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Merge pull request #28632 from amd:fast_scharr_deriv

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video: Optimized ScharrDeriv#28632

- Move to CV_SIMD_SCALABLE
- avx2 & avx512 dispatch added for ScharrDeriv

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Madan mohan Manokar
2026-03-13 15:34:46 +05:30
committed by GitHub
parent 8f9c7c0f72
commit 00833f98d0
5 changed files with 145 additions and 79 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/video/CMakeLists.txt
+1
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@@ -1,4 +1,5 @@
set(the_description "Video Analysis")
ocv_add_dispatched_file(lkpyramid SSE2 SSE4_1 AVX2 AVX512_SKX AVX512_ICL)
ocv_define_module(video
opencv_imgproc
OPTIONAL
modules/video/src/lkpyramid.cpp
+2 -79
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@@ -12,6 +12,7 @@
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2026, Advanced Micro Devices, 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,
@@ -73,85 +74,7 @@ static void calcScharrDeriv(const cv::Mat& src, cv::Mat& dst)
void cv::detail::ScharrDerivInvoker::operator()(const Range& range) const
{
using cv::detail::deriv_type;
int rows = src.rows, cols = src.cols, cn = src.channels(), colsn = cols*cn;
int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
deriv_type *trow0 = alignPtr(_tempBuf.data() + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
#if CV_SIMD128
v_int16x8 c3 = v_setall_s16(3), c10 = v_setall_s16(10);
#endif
for( y = range.start; y < range.end; y++ )
{
const uchar* srow0 = src.ptr<uchar>(y > 0 ? y-1 : rows > 1 ? 1 : 0);
const uchar* srow1 = src.ptr<uchar>(y);
const uchar* srow2 = src.ptr<uchar>(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0);
deriv_type* drow = (deriv_type *)dst.ptr<deriv_type>(y);
// do vertical convolution
x = 0;
#if CV_SIMD128
{
for( ; x <= colsn - 8; x += 8 )
{
v_int16x8 s0 = v_reinterpret_as_s16(v_load_expand(srow0 + x));
v_int16x8 s1 = v_reinterpret_as_s16(v_load_expand(srow1 + x));
v_int16x8 s2 = v_reinterpret_as_s16(v_load_expand(srow2 + x));
v_int16x8 t1 = v_sub(s2, s0);
v_int16x8 t0 = v_add(v_mul_wrap(v_add(s0, s2), c3), v_mul_wrap(s1, c10));
v_store(trow0 + x, t0);
v_store(trow1 + x, t1);
}
}
#endif
for( ; x < colsn; x++ )
{
int t0 = (srow0[x] + srow2[x])*3 + srow1[x]*10;
int t1 = srow2[x] - srow0[x];
trow0[x] = (deriv_type)t0;
trow1[x] = (deriv_type)t1;
}
// make border
int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn;
for( int k = 0; k < cn; k++ )
{
trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k];
trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k];
}
// do horizontal convolution, interleave the results and store them to dst
x = 0;
#if CV_SIMD128
{
for( ; x <= colsn - 8; x += 8 )
{
v_int16x8 s0 = v_load(trow0 + x - cn);
v_int16x8 s1 = v_load(trow0 + x + cn);
v_int16x8 s2 = v_load(trow1 + x - cn);
v_int16x8 s3 = v_load(trow1 + x);
v_int16x8 s4 = v_load(trow1 + x + cn);
v_int16x8 t0 = v_sub(s1, s0);
v_int16x8 t1 = v_add(v_mul_wrap(v_add(s2, s4), c3), v_mul_wrap(s3, c10));
v_store_interleave((drow + x*2), t0, t1);
}
}
#endif
for( ; x < colsn; x++ )
{
deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]);
deriv_type t1 = (deriv_type)((trow1[x+cn] + trow1[x-cn])*3 + trow1[x]*10);
drow[x*2] = t0; drow[x*2+1] = t1;
}
}
ScharrDerivInvoker_impl(src, const_cast<Mat&>(dst), range);
}
cv::detail::LKTrackerInvoker::LKTrackerInvoker(
modules/video/src/lkpyramid.dispatch.cpp
+22
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@@ -0,0 +1,22 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// Copyright (C) 2026, Advanced Micro Devices, Inc., all rights reserved.
#include "precomp.hpp"
#include "lkpyramid.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include "lkpyramid.simd.hpp"
