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278 lines
14 KiB
C++
278 lines
14 KiB
C++
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//============================================================================
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// Name : HighDynamicRange_RetinaCompression.cpp
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// Author : Alexandre Benoit (benoit.alexandre.vision@gmail.com)
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// Version : 0.1
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// Copyright : Alexandre Benoit, LISTIC Lab, july 2011
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// Description : HighDynamicRange compression (tone mapping) with the help of the Gipsa/Listic's retina in C++, Ansi-style
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//============================================================================
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#include <iostream>
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#include <cstring>
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#include "opencv2/opencv.hpp"
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void help(std::string errorMessage)
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{
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std::cout<<"Program init error : "<<errorMessage<<std::endl;
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std::cout<<"\nProgram call procedure : ./OpenEXRimages_HighDynamicRange_Retina_toneMapping [OpenEXR image to process]"<<std::endl;
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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;
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std::cout<<"\nExamples:"<<std::endl;
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std::cout<<"\t-Image processing : ./OpenEXRimages_HighDynamicRange_Retina_toneMapping memorial.exr"<<std::endl;
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}
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// simple procedure for 1D curve tracing
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void drawPlot(const cv::Mat curve, const std::string figureTitle, const int lowerLimit, const int upperLimit)
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{
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//std::cout<<"curve size(h,w) = "<<curve.size().height<<", "<<curve.size().width<<std::endl;
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cv::Mat displayedCurveImage = cv::Mat::ones(200, curve.size().height, CV_8U);
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cv::Mat windowNormalizedCurve;
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normalize(curve, windowNormalizedCurve, 0, 200, CV_MINMAX, CV_32F);
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displayedCurveImage = cv::Scalar::all(255); // set a white background
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int binW = cvRound((double)displayedCurveImage.cols/curve.size().height);
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for( int i = 0; i < curve.size().height; i++ )
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rectangle( displayedCurveImage, cv::Point(i*binW, displayedCurveImage.rows),
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cv::Point((i+1)*binW, displayedCurveImage.rows - cvRound(windowNormalizedCurve.at<float>(i))),
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cv::Scalar::all(0), -1, 8, 0 );
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rectangle( displayedCurveImage, cv::Point(0, 0),
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cv::Point((lowerLimit)*binW, 200),
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cv::Scalar::all(128), -1, 8, 0 );
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rectangle( displayedCurveImage, cv::Point(displayedCurveImage.cols, 0),
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cv::Point((upperLimit)*binW, 200),
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cv::Scalar::all(128), -1, 8, 0 );
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cv::imshow(figureTitle, displayedCurveImage);
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}
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/*
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* objective : get the gray level map of the input image and rescale it to the range [0-255]
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*/void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, const float histogramClippingLimit)
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{
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// adjust output matrix wrt the input size but single channel
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std::cout<<"Input image rescaling with histogram edges cutting (in order to eliminate bad pixels created during the HDR image creation) :"<<std::endl;
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//std::cout<<"=> image size (h,w,channels) = "<<inputMat.size().height<<", "<<inputMat.size().width<<", "<<inputMat.channels()<<std::endl;
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//std::cout<<"=> pixel coding (nbchannel, bytes per channel) = "<<inputMat.elemSize()/inputMat.elemSize1()<<", "<<inputMat.elemSize1()<<std::endl;
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// rescale between 0-255, keeping floating point values
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cv::normalize(inputMat, outputMat, 0.0, 255.0, cv::NORM_MINMAX);
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// extract a 8bit image that will be used for histogram edge cut
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cv::Mat intGrayImage;
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if (inputMat.channels()==1)
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{
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outputMat.convertTo(intGrayImage, CV_8U);
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}else
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{
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cv::Mat rgbIntImg;
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outputMat.convertTo(rgbIntImg, CV_8UC3);
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cvtColor(rgbIntImg, intGrayImage, CV_BGR2GRAY);
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}
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// get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
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cv::Mat dst, hist;
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int histSize = 256;
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calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
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cv::Mat normalizedHist;
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normalize(hist, normalizedHist, 1, 0, cv::NORM_L1, CV_32F); // normalize histogram so that its sum equals 1
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double min_val, max_val;
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CvMat histArr(normalizedHist);
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cvMinMaxLoc(&histArr, &min_val, &max_val);
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//std::cout<<"Hist max,min = "<<max_val<<", "<<min_val<<std::endl;
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// compute density probability
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cv::Mat denseProb=cv::Mat::zeros(normalizedHist.size(), CV_32F);
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denseProb.at<float>(0)=normalizedHist.at<float>(0);
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int histLowerLimit=0, histUpperLimit=0;
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for (int i=1;i<normalizedHist.size().height;++i)
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{
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denseProb.at<float>(i)=denseProb.at<float>(i-1)+normalizedHist.at<float>(i);
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//std::cout<<normalizedHist.at<float>(i)<<", "<<denseProb.at<float>(i)<<std::endl;
