转自:http://www.2cto.com/kf/201312/267308.html
Mask Operation filter2D函数 Last Edit 2013/12/24 所谓的Mask Operation就是滤波。 第一步:建立Mask:
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Mat kern = (Mat_< char >( 3 , 3 ) << 0 , - 1 , 0 , - 1 , 5 , - 1 , 0 , - 1 , 0 );</ char > |
Mat_是一个模板,建立了一个3*3的矩阵,矩阵的值在-128~127.
第二步:使用filter2D. 函数原型:
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void filter2D(InputArray src, //要进行滤波的图像 OutputArray dst, //滤波后的图像 int ddepth, //原图像的深度 src.depth() InputArray kernel, //第一步建立的Mask Point anchor=Point(- 1 ,- 1 ), //Mask的中心点 double delta= 0 , //Optional value added to the filtered pixels before storing them in dst int borderType=BORDER_DEFAULT ) |
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filter2D(I, K, I.depth(), kern ); |
以下是OpenCV2.0提供的sample:
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#include <opencv2 core= "" core.hpp= "" > #include <opencv2 highgui= "" highgui.hpp= "" > #include <opencv2 imgproc= "" imgproc.hpp= "" > #include <iostream> using namespace std; using namespace cv; void help( char * progName) { cout << endl << "This program shows how to filter images with mask: the write it yourself and the" << "filter2d way. " << endl << "Usage:" << endl << progName << " [image_name -- default lena.jpg] [G -- grayscale] " << endl << endl; } void Sharpen( const Mat& myImage,Mat& Result); int main( int argc, char * argv[]) { help(argv[ 0 ]); const char * filename = argc >= 2 ? argv[ 1 ] : "lena.jpg" ; Mat I, J, K; if (argc >= 3 && !strcmp( "G" , argv[ 2 ])) I = imread( filename, CV_LOAD_IMAGE_GRAYSCALE); else I = imread( filename, CV_LOAD_IMAGE_COLOR); namedWindow( "Input" , CV_WINDOW_AUTOSIZE); namedWindow( "Output" , CV_WINDOW_AUTOSIZE); imshow( "Input" , I); double t = ( double )getTickCount(); Sharpen(I, J); t = (( double )getTickCount() - t)/getTickFrequency(); cout << "Hand written function times passed in seconds: " << t << endl; imshow( "Output" , J); cvWaitKey( 0 ); Mat kern = (Mat_< char >( 3 , 3 ) << 0 , - 1 , 0 , - 1 , 5 , - 1 , 0 , - 1 , 0 ); t = ( double )getTickCount(); filter2D(I, K, I.depth(), kern ); t = (( double )getTickCount() - t)/getTickFrequency(); cout << "Built-in filter2D time passed in seconds: " << t << endl; imshow( "Output" , K); cvWaitKey( 0 ); return 0 ; } void Sharpen( const Mat& myImage,Mat& Result) { CV_Assert(myImage.depth() == CV_8U); // accept only uchar images const int nChannels = myImage.channels(); Result.create(myImage.size(),myImage.type()); for ( int j = 1 ; j < myImage.rows- 1 ; ++j) { const uchar* previous = myImage.ptr<uchar>(j - 1 ); const uchar* current = myImage.ptr<uchar>(j ); const uchar* next = myImage.ptr<uchar>(j + 1 ); uchar* output = Result.ptr<uchar>(j); for ( int i= nChannels;i < nChannels*(myImage.cols- 1 ); ++i) { *output++ = saturate_cast<uchar>( 5 *current[i] -current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]); } } Result.row( 0 ).setTo(Scalar( 0 )); Result.row(Result.rows- 1 ).setTo(Scalar( 0 )); Result.col( 0 ).setTo(Scalar( 0 )); Result.col(Result.cols- 1 ).setTo(Scalar( 0 )); }</uchar></uchar></uchar></uchar></uchar></ char ></iostream></opencv2></o |