1171 lines
50 KiB
C++
1171 lines
50 KiB
C++
/*! \file imgproc.hpp
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\brief The Image Processing
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*/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_IMGPROC_HPP__
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#define __OPENCV_IMGPROC_HPP__
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#include "opencv2/core/core.hpp"
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#include "opencv2/imgproc/types_c.h"
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#ifdef __cplusplus
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/*! \namespace cv
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Namespace where all the C++ OpenCV functionality resides
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*/
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namespace cv
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{
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//! various border interpolation methods
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enum { BORDER_REPLICATE=IPL_BORDER_REPLICATE, BORDER_CONSTANT=IPL_BORDER_CONSTANT,
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BORDER_REFLECT=IPL_BORDER_REFLECT, BORDER_WRAP=IPL_BORDER_WRAP,
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BORDER_REFLECT_101=IPL_BORDER_REFLECT_101, BORDER_REFLECT101=BORDER_REFLECT_101,
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BORDER_TRANSPARENT=IPL_BORDER_TRANSPARENT,
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BORDER_DEFAULT=BORDER_REFLECT_101, BORDER_ISOLATED=16 };
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//! 1D interpolation function: returns coordinate of the "donor" pixel for the specified location p.
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CV_EXPORTS_W int borderInterpolate( int p, int len, int borderType );
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/*!
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The Base Class for 1D or Row-wise Filters
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This is the base class for linear or non-linear filters that process 1D data.
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In particular, such filters are used for the "horizontal" filtering parts in separable filters.
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Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
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and those pointers can be used directly or within cv::FilterEngine.
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*/
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class CV_EXPORTS BaseRowFilter
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{
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public:
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//! the default constructor
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BaseRowFilter();
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//! the destructor
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virtual ~BaseRowFilter();
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//! the filtering operator. Must be overrided in the derived classes. The horizontal border interpolation is done outside of the class.
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virtual void operator()(const uchar* src, uchar* dst,
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int width, int cn) = 0;
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int ksize, anchor;
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};
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/*!
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The Base Class for Column-wise Filters
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This is the base class for linear or non-linear filters that process columns of 2D arrays.
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Such filters are used for the "vertical" filtering parts in separable filters.
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Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
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and those pointers can be used directly or within cv::FilterEngine.
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Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
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i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
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must be called (e.g. the method is called by cv::FilterEngine)
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*/
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class CV_EXPORTS BaseColumnFilter
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{
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public:
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//! the default constructor
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BaseColumnFilter();
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//! the destructor
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virtual ~BaseColumnFilter();
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//! the filtering operator. Must be overrided in the derived classes. The vertical border interpolation is done outside of the class.
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virtual void operator()(const uchar** src, uchar* dst, int dststep,
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int dstcount, int width) = 0;
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//! resets the internal buffers, if any
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virtual void reset();
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int ksize, anchor;
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};
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/*!
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The Base Class for Non-Separable 2D Filters.
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This is the base class for linear or non-linear 2D filters.
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Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
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and those pointers can be used directly or within cv::FilterEngine.
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Similar to cv::BaseColumnFilter, the class may have some context information,
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that should be reset using BaseFilter::reset() method before processing the new array.
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*/
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class CV_EXPORTS BaseFilter
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{
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public:
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//! the default constructor
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BaseFilter();
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//! the destructor
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virtual ~BaseFilter();
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//! the filtering operator. The horizontal and the vertical border interpolation is done outside of the class.
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virtual void operator()(const uchar** src, uchar* dst, int dststep,
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int dstcount, int width, int cn) = 0;
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//! resets the internal buffers, if any
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virtual void reset();
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Size ksize;
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Point anchor;
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};
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/*!
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The Main Class for Image Filtering.
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The class can be used to apply an arbitrary filtering operation to an image.
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It contains all the necessary intermediate buffers, it computes extrapolated values
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of the "virtual" pixels outside of the image etc.
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Pointers to the initialized cv::FilterEngine instances
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are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
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cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
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cv::createBoxFilter() and cv::createMorphologyFilter().
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Using the class you can process large images by parts and build complex pipelines
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that include filtering as some of the stages. If all you need is to apply some pre-defined
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filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
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functions that create FilterEngine internally.
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Here is the example on how to use the class to implement Laplacian operator, which is the sum of
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second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
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\code
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void laplace_f(const Mat& src, Mat& dst)
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{
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CV_Assert( src.type() == CV_32F );
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// make sure the destination array has the proper size and type
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dst.create(src.size(), src.type());
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// get the derivative and smooth kernels for d2I/dx2.
