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AVX optimized implementation of resize and warp functions migrated to separate file

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This commit is contained in:
Vitaly Tuzov 2017-07-03 13:49:16 +03:00
parent 20f603a217
commit 3681dcef1a
4 changed files with 569 additions and 360 deletions
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289
modules/imgproc/src/imgwarp.avx2.cpp Normal file
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@ -0,0 +1,289 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* ////////////////////////////////////////////////////////////////////
//
// Geometrical transforms on images and matrices: rotation, zoom etc.
//
// */
#include "precomp.hpp"
#include "imgwarp.hpp"
namespace cv
{
namespace opt_AVX2
{
class resizeNNInvokerAVX4 :
public ParallelLoopBody
{
public:
resizeNNInvokerAVX4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=AVX
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
int avxWidth = width - (width & 0x7);
const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
if(((int64)(dst.data + dst.step) & 0x1f) == 0)
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 8)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
_mm256_maskstore_epi32((int*)D, mask, pixels);
D += 32;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
else
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 8)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
_mm256_storeu_si256((__m256i*)D, pixels);
D += 32;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerAVX4(const resizeNNInvokerAVX4&);
resizeNNInvokerAVX4& operator=(const resizeNNInvokerAVX4&);
};
class resizeNNInvokerAVX2 :
public ParallelLoopBody
{
public:
resizeNNInvokerAVX2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=AVX
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
//int avxWidth = (width - 1) - ((width - 1) & 0x7);
int avxWidth = width - (width & 0xf);
const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
const __m256i CV_DECL_ALIGNED(64) shuffle_mask = _mm256_set_epi8(15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0,
15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0);
const __m256i CV_DECL_ALIGNED(64) permute_mask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
const __m256i CV_DECL_ALIGNED(64) shift_shuffle_mask = _mm256_set_epi8(13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2,
13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2);
if(((int64)(dst.data + dst.step) & 0x1f) == 0)
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
const uchar* S2 = S - 2;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 16)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
_mm256_maskstore_epi32((int*)D, mask, ints_permuted);
D += 32;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
else
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
const uchar* S2 = S - 2;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 16)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
_mm256_storeu_si256((__m256i*)D, ints_permuted);
D += 32;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerAVX2(const resizeNNInvokerAVX2&);
resizeNNInvokerAVX2& operator=(const resizeNNInvokerAVX2&);
};
void resizeNN2_AVX2(const Range& range, const Mat& src, Mat &dst, int *x_ofs, int pix_size4, double ify)
{
resizeNNInvokerAVX2 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total() / (double)(1 << 16));
}
void resizeNN4_AVX2(const Range& range, const Mat& src, Mat &dst, int *x_ofs, int pix_size4, double ify)
{
resizeNNInvokerAVX4 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total() / (double)(1 << 16));
}
int warpAffineBlockline(int *adelta, int *bdelta, short* xy, short* alpha, int X0, int Y0, int bw)
