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Merge pull request #29123 from asmorkalov:as/divSpectrums
Move divSpectrums to core module.
This commit is contained in:
Alexander Smorkalov
GitHub
@@ -2284,6 +2284,22 @@ or not (false).
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CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c,
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int flags, bool conjB = false);
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/** @brief Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
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*
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* The function cv::divSpectrums performs the per-element division of the first array by the second array.
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* The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
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*
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* @param a first input array.
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* @param b second input array of the same size and type as src1 .
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* @param c output array of the same size and type as src1 .
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* @param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
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* each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
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* @param conjB optional flag that conjugates the second input array before the multiplication (true)
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* or not (false).
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*/
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CV_EXPORTS_W void divSpectrums(InputArray a, InputArray b, OutputArray c,
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int flags, bool conjB = false);
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/** @brief Returns the optimal DFT size for a given vector size.
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DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
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@@ -3,8 +3,8 @@
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{
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"": [
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"absdiff", "add", "addWeighted", "bitwise_and", "bitwise_not", "bitwise_or", "bitwise_xor", "cartToPolar",
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"compare", "convertScaleAbs", "copyMakeBorder", "countNonZero", "determinant", "dft", "divide", "eigen",
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"exp", "flip", "getOptimalDFTSize","gemm", "hconcat", "inRange", "invert", "kmeans", "log", "magnitude",
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"compare", "convertScaleAbs", "copyMakeBorder", "countNonZero", "determinant", "dft", "divide", "divSpectrums",
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"eigen", "exp", "flip", "getOptimalDFTSize","gemm", "hconcat", "inRange", "invert", "kmeans", "log", "magnitude",
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"max", "mean", "meanStdDev", "merge", "min", "minMaxLoc", "mixChannels", "multiply", "norm", "normalize",
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"perspectiveTransform", "polarToCart", "pow", "randn", "randu", "reduce", "repeat", "rotate", "setIdentity", "setRNGSeed",
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"solve", "solvePoly", "split", "sqrt", "subtract", "trace", "transform", "transpose", "vconcat",
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@@ -3773,6 +3773,202 @@ void cv::mulSpectrums( InputArray _srcA, InputArray _srcB,
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}
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}
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void cv::divSpectrums( InputArray _srcA, InputArray _srcB, OutputArray _dst, int flags, bool conjB)
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{
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Mat srcA = _srcA.getMat(), srcB = _srcB.getMat();
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int depth = srcA.depth(), cn = srcA.channels(), type = srcA.type();
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int rows = srcA.rows, cols = srcA.cols;
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int j, k;
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CV_Assert( type == srcB.type() && srcA.size() == srcB.size() );
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CV_Assert( type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2 );
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_dst.create( srcA.rows, srcA.cols, type );
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Mat dst = _dst.getMat();
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CV_Assert(dst.data != srcA.data); // non-inplace check
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CV_Assert(dst.data != srcB.data); // non-inplace check
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bool is_1d = (flags & DFT_ROWS) || (rows == 1 || (cols == 1 &&
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srcA.isContinuous() && srcB.isContinuous() && dst.isContinuous()));
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if( is_1d && !(flags & DFT_ROWS) )
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cols = cols + rows - 1, rows = 1;
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int ncols = cols*cn;
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int j0 = cn == 1;
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int j1 = ncols - (cols % 2 == 0 && cn == 1);
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if( depth == CV_32F )
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{
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const float* dataA = srcA.ptr<float>();
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const float* dataB = srcB.ptr<float>();
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float* dataC = dst.ptr<float>();
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float eps = FLT_EPSILON; // prevent div0 problems
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size_t stepA = srcA.step/sizeof(dataA[0]);
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size_t stepB = srcB.step/sizeof(dataB[0]);
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size_t stepC = dst.step/sizeof(dataC[0]);
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if( !is_1d && cn == 1 )
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{
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for( k = 0; k < (cols % 2 ? 1 : 2); k++ )
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{
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if( k == 1 )
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dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
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dataC[0] = dataA[0] / (dataB[0] + eps);
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if( rows % 2 == 0 )
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dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
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if( !conjB )
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for( j = 1; j <= rows - 2; j += 2 )
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{
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double denom = (double)dataB[j*stepB]*dataB[j*stepB] +
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(double)dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + (double)eps;
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double re = (double)dataA[j*stepA]*dataB[j*stepB] +
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(double)dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
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double im = (double)dataA[(j+1)*stepA]*dataB[j*stepB] -
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(double)dataA[j*stepA]*dataB[(j+1)*stepB];
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dataC[j*stepC] = (float)(re / denom);
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dataC[(j+1)*stepC] = (float)(im / denom);
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}
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else
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for( j = 1; j <= rows - 2; j += 2 )
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{
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double denom = (double)dataB[j*stepB]*dataB[j*stepB] +
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(double)dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + (double)eps;
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double re = (double)dataA[j*stepA]*dataB[j*stepB] -
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(double)dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
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double im = (double)dataA[(j+1)*stepA]*dataB[j*stepB] +
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(double)dataA[j*stepA]*dataB[(j+1)*stepB];
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dataC[j*stepC] = (float)(re / denom);
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dataC[(j+1)*stepC] = (float)(im / denom);
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}
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if( k == 1 )
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dataA -= cols - 1, dataB -= cols - 1, dataC -= cols - 1;
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}
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}
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for( ; rows--; dataA += stepA, dataB += stepB, dataC += stepC )
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{
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if( is_1d && cn == 1 )
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{
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dataC[0] = dataA[0] / (dataB[0] + eps);
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if( cols % 2 == 0 )
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dataC[j1] = dataA[j1] / (dataB[j1] + eps);
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}
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if( !conjB )
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for( j = j0; j < j1; j += 2 )
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{
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double denom = (double)dataB[j]*dataB[j] + (double)dataB[j+1]*dataB[j+1] + (double)eps;
