556 lines
24 KiB
C++
556 lines
24 KiB
C++
/*#******************************************************************************
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** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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**
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** By downloading, copying, installing or using the software you agree to this license.
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** If you do not agree to this license, do not download, install,
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** copy or use the software.
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**
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**
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** HVStools : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
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** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping.
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**
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** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
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**
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** Creation - enhancement process 2007-2011
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** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
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**
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** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
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** Refer to the following research paper for more information:
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** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
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** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
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** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
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**
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** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
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** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
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** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
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** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
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** ====> more informations in the above cited Jeanny Heraults's book.
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**
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** License Agreement
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** For Open Source Computer Vision Library
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**
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** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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**
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** For Human Visual System tools (hvstools)
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** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
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**
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** Third party copyrights are property of their respective owners.
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**
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** Redistribution and use in source and binary forms, with or without modification,
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** are permitted provided that the following conditions are met:
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**
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** * Redistributions of source code must retain the above copyright notice,
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** this list of conditions and the following disclaimer.
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**
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** * Redistributions in binary form must reproduce the above copyright notice,
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** this list of conditions and the following disclaimer in the documentation
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** and/or other materials provided with the distribution.
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**
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** * The name of the copyright holders may not be used to endorse or promote products
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** derived from this software without specific prior written permission.
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**
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** This software is provided by the copyright holders and contributors "as is" and
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** any express or implied warranties, including, but not limited to, the implied
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** warranties of merchantability and fitness for a particular purpose are disclaimed.
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** In no event shall the Intel Corporation or contributors be liable for any direct,
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** indirect, incidental, special, exemplary, or consequential damages
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** (including, but not limited to, procurement of substitute goods or services;
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** loss of use, data, or profits; or business interruption) however caused
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** and on any theory of liability, whether in contract, strict liability,
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** or tort (including negligence or otherwise) arising in any way out of
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** the use of this software, even if advised of the possibility of such damage.
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*******************************************************************************/
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#ifndef __TEMPLATEBUFFER_HPP__
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#define __TEMPLATEBUFFER_HPP__
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#include <valarray>
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#include <cstdlib>
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#include <iostream>
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#include <cmath>
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//// If a parallelization method is available then, you should define MAKE_PARALLEL, in the other case, the classical serial code will be used
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#define MAKE_PARALLEL
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// ==> then include required includes
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#ifdef MAKE_PARALLEL
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// ==> declare usefull generic tools
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template <class type>
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class Parallel_clipBufferValues: public cv::ParallelLoopBody
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{
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private:
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type *bufferToClip;
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type minValue, maxValue;
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public:
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Parallel_clipBufferValues(type* bufferToProcess, const type min, const type max)
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: bufferToClip(bufferToProcess), minValue(min), maxValue(max){}
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virtual void operator()( const cv::Range &r ) const {
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register type *inputOutputBufferPTR=bufferToClip+r.start;
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for (register int jf = r.start; jf != r.end; ++jf, ++inputOutputBufferPTR)
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{
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if (*inputOutputBufferPTR>maxValue)
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*inputOutputBufferPTR=maxValue;
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else if (*inputOutputBufferPTR<minValue)
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*inputOutputBufferPTR=minValue;
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}
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}
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};
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#endif
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//#define __TEMPLATEBUFFERDEBUG //define TEMPLATEBUFFERDEBUG in order to display debug information
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namespace cv
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{
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/**
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* @class TemplateBuffer
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* @brief this class is a simple template memory buffer which contains basic functions to get information on or normalize the buffer content
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* note that thanks to the parent STL template class "valarray", it is possible to perform easily operations on the full array such as addition, product etc.
