apply default parameters of user interaction variables updated with interface sliderĬv::namedWindow("Processing configuration",1) Ĭv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat) Ĭv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors) Ĭv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams) Ĭv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams) Ĭv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams) HistogramClippingValue=0 // default value. Retina->activateMovingContoursProcessing(false) desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here Std::coutwrite("RetinaDefaultParameters.xml") Std::cout fastToneMapper=createRetinaFastToneMapping(inputImage.size()) create a fast retina tone mapper (Meyla&al algorithm) Retina = cv::bioinspired::createRetina(inputImage.size()) Retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0) Įlse// -> else allocate "classical" retina : * -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision) * create a retina instance with default parameters setup, uncomment the initialisation you wanna test Program start in a try/catch safety context (Retina may throw errors) Help("Input image could not be loaded, aborting") Imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage) apply tone mapping with this program ***" image size (h,w) = "8bits linear rescaling ", inputImage) generate an OpenEXR image with tools like ***" 3. take a set of photos from the same viewpoint using bracketing ***" 2. see on a 8bit display a more than 8bits coded (up to 16bits) image with details in high and low luminance ranges" It applies a spectral whithening (mid-frequency details enhancement)" high frequency spatio-temporal noise reduction" low frequency luminance to be reduced (luminance range compression)" local logarithmic luminance compression allows details to be enhanced in low light conditions\n" reports comments/remarks at " more informations and papers at : " 1.
Std::cout the main application is tone mapping of HDR images (i.e. Img = Mat::zeros(height, width, CV_8UC3) NamedWindow("Display Image", WINDOW_AUTOSIZE) Mat img(height, width, CV_8UC3, Scalar(0))
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