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mshabuninmshabunin
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Split stat.cpp into smaller pieces
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‎modules/core/src/batch_distance.cpp‎

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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "precomp.hpp"
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#include "stat.hpp"
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namespace cv
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{
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template<typename _Tp, typename _Rt>
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void batchDistL1_(const _Tp* src1, const _Tp* src2, size_t step2,
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int nvecs, int len, _Rt* dist, const uchar* mask)
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{
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step2 /= sizeof(src2[0]);
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if( !mask )
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{
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for( int i = 0; i < nvecs; i++ )
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dist[i] = normL1<_Tp, _Rt>(src1, src2 + step2*i, len);
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}
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else
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{
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_Rt val0 = std::numeric_limits<_Rt>::max();
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for( int i = 0; i < nvecs; i++ )
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dist[i] = mask[i] ? normL1<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
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}
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}
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template<typename _Tp, typename _Rt>
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void batchDistL2Sqr_(const _Tp* src1, const _Tp* src2, size_t step2,
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int nvecs, int len, _Rt* dist, const uchar* mask)
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{
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step2 /= sizeof(src2[0]);
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if( !mask )
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{
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for( int i = 0; i < nvecs; i++ )
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dist[i] = normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len);
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}
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else
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{
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_Rt val0 = std::numeric_limits<_Rt>::max();
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for( int i = 0; i < nvecs; i++ )
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dist[i] = mask[i] ? normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
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}
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}
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template<typename _Tp, typename _Rt>
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void batchDistL2_(const _Tp* src1, const _Tp* src2, size_t step2,
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int nvecs, int len, _Rt* dist, const uchar* mask)
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{
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step2 /= sizeof(src2[0]);
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if( !mask )
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{
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for( int i = 0; i < nvecs; i++ )
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dist[i] = std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len));
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}
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else
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{
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_Rt val0 = std::numeric_limits<_Rt>::max();
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for( int i = 0; i < nvecs; i++ )
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dist[i] = mask[i] ? std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len)) : val0;
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}
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}
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static void batchDistHamming(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, int* dist, const uchar* mask)
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{
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step2 /= sizeof(src2[0]);
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if( !mask )
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{
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for( int i = 0; i < nvecs; i++ )
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dist[i] = hal::normHamming(src1, src2 + step2*i, len);
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}
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else
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{
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int val0 = INT_MAX;
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for( int i = 0; i < nvecs; i++ )
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{
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if (mask[i])
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dist[i] = hal::normHamming(src1, src2 + step2*i, len);
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else
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dist[i] = val0;
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}
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}
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}
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static void batchDistHamming2(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, int* dist, const uchar* mask)
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{
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step2 /= sizeof(src2[0]);
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if( !mask )
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{
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for( int i = 0; i < nvecs; i++ )
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dist[i] = hal::normHamming(src1, src2 + step2*i, len, 2);
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}
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else
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{
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int val0 = INT_MAX;
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for( int i = 0; i < nvecs; i++ )
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{
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if (mask[i])
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dist[i] = hal::normHamming(src1, src2 + step2*i, len, 2);
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else
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dist[i] = val0;
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}
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}
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}
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static void batchDistL1_8u32s(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, int* dist, const uchar* mask)
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{
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batchDistL1_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL1_8u32f(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL1_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL2Sqr_8u32s(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, int* dist, const uchar* mask)
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{
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batchDistL2Sqr_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL2Sqr_8u32f(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL2Sqr_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL2_8u32f(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL2_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL1_32f(const float* src1, const float* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL1_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL2Sqr_32f(const float* src1, const float* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL2Sqr_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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static void batchDistL2_32f(const float* src1, const float* src2, size_t step2,
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int nvecs, int len, float* dist, const uchar* mask)
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{
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batchDistL2_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
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}
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typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, uchar* dist, const uchar* mask);
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struct BatchDistInvoker : public ParallelLoopBody
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{
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BatchDistInvoker( const Mat& _src1, const Mat& _src2,
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Mat& _dist, Mat& _nidx, int _K,
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const Mat& _mask, int _update,
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BatchDistFunc _func)
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{
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src1 = &_src1;
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src2 = &_src2;
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dist = &_dist;
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nidx = &_nidx;
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K = _K;
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mask = &_mask;
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update = _update;
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func = _func;
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}
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void operator()(const Range& range) const
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{
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AutoBuffer<int> buf(src2->rows);
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int* bufptr = buf;
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for( int i = range.start; i < range.end; i++ )
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{
