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Merge pull request #24 from albenoit/master

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added samples to use retina in its basic form and for HDR tone mapping
pull/33/head
Alexandre Benoit 11 years ago
parent 9ecdd12311 3ae595c1a7
commit ed3e450695
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  1. 305
      modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
  2. 159
      modules/bioinspired/samples/retinaDemo.cpp

305
modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
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//============================================================================
// Name : OpenEXRimages_HDR_Retina_toneMapping.cpp
// Author : Alexandre Benoit (benoit.alexandre.vision@gmail.com)
// Version : 0.1
// Copyright : Alexandre Benoit, LISTIC Lab, july 2011
// Description : HighDynamicRange retina tone mapping with the help of the Gipsa/Listic's retina in C++, Ansi-style
//============================================================================
#include <iostream>
#include <cstring>
#include "opencv2/bioinspired.hpp" // retina based algorithms
#include "opencv2/imgproc.hpp" // cvCvtcolor function
#include "opencv2/highgui.hpp" // display
static void help(std::string errorMessage)
{
std::cout<<"Program init error : "<<errorMessage<<std::endl;
std::cout<<"\nProgram call procedure : ./OpenEXRimages_HDR_Retina_toneMapping [OpenEXR image to process]"<<std::endl;
std::cout<<"\t[OpenEXR image to process] : the input HDR image to process, must be an OpenEXR format, see http://www.openexr.com/ to get some samples or create your own using camera bracketing and Photoshop or equivalent software for OpenEXR image synthesis"<<std::endl;
std::cout<<"\nExamples:"<<std::endl;
std::cout<<"\t-Image processing : ./OpenEXRimages_HDR_Retina_toneMapping memorial.exr"<<std::endl;
}
// simple procedure for 1D curve tracing
static void drawPlot(const cv::Mat curve, const std::string figureTitle, const int lowerLimit, const int upperLimit)
{
//std::cout<<"curve size(h,w) = "<<curve.size().height<<", "<<curve.size().width<<std::endl;
cv::Mat displayedCurveImage = cv::Mat::ones(200, curve.size().height, CV_8U);
cv::Mat windowNormalizedCurve;
normalize(curve, windowNormalizedCurve, 0, 200, cv::NORM_MINMAX, CV_32F);
displayedCurveImage = cv::Scalar::all(255); // set a white background
int binW = cvRound((double)displayedCurveImage.cols/curve.size().height);
for( int i = 0; i < curve.size().height; i++ )
rectangle( displayedCurveImage, cv::Point(i*binW, displayedCurveImage.rows),
cv::Point((i+1)*binW, displayedCurveImage.rows - cvRound(windowNormalizedCurve.at<float>(i))),
cv::Scalar::all(0), -1, 8, 0 );
rectangle( displayedCurveImage, cv::Point(0, 0),
cv::Point((lowerLimit)*binW, 200),
cv::Scalar::all(128), -1, 8, 0 );
rectangle( displayedCurveImage, cv::Point(displayedCurveImage.cols, 0),
cv::Point((upperLimit)*binW, 200),
cv::Scalar::all(128), -1, 8, 0 );
cv::imshow(figureTitle, displayedCurveImage);
}
/*
* objective : get the gray level map of the input image and rescale it to the range [0-255]
*/
static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, const float histogramClippingLimit)
{
// adjust output matrix wrt the input size but single channel
std::cout<<"Input image rescaling with histogram edges cutting (in order to eliminate bad pixels created during the HDR image creation) :"<<std::endl;
//std::cout<<"=> image size (h,w,channels) = "<<inputMat.size().height<<", "<<inputMat.size().width<<", "<<inputMat.channels()<<std::endl;
//std::cout<<"=> pixel coding (nbchannel, bytes per channel) = "<<inputMat.elemSize()/inputMat.elemSize1()<<", "<<inputMat.elemSize1()<<std::endl;
// rescale between 0-255, keeping floating point values
cv::normalize(inputMat, outputMat, 0.0, 255.0, cv::NORM_MINMAX);
// extract a 8bit image that will be used for histogram edge cut
cv::Mat intGrayImage;
if (inputMat.channels()==1)
{
outputMat.convertTo(intGrayImage, CV_8U);
}else
{
cv::Mat rgbIntImg;
outputMat.convertTo(rgbIntImg, CV_8UC3);
cvtColor(rgbIntImg, intGrayImage, cv::COLOR_BGR2GRAY);
}
// get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
cv::Mat dst, hist;
int histSize = 256;
calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
cv::Mat normalizedHist;
normalize(hist, normalizedHist, 1, 0, cv::NORM_L1, CV_32F); // normalize histogram so that its sum equals 1
double min_val, max_val;
minMaxLoc(normalizedHist, &min_val, &max_val);
//std::cout<<"Hist max,min = "<<max_val<<", "<<min_val<<std::endl;
// compute density probability
cv::Mat denseProb=cv::Mat::zeros(normalizedHist.size(), CV_32F);
denseProb.at<float>(0)=normalizedHist.at<float>(0);
int histLowerLimit=0, histUpperLimit=0;
for (int i=1;i<normalizedHist.size().height;++i)
{
