305 3522
RyoheiHagimoto 0:0e0631af0305 3523 @sa findContours
RyoheiHagimoto 0:0e0631af0305 3524 */
RyoheiHagimoto 0:0e0631af0305 3525 CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
RyoheiHagimoto 0:0e0631af0305 3526 Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
RyoheiHagimoto 0:0e0631af0305 3527 Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
RyoheiHagimoto 0:0e0631af0305 3528 int flags = 4 );
RyoheiHagimoto 0:0e0631af0305 3529
RyoheiHagimoto 0:0e0631af0305 3530 /** @brief Converts an image from one color space to another.
RyoheiHagimoto 0:0e0631af0305 3531
RyoheiHagimoto 0:0e0631af0305 3532 The function converts an input image from one color space to another. In case of a transformation
RyoheiHagimoto 0:0e0631af0305 3533 to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
RyoheiHagimoto 0:0e0631af0305 3534 that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
RyoheiHagimoto 0:0e0631af0305 3535 bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
RyoheiHagimoto 0:0e0631af0305 3536 component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
RyoheiHagimoto 0:0e0631af0305 3537 sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
RyoheiHagimoto 0:0e0631af0305 3538
RyoheiHagimoto 0:0e0631af0305 3539 The conventional ranges for R, G, and B channel values are:
RyoheiHagimoto 0:0e0631af0305 3540 - 0 to 255 for CV_8U images
RyoheiHagimoto 0:0e0631af0305 3541 - 0 to 65535 for CV_16U images
RyoheiHagimoto 0:0e0631af0305 3542 - 0 to 1 for CV_32F images
RyoheiHagimoto 0:0e0631af0305 3543
RyoheiHagimoto 0:0e0631af0305 3544 In case of linear transformations, the range does not matter. But in case of a non-linear
RyoheiHagimoto 0:0e0631af0305 3545 transformation, an input RGB image should be normalized to the proper value range to get the correct
RyoheiHagimoto 0:0e0631af0305 3546 results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
RyoheiHagimoto 0:0e0631af0305 3547 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
RyoheiHagimoto 0:0e0631af0305 3548 have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor ,
RyoheiHagimoto 0:0e0631af0305 3549 you need first to scale the image down:
RyoheiHagimoto 0:0e0631af0305 3550 @code
RyoheiHagimoto 0:0e0631af0305 3551 img *= 1./255;
RyoheiHagimoto 0:0e0631af0305 3552 cvtColor(img, img, COLOR_BGR2Luv);
RyoheiHagimoto 0:0e0631af0305 3553 @endcode
RyoheiHagimoto 0:0e0631af0305 3554 If you use cvtColor with 8-bit images, the conversion will have some information lost. For many
RyoheiHagimoto 0:0e0631af0305 3555 applications, this will not be noticeable but it is recommended to use 32-bit images in applications
RyoheiHagimoto 0:0e0631af0305 3556 that need the full range of colors or that convert an image before an operation and then convert
RyoheiHagimoto 0:0e0631af0305 3557 back.
RyoheiHagimoto 0:0e0631af0305 3558
RyoheiHagimoto 0:0e0631af0305 3559 If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
RyoheiHagimoto 0:0e0631af0305 3560 range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
RyoheiHagimoto 0:0e0631af0305 3561
RyoheiHagimoto 0:0e0631af0305 3562 @param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
RyoheiHagimoto 0:0e0631af0305 3563 floating-point.
RyoheiHagimoto 0:0e0631af0305 3564 @param dst output image of the same size and depth as src.
RyoheiHagimoto 0:0e0631af0305 3565 @param code color space conversion code (see cv::ColorConversionCodes).
RyoheiHagimoto 0:0e0631af0305 3566 @param dstCn number of channels in the destination image; if the parameter is 0, the number of the
RyoheiHagimoto 0:0e0631af0305 3567 channels is derived automatically from src and code.
RyoheiHagimoto 0:0e0631af0305 3568
RyoheiHagimoto 0:0e0631af0305 3569 @see @ref imgproc_color_conversions
RyoheiHagimoto 0:0e0631af0305 3570 */
RyoheiHagimoto 0:0e0631af0305 3571 CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 );
RyoheiHagimoto 0:0e0631af0305 3572
RyoheiHagimoto 0:0e0631af0305 3573 //! @} imgproc_misc
RyoheiHagimoto 0:0e0631af0305 3574
RyoheiHagimoto 0:0e0631af0305 3575 // main function for all demosaicing procceses
RyoheiHagimoto 0:0e0631af0305 3576 CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0);
RyoheiHagimoto 0:0e0631af0305 3577
RyoheiHagimoto 0:0e0631af0305 3578 //! @addtogroup imgproc_shape
RyoheiHagimoto 0:0e0631af0305 3579 //! @{
RyoheiHagimoto 0:0e0631af0305 3580
RyoheiHagimoto 0:0e0631af0305 3581 /** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
RyoheiHagimoto 0:0e0631af0305 3582
RyoheiHagimoto 0:0e0631af0305 3583 The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
RyoheiHagimoto 0:0e0631af0305 3584 results are returned in the structure cv::Moments.
RyoheiHagimoto 0:0e0631af0305 3585
RyoheiHagimoto 0:0e0631af0305 3586 @param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
RyoheiHagimoto 0:0e0631af0305 3587 \f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
RyoheiHagimoto 0:0e0631af0305 3588 @param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
RyoheiHagimoto 0:0e0631af0305 3589 used for images only.
RyoheiHagimoto 0:0e0631af0305 3590 @returns moments.
RyoheiHagimoto 0:0e0631af0305 3591
RyoheiHagimoto 0:0e0631af0305 3592 @note Only applicable to contour moments calculations from Python bindings: Note that the numpy
RyoheiHagimoto 0:0e0631af0305 3593 type for the input array should be either np.int32 or np.float32.
RyoheiHagimoto 0:0e0631af0305 3594
RyoheiHagimoto 0:0e0631af0305 3595 @sa contourArea, arcLength
RyoheiHagimoto 0:0e0631af0305 3596 */
RyoheiHagimoto 0:0e0631af0305 3597 CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false );
RyoheiHagimoto 0:0e0631af0305 3598
RyoheiHagimoto 0:0e0631af0305 3599 /** @brief Calculates seven Hu invariants.
RyoheiHagimoto 0:0e0631af0305 3600
RyoheiHagimoto 0:0e0631af0305 3601 The function calculates seven Hu invariants (introduced in @cite Hu62; see also
RyoheiHagimoto 0:0e0631af0305 3602 <http://en.wikipedia.org/wiki/Image_moment>) defined as:
RyoheiHagimoto 0:0e0631af0305 3603
RyoheiHagimoto 0:0e0631af0305 3604 \f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 3605
RyoheiHagimoto 0:0e0631af0305 3606 where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ .
RyoheiHagimoto 0:0e0631af0305 3607
RyoheiHagimoto 0:0e0631af0305 3608 These values are proved to be invariants to the image scale, rotation, and reflection except the
RyoheiHagimoto 0:0e0631af0305 3609 seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of
RyoheiHagimoto 0:0e0631af0305 3610 infinite image resolution. In case of raster images, the computed Hu invariants for the original and
RyoheiHagimoto 0:0e0631af0305 3611 transformed images are a bit different.
RyoheiHagimoto 0:0e0631af0305 3612
RyoheiHagimoto 0:0e0631af0305 3613 @param moments Input moments computed with moments .
RyoheiHagimoto 0:0e0631af0305 3614 @param hu Output Hu invariants.
RyoheiHagimoto 0:0e0631af0305 3615
RyoheiHagimoto 0:0e0631af0305 3616 @sa matchShapes
RyoheiHagimoto 0:0e0631af0305 3617 */
RyoheiHagimoto 0:0e0631af0305 3618 CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
RyoheiHagimoto 0:0e0631af0305 3619
RyoheiHagimoto 0:0e0631af0305 3620 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3621 CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
RyoheiHagimoto 0:0e0631af0305 3622
RyoheiHagimoto 0:0e0631af0305 3623 //! @} imgproc_shape
RyoheiHagimoto 0:0e0631af0305 3624
RyoheiHagimoto 0:0e0631af0305 3625 //! @addtogroup imgproc_object
RyoheiHagimoto 0:0e0631af0305 3626 //! @{
RyoheiHagimoto 0:0e0631af0305 3627
RyoheiHagimoto 0:0e0631af0305 3628 //! type of the template matching operation
RyoheiHagimoto 0:0e0631af0305 3629 enum TemplateMatchModes {
RyoheiHagimoto 0:0e0631af0305 3630 TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f]
RyoheiHagimoto 0:0e0631af0305 3631 TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
RyoheiHagimoto 0:0e0631af0305 3632 TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f]
RyoheiHagimoto 0:0e0631af0305 3633 TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
RyoheiHagimoto 0:0e0631af0305 3634 TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f]
RyoheiHagimoto 0:0e0631af0305 3635 //!< where
RyoheiHagimoto 0:0e0631af0305 3636 //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f]
RyoheiHagimoto 0:0e0631af0305 3637 TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f]
RyoheiHagimoto 0:0e0631af0305 3638 };
RyoheiHagimoto 0:0e0631af0305 3639
RyoheiHagimoto 0:0e0631af0305 3640 /** @brief Compares a template against overlapped image regions.
RyoheiHagimoto 0:0e0631af0305 3641
RyoheiHagimoto 0:0e0631af0305 3642 The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
RyoheiHagimoto 0:0e0631af0305 3643 templ using the specified method and stores the comparison results in result . Here are the formulae
RyoheiHagimoto 0:0e0631af0305 3644 for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation
RyoheiHagimoto 0:0e0631af0305 3645 is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
RyoheiHagimoto 0:0e0631af0305 3646
RyoheiHagimoto 0:0e0631af0305 3647 After the function finishes the comparison, the best matches can be found as global minimums (when
RyoheiHagimoto 0:0e0631af0305 3648 TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the
RyoheiHagimoto 0:0e0631af0305 3649 minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
RyoheiHagimoto 0:0e0631af0305 3650 the denominator is done over all of the channels and separate mean values are used for each channel.
RyoheiHagimoto 0:0e0631af0305 3651 That is, the function can take a color template and a color image. The result will still be a
RyoheiHagimoto 0:0e0631af0305 3652 single-channel image, which is easier to analyze.
RyoheiHagimoto 0:0e0631af0305 3653
RyoheiHagimoto 0:0e0631af0305 3654 @param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
RyoheiHagimoto 0:0e0631af0305 3655 @param templ Searched template. It must be not greater than the source image and have the same
RyoheiHagimoto 0:0e0631af0305 3656 data type.
RyoheiHagimoto 0:0e0631af0305 3657 @param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
RyoheiHagimoto 0:0e0631af0305 3658 is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
RyoheiHagimoto 0:0e0631af0305 3659 @param method Parameter specifying the comparison method, see cv::TemplateMatchModes
RyoheiHagimoto 0:0e0631af0305 3660 @param mask Mask of searched template. It must have the same datatype and size with templ. It is
RyoheiHagimoto 0:0e0631af0305 3661 not set by default.
