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7ea5029ae5
* model is not learned when grabcut is called with GC_EVAL * fixed test, was writing to wrong file. * modified patch by Iwan Paolucci; added GC_EVAL_FREEZE_MODEL in addition to GC_EVAL (which semantics is retained)
5026 lines
235 KiB
C++
5026 lines
235 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_IMGPROC_HPP
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#define OPENCV_IMGPROC_HPP
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#include "opencv2/core.hpp"
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/**
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@defgroup imgproc Image processing
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@{
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@defgroup imgproc_filter Image Filtering
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Functions and classes described in this section are used to perform various linear or non-linear
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filtering operations on 2D images (represented as Mat's). It means that for each pixel location
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\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
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compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
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morphological operations, it is the minimum or maximum values, and so on. The computed response is
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stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
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will be of the same size as the input image. Normally, the functions support multi-channel arrays,
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in which case every channel is processed independently. Therefore, the output image will also have
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the same number of channels as the input one.
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Another common feature of the functions and classes described in this section is that, unlike
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simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
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example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
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processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
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of the image. You can let these pixels be the same as the left-most image pixels ("replicated
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border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
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border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
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For details, see #BorderTypes
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@anchor filter_depths
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### Depth combinations
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Input depth (src.depth()) | Output depth (ddepth)
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--------------------------|----------------------
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CV_8U | -1/CV_16S/CV_32F/CV_64F
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CV_16U/CV_16S | -1/CV_32F/CV_64F
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CV_32F | -1/CV_32F/CV_64F
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CV_64F | -1/CV_64F
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@note when ddepth=-1, the output image will have the same depth as the source.
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@defgroup imgproc_transform Geometric Image Transformations
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The functions in this section perform various geometrical transformations of 2D images. They do not
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change the image content but deform the pixel grid and map this deformed grid to the destination
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image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
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destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
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functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
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pixel value:
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\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]
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In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
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\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
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\f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.
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The actual implementations of the geometrical transformations, from the most generic remap and to
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the simplest and the fastest resize, need to solve two main problems with the above formula:
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- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
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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
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of them may fall outside of the image. In this case, an extrapolation method needs to be used.
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OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
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addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in
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the destination image will not be modified at all.
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- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
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numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
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transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
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coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
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nearest integer coordinates and the corresponding pixel can be used. This is called a
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nearest-neighbor interpolation. However, a better result can be achieved by using more
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sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
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where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
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f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
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interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
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resize for details.
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@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images.
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@defgroup imgproc_misc Miscellaneous Image Transformations
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@defgroup imgproc_draw Drawing Functions
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Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
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rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
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the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
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for color images and brightness for grayscale images. For color images, the channel ordering is
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normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
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color using the Scalar constructor, it should look like:
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\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]
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If you are using your own image rendering and I/O functions, you can use any channel ordering. The
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drawing functions process each channel independently and do not depend on the channel order or even
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on the used color space. The whole image can be converted from BGR to RGB or to a different color
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space using cvtColor .
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If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
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many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
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that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
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fractional bits is specified by the shift parameter and the real point coordinates are calculated as
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\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
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especially effective when rendering antialiased shapes.
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@note The functions do not support alpha-transparency when the target image is 4-channel. In this
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case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
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semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
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image.
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@defgroup imgproc_colormap ColorMaps in OpenCV
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The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
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sensitive to observing changes between colors, so you often need to recolor your grayscale images to
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get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
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computer vision application.
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In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
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code reads the path to an image from command line, applies a Jet colormap on it and shows the
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result:
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@code
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/highgui.hpp>
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using namespace cv;
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#include <iostream>
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using namespace std;
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int main(int argc, const char *argv[])
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{
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// We need an input image. (can be grayscale or color)
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if (argc < 2)
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{
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cerr << "We need an image to process here. Please run: colorMap [path_to_image]" << endl;
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return -1;
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}
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Mat img_in = imread(argv[1]);
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if(img_in.empty())
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{
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cerr << "Sample image (" << argv[1] << ") is empty. Please adjust your path, so it points to a valid input image!" << endl;
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return -1;
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}
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// Holds the colormap version of the image:
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Mat img_color;
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// Apply the colormap:
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applyColorMap(img_in, img_color, COLORMAP_JET);
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// Show the result:
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imshow("colorMap", img_color);
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waitKey(0);
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return 0;
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}
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@endcode
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@see #ColormapTypes
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@defgroup imgproc_subdiv2d Planar Subdivision
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The Subdiv2D class described in this section is used to perform various planar subdivision on
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a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
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using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram.
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In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi
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diagram with red lines.
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The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
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location of points on the plane, building special graphs (such as NNG,RNG), and so forth.
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@defgroup imgproc_hist Histograms
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@defgroup imgproc_shape Structural Analysis and Shape Descriptors
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@defgroup imgproc_motion Motion Analysis and Object Tracking
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@defgroup imgproc_feature Feature Detection
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@defgroup imgproc_object Object Detection
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@defgroup imgproc_c C API
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@defgroup imgproc_hal Hardware Acceleration Layer
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@{
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@defgroup imgproc_hal_functions Functions
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@defgroup imgproc_hal_interface Interface
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@}
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@}
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*/
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namespace cv
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{
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/** @addtogroup imgproc
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@{
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*/
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//! @addtogroup imgproc_filter
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//! @{
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//! type of morphological operation
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enum MorphTypes{
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MORPH_ERODE = 0, //!< see #erode
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MORPH_DILATE = 1, //!< see #dilate
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MORPH_OPEN = 2, //!< an opening operation
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//!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f]
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MORPH_CLOSE = 3, //!< a closing operation
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//!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f]
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MORPH_GRADIENT = 4, //!< a morphological gradient
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//!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f]
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MORPH_TOPHAT = 5, //!< "top hat"
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//!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f]
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MORPH_BLACKHAT = 6, //!< "black hat"
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//!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f]
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MORPH_HITMISS = 7 //!< "hit or miss"
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//!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation
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};
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//! shape of the structuring element
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enum MorphShapes {
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MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f]
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MORPH_CROSS = 1, //!< a cross-shaped structuring element:
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//!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f]
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MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed
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//!< into the rectangle Rect(0, 0, esize.width, 0.esize.height)
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};
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//! @} imgproc_filter
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//! @addtogroup imgproc_transform
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//! @{
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//! interpolation algorithm
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enum InterpolationFlags{
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/** nearest neighbor interpolation */
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INTER_NEAREST = 0,
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/** bilinear interpolation */
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INTER_LINEAR = 1,
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/** bicubic interpolation */
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INTER_CUBIC = 2,
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/** resampling using pixel area relation. It may be a preferred method for image decimation, as
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it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
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method. */
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INTER_AREA = 3,
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/** Lanczos interpolation over 8x8 neighborhood */
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INTER_LANCZOS4 = 4,
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/** Bit exact bilinear interpolation */
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INTER_LINEAR_EXACT = 5,
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/** mask for interpolation codes */
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INTER_MAX = 7,
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/** flag, fills all of the destination image pixels. If some of them correspond to outliers in the
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source image, they are set to zero */
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WARP_FILL_OUTLIERS = 8,
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/** flag, inverse transformation
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For example, #linearPolar or #logPolar transforms:
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- flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$
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- flag is set: \f$dst(x,y) = src( \rho , \phi )\f$
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*/
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WARP_INVERSE_MAP = 16
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};
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/** \brief Specify the polar mapping mode
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@sa warpPolar
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*/
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enum WarpPolarMode
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{
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WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space.
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WARP_POLAR_LOG = 256 ///< Remaps an image to/from semilog-polar space.
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};
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enum InterpolationMasks {
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INTER_BITS = 5,
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INTER_BITS2 = INTER_BITS * 2,
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INTER_TAB_SIZE = 1 << INTER_BITS,
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INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE
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};
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//! @} imgproc_transform
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//! @addtogroup imgproc_misc
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//! @{
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//! Distance types for Distance Transform and M-estimators
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//! @see distanceTransform, fitLine
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enum DistanceTypes {
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DIST_USER = -1, //!< User defined distance
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DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2|
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DIST_L2 = 2, //!< the simple euclidean distance
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DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|)
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DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
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DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
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DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
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DIST_HUBER = 7 //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
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};
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//! Mask size for distance transform
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enum DistanceTransformMasks {
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DIST_MASK_3 = 3, //!< mask=3
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DIST_MASK_5 = 5, //!< mask=5
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DIST_MASK_PRECISE = 0 //!<
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};
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//! type of the threshold operation
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//! 
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enum ThresholdTypes {
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THRESH_BINARY = 0, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
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THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
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THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
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THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
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THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
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THRESH_MASK = 7,
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THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
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THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
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};
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//! adaptive threshold algorithm
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//! @see adaptiveThreshold
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enum AdaptiveThresholdTypes {
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/** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times
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\texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */
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ADAPTIVE_THRESH_MEAN_C = 0,
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/** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian
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window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$
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minus C . The default sigma (standard deviation) is used for the specified blockSize . See
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#getGaussianKernel*/
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ADAPTIVE_THRESH_GAUSSIAN_C = 1
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};
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//! cv::undistort mode
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enum UndistortTypes {
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PROJ_SPHERICAL_ORTHO = 0,
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PROJ_SPHERICAL_EQRECT = 1
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};
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//! class of the pixel in GrabCut algorithm
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enum GrabCutClasses {
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GC_BGD = 0, //!< an obvious background pixels
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GC_FGD = 1, //!< an obvious foreground (object) pixel
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GC_PR_BGD = 2, //!< a possible background pixel
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GC_PR_FGD = 3 //!< a possible foreground pixel
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};
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//! GrabCut algorithm flags
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enum GrabCutModes {
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/** The function initializes the state and the mask using the provided rectangle. After that it
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runs iterCount iterations of the algorithm. */
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GC_INIT_WITH_RECT = 0,
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/** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT
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and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are
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automatically initialized with GC_BGD .*/
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GC_INIT_WITH_MASK = 1,
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/** The value means that the algorithm should just resume. */
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GC_EVAL = 2,
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/** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */
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GC_EVAL_FREEZE_MODEL = 3
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};
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//! distanceTransform algorithm flags
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enum DistanceTransformLabelTypes {
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/** each connected component of zeros in src (as well as all the non-zero pixels closest to the
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connected component) will be assigned the same label */
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DIST_LABEL_CCOMP = 0,
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/** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */
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DIST_LABEL_PIXEL = 1
|
|
};
|
|
|
|
//! floodfill algorithm flags
|
|
enum FloodFillFlags {
|
|
/** If set, the difference between the current pixel and seed pixel is considered. Otherwise,
|
|
the difference between neighbor pixels is considered (that is, the range is floating). */
|
|
FLOODFILL_FIXED_RANGE = 1 << 16,
|
|
/** If set, the function does not change the image ( newVal is ignored), and only fills the
|
|
mask with the value specified in bits 8-16 of flags as described above. This option only make
|
|
sense in function variants that have the mask parameter. */
|
|
FLOODFILL_MASK_ONLY = 1 << 17
|
|
};
|
|
|
|
//! @} imgproc_misc
|
|
|
|
//! @addtogroup imgproc_shape
|
|
//! @{
|
|
|
|
//! connected components algorithm output formats
|
|
enum ConnectedComponentsTypes {
|
|
CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding
|
|
//!< box in the horizontal direction.
|
|
CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding
|
|
//!< box in the vertical direction.
|
|
CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box
|
|
CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box
|
|
CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component
|
|
CC_STAT_MAX = 5
|
|
};
|
|
|
|
//! connected components algorithm
|
|
enum ConnectedComponentsAlgorithmsTypes {
|
|
CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
|
|
CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
|
|
CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
|
|
};
|
|
|
|
//! mode of the contour retrieval algorithm
|
|
enum RetrievalModes {
|
|
/** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
|
|
all the contours. */
|
|
RETR_EXTERNAL = 0,
|
|
/** retrieves all of the contours without establishing any hierarchical relationships. */
|
|
RETR_LIST = 1,
|
|
/** retrieves all of the contours and organizes them into a two-level hierarchy. At the top
|
|
level, there are external boundaries of the components. At the second level, there are
|
|
boundaries of the holes. If there is another contour inside a hole of a connected component, it
|
|
is still put at the top level. */
|
|
RETR_CCOMP = 2,
|
|
/** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/
|
|
RETR_TREE = 3,
|
|
RETR_FLOODFILL = 4 //!<
|
|
};
|
|
|
|
//! the contour approximation algorithm
|
|
enum ContourApproximationModes {
|
|
/** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and
|
|
(x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is,
|
|
max(abs(x1-x2),abs(y2-y1))==1. */
|
|
CHAIN_APPROX_NONE = 1,
|
|
/** compresses horizontal, vertical, and diagonal segments and leaves only their end points.
|
|
For example, an up-right rectangular contour is encoded with 4 points. */
|
|
CHAIN_APPROX_SIMPLE = 2,
|
|
/** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
|
|
CHAIN_APPROX_TC89_L1 = 3,
|
|
/** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
|
|
CHAIN_APPROX_TC89_KCOS = 4
|
|
};
|
|
|
|
/** @brief Shape matching methods
|
|
|
|
\f$A\f$ denotes object1,\f$B\f$ denotes object2
|
|
|
|
\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$
|
|
|
|
and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively.
|
|
*/
|
|
enum ShapeMatchModes {
|
|
CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f]
|
|
CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f]
|
|
CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
|
|
};
|
|
|
|
//! @} imgproc_shape
|
|
|
|
//! Variants of a Hough transform
|
|
enum HoughModes {
|
|
|
|
/** classical or standard Hough transform. Every line is represented by two floating-point
|
|
numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line,
|
|
and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must
|
|
be (the created sequence will be) of CV_32FC2 type */
|
|
HOUGH_STANDARD = 0,
|
|
/** probabilistic Hough transform (more efficient in case if the picture contains a few long
|
|
linear segments). It returns line segments rather than the whole line. Each segment is
|
|
represented by starting and ending points, and the matrix must be (the created sequence will
|
|
be) of the CV_32SC4 type. */
|
|
HOUGH_PROBABILISTIC = 1,
|
|
/** multi-scale variant of the classical Hough transform. The lines are encoded the same way as
|
|
HOUGH_STANDARD. */
|
|
HOUGH_MULTI_SCALE = 2,
|
|
HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90
|
|
};
|
|
|
|
//! Variants of Line Segment %Detector
|
|
//! @ingroup imgproc_feature
|
|
enum LineSegmentDetectorModes {
|
|
LSD_REFINE_NONE = 0, //!< No refinement applied
|
|
LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
|
|
LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are
|
|
//!< refined through increase of precision, decrement in size, etc.
|
|
};
|
|
|
|
/** Histogram comparison methods
|
|
@ingroup imgproc_hist
|
|
*/
|
|
enum HistCompMethods {
|
|
/** Correlation
|
|
\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]
|
|
where
|
|
\f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f]
|
|
and \f$N\f$ is a total number of histogram bins. */
|
|
HISTCMP_CORREL = 0,
|
|
/** Chi-Square
|
|
\f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */
|
|
HISTCMP_CHISQR = 1,
|
|
/** Intersection
|
|
\f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */
|
|
HISTCMP_INTERSECT = 2,
|
|
/** Bhattacharyya distance
|
|
(In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.)
|
|
\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] */
|
|
HISTCMP_BHATTACHARYYA = 3,
|
|
HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA
|
|
/** Alternative Chi-Square
|
|
\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]
|
|
This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */
|
|
HISTCMP_CHISQR_ALT = 4,
|
|
/** Kullback-Leibler divergence
|
|
\f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */
|
|
HISTCMP_KL_DIV = 5
|
|
};
|
|
|
|
/** the color conversion code
|
|
@see @ref imgproc_color_conversions
|
|
@ingroup imgproc_misc
|
|
*/
|
|
enum ColorConversionCodes {
|
|
COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image
|
|
COLOR_RGB2RGBA = COLOR_BGR2BGRA,
|
|
|
|
COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image
|
|
COLOR_RGBA2RGB = COLOR_BGRA2BGR,
|
|
|
|
COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
|
|
COLOR_RGB2BGRA = COLOR_BGR2RGBA,
|
|
|
|
COLOR_RGBA2BGR = 3,
|
|
COLOR_BGRA2RGB = COLOR_RGBA2BGR,
|
|
|
|
COLOR_BGR2RGB = 4,
|
|
COLOR_RGB2BGR = COLOR_BGR2RGB,
|
|
|
|
COLOR_BGRA2RGBA = 5,
|
|
COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
|
|
|
|
COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
|
|
COLOR_RGB2GRAY = 7,
|
|
COLOR_GRAY2BGR = 8,
|
|
COLOR_GRAY2RGB = COLOR_GRAY2BGR,
|
|
COLOR_GRAY2BGRA = 9,
|
|
COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
|
|
COLOR_BGRA2GRAY = 10,
|
|
COLOR_RGBA2GRAY = 11,
|
|
|
|
COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
|
|
COLOR_RGB2BGR565 = 13,
|
|
COLOR_BGR5652BGR = 14,
|
|
COLOR_BGR5652RGB = 15,
|
|
COLOR_BGRA2BGR565 = 16,
|
|
COLOR_RGBA2BGR565 = 17,
|
|
COLOR_BGR5652BGRA = 18,
|
|
COLOR_BGR5652RGBA = 19,
|
|
|
|
COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images)
|
|
COLOR_BGR5652GRAY = 21,
|
|
|
|
COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images)
|
|
COLOR_RGB2BGR555 = 23,
|
|
COLOR_BGR5552BGR = 24,
|
|
COLOR_BGR5552RGB = 25,
|
|
COLOR_BGRA2BGR555 = 26,
|
|
COLOR_RGBA2BGR555 = 27,
|
|
COLOR_BGR5552BGRA = 28,
|
|
COLOR_BGR5552RGBA = 29,
|
|
|
|
COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images)
|
|
COLOR_BGR5552GRAY = 31,
|
|
|
|
COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
|
|
COLOR_RGB2XYZ = 33,
|
|
COLOR_XYZ2BGR = 34,
|
|
COLOR_XYZ2RGB = 35,
|
|
|
|
COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
|
|
COLOR_RGB2YCrCb = 37,
|
|
COLOR_YCrCb2BGR = 38,
|
|
COLOR_YCrCb2RGB = 39,
|
|
|
|
COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
|
|
COLOR_RGB2HSV = 41,
|
|
|
|
COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
|
|
COLOR_RGB2Lab = 45,
|
|
|
|
COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
|
|
COLOR_RGB2Luv = 51,
|
|
COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
|
|
COLOR_RGB2HLS = 53,
|
|
|
|
COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
|
|
COLOR_HSV2RGB = 55,
|
|
|
|
COLOR_Lab2BGR = 56,
|
|
COLOR_Lab2RGB = 57,
|
|
COLOR_Luv2BGR = 58,
|
|
COLOR_Luv2RGB = 59,
|
|
COLOR_HLS2BGR = 60,
|
|
COLOR_HLS2RGB = 61,
|
|
|
|
COLOR_BGR2HSV_FULL = 66, //!<
|
|
COLOR_RGB2HSV_FULL = 67,
|
|
COLOR_BGR2HLS_FULL = 68,
|
|
COLOR_RGB2HLS_FULL = 69,
|
|
|
|
COLOR_HSV2BGR_FULL = 70,
|
|
COLOR_HSV2RGB_FULL = 71,
|
|
COLOR_HLS2BGR_FULL = 72,
|
|
COLOR_HLS2RGB_FULL = 73,
|
|
|
|
COLOR_LBGR2Lab = 74,
|
|
COLOR_LRGB2Lab = 75,
|
|
COLOR_LBGR2Luv = 76,
|
|
COLOR_LRGB2Luv = 77,
|
|
|
|
COLOR_Lab2LBGR = 78,
|
|
COLOR_Lab2LRGB = 79,
|
|
COLOR_Luv2LBGR = 80,
|
|
COLOR_Luv2LRGB = 81,
|
|
|
|
COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV
|
|
COLOR_RGB2YUV = 83,
|
|
COLOR_YUV2BGR = 84,
|
|
COLOR_YUV2RGB = 85,
|
|
|
|
//! YUV 4:2:0 family to RGB
|
|
COLOR_YUV2RGB_NV12 = 90,
|
|
COLOR_YUV2BGR_NV12 = 91,
|
|
COLOR_YUV2RGB_NV21 = 92,
|
|
COLOR_YUV2BGR_NV21 = 93,
|
|
COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
|
|
COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
|
|
|
|
COLOR_YUV2RGBA_NV12 = 94,
|
|
COLOR_YUV2BGRA_NV12 = 95,
|
|
COLOR_YUV2RGBA_NV21 = 96,
|
|
COLOR_YUV2BGRA_NV21 = 97,
|
|
COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
|
|
COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
|
|
|
|
COLOR_YUV2RGB_YV12 = 98,
|
|
COLOR_YUV2BGR_YV12 = 99,
|
|
COLOR_YUV2RGB_IYUV = 100,
|
|
COLOR_YUV2BGR_IYUV = 101,
|
|
COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
|
|
COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
|
|
COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
|
|
COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
|
|
|
|
COLOR_YUV2RGBA_YV12 = 102,
|
|
COLOR_YUV2BGRA_YV12 = 103,
|
|
COLOR_YUV2RGBA_IYUV = 104,
|
|
COLOR_YUV2BGRA_IYUV = 105,
|
|
COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
|
|
COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
|
|
COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
|
|
COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
|
|
|
|
COLOR_YUV2GRAY_420 = 106,
|
|
COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
|
|
COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
|
|
|
|
//! YUV 4:2:2 family to RGB
|
|
COLOR_YUV2RGB_UYVY = 107,
|
|
COLOR_YUV2BGR_UYVY = 108,
|
|
//COLOR_YUV2RGB_VYUY = 109,
|
|
//COLOR_YUV2BGR_VYUY = 110,
|
|
COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
|
|
COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
|
|
COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
|
|
COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
|
|
|
|
COLOR_YUV2RGBA_UYVY = 111,
|
|
COLOR_YUV2BGRA_UYVY = 112,
|
|
//COLOR_YUV2RGBA_VYUY = 113,
|
|
//COLOR_YUV2BGRA_VYUY = 114,
|
|
COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
|
|
COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
|
|
COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
|
|
COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
|
|
|
|
COLOR_YUV2RGB_YUY2 = 115,
|
|
COLOR_YUV2BGR_YUY2 = 116,
|
|
COLOR_YUV2RGB_YVYU = 117,
|
|
COLOR_YUV2BGR_YVYU = 118,
|
|
COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
|
|
COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
|
|
COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
|
|
COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
|
|
|
|
COLOR_YUV2RGBA_YUY2 = 119,
|
|
COLOR_YUV2BGRA_YUY2 = 120,
|
|
COLOR_YUV2RGBA_YVYU = 121,
|
|
COLOR_YUV2BGRA_YVYU = 122,
|
|
COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
|
|
COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
|
|
COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
|
|
COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
|
|
|
|
COLOR_YUV2GRAY_UYVY = 123,
|
|
COLOR_YUV2GRAY_YUY2 = 124,
|
|
//CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
|
|
COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
|
|
COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
|
|
COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
|
|
COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
|
|
COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
|
|
|
|
//! alpha premultiplication
|
|
COLOR_RGBA2mRGBA = 125,
|
|
COLOR_mRGBA2RGBA = 126,
|
|
|
|
//! RGB to YUV 4:2:0 family
|
|
COLOR_RGB2YUV_I420 = 127,
|
|
COLOR_BGR2YUV_I420 = 128,
|
|
COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
|
|
COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
|
|
|
|
COLOR_RGBA2YUV_I420 = 129,
|
|
COLOR_BGRA2YUV_I420 = 130,
|
|
COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
|
|
COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
|
|
COLOR_RGB2YUV_YV12 = 131,
|
|
COLOR_BGR2YUV_YV12 = 132,
|
|
COLOR_RGBA2YUV_YV12 = 133,
|
|
COLOR_BGRA2YUV_YV12 = 134,
|
|
|
|
//! Demosaicing
|
|
COLOR_BayerBG2BGR = 46,
|
|
COLOR_BayerGB2BGR = 47,
|
|
COLOR_BayerRG2BGR = 48,
|
|
COLOR_BayerGR2BGR = 49,
|
|
|
|
COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
|
|
COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
|
|
COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
|
|
COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
|
|
|
|
COLOR_BayerBG2GRAY = 86,
|
|
COLOR_BayerGB2GRAY = 87,
|
|
COLOR_BayerRG2GRAY = 88,
|
|
COLOR_BayerGR2GRAY = 89,
|
|
|
|
//! Demosaicing using Variable Number of Gradients
|
|
COLOR_BayerBG2BGR_VNG = 62,
|
|
COLOR_BayerGB2BGR_VNG = 63,
|
|
COLOR_BayerRG2BGR_VNG = 64,
|
|
COLOR_BayerGR2BGR_VNG = 65,
|
|
|
|
COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
|
|
COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
|
|
COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
|
|
COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
|
|
|
|
//! Edge-Aware Demosaicing
|
|
COLOR_BayerBG2BGR_EA = 135,
|
|
COLOR_BayerGB2BGR_EA = 136,
|
|
COLOR_BayerRG2BGR_EA = 137,
|
|
COLOR_BayerGR2BGR_EA = 138,
|
|
|
|
COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
|
|
COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
|
|
COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
|
|
COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
|
|
|
|
//! Demosaicing with alpha channel
|
|
COLOR_BayerBG2BGRA = 139,
|
|
COLOR_BayerGB2BGRA = 140,
|
|
COLOR_BayerRG2BGRA = 141,
|
|
COLOR_BayerGR2BGRA = 142,
|
|
|
|
COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
|
|
COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
|
|
COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
|
|
COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
|
|
|
|
COLOR_COLORCVT_MAX = 143
|
|
};
|
|
|
|
/** types of intersection between rectangles
|
|
@ingroup imgproc_shape
|
|
*/
|
|
enum RectanglesIntersectTypes {
|
|
INTERSECT_NONE = 0, //!< No intersection
|
|
INTERSECT_PARTIAL = 1, //!< There is a partial intersection
|
|
INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other
|
|
};
|
|
|
|
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
|
|
class CV_EXPORTS GeneralizedHough : public Algorithm
|
|
{
|
|
public:
|
|
//! set template to search
|
|
virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0;
|
|
virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
|
|
|
|
//! find template on image
|
|
virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0;
|
|
virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0;
|
|
|
|
//! Canny low threshold.
|
|
virtual void setCannyLowThresh(int cannyLowThresh) = 0;
|
|
virtual int getCannyLowThresh() const = 0;
|
|
|
|
//! Canny high threshold.
|
|
virtual void setCannyHighThresh(int cannyHighThresh) = 0;
|
|
virtual int getCannyHighThresh() const = 0;
|
|
|
|
//! Minimum distance between the centers of the detected objects.
|
|
virtual void setMinDist(double minDist) = 0;
|
|
virtual double getMinDist() const = 0;
|
|
|
|
//! Inverse ratio of the accumulator resolution to the image resolution.
|
|
virtual void setDp(double dp) = 0;
|
|
virtual double getDp() const = 0;
|
|
|
|
//! Maximal size of inner buffers.
|
|
virtual void setMaxBufferSize(int maxBufferSize) = 0;
|
|
virtual int getMaxBufferSize() const = 0;
|
|
};
|
|
|
|
//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
|
|
//! Detects position only without translation and rotation
|
|
class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough
|
|
{
|
|
public:
|
|
//! R-Table levels.
|
|
virtual void setLevels(int levels) = 0;
|
|
virtual int getLevels() const = 0;
|
|
|
|
//! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
|
|
virtual void setVotesThreshold(int votesThreshold) = 0;
|
|
virtual int getVotesThreshold() const = 0;
|
|
};
|
|
|
|
//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
|
|
//! Detects position, translation and rotation
|
|
class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough
|
|
{
|
|
public:
|
|
//! Angle difference in degrees between two points in feature.
|
|
virtual void setXi(double xi) = 0;
|
|
virtual double getXi() const = 0;
|
|
|
|
//! Feature table levels.
|
|
virtual void setLevels(int levels) = 0;
|
|
virtual int getLevels() const = 0;
|
|
|
|
//! Maximal difference between angles that treated as equal.
|
|
virtual void setAngleEpsilon(double angleEpsilon) = 0;
|
|
virtual double getAngleEpsilon() const = 0;
|
|
|
|
//! Minimal rotation angle to detect in degrees.
|
|
virtual void setMinAngle(double minAngle) = 0;
|
|
virtual double getMinAngle() const = 0;
|
|
|
|
//! Maximal rotation angle to detect in degrees.
|
|
virtual void setMaxAngle(double maxAngle) = 0;
|
|
virtual double getMaxAngle() const = 0;
|
|
|
|
//! Angle step in degrees.
|
|
virtual void setAngleStep(double angleStep) = 0;
|
|
virtual double getAngleStep() const = 0;
|
|
|
|
//! Angle votes threshold.
|
|
virtual void setAngleThresh(int angleThresh) = 0;
|
|
virtual int getAngleThresh() const = 0;
|
|
|
|
//! Minimal scale to detect.
|
|
virtual void setMinScale(double minScale) = 0;
|
|
virtual double getMinScale() const = 0;
|
|
|
|
//! Maximal scale to detect.
|
|
virtual void setMaxScale(double maxScale) = 0;
|
|
virtual double getMaxScale() const = 0;
|
|
|
|
//! Scale step.
|
|
virtual void setScaleStep(double scaleStep) = 0;
|
|
virtual double getScaleStep() const = 0;
|
|
|
|
//! Scale votes threshold.
|
|
virtual void setScaleThresh(int scaleThresh) = 0;
|
|
virtual int getScaleThresh() const = 0;
|
|
|
|
//! Position votes threshold.
|
|
virtual void setPosThresh(int posThresh) = 0;
|
|
virtual int getPosThresh() const = 0;
|
|
};
|
|
|
|
/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. :
|
|
*/
|
|
class CV_EXPORTS_W CLAHE : public Algorithm
|
|
{
|
|
public:
|
|
/** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.
|
|
|
|
@param src Source image with CV_8UC1 type.
|
|
@param dst Destination image.
|
|
*/
|
|
CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
|
|
|
|
/** @brief Sets threshold for contrast limiting.
|
|
|
|
@param clipLimit threshold value.
|
|
*/
|
|
CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
|
|
|
|
//! Returns threshold value for contrast limiting.
|
|
CV_WRAP virtual double getClipLimit() const = 0;
|
|
|
|
/** @brief Sets size of grid for histogram equalization. Input image will be divided into
|
|
equally sized rectangular tiles.
|
|
|
|
@param tileGridSize defines the number of tiles in row and column.
|
|
*/
|
|
CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0;
|
|
|
|
//!@brief Returns Size defines the number of tiles in row and column.
|
|
CV_WRAP virtual Size getTilesGridSize() const = 0;
|
|
|
|
CV_WRAP virtual void collectGarbage() = 0;
|
|
};
|
|
|
|
|
|
//! @addtogroup imgproc_subdiv2d
|
|
//! @{
|
|
|
|
class CV_EXPORTS_W Subdiv2D
|
|
{
|
|
public:
|
|
/** Subdiv2D point location cases */
|
|
enum { PTLOC_ERROR = -2, //!< Point location error
|
|
PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect
|
|
PTLOC_INSIDE = 0, //!< Point inside some facet
|
|
PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices
|
|
PTLOC_ON_EDGE = 2 //!< Point on some edge
|
|
};
|
|
|
|
/** Subdiv2D edge type navigation (see: getEdge()) */
|
|
enum { NEXT_AROUND_ORG = 0x00,
|
|
NEXT_AROUND_DST = 0x22,
|
|
PREV_AROUND_ORG = 0x11,
|
|
PREV_AROUND_DST = 0x33,
|
|
NEXT_AROUND_LEFT = 0x13,
|
|
NEXT_AROUND_RIGHT = 0x31,
|
|
PREV_AROUND_LEFT = 0x20,
|
|
PREV_AROUND_RIGHT = 0x02
|
|
};
|
|
|
|
/** creates an empty Subdiv2D object.
|
|
To create a new empty Delaunay subdivision you need to use the #initDelaunay function.
|
|
*/
|
|
CV_WRAP Subdiv2D();
|
|
|
|
/** @overload
|
|
|
|
@param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
|
|
|
|
The function creates an empty Delaunay subdivision where 2D points can be added using the function
|
|
insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime
|
|
error is raised.
|
|
*/
|
|
CV_WRAP Subdiv2D(Rect rect);
|
|
|
|
/** @brief Creates a new empty Delaunay subdivision
|
|
|
|
@param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
|
|
|
|
*/
|
|
CV_WRAP void initDelaunay(Rect rect);
|
|
|
|
/** @brief Insert a single point into a Delaunay triangulation.
|
|
|
|
@param pt Point to insert.
|
|
|
|
The function inserts a single point into a subdivision and modifies the subdivision topology
|
|
appropriately. If a point with the same coordinates exists already, no new point is added.
|
|
@returns the ID of the point.
|
|
|
|
@note If the point is outside of the triangulation specified rect a runtime error is raised.
|
|
*/
|
|
CV_WRAP int insert(Point2f pt);
|
|
|
|
/** @brief Insert multiple points into a Delaunay triangulation.
|
|
|
|
@param ptvec Points to insert.
|
|
|
|
The function inserts a vector of points into a subdivision and modifies the subdivision topology
|
|
appropriately.
|
|
*/
|
|
CV_WRAP void insert(const std::vector<Point2f>& ptvec);
|
|
|
|
/** @brief Returns the location of a point within a Delaunay triangulation.
|
|
|
|
@param pt Point to locate.
|
|
@param edge Output edge that the point belongs to or is located to the right of it.
|
|
@param vertex Optional output vertex the input point coincides with.
|
|
|
|
The function locates the input point within the subdivision and gives one of the triangle edges
|
|
or vertices.
|
|
|
|
@returns an integer which specify one of the following five cases for point location:
|
|
- The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of
|
|
edges of the facet.
|
|
- The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge.
|
|
- The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and
|
|
vertex will contain a pointer to the vertex.
|
|
- The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT
|
|
and no pointers are filled.
|
|
- One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error
|
|
processing mode is selected, #PTLOC_ERROR is returned.
|
|
*/
|
|
CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
|
|
|
|
/** @brief Finds the subdivision vertex closest to the given point.
|
|
|
|
@param pt Input point.
|
|
@param nearestPt Output subdivision vertex point.
|
|
|
|
The function is another function that locates the input point within the subdivision. It finds the
|
|
subdivision vertex that is the closest to the input point. It is not necessarily one of vertices
|
|
of the facet containing the input point, though the facet (located using locate() ) is used as a
|
|
starting point.
|
|
|
|
@returns vertex ID.
|
|
*/
|
|
CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0);
|
|
|
|
/** @brief Returns a list of all edges.
|
|
|
|
@param edgeList Output vector.
|
|
|
|
The function gives each edge as a 4 numbers vector, where each two are one of the edge
|
|
vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3].
|
|
*/
|
|
CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const;
|
|
|
|
/** @brief Returns a list of the leading edge ID connected to each triangle.
|
|
|
|
@param leadingEdgeList Output vector.
|
|
|
|
The function gives one edge ID for each triangle.
|
|
*/
|
|
CV_WRAP void getLeadingEdgeList(CV_OUT std::vector<int>& leadingEdgeList) const;
|
|
|
|
/** @brief Returns a list of all triangles.
|
|
|
|
@param triangleList Output vector.
|
|
|
|
The function gives each triangle as a 6 numbers vector, where each two are one of the triangle
|
|
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].
|
|
*/
|
|
CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const;
|
|
|
|
/** @brief Returns a list of all Voroni facets.
|
|
|
|
@param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector.
|
|
@param facetList Output vector of the Voroni facets.
|
|
@param facetCenters Output vector of the Voroni facets center points.
|
|
|
|
*/
|
|
CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList,
|
|
CV_OUT std::vector<Point2f>& facetCenters);
|
|
|
|
/** @brief Returns vertex location from vertex ID.
|
|
|
|
@param vertex vertex ID.
|
|
@param firstEdge Optional. The first edge ID which is connected to the vertex.
|
|
@returns vertex (x,y)
|
|
|
|
*/
|
|
CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const;
|
|
|
|
/** @brief Returns one of the edges related to the given edge.
|
|
|
|
@param edge Subdivision edge ID.
|
|
@param nextEdgeType Parameter specifying which of the related edges to return.
|
|
The following values are possible:
|
|
- NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge)
|
|
- NEXT_AROUND_DST next around the edge vertex ( eDnext )
|
|
- PREV_AROUND_ORG previous around the edge origin (reversed eRnext )
|
|
- PREV_AROUND_DST previous around the edge destination (reversed eLnext )
|
|
- NEXT_AROUND_LEFT next around the left facet ( eLnext )
|
|
- NEXT_AROUND_RIGHT next around the right facet ( eRnext )
|
|
- PREV_AROUND_LEFT previous around the left facet (reversed eOnext )
|
|
- PREV_AROUND_RIGHT previous around the right facet (reversed eDnext )
|
|
|
|
|
|
|
|
@returns edge ID related to the input edge.
|
|
*/
|
|
CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
|
|
|
|
/** @brief Returns next edge around the edge origin.
|
|
|
|
@param edge Subdivision edge ID.
|
|
|
|
@returns an integer which is next edge ID around the edge origin: eOnext on the
|
|
picture above if e is the input edge).
|
|
*/
|
|
CV_WRAP int nextEdge(int edge) const;
|
|
|
|
/** @brief Returns another edge of the same quad-edge.
|
|
|
|
@param edge Subdivision edge ID.
|
|
@param rotate Parameter specifying which of the edges of the same quad-edge as the input
|
|
one to return. The following values are possible:
|
|
- 0 - the input edge ( e on the picture below if e is the input edge)
|
|
- 1 - the rotated edge ( eRot )
|
|
- 2 - the reversed edge (reversed e (in green))
|
|
- 3 - the reversed rotated edge (reversed eRot (in green))
|
|
|
|
@returns one of the edges ID of the same quad-edge as the input edge.
|
|
*/
|
|
CV_WRAP int rotateEdge(int edge, int rotate) const;
|
|
CV_WRAP int symEdge(int edge) const;
|
|
|
|
/** @brief Returns the edge origin.
|
|
|
|
@param edge Subdivision edge ID.
|
|
@param orgpt Output vertex location.
|
|
|
|
@returns vertex ID.
|
|
*/
|
|
CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const;
|
|
|
|
/** @brief Returns the edge destination.
|
|
|
|
@param edge Subdivision edge ID.
|
|
@param dstpt Output vertex location.
|
|
|
|
@returns vertex ID.
|
|
*/
|
|
CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const;
|
|
|
|
protected:
|
|
int newEdge();
|
|
void deleteEdge(int edge);
|
|
int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0);
|
|
void deletePoint(int vtx);
|
|
void setEdgePoints( int edge, int orgPt, int dstPt );
|
|
void splice( int edgeA, int edgeB );
|
|
int connectEdges( int edgeA, int edgeB );
|
|
void swapEdges( int edge );
|
|
int isRightOf(Point2f pt, int edge) const;
|
|
void calcVoronoi();
|
|
void clearVoronoi();
|
|
void checkSubdiv() const;
|
|
|
|
struct CV_EXPORTS Vertex
|
|
{
|
|
Vertex();
|
|
Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0);
|
|
bool isvirtual() const;
|
|
bool isfree() const;
|
|
|
|
int firstEdge;
|
|
int type;
|
|
Point2f pt;
|
|
};
|
|
|
|
struct CV_EXPORTS QuadEdge
|
|
{
|
|
QuadEdge();
|
|
QuadEdge(int edgeidx);
|
|
bool isfree() const;
|
|
|
|
int next[4];
|
|
int pt[4];
|
|
};
|
|
|
|
//! All of the vertices
|
|
std::vector<Vertex> vtx;
|
|
//! All of the edges
|
|
std::vector<QuadEdge> qedges;
|
|
int freeQEdge;
|
|
int freePoint;
|
|
bool validGeometry;
|
|
|
|
int recentEdge;
|
|
//! Top left corner of the bounding rect
|
|
Point2f topLeft;
|
|
//! Bottom right corner of the bounding rect
|
|
Point2f bottomRight;
|
|
};
|
|
|
|
//! @} imgproc_subdiv2d
|
|
|
|
//! @addtogroup imgproc_feature
|
|
//! @{
|
|
|
|
/** @example lsd_lines.cpp
|
|
An example using the LineSegmentDetector
|
|
\image html building_lsd.png "Sample output image" width=434 height=300
|
|
*/
|
|
|
|
/** @brief Line segment detector class
|
|
|
|
following the algorithm described at @cite Rafael12 .
|
|
*/
|
|
class CV_EXPORTS_W LineSegmentDetector : public Algorithm
|
|
{
|
|
public:
|
|
|
|
/** @brief Finds lines in the input image.
|
|
|
|
This is the output of the default parameters of the algorithm on the above shown image.
|
|
|
|
|
|
|
|
@param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
|
|
`lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
|
|
@param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where
|
|
Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
|
|
oriented depending on the gradient.
|
|
@param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
|
|
@param prec Vector of precisions with which the lines are found.
|
|
@param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
|
|
bigger the value, logarithmically better the detection.
|
|
- -1 corresponds to 10 mean false alarms
|
|
- 0 corresponds to 1 mean false alarm
|
|
- 1 corresponds to 0.1 mean false alarms
|
|
This vector will be calculated only when the objects type is #LSD_REFINE_ADV.
|
|
*/
|
|
CV_WRAP virtual void detect(InputArray _image, OutputArray _lines,
|
|
OutputArray width = noArray(), OutputArray prec = noArray(),
|
|
OutputArray nfa = noArray()) = 0;
|
|
|
|
/** @brief Draws the line segments on a given image.
