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201 KiB
4271 lines
201 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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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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// 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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// 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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// * Redistribution's of source code must retain the above copyright notice, |
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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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// This software is provided by the copyright holders and contributors "as is" and |
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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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/** |
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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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|
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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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|
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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 cv::BorderTypes |
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|
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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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|
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@note when ddepth=-1, the output image will have the same depth as the source. |
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|
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@defgroup imgproc_transform Geometric Image Transformations |
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|
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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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|
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\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] |
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|
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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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|
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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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|
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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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|
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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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@defgroup imgproc_misc Miscellaneous Image Transformations |
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@defgroup imgproc_draw Drawing Functions |
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|
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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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|
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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 cv::ColormapTypes |
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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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@} |
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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 cv::erode |
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MORPH_DILATE = 1, //!< see cv::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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}; |
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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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/** 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, polar transforms: |
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- flag is __not__ set: \f$dst( \phi , \rho ) = src(x,y)\f$ |
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- flag is set: \f$dst(x,y) = src( \phi , \rho )\f$ |
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*/ |
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WARP_INVERSE_MAP = 16 |
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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 cv::distanceTransform, cv::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 cv::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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cv::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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}; |
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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 |
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}; |
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//! floodfill algorithm flags |
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enum FloodFillFlags { |
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/** If set, the difference between the current pixel and seed pixel is considered. Otherwise, |
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the difference between neighbor pixels is considered (that is, the range is floating). */ |
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FLOODFILL_FIXED_RANGE = 1 << 16, |
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/** If set, the function does not change the image ( newVal is ignored), and only fills the |
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mask with the value specified in bits 8-16 of flags as described above. This option only make |
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sense in function variants that have the mask parameter. */ |
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FLOODFILL_MASK_ONLY = 1 << 17 |
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}; |
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//! @} imgproc_misc |
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|
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//! @addtogroup imgproc_shape |
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//! @{ |
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|
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//! connected components algorithm output formats |
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enum ConnectedComponentsTypes { |
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CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding |
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//!< box in the horizontal direction. |
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CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding |
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//!< box in the vertical direction. |
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CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box |
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CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box |
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CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component |