#include "lkpyramid.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL based on CMakeLists.txt
namespace cv {
namespace detail {
void ScharrDerivInvoker_impl(const Mat& src, Mat& dst, const Range& range)
{
CV_CPU_DISPATCH(ScharrDerivInvoker_SIMD, (src, dst, range), CV_CPU_DISPATCH_MODES_ALL);
}
} // namespace detail
} // namespace cv
modules/video/src/lkpyramid.hpp
+2
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@@ -7,6 +7,8 @@ namespace detail
typedef short deriv_type;
void ScharrDerivInvoker_impl(const Mat& src, Mat& dst, const Range& range);
struct ScharrDerivInvoker : ParallelLoopBody
{
ScharrDerivInvoker(const Mat& _src, const Mat& _dst)
modules/video/src/lkpyramid.simd.hpp
+118
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@@ -0,0 +1,118 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// Copyright (C) 2026, Advanced Micro Devices, Inc., all rights reserved.
#include "precomp.hpp"
#include "lkpyramid.hpp"
#include "opencv2/core/hal/intrin.hpp"
namespace cv {
namespace detail {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
// SIMD-optimized implementation
void ScharrDerivInvoker_SIMD(const Mat& src, Mat& dst, const Range& range);
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace detail
} // namespace cv
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
namespace cv {
namespace detail {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
void ScharrDerivInvoker_SIMD(const Mat& src, Mat& dst, const Range& range)
{
typedef short deriv_type;
int rows = src.rows, cols = src.cols, cn = src.channels(), colsn = cols*cn;
int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
deriv_type *trow0 = alignPtr(_tempBuf.data() + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
#if (CV_SIMD)
const int vlanes = VTraits<v_int16>::vlanes();
v_int16 c3 = vx_setall_s16(3), c10 = vx_setall_s16(10);
#endif
for( y = range.start; y < range.end; y++ )
{
const uchar* srow0 = src.ptr<uchar>(y > 0 ? y-1 : rows > 1 ? 1 : 0);
const uchar* srow1 = src.ptr<uchar>(y);
const uchar* srow2 = src.ptr<uchar>(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0);
deriv_type* drow = (deriv_type *)dst.ptr<deriv_type>(y);
// do vertical convolution
x = 0;
#if (CV_SIMD)
{
for( ; x <= colsn - vlanes; x += vlanes )
{
v_int16 s0 = v_reinterpret_as_s16(vx_load_expand(srow0 + x));
v_int16 s1 = v_reinterpret_as_s16(vx_load_expand(srow1 + x));
v_int16 s2 = v_reinterpret_as_s16(vx_load_expand(srow2 + x));
v_int16 t1 = v_sub(s2, s0);
v_int16 t0 = v_add(v_mul_wrap(v_add(s0, s2), c3), v_mul_wrap(s1, c10));
v_store(trow0 + x, t0);
v_store(trow1 + x, t1);
}
}
#endif
for( ; x < colsn; x++ )
{
int t0 = (srow0[x] + srow2[x])*3 + srow1[x]*10;
int t1 = srow2[x] - srow0[x];
trow0[x] = (deriv_type)t0;
trow1[x] = (deriv_type)t1;
}
// make border
int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn;
for( int k = 0; k < cn; k++ )
{
trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k];
trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k];
}
// do horizontal convolution, interleave the results and store them to dst
x = 0;
#if (CV_SIMD)
{
for( ; x <= colsn - vlanes; x += vlanes )
{
v_int16 s0 = vx_load(trow0 + x - cn);
v_int16 s1 = vx_load(trow0 + x + cn);
v_int16 s2 = vx_load(trow1 + x - cn);
v_int16 s3 = vx_load(trow1 + x);
v_int16 s4 = vx_load(trow1 + x + cn);
v_int16 t0 = v_sub(s1, s0);
v_int16 t1 = v_add(v_mul_wrap(v_add(s2, s4), c3), v_mul_wrap(s3, c10));
v_store_interleave((drow + x*2), t0, t1);
}
}
#endif
for( ; x < colsn; x++ )
{
deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]);
deriv_type t1 = (deriv_type)((trow1[x+cn] + trow1[x-cn])*3 + trow1[x]*10);
drow[x*2] = t0; drow[x*2+1] = t1;
}
}
}
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace detail
} // namespace cv
#endif // CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY