Exemplo n.º 1
0
void CameraCanvas::refreshFrame(){
    if(!isSwitchedOn) return;
    if(!hasStreamInput) waitForRealInput();
    loadNewFrame();
    processNewData();   
    
    cv::imshow(this->canvasName, working_matrix);
    cv::imshow(t_str, working_threshold);
    cv::waitKey(1);
    usleep(15);
}
Exemplo n.º 2
0
int main(int argc, char* argv[]) {
    // welcome message
    std::cout<<"*********************************************************************************"<<std::endl;
    std::cout<<"* Retina demonstration for High Dynamic Range compression (tone-mapping) : demonstrates the use of a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
    std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
    std::cout<<"* This demo focuses demonstration of the dynamic compression capabilities of the model"<<std::endl;
    std::cout<<"* => the main application is tone mapping of HDR images (i.e. see on a 8bit display a more than 8bits coded (up to 16bits) image with details in high and low luminance ranges"<<std::endl;
    std::cout<<"* The retina model still have the following properties:"<<std::endl;
    std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
    std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
    std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
    std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
    std::cout<<"* for more information, reer to the following papers :"<<std::endl;
    std::cout<<"* Benoit A., Caplier A., Durette B., Herault, J., \"USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING\", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011"<<std::endl;
    std::cout<<"* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891."<<std::endl;
    std::cout<<"* => reports comments/remarks at [email protected]"<<std::endl;
    std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
    std::cout<<"*********************************************************************************"<<std::endl;
    std::cout<<"** WARNING : this sample requires OpenCV to be configured with OpenEXR support **"<<std::endl;
    std::cout<<"*********************************************************************************"<<std::endl;
    std::cout<<"*** You can use free tools to generate OpenEXR images from images sets   :    ***"<<std::endl;
    std::cout<<"*** =>  1. take a set of photos from the same viewpoint using bracketing      ***"<<std::endl;
    std::cout<<"*** =>  2. generate an OpenEXR image with tools like qtpfsgui.sourceforge.net ***"<<std::endl;
    std::cout<<"*** =>  3. apply tone mapping with this program                               ***"<<std::endl;
    std::cout<<"*********************************************************************************"<<std::endl;

    // basic input arguments checking
    if (argc<4)
    {
        help("bad number of parameter");
        return -1;
    }

    bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing

    int startFrameIndex=0, endFrameIndex=0, currentFrameIndex=0;
    sscanf(argv[2], "%d", &startFrameIndex);
    sscanf(argv[3], "%d", &endFrameIndex);
    std::string inputImageNamePrototype(argv[1]);

    //////////////////////////////////////////////////////////////////////////////
    // checking input media type (still image, video file, live video acquisition)
    std::cout<<"RetinaDemo: setting up system with first image..."<<std::endl;
    loadNewFrame(inputImageNamePrototype, startFrameIndex, true);

    if (inputImage.empty())
    {
        help("could not load image, program end");
        return -1;
    }

    //////////////////////////////////////////////////////////////////////////////
    // Program start in a try/catch safety context (Retina may throw errors)
    try
    {
        /* create a retina instance with default parameters setup, uncomment the initialisation you wanna test
         * -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
         */
        if (useLogSampling)
        {
            retina = new cv::Retina(inputImage.size(),true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
        }
        else// -> else allocate "classical" retina :
            retina = new cv::Retina(inputImage.size());

        // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
        retina->write("RetinaDefaultParameters.xml");

        // desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
        retina->activateMovingContoursProcessing(false);

        // declare retina output buffers
        cv::Mat retinaOutput_parvo;

        /////////////////////////////////////////////
        // prepare displays and interactions
        histogramClippingValue=0; // default value... updated with interface slider

        std::string retinaInputCorrected("Retina input image (with cut edges histogram for basic pixels error avoidance)");
        cv::namedWindow(retinaInputCorrected,1);
        cv::createTrackbar("histogram edges clipping limit", "Retina input image (with cut edges histogram for basic pixels error avoidance)",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);

        std::string RetinaParvoWindow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping");
        cv::namedWindow(RetinaParvoWindow, 1);
        colorSaturationFactor=3;
        cv::createTrackbar("Color saturation", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &colorSaturationFactor,5,callback_saturateColors);

        retinaHcellsGain=40;
        cv::createTrackbar("Hcells gain", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping",&retinaHcellsGain,100,callBack_updateRetinaParams);

        localAdaptation_photoreceptors=197;
        localAdaptation_Gcells=190;
        cv::createTrackbar("Ph sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
        cv::createTrackbar("Gcells sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_Gcells,199,callBack_updateRetinaParams);

        std::string powerTransformedInput("EXR image with basic processing : 16bits=>8bits with gamma correction");

        /////////////////////////////////////////////
        // apply default parameters of user interaction variables
        callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
        callback_saturateColors(1, NULL);

        // processing loop with stop condition
        currentFrameIndex=startFrameIndex;
        while(currentFrameIndex <= endFrameIndex)
        {
            loadNewFrame(inputImageNamePrototype, currentFrameIndex, false);

            if (inputImage.empty())
            {
                std::cout<<"Could not load new image (index = "<<currentFrameIndex<<"), program end"<<std::endl;
                return -1;
            }
            // display input & process standard power transformation
            imshow("EXR image original image, 16bits=>8bits linear rescaling ", imageInputRescaled);
            cv::Mat gammaTransformedImage;
            cv::pow(imageInputRescaled, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
            imshow(powerTransformedInput, gammaTransformedImage);
            // run retina filter
            retina->run(imageInputRescaled);
            // Retrieve and display retina output
            retina->getParvo(retinaOutput_parvo);
            cv::imshow(retinaInputCorrected, imageInputRescaled/255.f);
            cv::imshow(RetinaParvoWindow, retinaOutput_parvo);
            cv::waitKey(4);
            // jump to next frame
            ++currentFrameIndex;
        }
    } catch(cv::Exception e)
    {
        std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
    }

    // Program end message
    std::cout<<"Retina demo end"<<std::endl;

    return 0;
}