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// for d2I/dy2 we could use the same kernels, just swapped
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Mat kd, ks;
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getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
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// let's process 10 source rows at once
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int DELTA = std::min(10, src.rows);
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Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
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dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
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Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
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dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
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int y = Fxx->start(src), dsty = 0, dy = 0;
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Fyy->start(src);
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const uchar* sptr = src.data + y*src.step;
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// allocate the buffers for the spatial image derivatives;
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// the buffers need to have more than DELTA rows, because at the
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// last iteration the output may take max(kd.rows-1,ks.rows-1)
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// rows more than the input.
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Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
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Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
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// inside the loop we always pass DELTA rows to the filter
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// (note that the "proceed" method takes care of possibe overflow, since
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// it was given the actual image height in the "start" method)
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// on output we can get:
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// * < DELTA rows (the initial buffer accumulation stage)
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// * = DELTA rows (settled state in the middle)
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// * > DELTA rows (then the input image is over, but we generate
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// "virtual" rows using the border mode and filter them)
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// this variable number of output rows is dy.
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// dsty is the current output row.
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// sptr is the pointer to the first input row in the portion to process
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for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
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{
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Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
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dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
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if( dy > 0 )
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{
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Mat dstripe = dst.rowRange(dsty, dsty + dy);
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add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
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}
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}
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}
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\endcode
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*/
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class CV_EXPORTS FilterEngine
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{
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public:
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//! the default constructor
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FilterEngine();
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//! the full constructor. Either _filter2D or both _rowFilter and _columnFilter must be non-empty.
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FilterEngine(const Ptr<BaseFilter>& _filter2D,
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const Ptr<BaseRowFilter>& _rowFilter,
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const Ptr<BaseColumnFilter>& _columnFilter,
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int srcType, int dstType, int bufType,
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int _rowBorderType=BORDER_REPLICATE,
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int _columnBorderType=-1,
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const Scalar& _borderValue=Scalar());
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//! the destructor
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virtual ~FilterEngine();
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//! reinitializes the engine. The previously assigned filters are released.
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void init(const Ptr<BaseFilter>& _filter2D,
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const Ptr<BaseRowFilter>& _rowFilter,
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const Ptr<BaseColumnFilter>& _columnFilter,
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int srcType, int dstType, int bufType,
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int _rowBorderType=BORDER_REPLICATE, int _columnBorderType=-1,
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const Scalar& _borderValue=Scalar());
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//! starts filtering of the specified ROI of an image of size wholeSize.
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virtual int start(Size wholeSize, Rect roi, int maxBufRows=-1);
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//! starts filtering of the specified ROI of the specified image.
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virtual int start(const Mat& src, const Rect& srcRoi=Rect(0,0,-1,-1),
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bool isolated=false, int maxBufRows=-1);
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//! processes the next srcCount rows of the image.
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virtual int proceed(const uchar* src, int srcStep, int srcCount,
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uchar* dst, int dstStep);
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//! applies filter to the specified ROI of the image. if srcRoi=(0,0,-1,-1), the whole image is filtered.
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virtual void apply( const Mat& src, Mat& dst,
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const Rect& srcRoi=Rect(0,0,-1,-1),
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Point dstOfs=Point(0,0),
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bool isolated=false);
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//! returns true if the filter is separable
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bool isSeparable() const { return (const BaseFilter*)filter2D == 0; }
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//! returns the number
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int remainingInputRows() const;
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int remainingOutputRows() const;
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int srcType, dstType, bufType;
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Size ksize;
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Point anchor;
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int maxWidth;
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Size wholeSize;
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Rect roi;
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int dx1, dx2;
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int rowBorderType, columnBorderType;
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vector<int> borderTab;
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int borderElemSize;
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vector<uchar> ringBuf;
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vector<uchar> srcRow;
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vector<uchar> constBorderValue;
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vector<uchar> constBorderRow;
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int bufStep, startY, startY0, endY, rowCount, dstY;
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vector<uchar*> rows;
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Ptr<BaseFilter> filter2D;
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Ptr<BaseRowFilter> rowFilter;
