opencv----计算图像旋转参数

// xuanzhuan.cpp : 定义控制台应用程序的入口点。
//

#include "StdAfx.h"

#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/legacy/legacy.hpp>
#include <iostream>
#include <vector>

using namespace cv;
using namespace std;

void fourier(cv::Mat in,cv::Mat &out_re,cv::Mat &out_im);//计算傅里叶变换，分别输出变换后结果的实部和虚部
void gonglvpu(cv::Mat in1,cv::Mat in2,cv::Mat out);//计算傅里叶变换后的功率谱
void pingyi(cv::Mat in_1,cv::Mat in_2,int &x,int &y);
void cart2polar(cv::Mat in,cv::Mat &out);
void polar2cart(cv::Mat in,cv::Mat out);
void fft2(IplImage *src, IplImage *dst);
void fft2shift(IplImage *src, IplImage *dst);
void test(cv::Mat in,cv::Mat out);//本函数可以实现计算图像傅里叶变换后功率谱的功能
int _tmain(int argc, _TCHAR* argv[])
{

cv::Mat image1=cv::imread("lena.bmp");
cv::Mat image2=cv::imread("5du.bmp");
cv::cvtColor(image1,image1,CV_RGB2GRAY);
cv::cvtColor(image2,image2,CV_RGB2GRAY);
cart2polar(image1,image1);
//image1=image1(cv::Rect(0,0,200,200));
cart2polar(image2,image2);
//image2=image2(cv::Rect(0,0,200,200));
cv::namedWindow("www");
cv::imshow("www",image2);
int xx=0,yy=0;
pingyi(image1,image2,xx,yy);
cout<<xx<<"     "<<yy<<endl;
int theta=((double)(yy+1)/image1.rows)*360;
cout<<"theta  "<<theta<<endl;
cv::waitKey();
/*
cv::Mat src1s[]={Mat_<float>(image1),cv::Mat::zeros(image1.size(),CV_32FC1)};
cv::Mat FA1;
cv::merge(src1s,2,FA1);
//cv::dft(FA1,FA1);
cv::split(FA1,src1s);
cv::magnitude(src1s[0],src1s[1],src1s[0]);
src1s[0]=src1s[0](cv::Rect(100,100,400,400));
cart2polar(src1s[0],src1s[0]);
//polar2cart(src1s[0],src1s[0]);
src1s[0]=src1s[0](cv::Rect(0,0,200,200));
cv::normalize(src1s[0],src1s[0],0,1,CV_MINMAX);

cv::Mat src2s[]={Mat_<float>(image2),cv::Mat::zeros(image2.size(),CV_32FC1)};
cv::Mat FA2;
cv::merge(src2s,2,FA2);
//cv::dft(FA2,FA2);
cv::split(FA2,src2s);
cv::magnitude(src2s[0],src2s[1],src2s[0]);
src2s[0]=src2s[0](cv::Rect(100,100,400,400));
cart2polar(src2s[0],src2s[0]);
src2s[0]=src2s[0](cv::Rect(0,0,200,200));
cv::normalize(src2s[0],src2s[0],0,1,CV_MINMAX);

//cv::normalize(src2s[0],src2s[0],0,1,CV_MINMAX);
cv::namedWindow("test");
cv::imshow("test",src2s[0]);
cv::waitKey();

int xx=0,yy=0;
pingyi(src1s[0],src2s[0],xx,yy);
cout<<xx<<"     "<<yy<<endl;

*/
return 0;
}

void test(cv::Mat in,cv::Mat out)
{
IplImage ss=in;
IplImage tt=in;
IplImage *src=&in.operator IplImage();          //源图像
IplImage *Image=&out.operator IplImage();
IplImage *Fourier;   //傅里叶系数
IplImage *dst ;

IplImage *ImageRe;
IplImage *ImageIm;
IplImage *ImageIm1;