denseProb.at<float>(i)=denseProb.at<float>(i-1)+normalizedHist.at<float>(i);
//std::cout<<normalizedHist.at<float>(i)<<", "<<denseProb.at<float>(i)<<std::endl;
if ( denseProb.at<float>(i)<histogramClippingLimit)
histLowerLimit=i;
if ( denseProb.at<float>(i)<1-histogramClippingLimit)
histUpperLimit=i;
}
// deduce min and max admitted gray levels
float minInputValue = (float)histLowerLimit/histSize*255;
float maxInputValue = (float)histUpperLimit/histSize*255;
std::cout<<"=> Histogram limits "
<<"\n\t"<<histogramClippingLimit*100<<"% index = "<<histLowerLimit<<" => normalizedHist value = "<<denseProb.at<float>(histLowerLimit)<<" => input gray level = "<<minInputValue
<<"\n\t"<<(1-histogramClippingLimit)*100<<"% index = "<<histUpperLimit<<" => normalizedHist value = "<<denseProb.at<float>(histUpperLimit)<<" => input gray level = "<<maxInputValue
<<std::endl;
//drawPlot(denseProb, "input histogram density probability", histLowerLimit, histUpperLimit);
drawPlot(normalizedHist, "input histogram", histLowerLimit, histUpperLimit);
// rescale image range [minInputValue-maxInputValue] to [0-255]
outputMat-=minInputValue;
outputMat*=255.0/(maxInputValue-minInputValue);
// cut original histogram and back project to original image
cv::threshold( outputMat, outputMat, 255.0, 255.0, 2 ); //THRESH_TRUNC, clips values above 255
cv::threshold( outputMat, outputMat, 0.0, 0.0, 3 ); //THRESH_TOZERO, clips values under 0
}
// basic callback method for interface management
cv::Mat inputImage;
cv::Mat imageInputRescaled;
int histogramClippingValue;
static void callBack_rescaleGrayLevelMat(int, void*)
{
std::cout<<"Histogram clipping value changed, current value = "<<histogramClippingValue<<std::endl;
rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)(histogramClippingValue/100.0));
normalize(imageInputRescaled, imageInputRescaled, 0.0, 255.0, cv::NORM_MINMAX);
}
cv::Ptr<cv::bioinspired::Retina> retina;
int retinaHcellsGain;
int localAdaptation_photoreceptors, localAdaptation_Gcells;
static void callBack_updateRetinaParams(int, void*)
{
retina->setupOPLandIPLParvoChannel(true, true, (float)(localAdaptation_photoreceptors/200.0), 0.5f, 0.43f, (float)retinaHcellsGain, 1.f, 7.f, (float)(localAdaptation_Gcells/200.0));
}
int colorSaturationFactor;
static void callback_saturateColors(int, void*)
{
retina->setColorSaturation(true, (float)colorSaturationFactor);
}
int main(int argc, char* argv[])
{
// welcome message
std::cout<<"*********************************************************************************"<<std::endl;
std::cout<<"* Retina demonstration for High Dynamic Range compression (tone-mapping) : demonstrates the use of a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
std::cout<<"* This demo focuses demonstration of the dynamic compression capabilities of the model"<<std::endl;
std::cout<<"* => the main application is tone mapping of HDR images (i.e. see on a 8bit display a more than 8bits coded (up to 16bits) image with details in high and low luminance ranges"<<std::endl;
std::cout<<"* The retina model still have the following properties:"<<std::endl;
std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
std::cout<<"* for more information, reer to the following papers :"<<std::endl;
std::cout<<"* 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"<<std::endl;
std::cout<<"* 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."<<std::endl;
std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
std::cout<<"*********************************************************************************"<<std::endl;
std::cout<<"** WARNING : this sample requires OpenCV to be configured with OpenEXR support **"<<std::endl;
std::cout<<"*********************************************************************************"<<std::endl;
std::cout<<"*** You can use free tools to generate OpenEXR images from images sets : ***"<<std::endl;
std::cout<<"*** => 1. take a set of photos from the same viewpoint using bracketing ***"<<std::endl;
std::cout<<"*** => 2. generate an OpenEXR image with tools like qtpfsgui.sourceforge.net ***"<<std::endl;
std::cout<<"*** => 3. apply tone mapping with this program ***"<<std::endl;
std::cout<<"*********************************************************************************"<<std::endl;
// basic input arguments checking
if (argc<2)
{
help("bad number of parameter");
return -1;
}
bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
int chosenMethod=0;
if (!strcmp(argv[argc-1], "fast"))
{
chosenMethod=1;
std::cout<<"Using fast method (no spectral whithning), adaptation of Meylan&al 2008 method"<<std::endl;
}
std::string inputImageName=argv[1];
//////////////////////////////////////////////////////////////////////////////
// checking input media type (still image, video file, live video acquisition)