|
|
@param _image The image, where the lines will be drawn. Should be bigger or equal to the image,
|
|
where the lines were found.
|
|
@param lines A vector of the lines that needed to be drawn.
|
|
*/
|
|
CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0;
|
|
|
|
/** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
|
|
|
|
@param size The size of the image, where lines1 and lines2 were found.
|
|
@param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
|
|
@param lines2 The second group of lines. They visualized in red color.
|
|
@param _image Optional image, where the lines will be drawn. The image should be color(3-channel)
|
|
in order for lines1 and lines2 to be drawn in the above mentioned colors.
|
|
*/
|
|
CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0;
|
|
|
|
virtual ~LineSegmentDetector() { }
|
|
};
|
|
|
|
/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
|
|
|
|
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
|
|
to edit those, as to tailor it for their own application.
|
|
|
|
@param _refine The way found lines will be refined, see #LineSegmentDetectorModes
|
|
@param _scale The scale of the image that will be used to find the lines. Range (0..1].
|
|
@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
|
|
@param _quant Bound to the quantization error on the gradient norm.
|
|
@param _ang_th Gradient angle tolerance in degrees.
|
|
@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement
|
|
is chosen.
|
|
@param _density_th Minimal density of aligned region points in the enclosing rectangle.
|
|
@param _n_bins Number of bins in pseudo-ordering of gradient modulus.
|
|
*/
|
|
CV_EXPORTS_W Ptr<LineSegmentDetector> createLineSegmentDetector(
|
|
int _refine = LSD_REFINE_STD, double _scale = 0.8,
|
|
double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5,
|
|
double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024);
|
|
|
|
//! @} imgproc_feature
|
|
|
|
//! @addtogroup imgproc_filter
|
|
//! @{
|
|
|
|
/** @brief Returns Gaussian filter coefficients.
|
|
|
|
The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
|
|
coefficients:
|
|
|
|
\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
|
|
|
|
where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
|
|
|
|
Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
|
|
smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
|
|
You may also use the higher-level GaussianBlur.
|
|
@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
|
|
@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
|
|
`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`.
|
|
@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
|
|
@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
|
|
*/
|
|
CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F );
|
|
|
|
/** @brief Returns filter coefficients for computing spatial image derivatives.
|
|
|
|
The function computes and returns the filter coefficients for spatial image derivatives. When
|
|
`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel
|
|
kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
|
|
|
|
@param kx Output matrix of row filter coefficients. It has the type ktype .
|
|
@param ky Output matrix of column filter coefficients. It has the type ktype .
|
|
@param dx Derivative order in respect of x.
|
|
@param dy Derivative order in respect of y.
|
|
@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
|
|
@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
|
|
Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
|
|
going to filter floating-point images, you are likely to use the normalized kernels. But if you
|
|
compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
|
|
all the fractional bits, you may want to set normalize=false .
|
|
@param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
|
|
*/
|
|
CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
|
|
int dx, int dy, int ksize,
|
|
bool normalize = false, int ktype = CV_32F );
|
|
|
|
/** @brief Returns Gabor filter coefficients.
|
|
|
|
For more details about gabor filter equations and parameters, see: [Gabor
|
|
Filter](http://en.wikipedia.org/wiki/Gabor_filter).
|
|
|
|
@param ksize Size of the filter returned.
|
|
@param sigma Standard deviation of the gaussian envelope.
|
|
@param theta Orientation of the normal to the parallel stripes of a Gabor function.
|
|
@param lambd Wavelength of the sinusoidal factor.
|
|
@param gamma Spatial aspect ratio.
|
|
@param psi Phase offset.
|
|
@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
|
|
*/
|
|
CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
|
|
double gamma, double psi = CV_PI*0.5, int ktype = CV_64F );
|
|
|
|
//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
|
|
static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
|
|
|
|
/** @brief Returns a structuring element of the specified size and shape for morphological operations.
|
|
|
|
The function constructs and returns the structuring element that can be further passed to #erode,
|
|
#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
|
|
the structuring element.
|
|
|
|
@param shape Element shape that could be one of #MorphShapes
|
|
@param ksize Size of the structuring element.
|
|
@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
|
|
anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
|
|
position. In other cases the anchor just regulates how much the result of the morphological
|
|
operation is shifted.
|
|
*/
|
|
CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
|
|
|
|
/** @example Smoothing.cpp
|
|
Sample code for simple filters
|
|
|
|
Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details
|
|
*/
|
|
/** @brief Blurs an image using the median filter.
|
|
|
|
The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
|
|
\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
|
|
In-place operation is supported.
|
|
|
|
@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes
|
|
|
|
@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
|
|
CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
|
|
@param dst destination array of the same size and type as src.
|
|
@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
|
|
@sa bilateralFilter, blur, boxFilter, GaussianBlur
|
|
*/
|
|
CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
|
|
|
|
/** @brief Blurs an image using a Gaussian filter.
|
|
|
|
The function convolves the source image with the specified Gaussian kernel. In-place filtering is
|
|
supported.
|
|
|
|
@param src input image; the image can have any number of channels, which are processed
|
|
independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
|
|
@param dst output image of the same size and type as src.
|
|
@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
|
|
positive and odd. Or, they can be zero's and then they are computed from sigma.
|
|
@param sigmaX Gaussian kernel standard deviation in X direction.
|
|
@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
|
|
equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
|
|
respectively (see #getGaussianKernel for details); to fully control the result regardless of
|
|
possible future modifications of all this semantics, it is recommended to specify all of ksize,
|
|
sigmaX, and sigmaY.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
|
|
@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
|
|
*/
|
|
CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize,
|
|
double sigmaX, double sigmaY = 0,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Applies the bilateral filter to an image.
|
|
|
|
The function applies bilateral filtering to the input image, as described in
|
|
http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
|
|
bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
|
|
very slow compared to most filters.
|
|
|
|
_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
|
|
10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
|
|
strong effect, making the image look "cartoonish".
|
|
|
|
_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
|
|
applications, and perhaps d=9 for offline applications that need heavy noise filtering.
|
|
|
|
This filter does not work inplace.
|
|
@param src Source 8-bit or floating-point, 1-channel or 3-channel image.
|
|
@param dst Destination image of the same size and type as src .
|
|
@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
|
|
it is computed from sigmaSpace.
|
|
@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
|
|
farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
|
|
in larger areas of semi-equal color.
|
|
@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
|
|
farther pixels will influence each other as long as their colors are close enough (see sigmaColor
|
|
). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
|
|
proportional to sigmaSpace.
|
|
@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
|
|
*/
|
|
CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
|
|
double sigmaColor, double sigmaSpace,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Blurs an image using the box filter.
|
|
|
|
The function smooths an image using the kernel:
|
|
|
|
\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]
|
|
|
|
where
|
|
|
|
\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f]
|
|
|
|
Unnormalized box filter is useful for computing various integral characteristics over each pixel
|
|
neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
|
|
algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.
|
|
|
|
@param src input image.
|
|
@param dst output image of the same size and type as src.
|
|
@param ddepth the output image depth (-1 to use src.depth()).
|
|
@param ksize blurring kernel size.
|
|
@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
|
|
center.
|
|
@param normalize flag, specifying whether the kernel is normalized by its area or not.
|
|
@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
|
|
@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral
|
|
*/
|
|
CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
|
|
Size ksize, Point anchor = Point(-1,-1),
|
|
bool normalize = true,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
|
|
|
|
For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
|
|
pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
|
|
|
|
The unnormalized square box filter can be useful in computing local image statistics such as the the local
|
|
variance and standard deviation around the neighborhood of a pixel.
|
|
|
|
@param _src input image
|
|
@param _dst output image of the same size and type as _src
|
|
@param ddepth the output image depth (-1 to use src.depth())
|
|
@param ksize kernel size
|
|
@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
|
|
center.
|
|
@param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
|
|
@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
|
|
@sa boxFilter
|
|
*/
|
|
CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
|
|
Size ksize, Point anchor = Point(-1, -1),
|
|
bool normalize = true,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Blurs an image using the normalized box filter.
|
|
|
|
The function smooths an image using the kernel:
|
|
|
|
\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]
|
|
|
|
The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(),
|
|
anchor, true, borderType)`.
|
|
|
|
@param src input image; it can have any number of channels, which are processed independently, but
|
|
the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
|
|
@param dst output image of the same size and type as src.
|
|
@param ksize blurring kernel size.
|
|
@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
|
|
center.
|
|
@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
|
|
@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur
|
|
*/
|
|
CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
|
|
Size ksize, Point anchor = Point(-1,-1),
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Convolves an image with the kernel.
|
|
|
|
The function applies an arbitrary linear filter to an image. In-place operation is supported. When
|
|
the aperture is partially outside the image, the function interpolates outlier pixel values
|
|
according to the specified border mode.
|
|
|
|
The function does actually compute correlation, not the convolution:
|
|
|
|
\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]
|
|
|
|
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
|
|
the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
|
|
anchor.y - 1)`.
|
|
|
|
The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or
|
|
larger) and the direct algorithm for small kernels.
|
|
|
|
@param src input image.
|
|
@param dst output image of the same size and the same number of channels as src.
|
|
@param ddepth desired depth of the destination image, see @ref filter_depths "combinations"
|
|
@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
|
|
matrix; if you want to apply different kernels to different channels, split the image into
|
|
separate color planes using split and process them individually.
|
|
@param anchor anchor of the kernel that indicates the relative position of a filtered point within
|
|
the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
|
|
is at the kernel center.
|
|
@param delta optional value added to the filtered pixels before storing them in dst.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
@sa sepFilter2D, dft, matchTemplate
|
|
*/
|
|
CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
|
|
InputArray kernel, Point anchor = Point(-1,-1),
|
|
double delta = 0, int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Applies a separable linear filter to an image.
|
|
|
|
The function applies a separable linear filter to the image. That is, first, every row of src is
|
|
filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
|
|
kernel kernelY. The final result shifted by delta is stored in dst .
|
|
|
|
@param src Source image.
|
|
@param dst Destination image of the same size and the same number of channels as src .
|
|
@param ddepth Destination image depth, see @ref filter_depths "combinations"
|
|
@param kernelX Coefficients for filtering each row.
|
|
@param kernelY Coefficients for filtering each column.
|
|
@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
|
|
is at the kernel center.
|
|
@param delta Value added to the filtered results before storing them.
|
|
@param borderType Pixel extrapolation method, see #BorderTypes
|
|
@sa filter2D, Sobel, GaussianBlur, boxFilter, blur
|
|
*/
|
|
CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
|
|
InputArray kernelX, InputArray kernelY,
|
|
Point anchor = Point(-1,-1),
|
|
double delta = 0, int borderType = BORDER_DEFAULT );
|
|
|
|
/** @example Sobel_Demo.cpp
|
|
Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
|
|
|
|
Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details
|
|
*/
|
|
/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
|
|
|
|
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
|
|
calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
|
|
kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
|
|
or the second x- or y- derivatives.
|
|
|
|
There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
|
|
filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
|
|
|
|
\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
|
|
|
|
for the x-derivative, or transposed for the y-derivative.
|
|
|
|
The function calculates an image derivative by convolving the image with the appropriate kernel:
|
|
|
|
\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
|
|
|
|
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
|
|
resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
|
|
or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
|
|
case corresponds to a kernel of:
|
|
|
|
\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
|
|
|
|
The second case corresponds to a kernel of:
|
|
|
|
\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
|
|
|
|
@param src input image.
|
|
@param dst output image of the same size and the same number of channels as src .
|
|
@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
|
|
8-bit input images it will result in truncated derivatives.
|
|
@param dx order of the derivative x.
|
|
@param dy order of the derivative y.
|
|
@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
|
|
@param scale optional scale factor for the computed derivative values; by default, no scaling is
|
|
applied (see #getDerivKernels for details).
|
|
@param delta optional delta value that is added to the results prior to storing them in dst.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
|
|
*/
|
|
CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
|
|
int dx, int dy, int ksize = 3,
|
|
double scale = 1, double delta = 0,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Calculates the first order image derivative in both x and y using a Sobel operator
|
|
|
|
Equivalent to calling:
|
|
|
|
@code
|
|
Sobel( src, dx, CV_16SC1, 1, 0, 3 );
|
|
Sobel( src, dy, CV_16SC1, 0, 1, 3 );
|
|
@endcode
|
|
|
|
@param src input image.
|
|
@param dx output image with first-order derivative in x.
|
|
@param dy output image with first-order derivative in y.
|
|
@param ksize size of Sobel kernel. It must be 3.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
|
|
@sa Sobel
|
|
*/
|
|
|
|
CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx,
|
|
OutputArray dy, int ksize = 3,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Calculates the first x- or y- image derivative using Scharr operator.
|
|
|
|
The function computes the first x- or y- spatial image derivative using the Scharr operator. The
|
|
call
|
|
|
|
\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
|
|
|
|
is equivalent to
|
|
|
|
\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f]
|
|
|
|
@param src input image.
|
|
@param dst output image of the same size and the same number of channels as src.
|
|
@param ddepth output image depth, see @ref filter_depths "combinations"
|
|
@param dx order of the derivative x.
|
|
@param dy order of the derivative y.
|
|
@param scale optional scale factor for the computed derivative values; by default, no scaling is
|
|
applied (see #getDerivKernels for details).
|
|
@param delta optional delta value that is added to the results prior to storing them in dst.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
@sa cartToPolar
|
|
*/
|
|
CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
|
|
int dx, int dy, double scale = 1, double delta = 0,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @example laplace.cpp
|
|
An example using Laplace transformations for edge detection
|
|
*/
|
|
|
|
/** @brief Calculates the Laplacian of an image.
|
|
|
|
The function calculates the Laplacian of the source image by adding up the second x and y
|
|
derivatives calculated using the Sobel operator:
|
|
|
|
\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
|
|
|
|
This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
|
|
with the following \f$3 \times 3\f$ aperture:
|
|
|
|
\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
|
|
|
|
@param src Source image.
|
|
@param dst Destination image of the same size and the same number of channels as src .
|
|
@param ddepth Desired depth of the destination image.
|
|
@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
|
|
details. The size must be positive and odd.
|
|
@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
|
|
applied. See #getDerivKernels for details.
|
|
@param delta Optional delta value that is added to the results prior to storing them in dst .
|
|
@param borderType Pixel extrapolation method, see #BorderTypes
|
|
@sa Sobel, Scharr
|
|
*/
|
|
CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
|
|
int ksize = 1, double scale = 1, double delta = 0,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
//! @} imgproc_filter
|
|
|
|
//! @addtogroup imgproc_feature
|
|
//! @{
|
|
|
|
/** @example edge.cpp
|
|
This program demonstrates usage of the Canny edge detector
|
|
|
|
Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
|
|
*/
|
|
|
|
/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
|
|
|
|
The function finds edges in the input image and marks them in the output map edges using the
|
|
Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
|
|
largest value is used to find initial segments of strong edges. See
|
|
<http://en.wikipedia.org/wiki/Canny_edge_detector>
|
|
|
|
@param image 8-bit input image.
|
|
@param edges output edge map; single channels 8-bit image, which has the same size as image .
|
|
@param threshold1 first threshold for the hysteresis procedure.
|
|
@param threshold2 second threshold for the hysteresis procedure.
|
|
@param apertureSize aperture size for the Sobel operator.
|
|
@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
|
|
\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
|
|
L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
|
|
L2gradient=false ).
|
|
*/
|
|
CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
|
|
double threshold1, double threshold2,
|
|
int apertureSize = 3, bool L2gradient = false );
|
|
|
|
/** \overload
|
|
|
|
Finds edges in an image using the Canny algorithm with custom image gradient.
|
|
|
|
@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
|
|
@param dy 16-bit y derivative of input image (same type as dx).
|
|
@param edges output edge map; single channels 8-bit image, which has the same size as image .
|
|
@param threshold1 first threshold for the hysteresis procedure.
|
|
@param threshold2 second threshold for the hysteresis procedure.
|
|
@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
|
|
\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
|
|
L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
|
|
L2gradient=false ).
|
|
*/
|
|
CV_EXPORTS_W void Canny( InputArray dx, InputArray dy,
|
|
OutputArray edges,
|
|
double threshold1, double threshold2,
|
|
bool L2gradient = false );
|
|
|
|
/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
|
|
|
|
The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
|
|
eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
|
|
of the formulae in the cornerEigenValsAndVecs description.
|
|
|
|
@param src Input single-channel 8-bit or floating-point image.
|
|
@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
|
|
src .
|
|
@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
|
|
@param ksize Aperture parameter for the Sobel operator.
|
|
@param borderType Pixel extrapolation method. See #BorderTypes.
|
|
*/
|
|
CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
|
|
int blockSize, int ksize = 3,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Harris corner detector.
|
|
|
|
The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
|
|
cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
|
|
matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
|
|
computes the following characteristic:
|
|
|
|
\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
|
|
|
|
Corners in the image can be found as the local maxima of this response map.
|
|
|
|
@param src Input single-channel 8-bit or floating-point image.
|
|
@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
|
|
size as src .
|
|
@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
|
|
@param ksize Aperture parameter for the Sobel operator.
|
|
@param k Harris detector free parameter. See the formula below.
|
|
@param borderType Pixel extrapolation method. See #BorderTypes.
|
|
*/
|
|
CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
|
|
int ksize, double k,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection.
|
|
|
|
For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize
|
|
neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as:
|
|
|
|
\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]
|
|
|
|
where the derivatives are computed using the Sobel operator.
|
|
|
|
After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as
|
|
\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where
|
|
|
|
- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$
|
|
- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$
|
|
- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$
|
|
|
|
The output of the function can be used for robust edge or corner detection.
|
|
|
|
@param src Input single-channel 8-bit or floating-point image.
|
|
@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
|
|
@param blockSize Neighborhood size (see details below).
|
|
@param ksize Aperture parameter for the Sobel operator.
|
|
@param borderType Pixel extrapolation method. See #BorderTypes.
|
|
|
|
@sa cornerMinEigenVal, cornerHarris, preCornerDetect
|
|
*/
|
|
CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
|
|
int blockSize, int ksize,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Calculates a feature map for corner detection.
|
|
|
|
The function calculates the complex spatial derivative-based function of the source image
|
|
|
|
\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]
|
|
|
|
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
|
|
derivatives, and \f$D_{xy}\f$ is the mixed derivative.
|
|
|
|
The corners can be found as local maximums of the functions, as shown below:
|
|
@code
|
|
Mat corners, dilated_corners;
|
|
preCornerDetect(image, corners, 3);
|
|
// dilation with 3x3 rectangular structuring element
|
|
dilate(corners, dilated_corners, Mat(), 1);
|
|
Mat corner_mask = corners == dilated_corners;
|
|
@endcode
|
|
|
|
@param src Source single-channel 8-bit of floating-point image.
|
|
@param dst Output image that has the type CV_32F and the same size as src .
|
|
@param ksize %Aperture size of the Sobel .
|
|
@param borderType Pixel extrapolation method. See #BorderTypes.
|
|
*/
|
|
CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
|
|
int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Refines the corner locations.
|
|
|
|
The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as
|
|
shown on the figure below.
|
|
|
|
|
|
|
|
Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
|
|
to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
|
|
subject to image and measurement noise. Consider the expression:
|
|
|
|
\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f]
|
|
|
|
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
|
|
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
|
|
with \f$\epsilon_i\f$ set to zero:
|
|
|
|
\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]
|
|
|
|
where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
|
|
gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
|
|
|
|
\f[q = G^{-1} \cdot b\f]
|
|
|
|
The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
|
|
until the center stays within a set threshold.
|
|
|
|
@param image Input image.
|
|
@param corners Initial coordinates of the input corners and refined coordinates provided for
|
|
output.
|
|
@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
|
|
then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used.
|
|
@param zeroZone Half of the size of the dead region in the middle of the search zone over which
|
|
the summation in the formula below is not done. It is used sometimes to avoid possible
|
|
singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
|
|
a size.
|
|
@param criteria Criteria for termination of the iterative process of corner refinement. That is,
|
|
the process of corner position refinement stops either after criteria.maxCount iterations or when
|
|
the corner position moves by less than criteria.epsilon on some iteration.
|
|
*/
|
|
CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
|
|
Size winSize, Size zeroZone,
|
|
TermCriteria criteria );
|
|
|
|
/** @brief Determines strong corners on an image.
|
|
|
|
The function finds the most prominent corners in the image or in the specified image region, as
|
|
described in @cite Shi94
|
|
|
|
- Function calculates the corner quality measure at every source image pixel using the
|
|
#cornerMinEigenVal or #cornerHarris .
|
|
- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
|
|
retained).
|
|
- The corners with the minimal eigenvalue less than
|
|
\f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
|
|
- The remaining corners are sorted by the quality measure in the descending order.
|
|
- Function throws away each corner for which there is a stronger corner at a distance less than
|
|
maxDistance.
|
|
|
|
The function can be used to initialize a point-based tracker of an object.
|
|
|
|
@note If the function is called with different values A and B of the parameter qualityLevel , and
|
|
A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
|
|
with qualityLevel=B .
|
|
|
|
@param image Input 8-bit or floating-point 32-bit, single-channel image.
|
|
@param corners Output vector of detected corners.
|
|
@param maxCorners Maximum number of corners to return. If there are more corners than are found,
|
|
the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
|
|
and all detected corners are returned.
|
|
@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
|
|
parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
|
|
(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
|
|
quality measure less than the product are rejected. For example, if the best corner has the
|
|
quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
|
|
less than 15 are rejected.
|
|
@param minDistance Minimum possible Euclidean distance between the returned corners.
|
|
@param mask Optional region of interest. If the image is not empty (it needs to have the type
|
|
CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
|
|
@param blockSize Size of an average block for computing a derivative covariation matrix over each
|
|
pixel neighborhood. See cornerEigenValsAndVecs .
|
|
@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
|
|
or #cornerMinEigenVal.
|
|
@param k Free parameter of the Harris detector.
|
|
|
|
@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
|
|
*/
|
|
|
|
CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
|
|
int maxCorners, double qualityLevel, double minDistance,
|
|
InputArray mask = noArray(), int blockSize = 3,
|
|
bool useHarrisDetector = false, double k = 0.04 );
|
|
|
|
CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
|
|
int maxCorners, double qualityLevel, double minDistance,
|
|
InputArray mask, int blockSize,
|
|
int gradientSize, bool useHarrisDetector = false,
|
|
double k = 0.04 );
|
|
/** @example houghlines.cpp
|
|
An example using the Hough line detector
|
|
 
|
|
*/
|
|
|
|
/** @brief Finds lines in a binary image using the standard Hough transform.
|
|
|
|
The function implements the standard or standard multi-scale Hough transform algorithm for line
|
|
detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
|
|
transform.
|
|
|
|
@param image 8-bit, single-channel binary source image. The image may be modified by the function.
|
|
@param lines Output vector of lines. Each line is represented by a two-element vector
|
|
\f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
|
|
the image). \f$\theta\f$ is the line rotation angle in radians (
|
|
\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
|
|
@param rho Distance resolution of the accumulator in pixels.
|
|
@param theta Angle resolution of the accumulator in radians.
|
|
@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
|
|
votes ( \f$>\texttt{threshold}\f$ ).
|
|
@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho .
|
|
The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
|
|
rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these
|
|
parameters should be positive.
|
|
@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
|
|
@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
|
|
Must fall between 0 and max_theta.
|
|
@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines.
|
|
Must fall between min_theta and CV_PI.
|
|
*/
|
|
CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
|
|
double rho, double theta, int threshold,
|
|
double srn = 0, double stn = 0,
|
|
double min_theta = 0, double max_theta = CV_PI );
|
|
|
|
/** @brief Finds line segments in a binary image using the probabilistic Hough transform.
|
|
|
|
The function implements the probabilistic Hough transform algorithm for line detection, described
|
|
in @cite Matas00
|
|
|
|
See the line detection example below:
|
|
|
|
@code
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
Mat src, dst, color_dst;
|
|
if( argc != 2 || !(src=imread(argv[1], 0)).data)
|
|
return -1;
|
|
|
|
Canny( src, dst, 50, 200, 3 );
|
|
cvtColor( dst, color_dst, COLOR_GRAY2BGR );
|
|
|
|
#if 0
|
|
vector<Vec2f> lines;
|
|
HoughLines( dst, lines, 1, CV_PI/180, 100 );
|
|
|
|
for( size_t i = 0; i < lines.size(); i++ )
|
|
{
|
|
float rho = lines[i][0];
|
|
float theta = lines[i][1];
|
|
double a = cos(theta), b = sin(theta);
|
|
double x0 = a*rho, y0 = b*rho;
|
|
Point pt1(cvRound(x0 + 1000*(-b)),
|
|
cvRound(y0 + 1000*(a)));
|
|
Point pt2(cvRound(x0 - 1000*(-b)),
|
|
cvRound(y0 - 1000*(a)));
|
|
line( color_dst, pt1, pt2, Scalar(0,0,255), 3, 8 );
|
|
}
|
|
#else
|
|
vector<Vec4i> lines;
|
|
HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 );
|
|
for( size_t i = 0; i < lines.size(); i++ )
|
|
{
|
|
line( color_dst, Point(lines[i][0], lines[i][1]),
|
|
Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 );
|
|
}
|
|
#endif
|
|
namedWindow( "Source", 1 );
|
|
imshow( "Source", src );
|
|
|
|
namedWindow( "Detected Lines", 1 );
|
|
imshow( "Detected Lines", color_dst );
|
|
|
|
waitKey(0);
|
|
return 0;
|
|
}
|
|
@endcode
|
|
This is a sample picture the function parameters have been tuned for:
|
|
|
|
|
|
|
|
And this is the output of the above program in case of the probabilistic Hough transform:
|
|
|
|
|
|
|
|
@param image 8-bit, single-channel binary source image. The image may be modified by the function.
|
|
@param lines Output vector of lines. Each line is represented by a 4-element vector
|
|
\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
|
|
line segment.
|
|
@param rho Distance resolution of the accumulator in pixels.
|
|
@param theta Angle resolution of the accumulator in radians.
|
|
@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
|
|
votes ( \f$>\texttt{threshold}\f$ ).
|
|
@param minLineLength Minimum line length. Line segments shorter than that are rejected.
|
|
@param maxLineGap Maximum allowed gap between points on the same line to link them.
|
|
|
|
@sa LineSegmentDetector
|
|
*/
|
|
CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
|
|
double rho, double theta, int threshold,
|
|
double minLineLength = 0, double maxLineGap = 0 );
|
|
|
|
/** @brief Finds lines in a set of points using the standard Hough transform.
|
|
|
|
The function finds lines in a set of points using a modification of the Hough transform.
|
|
@include snippets/imgproc_HoughLinesPointSet.cpp
|
|
@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2.
|
|
@param _lines Output vector of found lines. Each vector is encoded as a vector<Vec3d> \f$(votes, rho, theta)\f$.
|
|
The larger the value of 'votes', the higher the reliability of the Hough line.
|
|
@param lines_max Max count of hough lines.
|
|
@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
|
|
votes ( \f$>\texttt{threshold}\f$ )
|
|
@param min_rho Minimum Distance value of the accumulator in pixels.
|
|
@param max_rho Maximum Distance value of the accumulator in pixels.
|
|
@param rho_step Distance resolution of the accumulator in pixels.
|
|
@param min_theta Minimum angle value of the accumulator in radians.
|
|
@param max_theta Maximum angle value of the accumulator in radians.
|
|
@param theta_step Angle resolution of the accumulator in radians.
|
|
*/
|
|
CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold,
|
|
double min_rho, double max_rho, double rho_step,
|
|
double min_theta, double max_theta, double theta_step );
|
|
|
|
/** @example houghcircles.cpp
|
|
An example using the Hough circle detector
|
|
*/
|
|
|
|
/** @brief Finds circles in a grayscale image using the Hough transform.
|
|
|
|
The function finds circles in a grayscale image using a modification of the Hough transform.
|
|
|
|
Example: :
|
|
@code
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
#include <math.h>
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
Mat img, gray;
|
|
if( argc != 2 || !(img=imread(argv[1], 1)).data)
|
|
return -1;
|
|
cvtColor(img, gray, COLOR_BGR2GRAY);
|
|
// smooth it, otherwise a lot of false circles may be detected
|
|
GaussianBlur( gray, gray, Size(9, 9), 2, 2 );
|
|
vector<Vec3f> circles;
|
|
HoughCircles(gray, circles, HOUGH_GRADIENT,
|
|
2, gray.rows/4, 200, 100 );
|
|
for( size_t i = 0; i < circles.size(); i++ )
|
|
{
|
|
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
|
|
int radius = cvRound(circles[i][2]);
|
|
// draw the circle center
|
|
circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 );
|
|
// draw the circle outline
|
|
circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 );
|
|
}
|
|
namedWindow( "circles", 1 );
|
|
imshow( "circles", img );
|
|
|
|
waitKey(0);
|
|
return 0;
|
|
}
|
|
@endcode
|
|
|
|
@note Usually the function detects the centers of circles well. However, it may fail to find correct
|
|
radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if
|
|
you know it. Or, you may set maxRadius to a negative number to return centers only without radius
|
|
search, and find the correct radius using an additional procedure.
|
|
|
|
@param image 8-bit, single-channel, grayscale input image.
|
|
@param circles Output vector of found circles. Each vector is encoded as a 3-element
|
|
floating-point vector \f$(x, y, radius)\f$ .
|
|
@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT
|
|
@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
|
|
dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
|
|
half as big width and height.
|
|
@param minDist Minimum distance between the centers of the detected circles. If the parameter is
|
|
too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
|
|
too large, some circles may be missed.
|
|
@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher
|
|
threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
|
|
@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the
|
|
accumulator threshold for the circle centers at the detection stage. The smaller it is, the more
|
|
false circles may be detected. Circles, corresponding to the larger accumulator values, will be
|
|
returned first.
|
|
@param minRadius Minimum circle radius.
|
|
@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns
|
|
centers without finding the radius.
|
|
|
|
@sa fitEllipse, minEnclosingCircle
|
|
*/
|
|
CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
|
|
int method, double dp, double minDist,
|
|
double param1 = 100, double param2 = 100,
|
|
int minRadius = 0, int maxRadius = 0 );
|
|
|
|
//! @} imgproc_feature
|
|
|
|
//! @addtogroup imgproc_filter
|
|
//! @{
|
|
|
|
/** @example morphology2.cpp
|
|
Advanced morphology Transformations sample code
|
|
|
|
Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details
|
|
*/
|
|
|
|
/** @brief Erodes an image by using a specific structuring element.
|
|
|
|
The function erodes the source image using the specified structuring element that determines the
|
|
shape of a pixel neighborhood over which the minimum is taken:
|
|
|
|
\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
|
|
|
|
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
|
|
case of multi-channel images, each channel is processed independently.
|
|
|
|
@param src input image; the number of channels can be arbitrary, but the depth should be one of
|
|
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
|
|
@param dst output image of the same size and type as src.
|
|
@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
|
|
structuring element is used. Kernel can be created using #getStructuringElement.
|
|
@param anchor position of the anchor within the element; default value (-1, -1) means that the
|
|
anchor is at the element center.
|
|
@param iterations number of times erosion is applied.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
@param borderValue border value in case of a constant border
|
|
@sa dilate, morphologyEx, getStructuringElement
|
|
*/
|
|
CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
|
|
Point anchor = Point(-1,-1), int iterations = 1,
|
|
int borderType = BORDER_CONSTANT,
|
|
const Scalar& borderValue = morphologyDefaultBorderValue() );
|
|
|
|
/** @example Morphology_1.cpp
|
|
Erosion and Dilation sample code
|
|
|
|
Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details
|
|
*/
|
|
/** @brief Dilates an image by using a specific structuring element.
|
|
|
|
The function dilates the source image using the specified structuring element that determines the
|
|
shape of a pixel neighborhood over which the maximum is taken:
|
|
\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
|
|
|
|
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
|
|
case of multi-channel images, each channel is processed independently.
|
|
|
|
@param src input image; the number of channels can be arbitrary, but the depth should be one of
|
|
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
|
|
@param dst output image of the same size and type as src.
|
|
@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
|
|
structuring element is used. Kernel can be created using #getStructuringElement
|
|
@param anchor position of the anchor within the element; default value (-1, -1) means that the
|
|
anchor is at the element center.
|
|
@param iterations number of times dilation is applied.
|
|
@param borderType pixel extrapolation method, see #BorderTypes
|
|
@param borderValue border value in case of a constant border
|
|
@sa erode, morphologyEx, getStructuringElement
|
|
*/
|
|
CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
|
|
Point anchor = Point(-1,-1), int iterations = 1,
|
|
int borderType = BORDER_CONSTANT,
|
|
const Scalar& borderValue = morphologyDefaultBorderValue() );
|
|
|
|
/** @brief Performs advanced morphological transformations.
|
|
|
|
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
|
|
basic operations.
|
|
|
|
Any of the operations can be done in-place. In case of multi-channel images, each channel is
|
|
processed independently.
|
|
|
|
@param src Source image. The number of channels can be arbitrary. The depth should be one of
|
|
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
|
|
@param dst Destination image of the same size and type as source image.
|
|
@param op Type of a morphological operation, see #MorphTypes
|
|
@param kernel Structuring element. It can be created using #getStructuringElement.
|
|
@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
|
|
kernel center.
|
|
@param iterations Number of times erosion and dilation are applied.
|
|
@param borderType Pixel extrapolation method, see #BorderTypes
|
|
@param borderValue Border value in case of a constant border. The default value has a special
|
|
meaning.
|
|
@sa dilate, erode, getStructuringElement
|
|
@note The number of iterations is the number of times erosion or dilatation operation will be applied.
|
|
For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
|
|
successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
|
|
*/
|
|
CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
|
|
int op, InputArray kernel,
|
|
Point anchor = Point(-1,-1), int iterations = 1,
|
|
int borderType = BORDER_CONSTANT,
|
|
const Scalar& borderValue = morphologyDefaultBorderValue() );
|
|
|
|
//! @} imgproc_filter
|
|
|
|
//! @addtogroup imgproc_transform
|
|
//! @{
|
|
|
|
/** @brief Resizes an image.
|
|
|
|
The function resize resizes the image src down to or up to the specified size. Note that the
|
|
initial dst type or size are not taken into account. Instead, the size and type are derived from
|
|
the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
|
|
you may call the function as follows:
|
|
@code
|
|
// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
|
|
resize(src, dst, dst.size(), 0, 0, interpolation);
|
|
@endcode
|
|
If you want to decimate the image by factor of 2 in each direction, you can call the function this
|
|
way:
|
|
@code
|
|
// specify fx and fy and let the function compute the destination image size.
|
|
resize(src, dst, Size(), 0.5, 0.5, interpolation);
|
|
@endcode
|
|
To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
|
|
enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR
|
|
(faster but still looks OK).
|
|
|
|
@param src input image.
|
|
@param dst output image; it has the size dsize (when it is non-zero) or the size computed from
|
|
src.size(), fx, and fy; the type of dst is the same as of src.
|
|
@param dsize output image size; if it equals zero, it is computed as:
|
|
\f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
|
|
Either dsize or both fx and fy must be non-zero.
|
|
@param fx scale factor along the horizontal axis; when it equals 0, it is computed as
|
|
\f[\texttt{(double)dsize.width/src.cols}\f]
|
|
@param fy scale factor along the vertical axis; when it equals 0, it is computed as
|
|
\f[\texttt{(double)dsize.height/src.rows}\f]
|
|
@param interpolation interpolation method, see #InterpolationFlags
|
|
|
|
@sa warpAffine, warpPerspective, remap
|
|
*/
|
|
CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
|
|
Size dsize, double fx = 0, double fy = 0,
|
|
int interpolation = INTER_LINEAR );
|
|
|
|
/** @brief Applies an affine transformation to an image.
|
|
|
|
The function warpAffine transforms the source image using the specified matrix:
|
|
|
|
\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]
|
|
|
|
when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
|
|
with #invertAffineTransform and then put in the formula above instead of M. The function cannot
|
|
operate in-place.
|
|
|
|
@param src input image.
|
|
@param dst output image that has the size dsize and the same type as src .
|
|
@param M \f$2\times 3\f$ transformation matrix.
|
|
@param dsize size of the output image.
|
|
@param flags combination of interpolation methods (see #InterpolationFlags) and the optional
|
|
flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
|
|
\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
|
|
@param borderMode pixel extrapolation method (see #BorderTypes); when
|
|
borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
|
|
the "outliers" in the source image are not modified by the function.
|
|
@param borderValue value used in case of a constant border; by default, it is 0.
|
|
|
|
@sa warpPerspective, resize, remap, getRectSubPix, transform
|
|
*/
|
|
CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
|
|
InputArray M, Size dsize,
|
|
int flags = INTER_LINEAR,
|
|
int borderMode = BORDER_CONSTANT,
|
|
const Scalar& borderValue = Scalar());
|
|
|
|
/** @example warpPerspective_demo.cpp
|
|
An example program shows using cv::findHomography and cv::warpPerspective for image warping
|
|
*/
|
|
/** @brief Applies a perspective transformation to an image.
|
|
|
|
The function warpPerspective transforms the source image using the specified matrix:
|
|
|
|
\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}} ,
|
|
\frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
|
|
|
|
when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
|
|
and then put in the formula above instead of M. The function cannot operate in-place.
|
|
|
|
@param src input image.
|
|
@param dst output image that has the size dsize and the same type as src .
|
|
@param M \f$3\times 3\f$ transformation matrix.
|
|
@param dsize size of the output image.
|
|
@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
|
|
optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
|
|
\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
|
|
@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
|
|
@param borderValue value used in case of a constant border; by default, it equals 0.
|
|
|
|
@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform
|
|
*/
|
|
CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
|
|
InputArray M, Size dsize,
|
|
int flags = INTER_LINEAR,
|
|
int borderMode = BORDER_CONSTANT,
|
|
const Scalar& borderValue = Scalar());
|
|
|
|
/** @brief Applies a generic geometrical transformation to an image.
|
|
|
|
The function remap transforms the source image using the specified map:
|
|
|
|
\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f]
|
|
|
|
where values of pixels with non-integer coordinates are computed using one of available
|
|
interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
|
|
in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
|
|
\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
|
|
convert from floating to fixed-point representations of a map is that they can yield much faster
|
|
(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
|
|
cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
|
|
|
|
This function cannot operate in-place.
|
|
|
|
@param src Source image.
|
|
@param dst Destination image. It has the same size as map1 and the same type as src .
|
|
@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
|
|
CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
|
|
representation to fixed-point for speed.
|
|
@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
|
|
if map1 is (x,y) points), respectively.
|
|
@param interpolation Interpolation method (see #InterpolationFlags). The method #INTER_AREA is
|
|
not supported by this function.
|
|
@param borderMode Pixel extrapolation method (see #BorderTypes). When
|
|
borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that
|
|
corresponds to the "outliers" in the source image are not modified by the function.
|
|
@param borderValue Value used in case of a constant border. By default, it is 0.
|
|
@note
|
|
Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
|
|
*/
|
|
CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
|
|
InputArray map1, InputArray map2,
|
|
int interpolation, int borderMode = BORDER_CONSTANT,
|
|
const Scalar& borderValue = Scalar());
|
|
|
|
/** @brief Converts image transformation maps from one representation to another.
|
|
|
|
The function converts a pair of maps for remap from one representation to another. The following
|
|
options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
|
|
supported:
|
|
|
|
- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the
|
|
most frequently used conversion operation, in which the original floating-point maps (see remap )
|
|
are converted to a more compact and much faster fixed-point representation. The first output array
|
|
contains the rounded coordinates and the second array (created only when nninterpolation=false )
|
|
contains indices in the interpolation tables.
|
|
|
|
- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but
|
|
the original maps are stored in one 2-channel matrix.
|
|
|
|
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
|
|
as the originals.
|
|
|
|
@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
|
|
@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
|
|
respectively.
|
|
@param dstmap1 The first output map that has the type dstmap1type and the same size as src .
|
|
@param dstmap2 The second output map.
|
|
@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
|
|
CV_32FC2 .
|
|
@param nninterpolation Flag indicating whether the fixed-point maps are used for the
|
|
nearest-neighbor or for a more complex interpolation.
|
|
|
|
@sa remap, undistort, initUndistortRectifyMap
|
|
*/
|
|
CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
|
|
OutputArray dstmap1, OutputArray dstmap2,
|
|
int dstmap1type, bool nninterpolation = false );
|
|
|
|
/** @brief Calculates an affine matrix of 2D rotation.
|
|
|
|
The function calculates the following matrix:
|
|
|
|
\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]
|
|
|
|
where
|
|
|
|
\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
|
|
|
|
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
|
|
|
|
@param center Center of the rotation in the source image.
|
|
@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
|
|
coordinate origin is assumed to be the top-left corner).
|
|
@param scale Isotropic scale factor.
|
|
|
|
@sa getAffineTransform, warpAffine, transform
|
|
*/
|
|
CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale );
|
|
|
|
//! returns 3x3 perspective transformation for the corresponding 4 point pairs.
|
|
CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] );
|
|
|
|
/** @brief Calculates an affine transform from three pairs of the corresponding points.
|
|
|
|
The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
|
|
|
|
\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
|
|
|
|
where
|
|
|
|
\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
|
|
|
|
@param src Coordinates of triangle vertices in the source image.
|
|
@param dst Coordinates of the corresponding triangle vertices in the destination image.
|
|
|
|
@sa warpAffine, transform
|
|
*/
|
|
CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
|
|
|
|
/** @brief Inverts an affine transformation.
|
|
|
|
The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
|
|
|
|
\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
|
|
|
|
The result is also a \f$2 \times 3\f$ matrix of the same type as M.
|
|
|
|
@param M Original affine transformation.
|
|
@param iM Output reverse affine transformation.
|
|
*/
|
|
CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
|
|
|
|
/** @brief Calculates a perspective transform from four pairs of the corresponding points.
|
|
|
|
The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
|
|
|
|
\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]
|
|
|
|
where
|
|
|
|
\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
|
|
|
|
@param src Coordinates of quadrangle vertices in the source image.