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Ptr<BaseColumnFilter> columnFilter;
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};
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//! type of the kernel
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enum { KERNEL_GENERAL=0, KERNEL_SYMMETRICAL=1, KERNEL_ASYMMETRICAL=2,
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KERNEL_SMOOTH=4, KERNEL_INTEGER=8 };
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//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
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CV_EXPORTS int getKernelType(InputArray kernel, Point anchor);
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//! returns the primitive row filter with the specified kernel
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CV_EXPORTS Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType,
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InputArray kernel, int anchor,
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int symmetryType);
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//! returns the primitive column filter with the specified kernel
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CV_EXPORTS Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType,
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InputArray kernel, int anchor,
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int symmetryType, double delta=0,
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int bits=0);
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//! returns 2D filter with the specified kernel
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CV_EXPORTS Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
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InputArray kernel,
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Point anchor=Point(-1,-1),
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double delta=0, int bits=0);
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//! returns the separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType,
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InputArray rowKernel, InputArray columnKernel,
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Point _anchor=Point(-1,-1), double delta=0,
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int _rowBorderType=BORDER_DEFAULT,
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int _columnBorderType=-1,
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const Scalar& _borderValue=Scalar());
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//! returns the non-separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine> createLinearFilter(int srcType, int dstType,
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InputArray kernel, Point _anchor=Point(-1,-1),
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double delta=0, int _rowBorderType=BORDER_DEFAULT,
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int _columnBorderType=-1, const Scalar& _borderValue=Scalar());
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//! returns the Gaussian kernel with the specified parameters
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CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F );
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//! returns the Gaussian filter engine
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CV_EXPORTS Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
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double sigma1, double sigma2=0,
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int borderType=BORDER_DEFAULT);
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//! initializes kernels of the generalized Sobel operator
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CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
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int dx, int dy, int ksize,
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bool normalize=false, int ktype=CV_32F );
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//! returns filter engine for the generalized Sobel operator
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CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
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int dx, int dy, int ksize,
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int borderType=BORDER_DEFAULT );
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//! returns horizontal 1D box filter
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CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
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int ksize, int anchor=-1);
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//! returns vertical 1D box filter
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CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
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int ksize, int anchor=-1,
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double scale=1);
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//! returns box filter engine
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CV_EXPORTS Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
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Point anchor=Point(-1,-1),
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bool normalize=true,
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int borderType=BORDER_DEFAULT);
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//! returns the Gabor kernel with the specified parameters
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CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
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double gamma, double psi=CV_PI*0.5, int ktype=CV_64F );
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//! type of morphological operation
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enum { MORPH_ERODE=CV_MOP_ERODE, MORPH_DILATE=CV_MOP_DILATE,
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MORPH_OPEN=CV_MOP_OPEN, MORPH_CLOSE=CV_MOP_CLOSE,
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MORPH_GRADIENT=CV_MOP_GRADIENT, MORPH_TOPHAT=CV_MOP_TOPHAT,
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MORPH_BLACKHAT=CV_MOP_BLACKHAT };
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//! returns horizontal 1D morphological filter
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CV_EXPORTS Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int ksize, int anchor=-1);
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//! returns vertical 1D morphological filter
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CV_EXPORTS Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int ksize, int anchor=-1);
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//! returns 2D morphological filter
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CV_EXPORTS Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
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Point anchor=Point(-1,-1));
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//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
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static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
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//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
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CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
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Point anchor=Point(-1,-1), int _rowBorderType=BORDER_CONSTANT,
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int _columnBorderType=-1,
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const Scalar& _borderValue=morphologyDefaultBorderValue());
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//! shape of the structuring element
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enum { MORPH_RECT=0, MORPH_CROSS=1, MORPH_ELLIPSE=2 };
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//! returns structuring element of the specified shape and size
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CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-1,-1));
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template<> CV_EXPORTS void Ptr<IplConvKernel>::delete_obj();
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//! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode
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CV_EXPORTS_W void copyMakeBorder( InputArray src, OutputArray dst,
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int top, int bottom, int left, int right,
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int borderType, const Scalar& value=Scalar() );
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//! smooths the image using median filter.
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CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
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//! smooths the image using Gaussian filter.
