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Merge pull request #13025 from alalek:move_shape_contrib

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Alexander Alekhin 2018-11-07 12:36:30 +00:00
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modules/python/test/test_shape.py
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#!/usr/bin/env python
import cv2 as cv
from tests_common import NewOpenCVTests
class shape_test(NewOpenCVTests):
def test_computeDistance(self):
a = self.get_sample('samples/data/shape_sample/1.png', cv.IMREAD_GRAYSCALE)
b = self.get_sample('samples/data/shape_sample/2.png', cv.IMREAD_GRAYSCALE)
ca, _ = cv.findContours(a, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
cb, _ = cv.findContours(b, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
hd = cv.createHausdorffDistanceExtractor()
sd = cv.createShapeContextDistanceExtractor()
d1 = hd.computeDistance(ca[0], cb[0])
d2 = sd.computeDistance(ca[0], cb[0])
self.assertAlmostEqual(d1, 26.4196891785, 3, "HausdorffDistanceExtractor")
self.assertAlmostEqual(d2, 0.25804194808, 3, "ShapeContextDistanceExtractor")
if __name__ == '__main__':
NewOpenCVTests.bootstrap()

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modules/shape/CMakeLists.txt
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set(the_description "Shape descriptors and matchers")
ocv_define_module(shape opencv_core opencv_imgproc opencv_calib3d WRAP python)

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modules/shape/include/opencv2/shape.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_SHAPE_HPP
#define OPENCV_SHAPE_HPP
#include "opencv2/shape/emdL1.hpp"
#include "opencv2/shape/shape_transformer.hpp"
#include "opencv2/shape/hist_cost.hpp"
#include "opencv2/shape/shape_distance.hpp"
/**
@defgroup shape Shape Distance and Matching
*/
#endif
/* End of file. */

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modules/shape/include/opencv2/shape/emdL1.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_EMD_L1_HPP
#define OPENCV_EMD_L1_HPP
#include "opencv2/core.hpp"
namespace cv
{
/****************************************************************************************\
* EMDL1 Function *
\****************************************************************************************/
//! @addtogroup shape
//! @{
/** @brief Computes the "minimal work" distance between two weighted point configurations base on the papers
"EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors", by Haibin
Ling and Kazunori Okuda; and "The Earth Mover's Distance is the Mallows Distance: Some Insights from
Statistics", by Elizaveta Levina and Peter Bickel.
@param signature1 First signature, a single column floating-point matrix. Each row is the value of
the histogram in each bin.
@param signature2 Second signature of the same format and size as signature1.
*/
CV_EXPORTS float EMDL1(InputArray signature1, InputArray signature2);
//! @}
}//namespace cv
#endif

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modules/shape/include/opencv2/shape/hist_cost.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_HIST_COST_HPP
#define OPENCV_HIST_COST_HPP
#include "opencv2/imgproc.hpp"
namespace cv
{
//! @addtogroup shape
//! @{
/** @brief Abstract base class for histogram cost algorithms.
*/
class CV_EXPORTS_W HistogramCostExtractor : public Algorithm
{
public:
CV_WRAP virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) = 0;
CV_WRAP virtual void setNDummies(int nDummies) = 0;
CV_WRAP virtual int getNDummies() const = 0;
CV_WRAP virtual void setDefaultCost(float defaultCost) = 0;
CV_WRAP virtual float getDefaultCost() const = 0;
};
/** @brief A norm based cost extraction. :
*/
class CV_EXPORTS_W NormHistogramCostExtractor : public HistogramCostExtractor
{
public:
CV_WRAP virtual void setNormFlag(int flag) = 0;
CV_WRAP virtual int getNormFlag() const = 0;
};
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createNormHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
/** @brief An EMD based cost extraction. :
*/
class CV_EXPORTS_W EMDHistogramCostExtractor : public HistogramCostExtractor
{
public:
CV_WRAP virtual void setNormFlag(int flag) = 0;
CV_WRAP virtual int getNormFlag() const = 0;
};
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createEMDHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
/** @brief An Chi based cost extraction. :
*/
class CV_EXPORTS_W ChiHistogramCostExtractor : public HistogramCostExtractor
{};
CV_EXPORTS_W Ptr<HistogramCostExtractor> createChiHistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
/** @brief An EMD-L1 based cost extraction. :
*/
class CV_EXPORTS_W EMDL1HistogramCostExtractor : public HistogramCostExtractor
{};
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createEMDL1HistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
//! @}
} // cv
#endif

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modules/shape/include/opencv2/shape/shape.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/shape.hpp"

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modules/shape/include/opencv2/shape/shape_distance.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_SHAPE_SHAPE_DISTANCE_HPP
#define OPENCV_SHAPE_SHAPE_DISTANCE_HPP
#include "opencv2/core.hpp"
#include "opencv2/shape/hist_cost.hpp"
#include "opencv2/shape/shape_transformer.hpp"
namespace cv
{
//! @addtogroup shape
//! @{
/** @example samples/cpp/shape_example.cpp
An example using shape distance algorithm
*/
/** @brief Abstract base class for shape distance algorithms.
*/
class CV_EXPORTS_W ShapeDistanceExtractor : public Algorithm
{
public:
/** @brief Compute the shape distance between two shapes defined by its contours.
@param contour1 Contour defining first shape.
@param contour2 Contour defining second shape.
*/
CV_WRAP virtual float computeDistance(InputArray contour1, InputArray contour2) = 0;
};
/***********************************************************************************/
/***********************************************************************************/
/***********************************************************************************/
/** @brief Implementation of the Shape Context descriptor and matching algorithm
proposed by Belongie et al. in "Shape Matching and Object Recognition Using Shape Contexts" (PAMI
2002). This implementation is packaged in a generic scheme, in order to allow you the
implementation of the common variations of the original pipeline.
*/
class CV_EXPORTS_W ShapeContextDistanceExtractor : public ShapeDistanceExtractor
{
public:
/** @brief Establish the number of angular bins for the Shape Context Descriptor used in the shape matching
pipeline.
@param nAngularBins The number of angular bins in the shape context descriptor.
*/
CV_WRAP virtual void setAngularBins(int nAngularBins) = 0;
CV_WRAP virtual int getAngularBins() const = 0;
/** @brief Establish the number of radial bins for the Shape Context Descriptor used in the shape matching
pipeline.
@param nRadialBins The number of radial bins in the shape context descriptor.
*/
CV_WRAP virtual void setRadialBins(int nRadialBins) = 0;
CV_WRAP virtual int getRadialBins() const = 0;
/** @brief Set the inner radius of the shape context descriptor.
@param innerRadius The value of the inner radius.
*/
CV_WRAP virtual void setInnerRadius(float innerRadius) = 0;
CV_WRAP virtual float getInnerRadius() const = 0;
/** @brief Set the outer radius of the shape context descriptor.
@param outerRadius The value of the outer radius.
*/
CV_WRAP virtual void setOuterRadius(float outerRadius) = 0;
CV_WRAP virtual float getOuterRadius() const = 0;
CV_WRAP virtual void setRotationInvariant(bool rotationInvariant) = 0;
CV_WRAP virtual bool getRotationInvariant() const = 0;
/** @brief Set the weight of the shape context distance in the final value of the shape distance. The shape
context distance between two shapes is defined as the symmetric sum of shape context matching costs
over best matching points. The final value of the shape distance is a user-defined linear
combination of the shape context distance, an image appearance distance, and a bending energy.
@param shapeContextWeight The weight of the shape context distance in the final distance value.
*/
CV_WRAP virtual void setShapeContextWeight(float shapeContextWeight) = 0;
CV_WRAP virtual float getShapeContextWeight() const = 0;
/** @brief Set the weight of the Image Appearance cost in the final value of the shape distance. The image
appearance cost is defined as the sum of squared brightness differences in Gaussian windows around
corresponding image points. The final value of the shape distance is a user-defined linear
combination of the shape context distance, an image appearance distance, and a bending energy. If
this value is set to a number different from 0, is mandatory to set the images that correspond to
each shape.
@param imageAppearanceWeight The weight of the appearance cost in the final distance value.
*/
CV_WRAP virtual void setImageAppearanceWeight(float imageAppearanceWeight) = 0;
CV_WRAP virtual float getImageAppearanceWeight() const = 0;
/** @brief Set the weight of the Bending Energy in the final value of the shape distance. The bending energy
definition depends on what transformation is being used to align the shapes. The final value of the
shape distance is a user-defined linear combination of the shape context distance, an image
appearance distance, and a bending energy.
@param bendingEnergyWeight The weight of the Bending Energy in the final distance value.
*/
CV_WRAP virtual void setBendingEnergyWeight(float bendingEnergyWeight) = 0;
CV_WRAP virtual float getBendingEnergyWeight() const = 0;
/** @brief Set the images that correspond to each shape. This images are used in the calculation of the Image
Appearance cost.
@param image1 Image corresponding to the shape defined by contours1.
@param image2 Image corresponding to the shape defined by contours2.
*/
CV_WRAP virtual void setImages(InputArray image1, InputArray image2) = 0;
CV_WRAP virtual void getImages(OutputArray image1, OutputArray image2) const = 0;
CV_WRAP virtual void setIterations(int iterations) = 0;
CV_WRAP virtual int getIterations() const = 0;
/** @brief Set the algorithm used for building the shape context descriptor cost matrix.
@param comparer Smart pointer to a HistogramCostExtractor, an algorithm that defines the cost
matrix between descriptors.
*/
CV_WRAP virtual void setCostExtractor(Ptr<HistogramCostExtractor> comparer) = 0;
CV_WRAP virtual Ptr<HistogramCostExtractor> getCostExtractor() const = 0;
/** @brief Set the value of the standard deviation for the Gaussian window for the image appearance cost.
@param sigma Standard Deviation.
*/
CV_WRAP virtual void setStdDev(float sigma) = 0;
CV_WRAP virtual float getStdDev() const = 0;
/** @brief Set the algorithm used for aligning the shapes.
@param transformer Smart pointer to a ShapeTransformer, an algorithm that defines the aligning
transformation.
*/
CV_WRAP virtual void setTransformAlgorithm(Ptr<ShapeTransformer> transformer) = 0;
CV_WRAP virtual Ptr<ShapeTransformer> getTransformAlgorithm() const = 0;
};
/* Complete constructor */
CV_EXPORTS_W Ptr<ShapeContextDistanceExtractor>
createShapeContextDistanceExtractor(int nAngularBins=12, int nRadialBins=4,
float innerRadius=0.2f, float outerRadius=2, int iterations=3,
const Ptr<HistogramCostExtractor> &comparer = createChiHistogramCostExtractor(),
const Ptr<ShapeTransformer> &transformer = createThinPlateSplineShapeTransformer());
/***********************************************************************************/
/***********************************************************************************/
/***********************************************************************************/
/** @brief A simple Hausdorff distance measure between shapes defined by contours
according to the paper "Comparing Images using the Hausdorff distance." by D.P. Huttenlocher, G.A.
Klanderman, and W.J. Rucklidge. (PAMI 1993). :
*/
class CV_EXPORTS_W HausdorffDistanceExtractor : public ShapeDistanceExtractor
{
public:
/** @brief Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm.
@param distanceFlag Flag indicating which norm is used to compute the Hausdorff distance
(NORM_L1, NORM_L2).
*/
CV_WRAP virtual void setDistanceFlag(int distanceFlag) = 0;
CV_WRAP virtual int getDistanceFlag() const = 0;
/** @brief This method sets the rank proportion (or fractional value) that establish the Kth ranked value of
the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare
shapes.
@param rankProportion fractional value (between 0 and 1).
*/
CV_WRAP virtual void setRankProportion(float rankProportion) = 0;
CV_WRAP virtual float getRankProportion() const = 0;
};
/* Constructor */
CV_EXPORTS_W Ptr<HausdorffDistanceExtractor> createHausdorffDistanceExtractor(int distanceFlag=cv::NORM_L2, float rankProp=0.6f);
//! @}
} // cv
#endif

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modules/shape/include/opencv2/shape/shape_transformer.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_SHAPE_SHAPE_TRANSFORM_HPP
#define OPENCV_SHAPE_SHAPE_TRANSFORM_HPP
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
namespace cv
{
//! @addtogroup shape
//! @{
/** @brief Abstract base class for shape transformation algorithms.
*/
class CV_EXPORTS_W ShapeTransformer : public Algorithm
{
public:
/** @brief Estimate the transformation parameters of the current transformer algorithm, based on point matches.
@param transformingShape Contour defining first shape.
@param targetShape Contour defining second shape (Target).
@param matches Standard vector of Matches between points.
*/
CV_WRAP virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape,
std::vector<DMatch>& matches) = 0;
/** @brief Apply a transformation, given a pre-estimated transformation parameters.
@param input Contour (set of points) to apply the transformation.
@param output Output contour.
*/
CV_WRAP virtual float applyTransformation(InputArray input, OutputArray output=noArray()) = 0;
/** @brief Apply a transformation, given a pre-estimated transformation parameters, to an Image.
@param transformingImage Input image.
@param output Output image.
@param flags Image interpolation method.
@param borderMode border style.
@param borderValue border value.
*/
CV_WRAP virtual void warpImage(InputArray transformingImage, OutputArray output,
int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT,
const Scalar& borderValue=Scalar()) const = 0;
};
/***********************************************************************************/
/***********************************************************************************/
/** @brief Definition of the transformation
occupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by
F.L. Bookstein (PAMI 1989). :
*/
class CV_EXPORTS_W ThinPlateSplineShapeTransformer : public ShapeTransformer
{
public:
/** @brief Set the regularization parameter for relaxing the exact interpolation requirements of the TPS
algorithm.
@param beta value of the regularization parameter.
*/
CV_WRAP virtual void setRegularizationParameter(double beta) = 0;
CV_WRAP virtual double getRegularizationParameter() const = 0;
};
/** Complete constructor */
CV_EXPORTS_W Ptr<ThinPlateSplineShapeTransformer>
createThinPlateSplineShapeTransformer(double regularizationParameter=0);
/***********************************************************************************/
/***********************************************************************************/
/** @brief Wrapper class for the OpenCV Affine Transformation algorithm. :
*/
class CV_EXPORTS_W AffineTransformer : public ShapeTransformer
{
public:
CV_WRAP virtual void setFullAffine(bool fullAffine) = 0;
CV_WRAP virtual bool getFullAffine() const = 0;
};
/** Complete constructor */
CV_EXPORTS_W Ptr<AffineTransformer> createAffineTransformer(bool fullAffine);
//! @}
} // cv
#endif

