Image processing 开Cv相位相关
我想计算OpenCV中函数的相位相关性。我已经找到了一些其他的库，我想用OpenCV来实现它 我的问题是不同的，因为我想计算两个阵列的相位相关性，都是360个元素。 我试图从文档中评估如何做到这一点，但我不知道我的方法是否好。 我的R矩阵没有规范化，如何规范化它，有必要吗？ 如果你给我举一些例子，我将不胜感激。 我执行此任务的职能：Image processing 开Cv相位相关,image-processing,opencv,signal-processing,Image Processing,Opencv,Signal Processing,我想计算OpenCV中函数的相位相关性。我已经找到了一些其他的库，我想用OpenCV来实现它 我的问题是不同的，因为我想计算两个阵列的相位相关性，都是360个元素。 我试图从文档中评估如何做到这一点，但我不知道我的方法是否好。 我的R矩阵没有规范化，如何规范化它，有必要吗？ 如果你给我举一些例子，我将不胜感激。 我执行此任务的职能： void calcPhaseCorrelation(int* x1, int* x2){ Mat array1 = Mat(1,360,DataType&l
void calcPhaseCorrelation(int* x1, int* x2){
Mat array1 = Mat(1,360,DataType<float>::type);
Mat array2 = Mat(1,360,DataType<float>::type);
uchar* begin = array1.data;
uchar* end = begin + (array1.step.p[0]/ sizeof(float)) * array1.size().height;
uchar *ptr = begin;
int ctr1 = 0, ctr2 = 0; //control in loops
while(ptr<end)
{
*ptr = (float)x1[ctr1];
ctr1++;
ptr++;
}
begin = array2.data;
end = begin + (array2.step.p[0]/ sizeof(float)) * array2.size().height;
ptr = begin;
while(ptr<end)
{
*ptr = x2[ctr2];
ctr2++;
ptr++;
}
Mat outputArray;
outputArray.create(abs(array1.rows - array2.rows)+1,
abs(array1.cols - array2.cols)+1, array1.type());
Size dftSize;
dftSize.width = getOptimalDFTSize(array1.cols + array2.cols - 1);
dftSize.height = getOptimalDFTSize(array1.rows + array2.rows - 1);
Mat resultA(dftSize, array1.type(), Scalar::all(0));
Mat resultB(dftSize, array2.type(), Scalar::all(0));
dft(array1,resultA);
dft(array2,resultB);
Mat R;
mulSpectrums(resultA,resultB,R,DFT_ROWS,true);
Mat NormR;
normalize(R,NormR);
idft(NormR,outputArray);
double minVal, maxVal;
Point minLoc, maxLoc;
minMaxLoc(outputArray,&minVal,&maxVal,&minLoc,&maxLoc);
std::cout<<"Min value: "<<minVal<<", max value: "<<maxVal<<std::endl;
std::cout<<"Min loc x: "<<minLoc.x<<", min loc y: "<<minLoc.y<<std::endl;
std::cout<<"Max loc x: "<<maxLoc.x<<", max loc y: "<<maxLoc.y<<std::endl;
}
void calcPhaseCorrelation（int*x1，int*x2）{
Mat array1=Mat（1360，数据类型：：type）；
Mat array2=Mat（1360，数据类型：：类型）；
uchar*begin=array1.data；
uchar*end=begin+（array1.step.p[0]/sizeof（float））*array1.size（）.height；
uchar*ptr=开始；
int ctr1=0，ctr2=0；//循环中的控件
虽然（ptr我实际上已经为OpenCV实现了这个方法，但不幸的是，在这个阶段，它只在SVN主干中实现
是一个使用新方法的样本
如果你想参考我的实现，你可以找到它
此外，这是使用它的另一个示例的测试用例
如果您想使用后备箱，您可以通过以下方式获得后备箱：
svn co https://code.ros.org/svn/opencv/trunk/opencv opencv-trunk
是针对Linux的CMake构建指南。是针对Windows的构建指南
编辑：
有补丁给你：）
我在我的代码中也发现了一些错误，所以现在应该纠正它们。它现在还应该支持一维相位相关。我还发现cv:：sqrt（）
函数存在问题，导致一些-nan出现，即使std:：sqrt（）
没有。我猜这可能是OpenCV的一个bug，也可能只是一个准确性问题。不过，我还没有深入研究它以找出答案
不幸的是，您不能仅仅使用svn update
来获取我的最新更改，因为我必须等待OpenCV开发人员应用此修补程序。因此，您不必等待，这里有一个修补程序，您可以应用于$（OpenCV\u SRC）/modules/imgproc/src/phasecorr.cpp
文件。将此文件另存为类似于phasecorr.patch
的文件，并将其放在OpenCV源目录的根目录中。这是一个简短的陆龟指南，用于在源树中创建/应用补丁
Index: modules/imgproc/src/phasecorr.cpp
===================================================================