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double re = (double)dataA[j]*dataB[j] + (double)dataA[j+1]*dataB[j+1];
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double im = (double)dataA[j+1]*dataB[j] - (double)dataA[j]*dataB[j+1];
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dataC[j] = (float)(re / denom);
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dataC[j+1] = (float)(im / denom);
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}
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else
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for( j = j0; j < j1; j += 2 )
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{
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double denom = (double)dataB[j]*dataB[j] + (double)dataB[j+1]*dataB[j+1] + (double)eps;
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double re = (double)dataA[j]*dataB[j] - (double)dataA[j+1]*dataB[j+1];
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double im = (double)dataA[j+1]*dataB[j] + (double)dataA[j]*dataB[j+1];
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dataC[j] = (float)(re / denom);
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dataC[j+1] = (float)(im / denom);
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}
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}
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}
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else
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{
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const double* dataA = srcA.ptr<double>();
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const double* dataB = srcB.ptr<double>();
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double* dataC = dst.ptr<double>();
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double eps = DBL_EPSILON; // prevent div0 problems
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size_t stepA = srcA.step/sizeof(dataA[0]);
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size_t stepB = srcB.step/sizeof(dataB[0]);
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size_t stepC = dst.step/sizeof(dataC[0]);
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if( !is_1d && cn == 1 )
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{
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for( k = 0; k < (cols % 2 ? 1 : 2); k++ )
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{
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if( k == 1 )
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dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
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dataC[0] = dataA[0] / (dataB[0] + eps);
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if( rows % 2 == 0 )
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dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
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if( !conjB )
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for( j = 1; j <= rows - 2; j += 2 )
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{
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double denom = dataB[j*stepB]*dataB[j*stepB] +
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dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + eps;
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double re = dataA[j*stepA]*dataB[j*stepB] +
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dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
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double im = dataA[(j+1)*stepA]*dataB[j*stepB] -
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dataA[j*stepA]*dataB[(j+1)*stepB];
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dataC[j*stepC] = re / denom;
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dataC[(j+1)*stepC] = im / denom;
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}
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else
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for( j = 1; j <= rows - 2; j += 2 )
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{
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double denom = dataB[j*stepB]*dataB[j*stepB] +
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dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + eps;
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double re = dataA[j*stepA]*dataB[j*stepB] -
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dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
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double im = dataA[(j+1)*stepA]*dataB[j*stepB] +
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dataA[j*stepA]*dataB[(j+1)*stepB];
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dataC[j*stepC] = re / denom;
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dataC[(j+1)*stepC] = im / denom;
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}
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if( k == 1 )
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dataA -= cols - 1, dataB -= cols - 1, dataC -= cols - 1;
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}
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}
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for( ; rows--; dataA += stepA, dataB += stepB, dataC += stepC )
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{
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if( is_1d && cn == 1 )
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{
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dataC[0] = dataA[0] / (dataB[0] + eps);
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if( cols % 2 == 0 )
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dataC[j1] = dataA[j1] / (dataB[j1] + eps);
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}
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if( !conjB )
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for( j = j0; j < j1; j += 2 )
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{
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double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
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double re = dataA[j]*dataB[j] + dataA[j+1]*dataB[j+1];
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double im = dataA[j+1]*dataB[j] - dataA[j]*dataB[j+1];
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dataC[j] = re / denom;
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dataC[j+1] = im / denom;
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}
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else
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for( j = j0; j < j1; j += 2 )
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{
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double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
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double re = dataA[j]*dataB[j] - dataA[j+1]*dataB[j+1];
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double im = dataA[j+1]*dataB[j] + dataA[j]*dataB[j+1];
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dataC[j] = re / denom;
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dataC[j+1] = im / denom;
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}
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}
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}
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}
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/****************************************************************************************\
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Discrete Cosine Transform
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@@ -879,9 +879,283 @@ void CxCore_MulSpectrumsTest::prepare_to_validation( int /*test_case_idx*/ )
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}
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}
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////////////////////// DivSpectrums ////////////////////////
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class CV_DivSpectrumsTest : public cvtest::ArrayTest
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{
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public:
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CV_DivSpectrumsTest();
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protected:
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void run_func();
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void get_test_array_types_and_sizes( int, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void prepare_to_validation( int test_case_idx );
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int flags;
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};
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CV_DivSpectrumsTest::CV_DivSpectrumsTest() : flags(0)
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{
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// Allocate test matrices.
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test_array[INPUT].push_back(NULL); // first input DFT as a CCS-packed array or complex matrix.
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test_array[INPUT].push_back(NULL); // second input DFT as a CCS-packed array or complex matrix.
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test_array[OUTPUT].push_back(NULL); // output DFT as a complex matrix.
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test_array[REF_OUTPUT].push_back(NULL); // reference output DFT as a complex matrix.
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test_array[TEMP].push_back(NULL); // first input DFT converted to a complex matrix.
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test_array[TEMP].push_back(NULL); // second input DFT converted to a complex matrix.
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test_array[TEMP].push_back(NULL); // output DFT as a CCV-packed array.
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}
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void CV_DivSpectrumsTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx, sizes, types);
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RNG& rng = ts->get_rng();
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// Get the flag of the input.
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const int rand_int_flags = cvtest::randInt(rng);
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flags = rand_int_flags & (CV_TEST_DXT_MUL_CONJ | DFT_ROWS);
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// Get input type.