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
class AffineTransformerImpl : public AffineTransformer
{
public:
/* Constructors */
AffineTransformerImpl()
{
fullAffine = true;
name_ = "ShapeTransformer.AFF";
transformCost = 0;
}
AffineTransformerImpl(bool _fullAffine)
{
fullAffine = _fullAffine;
name_ = "ShapeTransformer.AFF";
transformCost = 0;
}
/* Destructor */
~AffineTransformerImpl()
{
}
//! the main operator
virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches) CV_OVERRIDE;
virtual float applyTransformation(InputArray input, OutputArray output=noArray()) CV_OVERRIDE;
virtual void warpImage(InputArray transformingImage, OutputArray output,
int flags, int borderMode, const Scalar& borderValue) const CV_OVERRIDE;
//! Setters/Getters
virtual void setFullAffine(bool _fullAffine) CV_OVERRIDE {fullAffine=_fullAffine;}
virtual bool getFullAffine() const CV_OVERRIDE {return fullAffine;}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "affine_type" << int(fullAffine);
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
fullAffine = int(fn["affine_type"])?true:false;
}
private:
bool fullAffine;
Mat affineMat;
float transformCost;
protected:
String name_;
};
void AffineTransformerImpl::warpImage(InputArray transformingImage, OutputArray output,
int flags, int borderMode, const Scalar& borderValue) const
{
CV_INSTRUMENT_REGION();
CV_Assert(!affineMat.empty());
warpAffine(transformingImage, output, affineMat, transformingImage.getMat().size(), flags, borderMode, borderValue);
}
static Mat _localAffineEstimate(const std::vector<Point2f>& shape1, const std::vector<Point2f>& shape2,
bool fullAfine)
{
Mat out(2,3,CV_32F);
int siz=2*(int)shape1.size();
if (fullAfine)
{
Mat matM(siz, 6, CV_32F);
Mat matP(siz,1,CV_32F);
int contPt=0;
for (int ii=0; ii<siz; ii++)
{
Mat therow = Mat::zeros(1,6,CV_32F);
if (ii%2==0)
{
therow.at<float>(0,0)=shape1[contPt].x;
therow.at<float>(0,1)=shape1[contPt].y;
therow.at<float>(0,2)=1;
therow.row(0).copyTo(matM.row(ii));
matP.at<float>(ii,0) = shape2[contPt].x;
}
else
{
therow.at<float>(0,3)=shape1[contPt].x;
therow.at<float>(0,4)=shape1[contPt].y;
therow.at<float>(0,5)=1;
therow.row(0).copyTo(matM.row(ii));
matP.at<float>(ii,0) = shape2[contPt].y;
contPt++;
}
}
Mat sol;
solve(matM, matP, sol, DECOMP_SVD);
out = sol.reshape(0,2);
}
else
{
Mat matM(siz, 4, CV_32F);
Mat matP(siz,1,CV_32F);
int contPt=0;
for (int ii=0; ii<siz; ii++)
{
Mat therow = Mat::zeros(1,4,CV_32F);
if (ii%2==0)
{
therow.at<float>(0,0)=shape1[contPt].x;
therow.at<float>(0,1)=shape1[contPt].y;
therow.at<float>(0,2)=1;
therow.row(0).copyTo(matM.row(ii));
matP.at<float>(ii,0) = shape2[contPt].x;
}
else
{
therow.at<float>(0,0)=shape1[contPt].y;
therow.at<float>(0,1)=-shape1[contPt].x;
therow.at<float>(0,3)=1;
therow.row(0).copyTo(matM.row(ii));
matP.at<float>(ii,0) = shape2[contPt].y;
contPt++;
}
}
Mat sol;
solve(matM, matP, sol, DECOMP_SVD);
out.at<float>(0,0)=sol.at<float>(0,0);
out.at<float>(0,1)=sol.at<float>(1,0);
out.at<float>(0,2)=sol.at<float>(2,0);
out.at<float>(1,0)=-sol.at<float>(1,0);
out.at<float>(1,1)=sol.at<float>(0,0);
out.at<float>(1,2)=sol.at<float>(3,0);
}
return out;
}
void AffineTransformerImpl::estimateTransformation(InputArray _pts1, InputArray _pts2, std::vector<DMatch>& _matches)
{
CV_INSTRUMENT_REGION();
Mat pts1 = _pts1.getMat();
Mat pts2 = _pts2.getMat();
CV_Assert((pts1.channels()==2) && (pts1.cols>0) && (pts2.channels()==2) && (pts2.cols>0));
CV_Assert(_matches.size()>1);
if (pts1.type() != CV_32F)
pts1.convertTo(pts1, CV_32F);
if (pts2.type() != CV_32F)
pts2.convertTo(pts2, CV_32F);
// Use only valid matchings //
std::vector<DMatch> matches;
for (size_t i=0; i<_matches.size(); i++)
{
if (_matches[i].queryIdx<pts1.cols &&
_matches[i].trainIdx<pts2.cols)
{
matches.push_back(_matches[i]);
}
}
// Organizing the correspondent points in vector style //
std::vector<Point2f> shape1; // transforming shape
std::vector<Point2f> shape2; // target shape
for (size_t i=0; i<matches.size(); i++)
{
Point2f pt1=pts1.at<Point2f>(0,matches[i].queryIdx);
shape1.push_back(pt1);
Point2f pt2=pts2.at<Point2f>(0,matches[i].trainIdx);
shape2.push_back(pt2);
}
Mat affine;
if (fullAffine)
{
estimateAffine2D(shape1, shape2).convertTo(affine, CV_32F);
} else
{
estimateAffinePartial2D(shape1, shape2).convertTo(affine, CV_32F);
}
if (affine.empty())
//In case there is not good solution, just give a LLS based one
affine = _localAffineEstimate(shape1, shape2, fullAffine);
affineMat = affine;
}
float AffineTransformerImpl::applyTransformation(InputArray inPts, OutputArray outPts)
{
CV_INSTRUMENT_REGION();
Mat pts1 = inPts.getMat();
CV_Assert((pts1.channels()==2) && (pts1.cols>0));
//Apply transformation in the complete set of points
Mat fAffine;
transform(pts1, fAffine, affineMat);
// Ensambling output //
if (outPts.needed())
{
outPts.create(1,fAffine.cols, CV_32FC2);
Mat outMat = outPts.getMat();
for (int i=0; i<fAffine.cols; i++)
outMat.at<Point2f>(0,i)=fAffine.at<Point2f>(0,i);
}
// Updating Transform Cost //
Mat Af(2, 2, CV_32F);
Af.at<float>(0,0)=affineMat.at<float>(0,0);
Af.at<float>(0,1)=affineMat.at<float>(1,0);
Af.at<float>(1,0)=affineMat.at<float>(0,1);
Af.at<float>(1,1)=affineMat.at<float>(1,1);
SVD mysvd(Af, SVD::NO_UV);
Mat singVals=mysvd.w;
transformCost=std::log((singVals.at<float>(0,0)+FLT_MIN)/(singVals.at<float>(1,0)+FLT_MIN));
return transformCost;
}
Ptr <AffineTransformer> createAffineTransformer(bool fullAffine)
{
return Ptr<AffineTransformer>( new AffineTransformerImpl(fullAffine) );
}
} // cv