RyoheiHagimoto 0:0e0631af0305 3662 */
RyoheiHagimoto 0:0e0631af0305 3663 CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
RyoheiHagimoto 0:0e0631af0305 3664 OutputArray result, int method, InputArray mask = noArray() );
RyoheiHagimoto 0:0e0631af0305 3665
RyoheiHagimoto 0:0e0631af0305 3666 //! @}
RyoheiHagimoto 0:0e0631af0305 3667
RyoheiHagimoto 0:0e0631af0305 3668 //! @addtogroup imgproc_shape
RyoheiHagimoto 0:0e0631af0305 3669 //! @{
RyoheiHagimoto 0:0e0631af0305 3670
RyoheiHagimoto 0:0e0631af0305 3671 /** @brief computes the connected components labeled image of boolean image
RyoheiHagimoto 0:0e0631af0305 3672
RyoheiHagimoto 0:0e0631af0305 3673 image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
RyoheiHagimoto 0:0e0631af0305 3674 represents the background label. ltype specifies the output label image type, an important
RyoheiHagimoto 0:0e0631af0305 3675 consideration based on the total number of labels or alternatively the total number of pixels in
RyoheiHagimoto 0:0e0631af0305 3676 the source image. ccltype specifies the connected components labeling algorithm to use, currently
RyoheiHagimoto 0:0e0631af0305 3677 Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
RyoheiHagimoto 0:0e0631af0305 3678 for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
RyoheiHagimoto 0:0e0631af0305 3679
RyoheiHagimoto 0:0e0631af0305 3680 @param image the 8-bit single-channel image to be labeled
RyoheiHagimoto 0:0e0631af0305 3681 @param labels destination labeled image
RyoheiHagimoto 0:0e0631af0305 3682 @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
RyoheiHagimoto 0:0e0631af0305 3683 @param ltype output image label type. Currently CV_32S and CV_16U are supported.
RyoheiHagimoto 0:0e0631af0305 3684 @param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
RyoheiHagimoto 0:0e0631af0305 3685 */
RyoheiHagimoto 0:0e0631af0305 3686 CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels,
RyoheiHagimoto 0:0e0631af0305 3687 int connectivity, int ltype, int ccltype);
RyoheiHagimoto 0:0e0631af0305 3688
RyoheiHagimoto 0:0e0631af0305 3689
RyoheiHagimoto 0:0e0631af0305 3690 /** @overload
RyoheiHagimoto 0:0e0631af0305 3691
RyoheiHagimoto 0:0e0631af0305 3692 @param image the 8-bit single-channel image to be labeled
RyoheiHagimoto 0:0e0631af0305 3693 @param labels destination labeled image
RyoheiHagimoto 0:0e0631af0305 3694 @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
RyoheiHagimoto 0:0e0631af0305 3695 @param ltype output image label type. Currently CV_32S and CV_16U are supported.
RyoheiHagimoto 0:0e0631af0305 3696 */
RyoheiHagimoto 0:0e0631af0305 3697 CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
RyoheiHagimoto 0:0e0631af0305 3698 int connectivity = 8, int ltype = CV_32S);
RyoheiHagimoto 0:0e0631af0305 3699
RyoheiHagimoto 0:0e0631af0305 3700
RyoheiHagimoto 0:0e0631af0305 3701 /** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
RyoheiHagimoto 0:0e0631af0305 3702
RyoheiHagimoto 0:0e0631af0305 3703 image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
RyoheiHagimoto 0:0e0631af0305 3704 represents the background label. ltype specifies the output label image type, an important
RyoheiHagimoto 0:0e0631af0305 3705 consideration based on the total number of labels or alternatively the total number of pixels in
RyoheiHagimoto 0:0e0631af0305 3706 the source image. ccltype specifies the connected components labeling algorithm to use, currently
RyoheiHagimoto 0:0e0631af0305 3707 Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
RyoheiHagimoto 0:0e0631af0305 3708 for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
RyoheiHagimoto 0:0e0631af0305 3709
RyoheiHagimoto 0:0e0631af0305 3710
RyoheiHagimoto 0:0e0631af0305 3711 @param image the 8-bit single-channel image to be labeled
RyoheiHagimoto 0:0e0631af0305 3712 @param labels destination labeled image
RyoheiHagimoto 0:0e0631af0305 3713 @param stats statistics output for each label, including the background label, see below for
RyoheiHagimoto 0:0e0631af0305 3714 available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
RyoheiHagimoto 0:0e0631af0305 3715 cv::ConnectedComponentsTypes. The data type is CV_32S.
RyoheiHagimoto 0:0e0631af0305 3716 @param centroids centroid output for each label, including the background label. Centroids are
RyoheiHagimoto 0:0e0631af0305 3717 accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
RyoheiHagimoto 0:0e0631af0305 3718 @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
RyoheiHagimoto 0:0e0631af0305 3719 @param ltype output image label type. Currently CV_32S and CV_16U are supported.
RyoheiHagimoto 0:0e0631af0305 3720 @param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
RyoheiHagimoto 0:0e0631af0305 3721 */
RyoheiHagimoto 0:0e0631af0305 3722 CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels,
RyoheiHagimoto 0:0e0631af0305 3723 OutputArray stats, OutputArray centroids,
RyoheiHagimoto 0:0e0631af0305 3724 int connectivity, int ltype, int ccltype);
RyoheiHagimoto 0:0e0631af0305 3725
RyoheiHagimoto 0:0e0631af0305 3726 /** @overload
RyoheiHagimoto 0:0e0631af0305 3727 @param image the 8-bit single-channel image to be labeled
RyoheiHagimoto 0:0e0631af0305 3728 @param labels destination labeled image
RyoheiHagimoto 0:0e0631af0305 3729 @param stats statistics output for each label, including the background label, see below for
RyoheiHagimoto 0:0e0631af0305 3730 available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
RyoheiHagimoto 0:0e0631af0305 3731 cv::ConnectedComponentsTypes. The data type is CV_32S.
RyoheiHagimoto 0:0e0631af0305 3732 @param centroids centroid output for each label, including the background label. Centroids are
RyoheiHagimoto 0:0e0631af0305 3733 accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
RyoheiHagimoto 0:0e0631af0305 3734 @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
RyoheiHagimoto 0:0e0631af0305 3735 @param ltype output image label type. Currently CV_32S and CV_16U are supported.
RyoheiHagimoto 0:0e0631af0305 3736 */
RyoheiHagimoto 0:0e0631af0305 3737 CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels,
RyoheiHagimoto 0:0e0631af0305 3738 OutputArray stats, OutputArray centroids,
RyoheiHagimoto 0:0e0631af0305 3739 int connectivity = 8, int ltype = CV_32S);
RyoheiHagimoto 0:0e0631af0305 3740
RyoheiHagimoto 0:0e0631af0305 3741
RyoheiHagimoto 0:0e0631af0305 3742 /** @brief Finds contours in a binary image.
RyoheiHagimoto 0:0e0631af0305 3743
RyoheiHagimoto 0:0e0631af0305 3744 The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
RyoheiHagimoto 0:0e0631af0305 3745 are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
RyoheiHagimoto 0:0e0631af0305 3746 OpenCV sample directory.
RyoheiHagimoto 0:0e0631af0305 3747
RyoheiHagimoto 0:0e0631af0305 3748 @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
RyoheiHagimoto 0:0e0631af0305 3749 pixels remain 0's, so the image is treated as binary . You can use cv::compare, cv::inRange, cv::threshold ,
RyoheiHagimoto 0:0e0631af0305 3750 cv::adaptiveThreshold, cv::Canny, and others to create a binary image out of a grayscale or color one.
RyoheiHagimoto 0:0e0631af0305 3751 If mode equals to cv::RETR_CCOMP or cv::RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
RyoheiHagimoto 0:0e0631af0305 3752 @param contours Detected contours. Each contour is stored as a vector of points (e.g.
RyoheiHagimoto 0:0e0631af0305 3753 std::vector<std::vector<cv::Point> >).
RyoheiHagimoto 0:0e0631af0305 3754 @param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
RyoheiHagimoto 0:0e0631af0305 3755 as many elements as the number of contours. For each i-th contour contours[i], the elements
RyoheiHagimoto 0:0e0631af0305 3756 hierarchy[i][0] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[i][3] are set to 0-based indices
RyoheiHagimoto 0:0e0631af0305 3757 in contours of the next and previous contours at the same hierarchical level, the first child
RyoheiHagimoto 0:0e0631af0305 3758 contour and the parent contour, respectively. If for the contour i there are no next, previous,
RyoheiHagimoto 0:0e0631af0305 3759 parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
RyoheiHagimoto 0:0e0631af0305 3760 @param mode Contour retrieval mode, see cv::RetrievalModes
RyoheiHagimoto 0:0e0631af0305 3761 @param method Contour approximation method, see cv::ContourApproximationModes
RyoheiHagimoto 0:0e0631af0305 3762 @param offset Optional offset by which every contour point is shifted. This is useful if the
RyoheiHagimoto 0:0e0631af0305 3763 contours are extracted from the image ROI and then they should be analyzed in the whole image
RyoheiHagimoto 0:0e0631af0305 3764 context.
RyoheiHagimoto 0:0e0631af0305 3765 */
RyoheiHagimoto 0:0e0631af0305 3766 CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
RyoheiHagimoto 0:0e0631af0305 3767 OutputArray hierarchy, int mode,
RyoheiHagimoto 0:0e0631af0305 3768 int method, Point offset = Point());
RyoheiHagimoto 0:0e0631af0305 3769
RyoheiHagimoto 0:0e0631af0305 3770 /** @overload */
RyoheiHagimoto 0:0e0631af0305 3771 CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
RyoheiHagimoto 0:0e0631af0305 3772 int mode, int method, Point offset = Point());
RyoheiHagimoto 0:0e0631af0305 3773
RyoheiHagimoto 0:0e0631af0305 3774 /** @brief Approximates a polygonal curve(s) with the specified precision.
RyoheiHagimoto 0:0e0631af0305 3775
RyoheiHagimoto 0:0e0631af0305 3776 The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
RyoheiHagimoto 0:0e0631af0305 3777 vertices so that the distance between them is less or equal to the specified precision. It uses the
RyoheiHagimoto 0:0e0631af0305 3778 Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
RyoheiHagimoto 0:0e0631af0305 3779
RyoheiHagimoto 0:0e0631af0305 3780 @param curve Input vector of a 2D point stored in std::vector or Mat
RyoheiHagimoto 0:0e0631af0305 3781 @param approxCurve Result of the approximation. The type should match the type of the input curve.
RyoheiHagimoto 0:0e0631af0305 3782 @param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
RyoheiHagimoto 0:0e0631af0305 3783 between the original curve and its approximation.
RyoheiHagimoto 0:0e0631af0305 3784 @param closed If true, the approximated curve is closed (its first and last vertices are
RyoheiHagimoto 0:0e0631af0305 3785 connected). Otherwise, it is not closed.
RyoheiHagimoto 0:0e0631af0305 3786 */
RyoheiHagimoto 0:0e0631af0305 3787 CV_EXPORTS_W void approxPolyDP( InputArray curve,
RyoheiHagimoto 0:0e0631af0305 3788 OutputArray approxCurve,
RyoheiHagimoto 0:0e0631af0305 3789 double epsilon, bool closed );
RyoheiHagimoto 0:0e0631af0305 3790
RyoheiHagimoto 0:0e0631af0305 3791 /** @brief Calculates a contour perimeter or a curve length.
RyoheiHagimoto 0:0e0631af0305 3792
RyoheiHagimoto 0:0e0631af0305 3793 The function computes a curve length or a closed contour perimeter.
RyoheiHagimoto 0:0e0631af0305 3794
RyoheiHagimoto 0:0e0631af0305 3795 @param curve Input vector of 2D points, stored in std::vector or Mat.
RyoheiHagimoto 0:0e0631af0305 3796 @param closed Flag indicating whether the curve is closed or not.
RyoheiHagimoto 0:0e0631af0305 3797 */
RyoheiHagimoto 0:0e0631af0305 3798 CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
RyoheiHagimoto 0:0e0631af0305 3799
RyoheiHagimoto 0:0e0631af0305 3800 /** @brief Calculates the up-right bounding rectangle of a point set.
RyoheiHagimoto 0:0e0631af0305 3801
RyoheiHagimoto 0:0e0631af0305 3802 The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
RyoheiHagimoto 0:0e0631af0305 3803
RyoheiHagimoto 0:0e0631af0305 3804 @param points Input 2D point set, stored in std::vector or Mat.
