学习opencv 第六章 习题十三

 1 #include "stdafx.h"
 2 #include "cv.h"
 3 #include "highgui.h"
 4 #include <iostream>
 5 
 6 using namespace cv;
 7 using namespace std;
 8 
 9 void speedy_convolution(const CvMat* A,const CvMat* B,CvMat* C);
10 
11 int main()
12 {
13     IplImage* img=cvLoadImage("C:/Users/shark/Desktop/fruits.jpg",0);
14     CvMat* src=cvCreateMat(img->height,img->width,CV_32FC1);
15     /*int data;
16     for(int i=0;i<img->height;i++)
17     {
18         for(int j=0;j<img->width;j++)
19         {
20             data=img->imageData[i*img->widthStep+j];
21             cvmSet(src,i,j,data);
22         }
23     }*/
24     //必须归一化矩阵的值为0-1之间(缩放比例在1/255.0附近效果最好,太小最后会全黑,接近1或大于1几乎是全白;
25     //(还未深入了解函数cvConvertScale的机理),缩放比例不能为1,打出目标图像的像素有正有负
26     cvConvertScale(img,src,1/255.0,0);  
27     
28 
29     CvMat* kernel=cvCreateMat(3,3,CV_32FC1);
30     cvSetReal2D(kernel,0,0,1.0/16);    cvSetReal2D(kernel,0,1,2.0/16); cvSetReal2D(kernel,0,2,1.0/16);    //注意设置值时必须加个.0否则1/16的值0
31     cvSetReal2D(kernel,1,0,2.0/16);    cvSetReal2D(kernel,1,1,4.0/16); cvSetReal2D(kernel,1,2,2.0/16);
32     cvSetReal2D(kernel,2,0,1.0/16);    cvSetReal2D(kernel,2,1,2.0/16); cvSetReal2D(kernel,2,2,1.0/16);
33     CvMat* C=cvCreateMat((src->rows+kernel->rows-1),(src->cols+kernel->cols-1),src->type);
34     speedy_convolution(src,kernel,C);
35 
36     IplImage* img_src=cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,1);
37     cvGetImage(src,img_src);
38     IplImage* img_dst=cvCreateImage(cvGetSize(C),IPL_DEPTH_32F,1);
39     cvGetImage(C,img_dst);
40 
41     cvNamedWindow("img_src");
42     cvShowImage("img_src",img_src);
43     cvNamedWindow("img");
44     cvShowImage("img",img);
45     cvNamedWindow("dst");
46     cvShowImage("dst",img_dst);
47     cvWaitKey();
48     return 0;
49 }
50 
51 void speedy_convolution(
52     const CvMat* A,
53     const CvMat* B,
54     CvMat* C
55     ){
56         int dft_M=cvGetOptimalDFTSize(A->rows+B->rows-1);
57         int dft_N=cvGetOptimalDFTSize(A->cols+B->cols-1);
58 
59         CvMat *dft_A=cvCreateMat(dft_M,dft_N,A->type);
60         CvMat *dft_B=cvCreateMat(dft_M,dft_N,B->type);
61         CvMat tmp;
62         cvGetSubRect(dft_A,&tmp,cvRect(0,0,A->cols,A->rows));
63         cvCopy(A,&tmp);
64         cvGetSubRect(dft_A,&tmp,cvRect(A->cols,0,dft_A->cols-A->cols,A->rows));
65         cvZero(&tmp);
66         cvDFT(dft_A,dft_A,CV_DXT_FORWARD,A->rows);
67 
68         cvGetSubRect(dft_B,&tmp,cvRect(0,0,B->cols,B->rows));
69         cvCopy(B,&tmp);
70         cvGetSubRect(dft_B,&tmp,cvRect(B->cols,0,dft_B->cols-B->cols,B->rows));
71         cvZero(&tmp);
72         cvDFT(dft_B,dft_B,CV_DXT_FORWARD,B->rows);
73 
74         cvMulSpectrums(dft_A,dft_B,dft_A,0);
75 
76         cvDFT(dft_A,dft_A,CV_DXT_INV_SCALE,C->rows);
77         cvGetSubRect(dft_A,&tmp,cvRect(0,0,C->cols,C->rows));
78         cvCopy(&tmp,C);
79         cvReleaseMat(&dft_A);
80         cvReleaseMat(&dft_B);
81 }

 1 #include "opencv2/core/core.hpp"
 2 #include "opencv2/imgproc/imgproc.hpp"
 3 #include "opencv2/highgui/highgui.hpp"
 4 #include <iostream>
 5 
 6 using namespace cv;
 7 using namespace std;
 8 
 9 //http://docs.opencv.org/modules/core/doc/operations_on_arrays.html#dft[2]
10 void convolveDFT(Mat A, Mat B, Mat& C)
11 {
12     // reallocate the output array if needed
13     C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
14     Size dftSize;
15     // calculate the size of DFT transform
16     dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
17     dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);
18 
19     // allocate temporary buffers and initialize them with 0‘s
20     Mat tempA(dftSize, A.type(), Scalar::all(0));
21     Mat tempB(dftSize, B.type(), Scalar::all(0));
22 
23     // copy A and B to the top-left corners of tempA and tempB, respectively
24     Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
25     A.copyTo(roiA);
26     Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
27     B.copyTo(roiB);
28 
29     // now transform the padded A & B in-place;
30     // use "nonzeroRows" hint for faster processing
31     dft(tempA, tempA, 0, A.rows);
32     dft(tempB, tempB, 0, B.rows);
33 
34     // multiply the spectrums;
35     // the function handles packed spectrum representations well
36     mulSpectrums(tempA, tempB, tempA, DFT_COMPLEX_OUTPUT);
37     //mulSpectrums(tempA, tempB, tempA, DFT_REAL_OUTPUT);
38 
39     // transform the product back from the frequency domain.
40     // Even though all the result rows will be non-zero,
41     // you need only the first C.rows of them, and thus you
42     // pass nonzeroRows == C.rows
43     dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);
44 
45     // now copy the result back to C.
46     tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);
47 
48     // all the temporary buffers will be deallocated automatically
49 }
50 
51 
52 int main(int argc, char* argv[])
53 {
54     const char* filename = argc >=2 ? argv[1] : "Lenna.png";
55 
56     Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
57     if( I.empty())
58         return -1;
59 
60     Mat kernel = (Mat_<float>(3,3) << 1, 1, 1, 1, 1, 1, 1, 1, 1);
61     cout << kernel;
62 
63     Mat floatI = Mat_<float>(I);// change image type into float
64     Mat filteredI;
65     convolveDFT(floatI, kernel, filteredI);
66     
67     normalize(filteredI, filteredI, 0, 1, CV_MINMAX); // Transform the matrix with float values into a
68                                             // viewable image form (float between values 0 and 1).
69     imshow("image", I);
70     imshow("filtered", filteredI);
71     waitKey(0);
72 
73 }
74 
75 //一是输出Mat C应声明为引用；二是其中的mulSpectrums函数的第四个参数flag值没有指定，应指定为DFT_COMPLEX_OUTPUT或是DFT_REAL_OUTPUT.
76 
77 //main函数中首先按灰度图读入图像，然后创造一个平滑核kernel，将输入图像转换成float类型(注意这步是必须的，因为dft只能处理浮点数)，在调用convolveDFT求出卷积结果后，将卷积结果归一化方便显示观看。
78 
79 //需要注意的是，一般求法中，利用核游走整个图像进行卷积运算，实际上进行的是相关运算，真正意义上的卷积，应该首先把核翻转180度，再在整个图像上进行游走。OpenCV中的filter2D实际上做的也只是相关，而非卷积。