797
modules/shape/src/emdL1.cpp
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@ -1,797 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* Implementation of an optimized EMD for histograms based in
* the papers "EMD-L1: An efficient and Robust Algorithm
* for comparing histogram-based descriptors", by Haibin Ling and
* Kazunori Okuda; and "The Earth Mover's Distance is the Mallows
* Distance: Some Insights from Statistics", by Elizaveta Levina and
* Peter Bickel, based on HAIBIN LING AND KAZUNORI OKADA implementation.
*/
#include "precomp.hpp"
#include "emdL1_def.hpp"
#include <limits>
/****************************************************************************************\
* EMDL1 Class *
\****************************************************************************************/
float EmdL1::getEMDL1(cv::Mat &sig1, cv::Mat &sig2)
{
// Initialization
CV_Assert((sig1.rows==sig2.rows) && (sig1.cols==sig2.cols) && (!sig1.empty()) && (!sig2.empty()));
if(!initBaseTrees(sig1.rows, 1))
return -1;
float *H1=new float[sig1.rows], *H2 = new float[sig2.rows];
for (int ii=0; ii<sig1.rows; ii++)
{
H1[ii]=sig1.at<float>(ii,0);
H2[ii]=sig2.at<float>(ii,0);
}
fillBaseTrees(H1,H2); // Initialize histograms
greedySolution(); // Construct an initial Basic Feasible solution
initBVTree(); // Initialize BVTree
// Iteration
bool bOptimal = false;
m_nItr = 0;
while(!bOptimal && m_nItr<nMaxIt)
{
// Derive U=(u_ij) for row i and column j
if(m_nItr==0) updateSubtree(m_pRoot);
else updateSubtree(m_pEnter->pChild);
// Optimality test
bOptimal = isOptimal();
// Find new solution
if(!bOptimal)
findNewSolution();
++m_nItr;
}
delete [] H1;
delete [] H2;
// Output the total flow
return compuTotalFlow();
}
void EmdL1::setMaxIteration(int _nMaxIt)
{
nMaxIt=_nMaxIt;
}
//-- SubFunctions called in the EMD algorithm
bool EmdL1::initBaseTrees(int n1, int n2, int n3)
{
if(binsDim1==n1 && binsDim2==n2 && binsDim3==n3)
return true;
binsDim1 = n1;
binsDim2 = n2;
binsDim3 = n3;
if(binsDim1==0 || binsDim2==0) dimension = 0;
else dimension = (binsDim3==0)?2:3;
if(dimension==2)
{
m_Nodes.resize(binsDim1);
m_EdgesUp.resize(binsDim1);
m_EdgesRight.resize(binsDim1);
for(int i1=0; i1<binsDim1; i1++)
{
m_Nodes[i1].resize(binsDim2);
m_EdgesUp[i1].resize(binsDim2);
m_EdgesRight[i1].resize(binsDim2);
}
m_NBVEdges.resize(binsDim1*binsDim2*4+2);
m_auxQueue.resize(binsDim1*binsDim2+2);
m_fromLoop.resize(binsDim1*binsDim2+2);
m_toLoop.resize(binsDim1*binsDim2+2);
}
else if(dimension==3)
{
m_3dNodes.resize(binsDim1);
m_3dEdgesUp.resize(binsDim1);
m_3dEdgesRight.resize(binsDim1);
m_3dEdgesDeep.resize(binsDim1);
for(int i1=0; i1<binsDim1; i1++)
{
m_3dNodes[i1].resize(binsDim2);
m_3dEdgesUp[i1].resize(binsDim2);
m_3dEdgesRight[i1].resize(binsDim2);
m_3dEdgesDeep[i1].resize(binsDim2);
for(int i2=0; i2<binsDim2; i2++)
{
m_3dNodes[i1][i2].resize(binsDim3);
m_3dEdgesUp[i1][i2].resize(binsDim3);
m_3dEdgesRight[i1][i2].resize(binsDim3);
m_3dEdgesDeep[i1][i2].resize(binsDim3);
}
}
m_NBVEdges.resize(binsDim1*binsDim2*binsDim3*6+4);
m_auxQueue.resize(binsDim1*binsDim2*binsDim3+4);
m_fromLoop.resize(binsDim1*binsDim2*binsDim3+4);
m_toLoop.resize(binsDim1*binsDim2*binsDim3+2);
}
else
return false;
return true;
}
bool EmdL1::fillBaseTrees(float *H1, float *H2)
{
//- Set global counters
m_pRoot = NULL;
// Graph initialization
float *p1 = H1;
float *p2 = H2;
if(dimension==2)
{
for(int c=0; c<binsDim2; c++)
{
for(int r=0; r<binsDim1; r++)
{
//- initialize nodes and links
m_Nodes[r][c].pos[0] = r;
m_Nodes[r][c].pos[1] = c;
m_Nodes[r][c].d = *(p1++)-*(p2++);
m_Nodes[r][c].pParent = NULL;
m_Nodes[r][c].pChild = NULL;
m_Nodes[r][c].iLevel = -1;
//- initialize edges
// to the right
m_EdgesRight[r][c].pParent = &(m_Nodes[r][c]);
m_EdgesRight[r][c].pChild = &(m_Nodes[r][(c+1)%binsDim2]);
m_EdgesRight[r][c].flow = 0;
m_EdgesRight[r][c].iDir = 1;
m_EdgesRight[r][c].pNxt = NULL;
// to the upward
m_EdgesUp[r][c].pParent = &(m_Nodes[r][c]);
m_EdgesUp[r][c].pChild = &(m_Nodes[(r+1)%binsDim1][c]);
m_EdgesUp[r][c].flow = 0;
m_EdgesUp[r][c].iDir = 1;
m_EdgesUp[r][c].pNxt = NULL;
}
}
}
else if(dimension==3)
{
for(int z=0; z<binsDim3; z++)
{
for(int c=0; c<binsDim2; c++)
{
for(int r=0; r<binsDim1; r++)
{
//- initialize nodes and edges
m_3dNodes[r][c][z].pos[0] = r;
m_3dNodes[r][c][z].pos[1] = c;
m_3dNodes[r][c][z].pos[2] = z;
m_3dNodes[r][c][z].d = *(p1++)-*(p2++);
m_3dNodes[r][c][z].pParent = NULL;
m_3dNodes[r][c][z].pChild = NULL;
m_3dNodes[r][c][z].iLevel = -1;
//- initialize edges
// to the upward
m_3dEdgesUp[r][c][z].pParent= &(m_3dNodes[r][c][z]);
m_3dEdgesUp[r][c][z].pChild = &(m_3dNodes[(r+1)%binsDim1][c][z]);
m_3dEdgesUp[r][c][z].flow = 0;
m_3dEdgesUp[r][c][z].iDir = 1;
m_3dEdgesUp[r][c][z].pNxt = NULL;
// to the right
m_3dEdgesRight[r][c][z].pParent = &(m_3dNodes[r][c][z]);
m_3dEdgesRight[r][c][z].pChild = &(m_3dNodes[r][(c+1)%binsDim2][z]);
m_3dEdgesRight[r][c][z].flow = 0;
m_3dEdgesRight[r][c][z].iDir = 1;
m_3dEdgesRight[r][c][z].pNxt = NULL;
// to the deep
m_3dEdgesDeep[r][c][z].pParent = &(m_3dNodes[r][c][z]);
m_3dEdgesDeep[r][c][z].pChild = &(m_3dNodes[r][c])[(z+1)%binsDim3];
m_3dEdgesDeep[r][c][z].flow = 0;
m_3dEdgesDeep[r][c][z].iDir = 1;
m_3dEdgesDeep[r][c][z].pNxt = NULL;
}
}
}
}
return true;
}
bool EmdL1::greedySolution()
{
return dimension==2?greedySolution2():greedySolution3();
}
bool EmdL1::greedySolution2()
{
//- Prepare auxiliary array, D=H1-H2
int c,r;
floatArray2D D(binsDim1);
for(r=0; r<binsDim1; r++)
{
D[r].resize(binsDim2);
for(c=0; c<binsDim2; c++) D[r][c] = m_Nodes[r][c].d;
}
// compute integrated values along each dimension
std::vector<float> d2s(binsDim2);
d2s[0] = 0;
for(c=0; c<binsDim2-1; c++)
{
d2s[c+1] = d2s[c];
for(r=0; r<binsDim1; r++) d2s[c+1]-= D[r][c];
}
std::vector<float> d1s(binsDim1);
d1s[0] = 0;
for(r=0; r<binsDim1-1; r++)
{
d1s[r+1] = d1s[r];
for(c=0; c<binsDim2; c++) d1s[r+1]-= D[r][c];
}
//- Greedy algorithm for initial solution
cvPEmdEdge pBV;
float dFlow;
bool bUpward = false;
nNBV = 0; // number of NON-BV edges
for(c=0; c<binsDim2-1; c++)
for(r=0; r<binsDim1; r++)
{
dFlow = D[r][c];
bUpward = (r<binsDim1-1) && (fabs(dFlow+d2s[c+1]) > fabs(dFlow+d1s[r+1])); // Move upward or right
// modify basic variables, record BV and related values
if(bUpward)
{
// move to up
pBV = &(m_EdgesUp[r][c]);
m_NBVEdges[nNBV++] = &(m_EdgesRight[r][c]);
D[r+1][c] += dFlow; // auxiliary matrix maintenance
d1s[r+1] += dFlow; // auxiliary matrix maintenance
}
else
{
// move to right, no other choice
pBV = &(m_EdgesRight[r][c]);
if(r<binsDim1-1)
m_NBVEdges[nNBV++] = &(m_EdgesUp[r][c]);
D[r][c+1] += dFlow; // auxiliary matrix maintenance
d2s[c+1] += dFlow; // auxiliary matrix maintenance
}
pBV->pParent->pChild = pBV;
pBV->flow = fabs(dFlow);
pBV->iDir = dFlow>0; // 1:outward, 0:inward
}
//- rightmost column, no choice but move upward
c = binsDim2-1;
for(r=0; r<binsDim1-1; r++)
{
dFlow = D[r][c];
pBV = &(m_EdgesUp[r][c]);
D[r+1][c] += dFlow; // auxiliary matrix maintenance
pBV->pParent->pChild= pBV;
pBV->flow = fabs(dFlow);
pBV->iDir = dFlow>0; // 1:outward, 0:inward
}
return true;
}
bool EmdL1::greedySolution3()
{
//- Prepare auxiliary array, D=H1-H2
int i1,i2,i3;
std::vector<floatArray2D> D(binsDim1);
for(i1=0; i1<binsDim1; i1++)
{
D[i1].resize(binsDim2);
for(i2=0; i2<binsDim2; i2++)
{
D[i1][i2].resize(binsDim3);
for(i3=0; i3<binsDim3; i3++)
D[i1][i2][i3] = m_3dNodes[i1][i2][i3].d;
}
}
// compute integrated values along each dimension
std::vector<float> d1s(binsDim1);
d1s[0] = 0;
for(i1=0; i1<binsDim1-1; i1++)
{
d1s[i1+1] = d1s[i1];
for(i2=0; i2<binsDim2; i2++)
{
for(i3=0; i3<binsDim3; i3++)
d1s[i1+1] -= D[i1][i2][i3];
}
}
std::vector<float> d2s(binsDim2);
d2s[0] = 0;
for(i2=0; i2<binsDim2-1; i2++)
{
d2s[i2+1] = d2s[i2];
for(i1=0; i1<binsDim1; i1++)
{
for(i3=0; i3<binsDim3; i3++)
d2s[i2+1] -= D[i1][i2][i3];
}
}
std::vector<float> d3s(binsDim3);
d3s[0] = 0;
for(i3=0; i3<binsDim3-1; i3++)
{
d3s[i3+1] = d3s[i3];
for(i1=0; i1<binsDim1; i1++)
{
for(i2=0; i2<binsDim2; i2++)
d3s[i3+1] -= D[i1][i2][i3];
}
}
//- Greedy algorithm for initial solution
cvPEmdEdge pBV;
float dFlow, f1,f2,f3;
nNBV = 0; // number of NON-BV edges
for(i3=0; i3<binsDim3; i3++)
{
for(i2=0; i2<binsDim2; i2++)
{
for(i1=0; i1<binsDim1; i1++)
{
if(i3==binsDim3-1 && i2==binsDim2-1 && i1==binsDim1-1) break;
//- determine which direction to move, either right or upward
dFlow = D[i1][i2][i3];
f1 = (i1<(binsDim1-1))?fabs(dFlow+d1s[i1+1]):std::numeric_limits<float>::max();
f2 = (i2<(binsDim2-1))?fabs(dFlow+d2s[i2+1]):std::numeric_limits<float>::max();
f3 = (i3<(binsDim3-1))?fabs(dFlow+d3s[i3+1]):std::numeric_limits<float>::max();
if(f1<f2 && f1<f3)
{
pBV = &(m_3dEdgesUp[i1][i2][i3]); // up
if(i2<binsDim2-1) m_NBVEdges[nNBV++] = &(m_3dEdgesRight[i1][i2][i3]); // right
if(i3<binsDim3-1) m_NBVEdges[nNBV++] = &(m_3dEdgesDeep[i1][i2][i3]); // deep
D[i1+1][i2][i3] += dFlow; // maintain auxiliary matrix
d1s[i1+1] += dFlow;
}
else if(f2<f3)
{
pBV = &(m_3dEdgesRight[i1][i2][i3]); // right
if(i1<binsDim1-1) m_NBVEdges[nNBV++] = &(m_3dEdgesUp[i1][i2][i3]); // up
if(i3<binsDim3-1) m_NBVEdges[nNBV++] = &(m_3dEdgesDeep[i1][i2][i3]); // deep
D[i1][i2+1][i3] += dFlow; // maintain auxiliary matrix
d2s[i2+1] += dFlow;
}
else
{
pBV = &(m_3dEdgesDeep[i1][i2][i3]); // deep
if(i2<binsDim2-1) m_NBVEdges[nNBV++] = &(m_3dEdgesRight[i1][i2][i3]); // right
if(i1<binsDim1-1) m_NBVEdges[nNBV++] = &(m_3dEdgesUp[i1][i2][i3]); // up
D[i1][i2][i3+1] += dFlow; // maintain auxiliary matrix
d3s[i3+1] += dFlow;
}
pBV->flow = fabs(dFlow);
pBV->iDir = dFlow>0; // 1:outward, 0:inward
pBV->pParent->pChild= pBV;
}
}
}
return true;
}
void EmdL1::initBVTree()
{
// initialize BVTree from the initial BF solution
//- Using the center of the graph as the root
int r = (int)(0.5*binsDim1-.5);
int c = (int)(0.5*binsDim2-.5);
int z = (int)(0.5*binsDim3-.5);
m_pRoot = dimension==2 ? &(m_Nodes[r][c]) : &(m_3dNodes[r][c][z]);
m_pRoot->u = 0;
m_pRoot->iLevel = 0;
m_pRoot->pParent= NULL;
m_pRoot->pPEdge = NULL;
//- Prepare a queue
m_auxQueue[0] = m_pRoot;
int nQueue = 1; // length of queue
int iQHead = 0; // head of queue
//- Recursively build subtrees
cvPEmdEdge pCurE=NULL, pNxtE=NULL;
cvPEmdNode pCurN=NULL, pNxtN=NULL;
int nBin = binsDim1*binsDim2*std::max(binsDim3,1);
while(iQHead<nQueue && nQueue<nBin)
{
pCurN = m_auxQueue[iQHead++]; // pop out from queue
r = pCurN->pos[0];
c = pCurN->pos[1];
z = pCurN->pos[2];
// check connection from itself
pCurE = pCurN->pChild; // the initial child from initial solution
if(pCurE)
{
pNxtN = pCurE->pChild;
pNxtN->pParent = pCurN;
pNxtN->pPEdge = pCurE;
m_auxQueue[nQueue++] = pNxtN;
}
// check four neighbor nodes
int nNB = dimension==2?4:6;
for(int k=0;k<nNB;k++)
{
if(dimension==2)
{
if(k==0 && c>0) pNxtN = &(m_Nodes[r][c-1]); // left
else if(k==1 && r>0) pNxtN = &(m_Nodes[r-1][c]); // down
else if(k==2 && c<binsDim2-1) pNxtN = &(m_Nodes[r][c+1]); // right
else if(k==3 && r<binsDim1-1) pNxtN = &(m_Nodes[r+1][c]); // up
else continue;
}
else if(dimension==3)
{
if(k==0 && c>0) pNxtN = &(m_3dNodes[r][c-1][z]); // left
else if(k==1 && c<binsDim2-1) pNxtN = &(m_3dNodes[r][c+1][z]); // right
else if(k==2 && r>0) pNxtN = &(m_3dNodes[r-1][c][z]); // down
else if(k==3 && r<binsDim1-1) pNxtN = &(m_3dNodes[r+1][c][z]); // up
else if(k==4 && z>0) pNxtN = &(m_3dNodes[r][c][z-1]); // shallow
else if(k==5 && z<binsDim3-1) pNxtN = &(m_3dNodes[r][c][z+1]); // deep
else continue;
}
if(pNxtN != pCurN->pParent)
{
CV_Assert(pNxtN != NULL);
pNxtE = pNxtN->pChild;
if(pNxtE && pNxtE->pChild==pCurN) // has connection
{
pNxtN->pParent = pCurN;
pNxtN->pPEdge = pNxtE;
pNxtN->pChild = NULL;
m_auxQueue[nQueue++] = pNxtN;
pNxtE->pParent = pCurN; // reverse direction
pNxtE->pChild = pNxtN;
pNxtE->iDir = !pNxtE->iDir;
if(pCurE) pCurE->pNxt = pNxtE; // add to edge list
else pCurN->pChild = pNxtE;
pCurE = pNxtE;
}
}
}
}
}
void EmdL1::updateSubtree(cvPEmdNode pRoot)
{
// Initialize auxiliary queue
m_auxQueue[0] = pRoot;
int nQueue = 1; // queue length
int iQHead = 0; // head of queue
// BFS browing
cvPEmdNode pCurN=NULL,pNxtN=NULL;
cvPEmdEdge pCurE=NULL;
while(iQHead<nQueue)
{
pCurN = m_auxQueue[iQHead++]; // pop out from queue
pCurE = pCurN->pChild;
// browsing all children
while(pCurE)
{
pNxtN = pCurE->pChild;
pNxtN->iLevel = pCurN->iLevel+1;
pNxtN->u = pCurE->iDir ? (pCurN->u - 1) : (pCurN->u + 1);
pCurE = pCurE->pNxt;
m_auxQueue[nQueue++] = pNxtN;
}
}
}
bool EmdL1::isOptimal()
{
int iC, iMinC = 0;
cvPEmdEdge pE;
m_pEnter = NULL;
m_iEnter = -1;
// test each NON-BV edges
for(int k=0; k<nNBV; ++k)
{
pE = m_NBVEdges[k];
iC = 1 - pE->pParent->u + pE->pChild->u;
if(iC<iMinC)
{
iMinC = iC;
m_iEnter= k;
}
else
{
// Try reversing the direction
iC = 1 + pE->pParent->u - pE->pChild->u;
if(iC<iMinC)
{
iMinC = iC;
m_iEnter= k;
}
}
}
if(m_iEnter>=0)
{
m_pEnter = m_NBVEdges[m_iEnter];
if(iMinC == (1 - m_pEnter->pChild->u + m_pEnter->pParent->u)) {
// reverse direction
cvPEmdNode pN = m_pEnter->pParent;
m_pEnter->pParent = m_pEnter->pChild;
m_pEnter->pChild = pN;
}
m_pEnter->iDir = 1;
}
return m_iEnter==-1;
}
void EmdL1::findNewSolution()
{
// Find loop formed by adding the Enter BV edge.
findLoopFromEnterBV();
// Modify flow values along the loop
cvPEmdEdge pE = NULL;
CV_Assert(m_pLeave != NULL);
float minFlow = m_pLeave->flow;
int k;
for(k=0; k<m_iFrom; k++)
{
pE = m_fromLoop[k];
if(pE->iDir) pE->flow += minFlow; // outward
else pE->flow -= minFlow; // inward
}
for(k=0; k<m_iTo; k++)
{
pE = m_toLoop[k];
if(pE->iDir) pE->flow -= minFlow; // outward
else pE->flow += minFlow; // inward
}
// Update BV Tree, removing the Leaving-BV edge
cvPEmdNode pLParentN = m_pLeave->pParent;
cvPEmdNode pLChildN = m_pLeave->pChild;
cvPEmdEdge pPreE = pLParentN->pChild;
if(pPreE==m_pLeave)
{
pLParentN->pChild = m_pLeave->pNxt; // Leaving-BV is the first child
}
else
{
while(pPreE->pNxt != m_pLeave)
pPreE = pPreE->pNxt;
pPreE->pNxt = m_pLeave->pNxt; // remove Leaving-BV from child list
}
pLChildN->pParent = NULL;
pLChildN->pPEdge = NULL;
m_NBVEdges[m_iEnter]= m_pLeave; // put the leaving-BV into the NBV array
// Add the Enter BV edge
cvPEmdNode pEParentN = m_pEnter->pParent;
cvPEmdNode pEChildN = m_pEnter->pChild;
m_pEnter->flow = minFlow;
m_pEnter->pNxt = pEParentN->pChild; // insert the Enter BV as the first child
pEParentN->pChild = m_pEnter; // of its parent
// Recursively update the tree start from pEChildN
cvPEmdNode pPreN = pEParentN;
cvPEmdNode pCurN = pEChildN;
cvPEmdNode pNxtN;
cvPEmdEdge pNxtE, pPreE0;
pPreE = m_pEnter;
while(pCurN)
{
pNxtN = pCurN->pParent;
pNxtE = pCurN->pPEdge;
pCurN->pParent = pPreN;
pCurN->pPEdge = pPreE;
if(pNxtN)
{
// remove the edge from pNxtN's child list
if(pNxtN->pChild==pNxtE)
{
pNxtN->pChild = pNxtE->pNxt; // first child
}
else
{
pPreE0 = pNxtN->pChild;
while(pPreE0->pNxt != pNxtE)
pPreE0 = pPreE0->pNxt;
pPreE0->pNxt = pNxtE->pNxt; // remove Leaving-BV from child list
}
// reverse the parent-child direction
pNxtE->pParent = pCurN;
pNxtE->pChild = pNxtN;
pNxtE->iDir = !pNxtE->iDir;
pNxtE->pNxt = pCurN->pChild;
pCurN->pChild = pNxtE;
pPreE = pNxtE;
pPreN = pCurN;
}
pCurN = pNxtN;
}
// Update U at the child of the Enter BV
pEChildN->u = m_pEnter->iDir?(pEParentN->u-1):(pEParentN->u + 1);
pEChildN->iLevel = pEParentN->iLevel+1;
}
void EmdL1::findLoopFromEnterBV()
{
// Initialize Leaving-BV edge
float minFlow = std::numeric_limits<float>::max();
cvPEmdEdge pE = NULL;
int iLFlag = 0; // 0: in the FROM list, 1: in the TO list
// Using two loop list to store the loop nodes
cvPEmdNode pFrom = m_pEnter->pParent;
cvPEmdNode pTo = m_pEnter->pChild;
m_iFrom = 0;
m_iTo = 0;
m_pLeave = NULL;
// Trace back to make pFrom and pTo at the same level
while(pFrom->iLevel > pTo->iLevel)
{
pE = pFrom->pPEdge;
m_fromLoop[m_iFrom++] = pE;
if(!pE->iDir && pE->flow<minFlow)
{
minFlow = pE->flow;
m_pLeave = pE;
iLFlag = 0; // 0: in the FROM list
}
pFrom = pFrom->pParent;
}
while(pTo->iLevel > pFrom->iLevel)
{
pE = pTo->pPEdge;
m_toLoop[m_iTo++] = pE;
if(pE->iDir && pE->flow<minFlow)
{
minFlow = pE->flow;
m_pLeave = pE;
iLFlag = 1; // 1: in the TO list
}
pTo = pTo->pParent;
}
// Trace pTo and pFrom simultaneously till find their common ancester
while(pTo!=pFrom)
{
pE = pFrom->pPEdge;
m_fromLoop[m_iFrom++] = pE;
if(!pE->iDir && pE->flow<minFlow)
{
minFlow = pE->flow;
m_pLeave = pE;
iLFlag = 0; // 0: in the FROM list, 1: in the TO list
}
pFrom = pFrom->pParent;
pE = pTo->pPEdge;
m_toLoop[m_iTo++] = pE;
if(pE->iDir && pE->flow<minFlow)
{
minFlow = pE->flow;
m_pLeave = pE;
iLFlag = 1; // 0: in the FROM list, 1: in the TO list
}
pTo = pTo->pParent;
}
// Reverse the direction of the Enter BV edge if necessary
if(iLFlag==0)
{
cvPEmdNode pN = m_pEnter->pParent;
m_pEnter->pParent = m_pEnter->pChild;
m_pEnter->pChild = pN;
m_pEnter->iDir = !m_pEnter->iDir;
}
}
float EmdL1::compuTotalFlow()
{
// Computing the total flow as the final distance
float f = 0;
// Initialize auxiliary queue
m_auxQueue[0] = m_pRoot;
int nQueue = 1; // length of queue
int iQHead = 0; // head of queue
// BFS browing the tree
cvPEmdNode pCurN=NULL,pNxtN=NULL;
cvPEmdEdge pCurE=NULL;
while(iQHead<nQueue)
{
pCurN = m_auxQueue[iQHead++]; // pop out from queue
pCurE = pCurN->pChild;
// browsing all children
while(pCurE)
{
f += pCurE->flow;
pNxtN = pCurE->pChild;
pCurE = pCurE->pNxt;
m_auxQueue[nQueue++] = pNxtN;
}
}
return f;
}
/****************************************************************************************\
* EMDL1 Function *
\****************************************************************************************/
float cv::EMDL1(InputArray _signature1, InputArray _signature2)
{
CV_INSTRUMENT_REGION();
Mat signature1 = _signature1.getMat(), signature2 = _signature2.getMat();
EmdL1 emdl1;
return emdl1.getEMDL1(signature1, signature2);
}

