From 154bc89d38b76c3a981354648941ee2ed7de7c06 Mon Sep 17 00:00:00 2001
From: Alexandre Benoit <benoit.alexandre.vision@gmail.com>
Date: Sun, 22 Jun 2014 15:58:17 +0200
Subject: [PATCH] added samples to use retina in its basic form and for HDR
 tone mapping

---
 .../OpenEXRimages_HDR_Retina_toneMapping.cpp  | 304 ++++++++++++++++++
 modules/bioinspired/samples/retinaDemo.cpp    | 158 +++++++++
 2 files changed, 462 insertions(+)
 create mode 100644 modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
 create mode 100644 modules/bioinspired/samples/retinaDemo.cpp

diff --git a/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp b/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
new file mode 100644
index 000000000..66cae7719
--- /dev/null
+++ b/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
@@ -0,0 +1,304 @@
+
+//============================================================================
+// 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;
+ }
diff --git a/modules/bioinspired/samples/retinaDemo.cpp b/modules/bioinspired/samples/retinaDemo.cpp
new file mode 100644
index 000000000..790e75383
--- /dev/null
+++ b/modules/bioinspired/samples/retinaDemo.cpp
@@ -0,0 +1,158 @@
+//============================================================================
+// 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;
+}