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363 lines
20 KiB
C++
363 lines
20 KiB
C++
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//============================================================================
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// Name : OpenEXRimages_HighDynamicRange_Retina_toneMapping_video.cpp
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// Author : Alexandre Benoit (benoit.alexandre.vision@gmail.com)
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// Version : 0.2
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// Copyright : Alexandre Benoit, LISTIC Lab, december 2011
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// Description : HighDynamicRange compression (tone mapping) for image sequences with the help of the Gipsa/Listic's retina in C++, Ansi-style
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// Known issues: the input OpenEXR sequences can have bad computed pixels that should be removed
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// => a simple method consists of cutting histogram edges (a slider for this on the UI is provided)
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// => however, in image sequences, this histogramm cut must be done in an elegant way from frame to frame... still not done...
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//============================================================================
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#include <iostream>
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#include <stdio.h>
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#include <cstring>
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#include "opencv2/contrib.hpp"
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#include "opencv2/highgui.hpp"
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static 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 sequence to process] [OPTIONNAL start frame] [OPTIONNAL end frame]"<<std::endl;
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std::cout<<"\t[OpenEXR image sequence to process] : std::sprintf style ready prototype filename of the input HDR images 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<<"\t\t => WARNING : image index number of digits cannot exceed 10"<<std::endl;
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std::cout<<"\t[start frame] : the starting frame tat should be considered"<<std::endl;
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std::cout<<"\t[end frame] : the ending frame tat should be considered"<<std::endl;
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std::cout<<"\nExamples:"<<std::endl;
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std::cout<<"\t-Image processing : ./OpenEXRimages_HighDynamicRange_Retina_toneMapping_video memorial%3d.exr 20 45"<<std::endl;
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std::cout<<"\t-Image processing : ./OpenEXRimages_HighDynamicRange_Retina_toneMapping_video memorial%3d.exr 20 45 log"<<std::endl;
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std::cout<<"\t ==> to process images from memorial020d.exr to memorial045d.exr"<<std::endl;
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}
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// simple procedure for 1D curve tracing
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static 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::NORM_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] if rescale0_255=TRUE, simply trunks else
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*/
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static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, const float histogramClippingLimit, const bool rescale0_255)
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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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// get min and max values to use afterwards if no 0-255 rescaling is used
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double maxInput, minInput, histNormRescalefactor=1.f;
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double histNormOffset=0.f;
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minMaxLoc(inputMat, &minInput, &maxInput);
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histNormRescalefactor=255.f/(maxInput-minInput);
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histNormOffset=minInput;
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std::cout<<"Hist max,min = "<<maxInput<<", "<<minInput<<" => scale, offset = "<<histNormRescalefactor<<", "<<histNormOffset<<std::endl;
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// rescale between 0-255, keeping floating point values
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cv::Mat normalisedImage;
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cv::normalize(inputMat, normalisedImage, 0.f, 255.f, cv::NORM_MINMAX);
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if (rescale0_255)
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normalisedImage.copyTo(outputMat);
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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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normalisedImage.convertTo(intGrayImage, CV_8U);
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}else
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{
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cv::Mat rgbIntImg;
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normalisedImage.convertTo(rgbIntImg, CV_8UC3);
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cv::cvtColor(rgbIntImg, intGrayImage, cv::COLOR_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.f, 0.f, cv::NORM_L1, CV_32F); // normalize histogram so that its sum equals 1
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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.f-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.f;
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float maxInputValue = (float)histUpperLimit/histSize*255.f;
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std::cout<<"=> Histogram limits "
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<<"\n\t"<<histogramClippingLimit*100.f<<"% index = "<<histLowerLimit<<" => normalizedHist value = "<<denseProb.at<float>(histLowerLimit)<<" => input gray level = "<<minInputValue
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<<"\n\t"<<(1.f-histogramClippingLimit)*100.f<<"% 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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if(rescale0_255) // rescale between 0-255 if asked to
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{
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cv::threshold( outputMat, outputMat, maxInputValue, maxInputValue, 2 ); //THRESH_TRUNC, clips values above maxInputValue
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cv::threshold( outputMat, outputMat, minInputValue, minInputValue, 3 ); //THRESH_TOZERO, clips values under minInputValue
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// rescale image range [minInputValue-maxInputValue] to [0-255]
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outputMat-=minInputValue;
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outputMat*=255.f/(maxInputValue-minInputValue);
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}else
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{
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inputMat.copyTo(outputMat);
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// update threshold in the initial input image range
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maxInputValue=(float)((maxInputValue-255.f)/histNormRescalefactor+maxInput);
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minInputValue=(float)(minInputValue/histNormRescalefactor+minInput);
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std::cout<<"===> Input Hist clipping values (max,min) = "<<maxInputValue<<", "<<minInputValue<<std::endl;
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cv::threshold( outputMat, outputMat, maxInputValue, maxInputValue, 2 ); //THRESH_TRUNC, clips values above maxInputValue
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cv::threshold( outputMat, outputMat, minInputValue, minInputValue, 3 ); //
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}
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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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float globalRescalefactor=1;
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cv::Scalar globalOffset=0;
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int histogramClippingValue;
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static 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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// rescale and process
