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:
Mat kern = (Mat_
(3,3) << 0, -1, 0,
-1, 5, -1,
0, -1, 0);
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：
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
)
filter2D(I, K, I.depth(), kern );
以下是OpenCV2.0提供的sample:
#include
#include
#include
#include
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_
(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
(j - 1); const uchar* current = myImage.ptr
(j ); const uchar* next = myImage.ptr
(j + 1); uchar* output = Result.ptr
(j); for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i) { *output++ = saturate_cast
(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)); }