|
|
@param dst Coordinates of the corresponding quadrangle vertices in the destination image.
|
|
|
|
@sa findHomography, warpPerspective, perspectiveTransform
|
|
*/
|
|
CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst );
|
|
|
|
CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
|
|
|
|
/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
|
|
|
|
The function getRectSubPix extracts pixels from src:
|
|
|
|
\f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
|
|
|
|
where the values of the pixels at non-integer coordinates are retrieved using bilinear
|
|
interpolation. Every channel of multi-channel images is processed independently. Also
|
|
the image should be a single channel or three channel image. While the center of the
|
|
rectangle must be inside the image, parts of the rectangle may be outside.
|
|
|
|
@param image Source image.
|
|
@param patchSize Size of the extracted patch.
|
|
@param center Floating point coordinates of the center of the extracted rectangle within the
|
|
source image. The center must be inside the image.
|
|
@param patch Extracted patch that has the size patchSize and the same number of channels as src .
|
|
@param patchType Depth of the extracted pixels. By default, they have the same depth as src .
|
|
|
|
@sa warpAffine, warpPerspective
|
|
*/
|
|
CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
|
|
Point2f center, OutputArray patch, int patchType = -1 );
|
|
|
|
/** @example polar_transforms.cpp
|
|
An example using the cv::linearPolar and cv::logPolar operations
|
|
*/
|
|
|
|
/** @brief Remaps an image to semilog-polar coordinates space.
|
|
|
|
@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
|
|
|
|
@internal
|
|
Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"):
|
|
\f[\begin{array}{l}
|
|
dst( \rho , \phi ) = src(x,y) \\
|
|
dst.size() \leftarrow src.size()
|
|
\end{array}\f]
|
|
|
|
where
|
|
\f[\begin{array}{l}
|
|
I = (dx,dy) = (x - center.x,y - center.y) \\
|
|
\rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
|
|
\phi = Kangle \cdot \texttt{angle} (I) \\
|
|
\end{array}\f]
|
|
|
|
and
|
|
\f[\begin{array}{l}
|
|
M = src.cols / log_e(maxRadius) \\
|
|
Kangle = src.rows / 2\Pi \\
|
|
\end{array}\f]
|
|
|
|
The function emulates the human "foveal" vision and can be used for fast scale and
|
|
rotation-invariant template matching, for object tracking and so forth.
|
|
@param src Source image
|
|
@param dst Destination image. It will have same size and type as src.
|
|
@param center The transformation center; where the output precision is maximal
|
|
@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
|
|
@param flags A combination of interpolation methods, see #InterpolationFlags
|
|
|
|
@note
|
|
- The function can not operate in-place.
|
|
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
|
|
|
|
@sa cv::linearPolar
|
|
@endinternal
|
|
*/
|
|
CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst,
|
|
Point2f center, double M, int flags );
|
|
|
|
/** @brief Remaps an image to polar coordinates space.
|
|
|
|
@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
|
|
|
|
@internal
|
|
Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"):
|
|
\f[\begin{array}{l}
|
|
dst( \rho , \phi ) = src(x,y) \\
|
|
dst.size() \leftarrow src.size()
|
|
\end{array}\f]
|
|
|
|
where
|
|
\f[\begin{array}{l}
|
|
I = (dx,dy) = (x - center.x,y - center.y) \\
|
|
\rho = Kmag \cdot \texttt{magnitude} (I) ,\\
|
|
\phi = angle \cdot \texttt{angle} (I)
|
|
\end{array}\f]
|
|
|
|
and
|
|
\f[\begin{array}{l}
|
|
Kx = src.cols / maxRadius \\
|
|
Ky = src.rows / 2\Pi
|
|
\end{array}\f]
|
|
|
|
|
|
@param src Source image
|
|
@param dst Destination image. It will have same size and type as src.
|
|
@param center The transformation center;
|
|
@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
|
|
@param flags A combination of interpolation methods, see #InterpolationFlags
|
|
|
|
@note
|
|
- The function can not operate in-place.
|
|
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
|
|
|
|
@sa cv::logPolar
|
|
@endinternal
|
|
*/
|
|
CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst,
|
|
Point2f center, double maxRadius, int flags );
|
|
|
|
|
|
/** \brief Remaps an image to polar or semilog-polar coordinates space
|
|
|
|
@anchor polar_remaps_reference_image
|
|
|
|
|
|
Transform the source image using the following transformation:
|
|
\f[
|
|
dst(\rho , \phi ) = src(x,y)
|
|
\f]
|
|
|
|
where
|
|
\f[
|
|
\begin{array}{l}
|
|
\vec{I} = (x - center.x, \;y - center.y) \\
|
|
\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\
|
|
\rho = \left\{\begin{matrix}
|
|
Klin \cdot \texttt{magnitude} (\vec{I}) & default \\
|
|
Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\
|
|
\end{matrix}\right.
|
|
\end{array}
|
|
\f]
|
|
|
|
and
|
|
\f[
|
|
\begin{array}{l}
|
|
Kangle = dsize.height / 2\Pi \\
|
|
Klin = dsize.width / maxRadius \\
|
|
Klog = dsize.width / log_e(maxRadius) \\
|
|
\end{array}
|
|
\f]
|
|
|
|
|
|
\par Linear vs semilog mapping
|
|
|
|
Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode.
|
|
|
|
Linear is the default mode.
|
|
|
|
The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision)
|
|
in contrast to peripheral vision where acuity is minor.
|
|
|
|
\par Option on `dsize`:
|
|
|
|
- if both values in `dsize <=0 ` (default),
|
|
the destination image will have (almost) same area of source bounding circle:
|
|
\f[\begin{array}{l}
|
|
dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\
|
|
dsize.width = \texttt{cvRound}(maxRadius) \\
|
|
dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\
|
|
\end{array}\f]
|
|
|
|
|
|
- if only `dsize.height <= 0`,
|
|
the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`:
|
|
\f[\begin{array}{l}
|
|
dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\
|
|
\end{array}
|
|
\f]
|
|
|
|
- if both values in `dsize > 0 `,
|
|
the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`.
|
|
|
|
|
|
\par Reverse mapping
|
|
|
|
You can get reverse mapping adding #WARP_INVERSE_MAP to `flags`
|
|
\snippet polar_transforms.cpp InverseMap
|
|
|
|
In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$:
|
|
\snippet polar_transforms.cpp InverseCoordinate
|
|
|
|
@param src Source image.
|
|
@param dst Destination image. It will have same type as src.
|
|
@param dsize The destination image size (see description for valid options).
|
|
@param center The transformation center.
|
|
@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
|
|
@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode.
|
|
- Add #WARP_POLAR_LINEAR to select linear polar mapping (default)
|
|
- Add #WARP_POLAR_LOG to select semilog polar mapping
|
|
- Add #WARP_INVERSE_MAP for reverse mapping.
|
|
@note
|
|
- The function can not operate in-place.
|
|
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
|
|
- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
|
|
|
|
@sa cv::remap
|
|
*/
|
|
CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize,
|
|
Point2f center, double maxRadius, int flags);
|
|
|
|
|
|
//! @} imgproc_transform
|
|
|
|
//! @addtogroup imgproc_misc
|
|
//! @{
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
|
|
OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
|
|
|
|
/** @brief Calculates the integral of an image.
|
|
|
|
The function calculates one or more integral images for the source image as follows:
|
|
|
|
\f[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\f]
|
|
|
|
\f[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\f]
|
|
|
|
\f[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\f]
|
|
|
|
Using these integral images, you can calculate sum, mean, and standard deviation over a specific
|
|
up-right or rotated rectangular region of the image in a constant time, for example:
|
|
|
|
\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]
|
|
|
|
It makes possible to do a fast blurring or fast block correlation with a variable window size, for
|
|
example. In case of multi-channel images, sums for each channel are accumulated independently.
|
|
|
|
As a practical example, the next figure shows the calculation of the integral of a straight
|
|
rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the
|
|
original image are shown, as well as the relative pixels in the integral images sum and tilted .
|
|
|
|
|
|
|
|
@param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f).
|
|
@param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f).
|
|
@param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision
|
|
floating-point (64f) array.
|
|
@param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with
|
|
the same data type as sum.
|
|
@param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
|
|
CV_64F.
|
|
@param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
|
|
*/
|
|
CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
|
|
OutputArray sqsum, OutputArray tilted,
|
|
int sdepth = -1, int sqdepth = -1 );
|
|
|
|
//! @} imgproc_misc
|
|
|
|
//! @addtogroup imgproc_motion
|
|
//! @{
|
|
|
|
/** @brief Adds an image to the accumulator image.
|
|
|
|
The function adds src or some of its elements to dst :
|
|
|
|
\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]
|
|
|
|
The function supports multi-channel images. Each channel is processed independently.
|
|
|
|
The function cv::accumulate can be used, for example, to collect statistics of a scene background
|
|
viewed by a still camera and for the further foreground-background segmentation.
|
|
|
|
@param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
|
|
@param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
|
|
@param mask Optional operation mask.
|
|
|
|
@sa accumulateSquare, accumulateProduct, accumulateWeighted
|
|
*/
|
|
CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
|
|
InputArray mask = noArray() );
|
|
|
|
/** @brief Adds the square of a source image to the accumulator image.
|
|
|
|
The function adds the input image src or its selected region, raised to a power of 2, to the
|
|
accumulator dst :
|
|
|
|
\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]
|
|
|
|
The function supports multi-channel images. Each channel is processed independently.
|
|
|
|
@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
|
|
@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
|
|
floating-point.
|
|
@param mask Optional operation mask.
|
|
|
|
@sa accumulateSquare, accumulateProduct, accumulateWeighted
|
|
*/
|
|
CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
|
|
InputArray mask = noArray() );
|
|
|
|
/** @brief Adds the per-element product of two input images to the accumulator image.
|
|
|
|
The function adds the product of two images or their selected regions to the accumulator dst :
|
|
|
|
\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]
|
|
|
|
The function supports multi-channel images. Each channel is processed independently.
|
|
|
|
@param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
|
|
@param src2 Second input image of the same type and the same size as src1 .
|
|
@param dst %Accumulator image with the same number of channels as input images, 32-bit or 64-bit
|
|
floating-point.
|
|
@param mask Optional operation mask.
|
|
|
|
@sa accumulate, accumulateSquare, accumulateWeighted
|
|
*/
|
|
CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
|
|
InputOutputArray dst, InputArray mask=noArray() );
|
|
|
|
/** @brief Updates a running average.
|
|
|
|
The function calculates the weighted sum of the input image src and the accumulator dst so that dst
|
|
becomes a running average of a frame sequence:
|
|
|
|
\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]
|
|
|
|
That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
|
|
The function supports multi-channel images. Each channel is processed independently.
|
|
|
|
@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
|
|
@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
|
|
floating-point.
|
|
@param alpha Weight of the input image.
|
|
@param mask Optional operation mask.
|
|
|
|
@sa accumulate, accumulateSquare, accumulateProduct
|
|
*/
|
|
CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
|
|
double alpha, InputArray mask = noArray() );
|
|
|
|
/** @brief The function is used to detect translational shifts that occur between two images.
|
|
|
|
The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
|
|
the frequency domain. It can be used for fast image registration as well as motion estimation. For
|
|
more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
|
|
|
|
Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
|
|
with getOptimalDFTSize.
|
|
|
|
The function performs the following equations:
|
|
- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
|
|
image to remove possible edge effects. This window is cached until the array size changes to speed
|
|
up processing time.
|
|
- Next it computes the forward DFTs of each source array:
|
|
\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f]
|
|
where \f$\mathcal{F}\f$ is the forward DFT.
|
|
- It then computes the cross-power spectrum of each frequency domain array:
|
|
\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f]
|
|
- Next the cross-correlation is converted back into the time domain via the inverse DFT:
|
|
\f[r = \mathcal{F}^{-1}\{R\}\f]
|
|
- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
|
|
achieve sub-pixel accuracy.
|
|
\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f]
|
|
- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5
|
|
centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single
|
|
peak) and will be smaller when there are multiple peaks.
|
|
|
|
@param src1 Source floating point array (CV_32FC1 or CV_64FC1)
|
|
@param src2 Source floating point array (CV_32FC1 or CV_64FC1)
|
|
@param window Floating point array with windowing coefficients to reduce edge effects (optional).
|
|
@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
|
|
@returns detected phase shift (sub-pixel) between the two arrays.
|
|
|
|
@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
|
|
*/
|
|
CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2,
|
|
InputArray window = noArray(), CV_OUT double* response = 0);
|
|
|
|
/** @brief This function computes a Hanning window coefficients in two dimensions.
|
|
|
|
See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
|
|
for more information.
|
|
|
|
An example is shown below:
|
|
@code
|
|
// create hanning window of size 100x100 and type CV_32F
|
|
Mat hann;
|
|
createHanningWindow(hann, Size(100, 100), CV_32F);
|
|
@endcode
|
|
@param dst Destination array to place Hann coefficients in
|
|
@param winSize The window size specifications (both width and height must be > 1)
|
|
@param type Created array type
|
|
*/
|
|
CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
|
|
|
|
//! @} imgproc_motion
|
|
|
|
//! @addtogroup imgproc_misc
|
|
//! @{
|
|
|
|
/** @brief Applies a fixed-level threshold to each array element.
|
|
|
|
The function applies fixed-level thresholding to a multiple-channel array. The function is typically
|
|
used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for
|
|
this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
|
|
values. There are several types of thresholding supported by the function. They are determined by
|
|
type parameter.
|
|
|
|
Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the
|
|
above values. In these cases, the function determines the optimal threshold value using the Otsu's
|
|
or Triangle algorithm and uses it instead of the specified thresh.
|
|
|
|
@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.
|
|
|
|
@param src input array (multiple-channel, 8-bit or 32-bit floating point).
|
|
@param dst output array of the same size and type and the same number of channels as src.
|
|
@param thresh threshold value.
|
|
@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding
|
|
types.
|
|
@param type thresholding type (see #ThresholdTypes).
|
|
@return the computed threshold value if Otsu's or Triangle methods used.
|
|
|
|
@sa adaptiveThreshold, findContours, compare, min, max
|
|
*/
|
|
CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
|
|
double thresh, double maxval, int type );
|
|
|
|
|
|
/** @brief Applies an adaptive threshold to an array.
|
|
|
|
The function transforms a grayscale image to a binary image according to the formulae:
|
|
- **THRESH_BINARY**
|
|
\f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
|
|
- **THRESH_BINARY_INV**
|
|
\f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
|
|
where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
|
|
|
|
The function can process the image in-place.
|
|
|
|
@param src Source 8-bit single-channel image.
|
|
@param dst Destination image of the same size and the same type as src.
|
|
@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
|
|
@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.
|
|
The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
|
|
@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,
|
|
see #ThresholdTypes.
|
|
@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
|
|
pixel: 3, 5, 7, and so on.
|
|
@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
|
|
is positive but may be zero or negative as well.
|
|
|
|
@sa threshold, blur, GaussianBlur
|
|
*/
|
|
CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
|
|
double maxValue, int adaptiveMethod,
|
|
int thresholdType, int blockSize, double C );
|
|
|
|
//! @} imgproc_misc
|
|
|
|
//! @addtogroup imgproc_filter
|
|
//! @{
|
|
|
|
/** @example Pyramids.cpp
|
|
An example using pyrDown and pyrUp functions
|
|
*/
|
|
/** @brief Blurs an image and downsamples it.
|
|
|
|
By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
|
|
any case, the following conditions should be satisfied:
|
|
|
|
\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f]
|
|
|
|
The function performs the downsampling step of the Gaussian pyramid construction. First, it
|
|
convolves the source image with the kernel:
|
|
|
|
\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]
|
|
|
|
Then, it downsamples the image by rejecting even rows and columns.
|
|
|
|
@param src input image.
|
|
@param dst output image; it has the specified size and the same type as src.
|
|
@param dstsize size of the output image.
|
|
@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
|
|
*/
|
|
CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
|
|
const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Upsamples an image and then blurs it.
|
|
|
|
By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
|
|
case, the following conditions should be satisfied:
|
|
|
|
\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]
|
|
|
|
The function performs the upsampling step of the Gaussian pyramid construction, though it can
|
|
actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
|
|
injecting even zero rows and columns and then convolves the result with the same kernel as in
|
|
pyrDown multiplied by 4.
|
|
|
|
@param src input image.
|
|
@param dst output image. It has the specified size and the same type as src .
|
|
@param dstsize size of the output image.
|
|
@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
|
|
*/
|
|
CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
|
|
const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
|
|
|
|
/** @brief Constructs the Gaussian pyramid for an image.
|
|
|
|
The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
|
|
pyrDown to the previously built pyramid layers, starting from `dst[0]==src`.
|
|
|
|
@param src Source image. Check pyrDown for the list of supported types.
|
|
@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the
|
|
same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
|
|
@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
|
|
@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
|
|
*/
|
|
CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
|
|
int maxlevel, int borderType = BORDER_DEFAULT );
|
|
|
|
//! @} imgproc_filter
|
|
|
|
//! @addtogroup imgproc_transform
|
|
//! @{
|
|
|
|
/** @brief Transforms an image to compensate for lens distortion.
|
|
|
|
The function transforms an image to compensate radial and tangential lens distortion.
|
|
|
|
The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap
|
|
(with bilinear interpolation). See the former function for details of the transformation being
|
|
performed.
|
|
|
|
Those pixels in the destination image, for which there is no correspondent pixels in the source
|
|
image, are filled with zeros (black color).
|
|
|
|
A particular subset of the source image that will be visible in the corrected image can be regulated
|
|
by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate
|
|
newCameraMatrix depending on your requirements.
|
|
|
|
The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
|
|
the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
|
|
f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
|
|
the same.
|
|
|
|
@param src Input (distorted) image.
|
|
@param dst Output (corrected) image that has the same size and type as src .
|
|
@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
|
|
@param distCoeffs Input vector of distortion coefficients
|
|
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
|
|
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
|
|
@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
|
|
cameraMatrix but you may additionally scale and shift the result by using a different matrix.
|
|
*/
|
|
CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
|
|
InputArray cameraMatrix,
|
|
InputArray distCoeffs,
|
|
InputArray newCameraMatrix = noArray() );
|
|
|
|
/** @brief Computes the undistortion and rectification transformation map.
|
|
|
|
The function computes the joint undistortion and rectification transformation and represents the
|
|
result in the form of maps for remap. The undistorted image looks like original, as if it is
|
|
captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
|
|
monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
|
|
#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
|
|
newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
|
|
|
|
Also, this new camera is oriented differently in the coordinate space, according to R. That, for
|
|
example, helps to align two heads of a stereo camera so that the epipolar lines on both images
|
|
become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
|
|
|
|
The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
|
|
is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
|
|
computes the corresponding coordinates in the source image (that is, in the original image from
|
|
camera). The following process is applied:
|
|
\f[
|
|
\begin{array}{l}
|
|
x \leftarrow (u - {c'}_x)/{f'}_x \\
|
|
y \leftarrow (v - {c'}_y)/{f'}_y \\
|
|
{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\
|
|
x' \leftarrow X/W \\
|
|
y' \leftarrow Y/W \\
|
|
r^2 \leftarrow x'^2 + y'^2 \\
|
|
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}
|
|
+ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\
|
|
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}
|
|
+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
|
|
s\vecthree{x'''}{y'''}{1} =
|
|
\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
|
|
{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
|
|
{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
|
|
map_x(u,v) \leftarrow x''' f_x + c_x \\
|
|
map_y(u,v) \leftarrow y''' f_y + c_y
|
|
\end{array}
|
|
\f]
|
|
where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
|
|
are the distortion coefficients.
|
|
|
|
In case of a stereo camera, this function is called twice: once for each camera head, after
|
|
stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera
|
|
was not calibrated, it is still possible to compute the rectification transformations directly from
|
|
the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes
|
|
homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
|
|
space. R can be computed from H as
|
|
\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
|
|
where cameraMatrix can be chosen arbitrarily.
|
|
|
|
@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
|
|
@param distCoeffs Input vector of distortion coefficients
|
|
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
|
|
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
|
|
@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
|
|
computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
|
|
is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
|
|
@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
|
|
@param size Undistorted image size.
|
|
@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
|
|
@param map1 The first output map.
|
|
@param map2 The second output map.
|
|
*/
|
|
CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
|
|
InputArray R, InputArray newCameraMatrix,
|
|
Size size, int m1type, OutputArray map1, OutputArray map2 );
|
|
|
|
//! initializes maps for #remap for wide-angle
|
|
CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
|
|
Size imageSize, int destImageWidth,
|
|
int m1type, OutputArray map1, OutputArray map2,
|
|
int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
|
|
|
|
/** @brief Returns the default new camera matrix.
|
|
|
|
The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
|
|
centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
|
|
|
|
In the latter case, the new camera matrix will be:
|
|
|
|
\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]
|
|
|
|
where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
|
|
|
|
By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
|
|
move the principal point. However, when you work with stereo, it is important to move the principal
|
|
points in both views to the same y-coordinate (which is required by most of stereo correspondence
|
|
algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
|
|
each view where the principal points are located at the center.
|
|
|
|
@param cameraMatrix Input camera matrix.