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const int rand_int_type = cvtest::randInt(rng);
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int type;
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if (rand_int_type % 4)
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{
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type = CV_32FC1;
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}
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else if (rand_int_type % 4 == 1)
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{
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type = CV_32FC2;
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}
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else if (rand_int_type % 4 == 2)
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{
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type = CV_64FC1;
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}
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else
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{
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type = CV_64FC2;
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}
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for( size_t i = 0; i < types.size(); i++ )
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{
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for( size_t j = 0; j < types[i].size(); j++ )
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{
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types[i][j] = type;
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}
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}
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// Inputs are CCS-packed arrays. Prepare outputs and temporary inputs as complex matrices.
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if( type == CV_32FC1 || type == CV_64FC1 )
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{
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types[OUTPUT][0] += CV_DEPTH_MAX;
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types[REF_OUTPUT][0] += CV_DEPTH_MAX;
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types[TEMP][0] += CV_DEPTH_MAX;
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types[TEMP][1] += CV_DEPTH_MAX;
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}
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}
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/// Helper function to convert a ccs array of depth_t into a complex matrix.
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template<typename depth_t>
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static void convert_from_ccs_helper( const Mat& src0, const Mat& src1, Mat& dst )
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{
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const int cn = src0.channels();
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int srcstep = cn;
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int dststep = 1;
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if( !dst.isContinuous() )
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dststep = (int)(dst.step/dst.elemSize());
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if( !src0.isContinuous() )
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srcstep = (int)(src0.step/src0.elemSize1());
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Complex<depth_t> *dst_data = dst.ptr<Complex<depth_t> >();
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const depth_t* src0_data = src0.ptr<depth_t>();
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const depth_t* src1_data = src1.ptr<depth_t>();
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dst_data->re = src0_data[0];
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dst_data->im = 0;
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const int n = dst.cols + dst.rows - 1;
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const int n2 = (n+1) >> 1;
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if( (n & 1) == 0 )
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{
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dst_data[n2*dststep].re = src0_data[(cn == 1 ? n-1 : n2)*srcstep];
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dst_data[n2*dststep].im = 0;
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}
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int delta0 = srcstep;
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int delta1 = delta0 + (cn == 1 ? srcstep : 1);
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if( cn == 1 )
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srcstep *= 2;
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for( int i = 1; i < n2; i++, delta0 += srcstep, delta1 += srcstep )
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{
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depth_t t0 = src0_data[delta0];
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depth_t t1 = src0_data[delta1];
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dst_data[i*dststep].re = t0;
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dst_data[i*dststep].im = t1;
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t0 = src1_data[delta0];
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t1 = -src1_data[delta1];
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dst_data[(n-i)*dststep].re = t0;
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dst_data[(n-i)*dststep].im = t1;
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}
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}
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/// Helper function to convert a ccs array into a complex matrix.
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static void convert_from_ccs( const Mat& src0, const Mat& src1, Mat& dst, const int flags )
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{
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if( dst.rows > 1 && (dst.cols > 1 || (flags & DFT_ROWS)) )
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{
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const int count = dst.rows;
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const int len = dst.cols;
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const bool is2d = (flags & DFT_ROWS) == 0;
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for( int i = 0; i < count; i++ )
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{
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const int j = !is2d || i == 0 ? i : count - i;
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const Mat& src0row = src0.row(i);
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const Mat& src1row = src1.row(j);
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Mat dstrow = dst.row(i);
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convert_from_ccs( src0row, src1row, dstrow, 0 );
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}
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if( is2d )
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{
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const Mat& src0row = src0.col(0);
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Mat dstrow = dst.col(0);
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convert_from_ccs( src0row, src0row, dstrow, 0 );
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if( (len & 1) == 0 )
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{
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const Mat& src0row_even = src0.col(src0.cols - 1);
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Mat dstrow_even = dst.col(len/2);
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convert_from_ccs( src0row_even, src0row_even, dstrow_even, 0 );
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}
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}
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}
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else
|
||||
{
|
||||
if( dst.depth() == CV_32F )
|
||||
{
|
||||
convert_from_ccs_helper<float>( src0, src1, dst );
|
||||
}
|
||||
else
|
||||
{
|
||||
convert_from_ccs_helper<double>( src0, src1, dst );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to compute complex number (nu_re + nu_im * i) / (de_re + de_im * i).
|
||||
static std::pair<double, double> divide_complex_numbers( const double nu_re, const double nu_im,
|
||||
const double de_re, const double de_im,
|
||||
const bool conj_de )
|
||||
{
|
||||
if ( conj_de )
|
||||
{
|
||||
return divide_complex_numbers( nu_re, nu_im, de_re, -de_im, false /* conj_de */ );
|
||||
}
|
||||
|
||||
const double result_de = de_re * de_re + de_im * de_im + DBL_EPSILON;
|
||||
const double result_re = nu_re * de_re + nu_im * de_im;
|
||||
const double result_im = nu_re * (-de_im) + nu_im * de_re;
|
||||
return std::pair<double, double>(result_re / result_de, result_im / result_de);
|
||||
}
|
||||
|
||||
/// Helper function to divide a DFT in src1 by a DFT in src2 with depths depth_t. The DFTs are
|
||||
/// complex matrices.