IplImage *ImageDst;

double m,M;
double scale;
double shift;
//src = cvLoadImage("lena.bmp",0);   //加载源图像，第二个参数表示将输入的图片转为单信道
Fourier = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,2);
dst = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,2);
ImageRe = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,1);
ImageIm = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,1);
ImageIm1 = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,1);
//Image = cvCreateImage(cvGetSize(src),src->depth,src->nChannels);
ImageDst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels);
fft2(src,Fourier);                  //傅里叶变换
fft2shift(Fourier, Image);          //中心化
//cvDFT(Fourier,dst,CV_DXT_INV_SCALE);//实现傅里叶逆变换，并对结果进行缩放

cvSplit(dst,ImageRe,ImageIm,0,0);
//cvNamedWindow("源图像",0);
//cvShowImage("源图像",src);
//对数组每个元素平方并存储在第二个参数中
cvPow(ImageRe,ImageRe,2);
cvPow(ImageIm,ImageIm1,2);
cvAdd(ImageRe,ImageIm1,ImageRe,NULL);
cvPow(ImageRe,ImageRe,0.5);
cvMinMaxLoc(ImageRe,&m,&M,NULL,NULL);
scale = 255/(M - m);
shift = -m * scale;
//将shift加在ImageRe各元素按比例缩放的结果上，存储为ImageDst
cvConvertScale(ImageRe,ImageDst,scale,shift);
cvNormalize(ImageIm,ImageIm, 0, 1, CV_MINMAX);
//cvNamedWindow("傅里叶谱",0);
//cvShowImage("傅里叶谱",Image);
//cvNamedWindow("傅里叶逆变换",0);
//cvShowImage("傅里叶逆变换",ImageDst);
//cvNamedWindow("test",0);
//cvShowImage("test",ImageIm);
cvWaitKey(0);
//cvReleaseImage(&src);
//cvReleaseImage(&Image);
cvReleaseImage(&ImageIm);
cvReleaseImage(&ImageRe);
cvReleaseImage(&Fourier);
cvReleaseImage(&dst);
cvReleaseImage(&ImageDst);
//    cvDestroyAllWindows();
}
void fourier(cv::Mat in,cv::Mat &out_re,cv::Mat &out_im)
{
Mat planes[] = {Mat_<float>(in), Mat::zeros(in.size(), CV_32FC1)};
Mat complexI;
merge(planes, 2, complexI);
cv::dft(complexI, complexI);
cv::split(complexI, planes);
out_re=planes[0];
out_im=planes[1];

}
void gonglvpu(cv::Mat in1,cv::Mat in2,cv::Mat out)
{
cv::magnitude(in1,in2,out);
/*
IplImage data_1=in1;
IplImage data_2=in2;
IplImage data_3=out;
IplImage* pI1  = &in1.operator IplImage();
IplImage* pI2  = &in2.operator IplImage();
IplImage* pI3  = &out.operator IplImage();
cvPow(pI1,pI1,2);
cvPow(pI2,pI2,2);
cvAdd(pI1,pI2,pI3);
cvPow(pI3,pI3,0.5);
*/
}
void pingyi(cv::Mat in_1,cv::Mat in_2,int &x,int &y)
{

cv::Mat padded=in_1;
Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
Mat complexI;
merge(planes, 2, complexI);
cv::dft(complexI, complexI);
cv::split(complexI, planes);

//////////////////////////////////

cv::Mat padded2=in_2;
Mat planes2[] = {Mat_<float>(padded2), Mat::zeros(padded2.size(), CV_32F)};
Mat complexI2;
merge(planes2, 2, complexI2);
cv::dft(complexI2, complexI2);
cv::split(complexI2, planes2);
//cv::normalize(planes[1],planes[1], 0, 1, CV_MINMAX);
////////////////////////////////////////////////////////
//src1变换后实部 planes[0]   虚部planes[1]
//src2变换后实部 planes2[0]  虚部planes2[1]

//互功率谱存放矩阵 实部  planes3[0]   虚部planes3[1]
//////////////////////////////////////////////////////////