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* @author Alexandre BENOIT (benoit.alexandre.vision@gmail.com), helped by Gelu IONESCU (gelu.ionescu@lis.inpg.fr)
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* creation date: september 2007
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*/
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template <class type> class TemplateBuffer : public std::valarray<type>
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{
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public:
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/**
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* constructor for monodimensional array
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* @param dim: the size of the vector
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*/
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TemplateBuffer(const size_t dim=0)
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: std::valarray<type>((type)0, dim)
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{
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_NBrows=1;
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_NBcolumns=dim;
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_NBdepths=1;
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_NBpixels=dim;
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_doubleNBpixels=2*dim;
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}
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/**
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* constructor by copy for monodimensional array
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* @param pVal: the pointer to a buffer to copy
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* @param dim: the size of the vector
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*/
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TemplateBuffer(const type* pVal, const size_t dim)
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: std::valarray<type>(pVal, dim)
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{
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_NBrows=1;
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_NBcolumns=dim;
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_NBdepths=1;
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_NBpixels=dim;
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_doubleNBpixels=2*dim;
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}
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/**
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* constructor for bidimensional array
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* @param dimRows: the size of the vector
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* @param dimColumns: the size of the vector
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* @param depth: the number of layers of the buffer in its third dimension (3 of color images, 1 for gray images.
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*/
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TemplateBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
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: std::valarray<type>((type)0, dimRows*dimColumns*depth)
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{
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#ifdef TEMPLATEBUFFERDEBUG
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std::cout<<"TemplateBuffer::TemplateBuffer: new buffer, size="<<dimRows<<", "<<dimColumns<<", "<<depth<<"valarraySize="<<this->size()<<std::endl;
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#endif
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_NBrows=dimRows;
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_NBcolumns=dimColumns;
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_NBdepths=depth;
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_NBpixels=dimRows*dimColumns;
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_doubleNBpixels=2*dimRows*dimColumns;
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//_createTableIndex();
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#ifdef TEMPLATEBUFFERDEBUG
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std::cout<<"TemplateBuffer::TemplateBuffer: construction successful"<<std::endl;
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#endif
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}
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/**
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* copy constructor
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* @param toCopy
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* @return thenconstructed instance
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*emplateBuffer(const TemplateBuffer &toCopy)
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:_NBrows(toCopy.getNBrows()),_NBcolumns(toCopy.getNBcolumns()),_NBdepths(toCopy.getNBdephs()), _NBpixels(toCopy.getNBpixels()), _doubleNBpixels(toCopy.getNBpixels()*2)
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//std::valarray<type>(toCopy)
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{
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memcpy(Buffer(), toCopy.Buffer(), this->size());
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}*/
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/**
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* destructor
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*/
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virtual ~TemplateBuffer()
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{
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#ifdef TEMPLATEBUFFERDEBUG
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std::cout<<"~TemplateBuffer"<<std::endl;
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#endif
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}
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/**
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* delete the buffer content (set zeros)
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*/
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inline void setZero(){std::valarray<type>::operator=(0);};//memset(Buffer(), 0, sizeof(type)*_NBpixels);};
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/**
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* @return the numbers of rows (height) of the images used by the object
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*/
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inline unsigned int getNBrows(){return (unsigned int)_NBrows;};
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/**
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* @return the numbers of columns (width) of the images used by the object
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*/
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inline unsigned int getNBcolumns(){return (unsigned int)_NBcolumns;};
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/**
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* @return the numbers of pixels (width*height) of the images used by the object
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*/
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inline unsigned int getNBpixels(){return (unsigned int)_NBpixels;};
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/**
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* @return the numbers of pixels (width*height) of the images used by the object
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*/
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inline unsigned int getDoubleNBpixels(){return (unsigned int)_doubleNBpixels;};
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/**
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* @return the numbers of depths (3rd dimension: 1 for gray images, 3 for rgb images) of the images used by the object
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*/
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inline unsigned int getDepthSize(){return (unsigned int)_NBdepths;};
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/**
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* resize the buffer and recompute table index etc.