|
||||
template <typename depth_t>
|
||||
static void div_complex_helper( const Mat& src1, const Mat& src2, Mat& dst, int flags )
|
||||
{
|
||||
CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
|
||||
dst.create( src1.rows, src1.cols, src1.type() );
|
||||
const int cn = src1.channels();
|
||||
int cols = src1.cols * cn;
|
||||
|
||||
for( int i = 0; i < dst.rows; i++ )
|
||||
{
|
||||
const depth_t *src1_data = src1.ptr<depth_t>(i);
|
||||
const depth_t *src2_data = src2.ptr<depth_t>(i);
|
||||
depth_t *dst_data = dst.ptr<depth_t>(i);
|
||||
for( int j = 0; j < cols; j += 2 )
|
||||
{
|
||||
std::pair<double, double> result =
|
||||
divide_complex_numbers( src1_data[j], src1_data[j + 1],
|
||||
src2_data[j], src2_data[j + 1],
|
||||
(flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
dst_data[j] = (depth_t)result.first;
|
||||
dst_data[j + 1] = (depth_t)result.second;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to divide a DFT in src1 by a DFT in src2. The DFTs are complex matrices.
|
||||
static void div_complex( const Mat& src1, const Mat& src2, Mat& dst, const int flags )
|
||||
{
|
||||
const int type = src1.type();
|
||||
CV_Assert( type == CV_32FC2 || type == CV_64FC2 );
|
||||
|
||||
if ( src1.depth() == CV_32F )
|
||||
{
|
||||
return div_complex_helper<float>( src1, src2, dst, flags );
|
||||
}
|
||||
else
|
||||
{
|
||||
return div_complex_helper<double>( src1, src2, dst, flags );
|
||||
}
|
||||
}
|
||||
|
||||
void CV_DivSpectrumsTest::prepare_to_validation( int /* test_case_idx */ )
|
||||
{
|
||||
Mat &src1 = test_mat[INPUT][0];
|
||||
Mat &src2 = test_mat[INPUT][1];
|
||||
Mat &ref_dst = test_mat[REF_OUTPUT][0];
|
||||
const int cn = src1.channels();
|
||||
// Inputs are CCS-packed arrays. Convert them to complex matrices and get the expected output
|
||||
// as a complex matrix.
|
||||
if( cn == 1 )
|
||||
{
|
||||
Mat &converted_src1 = test_mat[TEMP][0];
|
||||
Mat &converted_src2 = test_mat[TEMP][1];
|
||||
convert_from_ccs( src1, src1, converted_src1, flags );
|
||||
convert_from_ccs( src2, src2, converted_src2, flags );
|
||||
div_complex( converted_src1, converted_src2, ref_dst, flags );
|
||||
}
|
||||
// Inputs are complex matrices. Get the expected output as a complex matrix.
|
||||
else
|
||||
{
|
||||
div_complex( src1, src2, ref_dst, flags );
|
||||
}
|
||||
}
|
||||
|
||||
void CV_DivSpectrumsTest::run_func()
|
||||
{
|
||||
const Mat &src1 = test_mat[INPUT][0];
|
||||
const Mat &src2 = test_mat[INPUT][1];
|
||||
const int cn = src1.channels();
|
||||
|
||||
// Inputs are CCS-packed arrays. Get the output as a CCS-packed array and convert it to a
|
||||
// complex matrix.
|
||||
if ( cn == 1 )
|
||||
{
|
||||
Mat &dst = test_mat[TEMP][2];
|
||||
cv::divSpectrums( src1, src2, dst, flags, (flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
Mat &converted_dst = test_mat[OUTPUT][0];
|
||||
convert_from_ccs( dst, dst, converted_dst, flags );
|
||||
}
|
||||
// Inputs are complex matrices. Get the output as a complex matrix.
|
||||
else
|
||||
{
|
||||
Mat &dst = test_mat[OUTPUT][0];
|
||||
cv::divSpectrums( src1, src2, dst, flags, (flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
}
|
||||
}
|
||||
|
||||
TEST(Core_DCT, accuracy) { CxCore_DCTTest test; test.safe_run(); }
|
||||
TEST(Core_DFT, accuracy) { CxCore_DFTTest test; test.safe_run(); }
|
||||
TEST(Core_MulSpectrums, accuracy) { CxCore_MulSpectrumsTest test; test.safe_run(); }
|
||||
TEST(Core_DivSpectrums, accuracy) { CV_DivSpectrumsTest test; test.safe_run(); }
|
||||
|
||||
|
||||
class Core_DFTComplexOutputTest : public cvtest::BaseTest
|
||||
{
|
||||
|
||||
@@ -2951,22 +2951,6 @@ An example is shown below:
|
||||
*/
|
||||
CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
|
||||
|
||||
/** @brief Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
|
||||
|
||||
The function cv::divSpectrums performs the per-element division of the first array by the second array.
|
||||
The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
|
||||
|
||||
@param a first input array.
|
||||
@param b second input array of the same size and type as src1 .
|
||||
@param c output array of the same size and type as src1 .
|
||||
@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
|
||||
each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
|
||||
@param conjB optional flag that conjugates the second input array before the multiplication (true)
|
||||
or not (false).