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if ( denseProb.at<float>(i)<histogramClippingLimit)
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histLowerLimit=i;
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if ( denseProb.at<float>(i)<1-histogramClippingLimit)
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histUpperLimit=i;
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}
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// deduce min and max admitted gray levels
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float minInputValue = (float)histLowerLimit/histSize*255;
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float maxInputValue = (float)histUpperLimit/histSize*255;
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std::cout<<"=> Histogram limits "
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<<"\n\t"<<histogramClippingLimit*100<<"% index = "<<histLowerLimit<<" => normalizedHist value = "<<denseProb.at<float>(histLowerLimit)<<" => input gray level = "<<minInputValue
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<<"\n\t"<<(1-histogramClippingLimit)*100<<"% index = "<<histUpperLimit<<" => normalizedHist value = "<<denseProb.at<float>(histUpperLimit)<<" => input gray level = "<<maxInputValue
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<<std::endl;
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//drawPlot(denseProb, "input histogram density probability", histLowerLimit, histUpperLimit);
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drawPlot(normalizedHist, "input histogram", histLowerLimit, histUpperLimit);
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// rescale image range [minInputValue-maxInputValue] to [0-255]
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outputMat-=minInputValue;
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outputMat*=255.0/(maxInputValue-minInputValue);
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// cut original histogram and back project to original image
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cv::threshold( outputMat, outputMat, 255.0, 255.0, 2 ); //THRESH_TRUNC, clips values above 255
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cv::threshold( outputMat, outputMat, 0.0, 0.0, 3 ); //THRESH_TOZERO, clips values under 0
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}
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// basic callback method for interface management
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cv::Mat inputImage;
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cv::Mat imageInputRescaled;
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int histogramClippingValue;
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void callBack_rescaleGrayLevelMat(int, void*)
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{
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std::cout<<"Histogram clipping value changed, current value = "<<histogramClippingValue<<std::endl;
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rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)(histogramClippingValue/100.0));
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normalize(imageInputRescaled, imageInputRescaled, 0.0, 255.0, cv::NORM_MINMAX);
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}
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cv::Ptr<cv::Retina> retina;
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int retinaHcellsGain;
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int localAdaptation_photoreceptors, localAdaptation_Gcells;
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void callBack_updateRetinaParams(int, void*)
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{
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retina->setupOPLandIPLParvoChannel(true, true, (float)(localAdaptation_photoreceptors/200.0), 0.5f, 0.43f, (float)retinaHcellsGain, 1.f, 7.f, (float)(localAdaptation_Gcells/200.0));
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}
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int colorSaturationFactor;
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void callback_saturateColors(int, void*)
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{
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retina->setColorSaturation(true, (float)colorSaturationFactor);
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}
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int main(int argc, char* argv[]) {
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// welcome message
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std::cout<<"*********************************************************************************"<<std::endl;
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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;
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std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
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std::cout<<"* This demo focuses demonstration of the dynamic compression capabilities of the model"<<std::endl;
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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;
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std::cout<<"* The retina model still have the following properties:"<<std::endl;
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std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
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std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
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std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
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std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
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std::cout<<"* for more information, reer to the following papers :"<<std::endl;
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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;
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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;
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std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
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std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
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std::cout<<"*********************************************************************************"<<std::endl;
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std::cout<<"** WARNING : this sample requires OpenCV to be configured with OpenEXR support **"<<std::endl;
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std::cout<<"*********************************************************************************"<<std::endl;
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std::cout<<"*** You can use free tools to generate OpenEXR images from images sets : ***"<<std::endl;
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std::cout<<"*** => 1. take a set of photos from the same viewpoint using bracketing ***"<<std::endl;
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std::cout<<"*** => 2. generate an OpenEXR image with tools like qtpfsgui.sourceforge.net ***"<<std::endl;
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std::cout<<"*** => 3. apply tone mapping with this program ***"<<std::endl;
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std::cout<<"*********************************************************************************"<<std::endl;
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// basic input arguments checking
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if (argc<2)
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{
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help("bad number of parameter");
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return -1;
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}
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bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
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std::string inputImageName=argv[1];