RyoheiHagimoto 0:0e0631af0305 3805 */
RyoheiHagimoto 0:0e0631af0305 3806 CV_EXPORTS_W Rect boundingRect( InputArray points );
RyoheiHagimoto 0:0e0631af0305 3807
RyoheiHagimoto 0:0e0631af0305 3808 /** @brief Calculates a contour area.
RyoheiHagimoto 0:0e0631af0305 3809
RyoheiHagimoto 0:0e0631af0305 3810 The function computes a contour area. Similarly to moments , the area is computed using the Green
RyoheiHagimoto 0:0e0631af0305 3811 formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
RyoheiHagimoto 0:0e0631af0305 3812 drawContours or fillPoly , can be different. Also, the function will most certainly give a wrong
RyoheiHagimoto 0:0e0631af0305 3813 results for contours with self-intersections.
RyoheiHagimoto 0:0e0631af0305 3814
RyoheiHagimoto 0:0e0631af0305 3815 Example:
RyoheiHagimoto 0:0e0631af0305 3816 @code
RyoheiHagimoto 0:0e0631af0305 3817 vector<Point> contour;
RyoheiHagimoto 0:0e0631af0305 3818 contour.push_back(Point2f(0, 0));
RyoheiHagimoto 0:0e0631af0305 3819 contour.push_back(Point2f(10, 0));
RyoheiHagimoto 0:0e0631af0305 3820 contour.push_back(Point2f(10, 10));
RyoheiHagimoto 0:0e0631af0305 3821 contour.push_back(Point2f(5, 4));
RyoheiHagimoto 0:0e0631af0305 3822
RyoheiHagimoto 0:0e0631af0305 3823 double area0 = contourArea(contour);
RyoheiHagimoto 0:0e0631af0305 3824 vector<Point> approx;
RyoheiHagimoto 0:0e0631af0305 3825 approxPolyDP(contour, approx, 5, true);
RyoheiHagimoto 0:0e0631af0305 3826 double area1 = contourArea(approx);
RyoheiHagimoto 0:0e0631af0305 3827
RyoheiHagimoto 0:0e0631af0305 3828 cout << "area0 =" << area0 << endl <<
RyoheiHagimoto 0:0e0631af0305 3829 "area1 =" << area1 << endl <<
RyoheiHagimoto 0:0e0631af0305 3830 "approx poly vertices" << approx.size() << endl;
RyoheiHagimoto 0:0e0631af0305 3831 @endcode
RyoheiHagimoto 0:0e0631af0305 3832 @param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
RyoheiHagimoto 0:0e0631af0305 3833 @param oriented Oriented area flag. If it is true, the function returns a signed area value,
RyoheiHagimoto 0:0e0631af0305 3834 depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
RyoheiHagimoto 0:0e0631af0305 3835 determine orientation of a contour by taking the sign of an area. By default, the parameter is
RyoheiHagimoto 0:0e0631af0305 3836 false, which means that the absolute value is returned.
RyoheiHagimoto 0:0e0631af0305 3837 */
RyoheiHagimoto 0:0e0631af0305 3838 CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false );
RyoheiHagimoto 0:0e0631af0305 3839
RyoheiHagimoto 0:0e0631af0305 3840 /** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
RyoheiHagimoto 0:0e0631af0305 3841
RyoheiHagimoto 0:0e0631af0305 3842 The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a
RyoheiHagimoto 0:0e0631af0305 3843 specified point set. See the OpenCV sample minarea.cpp . Developer should keep in mind that the
RyoheiHagimoto 0:0e0631af0305 3844 returned rotatedRect can contain negative indices when data is close to the containing Mat element
RyoheiHagimoto 0:0e0631af0305 3845 boundary.
RyoheiHagimoto 0:0e0631af0305 3846
RyoheiHagimoto 0:0e0631af0305 3847 @param points Input vector of 2D points, stored in std::vector\<\> or Mat
RyoheiHagimoto 0:0e0631af0305 3848 */
RyoheiHagimoto 0:0e0631af0305 3849 CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
RyoheiHagimoto 0:0e0631af0305 3850
RyoheiHagimoto 0:0e0631af0305 3851 /** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
RyoheiHagimoto 0:0e0631af0305 3852
RyoheiHagimoto 0:0e0631af0305 3853 The function finds the four vertices of a rotated rectangle. This function is useful to draw the
RyoheiHagimoto 0:0e0631af0305 3854 rectangle. In C++, instead of using this function, you can directly use box.points() method. Please
RyoheiHagimoto 0:0e0631af0305 3855 visit the [tutorial on bounding
RyoheiHagimoto 0:0e0631af0305 3856 rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles)
RyoheiHagimoto 0:0e0631af0305 3857 for more information.
RyoheiHagimoto 0:0e0631af0305 3858
RyoheiHagimoto 0:0e0631af0305 3859 @param box The input rotated rectangle. It may be the output of
RyoheiHagimoto 0:0e0631af0305 3860 @param points The output array of four vertices of rectangles.
RyoheiHagimoto 0:0e0631af0305 3861 */
RyoheiHagimoto 0:0e0631af0305 3862 CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points);
RyoheiHagimoto 0:0e0631af0305 3863
RyoheiHagimoto 0:0e0631af0305 3864 /** @brief Finds a circle of the minimum area enclosing a 2D point set.
RyoheiHagimoto 0:0e0631af0305 3865
RyoheiHagimoto 0:0e0631af0305 3866 The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. See
RyoheiHagimoto 0:0e0631af0305 3867 the OpenCV sample minarea.cpp .
RyoheiHagimoto 0:0e0631af0305 3868
RyoheiHagimoto 0:0e0631af0305 3869 @param points Input vector of 2D points, stored in std::vector\<\> or Mat
RyoheiHagimoto 0:0e0631af0305 3870 @param center Output center of the circle.
RyoheiHagimoto 0:0e0631af0305 3871 @param radius Output radius of the circle.
RyoheiHagimoto 0:0e0631af0305 3872 */
RyoheiHagimoto 0:0e0631af0305 3873 CV_EXPORTS_W void minEnclosingCircle( InputArray points,
RyoheiHagimoto 0:0e0631af0305 3874 CV_OUT Point2f& center, CV_OUT float& radius );
RyoheiHagimoto 0:0e0631af0305 3875
RyoheiHagimoto 0:0e0631af0305 3876 /** @example minarea.cpp
RyoheiHagimoto 0:0e0631af0305 3877 */
RyoheiHagimoto 0:0e0631af0305 3878
RyoheiHagimoto 0:0e0631af0305 3879 /** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
RyoheiHagimoto 0:0e0631af0305 3880
RyoheiHagimoto 0:0e0631af0305 3881 The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
RyoheiHagimoto 0:0e0631af0305 3882 area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
RyoheiHagimoto 0:0e0631af0305 3883 *red* and the enclosing triangle in *yellow*.
RyoheiHagimoto 0:0e0631af0305 3884
RyoheiHagimoto 0:0e0631af0305 3885 ![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png)
RyoheiHagimoto 0:0e0631af0305 3886
RyoheiHagimoto 0:0e0631af0305 3887 The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's
RyoheiHagimoto 0:0e0631af0305 3888 @cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal
RyoheiHagimoto 0:0e0631af0305 3889 enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function
RyoheiHagimoto 0:0e0631af0305 3890 takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
RyoheiHagimoto 0:0e0631af0305 3891 2D point set is required. The complexity of the convexHull function is \f$O(n log(n))\f$ which is higher
RyoheiHagimoto 0:0e0631af0305 3892 than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$.
RyoheiHagimoto 0:0e0631af0305 3893
RyoheiHagimoto 0:0e0631af0305 3894 @param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat
RyoheiHagimoto 0:0e0631af0305 3895 @param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
RyoheiHagimoto 0:0e0631af0305 3896 of the OutputArray must be CV_32F.
RyoheiHagimoto 0:0e0631af0305 3897 */
RyoheiHagimoto 0:0e0631af0305 3898 CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle );
RyoheiHagimoto 0:0e0631af0305 3899
RyoheiHagimoto 0:0e0631af0305 3900 /** @brief Compares two shapes.
RyoheiHagimoto 0:0e0631af0305 3901
RyoheiHagimoto 0:0e0631af0305 3902 The function compares two shapes. All three implemented methods use the Hu invariants (see cv::HuMoments)
RyoheiHagimoto 0:0e0631af0305 3903
RyoheiHagimoto 0:0e0631af0305 3904 @param contour1 First contour or grayscale image.
RyoheiHagimoto 0:0e0631af0305 3905 @param contour2 Second contour or grayscale image.
RyoheiHagimoto 0:0e0631af0305 3906 @param method Comparison method, see ::ShapeMatchModes
RyoheiHagimoto 0:0e0631af0305 3907 @param parameter Method-specific parameter (not supported now).
RyoheiHagimoto 0:0e0631af0305 3908 */
RyoheiHagimoto 0:0e0631af0305 3909 CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
RyoheiHagimoto 0:0e0631af0305 3910 int method, double parameter );
RyoheiHagimoto 0:0e0631af0305 3911
RyoheiHagimoto 0:0e0631af0305 3912 /** @example convexhull.cpp
RyoheiHagimoto 0:0e0631af0305 3913 An example using the convexHull functionality
RyoheiHagimoto 0:0e0631af0305 3914 */
RyoheiHagimoto 0:0e0631af0305 3915
RyoheiHagimoto 0:0e0631af0305 3916 /** @brief Finds the convex hull of a point set.
RyoheiHagimoto 0:0e0631af0305 3917
RyoheiHagimoto 0:0e0631af0305 3918 The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82
RyoheiHagimoto 0:0e0631af0305 3919 that has *O(N logN)* complexity in the current implementation. See the OpenCV sample convexhull.cpp
RyoheiHagimoto 0:0e0631af0305 3920 that demonstrates the usage of different function variants.
RyoheiHagimoto 0:0e0631af0305 3921
RyoheiHagimoto 0:0e0631af0305 3922 @param points Input 2D point set, stored in std::vector or Mat.
RyoheiHagimoto 0:0e0631af0305 3923 @param hull Output convex hull. It is either an integer vector of indices or vector of points. In
RyoheiHagimoto 0:0e0631af0305 3924 the first case, the hull elements are 0-based indices of the convex hull points in the original
RyoheiHagimoto 0:0e0631af0305 3925 array (since the set of convex hull points is a subset of the original point set). In the second
RyoheiHagimoto 0:0e0631af0305 3926 case, hull elements are the convex hull points themselves.
RyoheiHagimoto 0:0e0631af0305 3927 @param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
RyoheiHagimoto 0:0e0631af0305 3928 Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
RyoheiHagimoto 0:0e0631af0305 3929 to the right, and its Y axis pointing upwards.
RyoheiHagimoto 0:0e0631af0305 3930 @param returnPoints Operation flag. In case of a matrix, when the flag is true, the function
RyoheiHagimoto 0:0e0631af0305 3931 returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
RyoheiHagimoto 0:0e0631af0305 3932 output array is std::vector, the flag is ignored, and the output depends on the type of the
RyoheiHagimoto 0:0e0631af0305 3933 vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies
RyoheiHagimoto 0:0e0631af0305 3934 returnPoints=true.
RyoheiHagimoto 0:0e0631af0305 3935 */
RyoheiHagimoto 0:0e0631af0305 3936 CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
RyoheiHagimoto 0:0e0631af0305 3937 bool clockwise = false, bool returnPoints = true );
RyoheiHagimoto 0:0e0631af0305 3938
RyoheiHagimoto 0:0e0631af0305 3939 /** @brief Finds the convexity defects of a contour.