|
||||
*/
|
||||
CV_EXPORTS_W void divSpectrums(InputArray a, InputArray b, OutputArray c,
|
||||
int flags, bool conjB = false);
|
||||
|
||||
//! @} imgproc_motion
|
||||
|
||||
//! @addtogroup imgproc_misc
|
||||
|
||||
@@ -35,7 +35,6 @@
|
||||
"dilate",
|
||||
"distanceTransform",
|
||||
"distanceTransformWithLabels",
|
||||
"divSpectrums",
|
||||
"drawContours",
|
||||
"drawMarker",
|
||||
"ellipse",
|
||||
|
||||
@@ -159,204 +159,6 @@ static void magSpectrums( InputArray _src, OutputArray _dst)
|
||||
}
|
||||
}
|
||||
|
||||
void divSpectrums( InputArray _srcA, InputArray _srcB, OutputArray _dst, int flags, bool conjB)
|
||||
{
|
||||
Mat srcA = _srcA.getMat(), srcB = _srcB.getMat();
|
||||
int depth = srcA.depth(), cn = srcA.channels(), type = srcA.type();
|
||||
int rows = srcA.rows, cols = srcA.cols;
|
||||
int j, k;
|
||||
|
||||
CV_Assert( type == srcB.type() && srcA.size() == srcB.size() );
|
||||
CV_Assert( type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2 );
|
||||
|
||||
_dst.create( srcA.rows, srcA.cols, type );
|
||||
Mat dst = _dst.getMat();
|
||||
|
||||
CV_Assert(dst.data != srcA.data); // non-inplace check
|
||||
CV_Assert(dst.data != srcB.data); // non-inplace check
|
||||
|
||||
bool is_1d = (flags & DFT_ROWS) || (rows == 1 || (cols == 1 &&
|
||||
srcA.isContinuous() && srcB.isContinuous() && dst.isContinuous()));
|
||||
|
||||
if( is_1d && !(flags & DFT_ROWS) )
|
||||
cols = cols + rows - 1, rows = 1;
|
||||
|
||||
int ncols = cols*cn;
|
||||
int j0 = cn == 1;
|
||||
int j1 = ncols - (cols % 2 == 0 && cn == 1);
|
||||
|
||||
if( depth == CV_32F )
|
||||
{
|
||||
const float* dataA = srcA.ptr<float>();
|
||||
const float* dataB = srcB.ptr<float>();
|
||||
float* dataC = dst.ptr<float>();
|
||||
float eps = FLT_EPSILON; // prevent div0 problems
|
||||
|
||||
size_t stepA = srcA.step/sizeof(dataA[0]);
|
||||
size_t stepB = srcB.step/sizeof(dataB[0]);
|
||||
size_t stepC = dst.step/sizeof(dataC[0]);
|
||||
|
||||
if( !is_1d && cn == 1 )
|
||||
{
|
||||
for( k = 0; k < (cols % 2 ? 1 : 2); k++ )
|
||||
{
|
||||
if( k == 1 )
|
||||
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
|
||||
dataC[0] = dataA[0] / (dataB[0] + eps);
|
||||
if( rows % 2 == 0 )
|
||||
dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
|
||||
if( !conjB )
|
||||
for( j = 1; j <= rows - 2; j += 2 )
|
||||
{
|
||||
double denom = (double)dataB[j*stepB]*dataB[j*stepB] +
|
||||
(double)dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + (double)eps;
|
||||
|
||||
double re = (double)dataA[j*stepA]*dataB[j*stepB] +
|
||||
(double)dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
double im = (double)dataA[(j+1)*stepA]*dataB[j*stepB] -
|
||||
(double)dataA[j*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
dataC[j*stepC] = (float)(re / denom);
|
||||
dataC[(j+1)*stepC] = (float)(im / denom);
|
||||
}
|
||||
else
|
||||
for( j = 1; j <= rows - 2; j += 2 )
|
||||
{
|
||||
|
||||
double denom = (double)dataB[j*stepB]*dataB[j*stepB] +
|
||||
(double)dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + (double)eps;
|
||||
|
||||
double re = (double)dataA[j*stepA]*dataB[j*stepB] -
|
||||
(double)dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
double im = (double)dataA[(j+1)*stepA]*dataB[j*stepB] +
|
||||
(double)dataA[j*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
dataC[j*stepC] = (float)(re / denom);
|
||||
dataC[(j+1)*stepC] = (float)(im / denom);
|
||||
}
|
||||
if( k == 1 )
|
||||
dataA -= cols - 1, dataB -= cols - 1, dataC -= cols - 1;
|
||||
}
|
||||
}
|
||||
|
||||
for( ; rows--; dataA += stepA, dataB += stepB, dataC += stepC )
|
||||
{
|
||||
if( is_1d && cn == 1 )
|
||||
{
|
||||
dataC[0] = dataA[0] / (dataB[0] + eps);
|
||||
if( cols % 2 == 0 )
|
||||
dataC[j1] = dataA[j1] / (dataB[j1] + eps);
|
||||
}
|
||||
|
||||
if( !conjB )
|
||||
for( j = j0; j < j1; j += 2 )
|
||||
{
|
||||
double denom = (double)dataB[j]*dataB[j] + (double)dataB[j+1]*dataB[j+1] + (double)eps;
|
||||
double re = (double)dataA[j]*dataB[j] + (double)dataA[j+1]*dataB[j+1];
|
||||
double im = (double)dataA[j+1]*dataB[j] - (double)dataA[j]*dataB[j+1];
|
||||
dataC[j] = (float)(re / denom);
|
||||
dataC[j+1] = (float)(im / denom);
|
||||
}
|
||||
else
|
||||
for( j = j0; j < j1; j += 2 )
|
||||
{
|
||||
double denom = (double)dataB[j]*dataB[j] + (double)dataB[j+1]*dataB[j+1] + (double)eps;
|
||||
double re = (double)dataA[j]*dataB[j] - (double)dataA[j+1]*dataB[j+1];
|
||||
double im = (double)dataA[j+1]*dataB[j] + (double)dataA[j]*dataB[j+1];
|
||||