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CC_STAT_MAX = 5 |
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}; |
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//! mode of the contour retrieval algorithm |
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enum RetrievalModes { |
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/** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for |
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all the contours. */ |
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RETR_EXTERNAL = 0, |
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/** retrieves all of the contours without establishing any hierarchical relationships. */ |
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RETR_LIST = 1, |
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/** retrieves all of the contours and organizes them into a two-level hierarchy. At the top |
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level, there are external boundaries of the components. At the second level, there are |
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boundaries of the holes. If there is another contour inside a hole of a connected component, it |
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is still put at the top level. */ |
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RETR_CCOMP = 2, |
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/** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ |
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RETR_TREE = 3, |
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RETR_FLOODFILL = 4 //!< |
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}; |
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//! the contour approximation algorithm |
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enum ContourApproximationModes { |
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/** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and |
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(x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, |
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max(abs(x1-x2),abs(y2-y1))==1. */ |
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CHAIN_APPROX_NONE = 1, |
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/** compresses horizontal, vertical, and diagonal segments and leaves only their end points. |
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For example, an up-right rectangular contour is encoded with 4 points. */ |
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CHAIN_APPROX_SIMPLE = 2, |
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/** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ |
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CHAIN_APPROX_TC89_L1 = 3, |
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/** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ |
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CHAIN_APPROX_TC89_KCOS = 4 |
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}; |
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|
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//! @} imgproc_shape |
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|
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//! Variants of a Hough transform |
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enum HoughModes { |
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|
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/** classical or standard Hough transform. Every line is represented by two floating-point |
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numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, |
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and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must |
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be (the created sequence will be) of CV_32FC2 type */ |
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HOUGH_STANDARD = 0, |
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/** probabilistic Hough transform (more efficient in case if the picture contains a few long |
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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, |
|
|
|
|
|
COLOR_COLORCVT_MAX = 139 |
|
}; |
|
|
|
/** 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 traslation 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, traslation 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; |
|
}; |
|
|
|
|
|
class CV_EXPORTS_W CLAHE : public Algorithm |
|
{ |
|
public: |
|
CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; |
|
|
|
CV_WRAP virtual void setClipLimit(double clipLimit) = 0; |
|
CV_WRAP virtual double getClipLimit() const = 0; |
|
|
|
CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; |
|
CV_WRAP virtual Size getTilesGridSize() const = 0; |
|
|
|
CV_WRAP virtual void collectGarbage() = 0; |
|
}; |
|
|
|
|
|
class CV_EXPORTS_W Subdiv2D |
|
{ |
|
public: |
|
enum { PTLOC_ERROR = -2, |
|
PTLOC_OUTSIDE_RECT = -1, |
|
PTLOC_INSIDE = 0, |
|
PTLOC_VERTEX = 1, |
|
PTLOC_ON_EDGE = 2 |
|
}; |
|
|
|
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 |
|
}; |
|
|
|
CV_WRAP Subdiv2D(); |
|
CV_WRAP Subdiv2D(Rect rect); |
|
CV_WRAP void initDelaunay(Rect rect); |
|
|
|
CV_WRAP int insert(Point2f pt); |
|
CV_WRAP void insert(const std::vector<Point2f>& ptvec); |
|
CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); |
|
|
|
CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); |
|
CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const; |
|
CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const; |
|
CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList, |
|
CV_OUT std::vector<Point2f>& facetCenters); |
|
|
|
CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; |
|
|
|
CV_WRAP int getEdge( int edge, int nextEdgeType ) const; |
|
CV_WRAP int nextEdge(int edge) const; |
|
CV_WRAP int rotateEdge(int edge, int rotate) const; |
|
CV_WRAP int symEdge(int edge) const; |
|
CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; |
|
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]; |
|
}; |
|
|
|
std::vector<Vertex> vtx; |
|
std::vector<QuadEdge> qedges; |
|
int freeQEdge; |
|
int freePoint; |
|
bool validGeometry; |
|
|
|
int recentEdge; |
|
Point2f topLeft; |
|
Point2f bottomRight; |
|
}; |
|
|
|
//! @addtogroup imgproc_feature |
|
//! @{ |
|
|
|
/** @example lsd_lines.cpp |
|
An example using the LineSegmentDetector |
|
*/ |
|
|
|
/** @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 liens 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 cv::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 advancent 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 cv::Scharr). Otherwise, Sobel |
|
kernels are generated (see cv::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 cv::erode, |
|
cv::dilate or cv::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 cv::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)); |
|
|
|
/** @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. |
|
|
|
@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 cv::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 cv::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 cv::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 smoothes 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 cv::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 cv::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 cv::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 smoothes 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 cv::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 cv::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 cv::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 cv::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 ); |