--- modules/imgproc/src/phasecorr.cpp (revision 6971)
+++ modules/imgproc/src/phasecorr.cpp (working copy)
@@ -83,8 +83,8 @@
for( j = 1; j <= rows - 2; j += 2 )
{
- dataDst[j*stepDst] = (float)((double)dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
- (double)dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
+ dataDst[j*stepDst] = (float)std::sqrt((double)dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
+ (double)dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
}
if( k == 1 )
@@ -103,7 +103,7 @@
for( j = j0; j < j1; j += 2 )
{
- dataDst[j] = (float)((double)dataSrc[j]*dataSrc[j] + (double)dataSrc[j+1]*dataSrc[j+1]);
+ dataDst[j] = (float)std::sqrt((double)dataSrc[j]*dataSrc[j] + (double)dataSrc[j+1]*dataSrc[j+1]);
}
}
}
@@ -127,8 +127,8 @@
for( j = 1; j <= rows - 2; j += 2 )
{
- dataDst[j*stepDst] = dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
- dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc];
+ dataDst[j*stepDst] = std::sqrt(dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
+ dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
}
if( k == 1 )
@@ -147,13 +147,10 @@
for( j = j0; j < j1; j += 2 )
{
- dataDst[j] = dataSrc[j]*dataSrc[j] + dataSrc[j+1]*dataSrc[j+1];
+ dataDst[j] = std::sqrt(dataSrc[j]*dataSrc[j] + dataSrc[j+1]*dataSrc[j+1]);
}
}
}
-
- // do batch sqrt to use SSE optimizations...
- cv::sqrt(dst, dst);
}
static void divSpectrums( InputArray _srcA, InputArray _srcB, OutputArray _dst, int flags, bool conjB)
@@ -196,9 +193,9 @@
{
if( k == 1 )
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( rows % 2 == 0 )
- dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / dataB[(rows-1)*stepB];
+ dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
if( !conjB )
for( j = 1; j <= rows - 2; j += 2 )
{
@@ -239,9 +236,9 @@
{
if( is_1d && cn == 1 )
{
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( cols % 2 == 0 )
- dataC[j1] = dataA[j1] / dataB[j1];
+ dataC[j1] = dataA[j1] / (dataB[j1] + eps);
}
if( !conjB )
@@ -281,9 +278,9 @@
{
if( k == 1 )
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( rows % 2 == 0 )
- dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / dataB[(rows-1)*stepB];
+ dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
if( !conjB )
for( j = 1; j <= rows - 2; j += 2 )
{
@@ -323,9 +320,9 @@
{
if( is_1d && cn == 1 )
{
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( cols % 2 == 0 )
- dataC[j1] = dataA[j1] / dataB[j1];
+ dataC[j1] = dataA[j1] / (dataB[j1] + eps);
}
if( !conjB )
@@ -354,31 +351,57 @@
static void fftShift(InputOutputArray _out)
{
Mat out = _out.getMat();
-
+
+ if(out.rows == 1 && out.cols == 1)
+ {
+ // trivially shifted.
+ return;
+ }
+
vector<Mat> planes;
split(out, planes);
-
+
int xMid = out.cols >> 1;
int yMid = out.rows >> 1;
-
- for(size_t i = 0; i < planes.size(); i++)
+
+ bool is_1d = xMid == 0 || yMid == 0;
+
+ if(is_1d)
{
- // perform quadrant swaps...