dataC[j] = (float)(re / denom);
|
||||
dataC[j+1] = (float)(im / denom);
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
const double* dataA = srcA.ptr<double>();
|
||||
const double* dataB = srcB.ptr<double>();
|
||||
double* dataC = dst.ptr<double>();
|
||||
double eps = DBL_EPSILON; // prevent div0 problems
|
||||
|
||||
size_t stepA = srcA.step/sizeof(dataA[0]);
|
||||
size_t stepB = srcB.step/sizeof(dataB[0]);
|
||||
size_t stepC = dst.step/sizeof(dataC[0]);
|
||||
|
||||
if( !is_1d && cn == 1 )
|
||||
{
|
||||
for( k = 0; k < (cols % 2 ? 1 : 2); k++ )
|
||||
{
|
||||
if( k == 1 )
|
||||
dataA += cols - 1, dataB += cols - 1, dataC += cols - 1;
|
||||
dataC[0] = dataA[0] / (dataB[0] + eps);
|
||||
if( rows % 2 == 0 )
|
||||
dataC[(rows-1)*stepC] = dataA[(rows-1)*stepA] / (dataB[(rows-1)*stepB] + eps);
|
||||
if( !conjB )
|
||||
for( j = 1; j <= rows - 2; j += 2 )
|
||||
{
|
||||
double denom = dataB[j*stepB]*dataB[j*stepB] +
|
||||
dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + eps;
|
||||
|
||||
double re = dataA[j*stepA]*dataB[j*stepB] +
|
||||
dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
double im = dataA[(j+1)*stepA]*dataB[j*stepB] -
|
||||
dataA[j*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
dataC[j*stepC] = re / denom;
|
||||
dataC[(j+1)*stepC] = im / denom;
|
||||
}
|
||||
else
|
||||
for( j = 1; j <= rows - 2; j += 2 )
|
||||
{
|
||||
double denom = dataB[j*stepB]*dataB[j*stepB] +
|
||||
dataB[(j+1)*stepB]*dataB[(j+1)*stepB] + eps;
|
||||
|
||||
double re = dataA[j*stepA]*dataB[j*stepB] -
|
||||
dataA[(j+1)*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
double im = dataA[(j+1)*stepA]*dataB[j*stepB] +
|
||||
dataA[j*stepA]*dataB[(j+1)*stepB];
|
||||
|
||||
dataC[j*stepC] = re / denom;
|
||||
dataC[(j+1)*stepC] = im / denom;
|
||||
}
|
||||
if( k == 1 )
|
||||
dataA -= cols - 1, dataB -= cols - 1, dataC -= cols - 1;
|
||||
}
|
||||
}
|
||||
|
||||
for( ; rows--; dataA += stepA, dataB += stepB, dataC += stepC )
|
||||
{
|
||||
if( is_1d && cn == 1 )
|
||||
{
|
||||
dataC[0] = dataA[0] / (dataB[0] + eps);
|
||||
if( cols % 2 == 0 )
|
||||
dataC[j1] = dataA[j1] / (dataB[j1] + eps);
|
||||
}
|
||||
|
||||
if( !conjB )
|
||||
for( j = j0; j < j1; j += 2 )
|
||||
{
|
||||
double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
|
||||
double re = dataA[j]*dataB[j] + dataA[j+1]*dataB[j+1];
|
||||
double im = dataA[j+1]*dataB[j] - dataA[j]*dataB[j+1];
|
||||
dataC[j] = re / denom;
|
||||
dataC[j+1] = im / denom;
|
||||
}
|
||||
else
|
||||
for( j = j0; j < j1; j += 2 )
|
||||
{
|
||||
double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
|
||||
double re = dataA[j]*dataB[j] - dataA[j+1]*dataB[j+1];
|
||||
double im = dataA[j+1]*dataB[j] + dataA[j]*dataB[j+1];
|
||||
dataC[j] = re / denom;
|
||||
dataC[j+1] = im / denom;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
static void fftShift(InputOutputArray _out)
|
||||
{
|
||||
Mat out = _out.getMat();
|
||||
|
||||
@@ -151,278 +151,4 @@ TEST(Imgproc_PhaseCorrelatorTest, float32_overflow) {
|
||||
EXPECT_NEAR(std::abs(phaseShift.y), 0.0, 1.0);
|
||||
}
|
||||
|
||||
////////////////////// DivSpectrums ////////////////////////
|
||||
class CV_DivSpectrumsTest : public cvtest::ArrayTest
|
||||
{
|
||||
public:
|
||||
CV_DivSpectrumsTest();
|
||||
protected:
|
||||
void run_func();
|
||||
void get_test_array_types_and_sizes( int, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
||||
void prepare_to_validation( int test_case_idx );
|
||||
int flags;
|
||||
};
|
||||
|
||||
|
||||
CV_DivSpectrumsTest::CV_DivSpectrumsTest() : flags(0)
|
||||
{
|
||||
// Allocate test matrices.
|
||||
test_array[INPUT].push_back(NULL); // first input DFT as a CCS-packed array or complex matrix.
|
||||
test_array[INPUT].push_back(NULL); // second input DFT as a CCS-packed array or complex matrix.
|
||||
test_array[OUTPUT].push_back(NULL); // output DFT as a complex matrix.
|
||||
test_array[REF_OUTPUT].push_back(NULL); // reference output DFT as a complex matrix.
|
||||
test_array[TEMP].push_back(NULL); // first input DFT converted to a complex matrix.
|
||||
test_array[TEMP].push_back(NULL); // second input DFT converted to a complex matrix.
|
||||
test_array[TEMP].push_back(NULL); // output DFT as a CCV-packed array.
|
||||
}
|
||||
|
||||
void CV_DivSpectrumsTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
||||
{
|
||||
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx, sizes, types);
|
||||
RNG& rng = ts->get_rng();
|
||||
|
||||
// Get the flag of the input.