|
|
@param imgsize Camera view image size in pixels.
|
|
@param centerPrincipalPoint Location of the principal point in the new camera matrix. The
|
|
parameter indicates whether this location should be at the image center or not.
|
|
*/
|
|
CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(),
|
|
bool centerPrincipalPoint = false );
|
|
|
|
/** @brief Computes the ideal point coordinates from the observed point coordinates.
|
|
|
|
The function is similar to #undistort and #initUndistortRectifyMap but it operates on a
|
|
sparse set of points instead of a raster image. Also the function performs a reverse transformation
|
|
to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
|
|
planar object, it does, up to a translation vector, if the proper R is specified.
|
|
|
|
For each observed point coordinate \f$(u, v)\f$ the function computes:
|
|
\f[
|
|
\begin{array}{l}
|
|
x^{"} \leftarrow (u - c_x)/f_x \\
|
|
y^{"} \leftarrow (v - c_y)/f_y \\
|
|
(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
|
|
{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
|
|
x \leftarrow X/W \\
|
|
y \leftarrow Y/W \\
|
|
\text{only performed if P is specified:} \\
|
|
u' \leftarrow x {f'}_x + {c'}_x \\
|
|
v' \leftarrow y {f'}_y + {c'}_y
|
|
\end{array}
|
|
\f]
|
|
|
|
where *undistort* is an approximate iterative algorithm that estimates the normalized original
|
|
point coordinates out of the normalized distorted point coordinates ("normalized" means that the
|
|
coordinates do not depend on the camera matrix).
|
|
|
|
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
|
|
|
|
@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
|
|
@param dst Output ideal point coordinates after undistortion and reverse perspective
|
|
transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
|
|
@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
|
|
@param distCoeffs Input vector of distortion coefficients
|
|
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
|
|
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
|
|
@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
|
|
#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
|
|
@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
|
|
#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
|
|
*/
|
|
CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
|
|
InputArray cameraMatrix, InputArray distCoeffs,
|
|
InputArray R = noArray(), InputArray P = noArray());
|
|
/** @overload
|
|
@note Default version of #undistortPoints does 5 iterations to compute undistorted points.
|
|
|
|
*/
|
|
CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst,
|
|
InputArray cameraMatrix, InputArray distCoeffs,
|
|
InputArray R, InputArray P, TermCriteria criteria);
|
|
|
|
//! @} imgproc_transform
|
|
|
|
//! @addtogroup imgproc_hist
|
|
//! @{
|
|
|
|
/** @example demhist.cpp
|
|
An example for creating histograms of an image
|
|
*/
|
|
|
|
/** @brief Calculates a histogram of a set of arrays.
|
|
|
|
The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used
|
|
to increment a histogram bin are taken from the corresponding input arrays at the same location. The
|
|
sample below shows how to compute a 2D Hue-Saturation histogram for a color image. :
|
|
@code
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
|
|
using namespace cv;
|
|
|
|
int main( int argc, char** argv )
|
|
{
|
|
Mat src, hsv;
|
|
if( argc != 2 || !(src=imread(argv[1], 1)).data )
|
|
return -1;
|
|
|
|
cvtColor(src, hsv, COLOR_BGR2HSV);
|
|
|
|
// Quantize the hue to 30 levels
|
|
// and the saturation to 32 levels
|
|
int hbins = 30, sbins = 32;
|
|
int histSize[] = {hbins, sbins};
|
|
// hue varies from 0 to 179, see cvtColor
|
|
float hranges[] = { 0, 180 };
|
|
// saturation varies from 0 (black-gray-white) to
|
|
// 255 (pure spectrum color)
|
|
float sranges[] = { 0, 256 };
|
|
const float* ranges[] = { hranges, sranges };
|
|
MatND hist;
|
|
// we compute the histogram from the 0-th and 1-st channels
|
|
int channels[] = {0, 1};
|
|
|
|
calcHist( &hsv, 1, channels, Mat(), // do not use mask
|
|
hist, 2, histSize, ranges,
|
|
true, // the histogram is uniform
|
|
false );
|
|
double maxVal=0;
|
|
minMaxLoc(hist, 0, &maxVal, 0, 0);
|
|
|
|
int scale = 10;
|
|
Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
|
|
|
|
for( int h = 0; h < hbins; h++ )
|
|
for( int s = 0; s < sbins; s++ )
|
|
{
|
|
float binVal = hist.at<float>(h, s);
|
|
int intensity = cvRound(binVal*255/maxVal);
|
|
rectangle( histImg, Point(h*scale, s*scale),
|
|
Point( (h+1)*scale - 1, (s+1)*scale - 1),
|
|
Scalar::all(intensity),
|
|
CV_FILLED );
|
|
}
|
|
|
|
namedWindow( "Source", 1 );
|
|
imshow( "Source", src );
|
|
|
|
namedWindow( "H-S Histogram", 1 );
|
|
imshow( "H-S Histogram", histImg );
|
|
waitKey();
|
|
}
|
|
@endcode
|
|
|
|
@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
|
|
size. Each of them can have an arbitrary number of channels.
|
|
@param nimages Number of source images.
|
|
@param channels List of the dims channels used to compute the histogram. The first array channels
|
|
are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
|
|
images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
|
|
@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
|
|
as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
|
|
@param hist Output histogram, which is a dense or sparse dims -dimensional array.
|
|
@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
|
|
(equal to 32 in the current OpenCV version).
|
|
@param histSize Array of histogram sizes in each dimension.
|
|
@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the
|
|
histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower
|
|
(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary
|
|
\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a
|
|
uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (
|
|
uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
|
|
\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$
|
|
. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not
|
|
counted in the histogram.
|
|
@param uniform Flag indicating whether the histogram is uniform or not (see above).
|
|
@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning
|
|
when it is allocated. This feature enables you to compute a single histogram from several sets of
|
|
arrays, or to update the histogram in time.
|
|
*/
|
|
CV_EXPORTS void calcHist( const Mat* images, int nimages,
|
|
const int* channels, InputArray mask,
|
|
OutputArray hist, int dims, const int* histSize,
|
|
const float** ranges, bool uniform = true, bool accumulate = false );
|
|
|
|
/** @overload
|
|
|
|
this variant uses %SparseMat for output
|
|
*/
|
|
CV_EXPORTS void calcHist( const Mat* images, int nimages,
|
|
const int* channels, InputArray mask,
|
|
SparseMat& hist, int dims,
|
|
const int* histSize, const float** ranges,
|
|
bool uniform = true, bool accumulate = false );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
|
|
const std::vector<int>& channels,
|
|
InputArray mask, OutputArray hist,
|
|
const std::vector<int>& histSize,
|
|
const std::vector<float>& ranges,
|
|
bool accumulate = false );
|
|
|
|
/** @brief Calculates the back projection of a histogram.
|
|
|
|
The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to
|
|
#calcHist , at each location (x, y) the function collects the values from the selected channels
|
|
in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
|
|
function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of
|
|
statistics, the function computes probability of each element value in respect with the empirical
|
|
probability distribution represented by the histogram. See how, for example, you can find and track
|
|
a bright-colored object in a scene:
|
|
|
|
- Before tracking, show the object to the camera so that it covers almost the whole frame.
|
|
Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant
|
|
colors in the object.
|
|
|
|
- When tracking, calculate a back projection of a hue plane of each input video frame using that
|
|
pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make
|
|
sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
|
|
|
|
- Find connected components in the resulting picture and choose, for example, the largest
|
|
component.
|
|
|
|
This is an approximate algorithm of the CamShift color object tracker.
|
|
|
|
@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
|
|
size. Each of them can have an arbitrary number of channels.
|
|
@param nimages Number of source images.
|
|
@param channels The list of channels used to compute the back projection. The number of channels
|
|
must match the histogram dimensionality. The first array channels are numerated from 0 to
|
|
images[0].channels()-1 , the second array channels are counted from images[0].channels() to
|
|
images[0].channels() + images[1].channels()-1, and so on.
|
|
@param hist Input histogram that can be dense or sparse.
|
|
@param backProject Destination back projection array that is a single-channel array of the same
|
|
size and depth as images[0] .
|
|
@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist .
|
|
@param scale Optional scale factor for the output back projection.
|
|
@param uniform Flag indicating whether the histogram is uniform or not (see above).
|
|
|
|
@sa calcHist, compareHist
|
|
*/
|
|
CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
|
|
const int* channels, InputArray hist,
|
|
OutputArray backProject, const float** ranges,
|
|
double scale = 1, bool uniform = true );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
|
|
const int* channels, const SparseMat& hist,
|
|
OutputArray backProject, const float** ranges,
|
|
double scale = 1, bool uniform = true );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
|
|
InputArray hist, OutputArray dst,
|
|
const std::vector<float>& ranges,
|
|
double scale );
|
|
|
|
/** @brief Compares two histograms.
|
|
|
|
The function cv::compareHist compares two dense or two sparse histograms using the specified method.
|
|
|
|
The function returns \f$d(H_1, H_2)\f$ .
|
|
|
|
While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
|
|
for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
|
|
problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
|
|
or more general sparse configurations of weighted points, consider using the #EMD function.
|
|
|
|
@param H1 First compared histogram.
|
|
@param H2 Second compared histogram of the same size as H1 .
|
|
@param method Comparison method, see #HistCompMethods
|
|
*/
|
|
CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
|
|
|
|
/** @brief Equalizes the histogram of a grayscale image.
|
|
|
|
The function equalizes the histogram of the input image using the following algorithm:
|
|
|
|
- Calculate the histogram \f$H\f$ for src .
|
|
- Normalize the histogram so that the sum of histogram bins is 255.
|
|
- Compute the integral of the histogram:
|
|
\f[H'_i = \sum _{0 \le j < i} H(j)\f]
|
|
- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
|
|
|
|
The algorithm normalizes the brightness and increases the contrast of the image.
|
|
|
|
@param src Source 8-bit single channel image.
|
|
@param dst Destination image of the same size and type as src .
|
|
*/
|
|
CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
|
|
|
|
/** @brief Computes the "minimal work" distance between two weighted point configurations.
|
|
|
|
The function computes the earth mover distance and/or a lower boundary of the distance between the
|
|
two weighted point configurations. One of the applications described in @cite RubnerSept98,
|
|
@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
|
|
problem that is solved using some modification of a simplex algorithm, thus the complexity is
|
|
exponential in the worst case, though, on average it is much faster. In the case of a real metric
|
|
the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
|
|
to determine roughly whether the two signatures are far enough so that they cannot relate to the
|
|
same object.
|
|
|
|
@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
|
|
Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
|
|
a single column (weights only) if the user-defined cost matrix is used. The weights must be
|
|
non-negative and have at least one non-zero value.
|
|
@param signature2 Second signature of the same format as signature1 , though the number of rows
|
|
may be different. The total weights may be different. In this case an extra "dummy" point is added
|
|
to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
|
|
value.
|
|
@param distType Used metric. See #DistanceTypes.
|
|
@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
|
|
is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
|
|
@param lowerBound Optional input/output parameter: lower boundary of a distance between the two
|
|
signatures that is a distance between mass centers. The lower boundary may not be calculated if
|
|
the user-defined cost matrix is used, the total weights of point configurations are not equal, or
|
|
if the signatures consist of weights only (the signature matrices have a single column). You
|
|
**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
|
|
equal to \*lowerBound (it means that the signatures are far enough), the function does not
|
|
calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
|
|
return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
|
|
should be set to 0.
|
|
@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
|
|
a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
|
|
*/
|
|
CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
|
|
int distType, InputArray cost=noArray(),
|
|
float* lowerBound = 0, OutputArray flow = noArray() );
|
|
|
|
CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2,
|
|
int distType, InputArray cost=noArray(),
|
|
CV_IN_OUT Ptr<float> lowerBound = Ptr<float>(), OutputArray flow = noArray() );
|
|
|
|
//! @} imgproc_hist
|
|
|
|
/** @example watershed.cpp
|
|
An example using the watershed algorithm
|
|
*/
|
|
|
|
/** @brief Performs a marker-based image segmentation using the watershed algorithm.
|
|
|
|
The function implements one of the variants of watershed, non-parametric marker-based segmentation
|
|
algorithm, described in @cite Meyer92 .
|
|
|
|
Before passing the image to the function, you have to roughly outline the desired regions in the
|
|
image markers with positive (\>0) indices. So, every region is represented as one or more connected
|
|
components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
|
|
mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of
|
|
the future image regions. All the other pixels in markers , whose relation to the outlined regions
|
|
is not known and should be defined by the algorithm, should be set to 0's. In the function output,
|
|
each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
|
|
regions.
|
|
|
|
@note Any two neighbor connected components are not necessarily separated by a watershed boundary
|
|
(-1's pixels); for example, they can touch each other in the initial marker image passed to the
|
|
function.
|
|
|
|
@param image Input 8-bit 3-channel image.
|
|
@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
|
|
size as image .
|
|
|
|
@sa findContours
|
|
|
|
@ingroup imgproc_misc
|
|
*/
|
|
CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
|
|
|
|
//! @addtogroup imgproc_filter
|
|
//! @{
|
|
|
|
/** @brief Performs initial step of meanshift segmentation of an image.
|
|
|
|
The function implements the filtering stage of meanshift segmentation, that is, the output of the
|
|
function is the filtered "posterized" image with color gradients and fine-grain texture flattened.
|
|
At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes
|
|
meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is
|
|
considered:
|
|
|
|
\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]
|
|
|
|
where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively
|
|
(though, the algorithm does not depend on the color space used, so any 3-component color space can
|
|
be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector
|
|
(R',G',B') are found and they act as the neighborhood center on the next iteration:
|
|
|
|
\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f]
|
|
|
|
After the iterations over, the color components of the initial pixel (that is, the pixel from where
|
|
the iterations started) are set to the final value (average color at the last iteration):
|
|
|
|
\f[I(X,Y) <- (R*,G*,B*)\f]
|
|
|
|
When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
|
|
run on the smallest layer first. After that, the results are propagated to the larger layer and the
|
|
iterations are run again only on those pixels where the layer colors differ by more than sr from the
|
|
lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
|
|
results will be actually different from the ones obtained by running the meanshift procedure on the
|
|
whole original image (i.e. when maxLevel==0).
|
|
|
|
@param src The source 8-bit, 3-channel image.
|
|
@param dst The destination image of the same format and the same size as the source.
|
|
@param sp The spatial window radius.
|
|
@param sr The color window radius.
|
|
@param maxLevel Maximum level of the pyramid for the segmentation.
|
|
@param termcrit Termination criteria: when to stop meanshift iterations.
|
|
*/
|
|
CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
|
|
double sp, double sr, int maxLevel = 1,
|
|
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
|
|
|
|
//! @}
|
|
|
|
//! @addtogroup imgproc_misc
|
|
//! @{
|
|
|
|
/** @example grabcut.cpp
|
|
An example using the GrabCut algorithm
|
|
|
|
*/
|
|
|
|
/** @brief Runs the GrabCut algorithm.
|
|
|
|
The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
|
|
|
|
@param img Input 8-bit 3-channel image.
|
|
@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
|
|
mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
|
|
@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
|
|
"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .
|
|
@param bgdModel Temporary array for the background model. Do not modify it while you are
|
|
processing the same image.
|
|
@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
|
|
processing the same image.
|
|
@param iterCount Number of iterations the algorithm should make before returning the result. Note
|
|
that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or
|
|
mode==GC_EVAL .
|
|
@param mode Operation mode that could be one of the #GrabCutModes
|
|
*/
|
|
CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
|
|
InputOutputArray bgdModel, InputOutputArray fgdModel,
|
|
int iterCount, int mode = GC_EVAL );
|
|
|
|
/** @example distrans.cpp
|
|
An example on using the distance transform\
|
|
*/
|
|
|
|
|
|
/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
|
|
|
|
The function cv::distanceTransform calculates the approximate or precise distance from every binary
|
|
image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
|
|
|
|
When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the
|
|
algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
|
|
|
|
In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
|
|
finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
|
|
diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
|
|
distance is calculated as a sum of these basic distances. Since the distance function should be
|
|
symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
|
|
the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
|
|
same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated
|
|
precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a
|
|
relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
|
|
uses the values suggested in the original paper:
|
|
- DIST_L1: `a = 1, b = 2`
|
|
- DIST_L2:
|
|
- `3 x 3`: `a=0.955, b=1.3693`
|
|
- `5 x 5`: `a=1, b=1.4, c=2.1969`
|
|
- DIST_C: `a = 1, b = 1`
|
|
|
|
Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a
|
|
more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
|
|
Note that both the precise and the approximate algorithms are linear on the number of pixels.
|
|
|
|
This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
|
|
but also identifies the nearest connected component consisting of zero pixels
|
|
(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the
|
|
component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function
|
|
automatically finds connected components of zero pixels in the input image and marks them with
|
|
distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and
|
|
marks all the zero pixels with distinct labels.
|
|
|
|
In this mode, the complexity is still linear. That is, the function provides a very fast way to
|
|
compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
|
|
approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported
|
|
yet.
|
|
|
|
@param src 8-bit, single-channel (binary) source image.
|
|
@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
|
|
single-channel image of the same size as src.
|
|
@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
|
|
CV_32SC1 and the same size as src.
|
|
@param distanceType Type of distance, see #DistanceTypes
|
|
@param maskSize Size of the distance transform mask, see #DistanceTransformMasks.
|
|
#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type,
|
|
the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
|
|
5\f$ or any larger aperture.
|
|
@param labelType Type of the label array to build, see #DistanceTransformLabelTypes.
|
|
*/
|
|
CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
|
|
OutputArray labels, int distanceType, int maskSize,
|
|
int labelType = DIST_LABEL_CCOMP );
|
|
|
|
/** @overload
|
|
@param src 8-bit, single-channel (binary) source image.
|
|
@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
|
|
single-channel image of the same size as src .
|
|
@param distanceType Type of distance, see #DistanceTypes
|
|
@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the
|
|
#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
|
|
the same result as \f$5\times 5\f$ or any larger aperture.
|
|
@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
|
|
the first variant of the function and distanceType == #DIST_L1.
|
|
*/
|
|
CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
|
|
int distanceType, int maskSize, int dstType=CV_32F);
|
|
|
|
/** @example ffilldemo.cpp
|
|
An example using the FloodFill technique
|
|
*/
|
|
|
|
/** @overload
|
|
|
|
variant without `mask` parameter
|
|
*/
|
|
CV_EXPORTS int floodFill( InputOutputArray image,
|
|
Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0,
|
|
Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
|
|
int flags = 4 );
|
|
|
|
/** @brief Fills a connected component with the given color.
|
|
|
|
The function cv::floodFill fills a connected component starting from the seed point with the specified
|
|
color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
|
|
pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if:
|
|
|
|
- in case of a grayscale image and floating range
|
|
\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f]
|
|
|
|
|
|
- in case of a grayscale image and fixed range
|
|
\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]
|
|
|
|
|
|
- in case of a color image and floating range
|
|
\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]
|
|
\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]
|
|
and
|
|
\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]
|
|
|
|
|
|
- in case of a color image and fixed range
|
|
\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]
|
|
\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]
|
|
and
|
|
\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]
|
|
|
|
|
|
where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the
|
|
component. That is, to be added to the connected component, a color/brightness of the pixel should
|
|
be close enough to:
|
|
- Color/brightness of one of its neighbors that already belong to the connected component in case
|
|
of a floating range.
|
|
- Color/brightness of the seed point in case of a fixed range.
|
|
|
|
Use these functions to either mark a connected component with the specified color in-place, or build
|
|
a mask and then extract the contour, or copy the region to another image, and so on.
|
|
|
|
@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
|
|
function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
|
|
the details below.
|
|
@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
|
|
taller than image. Since this is both an input and output parameter, you must take responsibility
|
|
of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
|
|
an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
|
|
mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
|
|
as described below. Additionally, the function fills the border of the mask with ones to simplify
|
|
internal processing. It is therefore possible to use the same mask in multiple calls to the function
|
|
to make sure the filled areas do not overlap.
|
|
@param seedPoint Starting point.
|
|
@param newVal New value of the repainted domain pixels.
|
|
@param loDiff Maximal lower brightness/color difference between the currently observed pixel and
|
|
one of its neighbors belonging to the component, or a seed pixel being added to the component.
|
|
@param upDiff Maximal upper brightness/color difference between the currently observed pixel and
|
|
one of its neighbors belonging to the component, or a seed pixel being added to the component.
|
|
@param rect Optional output parameter set by the function to the minimum bounding rectangle of the
|
|
repainted domain.
|
|
@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
|
|
4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
|
|
connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
|
|
will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
|
|
the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest
|
|
neighbours and fill the mask with a value of 255. The following additional options occupy higher
|
|
bits and therefore may be further combined with the connectivity and mask fill values using
|
|
bit-wise or (|), see #FloodFillFlags.
|
|
|
|
@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the
|
|
pixel \f$(x+1, y+1)\f$ in the mask .
|
|
|
|
@sa findContours
|
|
*/
|
|
CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
|
|
Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
|
|
Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
|
|
int flags = 4 );
|
|
|
|
/** @brief Converts an image from one color space to another.