RyoheiHagimoto 0:0e0631af0305 3940
RyoheiHagimoto 0:0e0631af0305 3941 The figure below displays convexity defects of a hand contour:
RyoheiHagimoto 0:0e0631af0305 3942
RyoheiHagimoto 0:0e0631af0305 3943 ![image](pics/defects.png)
RyoheiHagimoto 0:0e0631af0305 3944
RyoheiHagimoto 0:0e0631af0305 3945 @param contour Input contour.
RyoheiHagimoto 0:0e0631af0305 3946 @param convexhull Convex hull obtained using convexHull that should contain indices of the contour
RyoheiHagimoto 0:0e0631af0305 3947 points that make the hull.
RyoheiHagimoto 0:0e0631af0305 3948 @param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
RyoheiHagimoto 0:0e0631af0305 3949 interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i):
RyoheiHagimoto 0:0e0631af0305 3950 (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
RyoheiHagimoto 0:0e0631af0305 3951 in the original contour of the convexity defect beginning, end and the farthest point, and
RyoheiHagimoto 0:0e0631af0305 3952 fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
RyoheiHagimoto 0:0e0631af0305 3953 farthest contour point and the hull. That is, to get the floating-point value of the depth will be
RyoheiHagimoto 0:0e0631af0305 3954 fixpt_depth/256.0.
RyoheiHagimoto 0:0e0631af0305 3955 */
RyoheiHagimoto 0:0e0631af0305 3956 CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects );
RyoheiHagimoto 0:0e0631af0305 3957
RyoheiHagimoto 0:0e0631af0305 3958 /** @brief Tests a contour convexity.
RyoheiHagimoto 0:0e0631af0305 3959
RyoheiHagimoto 0:0e0631af0305 3960 The function tests whether the input contour is convex or not. The contour must be simple, that is,
RyoheiHagimoto 0:0e0631af0305 3961 without self-intersections. Otherwise, the function output is undefined.
RyoheiHagimoto 0:0e0631af0305 3962
RyoheiHagimoto 0:0e0631af0305 3963 @param contour Input vector of 2D points, stored in std::vector\<\> or Mat
RyoheiHagimoto 0:0e0631af0305 3964 */
RyoheiHagimoto 0:0e0631af0305 3965 CV_EXPORTS_W bool isContourConvex( InputArray contour );
RyoheiHagimoto 0:0e0631af0305 3966
RyoheiHagimoto 0:0e0631af0305 3967 //! finds intersection of two convex polygons
RyoheiHagimoto 0:0e0631af0305 3968 CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
RyoheiHagimoto 0:0e0631af0305 3969 OutputArray _p12, bool handleNested = true );
RyoheiHagimoto 0:0e0631af0305 3970
RyoheiHagimoto 0:0e0631af0305 3971 /** @example fitellipse.cpp
RyoheiHagimoto 0:0e0631af0305 3972 An example using the fitEllipse technique
RyoheiHagimoto 0:0e0631af0305 3973 */
RyoheiHagimoto 0:0e0631af0305 3974
RyoheiHagimoto 0:0e0631af0305 3975 /** @brief Fits an ellipse around a set of 2D points.
RyoheiHagimoto 0:0e0631af0305 3976
RyoheiHagimoto 0:0e0631af0305 3977 The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of
RyoheiHagimoto 0:0e0631af0305 3978 all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95
RyoheiHagimoto 0:0e0631af0305 3979 is used. Developer should keep in mind that it is possible that the returned
RyoheiHagimoto 0:0e0631af0305 3980 ellipse/rotatedRect data contains negative indices, due to the data points being close to the
RyoheiHagimoto 0:0e0631af0305 3981 border of the containing Mat element.
RyoheiHagimoto 0:0e0631af0305 3982
RyoheiHagimoto 0:0e0631af0305 3983 @param points Input 2D point set, stored in std::vector\<\> or Mat
RyoheiHagimoto 0:0e0631af0305 3984 */
RyoheiHagimoto 0:0e0631af0305 3985 CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
RyoheiHagimoto 0:0e0631af0305 3986
RyoheiHagimoto 0:0e0631af0305 3987 /** @brief Fits a line to a 2D or 3D point set.
RyoheiHagimoto 0:0e0631af0305 3988
RyoheiHagimoto 0:0e0631af0305 3989 The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
RyoheiHagimoto 0:0e0631af0305 3990 \f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one
RyoheiHagimoto 0:0e0631af0305 3991 of the following:
RyoheiHagimoto 0:0e0631af0305 3992 - DIST_L2
RyoheiHagimoto 0:0e0631af0305 3993 \f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f]
RyoheiHagimoto 0:0e0631af0305 3994 - DIST_L1
RyoheiHagimoto 0:0e0631af0305 3995 \f[\rho (r) = r\f]
RyoheiHagimoto 0:0e0631af0305 3996 - DIST_L12
RyoheiHagimoto 0:0e0631af0305 3997 \f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
RyoheiHagimoto 0:0e0631af0305 3998 - DIST_FAIR
RyoheiHagimoto 0:0e0631af0305 3999 \f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f]
RyoheiHagimoto 0:0e0631af0305 4000 - DIST_WELSCH
RyoheiHagimoto 0:0e0631af0305 4001 \f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f]
RyoheiHagimoto 0:0e0631af0305 4002 - DIST_HUBER
RyoheiHagimoto 0:0e0631af0305 4003 \f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
RyoheiHagimoto 0:0e0631af0305 4004
RyoheiHagimoto 0:0e0631af0305 4005 The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
RyoheiHagimoto 0:0e0631af0305 4006 that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
RyoheiHagimoto 0:0e0631af0305 4007 weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
RyoheiHagimoto 0:0e0631af0305 4008
RyoheiHagimoto 0:0e0631af0305 4009 @param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat.
RyoheiHagimoto 0:0e0631af0305 4010 @param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
RyoheiHagimoto 0:0e0631af0305 4011 (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
RyoheiHagimoto 0:0e0631af0305 4012 (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
RyoheiHagimoto 0:0e0631af0305 4013 Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
RyoheiHagimoto 0:0e0631af0305 4014 and (x0, y0, z0) is a point on the line.
RyoheiHagimoto 0:0e0631af0305 4015 @param distType Distance used by the M-estimator, see cv::DistanceTypes
RyoheiHagimoto 0:0e0631af0305 4016 @param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
RyoheiHagimoto 0:0e0631af0305 4017 is chosen.
RyoheiHagimoto 0:0e0631af0305 4018 @param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
RyoheiHagimoto 0:0e0631af0305 4019 @param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
RyoheiHagimoto 0:0e0631af0305 4020 */
RyoheiHagimoto 0:0e0631af0305 4021 CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
RyoheiHagimoto 0:0e0631af0305 4022 double param, double reps, double aeps );
RyoheiHagimoto 0:0e0631af0305 4023
RyoheiHagimoto 0:0e0631af0305 4024 /** @brief Performs a point-in-contour test.
RyoheiHagimoto 0:0e0631af0305 4025
RyoheiHagimoto 0:0e0631af0305 4026 The function determines whether the point is inside a contour, outside, or lies on an edge (or
RyoheiHagimoto 0:0e0631af0305 4027 coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
RyoheiHagimoto 0:0e0631af0305 4028 value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
RyoheiHagimoto 0:0e0631af0305 4029 Otherwise, the return value is a signed distance between the point and the nearest contour edge.
RyoheiHagimoto 0:0e0631af0305 4030
RyoheiHagimoto 0:0e0631af0305 4031 See below a sample output of the function where each image pixel is tested against the contour:
RyoheiHagimoto 0:0e0631af0305 4032
RyoheiHagimoto 0:0e0631af0305 4033 ![sample output](pics/pointpolygon.png)
RyoheiHagimoto 0:0e0631af0305 4034
RyoheiHagimoto 0:0e0631af0305 4035 @param contour Input contour.
RyoheiHagimoto 0:0e0631af0305 4036 @param pt Point tested against the contour.
RyoheiHagimoto 0:0e0631af0305 4037 @param measureDist If true, the function estimates the signed distance from the point to the
RyoheiHagimoto 0:0e0631af0305 4038 nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
RyoheiHagimoto 0:0e0631af0305 4039 */
RyoheiHagimoto 0:0e0631af0305 4040 CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
RyoheiHagimoto 0:0e0631af0305 4041
RyoheiHagimoto 0:0e0631af0305 4042 /** @brief Finds out if there is any intersection between two rotated rectangles.
RyoheiHagimoto 0:0e0631af0305 4043
RyoheiHagimoto 0:0e0631af0305 4044 If there is then the vertices of the interesecting region are returned as well.
RyoheiHagimoto 0:0e0631af0305 4045
RyoheiHagimoto 0:0e0631af0305 4046 Below are some examples of intersection configurations. The hatched pattern indicates the
RyoheiHagimoto 0:0e0631af0305 4047 intersecting region and the red vertices are returned by the function.
RyoheiHagimoto 0:0e0631af0305 4048
RyoheiHagimoto 0:0e0631af0305 4049 ![intersection examples](pics/intersection.png)
RyoheiHagimoto 0:0e0631af0305 4050
RyoheiHagimoto 0:0e0631af0305 4051 @param rect1 First rectangle
RyoheiHagimoto 0:0e0631af0305 4052 @param rect2 Second rectangle
RyoheiHagimoto 0:0e0631af0305 4053 @param intersectingRegion The output array of the verticies of the intersecting region. It returns
RyoheiHagimoto 0:0e0631af0305 4054 at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2.
RyoheiHagimoto 0:0e0631af0305 4055 @returns One of cv::RectanglesIntersectTypes
RyoheiHagimoto 0:0e0631af0305 4056 */
RyoheiHagimoto 0:0e0631af0305 4057 CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion );
RyoheiHagimoto 0:0e0631af0305 4058
RyoheiHagimoto 0:0e0631af0305 4059 //! @} imgproc_shape
RyoheiHagimoto 0:0e0631af0305 4060
RyoheiHagimoto 0:0e0631af0305 4061 CV_EXPORTS_W Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
RyoheiHagimoto 0:0e0631af0305 4062
RyoheiHagimoto 0:0e0631af0305 4063 //! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
RyoheiHagimoto 0:0e0631af0305 4064 //! Detects position only without traslation and rotation
RyoheiHagimoto 0:0e0631af0305 4065 CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
RyoheiHagimoto 0:0e0631af0305 4066
RyoheiHagimoto 0:0e0631af0305 4067 //! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
RyoheiHagimoto 0:0e0631af0305 4068 //! Detects position, traslation and rotation
RyoheiHagimoto 0:0e0631af0305 4069 CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
RyoheiHagimoto 0:0e0631af0305 4070
RyoheiHagimoto 0:0e0631af0305 4071 //! Performs linear blending of two images
RyoheiHagimoto 0:0e0631af0305 4072 CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
RyoheiHagimoto 0:0e0631af0305 4073
RyoheiHagimoto 0:0e0631af0305 4074 //! @addtogroup imgproc_colormap
RyoheiHagimoto 0:0e0631af0305 4075 //! @{
RyoheiHagimoto 0:0e0631af0305 4076
RyoheiHagimoto 0:0e0631af0305 4077 //! GNU Octave/MATLAB equivalent colormaps
RyoheiHagimoto 0:0e0631af0305 4078 enum ColormapTypes
RyoheiHagimoto 0:0e0631af0305 4079 {
RyoheiHagimoto 0:0e0631af0305 4080 COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg)
RyoheiHagimoto 0:0e0631af0305 4081 COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg)
RyoheiHagimoto 0:0e0631af0305 4082 COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg)
RyoheiHagimoto 0:0e0631af0305 4083 COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg)
RyoheiHagimoto 0:0e0631af0305 4084 COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg)
RyoheiHagimoto 0:0e0631af0305 4085 COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg)
RyoheiHagimoto 0:0e0631af0305 4086 COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg)
RyoheiHagimoto 0:0e0631af0305 4087 COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg)
RyoheiHagimoto 0:0e0631af0305 4088 COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg)
RyoheiHagimoto 0:0e0631af0305 4089 COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg)
RyoheiHagimoto 0:0e0631af0305 4090 COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg)
RyoheiHagimoto 0:0e0631af0305 4091 COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg)
RyoheiHagimoto 0:0e0631af0305 4092 COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg)
RyoheiHagimoto 0:0e0631af0305 4093 };
RyoheiHagimoto 0:0e0631af0305 4094
RyoheiHagimoto 0:0e0631af0305 4095 /** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
RyoheiHagimoto 0:0e0631af0305 4096
RyoheiHagimoto 0:0e0631af0305 4097 @param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
RyoheiHagimoto 0:0e0631af0305 4098 @param dst The result is the colormapped source image. Note: Mat::create is called on dst.
