opencv----计算图像旋转参数

弄这个弄了好几天，一直想的复杂了

其实很简单，其实并不难

先上代码吧，不过里面有很多多余的函数，不过之后计算平移+旋转的时候会用到

// 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);
	*/