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