148
modules/shape/src/emdL1_def.hpp
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@ -1,148 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <stdlib.h>
#include <math.h>
#include <vector>
/****************************************************************************************\
* For EMDL1 Framework *
\****************************************************************************************/
typedef struct cvEMDEdge* cvPEmdEdge;
typedef struct cvEMDNode* cvPEmdNode;
struct cvEMDNode
{
int pos[3]; // grid position
float d; // initial value
int u;
// tree maintenance
int iLevel; // level in the tree, 0 means root
cvPEmdNode pParent; // pointer to its parent
cvPEmdEdge pChild;
cvPEmdEdge pPEdge; // point to the edge coming out from its parent
};
struct cvEMDEdge
{
float flow; // initial value
int iDir; // 1:outward, 0:inward
// tree maintenance
cvPEmdNode pParent; // point to its parent
cvPEmdNode pChild; // the child node
cvPEmdEdge pNxt; // next child/edge
};
typedef std::vector<cvEMDNode> cvEMDNodeArray;
typedef std::vector<cvEMDEdge> cvEMDEdgeArray;
typedef std::vector<cvEMDNodeArray> cvEMDNodeArray2D;
typedef std::vector<cvEMDEdgeArray> cvEMDEdgeArray2D;
typedef std::vector<float> floatArray;
typedef std::vector<floatArray> floatArray2D;
/****************************************************************************************\
* EMDL1 Class *
\****************************************************************************************/
class EmdL1
{
public:
EmdL1()
{
m_pRoot = NULL;
binsDim1 = 0;
binsDim2 = 0;
binsDim3 = 0;
dimension = 0;
nMaxIt = 500;
m_pLeave = 0;
m_iEnter = 0;
nNBV = 0;
m_nItr = 0;
m_iTo = 0;
m_iFrom = 0;
m_pEnter = 0;
}
~EmdL1()
{
}
float getEMDL1(cv::Mat &sig1, cv::Mat &sig2);
void setMaxIteration(int _nMaxIt);
private:
//-- SubFunctions called in the EMD algorithm
bool initBaseTrees(int n1=0, int n2=0, int n3=0);
bool fillBaseTrees(float *H1, float *H2);
bool greedySolution();
bool greedySolution2();
bool greedySolution3();
void initBVTree();
void updateSubtree(cvPEmdNode pRoot);
bool isOptimal();
void findNewSolution();
void findLoopFromEnterBV();
float compuTotalFlow();
private:
int dimension;
int binsDim1, binsDim2, binsDim3; // the histogram contains m_n1 rows and m_n2 columns
int nNBV; // number of Non-Basic Variables (NBV)
int nMaxIt;
cvEMDNodeArray2D m_Nodes; // all nodes
cvEMDEdgeArray2D m_EdgesRight; // all edges to right
cvEMDEdgeArray2D m_EdgesUp; // all edges to upward
std::vector<cvEMDNodeArray2D> m_3dNodes; // all nodes for 3D
std::vector<cvEMDEdgeArray2D> m_3dEdgesRight; // all edges to right, 3D
std::vector<cvEMDEdgeArray2D> m_3dEdgesUp; // all edges to upward, 3D
std::vector<cvEMDEdgeArray2D> m_3dEdgesDeep; // all edges to deep, 3D
std::vector<cvPEmdEdge> m_NBVEdges; // pointers to all NON-BV edges
std::vector<cvPEmdNode> m_auxQueue; // auxiliary node queue
cvPEmdNode m_pRoot; // root of the BV Tree
cvPEmdEdge m_pEnter; // Enter BV edge
int m_iEnter; // Enter BV edge, index in m_NBVEdges
cvPEmdEdge m_pLeave; // Leave BV edge
int m_nItr; // number of iteration
// auxiliary variables for searching a new loop
std::vector<cvPEmdEdge> m_fromLoop;
std::vector<cvPEmdEdge> m_toLoop;
int m_iFrom;
int m_iTo;
};

157
modules/shape/src/haus_dis.cpp
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@ -1,157 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
class HausdorffDistanceExtractorImpl CV_FINAL : public HausdorffDistanceExtractor
{
public:
/* Constructor */
HausdorffDistanceExtractorImpl(int _distanceFlag = NORM_L1, float _rankProportion=0.6)
{
distanceFlag = _distanceFlag;
rankProportion = _rankProportion;
name_ = "ShapeDistanceExtractor.HAU";
}
/* Destructor */
~HausdorffDistanceExtractorImpl()
{
}
//! the main operator
virtual float computeDistance(InputArray contour1, InputArray contour2) CV_OVERRIDE;
//! Setters/Getters
virtual void setDistanceFlag(int _distanceFlag) CV_OVERRIDE {distanceFlag=_distanceFlag;}
virtual int getDistanceFlag() const CV_OVERRIDE {return distanceFlag;}
virtual void setRankProportion(float _rankProportion) CV_OVERRIDE
{
CV_Assert((_rankProportion>0) && (_rankProportion<=1));
rankProportion=_rankProportion;
}
virtual float getRankProportion() const CV_OVERRIDE {return rankProportion;}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "distance" << distanceFlag
<< "rank" << rankProportion;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
distanceFlag = (int)fn["distance"];
rankProportion = (float)fn["rank"];
}
private:
int distanceFlag;
float rankProportion;
protected:
String name_;
};
//! Hausdorff distance for a pair of set of points
static float _apply(const Mat &set1, const Mat &set2, int distType, double propRank)
{
// Building distance matrix //
Mat disMat(set1.cols, set2.cols, CV_32F);
int K = int(propRank*(disMat.rows-1));
for (int r=0; r<disMat.rows; r++)
{
for (int c=0; c<disMat.cols; c++)
{
Point2f diff = set1.at<Point2f>(0,r)-set2.at<Point2f>(0,c);
disMat.at<float>(r,c) = (float)norm(Mat(diff), distType);
}
}
Mat shortest(disMat.rows,1,CV_32F);
for (int ii=0; ii<disMat.rows; ii++)
{
Mat therow = disMat.row(ii);
double mindis;
minMaxIdx(therow, &mindis);
shortest.at<float>(ii,0) = float(mindis);
}
Mat sorted;
cv::sort(shortest, sorted, SORT_EVERY_ROW | SORT_DESCENDING);
return sorted.at<float>(K,0);
}
float HausdorffDistanceExtractorImpl::computeDistance(InputArray contour1, InputArray contour2)
{
CV_INSTRUMENT_REGION();
Mat set1=contour1.getMat(), set2=contour2.getMat();
if (set1.type() != CV_32F)
set1.convertTo(set1, CV_32F);
if (set2.type() != CV_32F)
set2.convertTo(set2, CV_32F);
CV_Assert((set1.channels()==2) && (set1.cols>0));
CV_Assert((set2.channels()==2) && (set2.cols>0));
// Force vectors column-based
if (set1.dims > 1)
set1 = set1.reshape(2, 1);
if (set2.dims > 1)
set2 = set2.reshape(2, 1);
return std::max( _apply(set1, set2, distanceFlag, rankProportion),
_apply(set2, set1, distanceFlag, rankProportion) );
}
Ptr <HausdorffDistanceExtractor> createHausdorffDistanceExtractor(int distanceFlag, float rankProp)
{
return Ptr<HausdorffDistanceExtractor>(new HausdorffDistanceExtractorImpl(distanceFlag, rankProp));
}
} // cv

549
modules/shape/src/hist_cost.cpp
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@ -1,549 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
/*! */
class NormHistogramCostExtractorImpl CV_FINAL : public NormHistogramCostExtractor
{
public:
/* Constructors */
NormHistogramCostExtractorImpl(int _flag, int _nDummies, float _defaultCost)
{
flag=_flag;
nDummies=_nDummies;
defaultCost=_defaultCost;
name_ = "HistogramCostExtractor.NOR";
}
/* Destructor */
~NormHistogramCostExtractorImpl() CV_OVERRIDE
{
}
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) CV_OVERRIDE;
//! Setters/Getters
void setNDummies(int _nDummies) CV_OVERRIDE
{
nDummies=_nDummies;
}
int getNDummies() const CV_OVERRIDE
{
return nDummies;
}
void setDefaultCost(float _defaultCost) CV_OVERRIDE
{
defaultCost=_defaultCost;
}
float getDefaultCost() const CV_OVERRIDE
{
return defaultCost;
}
virtual void setNormFlag(int _flag) CV_OVERRIDE
{
flag=_flag;
}
virtual int getNormFlag() const CV_OVERRIDE
{
return flag;
}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "flag" << flag
<< "dummies" << nDummies
<< "default" << defaultCost;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
flag = (int)fn["flag"];
nDummies = (int)fn["dummies"];
defaultCost = (float)fn["default"];
}
private:
int flag;
int nDummies;
float defaultCost;
protected:
String name_;
};
void NormHistogramCostExtractorImpl::buildCostMatrix(InputArray _descriptors1, InputArray _descriptors2, OutputArray _costMatrix)
{
CV_INSTRUMENT_REGION();
// size of the costMatrix with dummies //
Mat descriptors1=_descriptors1.getMat();
Mat descriptors2=_descriptors2.getMat();
int costrows = std::max(descriptors1.rows, descriptors2.rows)+nDummies;
_costMatrix.create(costrows, costrows, CV_32F);
Mat costMatrix=_costMatrix.getMat();
// Obtain copies of the descriptors //
cv::Mat scd1 = descriptors1.clone();
cv::Mat scd2 = descriptors2.clone();
// row normalization //
for(int i=0; i<scd1.rows; i++)
{
scd1.row(i)/=(sum(scd1.row(i))[0]+FLT_EPSILON);
}
for(int i=0; i<scd2.rows; i++)
{
scd2.row(i)/=(sum(scd2.row(i))[0]+FLT_EPSILON);
}
// Compute the Cost Matrix //
for(int i=0; i<costrows; i++)
{
for(int j=0; j<costrows; j++)
{
if (i<scd1.rows && j<scd2.rows)
{
Mat columnDiff = scd1.row(i)-scd2.row(j);
costMatrix.at<float>(i,j)=(float)norm(columnDiff, flag);
}
else
{
costMatrix.at<float>(i,j)=defaultCost;
}
}
}
}
Ptr <HistogramCostExtractor> createNormHistogramCostExtractor(int flag, int nDummies, float defaultCost)
{
return Ptr <HistogramCostExtractor>( new NormHistogramCostExtractorImpl(flag, nDummies, defaultCost) );
}
/*! */
class EMDHistogramCostExtractorImpl CV_FINAL : public EMDHistogramCostExtractor
{
public:
/* Constructors */
EMDHistogramCostExtractorImpl(int _flag, int _nDummies, float _defaultCost)
{
flag=_flag;
nDummies=_nDummies;
defaultCost=_defaultCost;
name_ = "HistogramCostExtractor.EMD";
}
/* Destructor */
~EMDHistogramCostExtractorImpl() CV_OVERRIDE
{
}
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) CV_OVERRIDE;
//! Setters/Getters
void setNDummies(int _nDummies) CV_OVERRIDE
{
nDummies=_nDummies;
}
int getNDummies() const CV_OVERRIDE
{
return nDummies;
}
void setDefaultCost(float _defaultCost) CV_OVERRIDE
{
defaultCost=_defaultCost;
}
float getDefaultCost() const CV_OVERRIDE
{
return defaultCost;
}
virtual void setNormFlag(int _flag) CV_OVERRIDE
{
flag=_flag;
}
virtual int getNormFlag() const CV_OVERRIDE
{
return flag;
}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "flag" << flag
<< "dummies" << nDummies
<< "default" << defaultCost;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
flag = (int)fn["flag"];
nDummies = (int)fn["dummies"];
defaultCost = (float)fn["default"];
}
private:
int flag;
int nDummies;
float defaultCost;
protected:
String name_;
};
void EMDHistogramCostExtractorImpl::buildCostMatrix(InputArray _descriptors1, InputArray _descriptors2, OutputArray _costMatrix)
{
CV_INSTRUMENT_REGION();
// size of the costMatrix with dummies //
Mat descriptors1=_descriptors1.getMat();
Mat descriptors2=_descriptors2.getMat();
int costrows = std::max(descriptors1.rows, descriptors2.rows)+nDummies;
_costMatrix.create(costrows, costrows, CV_32F);
Mat costMatrix=_costMatrix.getMat();
// Obtain copies of the descriptors //
cv::Mat scd1=descriptors1.clone();
cv::Mat scd2=descriptors2.clone();
// row normalization //
for(int i=0; i<scd1.rows; i++)
{
cv::Mat row = scd1.row(i);
scd1.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
for(int i=0; i<scd2.rows; i++)
{
cv::Mat row = scd2.row(i);
scd2.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
// Compute the Cost Matrix //
for(int i=0; i<costrows; i++)
{
for(int j=0; j<costrows; j++)
{
if (i<scd1.rows && j<scd2.rows)
{
cv::Mat sig1(scd1.cols,2,CV_32F), sig2(scd2.cols,2,CV_32F);
sig1.col(0)=scd1.row(i).t();
sig2.col(0)=scd2.row(j).t();
for (int k=0; k<sig1.rows; k++)
{
sig1.at<float>(k,1)=float(k);
}
for (int k=0; k<sig2.rows; k++)
{
sig2.at<float>(k,1)=float(k);
}
costMatrix.at<float>(i,j) = cv::EMD(sig1, sig2, flag);
}
else
{
costMatrix.at<float>(i,j) = defaultCost;
}
}
}
}
Ptr <HistogramCostExtractor> createEMDHistogramCostExtractor(int flag, int nDummies, float defaultCost)
{
return Ptr <HistogramCostExtractor>( new EMDHistogramCostExtractorImpl(flag, nDummies, defaultCost) );
}
/*! */
class ChiHistogramCostExtractorImpl CV_FINAL : public ChiHistogramCostExtractor
{
public:
/* Constructors */
ChiHistogramCostExtractorImpl(int _nDummies, float _defaultCost)
{
name_ = "HistogramCostExtractor.CHI";
nDummies=_nDummies;
defaultCost=_defaultCost;
}
/* Destructor */
~ChiHistogramCostExtractorImpl() CV_OVERRIDE
{
}
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) CV_OVERRIDE;
//! setters / getters
void setNDummies(int _nDummies) CV_OVERRIDE
{
nDummies=_nDummies;
}
int getNDummies() const CV_OVERRIDE
{
return nDummies;
}
void setDefaultCost(float _defaultCost) CV_OVERRIDE
{
defaultCost=_defaultCost;
}
float getDefaultCost() const CV_OVERRIDE
{
return defaultCost;
}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "dummies" << nDummies
<< "default" << defaultCost;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
nDummies = (int)fn["dummies"];
defaultCost = (float)fn["default"];
}
protected:
String name_;
int nDummies;
float defaultCost;
};
void ChiHistogramCostExtractorImpl::buildCostMatrix(InputArray _descriptors1, InputArray _descriptors2, OutputArray _costMatrix)
{
CV_INSTRUMENT_REGION();
// size of the costMatrix with dummies //
Mat descriptors1=_descriptors1.getMat();
Mat descriptors2=_descriptors2.getMat();
int costrows = std::max(descriptors1.rows, descriptors2.rows)+nDummies;
_costMatrix.create(costrows, costrows, CV_32FC1);
Mat costMatrix=_costMatrix.getMat();
// Obtain copies of the descriptors //
cv::Mat scd1=descriptors1.clone();
cv::Mat scd2=descriptors2.clone();
// row normalization //
for(int i=0; i<scd1.rows; i++)
{
cv::Mat row = scd1.row(i);
scd1.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
for(int i=0; i<scd2.rows; i++)
{
cv::Mat row = scd2.row(i);
scd2.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
// Compute the Cost Matrix //
for(int i=0; i<costrows; i++)
{
for(int j=0; j<costrows; j++)
{
if (i<scd1.rows && j<scd2.rows)
{
float csum = 0;
for(int k=0; k<scd2.cols; k++)
{
float resta=scd1.at<float>(i,k)-scd2.at<float>(j,k);
float suma=scd1.at<float>(i,k)+scd2.at<float>(j,k);
csum += resta*resta/(FLT_EPSILON+suma);
}
costMatrix.at<float>(i,j)=csum/2;
}
else
{
costMatrix.at<float>(i,j)=defaultCost;
}
}
}
}
Ptr <HistogramCostExtractor> createChiHistogramCostExtractor(int nDummies, float defaultCost)
{
return Ptr <HistogramCostExtractor>( new ChiHistogramCostExtractorImpl(nDummies, defaultCost) );
}
/*! */
class EMDL1HistogramCostExtractorImpl CV_FINAL : public EMDL1HistogramCostExtractor
{
public:
/* Constructors */
EMDL1HistogramCostExtractorImpl(int _nDummies, float _defaultCost)
{
name_ = "HistogramCostExtractor.CHI";
nDummies=_nDummies;
defaultCost=_defaultCost;
}
/* Destructor */
~EMDL1HistogramCostExtractorImpl() CV_OVERRIDE
{
}
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) CV_OVERRIDE;
//! setters / getters
void setNDummies(int _nDummies) CV_OVERRIDE
{
nDummies=_nDummies;
}
int getNDummies() const CV_OVERRIDE
{
return nDummies;
}
void setDefaultCost(float _defaultCost) CV_OVERRIDE
{
defaultCost=_defaultCost;
}
float getDefaultCost() const CV_OVERRIDE
{
return defaultCost;
}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "dummies" << nDummies
<< "default" << defaultCost;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
nDummies = (int)fn["dummies"];
defaultCost = (float)fn["default"];
}
protected:
String name_;
int nDummies;
float defaultCost;
};
void EMDL1HistogramCostExtractorImpl::buildCostMatrix(InputArray _descriptors1, InputArray _descriptors2, OutputArray _costMatrix)
{
CV_INSTRUMENT_REGION();
// size of the costMatrix with dummies //
Mat descriptors1=_descriptors1.getMat();
Mat descriptors2=_descriptors2.getMat();
int costrows = std::max(descriptors1.rows, descriptors2.rows)+nDummies;
_costMatrix.create(costrows, costrows, CV_32F);
Mat costMatrix=_costMatrix.getMat();
// Obtain copies of the descriptors //
cv::Mat scd1=descriptors1.clone();
cv::Mat scd2=descriptors2.clone();
// row normalization //
for(int i=0; i<scd1.rows; i++)
{
cv::Mat row = scd1.row(i);
scd1.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
for(int i=0; i<scd2.rows; i++)
{
cv::Mat row = scd2.row(i);
scd2.row(i)/=(sum(row)[0]+FLT_EPSILON);
}
// Compute the Cost Matrix //
for(int i=0; i<costrows; i++)
{
for(int j=0; j<costrows; j++)
{
if (i<scd1.rows && j<scd2.rows)
{
cv::Mat sig1(scd1.cols,1,CV_32F), sig2(scd2.cols,1,CV_32F);
sig1.col(0)=scd1.row(i).t();
sig2.col(0)=scd2.row(j).t();
costMatrix.at<float>(i,j) = cv::EMDL1(sig1, sig2);
}
else
{
costMatrix.at<float>(i,j) = defaultCost;
}
}
}
}
Ptr <HistogramCostExtractor> createEMDL1HistogramCostExtractor(int nDummies, float defaultCost)
{
return Ptr <HistogramCostExtractor>( new EMDL1HistogramCostExtractorImpl(nDummies, defaultCost) );
}
} // cv

59
modules/shape/src/precomp.hpp
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@ -1,59 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include <vector>
#include <cmath>
#include <iostream>
#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/shape.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
#include "opencv2/opencv_modules.hpp"
#endif

