void CameraCanvas::refreshFrame(){ if(!isSwitchedOn) return; if(!hasStreamInput) waitForRealInput(); loadNewFrame(); processNewData(); cv::imshow(this->canvasName, working_matrix); cv::imshow(t_str, working_threshold); cv::waitKey(1); usleep(15); }
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<4) { help("bad number of parameter"); return -1; } bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing int startFrameIndex=0, endFrameIndex=0, currentFrameIndex=0; sscanf(argv[2], "%d", &startFrameIndex); sscanf(argv[3], "%d", &endFrameIndex); std::string inputImageNamePrototype(argv[1]); ////////////////////////////////////////////////////////////////////////////// // checking input media type (still image, video file, live video acquisition) std::cout<<"RetinaDemo: setting up system with first image..."<<std::endl; loadNewFrame(inputImageNamePrototype, startFrameIndex, true); if (inputImage.empty()) { help("could not load image, program end"); 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 = new cv::Retina(inputImage.size(),true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0); } else// -> else allocate "classical" retina : retina = new cv::Retina(inputImage.size()); // 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 std::string retinaInputCorrected("Retina input image (with cut edges histogram for basic pixels error avoidance)"); cv::namedWindow(retinaInputCorrected,1); cv::createTrackbar("histogram edges clipping limit", "Retina input image (with cut edges histogram for basic pixels error avoidance)",&histogramClippingValue,50,callBack_rescaleGrayLevelMat); std::string RetinaParvoWindow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping"); cv::namedWindow(RetinaParvoWindow, 1); colorSaturationFactor=3; cv::createTrackbar("Color saturation", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &colorSaturationFactor,5,callback_saturateColors); retinaHcellsGain=40; cv::createTrackbar("Hcells gain", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping",&retinaHcellsGain,100,callBack_updateRetinaParams); localAdaptation_photoreceptors=197; localAdaptation_Gcells=190; cv::createTrackbar("Ph sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams); cv::createTrackbar("Gcells sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_Gcells,199,callBack_updateRetinaParams); std::string powerTransformedInput("EXR image with basic processing : 16bits=>8bits with gamma correction"); ///////////////////////////////////////////// // apply default parameters of user interaction variables callBack_updateRetinaParams(1,NULL); // first call for default parameters setup callback_saturateColors(1, NULL); // processing loop with stop condition currentFrameIndex=startFrameIndex; while(currentFrameIndex <= endFrameIndex) { loadNewFrame(inputImageNamePrototype, currentFrameIndex, false); if (inputImage.empty()) { std::cout<<"Could not load new image (index = "<<currentFrameIndex<<"), program end"<<std::endl; return -1; } // display input & process standard power transformation imshow("EXR image original image, 16bits=>8bits linear rescaling ", imageInputRescaled); cv::Mat gammaTransformedImage; cv::pow(imageInputRescaled, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5) imshow(powerTransformedInput, gammaTransformedImage); // run retina filter retina->run(imageInputRescaled); // Retrieve and display retina output retina->getParvo(retinaOutput_parvo); cv::imshow(retinaInputCorrected, imageInputRescaled/255.f); cv::imshow(RetinaParvoWindow, retinaOutput_parvo); cv::waitKey(4); // jump to next frame ++currentFrameIndex; } } 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; }