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*/
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void resizeBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
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{
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this->resize(dimRows*dimColumns*depth);
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_NBrows=dimRows;
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_NBcolumns=dimColumns;
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_NBdepths=depth;
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_NBpixels=dimRows*dimColumns;
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_doubleNBpixels=2*dimRows*dimColumns;
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}
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inline TemplateBuffer<type> & operator=(const std::valarray<type> &b)
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{
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//std::cout<<"TemplateBuffer<type> & operator= affect vector: "<<std::endl;
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std::valarray<type>::operator=(b);
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return *this;
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}
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inline TemplateBuffer<type> & operator=(const type &b)
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{
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//std::cout<<"TemplateBuffer<type> & operator= affect value: "<<b<<std::endl;
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std::valarray<type>::operator=(b);
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return *this;
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}
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/* inline const type &operator[](const unsigned int &b)
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{
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return (*this)[b];
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}
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*/
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/**
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* @return the buffer adress in non const mode
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*/
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inline type* Buffer() { return &(*this)[0]; }
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///////////////////////////////////////////////////////
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// Standard Image manipulation functions
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/**
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* standard 0 to 255 image normalization function
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* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
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* @param maxOutputValue: the maximum output value
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*/
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static void normalizeGrayOutput_0_maxOutputValue(type *inputOutputBuffer, const size_t nbPixels, const type maxOutputValue=(type)255.0);
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/**
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* standard 0 to 255 image normalization function
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* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
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* @param maxOutputValue: the maximum output value
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*/
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void normalizeGrayOutput_0_maxOutputValue(const type maxOutputValue=(type)255.0){normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);};
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/**
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* sigmoide image normalization function (saturates min and max values)
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* @param meanValue: specifies the mean value of th pixels to be processed
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* @param sensitivity: strenght of the sigmoide
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* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param maxOutputValue: the maximum output value
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*/
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static void normalizeGrayOutputCentredSigmoide(const type meanValue, const type sensitivity, const type maxOutputValue, type *inputPicture, type *outputBuffer, const unsigned int nbPixels);
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/**
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* sigmoide image normalization function on the current buffer (saturates min and max values)
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* @param meanValue: specifies the mean value of th pixels to be processed
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* @param sensitivity: strenght of the sigmoide
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* @param maxOutputValue: the maximum output value
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*/
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inline void normalizeGrayOutputCentredSigmoide(const type meanValue=(type)0.0, const type sensitivity=(type)2.0, const type maxOutputValue=(type)255.0){ (void)maxOutputValue; normalizeGrayOutputCentredSigmoide(meanValue, sensitivity, 255.0, this->Buffer(), this->Buffer(), this->getNBpixels());};
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/**
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* sigmoide image normalization function (saturates min and max values), in this function, the sigmoide is centered on low values (high saturation of the medium and high values
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* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
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* @param sensitivity: strenght of the sigmoide
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* @param maxOutputValue: the maximum output value
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*/
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void normalizeGrayOutputNearZeroCentreredSigmoide(type *inputPicture=(type*)NULL, type *outputBuffer=(type*)NULL, const type sensitivity=(type)40, const type maxOutputValue=(type)255.0);
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/**
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* center and reduct the image (image-mean)/std
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* @param inputOutputBuffer: the image to be normalized if no parameter, the result is rewrited on it
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*/