793
modules/shape/src/sc_dis.cpp
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@ -1,793 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* Implementation of the paper Shape Matching and Object Recognition Using Shape Contexts
* Belongie et al., 2002 by Juan Manuel Perez for GSoC 2013.
*/
#include "precomp.hpp"
#include "opencv2/core.hpp"
#include "scd_def.hpp"
#include <limits>
namespace cv
{
class ShapeContextDistanceExtractorImpl : public ShapeContextDistanceExtractor
{
public:
/* Constructors */
ShapeContextDistanceExtractorImpl(int _nAngularBins, int _nRadialBins, float _innerRadius, float _outerRadius, int _iterations,
const Ptr<HistogramCostExtractor> &_comparer, const Ptr<ShapeTransformer> &_transformer)
{
nAngularBins=_nAngularBins;
nRadialBins=_nRadialBins;
innerRadius=_innerRadius;
outerRadius=_outerRadius;
rotationInvariant=false;
comparer=_comparer;
iterations=_iterations;
transformer=_transformer;
bendingEnergyWeight=0.3f;
imageAppearanceWeight=0.0f;
shapeContextWeight=1.0f;
sigma=10.0f;
name_ = "ShapeDistanceExtractor.SCD";
costFlag = 0;
}
/* Destructor */
~ShapeContextDistanceExtractorImpl() CV_OVERRIDE
{
}
//! the main operator
virtual float computeDistance(InputArray contour1, InputArray contour2) CV_OVERRIDE;
//! Setters/Getters
virtual void setAngularBins(int _nAngularBins) CV_OVERRIDE { CV_Assert(_nAngularBins>0); nAngularBins=_nAngularBins; }
virtual int getAngularBins() const CV_OVERRIDE { return nAngularBins; }
virtual void setRadialBins(int _nRadialBins) CV_OVERRIDE { CV_Assert(_nRadialBins>0); nRadialBins=_nRadialBins; }
virtual int getRadialBins() const CV_OVERRIDE { return nRadialBins; }
virtual void setInnerRadius(float _innerRadius) CV_OVERRIDE { CV_Assert(_innerRadius>0); innerRadius=_innerRadius; }
virtual float getInnerRadius() const CV_OVERRIDE { return innerRadius; }
virtual void setOuterRadius(float _outerRadius) CV_OVERRIDE { CV_Assert(_outerRadius>0); outerRadius=_outerRadius; }
virtual float getOuterRadius() const CV_OVERRIDE { return outerRadius; }
virtual void setRotationInvariant(bool _rotationInvariant) CV_OVERRIDE { rotationInvariant=_rotationInvariant; }
virtual bool getRotationInvariant() const CV_OVERRIDE { return rotationInvariant; }
virtual void setCostExtractor(Ptr<HistogramCostExtractor> _comparer) CV_OVERRIDE { comparer = _comparer; }
virtual Ptr<HistogramCostExtractor> getCostExtractor() const CV_OVERRIDE { return comparer; }
virtual void setShapeContextWeight(float _shapeContextWeight) CV_OVERRIDE { shapeContextWeight=_shapeContextWeight; }
virtual float getShapeContextWeight() const CV_OVERRIDE { return shapeContextWeight; }
virtual void setImageAppearanceWeight(float _imageAppearanceWeight) CV_OVERRIDE { imageAppearanceWeight=_imageAppearanceWeight; }
virtual float getImageAppearanceWeight() const CV_OVERRIDE { return imageAppearanceWeight; }
virtual void setBendingEnergyWeight(float _bendingEnergyWeight) CV_OVERRIDE { bendingEnergyWeight=_bendingEnergyWeight; }
virtual float getBendingEnergyWeight() const CV_OVERRIDE { return bendingEnergyWeight; }
virtual void setStdDev(float _sigma) CV_OVERRIDE { sigma=_sigma; }
virtual float getStdDev() const CV_OVERRIDE { return sigma; }
virtual void setImages(InputArray _image1, InputArray _image2) CV_OVERRIDE
{
Mat image1_=_image1.getMat(), image2_=_image2.getMat();
CV_Assert((image1_.depth()==0) && (image2_.depth()==0));
image1=image1_;
image2=image2_;
}
virtual void getImages(OutputArray _image1, OutputArray _image2) const CV_OVERRIDE
{
CV_Assert((!image1.empty()) && (!image2.empty()));
image1.copyTo(_image1);
image2.copyTo(_image2);
}
virtual void setIterations(int _iterations) CV_OVERRIDE {CV_Assert(_iterations>0); iterations=_iterations;}
virtual int getIterations() const CV_OVERRIDE {return iterations;}
virtual void setTransformAlgorithm(Ptr<ShapeTransformer> _transformer) CV_OVERRIDE {transformer=_transformer;}
virtual Ptr<ShapeTransformer> getTransformAlgorithm() const CV_OVERRIDE {return transformer;}
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "nRads" << nRadialBins
<< "nAngs" << nAngularBins
<< "iters" << iterations
<< "img_1" << image1
<< "img_2" << image2
<< "beWei" << bendingEnergyWeight
<< "scWei" << shapeContextWeight
<< "iaWei" << imageAppearanceWeight
<< "costF" << costFlag
<< "rotIn" << rotationInvariant
<< "sigma" << sigma;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
nRadialBins = (int)fn["nRads"];
nAngularBins = (int)fn["nAngs"];
iterations = (int)fn["iters"];
bendingEnergyWeight = (float)fn["beWei"];
shapeContextWeight = (float)fn["scWei"];
imageAppearanceWeight = (float)fn["iaWei"];
costFlag = (int)fn["costF"];
sigma = (float)fn["sigma"];
}
protected:
int nAngularBins;
int nRadialBins;
float innerRadius;
float outerRadius;
bool rotationInvariant;
int costFlag;
int iterations;
Ptr<ShapeTransformer> transformer;
Ptr<HistogramCostExtractor> comparer;
Mat image1;
Mat image2;
float bendingEnergyWeight;
float imageAppearanceWeight;
float shapeContextWeight;
float sigma;
String name_;
};
float ShapeContextDistanceExtractorImpl::computeDistance(InputArray contour1, InputArray contour2)
{
CV_INSTRUMENT_REGION();
// Checking //
Mat sset1=contour1.getMat(), sset2=contour2.getMat(), set1, set2;
if (set1.type() != CV_32F)
sset1.convertTo(set1, CV_32F);
else
sset1.copyTo(set1);
if (set2.type() != CV_32F)
sset2.convertTo(set2, CV_32F);
else
sset2.copyTo(set2);
CV_Assert((set1.channels()==2) && (set1.cols>0));
CV_Assert((set2.channels()==2) && (set2.cols>0));
// Force vectors column-based
if (set1.dims > 1)
set1 = set1.reshape(2, 1);
if (set2.dims > 1)
set2 = set2.reshape(2, 1);
if (imageAppearanceWeight!=0)
{
CV_Assert((!image1.empty()) && (!image2.empty()));
}
// Initializing Extractor, Descriptor structures and Matcher //
SCD set1SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
Mat set1SCD;
SCD set2SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
Mat set2SCD;
SCDMatcher matcher;
std::vector<DMatch> matches;
// Distance components (The output is a linear combination of these 3) //
float sDistance=0, bEnergy=0, iAppearance=0;
float beta;
// Initializing some variables //
std::vector<int> inliers1, inliers2;
Ptr<ThinPlateSplineShapeTransformer> transDown = transformer.dynamicCast<ThinPlateSplineShapeTransformer>();
Mat warpedImage;
int ii, jj, pt;
for (ii=0; ii<iterations; ii++)
{
// Extract SCD descriptor in the set1 //
set1SCE.extractSCD(set1, set1SCD, inliers1);
// Extract SCD descriptor of the set2 (TARGET) //
set2SCE.extractSCD(set2, set2SCD, inliers2, set1SCE.getMeanDistance());
// regularization parameter with annealing rate annRate //
beta=set1SCE.getMeanDistance();
beta *= beta;
// match //
matcher.matchDescriptors(set1SCD, set2SCD, matches, comparer, inliers1, inliers2);
// apply TPS transform //
if ( !transDown.empty() )
transDown->setRegularizationParameter(beta);
transformer->estimateTransformation(set1, set2, matches);
bEnergy += transformer->applyTransformation(set1, set1);
// Image appearance //
if (imageAppearanceWeight!=0)
{
// Have to accumulate the transformation along all the iterations
if (ii==0)
{
if ( !transDown.empty() )
{
image2.copyTo(warpedImage);
}
else
{
image1.copyTo(warpedImage);
}
}
transformer->warpImage(warpedImage, warpedImage);
}
}
Mat gaussWindow, diffIm;
if (imageAppearanceWeight!=0)
{
// compute appearance cost
if ( !transDown.empty() )
{
resize(warpedImage, warpedImage, image1.size(), 0, 0, INTER_LINEAR_EXACT);
Mat temp=(warpedImage-image1);
multiply(temp, temp, diffIm);
}
else
{
resize(warpedImage, warpedImage, image2.size(), 0, 0, INTER_LINEAR_EXACT);
Mat temp=(warpedImage-image2);
multiply(temp, temp, diffIm);
}
gaussWindow = Mat::zeros(warpedImage.rows, warpedImage.cols, CV_32F);
for (pt=0; pt<sset1.cols; pt++)
{
Point2f p = sset1.at<Point2f>(0,pt);
for (ii=0; ii<diffIm.rows; ii++)
{
for (jj=0; jj<diffIm.cols; jj++)
{
float val = float(std::exp( -float( (p.x-jj)*(p.x-jj) + (p.y-ii)*(p.y-ii) )/(2*sigma*sigma) ) / (sigma*sigma*2*CV_PI));
gaussWindow.at<float>(ii,jj) += val;
}
}
}
Mat appIm(diffIm.rows, diffIm.cols, CV_32F);
for (ii=0; ii<diffIm.rows; ii++)
{
for (jj=0; jj<diffIm.cols; jj++)
{
float elema=float( diffIm.at<uchar>(ii,jj) )/255;
float elemb=gaussWindow.at<float>(ii,jj);
appIm.at<float>(ii,jj) = elema*elemb;
}
}
iAppearance = float(cv::sum(appIm)[0]/sset1.cols);
}
sDistance = matcher.getMatchingCost();
return (sDistance*shapeContextWeight+bEnergy*bendingEnergyWeight+iAppearance*imageAppearanceWeight);
}
Ptr <ShapeContextDistanceExtractor> createShapeContextDistanceExtractor(int nAngularBins, int nRadialBins, float innerRadius, float outerRadius, int iterations,
const Ptr<HistogramCostExtractor> &comparer, const Ptr<ShapeTransformer> &transformer)
{
return Ptr <ShapeContextDistanceExtractor> ( new ShapeContextDistanceExtractorImpl(nAngularBins, nRadialBins, innerRadius,
outerRadius, iterations, comparer, transformer) );
}
//! SCD
void SCD::extractSCD(cv::Mat &contour, cv::Mat &descriptors, const std::vector<int> &queryInliers, const float _meanDistance)
{
cv::Mat contourMat = contour;
cv::Mat disMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);
cv::Mat angleMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);
std::vector<double> logspaces, angspaces;
logarithmicSpaces(logspaces);
angularSpaces(angspaces);
buildNormalizedDistanceMatrix(contourMat, disMatrix, queryInliers, _meanDistance);
buildAngleMatrix(contourMat, angleMatrix);
// Now, build the descriptor matrix (each row is a point) //
descriptors = cv::Mat::zeros(contourMat.cols, descriptorSize(), CV_32F);
for (int ptidx=0; ptidx<contourMat.cols; ptidx++)
{
for (int cmp=0; cmp<contourMat.cols; cmp++)
{
if (ptidx==cmp) continue;
if ((int)queryInliers.size()>0)
{
if (queryInliers[ptidx]==0 || queryInliers[cmp]==0) continue; //avoid outliers
}
int angidx=-1, radidx=-1;
for (int i=0; i<nRadialBins; i++)
{
if (disMatrix.at<float>(ptidx, cmp)<logspaces[i])
{
radidx=i;
break;
}
}
for (int i=0; i<nAngularBins; i++)
{
if (angleMatrix.at<float>(ptidx, cmp)<angspaces[i])
{
angidx=i;
break;
}
}
if (angidx!=-1 && radidx!=-1)
{
int idx = angidx+radidx*nAngularBins;
descriptors.at<float>(ptidx, idx)++;
}
}
}
}
void SCD::logarithmicSpaces(std::vector<double> &vecSpaces) const
{
double logmin=log10(innerRadius);
double logmax=log10(outerRadius);
double delta=(logmax-logmin)/(nRadialBins-1);
double accdelta=0;
for (int i=0; i<nRadialBins; i++)
{
double val = std::pow(10,logmin+accdelta);
vecSpaces.push_back(val);
accdelta += delta;
}
}
void SCD::angularSpaces(std::vector<double> &vecSpaces) const
{
double delta=2*CV_PI/nAngularBins;
double val=0;
for (int i=0; i<nAngularBins; i++)
{
val += delta;
vecSpaces.push_back(val);
}
}
void SCD::buildNormalizedDistanceMatrix(cv::Mat &contour, cv::Mat &disMatrix, const std::vector<int> &queryInliers, const float _meanDistance)
{
cv::Mat contourMat = contour;
cv::Mat mask(disMatrix.rows, disMatrix.cols, CV_8U);
for (int i=0; i<contourMat.cols; i++)
{
for (int j=0; j<contourMat.cols; j++)
{
disMatrix.at<float>(i,j) = (float)norm( cv::Mat(contourMat.at<cv::Point2f>(0,i)-contourMat.at<cv::Point2f>(0,j)), cv::NORM_L2 );
if (_meanDistance<0)
{
if (queryInliers.size()>0)
{
mask.at<char>(i,j)=char(queryInliers[j] && queryInliers[i]);
}
else
{
mask.at<char>(i,j)=1;
}
}
}
}
if (_meanDistance<0)
{
meanDistance=(float)mean(disMatrix, mask)[0];
}
else
{
meanDistance=_meanDistance;
}
disMatrix/=meanDistance+FLT_EPSILON;
}
void SCD::buildAngleMatrix(cv::Mat &contour, cv::Mat &angleMatrix) const
{
cv::Mat contourMat = contour;
// if descriptor is rotationInvariant compute massCenter //
cv::Point2f massCenter(0,0);
if (rotationInvariant)
{
for (int i=0; i<contourMat.cols; i++)
{
massCenter+=contourMat.at<cv::Point2f>(0,i);
}
massCenter.x=massCenter.x/(float)contourMat.cols;
massCenter.y=massCenter.y/(float)contourMat.cols;
}
for (int i=0; i<contourMat.cols; i++)
{
for (int j=0; j<contourMat.cols; j++)
{
if (i==j)
{
angleMatrix.at<float>(i,j)=0.0;
}
else
{
cv::Point2f dif = contourMat.at<cv::Point2f>(0,i) - contourMat.at<cv::Point2f>(0,j);
angleMatrix.at<float>(i,j) = std::atan2(dif.y, dif.x);
if (rotationInvariant)
{
cv::Point2f refPt = contourMat.at<cv::Point2f>(0,i) - massCenter;
float refAngle = atan2(refPt.y, refPt.x);
angleMatrix.at<float>(i,j) -= refAngle;
}
angleMatrix.at<float>(i,j) = float(fmod(double(angleMatrix.at<float>(i,j)+(double)FLT_EPSILON),2*CV_PI)+CV_PI);
}
}
}
}
//! SCDMatcher
void SCDMatcher::matchDescriptors(cv::Mat &descriptors1, cv::Mat &descriptors2, std::vector<cv::DMatch> &matches,
cv::Ptr<cv::HistogramCostExtractor> &comparer, std::vector<int> &inliers1, std::vector<int> &inliers2)
{
matches.clear();
// Build the cost Matrix between descriptors //
cv::Mat costMat;
buildCostMatrix(descriptors1, descriptors2, costMat, comparer);
// Solve the matching problem using the hungarian method //
hungarian(costMat, matches, inliers1, inliers2, descriptors1.rows, descriptors2.rows);
}
void SCDMatcher::buildCostMatrix(const cv::Mat &descriptors1, const cv::Mat &descriptors2,
cv::Mat &costMatrix, cv::Ptr<cv::HistogramCostExtractor> &comparer) const
{
CV_INSTRUMENT_REGION();
comparer->buildCostMatrix(descriptors1, descriptors2, costMatrix);
}
void SCDMatcher::hungarian(cv::Mat &costMatrix, std::vector<cv::DMatch> &outMatches, std::vector<int> &inliers1,
std::vector<int> &inliers2, int sizeScd1, int sizeScd2)
{
std::vector<int> free(costMatrix.rows, 0), collist(costMatrix.rows, 0);
std::vector<int> matches(costMatrix.rows, 0), colsol(costMatrix.rows), rowsol(costMatrix.rows);
std::vector<float> d(costMatrix.rows), pred(costMatrix.rows), v(costMatrix.rows);
const float LOWV = 1e-10f;
bool unassignedfound;
int i=0, imin=0, numfree=0, prvnumfree=0, f=0, i0=0, k=0, freerow=0;
int j=0, j1=0, j2=0, endofpath=0, last=0, low=0, up=0;
float min=0, h=0, umin=0, usubmin=0, v2=0;
// COLUMN REDUCTION //
for (j = costMatrix.rows-1; j >= 0; j--)
{
// find minimum cost over rows.
min = costMatrix.at<float>(0,j);
imin = 0;
for (i = 1; i < costMatrix.rows; i++)
if (costMatrix.at<float>(i,j) < min)
{
min = costMatrix.at<float>(i,j);
imin = i;
}
v[j] = min;
if (++matches[imin] == 1)
{
rowsol[imin] = j;
colsol[j] = imin;
}
else
{
colsol[j]=-1;
}
}
// REDUCTION TRANSFER //
for (i=0; i<costMatrix.rows; i++)
{
if (matches[i] == 0)
{
free[numfree++] = i;
}
else
{
if (matches[i] == 1)
{
j1=rowsol[i];
min=std::numeric_limits<float>::max();
for (j=0; j<costMatrix.rows; j++)
{
if (j!=j1)
{
if (costMatrix.at<float>(i,j)-v[j] < min)
{
min=costMatrix.at<float>(i,j)-v[j];
}
}
}
v[j1] = v[j1]-min;
}
}
}
// AUGMENTING ROW REDUCTION //
int loopcnt = 0;
do
{
loopcnt++;
k=0;
prvnumfree=numfree;
numfree=0;
while (k < prvnumfree)
{
i=free[k];
k++;
umin = costMatrix.at<float>(i,0)-v[0];
j1=0;
usubmin = std::numeric_limits<float>::max();
for (j=1; j<costMatrix.rows; j++)
{
h = costMatrix.at<float>(i,j)-v[j];
if (h < usubmin)
{
if (h >= umin)
{
usubmin = h;
j2 = j;
}
else
{
usubmin = umin;
umin = h;
j2 = j1;
j1 = j;
}
}
}
i0 = colsol[j1];
if (fabs(umin-usubmin) > LOWV) //if( umin < usubmin )
{
v[j1] = v[j1] - (usubmin - umin);
}
else // minimum and subminimum equal.
{
if (i0 >= 0) // minimum column j1 is assigned.
{
j1 = j2;
i0 = colsol[j2];
}
}
// (re-)assign i to j1, possibly de-assigning an i0.
rowsol[i]=j1;
colsol[j1]=i;
if (i0 >= 0)
{
//if( umin < usubmin )
if (fabs(umin-usubmin) > LOWV)
{
free[--k] = i0;
}
else
{
free[numfree++] = i0;
}
}
}
}while (loopcnt<2); // repeat once.
// AUGMENT SOLUTION for each free row //
for (f = 0; f<numfree; f++)
{
freerow = free[f]; // start row of augmenting path.
// Dijkstra shortest path algorithm.
// runs until unassigned column added to shortest path tree.
for (j = 0; j < costMatrix.rows; j++)
{
d[j] = costMatrix.at<float>(freerow,j) - v[j];
pred[j] = float(freerow);
collist[j] = j; // init column list.
}
low=0; // columns in 0..low-1 are ready, now none.
up=0; // columns in low..up-1 are to be scanned for current minimum, now none.
unassignedfound = false;
do
{
if (up == low)
{
last=low-1;
min = d[collist[up++]];
for (k = up; k < costMatrix.rows; k++)
{
j = collist[k];
h = d[j];
if (h <= min)
{
if (h < min) // new minimum.
{
up = low; // restart list at index low.
min = h;
}
collist[k] = collist[up];
collist[up++] = j;
}
}
for (k=low; k<up; k++)
{
if (colsol[collist[k]] < 0)
{
endofpath = collist[k];
unassignedfound = true;
break;
}
}
}
if (!unassignedfound)
{
// update 'distances' between freerow and all unscanned columns, via next scanned column.
j1 = collist[low];
low++;
i = colsol[j1];
h = costMatrix.at<float>(i,j1)-v[j1]-min;
for (k = up; k < costMatrix.rows; k++)
{
j = collist[k];
v2 = costMatrix.at<float>(i,j) - v[j] - h;
if (v2 < d[j])
{
pred[j] = float(i);
if (v2 == min)
{
if (colsol[j] < 0)
{
// if unassigned, shortest augmenting path is complete.
endofpath = j;
unassignedfound = true;
break;
}
else
{
collist[k] = collist[up];
collist[up++] = j;
}
}
d[j] = v2;
}
}
}
}while (!unassignedfound);
// update column prices.
for (k = 0; k <= last; k++)
{
j1 = collist[k];
v[j1] = v[j1] + d[j1] - min;
}
// reset row and column assignments along the alternating path.
do
{
i = int(pred[endofpath]);
colsol[endofpath] = i;
j1 = endofpath;
endofpath = rowsol[i];
rowsol[i] = j1;
}while (i != freerow);
}
// calculate symmetric shape context cost
cv::Mat trueCostMatrix(costMatrix, cv::Rect(0,0,sizeScd1, sizeScd2));
CV_Assert(!trueCostMatrix.empty());
float leftcost = 0;
for (int nrow=0; nrow<trueCostMatrix.rows; nrow++)
{
double minval;
minMaxIdx(trueCostMatrix.row(nrow), &minval);
leftcost+=float(minval);
}
leftcost /= trueCostMatrix.rows;
float rightcost = 0;
for (int ncol=0; ncol<trueCostMatrix.cols; ncol++)
{
double minval;
minMaxIdx(trueCostMatrix.col(ncol), &minval);
rightcost+=float(minval);
}
rightcost /= trueCostMatrix.cols;
minMatchCost = std::max(leftcost,rightcost);
// Save in a DMatch vector
for (i=0;i<costMatrix.cols;i++)
{
cv::DMatch singleMatch(colsol[i],i,costMatrix.at<float>(colsol[i],i));//queryIdx,trainIdx,distance
outMatches.push_back(singleMatch);
}
// Update inliers
inliers1.reserve(sizeScd1);
for (size_t kc = 0; kc<inliers1.size(); kc++)
{
if (rowsol[kc]<sizeScd2) // if a real match
inliers1[kc]=1;
else
inliers1[kc]=0;
}
inliers2.reserve(sizeScd2);
for (size_t kc = 0; kc<inliers2.size(); kc++)
{
if (colsol[kc]<sizeScd1) // if a real match
inliers2[kc]=1;
else
inliers2[kc]=0;
}
}
}