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CV_EXPORTS_W void GaussianBlur( InputArray src,
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OutputArray dst, Size ksize,
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double sigma1, double sigma2=0,
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int borderType=BORDER_DEFAULT );
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//! smooths the image using bilateral filter
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CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
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double sigmaColor, double sigmaSpace,
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int borderType=BORDER_DEFAULT );
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//! smooths the image using the box filter. Each pixel is processed in O(1) time
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CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
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Size ksize, Point anchor=Point(-1,-1),
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bool normalize=true,
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int borderType=BORDER_DEFAULT );
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//! a synonym for normalized box filter
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CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
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Size ksize, Point anchor=Point(-1,-1),
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int borderType=BORDER_DEFAULT );
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//! applies non-separable 2D linear filter to the image
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CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
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InputArray kernel, Point anchor=Point(-1,-1),
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double delta=0, int borderType=BORDER_DEFAULT );
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//! applies separable 2D linear filter to the image
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CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
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InputArray kernelX, InputArray kernelY,
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Point anchor=Point(-1,-1),
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double delta=0, int borderType=BORDER_DEFAULT );
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//! applies generalized Sobel operator to the image
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CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
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int dx, int dy, int ksize=3,
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double scale=1, double delta=0,
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int borderType=BORDER_DEFAULT );
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//! applies the vertical or horizontal Scharr operator to the image
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CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
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int dx, int dy, double scale=1, double delta=0,
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int borderType=BORDER_DEFAULT );
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//! applies Laplacian operator to the image
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CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
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int ksize=1, double scale=1, double delta=0,
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int borderType=BORDER_DEFAULT );
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//! applies Canny edge detector and produces the edge map.
|
|
CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
|
|
double threshold1, double threshold2,
|
|
int apertureSize=3, bool L2gradient=false );
|
|
|
|
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
|
|
CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
|
|
int blockSize, int ksize=3,
|
|
int borderType=BORDER_DEFAULT );
|
|
|
|
//! computes Harris cornerness criteria at each image pixel
|
|
CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
|
|
int ksize, double k,
|
|
int borderType=BORDER_DEFAULT );
|
|
|
|
// low-level function for computing eigenvalues and eigenvectors of 2x2 matrices
|
|
CV_EXPORTS void eigen2x2( const float* a, float* e, int n );
|
|
|
|
//! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix at each pixel. The output is stored as 6-channel matrix.
|
|
CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
|
|
int blockSize, int ksize,
|
|
int borderType=BORDER_DEFAULT );
|
|
|
|
//! computes another complex cornerness criteria at each pixel
|
|
CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
|
|
int borderType=BORDER_DEFAULT );
|
|
|
|
//! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
|
|
CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
|
|
Size winSize, Size zeroZone,
|
|
TermCriteria criteria );
|
|
|
|
//! finds the strong enough corners where the cornerMinEigenVal() or cornerHarris() report the local maxima
|
|
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 );
|
|
|
|
//! finds lines in the black-n-white image using the standard or pyramid Hough transform
|
|
CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
|
|
double rho, double theta, int threshold,
|
|
double srn=0, double stn=0 );
|
|
|
|
//! finds line segments in the black-n-white image using probabalistic Hough transform
|
|
CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
|
|
double rho, double theta, int threshold,
|
|
double minLineLength=0, double maxLineGap=0 );
|
|
|
|
//! finds circles in the grayscale image using 2+1 gradient Hough transform
|
|
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 );
|
|
|
|
//! erodes the image (applies the local minimum operator)
|
|
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() );
|
|
|
|
//! dilates the image (applies the local maximum operator)
|
|
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() );
|
|
|
|
//! applies an advanced morphological operation to the image
|
|
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() );
|
|
|
|
//! interpolation algorithm
|
|
enum
|
|
{
|
|
INTER_NEAREST=CV_INTER_NN, //!< nearest neighbor interpolation
|
|
INTER_LINEAR=CV_INTER_LINEAR, //!< bilinear interpolation
|
|
INTER_CUBIC=CV_INTER_CUBIC, //!< bicubic interpolation
|
|
INTER_AREA=CV_INTER_AREA, //!< area-based (or super) interpolation
|
|
INTER_LANCZOS4=CV_INTER_LANCZOS4, //!< Lanczos interpolation over 8x8 neighborhood
|
|
INTER_MAX=7,
|
|
WARP_INVERSE_MAP=CV_WARP_INVERSE_MAP
|
|
};
|
|
|
|
//! resizes the image
|
|
CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
|
|
Size dsize, double fx=0, double fy=0,
|
|
int interpolation=INTER_LINEAR );
|
|
|
|
//! warps the image using affine transformation
|
|
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());
|
|
|
|
//! warps the image using perspective transformation
|
|
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());
|
|
|
|
enum
|
|
{
|
|
INTER_BITS=5, INTER_BITS2=INTER_BITS*2,
|
|
INTER_TAB_SIZE=(1<<INTER_BITS),
|
|
INTER_TAB_SIZE2=INTER_TAB_SIZE*INTER_TAB_SIZE
|
|
};
|
|
|
|
//! warps the image using the precomputed maps. The maps are stored in either floating-point or integer fixed-point format
|
|
CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
|
|
InputArray map1, InputArray map2,
|
|
int interpolation, int borderMode=BORDER_CONSTANT,
|
|
const Scalar& borderValue=Scalar());
|
|
|
|
//! converts maps for remap from floating-point to fixed-point format or backwards
|
|
CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
|
|
OutputArray dstmap1, OutputArray dstmap2,
|
|
int dstmap1type, bool nninterpolation=false );
|
|
|
|
//! returns 2x3 affine transformation matrix for the planar rotation.
|
|
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[] );
|
|
//! returns 2x3 affine transformation for the corresponding 3 point pairs.
|
|
CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
|
|
//! computes 2x3 affine transformation matrix that is inverse to the specified 2x3 affine transformation.
|
|
CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
|
|
|
|
CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst );
|
|
CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
|
|
|
|
//! extracts rectangle from the image at sub-pixel location
|
|
CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
|
|
Point2f center, OutputArray patch, int patchType=-1 );
|
|
|
|
//! computes the integral image
|
|
CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth=-1 );
|
|
|
|
//! computes the integral image and integral for the squared image
|
|
CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
|
|
OutputArray sqsum, int sdepth=-1 );
|
|
//! computes the integral image, integral for the squared image and the tilted integral image
|
|
CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
|
|
OutputArray sqsum, OutputArray tilted,
|
|
int sdepth=-1 );
|
|
|
|
//! adds image to the accumulator (dst += src). Unlike cv::add, dst and src can have different types.
|
|
CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
|
|
InputArray mask=noArray() );
|
|
//! adds squared src image to the accumulator (dst += src*src).
|
|
CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
|
|
InputArray mask=noArray() );
|
|
//! adds product of the 2 images to the accumulator (dst += src1*src2).
|
|
CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
|
|
InputOutputArray dst, InputArray mask=noArray() );
|
|
//! updates the running average (dst = dst*(1-alpha) + src*alpha)
|
|
CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
|
|
double alpha, InputArray mask=noArray() );
|
|
|
|
CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, InputArray window = noArray());
|
|
CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
|
|
|
|
//! type of the threshold operation
|
|
enum { THRESH_BINARY=CV_THRESH_BINARY, THRESH_BINARY_INV=CV_THRESH_BINARY_INV,
|
|
THRESH_TRUNC=CV_THRESH_TRUNC, THRESH_TOZERO=CV_THRESH_TOZERO,
|
|
THRESH_TOZERO_INV=CV_THRESH_TOZERO_INV, THRESH_MASK=CV_THRESH_MASK,
|
|
THRESH_OTSU=CV_THRESH_OTSU };
|
|
|
|
//! applies fixed threshold to the image
|
|
CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
|
|
double thresh, double maxval, int type );
|
|
|
|
//! adaptive threshold algorithm
|
|
enum { ADAPTIVE_THRESH_MEAN_C=0, ADAPTIVE_THRESH_GAUSSIAN_C=1 };
|
|
|
|
//! applies variable (adaptive) threshold to the image
|
|
CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
|
|
double maxValue, int adaptiveMethod,
|
|
int thresholdType, int blockSize, double C );
|
|
|
|
//! smooths and downsamples the image
|
|
CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
|
|
const Size& dstsize=Size());
|
|
//! upsamples and smoothes the image
|
|
CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
|
|
const Size& dstsize=Size());
|
|
|
|
//! builds the gaussian pyramid using pyrDown() as a basic operation
|
|
CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, int maxlevel );
|
|
|
|
//! corrects lens distortion for the given camera matrix and distortion coefficients
|
|
CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
|
|
InputArray cameraMatrix,
|
|
InputArray distCoeffs,
|
|
InputArray newCameraMatrix=noArray() );
|
|
|
|
//! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
|
|
CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
|
|
InputArray R, InputArray newCameraMatrix,
|
|
Size size, int m1type, OutputArray map1, OutputArray map2 );
|
|
|
|
enum
|
|
{
|
|
PROJ_SPHERICAL_ORTHO = 0,
|
|
PROJ_SPHERICAL_EQRECT = 1
|
|
};
|
|
|
|
//! 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);
|
|
|
|
//! returns the default new camera matrix (by default it is the same as cameraMatrix unless centerPricipalPoint=true)
|
|
CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize=Size(),
|
|
bool centerPrincipalPoint=false );
|
|
|
|
//! returns points' coordinates after lens distortion correction
|
|
CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
|
|
InputArray cameraMatrix, InputArray distCoeffs,
|
|
InputArray R=noArray(), InputArray P=noArray());
|
|
|
|
template<> CV_EXPORTS void Ptr<CvHistogram>::delete_obj();
|
|
|
|
//! computes the joint dense histogram for a set of images.