{
const int AB_BITS = MAX(10, (int)INTER_BITS);
int x1 = 0;
__m256i fxy_mask = _mm256_set1_epi32(INTER_TAB_SIZE - 1);
__m256i XX = _mm256_set1_epi32(X0), YY = _mm256_set1_epi32(Y0);
for (; x1 <= bw - 16; x1 += 16)
{
__m256i tx0, tx1, ty0, ty1;
tx0 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(adelta + x1)), XX);
ty0 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(bdelta + x1)), YY);
tx1 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(adelta + x1 + 8)), XX);
ty1 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(bdelta + x1 + 8)), YY);
tx0 = _mm256_srai_epi32(tx0, AB_BITS - INTER_BITS);
ty0 = _mm256_srai_epi32(ty0, AB_BITS - INTER_BITS);
tx1 = _mm256_srai_epi32(tx1, AB_BITS - INTER_BITS);
ty1 = _mm256_srai_epi32(ty1, AB_BITS - INTER_BITS);
__m256i fx_ = _mm256_packs_epi32(_mm256_and_si256(tx0, fxy_mask),
_mm256_and_si256(tx1, fxy_mask));
__m256i fy_ = _mm256_packs_epi32(_mm256_and_si256(ty0, fxy_mask),
_mm256_and_si256(ty1, fxy_mask));
tx0 = _mm256_packs_epi32(_mm256_srai_epi32(tx0, INTER_BITS),
_mm256_srai_epi32(tx1, INTER_BITS));
ty0 = _mm256_packs_epi32(_mm256_srai_epi32(ty0, INTER_BITS),
_mm256_srai_epi32(ty1, INTER_BITS));
fx_ = _mm256_adds_epi16(fx_, _mm256_slli_epi16(fy_, INTER_BITS));
fx_ = _mm256_permute4x64_epi64(fx_, (3 << 6) + (1 << 4) + (2 << 2) + 0);
_mm256_storeu_si256((__m256i*)(xy + x1 * 2), _mm256_unpacklo_epi16(tx0, ty0));
_mm256_storeu_si256((__m256i*)(xy + x1 * 2 + 16), _mm256_unpackhi_epi16(tx0, ty0));
_mm256_storeu_si256((__m256i*)(alpha + x1), fx_);
}
_mm256_zeroupper();
return x1;
}
}
}
/* End of file. */

375
modules/imgproc/src/imgwarp.cpp
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@ -52,6 +52,7 @@
#include "hal_replacement.hpp"
#include "opencv2/core/openvx/ovx_defs.hpp"
#include "imgwarp.hpp"
using namespace cv;
@ -417,308 +418,6 @@ private:
resizeNNInvoker& operator=(const resizeNNInvoker&);
};
#if CV_AVX2
class resizeNNInvokerAVX4 :
public ParallelLoopBody
{
public:
resizeNNInvokerAVX4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=AVX
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x, pix_size = (int)src.elemSize();
int width = dsize.width;
int avxWidth = width - (width & 0x7);
const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
if(((int64)(dst.data + dst.step) & 0x1f) == 0)
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 8)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
_mm256_maskstore_epi32((int*)D, mask, pixels);
D += 32;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
else
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 8)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
_mm256_storeu_si256((__m256i*)D, pixels);
D += 32;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerAVX4(const resizeNNInvokerAVX4&);
resizeNNInvokerAVX4& operator=(const resizeNNInvokerAVX4&);
};
class resizeNNInvokerAVX2 :
public ParallelLoopBody
{
public:
resizeNNInvokerAVX2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=AVX
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x, pix_size = (int)src.elemSize();
int width = dsize.width;
//int avxWidth = (width - 1) - ((width - 1) & 0x7);
int avxWidth = width - (width & 0xf);
const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
const __m256i CV_DECL_ALIGNED(64) shuffle_mask = _mm256_set_epi8(15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0,
15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0);
const __m256i CV_DECL_ALIGNED(64) permute_mask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
const __m256i CV_DECL_ALIGNED(64) shift_shuffle_mask = _mm256_set_epi8(13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2,
13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2);