132
modules/shape/src/scd_def.hpp
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@ -1,132 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <stdlib.h>
#include <math.h>
#include <vector>
namespace cv
{
/*
* ShapeContextDescriptor class
*/
class SCD
{
public:
//! the full constructor taking all the necessary parameters
explicit SCD(int _nAngularBins=12, int _nRadialBins=5,
double _innerRadius=0.1, double _outerRadius=1, bool _rotationInvariant=false)
{
setAngularBins(_nAngularBins);
setRadialBins(_nRadialBins);
setInnerRadius(_innerRadius);
setOuterRadius(_outerRadius);
setRotationInvariant(_rotationInvariant);
meanDistance = 0;
}
void extractSCD(cv::Mat& contour, cv::Mat& descriptors,
const std::vector<int>& queryInliers=std::vector<int>(),
const float _meanDistance=-1);
int descriptorSize() {return nAngularBins*nRadialBins;}
void setAngularBins(int angularBins) { nAngularBins=angularBins; }
void setRadialBins(int radialBins) { nRadialBins=radialBins; }
void setInnerRadius(double _innerRadius) { innerRadius=_innerRadius; }
void setOuterRadius(double _outerRadius) { outerRadius=_outerRadius; }
void setRotationInvariant(bool _rotationInvariant) { rotationInvariant=_rotationInvariant; }
int getAngularBins() const { return nAngularBins; }
int getRadialBins() const { return nRadialBins; }
double getInnerRadius() const { return innerRadius; }
double getOuterRadius() const { return outerRadius; }
bool getRotationInvariant() const { return rotationInvariant; }
float getMeanDistance() const { return meanDistance; }
private:
int nAngularBins;
int nRadialBins;
double innerRadius;
double outerRadius;
bool rotationInvariant;
float meanDistance;
protected:
void logarithmicSpaces(std::vector<double>& vecSpaces) const;
void angularSpaces(std::vector<double>& vecSpaces) const;
void buildNormalizedDistanceMatrix(cv::Mat& contour,
cv::Mat& disMatrix, const std::vector<int> &queryInliers,
const float _meanDistance=-1);
void buildAngleMatrix(cv::Mat& contour,
cv::Mat& angleMatrix) const;
};
/*
* Matcher
*/
class SCDMatcher
{
public:
// the full constructor
SCDMatcher() : minMatchCost(0)
{
}
// the matcher function using Hungarian method
void matchDescriptors(cv::Mat& descriptors1, cv::Mat& descriptors2, std::vector<cv::DMatch>& matches, cv::Ptr<cv::HistogramCostExtractor>& comparer,
std::vector<int>& inliers1, std::vector<int> &inliers2);
// matching cost
float getMatchingCost() const {return minMatchCost;}
private:
float minMatchCost;
protected:
void buildCostMatrix(const cv::Mat& descriptors1, const cv::Mat& descriptors2,
cv::Mat& costMatrix, cv::Ptr<cv::HistogramCostExtractor>& comparer) const;
void hungarian(cv::Mat& costMatrix, std::vector<cv::DMatch>& outMatches, std::vector<int> &inliers1,
std::vector<int> &inliers2, int sizeScd1=0, int sizeScd2=0);
};
}

295
modules/shape/src/tps_trans.cpp
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@ -1,295 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
class ThinPlateSplineShapeTransformerImpl CV_FINAL : public ThinPlateSplineShapeTransformer
{
public:
/* Constructors */
ThinPlateSplineShapeTransformerImpl()
{
regularizationParameter=0;
name_ = "ShapeTransformer.TPS";
tpsComputed=false;
transformCost = 0;
}
ThinPlateSplineShapeTransformerImpl(double _regularizationParameter)
{
regularizationParameter=_regularizationParameter;
name_ = "ShapeTransformer.TPS";
tpsComputed=false;
transformCost = 0;
}
/* Destructor */
~ThinPlateSplineShapeTransformerImpl() CV_OVERRIDE
{
}
//! the main operators
virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches) CV_OVERRIDE;
virtual float applyTransformation(InputArray inPts, OutputArray output=noArray()) CV_OVERRIDE;
virtual void warpImage(InputArray transformingImage, OutputArray output,
int flags, int borderMode, const Scalar& borderValue) const CV_OVERRIDE;
//! Setters/Getters
virtual void setRegularizationParameter(double _regularizationParameter) CV_OVERRIDE { regularizationParameter=_regularizationParameter; }
virtual double getRegularizationParameter() const CV_OVERRIDE { return regularizationParameter; }
//! write/read
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
writeFormat(fs);
fs << "name" << name_
<< "regularization" << regularizationParameter;
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
regularizationParameter = (int)fn["regularization"];
}
private:
bool tpsComputed;
double regularizationParameter;
float transformCost;
Mat tpsParameters;
Mat shapeReference;
protected:
String name_;
};
static float distance(Point2f p, Point2f q)
{
Point2f diff = p - q;
float norma = diff.x*diff.x + diff.y*diff.y;// - 2*diff.x*diff.y;
if (norma<0) norma=0;
//else norma = std::sqrt(norma);
norma = norma*std::log(norma+FLT_EPSILON);
return norma;
}
static Point2f _applyTransformation(const Mat &shapeRef, const Point2f point, const Mat &tpsParameters)
{
Point2f out;
for (int i=0; i<2; i++)
{
float a1=tpsParameters.at<float>(tpsParameters.rows-3,i);
float ax=tpsParameters.at<float>(tpsParameters.rows-2,i);
float ay=tpsParameters.at<float>(tpsParameters.rows-1,i);
float affine=a1+ax*point.x+ay*point.y;
float nonrigid=0;
for (int j=0; j<shapeRef.rows; j++)
{
nonrigid+=tpsParameters.at<float>(j,i)*
distance(Point2f(shapeRef.at<float>(j,0),shapeRef.at<float>(j,1)),
point);
}
if (i==0)
{
out.x=affine+nonrigid;
}
if (i==1)
{
out.y=affine+nonrigid;
}
}
return out;
}
/* public methods */
void ThinPlateSplineShapeTransformerImpl::warpImage(InputArray transformingImage, OutputArray output,
int flags, int borderMode, const Scalar& borderValue) const
{
CV_INSTRUMENT_REGION();
CV_Assert(tpsComputed==true);
Mat theinput = transformingImage.getMat();
Mat mapX(theinput.rows, theinput.cols, CV_32FC1);
Mat mapY(theinput.rows, theinput.cols, CV_32FC1);
for (int row = 0; row < theinput.rows; row++)
{
for (int col = 0; col < theinput.cols; col++)
{
Point2f pt = _applyTransformation(shapeReference, Point2f(float(col), float(row)), tpsParameters);
mapX.at<float>(row, col) = pt.x;
mapY.at<float>(row, col) = pt.y;
}
}
remap(transformingImage, output, mapX, mapY, flags, borderMode, borderValue);
}
float ThinPlateSplineShapeTransformerImpl::applyTransformation(InputArray inPts, OutputArray outPts)
{
CV_INSTRUMENT_REGION();
CV_Assert(tpsComputed);
Mat pts1 = inPts.getMat();
CV_Assert((pts1.channels()==2) && (pts1.cols>0));
//Apply transformation in the complete set of points
// Ensambling output //
if (outPts.needed())
{
outPts.create(1,pts1.cols, CV_32FC2);
Mat outMat = outPts.getMat();
for (int i=0; i<pts1.cols; i++)
{
Point2f pt=pts1.at<Point2f>(0,i);
outMat.at<Point2f>(0,i)=_applyTransformation(shapeReference, pt, tpsParameters);
}
}
return transformCost;
}
void ThinPlateSplineShapeTransformerImpl::estimateTransformation(InputArray _pts1, InputArray _pts2,
std::vector<DMatch>& _matches )
{
CV_INSTRUMENT_REGION();
Mat pts1 = _pts1.getMat();
Mat pts2 = _pts2.getMat();
CV_Assert((pts1.channels()==2) && (pts1.cols>0) && (pts2.channels()==2) && (pts2.cols>0));
CV_Assert(_matches.size()>1);
if (pts1.type() != CV_32F)
pts1.convertTo(pts1, CV_32F);
if (pts2.type() != CV_32F)
pts2.convertTo(pts2, CV_32F);
// Use only valid matchings //
std::vector<DMatch> matches;
for (size_t i=0; i<_matches.size(); i++)
{
if (_matches[i].queryIdx<pts1.cols &&
_matches[i].trainIdx<pts2.cols)
{
matches.push_back(_matches[i]);
}
}
// Organizing the correspondent points in matrix style //
Mat shape1((int)matches.size(),2,CV_32F); // transforming shape
Mat shape2((int)matches.size(),2,CV_32F); // target shape
for (int i=0, end = (int)matches.size(); i<end; i++)
{
Point2f pt1=pts1.at<Point2f>(0,matches[i].queryIdx);
shape1.at<float>(i,0) = pt1.x;
shape1.at<float>(i,1) = pt1.y;
Point2f pt2=pts2.at<Point2f>(0,matches[i].trainIdx);
shape2.at<float>(i,0) = pt2.x;
shape2.at<float>(i,1) = pt2.y;
}
shape1.copyTo(shapeReference);
// Building the matrices for solving the L*(w|a)=(v|0) problem with L={[K|P];[P'|0]}
//Building K and P (Needed to build L)
Mat matK((int)matches.size(),(int)matches.size(),CV_32F);
Mat matP((int)matches.size(),3,CV_32F);
for (int i=0, end=(int)matches.size(); i<end; i++)
{
for (int j=0; j<end; j++)
{
if (i==j)
{
matK.at<float>(i,j)=float(regularizationParameter);
}
else
{
matK.at<float>(i,j) = distance(Point2f(shape1.at<float>(i,0),shape1.at<float>(i,1)),
Point2f(shape1.at<float>(j,0),shape1.at<float>(j,1)));
}
}
matP.at<float>(i,0) = 1;
matP.at<float>(i,1) = shape1.at<float>(i,0);
matP.at<float>(i,2) = shape1.at<float>(i,1);
}
//Building L
Mat matL=Mat::zeros((int)matches.size()+3,(int)matches.size()+3,CV_32F);
Mat matLroi(matL, Rect(0,0,(int)matches.size(),(int)matches.size())); //roi for K
matK.copyTo(matLroi);
matLroi = Mat(matL,Rect((int)matches.size(),0,3,(int)matches.size())); //roi for P
matP.copyTo(matLroi);
Mat matPt;
transpose(matP,matPt);
matLroi = Mat(matL,Rect(0,(int)matches.size(),(int)matches.size(),3)); //roi for P'
matPt.copyTo(matLroi);
//Building B (v|0)
Mat matB = Mat::zeros((int)matches.size()+3,2,CV_32F);
for (int i=0, end = (int)matches.size(); i<end; i++)
{
matB.at<float>(i,0) = shape2.at<float>(i,0); //x's
matB.at<float>(i,1) = shape2.at<float>(i,1); //y's
}
//Obtaining transformation params (w|a)
solve(matL, matB, tpsParameters, DECOMP_LU);
//tpsParameters = matL.inv()*matB;
//Setting transform Cost and Shape reference
Mat w(tpsParameters, Rect(0,0,2,tpsParameters.rows-3));
Mat Q=w.t()*matK*w;
transformCost=fabs(Q.at<float>(0,0)*Q.at<float>(1,1));//fabs(mean(Q.diag(0))[0]);//std::max(Q.at<float>(0,0),Q.at<float>(1,1));
tpsComputed=true;
}
Ptr <ThinPlateSplineShapeTransformer> createThinPlateSplineShapeTransformer(double regularizationParameter)
{
return Ptr<ThinPlateSplineShapeTransformer>( new ThinPlateSplineShapeTransformerImpl(regularizationParameter) );
}
} // cv