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func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols,
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K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0);
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if( K > 0 )
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{
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int* nidxptr = nidx->ptr<int>(i);
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// since positive float's can be compared just like int's,
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// we handle both CV_32S and CV_32F cases with a single branch
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int* distptr = (int*)dist->ptr(i);
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int j, k;
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for( j = 0; j < src2->rows; j++ )
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{
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int d = bufptr[j];
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if( d < distptr[K-1] )
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{
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for( k = K-2; k >= 0 && distptr[k] > d; k-- )
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{
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nidxptr[k+1] = nidxptr[k];
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distptr[k+1] = distptr[k];
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}
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nidxptr[k+1] = j + update;
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distptr[k+1] = d;
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}
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}
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}
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}
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}
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const Mat *src1;
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const Mat *src2;
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Mat *dist;
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Mat *nidx;
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const Mat *mask;
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int K;
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int update;
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BatchDistFunc func;
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};
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}
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void cv::batchDistance( InputArray _src1, InputArray _src2,
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OutputArray _dist, int dtype, OutputArray _nidx,
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int normType, int K, InputArray _mask,
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int update, bool crosscheck )
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{
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CV_INSTRUMENT_REGION()
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Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
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int type = src1.type();
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CV_Assert( type == src2.type() && src1.cols == src2.cols &&
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(type == CV_32F || type == CV_8U));
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CV_Assert( _nidx.needed() == (K > 0) );
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if( dtype == -1 )
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{
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dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
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}
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CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F);
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K = std::min(K, src2.rows);
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_dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype);
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Mat dist = _dist.getMat(), nidx;
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if( _nidx.needed() )
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{
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_nidx.create(dist.size(), CV_32S);
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nidx = _nidx.getMat();
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}
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if( update == 0 && K > 0 )
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{
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dist = Scalar::all(dtype == CV_32S ? (double)INT_MAX : (double)FLT_MAX);
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nidx = Scalar::all(-1);
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}
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if( crosscheck )
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{
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CV_Assert( K == 1 && update == 0 && mask.empty() );
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Mat tdist, tidx;
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batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
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// if an idx-th element from src1 appeared to be the nearest to i-th element of src2,
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// we update the minimum mutual distance between idx-th element of src1 and the whole src2 set.
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// As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest
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// to i*-th element of src2 and i*-th element of src2 is the closest to idx-th element of src1.
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// If nidx[idx] = -1, it means that there is no such ideal couple for it in src2.
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// This O(N) procedure is called cross-check and it helps to eliminate some false matches.
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if( dtype == CV_32S )
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{
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for( int i = 0; i < tdist.rows; i++ )
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{
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int idx = tidx.at<int>(i);
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int d = tdist.at<int>(i), d0 = dist.at<int>(idx);
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if( d < d0 )
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{
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dist.at<int>(idx) = d;
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nidx.at<int>(idx) = i + update;
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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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for( int i = 0; i < tdist.rows; i++ )
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{
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int idx = tidx.at<int>(i);
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float d = tdist.at<float>(i), d0 = dist.at<float>(idx);
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if( d < d0 )
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{
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dist.at<float>(idx) = d;
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nidx.at<int>(idx) = i + update;
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}
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}
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}
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return;
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}
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BatchDistFunc func = 0;
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if( type == CV_8U )
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{
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if( normType == NORM_L1 && dtype == CV_32S )
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func = (BatchDistFunc)batchDistL1_8u32s;
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else if( normType == NORM_L1 && dtype == CV_32F )
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func = (BatchDistFunc)batchDistL1_8u32f;
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else if( normType == NORM_L2SQR && dtype == CV_32S )
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func = (BatchDistFunc)batchDistL2Sqr_8u32s;
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else if( normType == NORM_L2SQR && dtype == CV_32F )
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func = (BatchDistFunc)batchDistL2Sqr_8u32f;
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else if( normType == NORM_L2 && dtype == CV_32F )
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func = (BatchDistFunc)batchDistL2_8u32f;
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else if( normType == NORM_HAMMING && dtype == CV_32S )
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func = (BatchDistFunc)batchDistHamming;
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else if( normType == NORM_HAMMING2 && dtype == CV_32S )
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func = (BatchDistFunc)batchDistHamming2;
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}
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else if( type == CV_32F && dtype == CV_32F )
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{
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if( normType == NORM_L1 )
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func = (BatchDistFunc)batchDistL1_32f;
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else if( normType == NORM_L2SQR )
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func = (BatchDistFunc)batchDistL2Sqr_32f;
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else if( normType == NORM_L2 )
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func = (BatchDistFunc)batchDistL2_32f;
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}
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if( func == 0 )
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CV_Error_(CV_StsUnsupportedFormat,
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("The combination of type=%d, dtype=%d and normType=%d is not supported",
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type, dtype, normType));
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parallel_for_(Range(0, src1.rows),
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BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
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}

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