|
|
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 );
|
|
|
|
//! computes the joint sparse histogram for a set of images.
|
|
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 );
|
|
|
|
CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
|
|
const vector<int>& channels,
|
|
InputArray mask, OutputArray hist,
|
|
const vector<int>& histSize,
|
|
const vector<float>& ranges,
|
|
bool accumulate=false );
|
|
|
|
//! computes back projection for the set of images
|
|
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 );
|
|
|
|
//! computes back projection for the set of images
|
|
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 );
|
|
|
|
CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const vector<int>& channels,
|
|
InputArray hist, OutputArray dst,
|
|
const vector<float>& ranges,
|
|
double scale );
|
|
|
|
/*CV_EXPORTS void calcBackProjectPatch( const Mat* images, int nimages, const int* channels,
|
|
InputArray hist, OutputArray dst, Size patchSize,
|
|
int method, double factor=1 );
|
|
|
|
CV_EXPORTS_W void calcBackProjectPatch( InputArrayOfArrays images, const vector<int>& channels,
|
|
InputArray hist, OutputArray dst, Size patchSize,
|
|
int method, double factor=1 );*/
|
|
|
|
//! compares two histograms stored in dense arrays
|
|
CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
|
|
|
|
//! compares two histograms stored in sparse arrays
|
|
CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
|
|
|
|
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
|
|
CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
|
|
|
|
CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
|
|
int distType, InputArray cost=noArray(),
|
|
float* lowerBound=0, OutputArray flow=noArray() );
|
|
|
|
//! segments the image using watershed algorithm
|
|
CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
|
|
|
|
//! filters image using meanshift algorithm
|
|
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) );
|
|
|
|
//! class of the pixel in GrabCut algorithm
|
|
enum
|
|
{
|
|
GC_BGD = 0, //!< background
|
|
GC_FGD = 1, //!< foreground
|
|
GC_PR_BGD = 2, //!< most probably background
|
|
GC_PR_FGD = 3 //!< most probably foreground
|
|
};
|
|
|
|
//! GrabCut algorithm flags
|
|
enum
|
|
{
|
|
GC_INIT_WITH_RECT = 0,
|
|
GC_INIT_WITH_MASK = 1,
|
|
GC_EVAL = 2
|
|
};
|
|
|
|
//! segments the image using GrabCut algorithm
|
|
CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
|
|
InputOutputArray bgdModel, InputOutputArray fgdModel,
|
|
int iterCount, int mode = GC_EVAL );
|
|
|
|
enum
|
|
{
|
|
DIST_LABEL_CCOMP = 0,
|
|
DIST_LABEL_PIXEL = 1
|
|
};
|
|
|
|
//! builds the discrete Voronoi diagram
|
|
CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
|
|
OutputArray labels, int distanceType, int maskSize,
|
|
int labelType=DIST_LABEL_CCOMP );
|
|
|
|
//! computes the distance transform map
|
|
CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
|
|
int distanceType, int maskSize );
|
|
|
|
enum { FLOODFILL_FIXED_RANGE = 1 << 16, FLOODFILL_MASK_ONLY = 1 << 17 };
|
|
|
|
//! fills the semi-uniform image region starting from the specified seed point
|
|
CV_EXPORTS int floodFill( InputOutputArray image,
|
|
Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
|
|
Scalar loDiff=Scalar(), Scalar upDiff=Scalar(),
|
|
int flags=4 );
|
|
|
|
//! fills the semi-uniform image region and/or the mask starting from the specified seed point
|
|
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 );
|
|
|
|
|
|
enum
|
|
{
|
|
COLOR_BGR2BGRA =0,
|
|
COLOR_RGB2RGBA =COLOR_BGR2BGRA,
|
|
|
|
COLOR_BGRA2BGR =1,
|
|
COLOR_RGBA2RGB =COLOR_BGRA2BGR,
|
|
|
|
COLOR_BGR2RGBA =2,
|
|
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,
|
|
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,
|
|
COLOR_RGB2BGR565 =13,
|
|
COLOR_BGR5652BGR =14,
|
|
COLOR_BGR5652RGB =15,