RyoheiHagimoto 0:0e0631af0305 4099 @param colormap The colormap to apply, see cv::ColormapTypes
RyoheiHagimoto 0:0e0631af0305 4100 */
RyoheiHagimoto 0:0e0631af0305 4101 CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
RyoheiHagimoto 0:0e0631af0305 4102
RyoheiHagimoto 0:0e0631af0305 4103 //! @} imgproc_colormap
RyoheiHagimoto 0:0e0631af0305 4104
RyoheiHagimoto 0:0e0631af0305 4105 //! @addtogroup imgproc_draw
RyoheiHagimoto 0:0e0631af0305 4106 //! @{
RyoheiHagimoto 0:0e0631af0305 4107
RyoheiHagimoto 0:0e0631af0305 4108 /** @brief Draws a line segment connecting two points.
RyoheiHagimoto 0:0e0631af0305 4109
RyoheiHagimoto 0:0e0631af0305 4110 The function line draws the line segment between pt1 and pt2 points in the image. The line is
RyoheiHagimoto 0:0e0631af0305 4111 clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
RyoheiHagimoto 0:0e0631af0305 4112 or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
RyoheiHagimoto 0:0e0631af0305 4113 lines are drawn using Gaussian filtering.
RyoheiHagimoto 0:0e0631af0305 4114
RyoheiHagimoto 0:0e0631af0305 4115 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4116 @param pt1 First point of the line segment.
RyoheiHagimoto 0:0e0631af0305 4117 @param pt2 Second point of the line segment.
RyoheiHagimoto 0:0e0631af0305 4118 @param color Line color.
RyoheiHagimoto 0:0e0631af0305 4119 @param thickness Line thickness.
RyoheiHagimoto 0:0e0631af0305 4120 @param lineType Type of the line, see cv::LineTypes.
RyoheiHagimoto 0:0e0631af0305 4121 @param shift Number of fractional bits in the point coordinates.
RyoheiHagimoto 0:0e0631af0305 4122 */
RyoheiHagimoto 0:0e0631af0305 4123 CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4124 int thickness = 1, int lineType = LINE_8, int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4125
RyoheiHagimoto 0:0e0631af0305 4126 /** @brief Draws a arrow segment pointing from the first point to the second one.
RyoheiHagimoto 0:0e0631af0305 4127
RyoheiHagimoto 0:0e0631af0305 4128 The function arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::line.
RyoheiHagimoto 0:0e0631af0305 4129
RyoheiHagimoto 0:0e0631af0305 4130 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4131 @param pt1 The point the arrow starts from.
RyoheiHagimoto 0:0e0631af0305 4132 @param pt2 The point the arrow points to.
RyoheiHagimoto 0:0e0631af0305 4133 @param color Line color.
RyoheiHagimoto 0:0e0631af0305 4134 @param thickness Line thickness.
RyoheiHagimoto 0:0e0631af0305 4135 @param line_type Type of the line, see cv::LineTypes
RyoheiHagimoto 0:0e0631af0305 4136 @param shift Number of fractional bits in the point coordinates.
RyoheiHagimoto 0:0e0631af0305 4137 @param tipLength The length of the arrow tip in relation to the arrow length
RyoheiHagimoto 0:0e0631af0305 4138 */
RyoheiHagimoto 0:0e0631af0305 4139 CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4140 int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
RyoheiHagimoto 0:0e0631af0305 4141
RyoheiHagimoto 0:0e0631af0305 4142 /** @brief Draws a simple, thick, or filled up-right rectangle.
RyoheiHagimoto 0:0e0631af0305 4143
RyoheiHagimoto 0:0e0631af0305 4144 The function rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
RyoheiHagimoto 0:0e0631af0305 4145 are pt1 and pt2.
RyoheiHagimoto 0:0e0631af0305 4146
RyoheiHagimoto 0:0e0631af0305 4147 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4148 @param pt1 Vertex of the rectangle.
RyoheiHagimoto 0:0e0631af0305 4149 @param pt2 Vertex of the rectangle opposite to pt1 .
RyoheiHagimoto 0:0e0631af0305 4150 @param color Rectangle color or brightness (grayscale image).
RyoheiHagimoto 0:0e0631af0305 4151 @param thickness Thickness of lines that make up the rectangle. Negative values, like CV_FILLED ,
RyoheiHagimoto 0:0e0631af0305 4152 mean that the function has to draw a filled rectangle.
RyoheiHagimoto 0:0e0631af0305 4153 @param lineType Type of the line. See the line description.
RyoheiHagimoto 0:0e0631af0305 4154 @param shift Number of fractional bits in the point coordinates.
RyoheiHagimoto 0:0e0631af0305 4155 */
RyoheiHagimoto 0:0e0631af0305 4156 CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
RyoheiHagimoto 0:0e0631af0305 4157 const Scalar& color, int thickness = 1,
RyoheiHagimoto 0:0e0631af0305 4158 int lineType = LINE_8, int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4159
RyoheiHagimoto 0:0e0631af0305 4160 /** @overload
RyoheiHagimoto 0:0e0631af0305 4161
RyoheiHagimoto 0:0e0631af0305 4162 use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and
RyoheiHagimoto 0:0e0631af0305 4163 r.br()-Point(1,1)` are opposite corners
RyoheiHagimoto 0:0e0631af0305 4164 */
RyoheiHagimoto 0:0e0631af0305 4165 CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
RyoheiHagimoto 0:0e0631af0305 4166 const Scalar& color, int thickness = 1,
RyoheiHagimoto 0:0e0631af0305 4167 int lineType = LINE_8, int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4168
RyoheiHagimoto 0:0e0631af0305 4169 /** @brief Draws a circle.
RyoheiHagimoto 0:0e0631af0305 4170
RyoheiHagimoto 0:0e0631af0305 4171 The function circle draws a simple or filled circle with a given center and radius.
RyoheiHagimoto 0:0e0631af0305 4172 @param img Image where the circle is drawn.
RyoheiHagimoto 0:0e0631af0305 4173 @param center Center of the circle.
RyoheiHagimoto 0:0e0631af0305 4174 @param radius Radius of the circle.
RyoheiHagimoto 0:0e0631af0305 4175 @param color Circle color.
RyoheiHagimoto 0:0e0631af0305 4176 @param thickness Thickness of the circle outline, if positive. Negative thickness means that a
RyoheiHagimoto 0:0e0631af0305 4177 filled circle is to be drawn.
RyoheiHagimoto 0:0e0631af0305 4178 @param lineType Type of the circle boundary. See the line description.
RyoheiHagimoto 0:0e0631af0305 4179 @param shift Number of fractional bits in the coordinates of the center and in the radius value.
RyoheiHagimoto 0:0e0631af0305 4180 */
RyoheiHagimoto 0:0e0631af0305 4181 CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
RyoheiHagimoto 0:0e0631af0305 4182 const Scalar& color, int thickness = 1,
RyoheiHagimoto 0:0e0631af0305 4183 int lineType = LINE_8, int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4184
RyoheiHagimoto 0:0e0631af0305 4185 /** @brief Draws a simple or thick elliptic arc or fills an ellipse sector.
RyoheiHagimoto 0:0e0631af0305 4186
RyoheiHagimoto 0:0e0631af0305 4187 The function cv::ellipse with less parameters draws an ellipse outline, a filled ellipse, an elliptic
RyoheiHagimoto 0:0e0631af0305 4188 arc, or a filled ellipse sector. A piecewise-linear curve is used to approximate the elliptic arc
RyoheiHagimoto 0:0e0631af0305 4189 boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
RyoheiHagimoto 0:0e0631af0305 4190 ellipse2Poly and then render it with polylines or fill it with fillPoly . If you use the first
RyoheiHagimoto 0:0e0631af0305 4191 variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and
RyoheiHagimoto 0:0e0631af0305 4192 endAngle=360 . The figure below explains the meaning of the parameters.
RyoheiHagimoto 0:0e0631af0305 4193
RyoheiHagimoto 0:0e0631af0305 4194 ![Parameters of Elliptic Arc](pics/ellipse.png)
RyoheiHagimoto 0:0e0631af0305 4195
RyoheiHagimoto 0:0e0631af0305 4196 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4197 @param center Center of the ellipse.
RyoheiHagimoto 0:0e0631af0305 4198 @param axes Half of the size of the ellipse main axes.
RyoheiHagimoto 0:0e0631af0305 4199 @param angle Ellipse rotation angle in degrees.
RyoheiHagimoto 0:0e0631af0305 4200 @param startAngle Starting angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4201 @param endAngle Ending angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4202 @param color Ellipse color.
RyoheiHagimoto 0:0e0631af0305 4203 @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
RyoheiHagimoto 0:0e0631af0305 4204 a filled ellipse sector is to be drawn.
RyoheiHagimoto 0:0e0631af0305 4205 @param lineType Type of the ellipse boundary. See the line description.
RyoheiHagimoto 0:0e0631af0305 4206 @param shift Number of fractional bits in the coordinates of the center and values of axes.
RyoheiHagimoto 0:0e0631af0305 4207 */
RyoheiHagimoto 0:0e0631af0305 4208 CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
RyoheiHagimoto 0:0e0631af0305 4209 double angle, double startAngle, double endAngle,
RyoheiHagimoto 0:0e0631af0305 4210 const Scalar& color, int thickness = 1,
RyoheiHagimoto 0:0e0631af0305 4211 int lineType = LINE_8, int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4212
RyoheiHagimoto 0:0e0631af0305 4213 /** @overload
RyoheiHagimoto 0:0e0631af0305 4214 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4215 @param box Alternative ellipse representation via RotatedRect. This means that the function draws
RyoheiHagimoto 0:0e0631af0305 4216 an ellipse inscribed in the rotated rectangle.
RyoheiHagimoto 0:0e0631af0305 4217 @param color Ellipse color.
RyoheiHagimoto 0:0e0631af0305 4218 @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
RyoheiHagimoto 0:0e0631af0305 4219 a filled ellipse sector is to be drawn.
RyoheiHagimoto 0:0e0631af0305 4220 @param lineType Type of the ellipse boundary. See the line description.
RyoheiHagimoto 0:0e0631af0305 4221 */
RyoheiHagimoto 0:0e0631af0305 4222 CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4223 int thickness = 1, int lineType = LINE_8);
RyoheiHagimoto 0:0e0631af0305 4224
RyoheiHagimoto 0:0e0631af0305 4225 /* ----------------------------------------------------------------------------------------- */
RyoheiHagimoto 0:0e0631af0305 4226 /* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */
RyoheiHagimoto 0:0e0631af0305 4227 /* ----------------------------------------------------------------------------------------- */
RyoheiHagimoto 0:0e0631af0305 4228
RyoheiHagimoto 0:0e0631af0305 4229 //! Possible set of marker types used for the cv::drawMarker function
RyoheiHagimoto 0:0e0631af0305 4230 enum MarkerTypes
RyoheiHagimoto 0:0e0631af0305 4231 {
RyoheiHagimoto 0:0e0631af0305 4232 MARKER_CROSS = 0, //!< A crosshair marker shape
RyoheiHagimoto 0:0e0631af0305 4233 MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape
RyoheiHagimoto 0:0e0631af0305 4234 MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross
RyoheiHagimoto 0:0e0631af0305 4235 MARKER_DIAMOND = 3, //!< A diamond marker shape
RyoheiHagimoto 0:0e0631af0305 4236 MARKER_SQUARE = 4, //!< A square marker shape
RyoheiHagimoto 0:0e0631af0305 4237 MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape
RyoheiHagimoto 0:0e0631af0305 4238 MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape
RyoheiHagimoto 0:0e0631af0305 4239 };
RyoheiHagimoto 0:0e0631af0305 4240
RyoheiHagimoto 0:0e0631af0305 4241 /** @brief Draws a marker on a predefined position in an image.
