OpenCV_4.2.0/opencv_contrib-4.2.0/modules/xphoto/samples/dct_image_denoising.cpp

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2024-07-25 16:47:56 +08:00
#include "opencv2/xphoto.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/imgproc/types_c.h"
const char* keys =
{
"{i || input image name}"
"{o || output image name}"
"{sigma || expected noise standard deviation}"
"{psize |16| expected noise standard deviation}"
};
int main( int argc, const char** argv )
{
bool printHelp = ( argc == 1 );
printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "--help" );
printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "-h" );
if ( printHelp )
{
printf("\nThis sample demonstrates dct-based image denoising\n"
"Call:\n"
" dct_image_denoising -i=<string> -sigma=<double> -psize=<int> [-o=<string>]\n\n");
return 0;
}
cv::CommandLineParser parser(argc, argv, keys);
if ( !parser.check() )
{
parser.printErrors();
return -1;
}
std::string inFilename = parser.get<std::string>("i");
std::string outFilename = parser.get<std::string>("o");
cv::Mat src = cv::imread(inFilename, 1);
if ( src.empty() )
{
printf("Cannot read image file: %s\n", inFilename.c_str());
return -1;
}
double sigma = parser.get<double>("sigma");
if (sigma == 0.0)
sigma = 15.0;
int psize = parser.get<int>("psize");
if (psize == 0)
psize = 16;
cv::Mat res(src.size(), src.type());
cv::xphoto::dctDenoising(src, res, sigma, psize);
if ( outFilename == "" )
{
cv::namedWindow("denoising result", 1);
cv::imshow("denoising result", res);
cv::waitKey(0);
}
else
cv::imwrite(outFilename, res);
return 0;
}