|
||||
const int rand_int_flags = cvtest::randInt(rng);
|
||||
flags = rand_int_flags & (CV_TEST_DXT_MUL_CONJ | DFT_ROWS);
|
||||
|
||||
// Get input type.
|
||||
const int rand_int_type = cvtest::randInt(rng);
|
||||
int type;
|
||||
|
||||
if (rand_int_type % 4)
|
||||
{
|
||||
type = CV_32FC1;
|
||||
}
|
||||
else if (rand_int_type % 4 == 1)
|
||||
{
|
||||
type = CV_32FC2;
|
||||
}
|
||||
else if (rand_int_type % 4 == 2)
|
||||
{
|
||||
type = CV_64FC1;
|
||||
}
|
||||
else
|
||||
{
|
||||
type = CV_64FC2;
|
||||
}
|
||||
|
||||
for( size_t i = 0; i < types.size(); i++ )
|
||||
{
|
||||
for( size_t j = 0; j < types[i].size(); j++ )
|
||||
{
|
||||
types[i][j] = type;
|
||||
}
|
||||
}
|
||||
|
||||
// Inputs are CCS-packed arrays. Prepare outputs and temporary inputs as complex matrices.
|
||||
if( type == CV_32FC1 || type == CV_64FC1 )
|
||||
{
|
||||
types[OUTPUT][0] += CV_DEPTH_MAX;
|
||||
types[REF_OUTPUT][0] += CV_DEPTH_MAX;
|
||||
types[TEMP][0] += CV_DEPTH_MAX;
|
||||
types[TEMP][1] += CV_DEPTH_MAX;
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to convert a ccs array of depth_t into a complex matrix.
|
||||
template<typename depth_t>
|
||||
static void convert_from_ccs_helper( const Mat& src0, const Mat& src1, Mat& dst )
|
||||
{
|
||||
const int cn = src0.channels();
|
||||
int srcstep = cn;
|
||||
int dststep = 1;
|
||||
|
||||
if( !dst.isContinuous() )
|
||||
dststep = (int)(dst.step/dst.elemSize());
|
||||
|
||||
if( !src0.isContinuous() )
|
||||
srcstep = (int)(src0.step/src0.elemSize1());
|
||||
|
||||
Complex<depth_t> *dst_data = dst.ptr<Complex<depth_t> >();
|
||||
const depth_t* src0_data = src0.ptr<depth_t>();
|
||||
const depth_t* src1_data = src1.ptr<depth_t>();
|
||||
dst_data->re = src0_data[0];
|
||||
dst_data->im = 0;
|
||||
const int n = dst.cols + dst.rows - 1;
|
||||
const int n2 = (n+1) >> 1;
|
||||
|
||||
if( (n & 1) == 0 )
|
||||
{
|
||||
dst_data[n2*dststep].re = src0_data[(cn == 1 ? n-1 : n2)*srcstep];
|
||||
dst_data[n2*dststep].im = 0;
|
||||
}
|
||||
|
||||
int delta0 = srcstep;
|
||||
int delta1 = delta0 + (cn == 1 ? srcstep : 1);
|
||||
|
||||
if( cn == 1 )
|
||||
srcstep *= 2;
|
||||
|
||||
for( int i = 1; i < n2; i++, delta0 += srcstep, delta1 += srcstep )
|
||||
{
|
||||
depth_t t0 = src0_data[delta0];
|
||||
depth_t t1 = src0_data[delta1];
|
||||
|
||||
dst_data[i*dststep].re = t0;
|
||||
dst_data[i*dststep].im = t1;
|
||||
|
||||
t0 = src1_data[delta0];
|
||||
t1 = -src1_data[delta1];
|
||||
|
||||
dst_data[(n-i)*dststep].re = t0;
|
||||
dst_data[(n-i)*dststep].im = t1;
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to convert a ccs array into a complex matrix.
|
||||
static void convert_from_ccs( const Mat& src0, const Mat& src1, Mat& dst, const int flags )
|
||||
{
|
||||
if( dst.rows > 1 && (dst.cols > 1 || (flags & DFT_ROWS)) )
|
||||
{
|
||||
const int count = dst.rows;
|
||||
const int len = dst.cols;
|
||||
const bool is2d = (flags & DFT_ROWS) == 0;
|
||||
for( int i = 0; i < count; i++ )
|
||||
{
|
||||
const int j = !is2d || i == 0 ? i : count - i;
|
||||
const Mat& src0row = src0.row(i);
|
||||
const Mat& src1row = src1.row(j);
|
||||
Mat dstrow = dst.row(i);
|
||||
convert_from_ccs( src0row, src1row, dstrow, 0 );
|
||||
}
|
||||
|
||||
if( is2d )
|
||||
{
|
||||
const Mat& src0row = src0.col(0);
|
||||
Mat dstrow = dst.col(0);
|
||||
convert_from_ccs( src0row, src0row, dstrow, 0 );
|
||||
|
||||
if( (len & 1) == 0 )
|
||||
{
|
||||
const Mat& src0row_even = src0.col(src0.cols - 1);
|
||||
Mat dstrow_even = dst.col(len/2);
|
||||
convert_from_ccs( src0row_even, src0row_even, dstrow_even, 0 );
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if( dst.depth() == CV_32F )
|
||||
{
|
||||
convert_from_ccs_helper<float>( src0, src1, dst );
|
||||
}
|
||||
else
|
||||
{
|
||||
convert_from_ccs_helper<double>( src0, src1, dst );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to compute complex number (nu_re + nu_im * i) / (de_re + de_im * i).