10
modules/shape/test/test_main.cpp
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@ -1,10 +0,0 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#if defined(HAVE_HPX)
#include <hpx/hpx_main.hpp>
#endif
CV_TEST_MAIN("cv")

10
modules/shape/test/test_precomp.hpp
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@ -1,10 +0,0 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/shape.hpp"
#endif

324
modules/shape/test/test_shape.cpp
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@ -1,324 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
template <typename T, typename compute>
class ShapeBaseTest : public cvtest::BaseTest
{
public:
typedef Point_<T> PointType;
ShapeBaseTest(int _NSN, int _NP, float _CURRENT_MAX_ACCUR)
: NSN(_NSN), NP(_NP), CURRENT_MAX_ACCUR(_CURRENT_MAX_ACCUR)
{
// generate file list
vector<string> shapeNames;
shapeNames.push_back("apple"); //ok
shapeNames.push_back("children"); // ok
shapeNames.push_back("device7"); // ok
shapeNames.push_back("Heart"); // ok
shapeNames.push_back("teddy"); // ok
for (vector<string>::const_iterator i = shapeNames.begin(); i != shapeNames.end(); ++i)
{
for (int j = 0; j < NSN; ++j)
{
std::stringstream filename;
filename << cvtest::TS::ptr()->get_data_path()
<< "shape/mpeg_test/" << *i << "-" << j + 1 << ".png";
filenames.push_back(filename.str());
}
}
// distance matrix
const int totalCount = (int)filenames.size();
distanceMat = Mat::zeros(totalCount, totalCount, CV_32F);
}
protected:
void run(int)
{
mpegTest();
displayMPEGResults();
}
vector<PointType> convertContourType(const Mat& currentQuery) const
{
if (currentQuery.empty()) {
return vector<PointType>();
}
vector<vector<Point> > _contoursQuery;
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
vector <PointType> contoursQuery;
for (size_t border=0; border<_contoursQuery.size(); border++)
{
for (size_t p=0; p<_contoursQuery[border].size(); p++)
{
contoursQuery.push_back(PointType((T)_contoursQuery[border][p].x,
(T)_contoursQuery[border][p].y));
}
}
// In case actual number of points is less than n
for (int add=(int)contoursQuery.size()-1; add<NP; add++)
{
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
}
// Uniformly sampling
cv::randShuffle(contoursQuery);
int nStart=NP;
vector<PointType> cont;
for (int i=0; i<nStart; i++)
{
cont.push_back(contoursQuery[i]);
}
return cont;
}
void mpegTest()
{
// query contours (normal v flipped, h flipped) and testing contour
vector<PointType> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
// reading query and computing its properties
for (vector<string>::const_iterator a = filenames.begin(); a != filenames.end(); ++a)
{
// read current image
int aIndex = (int)(a - filenames.begin());
Mat currentQuery = imread(*a, IMREAD_GRAYSCALE);
Mat flippedHQuery, flippedVQuery;
flip(currentQuery, flippedHQuery, 0);
flip(currentQuery, flippedVQuery, 1);
// compute border of the query and its flipped versions
contoursQuery1=convertContourType(currentQuery);
contoursQuery2=convertContourType(flippedHQuery);
contoursQuery3=convertContourType(flippedVQuery);
// compare with all the rest of the images: testing
for (vector<string>::const_iterator b = filenames.begin(); b != filenames.end(); ++b)
{
int bIndex = (int)(b - filenames.begin());
float distance = 0;
// skip self-comparisson
if (a != b)
{
// read testing image
Mat currentTest = imread(*b, IMREAD_GRAYSCALE);
// compute border of the testing
contoursTesting=convertContourType(currentTest);
// compute shape distance
distance = cmp(contoursQuery1, contoursQuery2,
contoursQuery3, contoursTesting);
}
distanceMat.at<float>(aIndex, bIndex) = distance;
}
}
}
void displayMPEGResults()
{
const int FIRST_MANY=2*NSN;
int corrects=0;
int divi=0;
for (int row=0; row<distanceMat.rows; row++)
{
if (row%NSN==0) //another group
{
divi+=NSN;
}
for (int col=divi-NSN; col<divi; col++)
{
int nsmall=0;
for (int i=0; i<distanceMat.cols; i++)
{
if (distanceMat.at<float>(row,col) > distanceMat.at<float>(row,i))
{
nsmall++;
}
}
if (nsmall<=FIRST_MANY)
{
corrects++;
}
}
}
float porc = 100*float(corrects)/(NSN*distanceMat.rows);
std::cout << "Test result: " << porc << "%" << std::endl;
if (porc >= CURRENT_MAX_ACCUR)
ts->set_failed_test_info(cvtest::TS::OK);
else
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
protected:
int NSN;
int NP;
float CURRENT_MAX_ACCUR;
vector<string> filenames;
Mat distanceMat;
compute cmp;
};
//------------------------------------------------------------------------
// Test Shape_SCD.regression
//------------------------------------------------------------------------
class computeShapeDistance_Chi
{
Ptr <ShapeContextDistanceExtractor> mysc;
public:
computeShapeDistance_Chi()
{
const int angularBins=12;
const int radialBins=4;
const float minRad=0.2f;
const float maxRad=2;
mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
mysc->setIterations(1);
mysc->setCostExtractor(createChiHistogramCostExtractor(30,0.15f));
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
}
float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
vector <Point2f>& query3, vector <Point2f>& testq)
{
return std::min(mysc->computeDistance(query1, testq),
std::min(mysc->computeDistance(query2, testq),
mysc->computeDistance(query3, testq)));
}
};
TEST(Shape_SCD, regression)
{
const int NSN_val=5;//10;//20; //number of shapes per class
const int NP_val=120; //number of points simplifying the contour
const float CURRENT_MAX_ACCUR_val=95; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
ShapeBaseTest<float, computeShapeDistance_Chi> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
test.safe_run();
}
//------------------------------------------------------------------------
// Test ShapeEMD_SCD.regression
//------------------------------------------------------------------------
class computeShapeDistance_EMD
{
Ptr <ShapeContextDistanceExtractor> mysc;
public:
computeShapeDistance_EMD()
{
const int angularBins=12;
const int radialBins=4;
const float minRad=0.2f;
const float maxRad=2;
mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
mysc->setIterations(1);
mysc->setCostExtractor( createEMDL1HistogramCostExtractor() );
mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
}
float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
vector <Point2f>& query3, vector <Point2f>& testq)
{
return std::min(mysc->computeDistance(query1, testq),
std::min(mysc->computeDistance(query2, testq),
mysc->computeDistance(query3, testq)));
}
};
TEST(ShapeEMD_SCD, regression)
{
const int NSN_val=5;//10;//20; //number of shapes per class
const int NP_val=100; //number of points simplifying the contour
const float CURRENT_MAX_ACCUR_val=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
ShapeBaseTest<float, computeShapeDistance_EMD> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
test.safe_run();
}
//------------------------------------------------------------------------
// Test Hauss.regression
//------------------------------------------------------------------------
class computeShapeDistance_Haussdorf
{
Ptr <HausdorffDistanceExtractor> haus;
public:
computeShapeDistance_Haussdorf()
{
haus = createHausdorffDistanceExtractor();
}
float operator()(vector<Point> &query1, vector<Point> &query2,
vector<Point> &query3, vector<Point> &testq)
{
return std::min(haus->computeDistance(query1,testq),
std::min(haus->computeDistance(query2,testq),
haus->computeDistance(query3,testq)));
}
};
TEST(Hauss, regression)
{
const int NSN_val=5;//10;//20; //number of shapes per class
const int NP_val = 180; //number of points simplifying the contour
const float CURRENT_MAX_ACCUR_val=85; //90% and 91% reached in several tests, 85 is fixed as minimum boundary
ShapeBaseTest<int, computeShapeDistance_Haussdorf> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
test.safe_run();
}
TEST(computeDistance, regression_4976)
{
Mat a = imread(cvtest::findDataFile("shape/samples/1.png"), 0);
Mat b = imread(cvtest::findDataFile("shape/samples/2.png"), 0);
vector<vector<Point> > ca,cb;
findContours(a, ca, cv::RETR_CCOMP, cv::CHAIN_APPROX_TC89_KCOS);
findContours(b, cb, cv::RETR_CCOMP, cv::CHAIN_APPROX_TC89_KCOS);
Ptr<HausdorffDistanceExtractor> hd = createHausdorffDistanceExtractor();
Ptr<ShapeContextDistanceExtractor> sd = createShapeContextDistanceExtractor();
double d1 = hd->computeDistance(ca[0],cb[0]);
double d2 = sd->computeDistance(ca[0],cb[0]);
EXPECT_NEAR(d1, 26.4196891785, 1e-3) << "HausdorffDistanceExtractor";
EXPECT_NEAR(d2, 0.25804194808, 1e-3) << "ShapeContextDistanceExtractor";
}
}} // namespace

1
samples/cpp/CMakeLists.txt
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@ -15,7 +15,6 @@ set(OPENCV_CPP_SAMPLES_REQUIRED_DEPS
opencv_calib3d
opencv_stitching
opencv_videostab
opencv_shape
${OPENCV_MODULES_PUBLIC}
${OpenCV_LIB_COMPONENTS})
ocv_check_dependencies(${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})

121
samples/cpp/shape_example.cpp
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/*
* shape_context.cpp -- Shape context demo for shape matching
*/
#include "opencv2/shape.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <opencv2/core/utility.hpp>
#include <iostream>
#include <string>
using namespace std;
using namespace cv;
static void help()
{
printf("\n"
"This program demonstrates a method for shape comparison based on Shape Context\n"
"You should run the program providing a number between 1 and 20 for selecting an image in the folder ../data/shape_sample.\n"
"Call\n"
"./shape_example [number between 1 and 20, 1 default]\n\n");
}
static vector<Point> simpleContour( const Mat& currentQuery, int n=300 )
{
vector<vector<Point> > _contoursQuery;
vector <Point> contoursQuery;
findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
for (size_t border=0; border<_contoursQuery.size(); border++)
{
for (size_t p=0; p<_contoursQuery[border].size(); p++)
{
contoursQuery.push_back( _contoursQuery[border][p] );
}
}
// In case actual number of points is less than n
int dummy=0;
for (int add=(int)contoursQuery.size()-1; add<n; add++)
{
contoursQuery.push_back(contoursQuery[dummy++]); //adding dummy values
}
// Uniformly sampling
cv::randShuffle(contoursQuery);
vector<Point> cont;
for (int i=0; i<n; i++)
{
cont.push_back(contoursQuery[i]);
}
return cont;
}
int main(int argc, char** argv)
{
string path = "../data/shape_sample/";
cv::CommandLineParser parser(argc, argv, "{help h||}{@input|1|}");
if (parser.has("help"))
{
help();
return 0;
}
int indexQuery = parser.get<int>("@input");
if (!parser.check())
{
parser.printErrors();
help();
return 1;
}
if (indexQuery < 1 || indexQuery > 20)
{
help();
return 1;
}
cv::Ptr <cv::ShapeContextDistanceExtractor> mysc = cv::createShapeContextDistanceExtractor();
Size sz2Sh(300,300);
stringstream queryName;
queryName<<path<<indexQuery<<".png";
Mat query=imread(queryName.str(), IMREAD_GRAYSCALE);
Mat queryToShow;
resize(query, queryToShow, sz2Sh, 0, 0, INTER_LINEAR_EXACT);
imshow("QUERY", queryToShow);
moveWindow("TEST", 0,0);
vector<Point> contQuery = simpleContour(query);
int bestMatch = 0;
float bestDis=FLT_MAX;
for ( int ii=1; ii<=20; ii++ )
{
if (ii==indexQuery) continue;
waitKey(30);
stringstream iiname;
iiname<<path<<ii<<".png";
cout<<"name: "<<iiname.str()<<endl;
Mat iiIm=imread(iiname.str(), 0);
Mat iiToShow;
resize(iiIm, iiToShow, sz2Sh, 0, 0, INTER_LINEAR_EXACT);
imshow("TEST", iiToShow);
moveWindow("TEST", sz2Sh.width+50,0);
vector<Point> contii = simpleContour(iiIm);
float dis = mysc->computeDistance( contQuery, contii );
if ( dis<bestDis )
{
bestMatch = ii;
bestDis = dis;
}
std::cout<<" distance between "<<queryName.str()<<" and "<<iiname.str()<<" is: "<<dis<<std::endl;
}
destroyWindow("TEST");
stringstream bestname;
bestname<<path<<bestMatch<<".png";
Mat iiIm=imread(bestname.str(), 0);
Mat bestToShow;
resize(iiIm, bestToShow, sz2Sh, 0, 0, INTER_LINEAR_EXACT);
imshow("BEST MATCH", bestToShow);
moveWindow("BEST MATCH", sz2Sh.width+50,0);
waitKey();
return 0;
}

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