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void centerReductImageLuminance(type *inputOutputBuffer=(type*)NULL);
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/**
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* @return standard deviation of the buffer
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*/
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double getStandardDeviation()
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{
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double standardDeviation=0;
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double meanValue=getMean();
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type *bufferPTR=Buffer();
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for (unsigned int i=0;i<this->size();++i)
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{
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double diff=(*(bufferPTR++)-meanValue);
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standardDeviation+=diff*diff;
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}
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return sqrt(standardDeviation/this->size());
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};
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/**
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* Clip buffer histogram
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* @param minRatio: the minimum ratio of the lower pixel values, range=[0,1] and lower than maxRatio
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* @param maxRatio: the aximum ratio of the higher pixel values, range=[0,1] and higher than minRatio
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*/
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void clipHistogram(double minRatio, double maxRatio, double maxOutputValue)
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{
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if (minRatio>=maxRatio)
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{
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std::cerr<<"TemplateBuffer::clipHistogram: minRatio must be inferior to maxRatio, buffer unchanged"<<std::endl;
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return;
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}
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/* minRatio=min(max(minRatio, 1.0),0.0);
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maxRatio=max(max(maxRatio, 0.0),1.0);
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*/
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// find the pixel value just above the threshold
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const double maxThreshold=this->max()*maxRatio;
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const double minThreshold=(this->max()-this->min())*minRatio+this->min();
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type *bufferPTR=this->Buffer();
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double deltaH=maxThreshold;
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double deltaL=maxThreshold;
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double updatedHighValue=maxThreshold;
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double updatedLowValue=maxThreshold;
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for (unsigned int i=0;i<this->size();++i)
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{
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double curentValue=(double)*(bufferPTR++);
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// updating "closest to the high threshold" pixel value
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double highValueTest=maxThreshold-curentValue;
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if (highValueTest>0)
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{
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if (deltaH>highValueTest)
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{
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deltaH=highValueTest;
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updatedHighValue=curentValue;
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}
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}
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// updating "closest to the low threshold" pixel value
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double lowValueTest=curentValue-minThreshold;
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if (lowValueTest>0)
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{
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if (deltaL>lowValueTest)
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{
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deltaL=lowValueTest;
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updatedLowValue=curentValue;
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}
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}
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}
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std::cout<<"Tdebug"<<std::endl;
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std::cout<<"deltaL="<<deltaL<<", deltaH="<<deltaH<<std::endl;
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std::cout<<"this->max()"<<this->max()<<"maxThreshold="<<maxThreshold<<"updatedHighValue="<<updatedHighValue<<std::endl;
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std::cout<<"this->min()"<<this->min()<<"minThreshold="<<minThreshold<<"updatedLowValue="<<updatedLowValue<<std::endl;
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// clipping values outside than the updated thresholds
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bufferPTR=this->Buffer();
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#ifdef MAKE_PARALLEL // call the TemplateBuffer multitreaded clipping method
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parallel_for_(cv::Range(0,this->size()), Parallel_clipBufferValues<type>(bufferPTR, updatedLowValue, updatedHighValue));
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#else
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for (unsigned int i=0;i<this->size();++i, ++bufferPTR)
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{
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if (*bufferPTR<updatedLowValue)
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*bufferPTR=updatedLowValue;
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else if (*bufferPTR>updatedHighValue)
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*bufferPTR=updatedHighValue;
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}
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#endif
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normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);