|
||||
static std::pair<double, double> divide_complex_numbers( const double nu_re, const double nu_im,
|
||||
const double de_re, const double de_im,
|
||||
const bool conj_de )
|
||||
{
|
||||
if ( conj_de )
|
||||
{
|
||||
return divide_complex_numbers( nu_re, nu_im, de_re, -de_im, false /* conj_de */ );
|
||||
}
|
||||
|
||||
const double result_de = de_re * de_re + de_im * de_im + DBL_EPSILON;
|
||||
const double result_re = nu_re * de_re + nu_im * de_im;
|
||||
const double result_im = nu_re * (-de_im) + nu_im * de_re;
|
||||
return std::pair<double, double>(result_re / result_de, result_im / result_de);
|
||||
}
|
||||
|
||||
/// Helper function to divide a DFT in src1 by a DFT in src2 with depths depth_t. The DFTs are
|
||||
/// complex matrices.
|
||||
template <typename depth_t>
|
||||
static void div_complex_helper( const Mat& src1, const Mat& src2, Mat& dst, int flags )
|
||||
{
|
||||
CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
|
||||
dst.create( src1.rows, src1.cols, src1.type() );
|
||||
const int cn = src1.channels();
|
||||
int cols = src1.cols * cn;
|
||||
|
||||
for( int i = 0; i < dst.rows; i++ )
|
||||
{
|
||||
const depth_t *src1_data = src1.ptr<depth_t>(i);
|
||||
const depth_t *src2_data = src2.ptr<depth_t>(i);
|
||||
depth_t *dst_data = dst.ptr<depth_t>(i);
|
||||
for( int j = 0; j < cols; j += 2 )
|
||||
{
|
||||
std::pair<double, double> result =
|
||||
divide_complex_numbers( src1_data[j], src1_data[j + 1],
|
||||
src2_data[j], src2_data[j + 1],
|
||||
(flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
dst_data[j] = (depth_t)result.first;
|
||||
dst_data[j + 1] = (depth_t)result.second;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to divide a DFT in src1 by a DFT in src2. The DFTs are complex matrices.
|
||||
static void div_complex( const Mat& src1, const Mat& src2, Mat& dst, const int flags )
|
||||
{
|
||||
const int type = src1.type();
|
||||
CV_Assert( type == CV_32FC2 || type == CV_64FC2 );
|
||||
|
||||
if ( src1.depth() == CV_32F )
|
||||
{
|
||||
return div_complex_helper<float>( src1, src2, dst, flags );
|
||||
}
|
||||
else
|
||||
{
|
||||
return div_complex_helper<double>( src1, src2, dst, flags );
|
||||
}
|
||||
}
|
||||
|
||||
void CV_DivSpectrumsTest::prepare_to_validation( int /* test_case_idx */ )
|
||||
{
|
||||
Mat &src1 = test_mat[INPUT][0];
|
||||
Mat &src2 = test_mat[INPUT][1];
|
||||
Mat &ref_dst = test_mat[REF_OUTPUT][0];
|
||||
const int cn = src1.channels();
|
||||
// Inputs are CCS-packed arrays. Convert them to complex matrices and get the expected output
|
||||
// as a complex matrix.
|
||||
if( cn == 1 )
|
||||
{
|
||||
Mat &converted_src1 = test_mat[TEMP][0];
|
||||
Mat &converted_src2 = test_mat[TEMP][1];
|
||||
convert_from_ccs( src1, src1, converted_src1, flags );
|
||||
convert_from_ccs( src2, src2, converted_src2, flags );
|
||||
div_complex( converted_src1, converted_src2, ref_dst, flags );
|
||||
}
|
||||
// Inputs are complex matrices. Get the expected output as a complex matrix.
|
||||
else
|
||||
{
|
||||
div_complex( src1, src2, ref_dst, flags );
|
||||
}
|
||||
}
|
||||
|
||||
void CV_DivSpectrumsTest::run_func()
|
||||
{
|
||||
const Mat &src1 = test_mat[INPUT][0];
|
||||
const Mat &src2 = test_mat[INPUT][1];
|
||||
const int cn = src1.channels();
|
||||
|
||||
// Inputs are CCS-packed arrays. Get the output as a CCS-packed array and convert it to a
|
||||
// complex matrix.
|
||||
if ( cn == 1 )
|
||||
{
|
||||
Mat &dst = test_mat[TEMP][2];
|
||||
cv::divSpectrums( src1, src2, dst, flags, (flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
Mat &converted_dst = test_mat[OUTPUT][0];
|
||||
convert_from_ccs( dst, dst, converted_dst, flags );
|
||||
}
|
||||
// Inputs are complex matrices. Get the output as a complex matrix.
|
||||
else
|
||||
{
|
||||
Mat &dst = test_mat[OUTPUT][0];
|
||||
cv::divSpectrums( src1, src2, dst, flags, (flags & CV_TEST_DXT_MUL_CONJ) != 0 );
|
||||
}
|
||||
}
|
||||
|
||||
TEST(Imgproc_DivSpectrums, accuracy) { CV_DivSpectrumsTest test; test.safe_run(); }
|
||||
|
||||
}} // namespace
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
core = {
|
||||
'': [
|
||||
'absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',
|
||||
'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen',
|
||||
'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'divSpectrums', 'eigen',
|
||||
'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude',
|
||||
'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize',
|
||||
'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed',
|
||||
@@ -49,7 +49,6 @@ imgproc = {
|
||||
'dilate',
|
||||
'distanceTransform',
|
||||
'distanceTransformWithLabels',
|
||||
'divSpectrums',
|
||||
'drawContours',
|
||||
'drawMarker',
|
||||
'ellipse',
|
||||
|
||||
Reference in New Issue
Block a user