本文整理汇总了C++中cv::Ptr::getParvo方法的典型用法代码示例。如果您正苦于以下问题：C++ Ptr::getParvo方法的具体用法？C++ Ptr::getParvo怎么用？C++ Ptr::getParvo使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv::Ptr的用法示例。
在下文中一共展示了Ptr::getParvo方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
//.........这里部分代码省略.........
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;
}示例2: main
//.........这里部分代码省略.........
imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage);
if (inputImage.empty())
{
help("Input image could not be loaded, aborting");
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 = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
retina = cv::bioinspired::createRetina(inputImage.size());
// create a fast retina tone mapper (Meyla&al algorithm)
std::cout<<"Allocating fast tone mapper..."<<std::endl;
//cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
std::cout<<"Fast tone mapper allocated"<<std::endl;
// 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
//inputRescaleMat = inputImage;
//outputRescaleMat = imageInputRescaled;
cv::namedWindow("Processing configuration",1);
cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
colorSaturationFactor=3;
cv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors);
retinaHcellsGain=40;
cv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams);
localAdaptation_photoreceptors=197;
localAdaptation_Gcells=190;
cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
/////////////////////////////////////////////
// apply default parameters of user interaction variables
rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100);
retina->setColorSaturation(true,(float)colorSaturationFactor);
callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
// processing loop with stop condition
bool continueProcessing=true;
while(continueProcessing)
{
// run retina filter
if (!chosenMethod)
{
retina->run(imageInputRescaled);
// Retrieve and display retina output
retina->getParvo(retinaOutput_parvo);
cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
cv::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
}
else
{
// apply the simplified hdr tone mapping method
cv::Mat fastToneMappingOutput;
retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
}
/*cv::Mat fastToneMappingOutput_specificObject;
fastToneMapper->setup(3.f, 1.5f, 1.f);
fastToneMapper->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput_specificObject);
cv::imshow("### Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput_specificObject);
*/
cv::waitKey(10);
}
}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;
}