- Mat tmp;
- Mat q0(planes[i], Rect(0, 0, xMid, yMid));
- Mat q1(planes[i], Rect(xMid, 0, xMid, yMid));
- Mat q2(planes[i], Rect(0, yMid, xMid, yMid));
- Mat q3(planes[i], Rect(xMid, yMid, xMid, yMid));
-
- q0.copyTo(tmp);
- q3.copyTo(q0);
- tmp.copyTo(q3);
-
- q1.copyTo(tmp);
- q2.copyTo(q1);
- tmp.copyTo(q2);
+ xMid = xMid + yMid;
+
+ for(size_t i = 0; i < planes.size(); i++)
+ {
+ Mat tmp;
+ Mat half0(planes[i], Rect(0, 0, xMid, 1));
+ Mat half1(planes[i], Rect(xMid, 0, xMid, 1));
+
+ half0.copyTo(tmp);
+ half1.copyTo(half0);
+ tmp.copyTo(half1);
+ }
}
-
+ else
+ {
+ for(size_t i = 0; i < planes.size(); i++)
+ {
+ // perform quadrant swaps...
+ Mat tmp;
+ Mat q0(planes[i], Rect(0, 0, xMid, yMid));
+ Mat q1(planes[i], Rect(xMid, 0, xMid, yMid));
+ Mat q2(planes[i], Rect(0, yMid, xMid, yMid));
+ Mat q3(planes[i], Rect(xMid, yMid, xMid, yMid));
+
+ q0.copyTo(tmp);
+ q3.copyTo(q0);
+ tmp.copyTo(q3);
+
+ q1.copyTo(tmp);
+ q2.copyTo(q1);
+ tmp.copyTo(q2);
+ }
+ }
+
merge(planes, out);
}
@@ -548,38 +571,67 @@
int rows = dst.rows;
int cols = dst.cols;
+ bool is_1d = rows == 1 || cols == 1;
+
+ if(is_1d)
+ {
+ cols = cols + rows - 1;
+ }
+
if(dst.depth() == CV_32F)
{
float* dstData = (float*)dst.data;
- for(int i = 0; i < rows; i++)
+ if(is_1d)
{
- double wr = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(rows - 1)));
- for(int j = 0; j < cols; j++)
+ for(int i = 0; i < cols; i++)
{
- double wc = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)j / (double)(cols - 1)));
- dstData[i*cols + j] = (float)(wr * wc);
+ double w = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(cols - 1)));
+ dstData[i] = (float)w;
}
}
+ else
+ {
+ for(int i = 0; i < rows; i++)
+ {
+ double wr = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(rows - 1)));
+ for(int j = 0; j < cols; j++)
+ {
+ double wc = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)j / (double)(cols - 1)));
+ dstData[i*cols + j] = (float)(wr * wc);
+ }
+ }
- // perform batch sqrt for SSE performance gains
- cv::sqrt(dst, dst);
+ // perform batch sqrt for SSE performance gains
+ cv::sqrt(dst, dst);
+ }
}
else
{
double* dstData = (double*)dst.data;
- for(int i = 0; i < rows; i++)
+ if(is_1d)
{
- double wr = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)i / (double)(rows - 1)));
- for(int j = 0; j < cols; j++)
+ for(int i = 0; i < cols; i++)
{
- double wc = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)j / (double)(cols - 1)));
- dstData[i*cols + j] = wr * wc;
+ double w = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(cols - 1)));
+ dstData[i] = w;
}
}
+ else
+ {
+ for(int i = 0; i < rows; i++)
+ {
+ double wr = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)i / (double)(rows - 1)));
+ for(int j = 0; j < cols; j++)
+ {
+ double wc = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)j / (double)(cols - 1)));
+ dstData[i*cols + j] = wr * wc;
+ }
+ }
- // perform batch sqrt for SSE performance gains
- cv::sqrt(dst, dst);
+ // perform batch sqrt for SSE performance gains
+ cv::sqrt(dst, dst);
+ }
}
}
索引：modules/imgproc/src/phasecorr.cpp
===================================================================
---模块/imgproc/src/phasecorr.cpp（版本6971）
+++模块/imgproc/src/phasecorr.cpp（工作副本）
@@ -83,8 +83,8 @@
对于（j=1；j1；
-
-对于（size_t i=0；iIndex: modules/imgproc/src/phasecorr.cpp
===================================================================
--- modules/imgproc/src/phasecorr.cpp (revision 6971)
+++ modules/imgproc/src/phasecorr.cpp (working copy)