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//////////////////////////////////////////////////////////////////////////////
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// checking input media type (still image, video file, live video acquisition)
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std::cout<<"RetinaDemo: processing image "<<inputImageName<<std::endl;
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// image processing case
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// declare the retina input buffer... that will be fed differently in regard of the input media
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inputImage = cv::imread(inputImageName, -1); // load image in RGB mode
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std::cout<<"=> image size (h,w) = "<<inputImage.size().height<<", "<<inputImage.size().width<<std::endl;
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if (!inputImage.total())
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{
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help("could not load image, program end");
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return -1;
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}
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// rescale between 0 and 1
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normalize(inputImage, inputImage, 0.0, 1.0, cv::NORM_MINMAX);
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cv::Mat gammaTransformedImage;
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cv::pow(inputImage, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
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imshow("EXR image original image, 16bits=>8bits linear rescaling ", inputImage);
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imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage);
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if (inputImage.empty())
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{
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help("Input image could not be loaded, aborting");
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return -1;
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}
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//////////////////////////////////////////////////////////////////////////////
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// Program start in a try/catch safety context (Retina may throw errors)
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try
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{
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/* create a retina instance with default parameters setup, uncomment the initialisation you wanna test
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* -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
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*/
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if (useLogSampling)
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{
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retina = new cv::Retina(inputImage.size(),true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
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}
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else// -> else allocate "classical" retina :
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retina = new cv::Retina(inputImage.size());
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// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
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retina->write("RetinaDefaultParameters.xml");
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// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
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retina->activateMovingContoursProcessing(false);
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// declare retina output buffers
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cv::Mat retinaOutput_parvo;
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/////////////////////////////////////////////
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// prepare displays and interactions
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histogramClippingValue=0; // default value... updated with interface slider
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//inputRescaleMat = inputImage;
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//outputRescaleMat = imageInputRescaled;
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cv::namedWindow("Retina input image (with cut edges histogram for basic pixels error avoidance)",1);
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cv::createTrackbar("histogram edges clipping limit", "Retina input image (with cut edges histogram for basic pixels error avoidance)",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
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cv::namedWindow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", 1);
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colorSaturationFactor=3;
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cv::createTrackbar("Color saturation", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &colorSaturationFactor,5,callback_saturateColors);
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retinaHcellsGain=40;
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cv::createTrackbar("Hcells gain", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping",&retinaHcellsGain,100,callBack_updateRetinaParams);
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localAdaptation_photoreceptors=197;
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localAdaptation_Gcells=190;
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cv::createTrackbar("Ph sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
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cv::createTrackbar("Gcells sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
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/////////////////////////////////////////////
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// apply default parameters of user interaction variables
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rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100);
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retina->setColorSaturation(true,(float)colorSaturationFactor);
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callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
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// processing loop with stop condition
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bool continueProcessing=true;
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while(continueProcessing)
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{
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// run retina filter
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retina->run(imageInputRescaled);
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// Retrieve and display retina output
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retina->getParvo(retinaOutput_parvo);
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cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
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cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
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cv::waitKey(10);
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}
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}catch(cv::Exception e)
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{
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std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
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}
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// Program end message
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std::cout<<"Retina demo end"<<std::endl;
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return 0;
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}
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