if(((int64)(dst.data + dst.step) & 0x1f) == 0)
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
const uchar* S2 = S - 2;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 16)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
_mm256_maskstore_epi32((int*)D, mask, ints_permuted);
D += 32;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
else
{
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
const uchar* S2 = S - 2;
#pragma unroll(4)
for(x = 0; x < avxWidth; x += 16)
{
const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
_mm256_storeu_si256((__m256i*)D, ints_permuted);
D += 32;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerAVX2(const resizeNNInvokerAVX2&);
resizeNNInvokerAVX2& operator=(const resizeNNInvokerAVX2&);
};
#endif
#if CV_SSE4_1
class resizeNNInvokerSSE2 :
public ParallelLoopBody
{
public:
resizeNNInvokerSSE2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=SSE4.2
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
int sseWidth = width - (width & 0x7);
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
for(x = 0; x < sseWidth; x += 8)
{
ushort imm = *(ushort*)(S + x_ofs[x + 0]);
pixels = _mm_insert_epi16(pixels, imm, 0);
imm = *(ushort*)(S + x_ofs[x + 1]);
pixels = _mm_insert_epi16(pixels, imm, 1);
imm = *(ushort*)(S + x_ofs[x + 2]);
pixels = _mm_insert_epi16(pixels, imm, 2);
imm = *(ushort*)(S + x_ofs[x + 3]);
pixels = _mm_insert_epi16(pixels, imm, 3);
imm = *(ushort*)(S + x_ofs[x + 4]);
pixels = _mm_insert_epi16(pixels, imm, 4);
imm = *(ushort*)(S + x_ofs[x + 5]);
pixels = _mm_insert_epi16(pixels, imm, 5);
imm = *(ushort*)(S + x_ofs[x + 6]);
pixels = _mm_insert_epi16(pixels, imm, 6);
imm = *(ushort*)(S + x_ofs[x + 7]);
pixels = _mm_insert_epi16(pixels, imm, 7);
_mm_storeu_si128((__m128i*)D, pixels);
D += 16;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerSSE2(const resizeNNInvokerSSE2&);
resizeNNInvokerSSE2& operator=(const resizeNNInvokerSSE2&);
};
class resizeNNInvokerSSE4 :
public ParallelLoopBody
{
public:
resizeNNInvokerSSE4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=SSE4.2
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
int sseWidth = width - (width & 0x3);
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
for(x = 0; x < sseWidth; x += 4)
{
int imm = *(int*)(S + x_ofs[x + 0]);
pixels = _mm_insert_epi32(pixels, imm, 0);
imm = *(int*)(S + x_ofs[x + 1]);
pixels = _mm_insert_epi32(pixels, imm, 1);
imm = *(int*)(S + x_ofs[x + 2]);
pixels = _mm_insert_epi32(pixels, imm, 2);
imm = *(int*)(S + x_ofs[x + 3]);
pixels = _mm_insert_epi32(pixels, imm, 3);
_mm_storeu_si128((__m128i*)D, pixels);
D += 16;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerSSE4(const resizeNNInvokerSSE4&);
resizeNNInvokerSSE4& operator=(const resizeNNInvokerSSE4&);
};
#endif
static void
resizeNN( const Mat& src, Mat& dst, double fx, double fy )
{
@ -737,35 +436,23 @@ resizeNN( const Mat& src, Mat& dst, double fx, double fy )
}
Range range(0, dsize.height);
#if CV_AVX2
if(checkHardwareSupport(CV_CPU_AVX2) && ((pix_size == 2) || (pix_size == 4)))
#if CV_TRY_AVX2
if(CV_CPU_HAS_SUPPORT_AVX2 && ((pix_size == 2) || (pix_size == 4)))
{
if(pix_size == 2)
{
resizeNNInvokerAVX2 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total()/(double)(1<<16));
}
else if (pix_size == 4)
{
resizeNNInvokerAVX4 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total()/(double)(1<<16));
}
opt_AVX2::resizeNN2_AVX2(range, src, dst, x_ofs, pix_size4, ify);
else
opt_AVX2::resizeNN4_AVX2(range, src, dst, x_ofs, pix_size4, ify);
}
else
#endif
#if CV_SSE4_1
if(checkHardwareSupport(CV_CPU_SSE4_1) && ((pix_size == 2) || (pix_size == 4)))
#if CV_TRY_SSE4_1
if(CV_CPU_HAS_SUPPORT_SSE4_1 && ((pix_size == 2) || (pix_size == 4)))
{
if(pix_size == 2)
{
