228 lines
10 KiB
C++
228 lines
10 KiB
C++
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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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// Copyright (C) 2013, OpenCV Foundation, 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_SHAPE_SHAPE_DISTANCE_HPP
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#define OPENCV_SHAPE_SHAPE_DISTANCE_HPP
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#include "opencv2/core.hpp"
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#include "opencv2/shape/hist_cost.hpp"
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#include "opencv2/shape/shape_transformer.hpp"
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namespace cv
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{
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//! @addtogroup shape
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//! @{
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/** @example modules/shape/samples/shape_example.cpp
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An example using shape distance algorithm
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*/
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/** @brief Abstract base class for shape distance algorithms.
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*/
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class CV_EXPORTS_W ShapeDistanceExtractor : public Algorithm
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{
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public:
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/** @brief Compute the shape distance between two shapes defined by its contours.
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@param contour1 Contour defining first shape.
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@param contour2 Contour defining second shape.
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*/
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CV_WRAP virtual float computeDistance(InputArray contour1, InputArray contour2) = 0;
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};
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/***********************************************************************************/
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/***********************************************************************************/
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/***********************************************************************************/
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/** @brief Implementation of the Shape Context descriptor and matching algorithm
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proposed by Belongie et al. in "Shape Matching and Object Recognition Using Shape Contexts" (PAMI
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2002). This implementation is packaged in a generic scheme, in order to allow you the
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implementation of the common variations of the original pipeline.
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*/
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class CV_EXPORTS_W ShapeContextDistanceExtractor : public ShapeDistanceExtractor
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{
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public:
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/** @brief Establish the number of angular bins for the Shape Context Descriptor used in the shape matching
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pipeline.
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@param nAngularBins The number of angular bins in the shape context descriptor.
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*/
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CV_WRAP virtual void setAngularBins(int nAngularBins) = 0;
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CV_WRAP virtual int getAngularBins() const = 0;
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/** @brief Establish the number of radial bins for the Shape Context Descriptor used in the shape matching
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pipeline.
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@param nRadialBins The number of radial bins in the shape context descriptor.
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*/
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CV_WRAP virtual void setRadialBins(int nRadialBins) = 0;
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CV_WRAP virtual int getRadialBins() const = 0;
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/** @brief Set the inner radius of the shape context descriptor.
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@param innerRadius The value of the inner radius.
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*/
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CV_WRAP virtual void setInnerRadius(float innerRadius) = 0;
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CV_WRAP virtual float getInnerRadius() const = 0;
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/** @brief Set the outer radius of the shape context descriptor.
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@param outerRadius The value of the outer radius.
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*/
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CV_WRAP virtual void setOuterRadius(float outerRadius) = 0;
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CV_WRAP virtual float getOuterRadius() const = 0;
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CV_WRAP virtual void setRotationInvariant(bool rotationInvariant) = 0;
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CV_WRAP virtual bool getRotationInvariant() const = 0;
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/** @brief Set the weight of the shape context distance in the final value of the shape distance. The shape
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context distance between two shapes is defined as the symmetric sum of shape context matching costs
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over best matching points. The final value of the shape distance is a user-defined linear
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combination of the shape context distance, an image appearance distance, and a bending energy.
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@param shapeContextWeight The weight of the shape context distance in the final distance value.
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*/
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CV_WRAP virtual void setShapeContextWeight(float shapeContextWeight) = 0;
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CV_WRAP virtual float getShapeContextWeight() const = 0;
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/** @brief Set the weight of the Image Appearance cost in the final value of the shape distance. The image
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appearance cost is defined as the sum of squared brightness differences in Gaussian windows around
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corresponding image points. The final value of the shape distance is a user-defined linear
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combination of the shape context distance, an image appearance distance, and a bending energy. If
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this value is set to a number different from 0, is mandatory to set the images that correspond to
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each shape.
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@param imageAppearanceWeight The weight of the appearance cost in the final distance value.