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}
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/**
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* @return the mean value of the vector
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*/
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inline double getMean(){return this->sum()/this->size();};
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protected:
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size_t _NBrows;
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size_t _NBcolumns;
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size_t _NBdepths;
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size_t _NBpixels;
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size_t _doubleNBpixels;
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// utilities
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static type _abs(const type x);
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};
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///////////////////////////////////////////////////////////////////////
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/// normalize output between 0 and 255, can be applied on images of different size that the declared size if nbPixels parameters is setted up;
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template <class type>
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void TemplateBuffer<type>::normalizeGrayOutput_0_maxOutputValue(type *inputOutputBuffer, const size_t processedPixels, const type maxOutputValue)
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{
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type maxValue=inputOutputBuffer[0], minValue=inputOutputBuffer[0];
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// get the min and max value
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register type *inputOutputBufferPTR=inputOutputBuffer;
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for (register size_t j = 0; j<processedPixels; ++j)
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{
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type pixValue = *(inputOutputBufferPTR++);
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if (maxValue < pixValue)
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maxValue = pixValue;
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else if (minValue > pixValue)
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minValue = pixValue;
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}
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// change the range of the data to 0->255
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type factor = maxOutputValue/(maxValue-minValue);
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type offset = (type)(-minValue*factor);
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inputOutputBufferPTR=inputOutputBuffer;
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for (register size_t j = 0; j < processedPixels; ++j, ++inputOutputBufferPTR)
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*inputOutputBufferPTR=*(inputOutputBufferPTR)*factor+offset;
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}
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// normalize data with a sigmoide close to 0 (saturates values for those superior to 0)
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template <class type>
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void TemplateBuffer<type>::normalizeGrayOutputNearZeroCentreredSigmoide(type *inputBuffer, type *outputBuffer, const type sensitivity, const type maxOutputValue)
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{
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if (inputBuffer==NULL)
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inputBuffer=Buffer();
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if (outputBuffer==NULL)
|
|
outputBuffer=Buffer();
|
|
|
|
type X0cube=sensitivity*sensitivity*sensitivity;
|
|
|
|
register type *inputBufferPTR=inputBuffer;
|
|
register type *outputBufferPTR=outputBuffer;
|
|
|
|
for (register size_t j = 0; j < _NBpixels; ++j, ++inputBufferPTR)
|
|
{
|
|
|
|
type currentCubeLuminance=*inputBufferPTR**inputBufferPTR**inputBufferPTR;
|
|
*(outputBufferPTR++)=maxOutputValue*currentCubeLuminance/(currentCubeLuminance+X0cube);
|
|
}
|
|
}
|
|
|
|
// normalize and adjust luminance with a centered to 128 sigmode
|
|
template <class type>
|
|
void TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide(const type meanValue, const type sensitivity, const type maxOutputValue, type *inputBuffer, type *outputBuffer, const unsigned int nbPixels)
|
|
{
|
|
|
|
if (sensitivity==1.0)
|
|
{
|
|
std::cerr<<"TemplateBuffer::TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide error: 2nd parameter (sensitivity) must not equal 0, copying original data..."<<std::endl;
|
|
memcpy(outputBuffer, inputBuffer, sizeof(type)*nbPixels);
|
|
return;
|
|
}
|
|
|
|
type X0=maxOutputValue/(sensitivity-(type)1.0);
|
|
|
|
register type *inputBufferPTR=inputBuffer;
|
|
register type *outputBufferPTR=outputBuffer;
|
|
|
|
for (register size_t j = 0; j < nbPixels; ++j, ++inputBufferPTR)
|
|
*(outputBufferPTR++)=(meanValue+(meanValue+X0)*(*(inputBufferPTR)-meanValue)/(_abs(*(inputBufferPTR)-meanValue)+X0));
|
|
|
|
}
|
|
|
|
// center and reduct the image (image-mean)/std
|
|
template <class type>
|
|
void TemplateBuffer<type>::centerReductImageLuminance(type *inputOutputBuffer)
|
|
{
|
|
// if outputBuffer unsassigned, the rewrite the buffer
|
|
if (inputOutputBuffer==NULL)
|
|
inputOutputBuffer=Buffer();
|
|
type meanValue=0, stdValue=0;
|
|
|
|
// compute mean value
|
|
for (register size_t j = 0; j < _NBpixels; ++j)
|
|
meanValue+=inputOutputBuffer[j];
|
|
meanValue/=((type)_NBpixels);
|
|
|
|
// compute std value
|
|
register type *inputOutputBufferPTR=inputOutputBuffer;
|
|
for (size_t index=0;index<_NBpixels;++index)
|
|
{
|
|
type inputMinusMean=*(inputOutputBufferPTR++)-meanValue;
|
|
stdValue+=inputMinusMean*inputMinusMean;
|
|
}
|
|
|
|
stdValue=sqrt(stdValue/((type)_NBpixels));
|
|
// adjust luminance in regard of mean and std value;
|
|
inputOutputBufferPTR=inputOutputBuffer;
|
|
for (size_t index=0;index<_NBpixels;++index, ++inputOutputBufferPTR)
|
|
*inputOutputBufferPTR=(*(inputOutputBufferPTR)-meanValue)/stdValue;
|
|
}
|
|
|
|
|
|
template <class type>
|
|
type TemplateBuffer<type>::_abs(const type x)
|
|
{
|
|
|
|
if (x>0)
|
|
return x;
|
|
else
|
|
return -x;
|
|
}
|
|
|
|
template < >
|
|
inline int TemplateBuffer<int>::_abs(const int x)
|
|
{
|
|
return std::abs(x);
|
|
}
|
|
template < >
|
|
inline double TemplateBuffer<double>::_abs(const double x)
|
|
{
|
|
return std::fabs(x);
|
|
}
|
|
|
|
template < >
|
|
inline float TemplateBuffer<float>::_abs(const float x)
|
|
{
|
|
return std::fabs(x);
|
|
}
|
|
|
|
}
|
|
#endif
|
|
|
|
|
|
|