|
|
|
/** @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 cv::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 cv::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 cv::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 cv::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 cv::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 |
|
An example on using the canny edge detector |
|
*/ |
|
|
|
/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . |
|
|
|
The function finds edges in the input image 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 ); |
|
|
|
/** @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 cv::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 cv::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 cv::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 cv::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. |
|
@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 ); |
|
|
|
/** @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 ); |
|
|
|
/** @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 ignore the returned radius, use only the center, 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 cv::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 CV_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 CV_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. |
|
|
|
@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 |
|
An example using the morphological operations |
|
*/ |
|
|
|
/** @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 cv::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() ); |
|
|
|
/** @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 cv::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 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 src\` . |
|
@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 op Type of a morphological operation, see cv::MorphTypes |
|
@param iterations Number of times erosion and dilation are applied. |
|
@param borderType Pixel extrapolation method, see cv::BorderTypes |
|
@param borderValue Border value in case of a constant border. The default value has a special |
|
meaning. |
|
@sa dilate, erode, getStructuringElement |
|
*/ |
|
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 cv::INTER_AREA interpolation, whereas to |
|
enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::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 cv::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 cv::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 cv::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 cv::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()); |
|
|
|
/** @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 cv::InterpolationFlags). The method INTER_AREA is |
|
not supported by this function. |
|
@param borderMode Pixel extrapolation method (see cv::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. |
|
*/ |
|
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[dst(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] |
|
|
|
where the values of the pixels at non-integer coordinates are retrieved using bilinear |
|
interpolation. Every channel of multi-channel images is processed independently. While the center of |
|
the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the |
|
replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of |
|
the image. |
|
|
|
@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 |
|
*/ |
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|
|
/** @brief Remaps an image to log-polar space. |
|
|
|
transforms the source image using the following transformation: |
|
\f[dst( \phi , \rho ) = src(x,y)\f] |
|
where |
|
\f[\rho = M \cdot \log{\sqrt{x^2 + y^2}} , \phi =atan(y/x)\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. The function can not operate |
|
in-place. |
|
|
|
@param src Source image |
|
@param dst Destination image |
|
@param center The transformation center; where the output precision is maximal |
|
@param M Magnitude scale parameter. |
|
@param flags A combination of interpolation methods, see cv::InterpolationFlags |
|
*/ |
|
CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, |
|
Point2f center, double M, int flags ); |
|
|
|
/** @brief Remaps an image to polar space. |
|
|
|
transforms the source image using the following transformation: |
|
\f[dst( \phi , \rho ) = src(x,y)\f] |
|
where |
|
\f[\rho = (src.width/maxRadius) \cdot \sqrt{x^2 + y^2} , \phi =atan(y/x)\f] |
|
|
|
The function can not operate in-place. |
|
|
|
@param src Source image |
|
@param dst Destination image |
|
@param center The transformation center; |
|
@param maxRadius Inverse magnitude scale parameter |
|
@param flags A combination of interpolation methods, see cv::InterpolationFlags |
|
*/ |
|
CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, |
|
Point2f center, double maxRadius, int flags ); |
|
|
|
//! @} imgproc_transform |
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|
|
//! @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 functions calculate 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. |
|
|
|
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 functions 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 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 accumulate( InputArray src, InputOutputArray dst, |
|
InputArray mask = noArray() ); |
|
|
|
/** @brief Adds the square of a source image to the accumulator. |
|
|
|
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. |
|
|
|
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 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 |
|
@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 single-channel array. The function is typically |
|
used to get a bi-level (binary) image out of a grayscale image ( cv::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 cv::THRESH_OTSU or cv::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 . The function returns the |
|
computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit |
|
images. |
|
|
|
@param src input array (single-channel, 8-bit or 32-bit floating point). |
|
@param dst output array of the same size and type 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 the cv::ThresholdTypes). |
|
|
|
@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 cv::AdaptiveThresholdTypes |
|
@param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, |
|
see cv::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 |
|
//! @{ |
|
|
|
/** @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 cv::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 cv::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 cv::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 cv::initUndistortRectifyMap (with unity R ) and cv::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 cv::getOptimalNewCameraMatrix to compute the appropriate |
|
newCameraMatrix depending on your requirements. |
|
|
|
The camera matrix and the distortion parameters can be determined using cv::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$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 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 |
