/*#****************************************************************************** ** 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. ** ** ** 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. ** 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. ** ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications) ** ** Creation - enhancement process 2007-2011 ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France ** ** 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). ** Refer to the following research paper for more information: ** 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 ** 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: ** 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. ** ** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author : ** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper: ** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 ** _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. ** ====> more informations in the above cited Jeanny Heraults's book. ** ** License Agreement ** For Open Source Computer Vision Library ** ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. ** ** For Human Visual System tools (hvstools) ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, 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: ** ** * Redistributions of source code must retain the above copyright notice, ** this list of conditions and the following disclaimer. ** ** * Redistributions 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. *******************************************************************************/ /* * Retina.cpp * * Created on: Jul 19, 2011 * Author: Alexandre Benoit */ #include "precomp.hpp" #include "retinafilter.hpp" #include namespace cv { Retina::Retina(const std::string parametersSaveFile, const cv::Size inputSize) { _retinaFilter = 0; _init(parametersSaveFile, inputSize, true, RETINA_COLOR_BAYER, false); } Retina::Retina(const std::string parametersSaveFile, const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { _retinaFilter = 0; _init(parametersSaveFile, inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); }; Retina::~Retina() { if (_retinaFilter) delete _retinaFilter; } void Retina::setColorSaturation(const bool saturateColors, const float colorSaturationValue) { _retinaFilter->setColorSaturation(saturateColors, colorSaturationValue); } void Retina::setup(std::string retinaParameterFile, const bool applyDefaultSetupOnFailure) { // open specified parameters file std::cout<<"Retina::setup: setting up retina from parameter file : "<>colorMode; currFn["normaliseOutput"]>>normaliseOutput; currFn["photoreceptorsLocalAdaptationSensitivity"]>>photoreceptorsLocalAdaptationSensitivity; currFn["photoreceptorsTemporalConstant"]>>photoreceptorsTemporalConstant; currFn["photoreceptorsSpatialConstant"]>>photoreceptorsSpatialConstant; currFn["horizontalCellsGain"]>>horizontalCellsGain; currFn["hcellsTemporalConstant"]>>hcellsTemporalConstant; currFn["hcellsSpatialConstant"]>>hcellsSpatialConstant; currFn["ganglionCellsSensitivity"]>>ganglionCellsSensitivity; setupOPLandIPLParvoChannel(colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity); // init retina IPL magno setup currFn=rootFn["IPLmagno"]; currFn["normaliseOutput"]>>normaliseOutput; float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k; currFn["parasolCells_beta"]>>parasolCells_beta; currFn["parasolCells_tau"]>>parasolCells_tau; currFn["parasolCells_k"]>>parasolCells_k; currFn["amacrinCellsTemporalCutFrequency"]>>amacrinCellsTemporalCutFrequency; currFn["V0CompressionParameter"]>>V0CompressionParameter; currFn["localAdaptintegration_tau"]>>localAdaptintegration_tau; currFn["localAdaptintegration_k"]>>localAdaptintegration_k; setupIPLMagnoChannel(normaliseOutput, parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k); }catch(Exception &e) { std::cout<<"Retina::setup: resetting retina with default parameters"<"< keeping current parameters"< colorMode : " << currFn["colorMode"].operator int() << "\n==> normalizeParvoOutput :" << currFn["normaliseOutput"].operator int() << "\n==> photoreceptorsLocalAdaptationSensitivity : " << currFn["photoreceptorsLocalAdaptationSensitivity"].operator float() << "\n==> photoreceptorsTemporalConstant : " << currFn["photoreceptorsTemporalConstant"].operator float() << "\n==> photoreceptorsSpatialConstant : " << currFn["photoreceptorsSpatialConstant"].operator