|
|
COLOR_BGRA2BGR565 =16,
|
|
COLOR_RGBA2BGR565 =17,
|
|
COLOR_BGR5652BGRA =18,
|
|
COLOR_BGR5652RGBA =19,
|
|
|
|
COLOR_GRAY2BGR565 =20,
|
|
COLOR_BGR5652GRAY =21,
|
|
|
|
COLOR_BGR2BGR555 =22,
|
|
COLOR_RGB2BGR555 =23,
|
|
COLOR_BGR5552BGR =24,
|
|
COLOR_BGR5552RGB =25,
|
|
COLOR_BGRA2BGR555 =26,
|
|
COLOR_RGBA2BGR555 =27,
|
|
COLOR_BGR5552BGRA =28,
|
|
COLOR_BGR5552RGBA =29,
|
|
|
|
COLOR_GRAY2BGR555 =30,
|
|
COLOR_BGR5552GRAY =31,
|
|
|
|
COLOR_BGR2XYZ =32,
|
|
COLOR_RGB2XYZ =33,
|
|
COLOR_XYZ2BGR =34,
|
|
COLOR_XYZ2RGB =35,
|
|
|
|
COLOR_BGR2YCrCb =36,
|
|
COLOR_RGB2YCrCb =37,
|
|
COLOR_YCrCb2BGR =38,
|
|
COLOR_YCrCb2RGB =39,
|
|
|
|
COLOR_BGR2HSV =40,
|
|
COLOR_RGB2HSV =41,
|
|
|
|
COLOR_BGR2Lab =44,
|
|
COLOR_RGB2Lab =45,
|
|
|
|
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_BGR2Luv =50,
|
|
COLOR_RGB2Luv =51,
|
|
COLOR_BGR2HLS =52,
|
|
COLOR_RGB2HLS =53,
|
|
|
|
COLOR_HSV2BGR =54,
|
|
COLOR_HSV2RGB =55,
|
|
|
|
COLOR_Lab2BGR =56,
|
|
COLOR_Lab2RGB =57,
|
|
COLOR_Luv2BGR =58,
|
|
COLOR_Luv2RGB =59,
|
|
COLOR_HLS2BGR =60,
|
|
COLOR_HLS2RGB =61,
|
|
|
|
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,
|
|
|
|
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,
|
|
COLOR_RGB2YUV = 83,
|
|
COLOR_YUV2BGR = 84,
|
|
COLOR_YUV2RGB = 85,
|
|
|
|
COLOR_BayerBG2GRAY = 86,
|
|
COLOR_BayerGB2GRAY = 87,
|
|
COLOR_BayerRG2GRAY = 88,
|
|
COLOR_BayerGR2GRAY = 89,
|
|
|
|
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_COLORCVT_MAX =100
|
|
};
|
|
|
|
|
|
//! converts image from one color space to another
|
|
CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 );
|
|
|
|
//! raster image moments
|
|
class CV_EXPORTS_W_MAP Moments
|
|
{
|
|
public:
|
|
//! the default constructor
|
|
Moments();
|
|
//! the full constructor
|
|
Moments(double m00, double m10, double m01, double m20, double m11,
|
|
double m02, double m30, double m21, double m12, double m03 );
|
|
//! the conversion from CvMoments
|
|
Moments( const CvMoments& moments );
|
|
//! the conversion to CvMoments
|
|
operator CvMoments() const;
|
|
|
|
//! spatial moments
|
|
CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
|
|
//! central moments
|
|
CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03;
|
|
//! central normalized moments
|
|
CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03;
|
|
};
|
|
|
|
//! computes moments of the rasterized shape or a vector of points
|
|
CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage=false );
|
|
|
|
//! computes 7 Hu invariants from the moments
|
|
CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
|
|
CV_EXPORTS_W void HuMoments( const Moments& m, CV_OUT OutputArray hu );
|
|
|
|
//! type of the template matching operation
|
|
enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF=4, TM_CCOEFF_NORMED=5 };
|
|
|
|
//! computes the proximity map for the raster template and the image where the template is searched for
|
|
CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
|
|
OutputArray result, int method );
|
|
|
|
//! mode of the contour retrieval algorithm
|
|
enum
|
|
{
|
|
RETR_EXTERNAL=CV_RETR_EXTERNAL, //!< retrieve only the most external (top-level) contours
|
|
RETR_LIST=CV_RETR_LIST, //!< retrieve all the contours without any hierarchical information
|
|
RETR_CCOMP=CV_RETR_CCOMP, //!< retrieve the connected components (that can possibly be nested)
|
|
RETR_TREE=CV_RETR_TREE, //!< retrieve all the contours and the whole hierarchy
|
|
RETR_FLOODFILL=CV_RETR_FLOODFILL
|
|
};
|
|
|
|
//! the contour approximation algorithm
|
|
enum
|
|
{
|
|
CHAIN_APPROX_NONE=CV_CHAIN_APPROX_NONE,
|
|
CHAIN_APPROX_SIMPLE=CV_CHAIN_APPROX_SIMPLE,
|
|
CHAIN_APPROX_TC89_L1=CV_CHAIN_APPROX_TC89_L1,
|
|
CHAIN_APPROX_TC89_KCOS=CV_CHAIN_APPROX_TC89_KCOS
|
|
};
|
|
|
|
//! retrieves contours and the hierarchical information from black-n-white image.