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inputImage+=globalOffset;
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inputImage*=globalRescalefactor;
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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
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rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100.f, true);
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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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static 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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static 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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// loadNewFrame : loads a n image wrt filename parameters. it also manages image rescaling/histogram edges cutting (acts differently at first image i.e. if firstTimeread=true)
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static void loadNewFrame(const std::string filenamePrototype, const int currentFileIndex, const bool firstTimeread)
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{
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char *currentImageName=NULL;
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currentImageName = (char*)malloc(sizeof(char)*filenamePrototype.size()+10);
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// grab the first frame
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sprintf(currentImageName, filenamePrototype.c_str(), currentFileIndex);
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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: reading image : "<<currentImageName<<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(currentImageName, -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.empty())
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{
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help("could not load image, program end");
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return;;
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}
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// rescaling/histogram clipping stage
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// rescale between 0 and 1
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// TODO : take care of this step !!! maybe disable of do this in a nicer way ... each successive image should get the same transformation... but it depends on the initial image format
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double maxInput, minInput;
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minMaxLoc(inputImage, &minInput, &maxInput);
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std::cout<<"ORIGINAL IMAGE pixels values range (max,min) : "<<maxInput<<", "<<minInput<<std::endl;
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if (firstTimeread)
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{
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/* the first time, get the pixel values range and rougthly update scaling value
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in order to center values around 128 and getting a range close to [0-255],
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=> actually using a little less in order to let some more flexibility in range evolves...
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*/
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double maxInput1, minInput1;
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minMaxLoc(inputImage, &minInput1, &maxInput1);
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std::cout<<"FIRST IMAGE pixels values range (max,min) : "<<maxInput1<<", "<<minInput1<<std::endl;
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globalRescalefactor=(float)(50.0/(maxInput1-minInput1)); // less than 255 for flexibility... experimental value to be carefull about
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double channelOffset = -1.5*minInput;
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globalOffset= cv::Scalar(channelOffset, channelOffset, channelOffset, channelOffset);
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}
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// call the generic input image rescaling callback
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callBack_rescaleGrayLevelMat(1,NULL);
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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<4)
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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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int startFrameIndex=0, endFrameIndex=0, currentFrameIndex=0;
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sscanf(argv[2], "%d", &startFrameIndex);
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sscanf(argv[3], "%d", &endFrameIndex);
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std::string inputImageNamePrototype(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: setting up system with first image..."<<std::endl;
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loadNewFrame(inputImageNamePrototype, startFrameIndex, true);
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if (inputImage.empty())
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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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//////////////////////////////////////////////////////////////////////////////
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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 = cv::createRetina(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 = cv::createRetina(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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std::string retinaInputCorrected("Retina input image (with cut edges histogram for basic pixels error avoidance)");
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cv::namedWindow(retinaInputCorrected,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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std::string RetinaParvoWindow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping");
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cv::namedWindow(RetinaParvoWindow, 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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std::string powerTransformedInput("EXR image with basic processing : 16bits=>8bits with gamma correction");
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/////////////////////////////////////////////
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// apply default parameters of user interaction variables
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callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
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callback_saturateColors(1, NULL);
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// processing loop with stop condition
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currentFrameIndex=startFrameIndex;
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while(currentFrameIndex <= endFrameIndex)
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{
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loadNewFrame(inputImageNamePrototype, currentFrameIndex, false);
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if (inputImage.empty())
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{
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std::cout<<"Could not load new image (index = "<<currentFrameIndex<<"), program end"<<std::endl;
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return -1;
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}
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// display input & process standard power transformation
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imshow("EXR image original image, 16bits=>8bits linear rescaling ", imageInputRescaled);
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cv::Mat gammaTransformedImage;
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cv::pow(imageInputRescaled, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
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imshow(powerTransformedInput, gammaTransformedImage);
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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(retinaInputCorrected, imageInputRescaled/255.f);
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cv::imshow(RetinaParvoWindow, retinaOutput_parvo);
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cv::waitKey(4);
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// jump to next frame
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++currentFrameIndex;
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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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