图像滤波

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

int main()
{
	// Read input image
	cv::Mat image= cv::imread("d:/test/opencv/boldt.jpg",0);
	if (!image.data)
		return 0; 

    // Display the image
	cv::namedWindow("Original Image");
	cv::imshow("Original Image",image);

	// Blur the image
	cv::Mat result;
	cv::GaussianBlur(image,result,cv::Size(5,5),1.5);

    // Display the blurred image
	cv::namedWindow("Gaussian filtered Image");
	cv::imshow("Gaussian filtered Image",result);

	// Get the gaussian kernel (1.5)
	cv::Mat gauss= cv::getGaussianKernel(9,1.5,CV_32F);
		  
	// Display kernel values
	cv::Mat_<float>::const_iterator it= gauss.begin<float>();  
	cv::Mat_<float>::const_iterator itend= gauss.end<float>();  
	std::cout << "[";
	for ( ; it!= itend; ++it) {
		std::cout << *it << " ";
	}
	std::cout << "]" << std::endl;

	// Get the gaussian kernel (0.5)
	gauss= cv::getGaussianKernel(9,0.5,CV_32F);
		  
	// Display kernel values
	it= gauss.begin<float>();  
	itend= gauss.end<float>();  
	std::cout << "[";
	for ( ; it!= itend; ++it) {
		std::cout << *it << " ";
	}
	std::cout << "]" << std::endl;

	// Get the gaussian kernel (2.5)
	gauss= cv::getGaussianKernel(9,2.5,CV_32F);
		  
	// Display kernel values
	it= gauss.begin<float>();  
	itend= gauss.end<float>();  
	std::cout << "[";
	for ( ; it!= itend; ++it) {
		std::cout << *it << " ";
	}
	std::cout << "]" << std::endl;

	// Get the Deriv kernel (2.5)
	cv::Mat kx, ky;
    cv::getDerivKernels(kx,ky,2,2,7,true);
		  
	// Display kernel values
	cv::Mat_<float>::const_iterator kit= kx.begin<float>();  
	cv::Mat_<float>::const_iterator kitend= kx.end<float>();  
	std::cout << "[";
	for ( ; kit!= kitend; ++kit) {
		std::cout << *kit << " ";
	}
	std::cout << "]" << std::endl;

	// Blur the image with a mean filter
	cv::blur(image,result,cv::Size(5,5));
		
    // Display the blurred image
	cv::namedWindow("Mean filtered Image");
	cv::imshow("Mean filtered Image",result);

	// Read input image with salt&pepper noise
	image= cv::imread("d:/test/opencv/salted.bmp",0);
	if (!image.data)
		return 0; 

    // Display the S&P image
	cv::namedWindow("S&P Image");
	cv::imshow("S&P Image",image);

	// Blur the image with a mean filter
	cv::blur(image,result,cv::Size(5,5));
		
    // Display the blurred image
	cv::namedWindow("Mean filtered S&P Image");
	cv::imshow("Mean filtered S&P Image",result);

	// Applying a median filter
	cv::medianBlur(image,result,5);
		
    // Display the blurred image
	cv::namedWindow("Median filtered S&P Image");
	cv::imshow("Median filtered S&P Image",result);

	// Reduce by 4 the size of the image (the wrong way)
	image= cv::imread("d:/test/opencv/boldt.jpg",0);
	cv::Mat reduced(image.rows/2,image.cols/2,CV_8U);

	for (int i=0; i<reduced.rows; i++)
		for (int j=0; j<reduced.cols; j++)
			reduced.at<uchar>(i,j)= image.at<uchar>(i*2,j*2);

    // Display the reduced image
	cv::namedWindow("Badly reduced Image");
	cv::imshow("Badly reduced Image",reduced);

	cv::waitKey();
	return 0;
}