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*/
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CV_WRAP virtual void setImageAppearanceWeight(float imageAppearanceWeight) = 0;
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CV_WRAP virtual float getImageAppearanceWeight() const = 0;
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/** @brief Set the weight of the Bending Energy in the final value of the shape distance. The bending energy
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definition depends on what transformation is being used to align the shapes. The final value of the
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shape distance is a user-defined linear combination of the shape context distance, an image
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appearance distance, and a bending energy.
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@param bendingEnergyWeight The weight of the Bending Energy in the final distance value.
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*/
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CV_WRAP virtual void setBendingEnergyWeight(float bendingEnergyWeight) = 0;
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CV_WRAP virtual float getBendingEnergyWeight() const = 0;
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/** @brief Set the images that correspond to each shape. This images are used in the calculation of the Image
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Appearance cost.
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@param image1 Image corresponding to the shape defined by contours1.
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@param image2 Image corresponding to the shape defined by contours2.
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*/
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CV_WRAP virtual void setImages(InputArray image1, InputArray image2) = 0;
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CV_WRAP virtual void getImages(OutputArray image1, OutputArray image2) const = 0;
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CV_WRAP virtual void setIterations(int iterations) = 0;
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CV_WRAP virtual int getIterations() const = 0;
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/** @brief Set the algorithm used for building the shape context descriptor cost matrix.
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@param comparer Smart pointer to a HistogramCostExtractor, an algorithm that defines the cost
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matrix between descriptors.
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*/
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CV_WRAP virtual void setCostExtractor(Ptr<HistogramCostExtractor> comparer) = 0;
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CV_WRAP virtual Ptr<HistogramCostExtractor> getCostExtractor() const = 0;
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/** @brief Set the value of the standard deviation for the Gaussian window for the image appearance cost.
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@param sigma Standard Deviation.
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*/
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CV_WRAP virtual void setStdDev(float sigma) = 0;
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CV_WRAP virtual float getStdDev() const = 0;
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/** @brief Set the algorithm used for aligning the shapes.
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@param transformer Smart pointer to a ShapeTransformer, an algorithm that defines the aligning
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transformation.
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*/
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CV_WRAP virtual void setTransformAlgorithm(Ptr<ShapeTransformer> transformer) = 0;
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CV_WRAP virtual Ptr<ShapeTransformer> getTransformAlgorithm() const = 0;
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};
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/* Complete constructor */
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CV_EXPORTS_W Ptr<ShapeContextDistanceExtractor>
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createShapeContextDistanceExtractor(int nAngularBins=12, int nRadialBins=4,
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float innerRadius=0.2f, float outerRadius=2, int iterations=3,
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const Ptr<HistogramCostExtractor> &comparer = createChiHistogramCostExtractor(),
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const Ptr<ShapeTransformer> &transformer = createThinPlateSplineShapeTransformer());
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/***********************************************************************************/
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/***********************************************************************************/
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/***********************************************************************************/
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/** @brief A simple Hausdorff distance measure between shapes defined by contours
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according to the paper "Comparing Images using the Hausdorff distance." by D.P. Huttenlocher, G.A.
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Klanderman, and W.J. Rucklidge. (PAMI 1993). :
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*/
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class CV_EXPORTS_W HausdorffDistanceExtractor : public ShapeDistanceExtractor
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{
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public:
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/** @brief Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm.
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@param distanceFlag Flag indicating which norm is used to compute the Hausdorff distance
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(NORM_L1, NORM_L2).
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*/
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CV_WRAP virtual void setDistanceFlag(int distanceFlag) = 0;
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CV_WRAP virtual int getDistanceFlag() const = 0;
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/** @brief This method sets the rank proportion (or fractional value) that establish the Kth ranked value of
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the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare
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shapes.
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@param rankProportion fractional value (between 0 and 1).
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*/
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CV_WRAP virtual void setRankProportion(float rankProportion) = 0;
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CV_WRAP virtual float getRankProportion() const = 0;
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};
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/* Constructor */
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CV_EXPORTS_W Ptr<HausdorffDistanceExtractor> createHausdorffDistanceExtractor(int distanceFlag=cv::NORM_L2, float rankProp=0.6f);
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//! @}
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} // cv
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#endif
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