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kradchen
2023-05-04 16:37:33 +08:00
commit 9c2b86c9bc
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.gitignore vendored Normal file
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build*/
.vscode/

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CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 3.16)
project(UR)
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_INCLUDE_CURRENT_DIR ON)
find_package(Aurora REQUIRED)
file(GLOB_RECURSE cpp_files ./src/*.cpp)
file(GLOB_RECURSE cxx_files ./src/*.cxx)
add_executable(UR ${cpp_files} ${cxx_files} ${Aurora_Source})
target_compile_options(UR PUBLIC ${Aurora_Complie_Options})
target_include_directories(UR PUBLIC ${Aurora_INCLUDE_DIRS})
target_link_libraries(UR PUBLIC ${Aurora_Libraries})
target_link_libraries(UR PUBLIC matio)

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src/main.cxx Normal file
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#define EIGEN_USE_MKL_ALL
#include <iostream>
#include <algorithm>
#include <complex>
#include "Matrix.h"
#include "Function.h"
#include "Function1D.h"
#include "Function2D.h"
#include "Function3D.h"
#include "MatlabReader.h"
int main()
{
MatlabReader m;
Input i;
MatchedFilter o;
m.read(&i, &o, "/home/krad/TestData/testCreateMatchedFilter.mat");
bool measuredCEused = true, findDefects = i.mParams->mFindDefects, removeOutliers = i.mRemoveOutliersFromCEMeasured;
Aurora::Matrix mFTime = Aurora::Matrix::fromRawData(i.mCe, 4000, 2304);
if (removeOutliers)
{
auto normSTD = Aurora::std(Aurora::abs(mFTime));
Aurora::nantoval(normSTD, 0.0);
auto sortSTD = Aurora::sort(normSTD);
int t = (int)std::round(0.4 * mFTime.getDimSize(1)) - 1;
t = t <= 0 ? 1.0 : t;
auto absFTime = abs(mFTime);
auto maxAbsFTime = max(absFTime);
auto maxFlag = maxAbsFTime < (0.1 * max(maxAbsFTime));
auto lessFlag = normSTD < sortSTD(0, t).toMatrix();
long maxCol, maxRow;
max(normSTD, Aurora::Column, maxRow, maxCol);
for (int j = 0; j < mFTime.getDimSize(1); ++j)
{
if ((bool)(lessFlag.getData()[j]) || (bool)(maxFlag.getData()[j]))
{
mFTime(Aurora::$, j) = mFTime(Aurora::$, maxCol);
}
}
}
auto matchedFilter = fft(mFTime);
auto sumDiff = Aurora::zeros(1, matchedFilter.getDimSize(1));
long minCol = 998, row;
auto absMatchedFilter = abs(matchedFilter);
auto highNoiseScore = mean(absMatchedFilter) * Aurora::std(absMatchedFilter);
// auto highNoiseScore = mean( abs(matchedFilter)) * Aurora::std(absMatchedFilter);
printf("highNoiseScore[998]: %f4, highNoiseScore[2053]:%f4\r\n", highNoiseScore.getData()[998], highNoiseScore.getData()[2053]);
auto medianNoise = median(highNoiseScore);
printf("median: %f , should be: 1724817516.8468074\r\n", medianNoise.getScalar());
min(abs(highNoiseScore - median(highNoiseScore)), Aurora::Column, row, minCol);
//
// minCol = 998;
auto maxMatchFilter = matchedFilter(Aurora::$, minCol).toMatrix();
for (int k = 0; k < matchedFilter.getDimSize(1); ++k)
{
sumDiff.getData()[k] = sum(abs(matchedFilter(Aurora::$, k).toMatrix() - maxMatchFilter)).getData()[0];
}
// printf("meanSumDiff\r\n");
// auto meanSumDiff = mean(sumDiff);
// printf("meanSumDiff finish\r\n");
{
double meansumDiff = mean(sumDiff).getScalar() ;
double stdSumDiff = Aurora::std(sumDiff).getScalar();
double sumDiffJ =meansumDiff + 2.596 * stdSumDiff;
// auto indexe = sumDiff > sumDiffJ;
printf("indexe finish\r\n");
for (int l = 0; l < sumDiff.getDataSize(); ++l)
{
if (sumDiff.getData()[l]> sumDiffJ)
{
matchedFilter(Aurora::$, l) = matchedFilter(Aurora::$, minCol);
}
}
printf("\r\n");
}
if (measuredCEused)
{
auto mFTime2 = real(ifft(-matchedFilter));
mFTime2 = mFTime2 / repmat(max(Aurora::abs(mFTime2)), mFTime2.getDimSize(0), 1);
mFTime2 = mFTime2 / repmat(sum(Aurora::abs(mFTime2)), mFTime2.getDimSize(0), 1);
matchedFilter = fft(mFTime2);
}
for (size_t i = 0; i < 10000; i += 500)
{
printf("index :%d,origin output:%f4,%f4, output:%f4,%f4\r\n",
i, o.mReal[i], o.mImag[i], matchedFilter.getData()[i * 2], matchedFilter.getData()[i * 2 + 1]);
}
return 0;
}