float() << "\n==> horizontalCellsGain : " << currFn["horizontalCellsGain"].operator float() << "\n==> hcellsTemporalConstant : " << currFn["hcellsTemporalConstant"].operator float() << "\n==> hcellsSpatialConstant : " << currFn["hcellsSpatialConstant"].operator float() << "\n==> parvoGanglionCellsSensitivity : " << currFn["ganglionCellsSensitivity"].operator float() <<"}\n"; // displaying IPL magno setup currFn=rootFn["IPLmagno"]; outmessage<<"Current Retina instance setup :" <<"\nIPLmagno"<<"{" << "\n==> normaliseOutput : " << currFn["normaliseOutput"].operator int() << "\n==> parasolCells_beta : " << currFn["parasolCells_beta"].operator float() << "\n==> parasolCells_tau : " << currFn["parasolCells_tau"].operator float() << "\n==> parasolCells_k : " << currFn["parasolCells_k"].operator float() << "\n==> amacrinCellsTemporalCutFrequency : " << currFn["amacrinCellsTemporalCutFrequency"].operator float() << "\n==> V0CompressionParameter : " << currFn["V0CompressionParameter"].operator float() << "\n==> localAdaptintegration_tau : " << currFn["localAdaptintegration_tau"].operator float() << "\n==> localAdaptintegration_k : " << currFn["localAdaptintegration_k"].operator float() <<"}"; } }catch(cv::Exception &e) { outmessage<<"Error reading parameters configuration file : "<setColorMode(colorMode); _retinaFilter->setPhotoreceptorsLocalAdaptationSensitivity(photoreceptorsLocalAdaptationSensitivity); _retinaFilter->setOPLandParvoParameters(0, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, HcellsTemporalConstant, HcellsSpatialConstant, ganglionCellsSensitivity); _retinaFilter->setParvoGanglionCellsLocalAdaptationSensitivity(ganglionCellsSensitivity); _retinaFilter->activateNormalizeParvoOutput_0_maxOutputValue(normaliseOutput); // save parameters in the xml parameters tree... if parameters file is already open if (!_parametersSaveFile.isOpened()) return; _parametersSaveFile<<"OPLandIPLparvo"<<"{"; _parametersSaveFile << "colorMode" << colorMode; _parametersSaveFile << "normaliseOutput" << normaliseOutput; _parametersSaveFile << "photoreceptorsLocalAdaptationSensitivity" << photoreceptorsLocalAdaptationSensitivity; _parametersSaveFile << "photoreceptorsTemporalConstant" << photoreceptorsTemporalConstant; _parametersSaveFile << "photoreceptorsSpatialConstant" << photoreceptorsSpatialConstant; _parametersSaveFile << "horizontalCellsGain" << horizontalCellsGain; _parametersSaveFile << "hcellsTemporalConstant" << HcellsTemporalConstant; _parametersSaveFile << "hcellsSpatialConstant" << HcellsSpatialConstant; _parametersSaveFile << "ganglionCellsSensitivity" << ganglionCellsSensitivity; _parametersSaveFile << "}"; } void Retina::setupIPLMagnoChannel(const bool normaliseOutput, const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float V0CompressionParameter, const float localAdaptintegration_tau, const float localAdaptintegration_k) { _retinaFilter->setMagnoCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k); _retinaFilter->activateNormalizeMagnoOutput_0_maxOutputValue(normaliseOutput); // save parameters in the xml parameters tree... if parameters file is already open if (!_parametersSaveFile.isOpened()) return; _parametersSaveFile<<"IPLmagno"<<"{"; _parametersSaveFile << "normaliseOutput" << normaliseOutput; _parametersSaveFile << "parasolCells_beta" << parasolCells_beta; _parametersSaveFile << "parasolCells_tau" << parasolCells_tau; _parametersSaveFile << "parasolCells_k" << parasolCells_k; _parametersSaveFile << "amacrinCellsTemporalCutFrequency" << amacrinCellsTemporalCutFrequency; _parametersSaveFile << "V0CompressionParameter" << V0CompressionParameter; _parametersSaveFile << "localAdaptintegration_tau" << localAdaptintegration_tau; _parametersSaveFile << "localAdaptintegration_k" << localAdaptintegration_k; _parametersSaveFile<<"}"; } void Retina::run(const cv::Mat &inputMatToConvert) { // first convert input image to the compatible format : std::valarray const bool colorMode = _convertCvMat2ValarrayBuffer(inputMatToConvert, _inputBuffer); // process the retina if (!