resizeNNInvokerSSE2 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total()/(double)(1<<16));
}
else if(pix_size == 4)
{
resizeNNInvokerSSE4 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total()/(double)(1<<16));
}
opt_SSE41::resizeNN2_SSE4_1(range, src, dst, x_ofs, pix_size4, ify);
else
opt_SSE41::resizeNN4_SSE4_1(range, src, dst, x_ofs, pix_size4, ify);
}
else
#endif
@ -5864,8 +5551,8 @@ public:
const int AB_BITS = MAX(10, (int)INTER_BITS);
const int AB_SCALE = 1 << AB_BITS;
int round_delta = interpolation == INTER_NEAREST ? AB_SCALE/2 : AB_SCALE/INTER_TAB_SIZE/2, x, y, x1, y1;
#if CV_AVX2
bool useAVX2 = checkHardwareSupport(CV_CPU_AVX2);
#if CV_TRY_AVX2
bool useAVX2 = CV_CPU_HAS_SUPPORT_AVX2;
#endif
#if CV_SSE2
bool useSSE2 = checkHardwareSupport(CV_CPU_SSE2);
@ -5947,41 +5634,9 @@ public:
{
short* alpha = A + y1*bw;
x1 = 0;
#if CV_AVX2
#if CV_TRY_AVX2
if ( useAVX2 )
{
__m256i fxy_mask = _mm256_set1_epi32(INTER_TAB_SIZE - 1);
__m256i XX = _mm256_set1_epi32(X0), YY = _mm256_set1_epi32(Y0);
for( ; x1 <= bw - 16; x1 += 16 )
{
__m256i tx0, tx1, ty0, ty1;
tx0 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(adelta + x + x1)), XX);
ty0 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(bdelta + x + x1)), YY);
tx1 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(adelta + x + x1 + 8)), XX);
ty1 = _mm256_add_epi32(_mm256_loadu_si256((const __m256i*)(bdelta + x + x1 + 8)), YY);
tx0 = _mm256_srai_epi32(tx0, AB_BITS - INTER_BITS);
ty0 = _mm256_srai_epi32(ty0, AB_BITS - INTER_BITS);
tx1 = _mm256_srai_epi32(tx1, AB_BITS - INTER_BITS);
ty1 = _mm256_srai_epi32(ty1, AB_BITS - INTER_BITS);
__m256i fx_ = _mm256_packs_epi32(_mm256_and_si256(tx0, fxy_mask),
_mm256_and_si256(tx1, fxy_mask));
__m256i fy_ = _mm256_packs_epi32(_mm256_and_si256(ty0, fxy_mask),
_mm256_and_si256(ty1, fxy_mask));
tx0 = _mm256_packs_epi32(_mm256_srai_epi32(tx0, INTER_BITS),
_mm256_srai_epi32(tx1, INTER_BITS));
ty0 = _mm256_packs_epi32(_mm256_srai_epi32(ty0, INTER_BITS),
_mm256_srai_epi32(ty1, INTER_BITS));
fx_ = _mm256_adds_epi16(fx_, _mm256_slli_epi16(fy_, INTER_BITS));
fx_ = _mm256_permute4x64_epi64(fx_, (3 << 6) + (1 << 4) + (2 << 2) + 0);
_mm256_storeu_si256((__m256i*)(xy + x1*2), _mm256_unpacklo_epi16(tx0, ty0));
_mm256_storeu_si256((__m256i*)(xy + x1*2 + 16), _mm256_unpackhi_epi16(tx0, ty0));
_mm256_storeu_si256((__m256i*)(alpha + x1), fx_);
}
_mm256_zeroupper();
}
x1 = opt_AVX2::warpAffineBlockline(adelta + x, bdelta + x, xy, alpha, X0, Y0, bw);
#endif
#if CV_SSE2
if( useSSE2 )

73
modules/imgproc/src/imgwarp.hpp Normal file
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@ -0,0 +1,73 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* ////////////////////////////////////////////////////////////////////
//
// Geometrical transforms on images and matrices: rotation, zoom etc.
//
// */
#ifndef OPENCV_IMGPROC_IMGWARP_HPP
#define OPENCV_IMGPROC_IMGWARP_HPP
#include "precomp.hpp"
namespace cv
{
namespace opt_AVX2
{
#if CV_TRY_AVX2
void resizeNN2_AVX2(const Range&, const Mat&, Mat&, int*, int, double);
void resizeNN4_AVX2(const Range&, const Mat&, Mat&, int*, int, double);
int warpAffineBlockline(int *adelta, int *bdelta, short* xy, short* alpha, int X0, int Y0, int bw);
#endif
}
namespace opt_SSE41
{
#if CV_TRY_SSE4_1
void resizeNN2_SSE4_1(const Range&, const Mat&, Mat&, int*, int, double);
void resizeNN4_SSE4_1(const Range&, const Mat&, Mat&, int*, int, double);
#endif
}
}
#endif
/* End of file. */

192
modules/imgproc/src/imgwarp.sse4_1.cpp Normal file
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@ -0,0 +1,192 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* ////////////////////////////////////////////////////////////////////
//
// Geometrical transforms on images and matrices: rotation, zoom etc.
