static void callBack_rescaleGrayLevelMat(int, void*)
{
std::cout<<"Histogram clipping value changed, current value = "<<histogramClippingValue<<std::endl;
// rescale and process
inputImage+=globalOffset;
inputImage*=globalRescalefactor;
inputImage+=cv::Scalar(50, 50, 50, 50); // WARNING value linked to the hardcoded value (200.0) used in the globalRescalefactor in order to center on the 128 mean value... experimental but... basic compromise
rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100.f, true);
}
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 [email protected]"<<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;
}
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);
}