|
cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, |
|
newCameraMatrix is normally set to P1 or P2 computed by cv::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 \\ x" \leftarrow x' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 x' y' + p_2(r^2 + 2 x'^2) \\ y" \leftarrow y' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' \\ 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$(k_1, k_2, p_1, p_2[, k_3])\f$ 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 cv::stereoCalibrate. But if the stereo camera |
|
was not calibrated, it is still possible to compute the rectification transformations directly from |
|
the fundamental matrix using cv::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$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 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 or CV_16SC2, see cv::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 cv::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 cv::undistort and cv::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. |
|
@code |
|
// (u,v) is the input point, (u', v') is the output point |
|
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1] |
|
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz] |
|
x" = (u - cx)/fx |
|
y" = (v - cy)/fy |
|
(x',y') = undistort(x",y",dist_coeffs) |
|
[X,Y,W]T = R*[x' y' 1]T |
|
x = X/W, y = Y/W |
|
// only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified |
|
u' = x*fx' + cx' |
|
v' = y*fy' + cy', |
|
@endcode |
|
where cv::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$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 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 |
|
cv::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). P1 or P2 computed by |
|
cv::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()); |
|
|
|
//! @} 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 functions calcHist calculate 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 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 cv::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 functions calcBackProject calculate the back project of the histogram. That is, similarly to |
|
cv::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 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 cv::calcHist, cv::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 compare 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 cv::EMD function. |
|
|
|
@param H1 First compared histogram. |
|
@param H2 Second compared histogram of the same size as H1 . |
|
@param method Comparison method, see cv::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. |
|
@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 . |
|
@param distType Used metric. See cv::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() ); |
|
|
|
//! @} 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 cv::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 cv::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 functions distanceTransform calculate 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 cv::DIST_C and cv::DIST_L1 types, the distance is calculated |
|
precisely, whereas for cv::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 cv::DistanceTypes |
|
@param maskSize Size of the distance transform mask, see cv::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 cv::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 cv::DistanceTypes |
|
@param maskSize Size of the distance transform mask, see cv::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 functions floodFill fill 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. 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 cv::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 cv::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 ); |
|
|
|
//! @} imgproc_misc |
|
|
|
// main function for all demosaicing procceses |
|
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. |
|
|
|
@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] |
|
}; |
|
|
|
/** @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 cv::TemplateMatchModes |
|
@param mask Mask of searched template. It must have the same datatype and size with templ. It is |
|
not set by default. |
|
*/ |
|
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. |
|
|
|
@param image the 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); |
|
|
|
/** @overload |
|
@param image the 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 |
|
cv::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.c in the |
|
OpenCV sample directory. |
|
|
|
@note Source image is modified by this function. Also, the function does not take into account |
|
1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm), |
|
therefore the contours touching the image border will be clipped. |
|
|
|
@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. |
|
The function modifies the image while extracting the contours. 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. |
|
@param hierarchy Optional output vector, 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] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[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 cv::RetrievalModes |
|
@param method Contour approximation method, see cv::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 functions approxPolyDP approximate 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. See the OpenCV sample minarea.cpp . 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 box.points() method. Please |
|
visit the [tutorial on bounding |
|
rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles) |
|
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. See |
|
the OpenCV sample minarea.cpp . |
|
|
|
@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 cv::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 functions find 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. See the OpenCV sample convexhull.cpp |
|
that demonstrates the usage of different function variants. |
|
|
|
@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=true, std::vector\<Point\> implies |
|
returnPoints=false. |
|
*/ |
|
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. cv::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 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 cv::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 interesecting 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 verticies 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 cv::RectanglesIntersectTypes |
|
*/ |
|
CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); |
|
|
|
//! @} imgproc_shape |
|
|
|
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 traslation 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, traslation and rotation |
|
CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil(); |
|
|
|
//! Performs linear blending of two images |
|
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 //!<  |
|
}; |
|
|
|
/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. |
|
|
|
@param src The source image, grayscale or colored does not matter. |
|
@param dst The result is the colormapped source image. Note: Mat::create is called on dst. |
|
@param colormap The colormap to apply, see cv::ColormapTypes |
|
*/ |
|
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); |
|
|
|
//! @} imgproc_colormap |
|
|
|
//! @addtogroup imgproc_draw |