_retinaFilter->runFilter(_inputBuffer, colorMode, false, colorMode, false)) throw cv::Exception(-1, "Retina cannot be applied, wrong input buffer size", "Retina::run", "Retina.h", 0); } void Retina::getParvo(cv::Mat &retinaOutput_parvo) { if (_retinaFilter->getColorMode()) { // reallocate output buffer (if necessary) _convertValarrayBuffer2cvMat(_retinaFilter->getColorOutput(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), true, retinaOutput_parvo); }else { // reallocate output buffer (if necessary) _convertValarrayBuffer2cvMat(_retinaFilter->getContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_parvo); } //retinaOutput_parvo/=255.0; } void Retina::getMagno(cv::Mat &retinaOutput_magno) { // reallocate output buffer (if necessary) _convertValarrayBuffer2cvMat(_retinaFilter->getMovingContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_magno); //retinaOutput_magno/=255.0; } // original API level data accessors void Retina::getMagno(std::valarray &){_retinaFilter->getMovingContours();} void Retina::getParvo(std::valarray &){_retinaFilter->getContours();} // private method called by constructirs void Retina::_init(const std::string parametersSaveFile, const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { _parametersSaveFileName = parametersSaveFile; // basic error check if (inputSize.height*inputSize.width <= 0) throw cv::Exception(-1, "Bad retina size setup : size height and with must be superior to zero", "Retina::setup", "Retina.h", 0); unsigned int nbPixels=inputSize.height*inputSize.width; // resize buffers if size does not match _inputBuffer.resize(nbPixels*3); // buffer supports gray images but also 3 channels color buffers... (larger is better...) // allocate the retina model if (_retinaFilter) delete _retinaFilter; _retinaFilter = new RetinaFilter(inputSize.height, inputSize.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); // prepare the parameter XML tree _parametersSaveFile.open(parametersSaveFile, cv::FileStorage::WRITE ); _parametersSaveFile<<"InputSize"<<"{"; _parametersSaveFile<<"height"<clearAllBuffers(); // report current configuration std::cout< &grayMatrixToConvert, const unsigned int nbRows, const unsigned int nbColumns, const bool colorMode, cv::Mat &outBuffer) { // fill output buffer with the valarray buffer const float *valarrayPTR=get_data(grayMatrixToConvert); if (!colorMode) { outBuffer.create(cv::Size(nbColumns, nbRows), CV_8U); for (unsigned int i=0;i(pixel)=(unsigned char)*(valarrayPTR++); } } }else { const unsigned int doubleNBpixels=_retinaFilter->getOutputNBpixels()*2; outBuffer.create(cv::Size(nbColumns, nbRows), CV_8UC3); for (unsigned int i=0;igetOutputNBpixels()); pixelValues[0]=(unsigned char)*(valarrayPTR+doubleNBpixels); outBuffer.at(pixel)=pixelValues; } } } } const bool Retina::_convertCvMat2ValarrayBuffer(const cv::Mat inputMatToConvert, std::valarray &outputValarrayMatrix) { // first check input consistency if (inputMatToConvert.empty()) throw cv::Exception(-1, "Retina cannot be applied, input buffer is empty", "Retina::run", "Retina.h", 0); // retreive color mode from image input int imageNumberOfChannels = inputMatToConvert.channels(); // convert to float AND fill the valarray buffer typedef float T; // define here the target pixel format, here, float const int dsttype = DataType::depth; // output buffer is float format if(imageNumberOfChannels==4) { // create a cv::Mat table (for RGBA planes) cv::Mat planes[] = { cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]), cv::Mat(inputMatToConvert.size(), dsttype) // last channel (alpha) does not point on the valarray (not usefull in our case) }; // split color cv::Mat in 4 planes... it fills valarray directely cv::split(cv::Mat_ >(inputMatToConvert), planes); }else if (imageNumberOfChannels==3) { // create a cv::Mat table (for RGB planes) cv::Mat planes[] = { cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]) }; // split color cv::Mat in 3 planes... it fills valarray directely cv::split(cv::Mat_ >(inputMatToConvert), planes); }else if(imageNumberOfChannels==1) { // create a cv::Mat header for the valarray cv::Mat dst(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]); inputMatToConvert.convertTo(dst, dsttype); } else CV_Error(CV_StsUnsupportedFormat, "input image must be single channel (gray levels), bgr format (color) or bgra (color with transparency which won't be considered"); return imageNumberOfChannels>1; // return bool : false for gray level image processing, true for color mode } void Retina::clearBuffers() {_retinaFilter->clearAllBuffers();} void Retina::activateMovingContoursProcessing(const bool activate){_retinaFilter->activateMovingContoursProcessing(activate);} void Retina::activateContoursProcessing(const bool activate){_retinaFilter->activateMovingContoursProcessing(activate);} } // end of namespace cv