RyoheiHagimoto 0:0e0631af0305 4242
RyoheiHagimoto 0:0e0631af0305 4243 The function drawMarker draws a marker on a given position in the image. For the moment several
RyoheiHagimoto 0:0e0631af0305 4244 marker types are supported, see cv::MarkerTypes for more information.
RyoheiHagimoto 0:0e0631af0305 4245
RyoheiHagimoto 0:0e0631af0305 4246 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4247 @param position The point where the crosshair is positioned.
RyoheiHagimoto 0:0e0631af0305 4248 @param color Line color.
RyoheiHagimoto 0:0e0631af0305 4249 @param markerType The specific type of marker you want to use, see cv::MarkerTypes
RyoheiHagimoto 0:0e0631af0305 4250 @param thickness Line thickness.
RyoheiHagimoto 0:0e0631af0305 4251 @param line_type Type of the line, see cv::LineTypes
RyoheiHagimoto 0:0e0631af0305 4252 @param markerSize The length of the marker axis [default = 20 pixels]
RyoheiHagimoto 0:0e0631af0305 4253 */
RyoheiHagimoto 0:0e0631af0305 4254 CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4255 int markerType = MARKER_CROSS, int markerSize=20, int thickness=1,
RyoheiHagimoto 0:0e0631af0305 4256 int line_type=8);
RyoheiHagimoto 0:0e0631af0305 4257
RyoheiHagimoto 0:0e0631af0305 4258 /* ----------------------------------------------------------------------------------------- */
RyoheiHagimoto 0:0e0631af0305 4259 /* END OF MARKER SECTION */
RyoheiHagimoto 0:0e0631af0305 4260 /* ----------------------------------------------------------------------------------------- */
RyoheiHagimoto 0:0e0631af0305 4261
RyoheiHagimoto 0:0e0631af0305 4262 /** @overload */
RyoheiHagimoto 0:0e0631af0305 4263 CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
RyoheiHagimoto 0:0e0631af0305 4264 const Scalar& color, int lineType = LINE_8,
RyoheiHagimoto 0:0e0631af0305 4265 int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4266
RyoheiHagimoto 0:0e0631af0305 4267 /** @brief Fills a convex polygon.
RyoheiHagimoto 0:0e0631af0305 4268
RyoheiHagimoto 0:0e0631af0305 4269 The function fillConvexPoly draws a filled convex polygon. This function is much faster than the
RyoheiHagimoto 0:0e0631af0305 4270 function cv::fillPoly . It can fill not only convex polygons but any monotonic polygon without
RyoheiHagimoto 0:0e0631af0305 4271 self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
RyoheiHagimoto 0:0e0631af0305 4272 twice at the most (though, its top-most and/or the bottom edge could be horizontal).
RyoheiHagimoto 0:0e0631af0305 4273
RyoheiHagimoto 0:0e0631af0305 4274 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4275 @param points Polygon vertices.
RyoheiHagimoto 0:0e0631af0305 4276 @param color Polygon color.
RyoheiHagimoto 0:0e0631af0305 4277 @param lineType Type of the polygon boundaries. See the line description.
RyoheiHagimoto 0:0e0631af0305 4278 @param shift Number of fractional bits in the vertex coordinates.
RyoheiHagimoto 0:0e0631af0305 4279 */
RyoheiHagimoto 0:0e0631af0305 4280 CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
RyoheiHagimoto 0:0e0631af0305 4281 const Scalar& color, int lineType = LINE_8,
RyoheiHagimoto 0:0e0631af0305 4282 int shift = 0);
RyoheiHagimoto 0:0e0631af0305 4283
RyoheiHagimoto 0:0e0631af0305 4284 /** @overload */
RyoheiHagimoto 0:0e0631af0305 4285 CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
RyoheiHagimoto 0:0e0631af0305 4286 const int* npts, int ncontours,
RyoheiHagimoto 0:0e0631af0305 4287 const Scalar& color, int lineType = LINE_8, int shift = 0,
RyoheiHagimoto 0:0e0631af0305 4288 Point offset = Point() );
RyoheiHagimoto 0:0e0631af0305 4289
RyoheiHagimoto 0:0e0631af0305 4290 /** @brief Fills the area bounded by one or more polygons.
RyoheiHagimoto 0:0e0631af0305 4291
RyoheiHagimoto 0:0e0631af0305 4292 The function fillPoly fills an area bounded by several polygonal contours. The function can fill
RyoheiHagimoto 0:0e0631af0305 4293 complex areas, for example, areas with holes, contours with self-intersections (some of their
RyoheiHagimoto 0:0e0631af0305 4294 parts), and so forth.
RyoheiHagimoto 0:0e0631af0305 4295
RyoheiHagimoto 0:0e0631af0305 4296 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4297 @param pts Array of polygons where each polygon is represented as an array of points.
RyoheiHagimoto 0:0e0631af0305 4298 @param color Polygon color.
RyoheiHagimoto 0:0e0631af0305 4299 @param lineType Type of the polygon boundaries. See the line description.
RyoheiHagimoto 0:0e0631af0305 4300 @param shift Number of fractional bits in the vertex coordinates.
RyoheiHagimoto 0:0e0631af0305 4301 @param offset Optional offset of all points of the contours.
RyoheiHagimoto 0:0e0631af0305 4302 */
RyoheiHagimoto 0:0e0631af0305 4303 CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
RyoheiHagimoto 0:0e0631af0305 4304 const Scalar& color, int lineType = LINE_8, int shift = 0,
RyoheiHagimoto 0:0e0631af0305 4305 Point offset = Point() );
RyoheiHagimoto 0:0e0631af0305 4306
RyoheiHagimoto 0:0e0631af0305 4307 /** @overload */
RyoheiHagimoto 0:0e0631af0305 4308 CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
RyoheiHagimoto 0:0e0631af0305 4309 int ncontours, bool isClosed, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4310 int thickness = 1, int lineType = LINE_8, int shift = 0 );
RyoheiHagimoto 0:0e0631af0305 4311
RyoheiHagimoto 0:0e0631af0305 4312 /** @brief Draws several polygonal curves.
RyoheiHagimoto 0:0e0631af0305 4313
RyoheiHagimoto 0:0e0631af0305 4314 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4315 @param pts Array of polygonal curves.
RyoheiHagimoto 0:0e0631af0305 4316 @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
RyoheiHagimoto 0:0e0631af0305 4317 the function draws a line from the last vertex of each curve to its first vertex.
RyoheiHagimoto 0:0e0631af0305 4318 @param color Polyline color.
RyoheiHagimoto 0:0e0631af0305 4319 @param thickness Thickness of the polyline edges.
RyoheiHagimoto 0:0e0631af0305 4320 @param lineType Type of the line segments. See the line description.
RyoheiHagimoto 0:0e0631af0305 4321 @param shift Number of fractional bits in the vertex coordinates.
RyoheiHagimoto 0:0e0631af0305 4322
RyoheiHagimoto 0:0e0631af0305 4323 The function polylines draws one or more polygonal curves.
RyoheiHagimoto 0:0e0631af0305 4324 */
RyoheiHagimoto 0:0e0631af0305 4325 CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
RyoheiHagimoto 0:0e0631af0305 4326 bool isClosed, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4327 int thickness = 1, int lineType = LINE_8, int shift = 0 );
RyoheiHagimoto 0:0e0631af0305 4328
RyoheiHagimoto 0:0e0631af0305 4329 /** @example contours2.cpp
RyoheiHagimoto 0:0e0631af0305 4330 An example using the drawContour functionality
RyoheiHagimoto 0:0e0631af0305 4331 */
RyoheiHagimoto 0:0e0631af0305 4332
RyoheiHagimoto 0:0e0631af0305 4333 /** @example segment_objects.cpp
RyoheiHagimoto 0:0e0631af0305 4334 An example using drawContours to clean up a background segmentation result
RyoheiHagimoto 0:0e0631af0305 4335 */
RyoheiHagimoto 0:0e0631af0305 4336
RyoheiHagimoto 0:0e0631af0305 4337 /** @brief Draws contours outlines or filled contours.
RyoheiHagimoto 0:0e0631af0305 4338
RyoheiHagimoto 0:0e0631af0305 4339 The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area
RyoheiHagimoto 0:0e0631af0305 4340 bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve
RyoheiHagimoto 0:0e0631af0305 4341 connected components from the binary image and label them: :
RyoheiHagimoto 0:0e0631af0305 4342 @code
RyoheiHagimoto 0:0e0631af0305 4343 #include "opencv2/imgproc.hpp"
RyoheiHagimoto 0:0e0631af0305 4344 #include "opencv2/highgui.hpp"
RyoheiHagimoto 0:0e0631af0305 4345
RyoheiHagimoto 0:0e0631af0305 4346 using namespace cv;
RyoheiHagimoto 0:0e0631af0305 4347 using namespace std;
RyoheiHagimoto 0:0e0631af0305 4348
RyoheiHagimoto 0:0e0631af0305 4349 int main( int argc, char** argv )
RyoheiHagimoto 0:0e0631af0305 4350 {
RyoheiHagimoto 0:0e0631af0305 4351 Mat src;
RyoheiHagimoto 0:0e0631af0305 4352 // the first command-line parameter must be a filename of the binary
RyoheiHagimoto 0:0e0631af0305 4353 // (black-n-white) image
RyoheiHagimoto 0:0e0631af0305 4354 if( argc != 2 || !(src=imread(argv[1], 0)).data)
RyoheiHagimoto 0:0e0631af0305 4355 return -1;
RyoheiHagimoto 0:0e0631af0305 4356
RyoheiHagimoto 0:0e0631af0305 4357 Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3);
RyoheiHagimoto 0:0e0631af0305 4358
RyoheiHagimoto 0:0e0631af0305 4359 src = src > 1;
RyoheiHagimoto 0:0e0631af0305 4360 namedWindow( "Source", 1 );
RyoheiHagimoto 0:0e0631af0305 4361 imshow( "Source", src );
RyoheiHagimoto 0:0e0631af0305 4362
RyoheiHagimoto 0:0e0631af0305 4363 vector<vector<Point> > contours;
RyoheiHagimoto 0:0e0631af0305 4364 vector<Vec4i> hierarchy;
RyoheiHagimoto 0:0e0631af0305 4365
RyoheiHagimoto 0:0e0631af0305 4366 findContours( src, contours, hierarchy,
RyoheiHagimoto 0:0e0631af0305 4367 RETR_CCOMP, CHAIN_APPROX_SIMPLE );
RyoheiHagimoto 0:0e0631af0305 4368
RyoheiHagimoto 0:0e0631af0305 4369 // iterate through all the top-level contours,
RyoheiHagimoto 0:0e0631af0305 4370 // draw each connected component with its own random color
RyoheiHagimoto 0:0e0631af0305 4371 int idx = 0;
RyoheiHagimoto 0:0e0631af0305 4372 for( ; idx >= 0; idx = hierarchy[idx][0] )
RyoheiHagimoto 0:0e0631af0305 4373 {
RyoheiHagimoto 0:0e0631af0305 4374 Scalar color( rand()&255, rand()&255, rand()&255 );
RyoheiHagimoto 0:0e0631af0305 4375 drawContours( dst, contours, idx, color, FILLED, 8, hierarchy );
RyoheiHagimoto 0:0e0631af0305 4376 }
RyoheiHagimoto 0:0e0631af0305 4377
RyoheiHagimoto 0:0e0631af0305 4378 namedWindow( "Components", 1 );
RyoheiHagimoto 0:0e0631af0305 4379 imshow( "Components", dst );
RyoheiHagimoto 0:0e0631af0305 4380 waitKey(0);
RyoheiHagimoto 0:0e0631af0305 4381 }
RyoheiHagimoto 0:0e0631af0305 4382 @endcode
RyoheiHagimoto 0:0e0631af0305 4383
RyoheiHagimoto 0:0e0631af0305 4384 @param image Destination image.