std::cout<<"RetinaDemo: processing image "<<inputImageName<<std::endl;
// image processing case
// declare the retina input buffer... that will be fed differently in regard of the input media
inputImage = cv::imread(inputImageName, -1); // load image in RGB mode
std::cout<<"=> image size (h,w) = "<<inputImage.size().height<<", "<<inputImage.size().width<<std::endl;
if (!inputImage.total())
{
help("could not load image, program end");
return -1;
}
// rescale between 0 and 1
normalize(inputImage, inputImage, 0.0, 1.0, cv::NORM_MINMAX);
cv::Mat gammaTransformedImage;
cv::pow(inputImage, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
imshow("EXR image original image, 16bits=>8bits linear rescaling ", inputImage);
imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage);
if (inputImage.empty())
{
help("Input image could not be loaded, aborting");
return -1;
}
//////////////////////////////////////////////////////////////////////////////
// Program start in a try/catch safety context (Retina may throw errors)
try
{
/* create a retina instance with default parameters setup, uncomment the initialisation you wanna test
* -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
*/
if (useLogSampling)
{
retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
retina = cv::bioinspired::createRetina(inputImage.size());
// create a fast retina tone mapper (Meyla&al algorithm)
std::cout<<"Allocating fast tone mapper..."<<std::endl;
//cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
std::cout<<"Fast tone mapper allocated"<<std::endl;
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
retina->write("RetinaDefaultParameters.xml");
// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
retina->activateMovingContoursProcessing(false);
// declare retina output buffers
cv::Mat retinaOutput_parvo;
/////////////////////////////////////////////
// prepare displays and interactions
histogramClippingValue=0; // default value... updated with interface slider
//inputRescaleMat = inputImage;
//outputRescaleMat = imageInputRescaled;
cv::namedWindow("Processing configuration",1);
cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
colorSaturationFactor=3;
cv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors);
retinaHcellsGain=40;
cv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams);
localAdaptation_photoreceptors=197;
localAdaptation_Gcells=190;
cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
/////////////////////////////////////////////
// apply default parameters of user interaction variables
rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100);
retina->setColorSaturation(true,(float)colorSaturationFactor);
callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
// processing loop with stop condition
bool continueProcessing=true;
while(continueProcessing)
{
// run retina filter
if (!chosenMethod)
{
retina->run(imageInputRescaled);
// Retrieve and display retina output
retina->getParvo(retinaOutput_parvo);
cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
cv::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
}
else
{
// apply the simplified hdr tone mapping method
cv::Mat fastToneMappingOutput;
retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
}
/*cv::Mat fastToneMappingOutput_specificObject;
fastToneMapper->setup(3.f, 1.5f, 1.f);
fastToneMapper->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput_specificObject);
cv::imshow("### Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput_specificObject);
*/
cv::waitKey(10);
}
}catch(cv::Exception e)
{
std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
}
// Program end message
std::cout<<"Retina demo end"<<std::endl;
return 0;
}

159
modules/bioinspired/samples/retinaDemo.cpp
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//============================================================================
// Name : retinademo.cpp
// Author : Alexandre Benoit, benoit.alexandre.vision@gmail.com
// Version : 0.1
// Copyright : LISTIC/GIPSA French Labs, july 2011
// Description : Gipsa/LISTIC Labs retina demo in C++, Ansi-style
//============================================================================
#include <iostream>
#include <cstring>
#include "opencv2/bioinspired.hpp"
#include "opencv2/highgui.hpp"
static void help(std::string errorMessage)
{
std::cout<<"Program init error : "<<errorMessage<<std::endl;
std::cout<<"\nProgram call procedure : retinaDemo [processing mode] [Optional : media target] [Optional LAST parameter: \"log\" to activate retina log sampling]"<<std::endl;
std::cout<<"\t[processing mode] :"<<std::endl;
std::cout<<"\t -image : for still image processing"<<std::endl;
std::cout<<"\t -video : for video stream processing"<<std::endl;
std::cout<<"\t[Optional : media target] :"<<std::endl;