|
|
|
|
The function converts an input image from one color space to another. In case of a transformation
|
|
to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
|
|
that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
|
|
bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
|
|
component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
|
|
sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
|
|
|
|
The conventional ranges for R, G, and B channel values are:
|
|
- 0 to 255 for CV_8U images
|
|
- 0 to 65535 for CV_16U images
|
|
- 0 to 1 for CV_32F images
|
|
|
|
In case of linear transformations, the range does not matter. But in case of a non-linear
|
|
transformation, an input RGB image should be normalized to the proper value range to get the correct
|
|
results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
|
|
32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
|
|
have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor ,
|
|
you need first to scale the image down:
|
|
@code
|
|
img *= 1./255;
|
|
cvtColor(img, img, COLOR_BGR2Luv);
|
|
@endcode
|
|
If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many
|
|
applications, this will not be noticeable but it is recommended to use 32-bit images in applications
|
|
that need the full range of colors or that convert an image before an operation and then convert
|
|
back.
|
|
|
|
If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
|
|
range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
|
|
|
|
@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
|
|
floating-point.
|
|
@param dst output image of the same size and depth as src.
|
|
@param code color space conversion code (see #ColorConversionCodes).
|
|
@param dstCn number of channels in the destination image; if the parameter is 0, the number of the
|
|
channels is derived automatically from src and code.
|
|
|
|
@see @ref imgproc_color_conversions
|
|
*/
|
|
CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 );
|
|
|
|
CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code );
|
|
|
|
//! @} imgproc_misc
|
|
|
|
// main function for all demosaicing processes
|
|
CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0);
|
|
|
|
//! @addtogroup imgproc_shape
|
|
//! @{
|
|
|
|
/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
|
|
|
|
The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
|
|
results are returned in the structure cv::Moments.
|
|
|
|
@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
|
|
\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
|
|
@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
|
|
used for images only.
|
|
@returns moments.
|
|
|
|
@note Only applicable to contour moments calculations from Python bindings: Note that the numpy
|
|
type for the input array should be either np.int32 or np.float32.
|
|
|
|
@sa contourArea, arcLength
|
|
*/
|
|
CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false );
|
|
|
|
/** @brief Calculates seven Hu invariants.
|
|
|
|
The function calculates seven Hu invariants (introduced in @cite Hu62; see also
|
|
<http://en.wikipedia.org/wiki/Image_moment>) defined as:
|
|
|
|
\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]
|
|
|
|
where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ .
|
|
|
|
These values are proved to be invariants to the image scale, rotation, and reflection except the
|
|
seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of
|
|
infinite image resolution. In case of raster images, the computed Hu invariants for the original and
|
|
transformed images are a bit different.
|
|
|
|
@param moments Input moments computed with moments .
|
|
@param hu Output Hu invariants.
|
|
|
|
@sa matchShapes
|
|
*/
|
|
CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
|
|
|
|
//! @} imgproc_shape
|
|
|
|
//! @addtogroup imgproc_object
|
|
//! @{
|
|
|
|
//! type of the template matching operation
|
|
enum TemplateMatchModes {
|
|
TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f]
|
|
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]
|
|
TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f]
|
|
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]
|
|
TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f]
|
|
//!< where
|
|
//!< \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]
|
|
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]
|
|
};
|
|
|
|
/** @example MatchTemplate_Demo.cpp
|
|
An example using Template Matching algorithm
|
|
*/
|
|
/** @brief Compares a template against overlapped image regions.
|
|
|
|
The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
|
|
templ using the specified method and stores the comparison results in result . Here are the formulae
|
|
for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation
|
|
is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
|
|
|
|
After the function finishes the comparison, the best matches can be found as global minimums (when
|
|
#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the
|
|
#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
|
|
the denominator is done over all of the channels and separate mean values are used for each channel.
|
|
That is, the function can take a color template and a color image. The result will still be a
|
|
single-channel image, which is easier to analyze.
|
|
|
|
@param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
|
|
@param templ Searched template. It must be not greater than the source image and have the same
|
|
data type.
|
|
@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
|
|
is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
|
|
@param method Parameter specifying the comparison method, see #TemplateMatchModes
|
|
@param mask Mask of searched template. It must have the same datatype and size with templ. It is
|
|
not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported.
|
|
*/
|
|
CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
|
|
OutputArray result, int method, InputArray mask = noArray() );
|
|
|
|
//! @}
|
|
|
|
//! @addtogroup imgproc_shape
|
|
//! @{
|
|
|
|
/** @brief computes the connected components labeled image of boolean image
|
|
|
|
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
|
|
represents the background label. ltype specifies the output label image type, an important
|
|
consideration based on the total number of labels or alternatively the total number of pixels in
|
|
the source image. ccltype specifies the connected components labeling algorithm to use, currently
|
|
Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
|
|
for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
|
|
This function uses parallel version of both Grana and Wu's algorithms if at least one allowed
|
|
parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
|
|
|
|
@param image the 8-bit single-channel image to be labeled
|
|
@param labels destination labeled image
|
|
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
|
|
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
|
|
@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
|
|
*/
|
|
CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels,
|
|
int connectivity, int ltype, int ccltype);
|
|
|
|
|
|
/** @overload
|
|
|
|
@param image the 8-bit single-channel image to be labeled
|
|
@param labels destination labeled image
|
|
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
|
|
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
|
|
*/
|
|
CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
|
|
int connectivity = 8, int ltype = CV_32S);
|
|
|
|
|
|
/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
|
|
|
|
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
|
|
represents the background label. ltype specifies the output label image type, an important
|
|
consideration based on the total number of labels or alternatively the total number of pixels in
|
|
the source image. ccltype specifies the connected components labeling algorithm to use, currently
|
|
Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
|
|
for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
|
|
This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed
|
|
parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
|
|
|
|
@param image the 8-bit single-channel image to be labeled
|
|
@param labels destination labeled image
|
|
@param stats statistics output for each label, including the background label, see below for
|
|
available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
|
|
#ConnectedComponentsTypes. The data type is CV_32S.
|
|
@param centroids centroid output for each label, including the background label. Centroids are
|
|
accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
|
|
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
|
|
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
|
|
@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).
|
|
*/
|
|
CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels,
|
|
OutputArray stats, OutputArray centroids,
|
|
int connectivity, int ltype, int ccltype);
|
|
|
|
/** @overload
|
|
@param image the 8-bit single-channel image to be labeled
|
|
@param labels destination labeled image
|
|
@param stats statistics output for each label, including the background label, see below for
|
|
available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
|
|
#ConnectedComponentsTypes. The data type is CV_32S.
|
|
@param centroids centroid output for each label, including the background label. Centroids are
|
|
accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
|
|
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
|
|
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
|
|
*/
|
|
CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels,
|
|
OutputArray stats, OutputArray centroids,
|
|
int connectivity = 8, int ltype = CV_32S);
|
|
|
|
|
|
/** @brief Finds contours in a binary image.
|
|
|
|
The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
|
|
are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
|
|
OpenCV sample directory.
|
|
@note Since opencv 3.2 source image is not modified by this function.
|
|
|
|
@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
|
|
pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
|
|
#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
|
|
If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
|
|
@param contours Detected contours. Each contour is stored as a vector of points (e.g.
|
|
std::vector<std::vector<cv::Point> >).
|
|
@param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
|
|
as many elements as the number of contours. For each i-th contour contours[i], the elements
|
|
hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
|
|
in contours of the next and previous contours at the same hierarchical level, the first child
|
|
contour and the parent contour, respectively. If for the contour i there are no next, previous,
|
|
parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
|
|
@param mode Contour retrieval mode, see #RetrievalModes
|
|
@param method Contour approximation method, see #ContourApproximationModes
|
|
@param offset Optional offset by which every contour point is shifted. This is useful if the
|
|
contours are extracted from the image ROI and then they should be analyzed in the whole image
|
|
context.
|
|
*/
|
|
CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
|
|
OutputArray hierarchy, int mode,
|
|
int method, Point offset = Point());
|
|
|
|
/** @overload */
|
|
CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
|
|
int mode, int method, Point offset = Point());
|
|
|
|
/** @brief Approximates a polygonal curve(s) with the specified precision.
|
|
|
|
The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
|
|
vertices so that the distance between them is less or equal to the specified precision. It uses the
|
|
Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
|
|
|
|
@param curve Input vector of a 2D point stored in std::vector or Mat
|
|
@param approxCurve Result of the approximation. The type should match the type of the input curve.
|
|
@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
|
|
between the original curve and its approximation.
|
|
@param closed If true, the approximated curve is closed (its first and last vertices are
|
|
connected). Otherwise, it is not closed.
|
|
*/
|
|
CV_EXPORTS_W void approxPolyDP( InputArray curve,
|
|
OutputArray approxCurve,
|
|
double epsilon, bool closed );
|
|
|
|
/** @brief Calculates a contour perimeter or a curve length.
|
|
|
|
The function computes a curve length or a closed contour perimeter.
|
|
|
|
@param curve Input vector of 2D points, stored in std::vector or Mat.
|
|
@param closed Flag indicating whether the curve is closed or not.
|
|
*/
|
|
CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
|
|
|
|
/** @brief Calculates the up-right bounding rectangle of a point set.
|
|
|
|
The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
|
|
|
|
@param points Input 2D point set, stored in std::vector or Mat.
|
|
*/
|
|
CV_EXPORTS_W Rect boundingRect( InputArray points );
|
|
|
|
/** @brief Calculates a contour area.
|
|
|
|
The function computes a contour area. Similarly to moments , the area is computed using the Green
|
|
formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
|
|
#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong
|
|
results for contours with self-intersections.
|
|
|
|
Example:
|
|
@code
|
|
vector<Point> contour;
|
|
contour.push_back(Point2f(0, 0));
|
|
contour.push_back(Point2f(10, 0));
|
|
contour.push_back(Point2f(10, 10));
|
|
contour.push_back(Point2f(5, 4));
|
|
|
|
double area0 = contourArea(contour);
|
|
vector<Point> approx;
|
|
approxPolyDP(contour, approx, 5, true);
|
|
double area1 = contourArea(approx);
|
|
|
|
cout << "area0 =" << area0 << endl <<
|
|
"area1 =" << area1 << endl <<
|
|
"approx poly vertices" << approx.size() << endl;
|
|
@endcode
|
|
@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
|
|
@param oriented Oriented area flag. If it is true, the function returns a signed area value,
|
|
depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
|
|
determine orientation of a contour by taking the sign of an area. By default, the parameter is
|
|
false, which means that the absolute value is returned.
|
|
*/
|
|
CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false );
|
|
|
|
/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
|
|
|
|
The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a
|
|
specified point set. Developer should keep in mind that the returned RotatedRect can contain negative
|
|
indices when data is close to the containing Mat element boundary.
|
|
|
|
@param points Input vector of 2D points, stored in std::vector\<\> or Mat
|
|
*/
|
|
CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
|
|
|
|
/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
|
|
|
|
The function finds the four vertices of a rotated rectangle. This function is useful to draw the
|
|
rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please
|
|
visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.
|
|
|
|
@param box The input rotated rectangle. It may be the output of
|
|
@param points The output array of four vertices of rectangles.
|
|
*/
|
|
CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points);
|
|
|
|
/** @brief Finds a circle of the minimum area enclosing a 2D point set.
|
|
|
|
The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.
|
|
|
|
@param points Input vector of 2D points, stored in std::vector\<\> or Mat
|
|
@param center Output center of the circle.
|
|
@param radius Output radius of the circle.
|
|
*/
|
|
CV_EXPORTS_W void minEnclosingCircle( InputArray points,
|
|
CV_OUT Point2f& center, CV_OUT float& radius );
|
|
|
|
/** @example minarea.cpp
|
|
*/
|
|
|
|
/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
|
|
|
|
The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
|
|
area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
|
|
*red* and the enclosing triangle in *yellow*.
|
|
|
|
|
|
|
|
The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's
|
|
@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal
|
|
enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function
|
|
takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
|
|
2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher
|
|
than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$.
|
|
|
|
@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat
|
|
@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
|
|
of the OutputArray must be CV_32F.
|
|
*/
|
|
CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle );
|
|
|
|
/** @brief Compares two shapes.
|
|
|
|
The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments)
|
|
|
|
@param contour1 First contour or grayscale image.
|
|
@param contour2 Second contour or grayscale image.
|
|
@param method Comparison method, see #ShapeMatchModes
|
|
@param parameter Method-specific parameter (not supported now).
|
|
*/
|
|
CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
|
|
int method, double parameter );
|
|
|
|
/** @example convexhull.cpp
|
|
An example using the convexHull functionality
|
|
*/
|
|
|
|
/** @brief Finds the convex hull of a point set.
|
|
|
|
The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82
|
|
that has *O(N logN)* complexity in the current implementation.
|
|
|
|
@param points Input 2D point set, stored in std::vector or Mat.
|
|
@param hull Output convex hull. It is either an integer vector of indices or vector of points. In
|
|
the first case, the hull elements are 0-based indices of the convex hull points in the original
|
|
array (since the set of convex hull points is a subset of the original point set). In the second
|
|
case, hull elements are the convex hull points themselves.
|
|
@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
|
|
Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
|
|
to the right, and its Y axis pointing upwards.
|
|
@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function
|
|
returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
|
|
output array is std::vector, the flag is ignored, and the output depends on the type of the
|
|
vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies
|
|
returnPoints=true.
|
|
|
|
@note `points` and `hull` should be different arrays, inplace processing isn't supported.
|
|
*/
|
|
CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
|
|
bool clockwise = false, bool returnPoints = true );
|
|
|
|
/** @brief Finds the convexity defects of a contour.
|
|
|
|
The figure below displays convexity defects of a hand contour:
|
|
|
|
|
|
|
|
@param contour Input contour.
|
|
@param convexhull Convex hull obtained using convexHull that should contain indices of the contour
|
|
points that make the hull.
|
|
@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
|
|
interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i):
|
|
(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
|
|
in the original contour of the convexity defect beginning, end and the farthest point, and
|
|
fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
|
|
farthest contour point and the hull. That is, to get the floating-point value of the depth will be
|
|
fixpt_depth/256.0.
|
|
*/
|
|
CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects );
|
|
|
|
/** @brief Tests a contour convexity.
|
|
|
|
The function tests whether the input contour is convex or not. The contour must be simple, that is,
|
|
without self-intersections. Otherwise, the function output is undefined.
|
|
|
|
@param contour Input vector of 2D points, stored in std::vector\<\> or Mat
|
|
*/
|
|
CV_EXPORTS_W bool isContourConvex( InputArray contour );
|
|
|
|
//! finds intersection of two convex polygons
|
|
CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
|
|
OutputArray _p12, bool handleNested = true );
|
|
|
|
/** @example fitellipse.cpp
|
|
An example using the fitEllipse technique
|
|
*/
|
|
|
|
/** @brief Fits an ellipse around a set of 2D points.
|
|
|
|
The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of
|
|
all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95
|
|
is used. Developer should keep in mind that it is possible that the returned
|
|
ellipse/rotatedRect data contains negative indices, due to the data points being close to the
|
|
border of the containing Mat element.
|
|
|
|
@param points Input 2D point set, stored in std::vector\<\> or Mat
|
|
*/
|
|
CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
|
|
|
|
/** @brief Fits an ellipse around a set of 2D points.
|
|
|
|
The function calculates the ellipse that fits a set of 2D points.
|
|
It returns the rotated rectangle in which the ellipse is inscribed.
|
|
The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used.
|
|
|
|
For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
|
|
which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
|
|
However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
|
|
the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
|
|
quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
|
|
If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used.
|
|
The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves
|
|
by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where
|
|
the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with
|
|
respect to x and y. The matrices are formed row by row applying the following to
|
|
each of the points in the set:
|
|
\f{align*}{
|
|
D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} &
|
|
D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} &
|
|
D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\}
|
|
\f}
|
|
The AMS method minimizes the cost function
|
|
\f{equation*}{
|
|
\epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T }
|
|
\f}
|
|
|
|
The minimum cost is found by solving the generalized eigenvalue problem.
|
|
|
|
\f{equation*}{
|
|
D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A
|
|
\f}
|
|
|
|
@param points Input 2D point set, stored in std::vector\<\> or Mat
|
|
*/
|
|
CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points );
|
|
|
|
|
|
/** @brief Fits an ellipse around a set of 2D points.
|
|
|
|
The function calculates the ellipse that fits a set of 2D points.
|
|
It returns the rotated rectangle in which the ellipse is inscribed.
|
|
The Direct least square (Direct) method by @cite Fitzgibbon1999 is used.
|
|
|
|
For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
|
|
which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
|
|
However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
|
|
the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
|
|
quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
|
|
The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$.
|
|
The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality
|
|
and as the coefficients can be arbitrarily scaled is not overly restrictive.
|
|
|
|
\f{equation*}{
|
|
\epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix}
|
|
0 & 0 & 2 & 0 & 0 & 0 \\
|
|
0 & -1 & 0 & 0 & 0 & 0 \\
|
|
2 & 0 & 0 & 0 & 0 & 0 \\
|
|
0 & 0 & 0 & 0 & 0 & 0 \\
|
|
0 & 0 & 0 & 0 & 0 & 0 \\
|
|
0 & 0 & 0 & 0 & 0 & 0
|
|
\end{matrix} \right)
|
|
\f}
|
|
|
|
The minimum cost is found by solving the generalized eigenvalue problem.
|
|
|
|
\f{equation*}{
|
|
D^T D A = \lambda \left( C\right) A
|
|
\f}
|
|
|
|
The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution
|
|
with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients
|
|
|
|
\f{equation*}{
|
|
A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u}
|
|
\f}
|
|
The scaling factor guarantees that \f$A^T C A =1\f$.
|
|
|
|
@param points Input 2D point set, stored in std::vector\<\> or Mat
|
|
*/
|
|
CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points );
|
|
|
|
/** @brief Fits a line to a 2D or 3D point set.
|
|
|
|
The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
|
|
\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
|
|
of the following:
|
|
- DIST_L2
|
|
\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f]
|
|
- DIST_L1
|
|
\f[\rho (r) = r\f]
|
|
- DIST_L12
|
|
\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
|
|
- DIST_FAIR
|
|
\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]
|
|
- DIST_WELSCH
|
|
\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]
|
|
- DIST_HUBER
|
|
\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
|
|
|
|
The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
|
|
that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
|
|
weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
|
|
|
|
@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat.
|
|
@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
|
|
(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
|
|
(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
|
|
Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
|
|
and (x0, y0, z0) is a point on the line.
|
|
@param distType Distance used by the M-estimator, see #DistanceTypes
|
|
@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
|
|
is chosen.
|
|
@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
|
|
@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
|
|
*/
|
|
CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
|
|
double param, double reps, double aeps );
|
|
|
|
/** @brief Performs a point-in-contour test.
|
|
|
|
The function determines whether the point is inside a contour, outside, or lies on an edge (or
|
|
coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
|
|
value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
|
|
Otherwise, the return value is a signed distance between the point and the nearest contour edge.
|
|
|
|
See below a sample output of the function where each image pixel is tested against the contour:
|
|
|
|
|
|
|
|
@param contour Input contour.
|
|
@param pt Point tested against the contour.
|
|
@param measureDist If true, the function estimates the signed distance from the point to the
|
|
nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
|
|
*/
|
|
CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
|
|
|
|
/** @brief Finds out if there is any intersection between two rotated rectangles.
|
|
|
|
If there is then the vertices of the intersecting region are returned as well.
|
|
|
|
Below are some examples of intersection configurations. The hatched pattern indicates the
|
|
intersecting region and the red vertices are returned by the function.
|
|
|
|
|
|
|
|
@param rect1 First rectangle
|
|
@param rect2 Second rectangle
|
|
@param intersectingRegion The output array of the vertices of the intersecting region. It returns
|
|
at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2.
|
|
@returns One of #RectanglesIntersectTypes
|
|
*/
|
|
CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion );
|
|
|
|
//! @} imgproc_shape
|
|
/** @brief Creates implementation for cv::CLAHE .
|
|
|
|
@param clipLimit Threshold for contrast limiting.
|
|
@param tileGridSize Size of grid for histogram equalization. Input image will be divided into
|
|
equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
|
|
*/
|
|
CV_EXPORTS_W Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
|
|
|
|
//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
|
|
//! Detects position only without translation and rotation
|
|
CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
|
|
|
|
//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
|
|
//! Detects position, translation and rotation
|
|
CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
|
|
|
|
//! Performs linear blending of two images:
|
|
//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f]
|
|
//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer.
|
|
//! @param src2 It has the same type and size as src1.
|
|
//! @param weights1 It has a type of CV_32FC1 and the same size with src1.
|
|
//! @param weights2 It has a type of CV_32FC1 and the same size with src1.
|
|
//! @param dst It is created if it does not have the same size and type with src1.
