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updated demos and tutorial regarding the Retina class transfer to bioinspired module.
This commit is contained in:
alexandre benoit
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@ -17,7 +17,7 @@ if(BUILD_DOCS AND HAVE_SPHINX)
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set(OPTIONAL_DOC_LIST "")
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set(OPENCV2_BASE_MODULES core imgproc highgui video calib3d features2d objdetect ml flann gpu photo stitching nonfree contrib legacy)
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set(OPENCV2_BASE_MODULES core imgproc highgui video calib3d features2d objdetect ml flann gpu photo stitching nonfree contrib legacy bioinspired)
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# build lists of modules to be documented
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set(OPENCV2_MODULES "")
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@ -156,17 +156,17 @@ As always, we would be happy to hear your comments and receive your contribution
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:width: 80pt
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:alt: gpu icon
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* :ref:`Table-Of-Content-Contrib`
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* :ref:`Table-Of-Content-Bioinspired`
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.. tabularcolumns:: m{100pt} m{300pt}
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.. cssclass:: toctableopencv
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=========== =======================================================
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|Contrib| Discover additional contribution to OpenCV.
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============= =======================================================
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|Bioinspired| Algorithms inspired from biological models.
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=========== =======================================================
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============= =======================================================
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.. |Contrib| image:: images/retina.jpg
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.. |Bioinspired| image:: images/retina.jpg
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:height: 80pt
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:width: 80pt
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:alt: gpu icon
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@ -219,6 +219,6 @@ As always, we would be happy to hear your comments and receive your contribution
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objdetect/table_of_content_objdetect/table_of_content_objdetect
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ml/table_of_content_ml/table_of_content_ml
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gpu/table_of_content_gpu/table_of_content_gpu
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contrib/table_of_content_contrib/table_of_content_contrib
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bioinspired/table_of_content_bioinspired/table_of_content_bioinspired
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ios/table_of_content_ios/table_of_content_ios
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general/table_of_content_general/table_of_content_general
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@ -52,6 +52,7 @@
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#include "opencv2/calib3d.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/contrib.hpp"
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#include "opencv2/bioinspired.hpp"
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#include "opencv2/ml.hpp"
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#endif
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@ -47,5 +47,5 @@
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#include "opencv2/bioinspired/retina.hpp"
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#include "opencv2/bioinspired/retinafasttonemapping.hpp"
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using namespace cv::hvstools;
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using namespace cv::hvstools; // used to avoid complex namespace inclusions cv::hvstools::Retina => cv::Retina preferred
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#endif
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@ -61,9 +61,12 @@ Here is an overview of the abstract Retina interface, allocate one instance with
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// parameters setup instance
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struct RetinaParameters; // this class is detailled later
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// main method for input frame processing
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// main method for input frame processing (all use method, can also perform High Dynamic Range tone mapping)
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void run (InputArray inputImage);
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// specific method aiming at correcting luminance only (faster High Dynamic Range tone mapping)
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void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)
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// output buffers retreival methods
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// -> foveal color vision details channel with luminance and noise correction
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void getParvo (OutputArray retinaOutput_parvo);
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@ -138,6 +141,10 @@ This retina filter code includes the research contributions of phd/research coll
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* take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book.
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* Meylan&al work on HDR tone mapping that is implemented as a specific method within the model :
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.. [Meylan2007] L. Meylan , D. Alleysson, S. Susstrunk, "A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images", Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
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Demos and experiments !
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=======================
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@ -161,12 +168,14 @@ Take a look at the provided C++ examples provided with OpenCV :
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Then, take a HDR image using bracketing with your camera and generate an OpenEXR image and then process it using the demo.
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Typical use, supposing that you have the OpenEXR image *memorial.exr* (present in the samples/cpp/ folder)
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Typical use, supposing that you have the OpenEXR image such as *memorial.exr* (present in the samples/cpp/ folder)
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**OpenCVReleaseFolder/bin/OpenEXRimages_HighDynamicRange_Retina_toneMapping memorial.exr**
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**OpenCVReleaseFolder/bin/OpenEXRimages_HighDynamicRange_Retina_toneMapping memorial.exr [optionnal: 'fast']**
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Note that some sliders are made available to allow you to play with luminance compression.
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If not using the 'fast' option, then, tone mapping is performed using the full retina model [Benoit2010]_. It includes spectral whitening that allows luminance energy to be reduced. When using the 'fast' option, then, a simpler method is used, it is an adaptation of the algorithm presented in [Meylan2007]_. This method gives also good results and is faster to process but it sometimes requires some more parameters adjustement.
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Methods description
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===================
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@ -275,7 +284,7 @@ Retina::printSetup
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Outputs a string showing the used parameters setup
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:return: a string which contains formatted parameters information
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:return: a string which contains formated parameters information
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Retina::run
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+++++++++++
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@ -286,6 +295,17 @@ Retina::run
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:param inputImage: the input Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)
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Retina::applyFastToneMapping
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++++++++++++++++++++++++++++
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.. ocv:function:: void Retina::applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)
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Method which processes an image in the aim to correct its luminance : correct backlight problems, enhance details in shadows. This method is designed to perform High Dynamic Range image tone mapping (compress >8bit/pixel images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model (simplified version of the run/getParvo methods call) since it does not include the spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral whitening and many other stuff. However, it works great for tone mapping and in a faster way.