@@ -83,8 +83,8 @@
for( j = 1; j <= rows - 2; j += 2 )
{
- dataDst[j*stepDst] = (float)((double)dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
- (double)dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
+ dataDst[j*stepDst] = (float)std::sqrt((double)dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
+ (double)dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
}
if( k == 1 )
@@ -103,7 +103,7 @@
for( j = j0; j < j1; j += 2 )
{
- dataDst[j] = (float)((double)dataSrc[j]*dataSrc[j] + (double)dataSrc[j+1]*dataSrc[j+1]);
+ dataDst[j] = (float)std::sqrt((double)dataSrc[j]*dataSrc[j] + (double)dataSrc[j+1]*dataSrc[j+1]);
}
}
}
@@ -127,8 +127,8 @@
for( j = 1; j <= rows - 2; j += 2 )
{
- dataDst[j*stepDst] = dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
- dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc];
+ dataDst[j*stepDst] = std::sqrt(dataSrc[j*stepSrc]*dataSrc[j*stepSrc] +
+ dataSrc[(j+1)*stepSrc]*dataSrc[(j+1)*stepSrc]);
}
if( k == 1 )
@@ -147,13 +147,10 @@
for( j = j0; j < j1; j += 2 )
{
- dataDst[j] = dataSrc[j]*dataSrc[j] + dataSrc[j+1]*dataSrc[j+1];
+ dataDst[j] = std::sqrt(dataSrc[j]*dataSrc[j] + dataSrc[j+1]*dataSrc[j+1]);
}
}
}
-
- // do batch sqrt to use SSE optimizations...
- cv::sqrt(dst, dst);
}
static void divSpectrums( InputArray _srcA, InputArray _srcB, OutputArray _dst, int flags, bool conjB)
@@ -196,9 +193,9 @@
{
if( k == 1 )
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( rows % 2 == 0 )
- dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / dataB[(rows-1)*stepB];
+ dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
if( !conjB )
for( j = 1; j <= rows - 2; j += 2 )
{
@@ -239,9 +236,9 @@
{
if( is_1d && cn == 1 )
{
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( cols % 2 == 0 )
- dataC[j1] = dataA[j1] / dataB[j1];
+ dataC[j1] = dataA[j1] / (dataB[j1] + eps);
}
if( !conjB )
@@ -281,9 +278,9 @@
{
if( k == 1 )
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( rows % 2 == 0 )
- dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / dataB[(rows-1)*stepB];
+ dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
if( !conjB )
for( j = 1; j <= rows - 2; j += 2 )
{
@@ -323,9 +320,9 @@
{
if( is_1d && cn == 1 )
{
- dataC[0] = dataA[0] / dataB[0];
+ dataC[0] = dataA[0] / (dataB[0] + eps);
if( cols % 2 == 0 )
- dataC[j1] = dataA[j1] / dataB[j1];
+ dataC[j1] = dataA[j1] / (dataB[j1] + eps);
}
if( !conjB )
@@ -354,31 +351,57 @@
static void fftShift(InputOutputArray _out)
{
Mat out = _out.getMat();
-
+
+ if(out.rows == 1 && out.cols == 1)
+ {
+ // trivially shifted.
+ return;
+ }
+
vector<Mat> planes;
split(out, planes);
-
+
int xMid = out.cols >> 1;
int yMid = out.rows >> 1;
-
- for(size_t i = 0; i < planes.size(); i++)
+
+ bool is_1d = xMid == 0 || yMid == 0;
+
+ if(is_1d)
{
- // perform quadrant swaps...
- Mat tmp;
- Mat q0(planes[i], Rect(0, 0, xMid, yMid));
- Mat q1(planes[i], Rect(xMid, 0, xMid, yMid));
- Mat q2(planes[i], Rect(0, yMid, xMid, yMid));
- Mat q3(planes[i], Rect(xMid, yMid, xMid, yMid));
-
- q0.copyTo(tmp);
- q3.copyTo(q0);
- tmp.copyTo(q3);
-
- q1.copyTo(tmp);
- q2.copyTo(q1);
- tmp.copyTo(q2);
+ xMid = xMid + yMid;
+
+ for(size_t i = 0; i < planes.size(); i++)
+ {
+ Mat tmp;
+ Mat half0(planes[i], Rect(0, 0, xMid, 1));
+ Mat half1(planes[i], Rect(xMid, 0, xMid, 1));
+
+ half0.copyTo(tmp);
+ half1.copyTo(half0);
+ tmp.copyTo(half1);
+ }
}
-
+ else
+ {
+ for(size_t i = 0; i < planes.size(); i++)
+ {
+ // perform quadrant swaps...