std::cout<<"\t if processing an image or video file, then, specify the path and filename of the target to process"<<std::endl;
std::cout<<"\t leave empty if processing video stream coming from a connected video device"<<std::endl;
std::cout<<"\t[Optional : activate retina log sampling] : an optional last parameter can be specified for retina spatial log sampling"<<std::endl;
std::cout<<"\t set \"log\" without quotes to activate this sampling, output frame size will be divided by 4"<<std::endl;
std::cout<<"\nExamples:"<<std::endl;
std::cout<<"\t-Image processing : ./retinaDemo -image lena.jpg"<<std::endl;
std::cout<<"\t-Image processing with log sampling : ./retinaDemo -image lena.jpg log"<<std::endl;
std::cout<<"\t-Video processing : ./retinaDemo -video myMovie.mp4"<<std::endl;
std::cout<<"\t-Live video processing : ./retinaDemo -video"<<std::endl;
std::cout<<"\nPlease start again with new parameters"<<std::endl;
}
int main(int argc, char* argv[])
{
// welcome message
std::cout<<"****************************************************"<<std::endl;
std::cout<<"* Retina demonstration : demonstrates the use of is a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
std::cout<<"* As a summary, these are the retina model properties:"<<std::endl;
std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
std::cout<<"* for more information, reer to the following papers :"<<std::endl;
std::cout<<"* 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"<<std::endl;
std::cout<<"* 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."<<std::endl;
std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
std::cout<<"****************************************************"<<std::endl;
std::cout<<" NOTE : this program generates the default retina parameters file 'RetinaDefaultParameters.xml'"<<std::endl;
std::cout<<" => you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<<std::endl;
// basic input arguments checking
if (argc<2)
{
help("bad number of parameter");
return -1;
}
bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
std::string inputMediaType=argv[1];
// declare the retina input buffer... that will be fed differently in regard of the input media
cv::Mat inputFrame;
cv::VideoCapture videoCapture; // in case a video media is used, its manager is declared here
//////////////////////////////////////////////////////////////////////////////
// checking input media type (still image, video file, live video acquisition)
if (!strcmp(inputMediaType.c_str(), "-image") && argc >= 3)
{
std::cout<<"RetinaDemo: processing image "<<argv[2]<<std::endl;
// image processing case
inputFrame = cv::imread(std::string(argv[2]), 1); // load image in RGB mode
}else
if (!strcmp(inputMediaType.c_str(), "-video"))
{
if (argc == 2 || (argc == 3 && useLogSampling)) // attempt to grab images from a video capture device
{
videoCapture.open(0);
}else// attempt to grab images from a video filestream
{
std::cout<<"RetinaDemo: processing video stream "<<argv[2]<<std::endl;
videoCapture.open(argv[2]);
}
// grab a first frame to check if everything is ok
videoCapture>>inputFrame;
}else
{
// bad command parameter
help("bad command parameter");
return -1;
}
if (inputFrame.empty())
{
help("Input media could not be loaded, aborting");
return -1;
}
//////////////////////////////////////////////////////////////////////////////
// Program start in a try/catch safety context (Retina may throw errors)
try
{
// create a retina instance with default parameters setup, uncomment the initialisation you wanna test
cv::Ptr<cv::bioinspired::Retina> myRetina;
// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
if (useLogSampling)
{
myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
myRetina = cv::bioinspired::createRetina(inputFrame.size());
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
myRetina->write("RetinaDefaultParameters.xml");
// load parameters if file exists
myRetina->setup("RetinaSpecificParameters.xml");
myRetina->clearBuffers();
// declare retina output buffers
cv::Mat retinaOutput_parvo;
cv::Mat retinaOutput_magno;
// processing loop with stop condition
bool continueProcessing=true; // FIXME : not yet managed during process...
while(continueProcessing)
{
// if using video stream, then, grabbing a new frame, else, input remains the same
if (videoCapture.isOpened())
videoCapture>>inputFrame;
// run retina filter
myRetina->run(inputFrame);
// Retrieve and display retina output
myRetina->getParvo(retinaOutput_parvo);
myRetina->getMagno(retinaOutput_magno);
cv::imshow("retina input", inputFrame);
cv::imshow("Retina Parvo", retinaOutput_parvo);
cv::imshow("Retina Magno", retinaOutput_magno);
cv::waitKey(5);
}
}catch(cv::Exception e)
{
std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
}
// Program end message
std::cout<<"Retina demo end"<<std::endl;
return 0;
}
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