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Check the demos and experiments section to see examples and the way to perform tone mapping using the original retina model and the method.
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:param inputImage: the input image to process (should be coded in float format : CV_32F, CV_32FC1, CV832F_C3, CV832F_C4, the 4th channel won't be considered).
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:param outputToneMappedImage: the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format).
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Retina::setColorSaturation
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++++++++++++++++++++++++++
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@ -633,7 +633,6 @@ CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
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CV_EXPORTS bool initModule_contrib();
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}
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#include "opencv2/contrib/retina.hpp"
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#include "opencv2/contrib/openfabmap.hpp"
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#endif
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@ -5,7 +5,7 @@
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SET(OPENCV_CPP_SAMPLES_REQUIRED_DEPS opencv_core opencv_flann opencv_imgproc
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opencv_highgui opencv_ml opencv_video opencv_objdetect opencv_photo opencv_nonfree opencv_softcascade
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opencv_features2d opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_videostab)
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opencv_features2d opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_videostab opencv_bioinspired)
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ocv_check_dependencies(${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
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@ -10,8 +10,9 @@
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#include <iostream>
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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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#include "opencv2/bioinspired.hpp" // retina based algorithms
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#include "opencv2/imgproc.hpp" // cvCvtcolor function
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#include "opencv2/highgui.hpp" // display
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static void help(std::string errorMessage)
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{
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@ -127,7 +128,7 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
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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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cv::Ptr<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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@ -175,6 +176,12 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
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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 chosenMethod=0;
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if (!strcmp(argv[argc-1], "fast"))
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{
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chosenMethod=1;
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std::cout<<"Using fast method (no spectral whithning), adaptation of Meylan&al 2008 method"<<std::endl;
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}
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std::string inputImageName=argv[1];
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@ -211,10 +218,15 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
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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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retina = createRetina(inputImage.size(),true, 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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retina = createRetina(inputImage.size());
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// create a fast retina tone mapper (Meyla&al algorithm)
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std::cout<<"Allocating fast tone mapper..."<<std::endl;
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//cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
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std::cout<<"Fast tone mapper allocated"<<std::endl;
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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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@ -230,20 +242,19 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
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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("Processing configuration",1);
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cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&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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cv::createTrackbar("Color saturation", "Processing configuration", &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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cv::createTrackbar("Hcells gain", "Processing configuration",&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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cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
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cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
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/////////////////////////////////////////////
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@ -257,11 +268,28 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
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while(continueProcessing)
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{
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// run retina filter
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if (!chosenMethod)
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{
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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::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
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cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
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}
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else
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{
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// apply the simplified hdr tone mapping method
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cv::Mat fastToneMappingOutput;
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retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
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cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
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}
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/*cv::Mat fastToneMappingOutput_specificObject;
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fastToneMapper->setup(3.f, 1.5f, 1.f);
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fastToneMapper->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput_specificObject);
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cv::imshow("### Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput_specificObject);
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*/
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cv::waitKey(10);
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}
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}catch(cv::Exception e)
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@ -14,8 +14,9 @@
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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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#include "opencv2/bioinspired.hpp" // retina based algorithms
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#include "opencv2/imgproc.hpp" // cvCvtcolor function
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#include "opencv2/highgui.hpp" // display
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static void help(std::string errorMessage)
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{
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@ -160,7 +161,7 @@ static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, con
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}
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cv::Ptr<cv::Retina> retina;
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cv::Ptr<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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@ -280,10 +281,10 @@ static void loadNewFrame(const std::string filenamePrototype, const int currentF
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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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retina = createRetina(inputImage.size(),true, 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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retina = 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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@ -9,7 +9,7 @@
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#include <iostream>
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#include <cstring>
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#include "opencv2/contrib.hpp"
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#include "opencv2/bioinspired.hpp"
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#include "opencv2/highgui.hpp"
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static void help(std::string errorMessage)
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@ -106,15 +106,15 @@ int main(int argc, char* argv[]) {
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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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cv::Ptr<cv::Retina> myRetina;
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cv::Ptr<Retina> myRetina;
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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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if (useLogSampling)
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{
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myRetina = cv::createRetina(inputFrame.size(), true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
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myRetina = createRetina(inputFrame.size(), true, 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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myRetina = cv::createRetina(inputFrame.size());
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myRetina = createRetina(inputFrame.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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myRetina->write("RetinaDefaultParameters.xml");
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@ -143,7 +143,8 @@ int main(int argc, char* argv[]) {
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cv::imshow("retina input", inputFrame);
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cv::imshow("Retina Parvo", retinaOutput_parvo);
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cv::imshow("Retina Magno", retinaOutput_magno);
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cv::waitKey(10);
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cv::waitKey(5);
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}
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}catch(cv::Exception e)
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{
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