//
// */
#include "precomp.hpp"
#include "imgwarp.hpp"
namespace cv
{
namespace opt_SSE41
{
class resizeNNInvokerSSE2 :
public ParallelLoopBody
{
public:
resizeNNInvokerSSE2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=SSE4.2
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
int sseWidth = width - (width & 0x7);
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
for(x = 0; x < sseWidth; x += 8)
{
ushort imm = *(ushort*)(S + x_ofs[x + 0]);
pixels = _mm_insert_epi16(pixels, imm, 0);
imm = *(ushort*)(S + x_ofs[x + 1]);
pixels = _mm_insert_epi16(pixels, imm, 1);
imm = *(ushort*)(S + x_ofs[x + 2]);
pixels = _mm_insert_epi16(pixels, imm, 2);
imm = *(ushort*)(S + x_ofs[x + 3]);
pixels = _mm_insert_epi16(pixels, imm, 3);
imm = *(ushort*)(S + x_ofs[x + 4]);
pixels = _mm_insert_epi16(pixels, imm, 4);
imm = *(ushort*)(S + x_ofs[x + 5]);
pixels = _mm_insert_epi16(pixels, imm, 5);
imm = *(ushort*)(S + x_ofs[x + 6]);
pixels = _mm_insert_epi16(pixels, imm, 6);
imm = *(ushort*)(S + x_ofs[x + 7]);
pixels = _mm_insert_epi16(pixels, imm, 7);
_mm_storeu_si128((__m128i*)D, pixels);
D += 16;
}
for(; x < width; x++)
{
*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerSSE2(const resizeNNInvokerSSE2&);
resizeNNInvokerSSE2& operator=(const resizeNNInvokerSSE2&);
};
class resizeNNInvokerSSE4 :
public ParallelLoopBody
{
public:
resizeNNInvokerSSE4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
ify(_ify)
{
}
#if defined(__INTEL_COMPILER)
#pragma optimization_parameter target_arch=SSE4.2
#endif
virtual void operator() (const Range& range) const
{
Size ssize = src.size(), dsize = dst.size();
int y, x;
int width = dsize.width;
int sseWidth = width - (width & 0x3);
for(y = range.start; y < range.end; y++)
{
uchar* D = dst.data + dst.step*y;
uchar* Dstart = D;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
const uchar* S = src.data + sy*src.step;
__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
for(x = 0; x < sseWidth; x += 4)
{
int imm = *(int*)(S + x_ofs[x + 0]);
pixels = _mm_insert_epi32(pixels, imm, 0);
imm = *(int*)(S + x_ofs[x + 1]);
pixels = _mm_insert_epi32(pixels, imm, 1);
imm = *(int*)(S + x_ofs[x + 2]);
pixels = _mm_insert_epi32(pixels, imm, 2);
imm = *(int*)(S + x_ofs[x + 3]);
pixels = _mm_insert_epi32(pixels, imm, 3);
_mm_storeu_si128((__m128i*)D, pixels);
D += 16;
}
for(; x < width; x++)
{
*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
}
}
}
private:
const Mat src;
Mat dst;
int* x_ofs, pix_size4;
double ify;
resizeNNInvokerSSE4(const resizeNNInvokerSSE4&);
resizeNNInvokerSSE4& operator=(const resizeNNInvokerSSE4&);
};
void resizeNN2_SSE4_1(const Range& range, const Mat& src, Mat &dst, int *x_ofs, int pix_size4, double ify)
{
resizeNNInvokerSSE2 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total() / (double)(1 << 16));
}
void resizeNN4_SSE4_1(const Range& range, const Mat& src, Mat &dst, int *x_ofs, int pix_size4, double ify)
{
resizeNNInvokerSSE4 invoker(src, dst, x_ofs, pix_size4, ify);
parallel_for_(range, invoker, dst.total() / (double)(1 << 16));
}
}
}
/* End of file. */