Mat planes3[] = {Mat::zeros(in_1.size(), CV_32F), Mat::zeros(in_1.size(), CV_32F)};

for(int i=0;i<in_1.rows-1;i++)
{
for(int j=0;j<in_1.cols-1;j++)
{
double r1=planes[0].at<float>(j,i);//获取像素值
double i1=planes[1].at<float>(j,i);
double r2=planes2[0].at<float>(j,i);
double i2=planes2[1].at<float>(j,i);

double r3    =r1*r2+i1*i2;//计算互功率谱的值
double i3    =r1*i2-r2*i1;
double abs   =sqrt((r3*r3)+(i3*i3));
double r_exp =r3/abs;
double i_exp =i3/abs;

planes3[0].at<float>(j,i)=r_exp;
planes3[1].at<float>(j,i)=i_exp;
}
}

Mat complexI3;
merge(planes3, 2, complexI3);
cv::dft(complexI3,complexI3,CV_DXT_INV_SCALE);
cv::split(complexI3,planes3);
magnitude(planes3[0], planes3[1], planes3[0]);
complexI3=planes3[0];
complexI3 += Scalar::all(1);
log(complexI3, complexI3);
cv::normalize(complexI3,complexI3, 0, 1, CV_MINMAX);

double max=0,min=0;
cv::Point minLoc,maxLoc;
cv::minMaxLoc(complexI3,&min,&max,&minLoc,&maxLoc);
//cv::imwrite("complexI3.bmp",complexI3);

x=maxLoc.x;
y=maxLoc.y;
cout<<min<<"   "<<max<<endl;
cout<<complexI3.at<float>(maxLoc)<<endl;
}
void cart2polar(cv::Mat in,cv::Mat &out)
{
IplImage tmp1=in;
IplImage* pI1  = &in.operator IplImage();
IplImage tmp2=out;
IplImage* pI2  = &out.operator IplImage();
cvLogPolar( pI1, pI2, cvPoint2D32f(pI1->width/2,pI1->height/2), 40, CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS );
//cvReleaseImage(&pI1);
//cvReleaseImage(&pI2);
}
void polar2cart(cv::Mat in,cv::Mat out)
{
IplImage tmp1=in;
IplImage* pI1  = &in.operator IplImage();
IplImage tmp2=out;
IplImage* pI2  = &out.operator IplImage();
cvLogPolar( pI1, pI2, cvPoint2D32f(pI1->width/2,pI1->height/2), 40, CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS+CV_WARP_INVERSE_MAP );
}
void fft2(IplImage *src, IplImage *dst)
{
IplImage *image_Re = 0, *image_Im = 0, *Fourier = 0; //实部、虚部

image_Re = cvCreateImage(cvGetSize(src), IPL_DEPTH_64F, 1);  //实部

image_Im = cvCreateImage(cvGetSize(src), IPL_DEPTH_64F, 1);  //虚部

Fourier = cvCreateImage(cvGetSize(src), IPL_DEPTH_64F, 2);//2 channels (image_Re, image_Im)

cvConvertScale(src, image_Re, 1, 0);// Real part conversion from u8 to 64f (double)

cvZero(image_Im);// Imaginary part (zeros)

cvMerge(image_Re, image_Im, 0, 0, Fourier);// Join real and imaginary parts and stock them in Fourier image

cvDFT(Fourier, dst, CV_DXT_FORWARD);// Application of the forward Fourier transform

cvReleaseImage(&image_Re);
cvReleaseImage(&image_Im);
cvReleaseImage(&Fourier);
}
void fft2shift(IplImage *src, IplImage *dst)
{
IplImage *image_Re = 0, *image_Im = 0;
int nRow, nCol, i, j, cy, cx;
double scale, shift, tmp13, tmp24;

image_Re = cvCreateImage(cvGetSize(src), IPL_DEPTH_64F, 1);

image_Im = cvCreateImage(cvGetSize(src), IPL_DEPTH_64F, 1);
cvSplit( src, image_Re, image_Im, 0, 0 );

//具体原理见冈萨雷斯数字图像处理p123
// Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
//计算傅里叶谱
cvPow( image_Re, image_Re, 2.0);
cvPow( image_Im, image_Im, 2.0);
cvAdd( image_Re, image_Im, image_Re);
cvPow( image_Re, image_Re, 0.5 );