RyoheiHagimoto 0:0e0631af0305 4385 @param contours All the input contours. Each contour is stored as a point vector.
RyoheiHagimoto 0:0e0631af0305 4386 @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
RyoheiHagimoto 0:0e0631af0305 4387 @param color Color of the contours.
RyoheiHagimoto 0:0e0631af0305 4388 @param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
RyoheiHagimoto 0:0e0631af0305 4389 thickness=CV_FILLED ), the contour interiors are drawn.
RyoheiHagimoto 0:0e0631af0305 4390 @param lineType Line connectivity. See cv::LineTypes.
RyoheiHagimoto 0:0e0631af0305 4391 @param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
RyoheiHagimoto 0:0e0631af0305 4392 some of the contours (see maxLevel ).
RyoheiHagimoto 0:0e0631af0305 4393 @param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
RyoheiHagimoto 0:0e0631af0305 4394 If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
RyoheiHagimoto 0:0e0631af0305 4395 draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
RyoheiHagimoto 0:0e0631af0305 4396 parameter is only taken into account when there is hierarchy available.
RyoheiHagimoto 0:0e0631af0305 4397 @param offset Optional contour shift parameter. Shift all the drawn contours by the specified
RyoheiHagimoto 0:0e0631af0305 4398 \f$\texttt{offset}=(dx,dy)\f$ .
RyoheiHagimoto 0:0e0631af0305 4399 */
RyoheiHagimoto 0:0e0631af0305 4400 CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
RyoheiHagimoto 0:0e0631af0305 4401 int contourIdx, const Scalar& color,
RyoheiHagimoto 0:0e0631af0305 4402 int thickness = 1, int lineType = LINE_8,
RyoheiHagimoto 0:0e0631af0305 4403 InputArray hierarchy = noArray(),
RyoheiHagimoto 0:0e0631af0305 4404 int maxLevel = INT_MAX, Point offset = Point() );
RyoheiHagimoto 0:0e0631af0305 4405
RyoheiHagimoto 0:0e0631af0305 4406 /** @brief Clips the line against the image rectangle.
RyoheiHagimoto 0:0e0631af0305 4407
RyoheiHagimoto 0:0e0631af0305 4408 The function cv::clipLine calculates a part of the line segment that is entirely within the specified
RyoheiHagimoto 0:0e0631af0305 4409 rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise,
RyoheiHagimoto 0:0e0631af0305 4410 it returns true .
RyoheiHagimoto 0:0e0631af0305 4411 @param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
RyoheiHagimoto 0:0e0631af0305 4412 @param pt1 First line point.
RyoheiHagimoto 0:0e0631af0305 4413 @param pt2 Second line point.
RyoheiHagimoto 0:0e0631af0305 4414 */
RyoheiHagimoto 0:0e0631af0305 4415 CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
RyoheiHagimoto 0:0e0631af0305 4416
RyoheiHagimoto 0:0e0631af0305 4417 /** @overload
RyoheiHagimoto 0:0e0631af0305 4418 @param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
RyoheiHagimoto 0:0e0631af0305 4419 @param pt1 First line point.
RyoheiHagimoto 0:0e0631af0305 4420 @param pt2 Second line point.
RyoheiHagimoto 0:0e0631af0305 4421 */
RyoheiHagimoto 0:0e0631af0305 4422 CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2);
RyoheiHagimoto 0:0e0631af0305 4423
RyoheiHagimoto 0:0e0631af0305 4424 /** @overload
RyoheiHagimoto 0:0e0631af0305 4425 @param imgRect Image rectangle.
RyoheiHagimoto 0:0e0631af0305 4426 @param pt1 First line point.
RyoheiHagimoto 0:0e0631af0305 4427 @param pt2 Second line point.
RyoheiHagimoto 0:0e0631af0305 4428 */
RyoheiHagimoto 0:0e0631af0305 4429 CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
RyoheiHagimoto 0:0e0631af0305 4430
RyoheiHagimoto 0:0e0631af0305 4431 /** @brief Approximates an elliptic arc with a polyline.
RyoheiHagimoto 0:0e0631af0305 4432
RyoheiHagimoto 0:0e0631af0305 4433 The function ellipse2Poly computes the vertices of a polyline that approximates the specified
RyoheiHagimoto 0:0e0631af0305 4434 elliptic arc. It is used by cv::ellipse.
RyoheiHagimoto 0:0e0631af0305 4435
RyoheiHagimoto 0:0e0631af0305 4436 @param center Center of the arc.
RyoheiHagimoto 0:0e0631af0305 4437 @param axes Half of the size of the ellipse main axes. See the ellipse for details.
RyoheiHagimoto 0:0e0631af0305 4438 @param angle Rotation angle of the ellipse in degrees. See the ellipse for details.
RyoheiHagimoto 0:0e0631af0305 4439 @param arcStart Starting angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4440 @param arcEnd Ending angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4441 @param delta Angle between the subsequent polyline vertices. It defines the approximation
RyoheiHagimoto 0:0e0631af0305 4442 accuracy.
RyoheiHagimoto 0:0e0631af0305 4443 @param pts Output vector of polyline vertices.
RyoheiHagimoto 0:0e0631af0305 4444 */
RyoheiHagimoto 0:0e0631af0305 4445 CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
RyoheiHagimoto 0:0e0631af0305 4446 int arcStart, int arcEnd, int delta,
RyoheiHagimoto 0:0e0631af0305 4447 CV_OUT std::vector<Point>& pts );
RyoheiHagimoto 0:0e0631af0305 4448
RyoheiHagimoto 0:0e0631af0305 4449 /** @overload
RyoheiHagimoto 0:0e0631af0305 4450 @param center Center of the arc.
RyoheiHagimoto 0:0e0631af0305 4451 @param axes Half of the size of the ellipse main axes. See the ellipse for details.
RyoheiHagimoto 0:0e0631af0305 4452 @param angle Rotation angle of the ellipse in degrees. See the ellipse for details.
RyoheiHagimoto 0:0e0631af0305 4453 @param arcStart Starting angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4454 @param arcEnd Ending angle of the elliptic arc in degrees.
RyoheiHagimoto 0:0e0631af0305 4455 @param delta Angle between the subsequent polyline vertices. It defines the approximation
RyoheiHagimoto 0:0e0631af0305 4456 accuracy.
RyoheiHagimoto 0:0e0631af0305 4457 @param pts Output vector of polyline vertices.
RyoheiHagimoto 0:0e0631af0305 4458 */
RyoheiHagimoto 0:0e0631af0305 4459 CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle,
RyoheiHagimoto 0:0e0631af0305 4460 int arcStart, int arcEnd, int delta,
RyoheiHagimoto 0:0e0631af0305 4461 CV_OUT std::vector<Point2d>& pts);
RyoheiHagimoto 0:0e0631af0305 4462
RyoheiHagimoto 0:0e0631af0305 4463 /** @brief Draws a text string.
RyoheiHagimoto 0:0e0631af0305 4464
RyoheiHagimoto 0:0e0631af0305 4465 The function putText renders the specified text string in the image. Symbols that cannot be rendered
RyoheiHagimoto 0:0e0631af0305 4466 using the specified font are replaced by question marks. See getTextSize for a text rendering code
RyoheiHagimoto 0:0e0631af0305 4467 example.
RyoheiHagimoto 0:0e0631af0305 4468
RyoheiHagimoto 0:0e0631af0305 4469 @param img Image.
RyoheiHagimoto 0:0e0631af0305 4470 @param text Text string to be drawn.
RyoheiHagimoto 0:0e0631af0305 4471 @param org Bottom-left corner of the text string in the image.
RyoheiHagimoto 0:0e0631af0305 4472 @param fontFace Font type, see cv::HersheyFonts.
RyoheiHagimoto 0:0e0631af0305 4473 @param fontScale Font scale factor that is multiplied by the font-specific base size.
RyoheiHagimoto 0:0e0631af0305 4474 @param color Text color.
RyoheiHagimoto 0:0e0631af0305 4475 @param thickness Thickness of the lines used to draw a text.
RyoheiHagimoto 0:0e0631af0305 4476 @param lineType Line type. See the line for details.
RyoheiHagimoto 0:0e0631af0305 4477 @param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
RyoheiHagimoto 0:0e0631af0305 4478 it is at the top-left corner.
RyoheiHagimoto 0:0e0631af0305 4479 */
RyoheiHagimoto 0:0e0631af0305 4480 CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
RyoheiHagimoto 0:0e0631af0305 4481 int fontFace, double fontScale, Scalar color,
RyoheiHagimoto 0:0e0631af0305 4482 int thickness = 1, int lineType = LINE_8,
RyoheiHagimoto 0:0e0631af0305 4483 bool bottomLeftOrigin = false );
RyoheiHagimoto 0:0e0631af0305 4484
RyoheiHagimoto 0:0e0631af0305 4485 /** @brief Calculates the width and height of a text string.
RyoheiHagimoto 0:0e0631af0305 4486
RyoheiHagimoto 0:0e0631af0305 4487 The function getTextSize calculates and returns the size of a box that contains the specified text.
RyoheiHagimoto 0:0e0631af0305 4488 That is, the following code renders some text, the tight box surrounding it, and the baseline: :
RyoheiHagimoto 0:0e0631af0305 4489 @code
RyoheiHagimoto 0:0e0631af0305 4490 String text = "Funny text inside the box";
RyoheiHagimoto 0:0e0631af0305 4491 int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
RyoheiHagimoto 0:0e0631af0305 4492 double fontScale = 2;
RyoheiHagimoto 0:0e0631af0305 4493 int thickness = 3;
RyoheiHagimoto 0:0e0631af0305 4494
RyoheiHagimoto 0:0e0631af0305 4495 Mat img(600, 800, CV_8UC3, Scalar::all(0));
RyoheiHagimoto 0:0e0631af0305 4496
RyoheiHagimoto 0:0e0631af0305 4497 int baseline=0;
RyoheiHagimoto 0:0e0631af0305 4498 Size textSize = getTextSize(text, fontFace,
RyoheiHagimoto 0:0e0631af0305 4499 fontScale, thickness, &baseline);
RyoheiHagimoto 0:0e0631af0305 4500 baseline += thickness;
RyoheiHagimoto 0:0e0631af0305 4501
RyoheiHagimoto 0:0e0631af0305 4502 // center the text
RyoheiHagimoto 0:0e0631af0305 4503 Point textOrg((img.cols - textSize.width)/2,
RyoheiHagimoto 0:0e0631af0305 4504 (img.rows + textSize.height)/2);
RyoheiHagimoto 0:0e0631af0305 4505
RyoheiHagimoto 0:0e0631af0305 4506 // draw the box
RyoheiHagimoto 0:0e0631af0305 4507 rectangle(img, textOrg + Point(0, baseline),
RyoheiHagimoto 0:0e0631af0305 4508 textOrg + Point(textSize.width, -textSize.height),
RyoheiHagimoto 0:0e0631af0305 4509 Scalar(0,0,255));
RyoheiHagimoto 0:0e0631af0305 4510 // ... and the baseline first
RyoheiHagimoto 0:0e0631af0305 4511 line(img, textOrg + Point(0, thickness),
RyoheiHagimoto 0:0e0631af0305 4512 textOrg + Point(textSize.width, thickness),
RyoheiHagimoto 0:0e0631af0305 4513 Scalar(0, 0, 255));
RyoheiHagimoto 0:0e0631af0305 4514
RyoheiHagimoto 0:0e0631af0305 4515 // then put the text itself
RyoheiHagimoto 0:0e0631af0305 4516 putText(img, text, textOrg, fontFace, fontScale,
RyoheiHagimoto 0:0e0631af0305 4517 Scalar::all(255), thickness, 8);
RyoheiHagimoto 0:0e0631af0305 4518 @endcode
RyoheiHagimoto 0:0e0631af0305 4519
RyoheiHagimoto 0:0e0631af0305 4520 @param text Input text string.