+ Mat tmp;
+ Mat q0(planes[i], Rect(0, 0, xMid, yMid));
+ Mat q1(planes[i], Rect(xMid, 0, xMid, yMid));
+ Mat q2(planes[i], Rect(0, yMid, xMid, yMid));
+ Mat q3(planes[i], Rect(xMid, yMid, xMid, yMid));
+
+ q0.copyTo(tmp);
+ q3.copyTo(q0);
+ tmp.copyTo(q3);
+
+ q1.copyTo(tmp);
+ q2.copyTo(q1);
+ tmp.copyTo(q2);
+ }
+ }
+
merge(planes, out);
}
@@ -548,38 +571,67 @@
int rows = dst.rows;
int cols = dst.cols;
+ bool is_1d = rows == 1 || cols == 1;
+
+ if(is_1d)
+ {
+ cols = cols + rows - 1;
+ }
+
if(dst.depth() == CV_32F)
{
float* dstData = (float*)dst.data;
- for(int i = 0; i < rows; i++)
+ if(is_1d)
{
- double wr = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(rows - 1)));
- for(int j = 0; j < cols; j++)
+ for(int i = 0; i < cols; i++)
{
- double wc = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)j / (double)(cols - 1)));
- dstData[i*cols + j] = (float)(wr * wc);
+ double w = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(cols - 1)));
+ dstData[i] = (float)w;
}
}
+ else
+ {
+ for(int i = 0; i < rows; i++)
+ {
+ double wr = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(rows - 1)));
+ for(int j = 0; j < cols; j++)
+ {
+ double wc = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)j / (double)(cols - 1)));
+ dstData[i*cols + j] = (float)(wr * wc);
+ }
+ }
- // perform batch sqrt for SSE performance gains
- cv::sqrt(dst, dst);
+ // perform batch sqrt for SSE performance gains
+ cv::sqrt(dst, dst);
+ }
}
else
{
double* dstData = (double*)dst.data;
- for(int i = 0; i < rows; i++)
+ if(is_1d)
{
- double wr = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)i / (double)(rows - 1)));
- for(int j = 0; j < cols; j++)
+ for(int i = 0; i < cols; i++)
{
- double wc = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)j / (double)(cols - 1)));
- dstData[i*cols + j] = wr * wc;
+ double w = 0.5 * (1.0f - cos(2.0f * CV_PI * (double)i / (double)(cols - 1)));
+ dstData[i] = w;
}
}
+ else
+ {
+ for(int i = 0; i < rows; i++)
+ {
+ double wr = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)i / (double)(rows - 1)));
+ for(int j = 0; j < cols; j++)
+ {
+ double wc = 0.5 * (1.0 - cos(2.0 * CV_PI * (double)j / (double)(cols - 1)));
+ dstData[i*cols + j] = wr * wc;
+ }
+ }
- // perform batch sqrt for SSE performance gains
- cv::sqrt(dst, dst);
+ // perform batch sqrt for SSE performance gains
+ cv::sqrt(dst, dst);
+ }
}
}
int main(int argc, char* argv[])
{
Mat firstArray = Mat::zeros(Size(360, 1), CV_64F);
Mat secondArray = Mat::zeros(Size(360, 1), CV_64F);
for(int i = 0; i < firstArray.cols; i++)
{
if(i < 8)
{
firstArray.at<double>(0, i) = 1;
}
if(i < 6)
{
secondArray.at<double>(0, i) = 1;
}
}
Mat hann;
createHanningWindow(hann, firstArray.size(), CV_64F);
Point2d shift = phaseCorrelate(firstArray, secondArray, hann);
std::cout<< "shift: " << shift.x << ";" << shift.y << std::endl;
return 0;
}