|
//! @{ |
|
|
|
/** @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 cv::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 arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::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 cv::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 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 CV_FILLED , |
|
mean that the function has to draw a filled rectangle. |
|
@param lineType Type of the line. See the line description. |
|
@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); |
|
|
|
/** @brief Draws a circle. |
|
|
|
The function 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 thickness means that a |
|
filled circle is to be drawn. |
|
@param lineType Type of the circle boundary. See the line description. |
|
@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 functions ellipse with less parameters draw an ellipse outline, a filled ellipse, an elliptic |
|
arc, or a filled ellipse sector. 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 . The figure below explains the meaning of the parameters. |
|
|
|
 |
|
|
|
@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 the line description. |
|
@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 the line description. |
|
*/ |
|
CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, |
|
int thickness = 1, int lineType = LINE_8); |
|
|
|
/** @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 fillConvexPoly draws a filled convex polygon. This function is much faster than the |
|
function cv::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 the line description. |
|
@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() ); |
|
|
|
/** @brief Fills the area bounded by one or more polygons. |
|
|
|
The function 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 the line description. |
|
@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 the line description. |
|
@param shift Number of fractional bits in the vertex coordinates. |
|
|
|
The function 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 using the drawContour functionality |
|
*/ |
|
|
|
/** @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=CV_FILLED ), the contour interiors are drawn. |
|
@param lineType Line connectivity. See cv::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$\texttt{offset}=(dx,dy)\f$ . |
|
*/ |
|
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 functions clipLine calculate a part of the line segment that is entirely within the specified |
|
rectangle. They return false if the line segment is completely outside the rectangle. Otherwise, |
|
they return 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 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 cv::ellipse. |
|
|
|
@param center Center of the arc. |
|
@param axes Half of the size of the ellipse main axes. See the ellipse for details. |
|
@param angle Rotation angle of the ellipse in degrees. See the 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 ); |
|
|
|
/** @brief Draws a text string. |
|
|
|
The function 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 cv::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 the line for details. |
|
@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 getTextSize calculates and returns the size of a box that contains the specified text. |
|
That is, the following code renders some text, the tight box surrounding it, and the baseline: : |
|
@code |
|
String text = "Funny text inside the box"; |
|
int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; |
|
double fontScale = 2; |
|
int thickness = 3; |
|
|
|
Mat img(600, 800, CV_8UC3, Scalar::all(0)); |
|
|
|
int baseline=0; |
|
Size textSize = getTextSize(text, fontFace, |
|
fontScale, thickness, &baseline); |
|
baseline += thickness; |
|
|
|
// center the text |
|
Point textOrg((img.cols - textSize.width)/2, |
|
(img.rows + textSize.height)/2); |
|
|
|
// draw the box |
|
rectangle(img, textOrg + Point(0, baseline), |
|
textOrg + Point(textSize.width, -textSize.height), |
|
Scalar(0,0,255)); |
|
// ... and the baseline first |
|
line(img, textOrg + Point(0, thickness), |
|
textOrg + Point(textSize.width, thickness), |
|
Scalar(0, 0, 255)); |
|
|
|
// then put the text itself |
|
putText(img, text, textOrg, fontFace, fontScale, |
|
Scalar::all(255), thickness, 8); |
|
@endcode |
|
|
|
@param text Input text string. |
|
@param fontFace Font to use, see cv::HersheyFonts. |
|
@param fontScale Font scale factor that is multiplied by the font-specific base size. |
|
@param thickness Thickness of lines used to render the text. See putText for details. |
|
@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text |
|
point. |
|
@return The size of a box that contains the specified text. |
|
|
|
@see cv::putText |
|
*/ |
|
CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, |
|
double fontScale, int thickness, |
|
CV_OUT int* baseLine); |
|
|
|
/** @brief Line iterator |
|
|
|
The class is used to iterate over all the pixels on the raster line |
|
segment connecting two specified points. |
|
|
|
The class LineIterator is used to get each pixel of a raster line. It |
|
can be treated as versatile implementation of the Bresenham algorithm |
|
where you can stop at each pixel and do some extra processing, for |
|
example, grab pixel values along the line or draw a line with an effect |
|
(for example, with XOR operation). |
|
|
|
The number of pixels along the line is stored in LineIterator::count. |
|
The method LineIterator::pos returns the current position in the image: |
|
|
|
@code{.cpp} |
|
// grabs pixels along the line (pt1, pt2) |
|
// from 8-bit 3-channel image to the buffer |
|
LineIterator it(img, pt1, pt2, 8); |
|
LineIterator it2 = it; |
|
vector<Vec3b> buf(it.count); |
|
|
|
for(int i = 0; i < it.count; i++, ++it) |
|
buf[i] = *(const Vec3b)*it; |
|
|
|
// alternative way of iterating through the line |
|
for(int i = 0; i < it2.count; i++, ++it2) |
|
{ |
|
Vec3b val = img.at<Vec3b>(it2.pos()); |
|
CV_Assert(buf[i] == val); |
|
} |
|
@endcode |
|
*/ |
|
class CV_EXPORTS LineIterator |
|
{ |
|
public: |
|
/** @brief intializes the iterator |
|
|
|
creates iterators for the line connecting pt1 and pt2 |
|
the line will be clipped on the image boundaries |
|
the line is 8-connected or 4-connected |
|
If leftToRight=true, then the iteration is always done |
|
from the left-most point to the right most, |
|
not to depend on the ordering of pt1 and pt2 parameters |
|
*/ |
|
LineIterator( const Mat& img, Point pt1, Point pt2, |
|
int connectivity = 8, bool leftToRight = false ); |
|
/** @brief returns pointer to the current pixel |
|
*/ |
|
uchar* operator *(); |
|
/** @brief prefix increment operator (++it). shifts iterator to the next pixel |
|
*/ |
|
LineIterator& operator ++(); |
|
/** @brief postfix increment operator (it++). shifts iterator to the next pixel |
|
*/ |
|
LineIterator operator ++(int); |
|
/** @brief returns coordinates of the current pixel |
|
*/ |
|
Point pos() const; |
|
|
|
uchar* ptr; |
|
const uchar* ptr0; |
|
int step, elemSize; |
|
int err, count; |
|
int minusDelta, plusDelta; |
|
int minusStep, plusStep; |
|
}; |
|
|
|
//! @cond IGNORED |
|
|
|
// === LineIterator implementation === |
|
|
|
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 |
|
|
|
//! @} imgproc_draw |
|
|
|
//! @} imgproc |
|
|
|
} // cv |
|
|
|
#ifndef DISABLE_OPENCV_24_COMPATIBILITY |
|
#include "opencv2/imgproc/imgproc_c.h" |
|
#endif |
|
|
|
#endif
|
|
|