RyoheiHagimoto 0:0e0631af0305 4521 @param fontFace Font to use, see cv::HersheyFonts.
RyoheiHagimoto 0:0e0631af0305 4522 @param fontScale Font scale factor that is multiplied by the font-specific base size.
RyoheiHagimoto 0:0e0631af0305 4523 @param thickness Thickness of lines used to render the text. See putText for details.
RyoheiHagimoto 0:0e0631af0305 4524 @param[out] baseLine y-coordinate of the baseline relative to the bottom-most text
RyoheiHagimoto 0:0e0631af0305 4525 point.
RyoheiHagimoto 0:0e0631af0305 4526 @return The size of a box that contains the specified text.
RyoheiHagimoto 0:0e0631af0305 4527
RyoheiHagimoto 0:0e0631af0305 4528 @see cv::putText
RyoheiHagimoto 0:0e0631af0305 4529 */
RyoheiHagimoto 0:0e0631af0305 4530 CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
RyoheiHagimoto 0:0e0631af0305 4531 double fontScale, int thickness,
RyoheiHagimoto 0:0e0631af0305 4532 CV_OUT int* baseLine);
RyoheiHagimoto 0:0e0631af0305 4533
RyoheiHagimoto 0:0e0631af0305 4534 /** @brief Line iterator
RyoheiHagimoto 0:0e0631af0305 4535
RyoheiHagimoto 0:0e0631af0305 4536 The class is used to iterate over all the pixels on the raster line
RyoheiHagimoto 0:0e0631af0305 4537 segment connecting two specified points.
RyoheiHagimoto 0:0e0631af0305 4538
RyoheiHagimoto 0:0e0631af0305 4539 The class LineIterator is used to get each pixel of a raster line. It
RyoheiHagimoto 0:0e0631af0305 4540 can be treated as versatile implementation of the Bresenham algorithm
RyoheiHagimoto 0:0e0631af0305 4541 where you can stop at each pixel and do some extra processing, for
RyoheiHagimoto 0:0e0631af0305 4542 example, grab pixel values along the line or draw a line with an effect
RyoheiHagimoto 0:0e0631af0305 4543 (for example, with XOR operation).
RyoheiHagimoto 0:0e0631af0305 4544
RyoheiHagimoto 0:0e0631af0305 4545 The number of pixels along the line is stored in LineIterator::count.
RyoheiHagimoto 0:0e0631af0305 4546 The method LineIterator::pos returns the current position in the image:
RyoheiHagimoto 0:0e0631af0305 4547
RyoheiHagimoto 0:0e0631af0305 4548 @code{.cpp}
RyoheiHagimoto 0:0e0631af0305 4549 // grabs pixels along the line (pt1, pt2)
RyoheiHagimoto 0:0e0631af0305 4550 // from 8-bit 3-channel image to the buffer
RyoheiHagimoto 0:0e0631af0305 4551 LineIterator it(img, pt1, pt2, 8);
RyoheiHagimoto 0:0e0631af0305 4552 LineIterator it2 = it;
RyoheiHagimoto 0:0e0631af0305 4553 vector<Vec3b> buf(it.count);
RyoheiHagimoto 0:0e0631af0305 4554
RyoheiHagimoto 0:0e0631af0305 4555 for(int i = 0; i < it.count; i++, ++it)
RyoheiHagimoto 0:0e0631af0305 4556 buf[i] = *(const Vec3b)*it;
RyoheiHagimoto 0:0e0631af0305 4557
RyoheiHagimoto 0:0e0631af0305 4558 // alternative way of iterating through the line
RyoheiHagimoto 0:0e0631af0305 4559 for(int i = 0; i < it2.count; i++, ++it2)
RyoheiHagimoto 0:0e0631af0305 4560 {
RyoheiHagimoto 0:0e0631af0305 4561 Vec3b val = img.at<Vec3b>(it2.pos());
RyoheiHagimoto 0:0e0631af0305 4562 CV_Assert(buf[i] == val);
RyoheiHagimoto 0:0e0631af0305 4563 }
RyoheiHagimoto 0:0e0631af0305 4564 @endcode
RyoheiHagimoto 0:0e0631af0305 4565 */
RyoheiHagimoto 0:0e0631af0305 4566 class CV_EXPORTS LineIterator
RyoheiHagimoto 0:0e0631af0305 4567 {
RyoheiHagimoto 0:0e0631af0305 4568 public:
RyoheiHagimoto 0:0e0631af0305 4569 /** @brief intializes the iterator
RyoheiHagimoto 0:0e0631af0305 4570
RyoheiHagimoto 0:0e0631af0305 4571 creates iterators for the line connecting pt1 and pt2
RyoheiHagimoto 0:0e0631af0305 4572 the line will be clipped on the image boundaries
RyoheiHagimoto 0:0e0631af0305 4573 the line is 8-connected or 4-connected
RyoheiHagimoto 0:0e0631af0305 4574 If leftToRight=true, then the iteration is always done
RyoheiHagimoto 0:0e0631af0305 4575 from the left-most point to the right most,
RyoheiHagimoto 0:0e0631af0305 4576 not to depend on the ordering of pt1 and pt2 parameters
RyoheiHagimoto 0:0e0631af0305 4577 */
RyoheiHagimoto 0:0e0631af0305 4578 LineIterator( const Mat& img, Point pt1, Point pt2,
RyoheiHagimoto 0:0e0631af0305 4579 int connectivity = 8, bool leftToRight = false );
RyoheiHagimoto 0:0e0631af0305 4580 /** @brief returns pointer to the current pixel
RyoheiHagimoto 0:0e0631af0305 4581 */
RyoheiHagimoto 0:0e0631af0305 4582 uchar* operator *();
RyoheiHagimoto 0:0e0631af0305 4583 /** @brief prefix increment operator (++it). shifts iterator to the next pixel
RyoheiHagimoto 0:0e0631af0305 4584 */
RyoheiHagimoto 0:0e0631af0305 4585 LineIterator& operator ++();
RyoheiHagimoto 0:0e0631af0305 4586 /** @brief postfix increment operator (it++). shifts iterator to the next pixel
RyoheiHagimoto 0:0e0631af0305 4587 */
RyoheiHagimoto 0:0e0631af0305 4588 LineIterator operator ++(int);
RyoheiHagimoto 0:0e0631af0305 4589 /** @brief returns coordinates of the current pixel
RyoheiHagimoto 0:0e0631af0305 4590 */
RyoheiHagimoto 0:0e0631af0305 4591 Point pos() const;
RyoheiHagimoto 0:0e0631af0305 4592
RyoheiHagimoto 0:0e0631af0305 4593 uchar* ptr;
RyoheiHagimoto 0:0e0631af0305 4594 const uchar* ptr0;
RyoheiHagimoto 0:0e0631af0305 4595 int step, elemSize;
RyoheiHagimoto 0:0e0631af0305 4596 int err, count;
RyoheiHagimoto 0:0e0631af0305 4597 int minusDelta, plusDelta;
RyoheiHagimoto 0:0e0631af0305 4598 int minusStep, plusStep;
RyoheiHagimoto 0:0e0631af0305 4599 };
RyoheiHagimoto 0:0e0631af0305 4600
RyoheiHagimoto 0:0e0631af0305 4601 //! @cond IGNORED
RyoheiHagimoto 0:0e0631af0305 4602
RyoheiHagimoto 0:0e0631af0305 4603 // === LineIterator implementation ===
RyoheiHagimoto 0:0e0631af0305 4604
RyoheiHagimoto 0:0e0631af0305 4605 inline
RyoheiHagimoto 0:0e0631af0305 4606 uchar* LineIterator::operator *()
RyoheiHagimoto 0:0e0631af0305 4607 {
RyoheiHagimoto 0:0e0631af0305 4608 return ptr;
RyoheiHagimoto 0:0e0631af0305 4609 }
RyoheiHagimoto 0:0e0631af0305 4610
RyoheiHagimoto 0:0e0631af0305 4611 inline
RyoheiHagimoto 0:0e0631af0305 4612 LineIterator& LineIterator::operator ++()
RyoheiHagimoto 0:0e0631af0305 4613 {
RyoheiHagimoto 0:0e0631af0305 4614 int mask = err < 0 ? -1 : 0;
RyoheiHagimoto 0:0e0631af0305 4615 err += minusDelta + (plusDelta & mask);
RyoheiHagimoto 0:0e0631af0305 4616 ptr += minusStep + (plusStep & mask);
RyoheiHagimoto 0:0e0631af0305 4617 return *this;
RyoheiHagimoto 0:0e0631af0305 4618 }
RyoheiHagimoto 0:0e0631af0305 4619
RyoheiHagimoto 0:0e0631af0305 4620 inline
RyoheiHagimoto 0:0e0631af0305 4621 LineIterator LineIterator::operator ++(int)
RyoheiHagimoto 0:0e0631af0305 4622 {
RyoheiHagimoto 0:0e0631af0305 4623 LineIterator it = *this;
RyoheiHagimoto 0:0e0631af0305 4624 ++(*this);
RyoheiHagimoto 0:0e0631af0305 4625 return it;
RyoheiHagimoto 0:0e0631af0305 4626 }
RyoheiHagimoto 0:0e0631af0305 4627
RyoheiHagimoto 0:0e0631af0305 4628 inline
RyoheiHagimoto 0:0e0631af0305 4629 Point LineIterator::pos() const
RyoheiHagimoto 0:0e0631af0305 4630 {
RyoheiHagimoto 0:0e0631af0305 4631 Point p;
RyoheiHagimoto 0:0e0631af0305 4632 p.y = (int)((ptr - ptr0)/step);
RyoheiHagimoto 0:0e0631af0305 4633 p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize);
RyoheiHagimoto 0:0e0631af0305 4634 return p;
RyoheiHagimoto 0:0e0631af0305 4635 }
RyoheiHagimoto 0:0e0631af0305 4636
RyoheiHagimoto 0:0e0631af0305 4637 //! @endcond
RyoheiHagimoto 0:0e0631af0305 4638
RyoheiHagimoto 0:0e0631af0305 4639 //! @} imgproc_draw
RyoheiHagimoto 0:0e0631af0305 4640
RyoheiHagimoto 0:0e0631af0305 4641 //! @} imgproc
RyoheiHagimoto 0:0e0631af0305 4642
RyoheiHagimoto 0:0e0631af0305 4643 } // cv
RyoheiHagimoto 0:0e0631af0305 4644
RyoheiHagimoto 0:0e0631af0305 4645 #ifndef DISABLE_OPENCV_24_COMPATIBILITY
RyoheiHagimoto 0:0e0631af0305 4646 #include "opencv2/imgproc/imgproc_c.h"
RyoheiHagimoto 0:0e0631af0305 4647 #endif
RyoheiHagimoto 0:0e0631af0305 4648
RyoheiHagimoto 0:0e0631af0305 4649 #endif