|
|
CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
|
|
|
|
//! @addtogroup imgproc_colormap
|
|
//! @{
|
|
|
|
//! GNU Octave/MATLAB equivalent colormaps
|
|
enum ColormapTypes
|
|
{
|
|
COLORMAP_AUTUMN = 0, //!< 
|
|
COLORMAP_BONE = 1, //!< 
|
|
COLORMAP_JET = 2, //!< 
|
|
COLORMAP_WINTER = 3, //!< 
|
|
COLORMAP_RAINBOW = 4, //!< 
|
|
COLORMAP_OCEAN = 5, //!< 
|
|
COLORMAP_SUMMER = 6, //!< 
|
|
COLORMAP_SPRING = 7, //!< 
|
|
COLORMAP_COOL = 8, //!< 
|
|
COLORMAP_HSV = 9, //!< 
|
|
COLORMAP_PINK = 10, //!< 
|
|
COLORMAP_HOT = 11, //!< 
|
|
COLORMAP_PARULA = 12 //!< 
|
|
};
|
|
|
|
/** @example falsecolor.cpp
|
|
An example using applyColorMap function
|
|
*/
|
|
/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
|
|
|
|
@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
|
|
@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
|
|
@param colormap The colormap to apply, see #ColormapTypes
|
|
*/
|
|
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
|
|
|
|
/** @brief Applies a user colormap on a given image.
|
|
|
|
@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
|
|
@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
|
|
@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256
|
|
*/
|
|
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor);
|
|
|
|
//! @} imgproc_colormap
|
|
|
|
//! @addtogroup imgproc_draw
|
|
//! @{
|
|
|
|
|
|
/** OpenCV color channel order is BGR[A] */
|
|
#define CV_RGB(r, g, b) cv::Scalar((b), (g), (r), 0)
|
|
|
|
/** @brief Draws a line segment connecting two points.
|
|
|
|
The function line draws the line segment between pt1 and pt2 points in the image. The line is
|
|
clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
|
|
or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
|
|
lines are drawn using Gaussian filtering.
|
|
|
|
@param img Image.
|
|
@param pt1 First point of the line segment.
|
|
@param pt2 Second point of the line segment.
|
|
@param color Line color.
|
|
@param thickness Line thickness.
|
|
@param lineType Type of the line. See #LineTypes.
|
|
@param shift Number of fractional bits in the point coordinates.
|
|
*/
|
|
CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
|
|
int thickness = 1, int lineType = LINE_8, int shift = 0);
|
|
|
|
/** @brief Draws a arrow segment pointing from the first point to the second one.
|
|
|
|
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
|
|
|
|
@param img Image.
|
|
@param pt1 The point the arrow starts from.
|
|
@param pt2 The point the arrow points to.
|
|
@param color Line color.
|
|
@param thickness Line thickness.
|
|
@param line_type Type of the line. See #LineTypes
|
|
@param shift Number of fractional bits in the point coordinates.
|
|
@param tipLength The length of the arrow tip in relation to the arrow length
|
|
*/
|
|
CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
|
|
int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
|
|
|
|
/** @brief Draws a simple, thick, or filled up-right rectangle.
|
|
|
|
The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
|
|
are pt1 and pt2.
|
|
|
|
@param img Image.
|
|
@param pt1 Vertex of the rectangle.
|
|
@param pt2 Vertex of the rectangle opposite to pt1 .
|
|
@param color Rectangle color or brightness (grayscale image).
|
|
@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
|
|
mean that the function has to draw a filled rectangle.
|
|
@param lineType Type of the line. See #LineTypes
|
|
@param shift Number of fractional bits in the point coordinates.
|
|
*/
|
|
CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
|
|
const Scalar& color, int thickness = 1,
|
|
int lineType = LINE_8, int shift = 0);
|
|
|
|
/** @overload
|
|
|
|
use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and
|
|
r.br()-Point(1,1)` are opposite corners
|
|
*/
|
|
CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
|
|
const Scalar& color, int thickness = 1,
|
|
int lineType = LINE_8, int shift = 0);
|
|
|
|
/** @example Drawing_2.cpp
|
|
An example using drawing functions
|
|
*/
|
|
/** @brief Draws a circle.
|
|
|
|
The function cv::circle draws a simple or filled circle with a given center and radius.
|
|
@param img Image where the circle is drawn.
|
|
@param center Center of the circle.
|
|
@param radius Radius of the circle.
|
|
@param color Circle color.
|
|
@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
|
|
mean that a filled circle is to be drawn.
|
|
@param lineType Type of the circle boundary. See #LineTypes
|
|
@param shift Number of fractional bits in the coordinates of the center and in the radius value.
|
|
*/
|
|
CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
|
|
const Scalar& color, int thickness = 1,
|
|
int lineType = LINE_8, int shift = 0);
|
|
|
|
/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector.
|
|
|
|
The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
|
|
arc, or a filled ellipse sector. The drawing code uses general parametric form.
|
|
A piecewise-linear curve is used to approximate the elliptic arc
|
|
boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
|
|
#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
|
|
variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and
|
|
`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains
|
|
the meaning of the parameters to draw the blue arc.
|
|
|
|
|
|
|
|
@param img Image.
|
|
@param center Center of the ellipse.
|
|
@param axes Half of the size of the ellipse main axes.
|
|
@param angle Ellipse rotation angle in degrees.
|
|
@param startAngle Starting angle of the elliptic arc in degrees.
|
|
@param endAngle Ending angle of the elliptic arc in degrees.
|
|
@param color Ellipse color.
|
|
@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
|
|
a filled ellipse sector is to be drawn.
|
|
@param lineType Type of the ellipse boundary. See #LineTypes
|
|
@param shift Number of fractional bits in the coordinates of the center and values of axes.
|
|
*/
|
|
CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
|
|
double angle, double startAngle, double endAngle,
|
|
const Scalar& color, int thickness = 1,
|
|
int lineType = LINE_8, int shift = 0);
|
|
|
|
/** @overload
|
|
@param img Image.
|
|
@param box Alternative ellipse representation via RotatedRect. This means that the function draws
|
|
an ellipse inscribed in the rotated rectangle.
|
|
@param color Ellipse color.
|
|
@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
|
|
a filled ellipse sector is to be drawn.
|
|
@param lineType Type of the ellipse boundary. See #LineTypes
|
|
*/
|
|
CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
|
|
int thickness = 1, int lineType = LINE_8);
|
|
|
|
/* ----------------------------------------------------------------------------------------- */
|
|
/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */
|
|
/* ----------------------------------------------------------------------------------------- */
|
|
|
|
//! Possible set of marker types used for the cv::drawMarker function
|
|
enum MarkerTypes
|
|
{
|
|
MARKER_CROSS = 0, //!< A crosshair marker shape
|
|
MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape
|
|
MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross
|
|
MARKER_DIAMOND = 3, //!< A diamond marker shape
|
|
MARKER_SQUARE = 4, //!< A square marker shape
|
|
MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape
|
|
MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape
|
|
};
|
|
|
|
/** @brief Draws a marker on a predefined position in an image.
|
|
|
|
The function cv::drawMarker draws a marker on a given position in the image. For the moment several
|
|
marker types are supported, see #MarkerTypes for more information.
|
|
|
|
@param img Image.
|
|
@param position The point where the crosshair is positioned.
|
|
@param color Line color.
|
|
@param markerType The specific type of marker you want to use, see #MarkerTypes
|
|
@param thickness Line thickness.
|
|
@param line_type Type of the line, See #LineTypes
|
|
@param markerSize The length of the marker axis [default = 20 pixels]
|
|
*/
|
|
CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color,
|
|
int markerType = MARKER_CROSS, int markerSize=20, int thickness=1,
|
|
int line_type=8);
|
|
|
|
/* ----------------------------------------------------------------------------------------- */
|
|
/* END OF MARKER SECTION */
|
|
/* ----------------------------------------------------------------------------------------- */
|
|
|
|
/** @overload */
|
|
CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
|
|
const Scalar& color, int lineType = LINE_8,
|
|
int shift = 0);
|
|
|
|
/** @brief Fills a convex polygon.
|
|
|
|
The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
|
|
function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
|
|
self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
|
|
twice at the most (though, its top-most and/or the bottom edge could be horizontal).
|
|
|
|
@param img Image.
|
|
@param points Polygon vertices.
|
|
@param color Polygon color.
|
|
@param lineType Type of the polygon boundaries. See #LineTypes
|
|
@param shift Number of fractional bits in the vertex coordinates.
|
|
*/
|
|
CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
|
|
const Scalar& color, int lineType = LINE_8,
|
|
int shift = 0);
|
|
|
|
/** @overload */
|
|
CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
|
|
const int* npts, int ncontours,
|
|
const Scalar& color, int lineType = LINE_8, int shift = 0,
|
|
Point offset = Point() );
|
|
|
|
/** @example Drawing_1.cpp
|
|
An example using drawing functions
|
|
*/
|
|
/** @brief Fills the area bounded by one or more polygons.
|
|
|
|
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
|
|
complex areas, for example, areas with holes, contours with self-intersections (some of their
|
|
parts), and so forth.
|
|
|
|
@param img Image.
|
|
@param pts Array of polygons where each polygon is represented as an array of points.
|
|
@param color Polygon color.
|
|
@param lineType Type of the polygon boundaries. See #LineTypes
|
|
@param shift Number of fractional bits in the vertex coordinates.
|
|
@param offset Optional offset of all points of the contours.
|
|
*/
|
|
CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
|
|
const Scalar& color, int lineType = LINE_8, int shift = 0,
|
|
Point offset = Point() );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
|
|
int ncontours, bool isClosed, const Scalar& color,
|
|
int thickness = 1, int lineType = LINE_8, int shift = 0 );
|
|
|
|
/** @brief Draws several polygonal curves.
|
|
|
|
@param img Image.
|
|
@param pts Array of polygonal curves.
|
|
@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
|
|
the function draws a line from the last vertex of each curve to its first vertex.
|
|
@param color Polyline color.
|
|
@param thickness Thickness of the polyline edges.
|
|
@param lineType Type of the line segments. See #LineTypes
|
|
@param shift Number of fractional bits in the vertex coordinates.
|
|
|
|
The function cv::polylines draws one or more polygonal curves.
|
|
*/
|
|
CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
|
|
bool isClosed, const Scalar& color,
|
|
int thickness = 1, int lineType = LINE_8, int shift = 0 );
|
|
|
|
/** @example contours2.cpp
|
|
An example program illustrates the use of cv::findContours and cv::drawContours
|
|
\image html WindowsQtContoursOutput.png "Screenshot of the program"
|
|
*/
|
|
|
|
/** @example segment_objects.cpp
|
|
An example using drawContours to clean up a background segmentation result
|
|
*/
|
|
|
|
/** @brief Draws contours outlines or filled contours.
|
|
|
|
The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area
|
|
bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve
|
|
connected components from the binary image and label them: :
|
|
@code
|
|
#include "opencv2/imgproc.hpp"
|
|
#include "opencv2/highgui.hpp"
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
int main( int argc, char** argv )
|
|
{
|
|
Mat src;
|
|
// the first command-line parameter must be a filename of the binary
|
|
// (black-n-white) image
|
|
if( argc != 2 || !(src=imread(argv[1], 0)).data)
|
|
return -1;
|
|
|
|
Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3);
|
|
|
|
src = src > 1;
|
|
namedWindow( "Source", 1 );
|
|
imshow( "Source", src );
|
|
|
|
vector<vector<Point> > contours;
|
|
vector<Vec4i> hierarchy;
|
|
|
|
findContours( src, contours, hierarchy,
|
|
RETR_CCOMP, CHAIN_APPROX_SIMPLE );
|
|
|
|
// iterate through all the top-level contours,
|
|
// draw each connected component with its own random color
|
|
int idx = 0;
|
|
for( ; idx >= 0; idx = hierarchy[idx][0] )
|
|
{
|
|
Scalar color( rand()&255, rand()&255, rand()&255 );
|
|
drawContours( dst, contours, idx, color, FILLED, 8, hierarchy );
|
|
}
|
|
|
|
namedWindow( "Components", 1 );
|
|
imshow( "Components", dst );
|
|
waitKey(0);
|
|
}
|
|
@endcode
|
|
|
|
@param image Destination image.
|
|
@param contours All the input contours. Each contour is stored as a point vector.
|
|
@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
|
|
@param color Color of the contours.
|
|
@param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
|
|
thickness=#FILLED ), the contour interiors are drawn.
|
|
@param lineType Line connectivity. See #LineTypes
|
|
@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
|
|
some of the contours (see maxLevel ).
|
|
@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
|
|
If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
|
|
draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
|
|
parameter is only taken into account when there is hierarchy available.
|
|
@param offset Optional contour shift parameter. Shift all the drawn contours by the specified
|
|
\f$\texttt{offset}=(dx,dy)\f$ .
|
|
@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly
|
|
even when no hierarchy date is provided. This is done by analyzing all the outlines together
|
|
using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
|
|
contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
|
|
of contours, or iterate over the collection using contourIdx parameter.
|
|
*/
|
|
CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
|
|
int contourIdx, const Scalar& color,
|
|
int thickness = 1, int lineType = LINE_8,
|
|
InputArray hierarchy = noArray(),
|
|
int maxLevel = INT_MAX, Point offset = Point() );
|
|
|
|
/** @brief Clips the line against the image rectangle.
|
|
|
|
The function cv::clipLine calculates a part of the line segment that is entirely within the specified
|
|
rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise,
|
|
it returns true .
|
|
@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
|
|
@param pt1 First line point.
|
|
@param pt2 Second line point.
|
|
*/
|
|
CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
|
|
|
|
/** @overload
|
|
@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
|
|
@param pt1 First line point.
|
|
@param pt2 Second line point.
|
|
*/
|
|
CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2);
|
|
|
|
/** @overload
|
|
@param imgRect Image rectangle.
|
|
@param pt1 First line point.
|
|
@param pt2 Second line point.
|
|
*/
|
|
CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
|
|
|
|
/** @brief Approximates an elliptic arc with a polyline.
|
|
|
|
The function ellipse2Poly computes the vertices of a polyline that approximates the specified
|
|
elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped.
|
|
|
|
@param center Center of the arc.
|
|
@param axes Half of the size of the ellipse main axes. See #ellipse for details.
|
|
@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
|
|
@param arcStart Starting angle of the elliptic arc in degrees.
|
|
@param arcEnd Ending angle of the elliptic arc in degrees.
|
|
@param delta Angle between the subsequent polyline vertices. It defines the approximation
|
|
accuracy.
|
|
@param pts Output vector of polyline vertices.
|
|
*/
|
|
CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
|
|
int arcStart, int arcEnd, int delta,
|
|
CV_OUT std::vector<Point>& pts );
|
|
|
|
/** @overload
|
|
@param center Center of the arc.
|
|
@param axes Half of the size of the ellipse main axes. See #ellipse for details.
|
|
@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
|
|
@param arcStart Starting angle of the elliptic arc in degrees.
|
|
@param arcEnd Ending angle of the elliptic arc in degrees.
|
|
@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy.
|
|
@param pts Output vector of polyline vertices.
|
|
*/
|
|
CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle,
|
|
int arcStart, int arcEnd, int delta,
|
|
CV_OUT std::vector<Point2d>& pts);
|
|
|
|
/** @brief Draws a text string.
|
|
|
|
The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
|
|
using the specified font are replaced by question marks. See #getTextSize for a text rendering code
|
|
example.
|
|
|
|
@param img Image.
|
|
@param text Text string to be drawn.
|
|
@param org Bottom-left corner of the text string in the image.
|
|
@param fontFace Font type, see #HersheyFonts.
|
|
@param fontScale Font scale factor that is multiplied by the font-specific base size.
|
|
@param color Text color.
|
|
@param thickness Thickness of the lines used to draw a text.
|
|
@param lineType Line type. See #LineTypes
|
|
@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
|
|
it is at the top-left corner.
|
|
*/
|
|
CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
|
|
int fontFace, double fontScale, Scalar color,
|
|
int thickness = 1, int lineType = LINE_8,
|
|
bool bottomLeftOrigin = false );
|
|
|
|
/** @brief Calculates the width and height of a text string.
|
|
|
|
The function cv::getTextSize calculates and returns the size of a box that contains the specified text.
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That is, the following code renders some text, the tight box surrounding it, and the baseline: :
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@code
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String text = "Funny text inside the box";
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int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
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double fontScale = 2;
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int thickness = 3;
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Mat img(600, 800, CV_8UC3, Scalar::all(0));
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int baseline=0;
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Size textSize = getTextSize(text, fontFace,
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fontScale, thickness, &baseline);
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baseline += thickness;
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// center the text
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Point textOrg((img.cols - textSize.width)/2,
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(img.rows + textSize.height)/2);
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// draw the box
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rectangle(img, textOrg + Point(0, baseline),
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textOrg + Point(textSize.width, -textSize.height),
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Scalar(0,0,255));
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// ... and the baseline first
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line(img, textOrg + Point(0, thickness),
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textOrg + Point(textSize.width, thickness),
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Scalar(0, 0, 255));
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// then put the text itself
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putText(img, text, textOrg, fontFace, fontScale,
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Scalar::all(255), thickness, 8);
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@endcode
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@param text Input text string.
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@param fontFace Font to use, see #HersheyFonts.
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@param fontScale Font scale factor that is multiplied by the font-specific base size.
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@param thickness Thickness of lines used to render the text. See #putText for details.
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@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text
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point.
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@return The size of a box that contains the specified text.
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@see putText
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*/
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CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
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double fontScale, int thickness,
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CV_OUT int* baseLine);
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/** @brief Calculates the font-specific size to use to achieve a given height in pixels.
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@param fontFace Font to use, see cv::HersheyFonts.
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@param pixelHeight Pixel height to compute the fontScale for
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@param thickness Thickness of lines used to render the text.See putText for details.
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@return The fontSize to use for cv::putText
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@see cv::putText
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*/
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CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace,
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const int pixelHeight,
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const int thickness = 1);
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/** @brief Line iterator
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The class is used to iterate over all the pixels on the raster line
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segment connecting two specified points.
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The class LineIterator is used to get each pixel of a raster line. It
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can be treated as versatile implementation of the Bresenham algorithm
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where you can stop at each pixel and do some extra processing, for
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example, grab pixel values along the line or draw a line with an effect
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(for example, with XOR operation).
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The number of pixels along the line is stored in LineIterator::count.
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The method LineIterator::pos returns the current position in the image:
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@code{.cpp}
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// grabs pixels along the line (pt1, pt2)
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// from 8-bit 3-channel image to the buffer
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LineIterator it(img, pt1, pt2, 8);
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LineIterator it2 = it;
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vector<Vec3b> buf(it.count);
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for(int i = 0; i < it.count; i++, ++it)
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buf[i] = *(const Vec3b)*it;
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// alternative way of iterating through the line
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for(int i = 0; i < it2.count; i++, ++it2)
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{
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Vec3b val = img.at<Vec3b>(it2.pos());
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CV_Assert(buf[i] == val);
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}
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@endcode
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*/
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class CV_EXPORTS LineIterator
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{
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public:
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/** @brief initializes the iterator
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creates iterators for the line connecting pt1 and pt2
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the line will be clipped on the image boundaries
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the line is 8-connected or 4-connected
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If leftToRight=true, then the iteration is always done
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from the left-most point to the right most,
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not to depend on the ordering of pt1 and pt2 parameters
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*/
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LineIterator( const Mat& img, Point pt1, Point pt2,
|
|
int connectivity = 8, bool leftToRight = false );
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|
/** @brief returns pointer to the current pixel
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|
*/
|
|
uchar* operator *();
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|
/** @brief prefix increment operator (++it). shifts iterator to the next pixel
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|
*/
|
|
LineIterator& operator ++();
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|
/** @brief postfix increment operator (it++). shifts iterator to the next pixel
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|
*/
|
|
LineIterator operator ++(int);
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|
/** @brief returns coordinates of the current pixel
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|
*/
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Point pos() const;
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uchar* ptr;
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|
const uchar* ptr0;
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|
int step, elemSize;
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|
int err, count;
|
|
int minusDelta, plusDelta;
|
|
int minusStep, plusStep;
|
|
};
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|
|
//! @cond IGNORED
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|
|
// === LineIterator implementation ===
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|
|
inline
|
|
uchar* LineIterator::operator *()
|
|
{
|
|
return ptr;
|
|
}
|
|
|
|
inline
|
|
LineIterator& LineIterator::operator ++()
|
|
{
|
|
int mask = err < 0 ? -1 : 0;
|
|
err += minusDelta + (plusDelta & mask);
|
|
ptr += minusStep + (plusStep & mask);
|
|
return *this;
|
|
}
|
|
|
|
inline
|
|
LineIterator LineIterator::operator ++(int)
|
|
{
|
|
LineIterator it = *this;
|
|
++(*this);
|
|
return it;
|
|
}
|
|
|
|
inline
|
|
Point LineIterator::pos() const
|
|
{
|
|
Point p;
|
|
p.y = (int)((ptr - ptr0)/step);
|
|
p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize);
|
|
return p;
|
|
}
|
|
|
|
//! @endcond
|
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|
|
//! @} imgproc_draw
|
|
|
|
//! @} imgproc
|
|
|
|
} // cv
|
|
|
|
#ifndef DISABLE_OPENCV_24_COMPATIBILITY
|
|
#include "opencv2/imgproc/imgproc_c.h"
|
|
#endif
|
|
|
|
#endif
|