//对数变换以增强灰度级细节(这种变换使以窄带低灰度输入图像值映射一宽带输出值，具体可见冈萨雷斯数字图像处理p62)
// Compute log(1 + Mag);
cvAddS( image_Re, cvScalar(1.0), image_Re ); // 1 + Mag
cvLog( image_Re, image_Re ); // log(1 + Mag)

//Rearrange the quadrants of Fourier image so that the origin is at the image center
nRow = src->height; nCol = src->width;
cx = nCol/2; cy = nRow/2; // image center

//CV_IMAGE_ELEM为OpenCV定义的宏，用来读取图像的像素值，这一部分就是进行中心变换
for( j = 0; j < cy; j++ ){
for( i = 0; i < cx; i++ ){
//中心化，将整体份成四块进行对角交换
tmp13 = CV_IMAGE_ELEM( image_Re, double, j, i);
CV_IMAGE_ELEM( image_Re, double, j, i) = CV_IMAGE_ELEM(image_Re, double, j+cy, i+cx);
CV_IMAGE_ELEM( image_Re, double, j+cy, i+cx) = tmp13;

tmp24 = CV_IMAGE_ELEM( image_Re, double, j, i+cx);
CV_IMAGE_ELEM( image_Re, double, j, i+cx) =CV_IMAGE_ELEM( image_Re, double, j+cy, i);
CV_IMAGE_ELEM( image_Re, double, j+cy, i) = tmp24;
}
}
//归一化处理将矩阵的元素值归一为[0,255]
//[(f(x,y)-minVal)/(maxVal-minVal)]*255
double minVal = 0, maxVal = 0;
// Localize minimum and maximum values
cvMinMaxLoc( image_Re, &minVal, &maxVal );
// Normalize image (0 - 255) to be observed as an u8 image
scale = 255/(maxVal - minVal);
shift = -minVal * scale;
cvConvertScale(image_Re, dst, scale, shift);
cvReleaseImage(&image_Re);
cvReleaseImage(&image_Im);

}

/*
for(int i=0;i<500;i++)
{
for(int j=0;j<500;j++)
{
if(i%20==0&&j%20==0)
cout<<src1_f_re.at<float>(i,j)<<endl;
}
}
*/

/*string name1="lena.bmp";
string name2="90du.bmp";
cv::Mat src1=imread(name1);
if(src1.channels()!=1)
cv::cvtColor(src1,src1,CV_RGB2GRAY);
cv::Mat src1_f_re   =cv::Mat::zeros(src1.size(),CV_32FC1);
cv::Mat src1_f_im   =cv::Mat::zeros(src1.size(),CV_32FC1);
cv::Mat src1_gonglv =cv::Mat::zeros(src1.size(),CV_32FC1);
cv::Mat src1_g_log  =cv::Mat::zeros(src1.size(),CV_32FC1);
fourier(src1,src1_f_re,src1_f_im);
gonglvpu(src1_f_re,src1_f_im,src1_gonglv);
cart2polar(src1_gonglv,src1_g_log);
cv::normalize(src1_g_log,src1_g_log, 0, 1, CV_MINMAX);

cv::Mat src2=imread(name2);
if(src2.channels()!=1)
cv::cvtColor(src2,src2,CV_RGB2GRAY);
cv::Mat src2_f_re   =cv::Mat::zeros(src2.size(),CV_32FC1);
cv::Mat src2_f_im   =cv::Mat::zeros(src2.size(),CV_32FC1);
cv::Mat src2_gonglv =cv::Mat::zeros(src2.size(),CV_32FC1);
cv::Mat src2_g_log  =cv::Mat::zeros(src2.size(),CV_32FC1);
fourier(src2,src2_f_re,src2_f_im);
gonglvpu(src2_f_re,src2_f_im,src2_gonglv);
cart2polar(src2_gonglv,src2_g_log);
cv::normalize(src2_g_log,src2_g_log, 0, 1, CV_MINMAX);
int gx=0,gy=0;

pingyi(src1_gonglv,src2_gonglv,gx,gy);
//cout<<"max  "<<max<<"  min  "<<min<<endl;
cout<<gx<<"   "<<gy<<endl;
//pingyi(src1,src2,gx,gy);
*/