|
|
CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
|
|
OutputArray hierarchy, int mode,
|
|
int method, Point offset=Point());
|
|
|
|
//! retrieves contours from black-n-white image.
|
|
CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
|
|
int mode, int method, Point offset=Point());
|
|
|
|
//! draws contours in the image
|
|
CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
|
|
int contourIdx, const Scalar& color,
|
|
int thickness=1, int lineType=8,
|
|
InputArray hierarchy=noArray(),
|
|
int maxLevel=INT_MAX, Point offset=Point() );
|
|
|
|
//! approximates contour or a curve using Douglas-Peucker algorithm
|
|
CV_EXPORTS_W void approxPolyDP( InputArray curve,
|
|
OutputArray approxCurve,
|
|
double epsilon, bool closed );
|
|
|
|
//! computes the contour perimeter (closed=true) or a curve length
|
|
CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
|
|
//! computes the bounding rectangle for a contour
|
|
CV_EXPORTS_W Rect boundingRect( InputArray points );
|
|
//! computes the contour area
|
|
CV_EXPORTS_W double contourArea( InputArray contour, bool oriented=false );
|
|
//! computes the minimal rotated rectangle for a set of points
|
|
CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
|
|
//! computes the minimal enclosing circle for a set of points
|
|
CV_EXPORTS_W void minEnclosingCircle( InputArray points,
|
|
CV_OUT Point2f& center, CV_OUT float& radius );
|
|
//! matches two contours using one of the available algorithms
|
|
CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
|
|
int method, double parameter );
|
|
//! computes convex hull for a set of 2D points.
|
|
CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
|
|
bool clockwise=false, bool returnPoints=true );
|
|
//! computes the contour convexity defects
|
|
CV_EXPORTS_W void convexityDefects( InputArray points, InputArray hull, OutputArray defects );
|
|
|
|
//! returns true iff the contour is convex. Does not support contours with self-intersection
|
|
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 );
|
|
|
|
//! fits ellipse to the set of 2D points
|
|
CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
|
|
|
|
//! fits line to the set of 2D points using M-estimator algorithm
|
|
CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
|
|
double param, double reps, double aeps );
|
|
//! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
|
|
CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
|
|
|
|
|
|
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 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 vector<Vec4f>& edgeList) const;
|
|
CV_WRAP void getTriangleList(CV_OUT vector<Vec6f>& triangleList) const;
|
|
CV_WRAP void getVoronoiFacetList(const vector<int>& idx, CV_OUT vector<vector<Point2f> >& facetList,
|
|
CV_OUT 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];
|
|
};
|
|
|
|
vector<Vertex> vtx;
|
|
vector<QuadEdge> qedges;
|
|
int freeQEdge;
|
|
int freePoint;
|
|
bool validGeometry;
|
|
|
|
int recentEdge;
|
|
Point2f topLeft;
|
|
Point2f bottomRight;
|
|
};
|
|
|
|
}
|
|
|
|
// 2009-01-12, Xavier Delacour <xavier.delacour@gmail.com>
|
|
|
|
struct lsh_hash {
|
|
int h1, h2;
|
|
};
|
|
|
|
struct CvLSHOperations
|
|
{
|
|
virtual ~CvLSHOperations() {}
|
|
|
|
virtual int vector_add(const void* data) = 0;
|
|
virtual void vector_remove(int i) = 0;
|
|
virtual const void* vector_lookup(int i) = 0;
|
|
virtual void vector_reserve(int n) = 0;
|
|
virtual unsigned int vector_count() = 0;
|
|
|
|
virtual void hash_insert(lsh_hash h, int l, int i) = 0;
|
|
virtual void hash_remove(lsh_hash h, int l, int i) = 0;
|
|
virtual int hash_lookup(lsh_hash h, int l, int* ret_i, int ret_i_max) = 0;
|
|
};
|
|
|
|
#endif /* __cplusplus */
|
|
|
|
#endif
|
|
|
|
/* End of file. */
|