mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-01-21 08:37:57 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #9875 from terfendail:fast_avx

Browse Source
This commit is contained in:
Vadim Pisarevsky 2017-10-27 12:53:59 +00:00
commit 1a495a5817
3 changed files with 316 additions and 55 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

184
modules/features2d/src/fast.avx2.cpp Normal file
View File

@ -0,0 +1,184 @@
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
Below is the original copyright and the references */
/*
Copyright (c) 2006, 2008 Edward Rosten
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
*Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
*Redistributions 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.
*Neither the name of the University of Cambridge nor the names of
its contributors may 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 COPYRIGHT OWNER 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.
*/
/*
The references are:
* Machine learning for high-speed corner detection,
E. Rosten and T. Drummond, ECCV 2006
* Faster and better: A machine learning approach to corner detection
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
*/
#include "precomp.hpp"
#include "fast.hpp"
#include "opencv2/core/hal/intrin.hpp"
namespace cv
{
namespace opt_AVX2
{
class FAST_t_patternSize16_AVX2_Impl: public FAST_t_patternSize16_AVX2
{
public:
FAST_t_patternSize16_AVX2_Impl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel):
cols(_cols), nonmax_suppression(_nonmax_suppression), pixel(_pixel)
{
//patternSize = 16
t256c = (char)_threshold;
threshold = std::min(std::max(_threshold, 0), 255);
}
virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners)
{
static const __m256i delta256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)(-128))), K16_256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)8));
const __m256i t256 = _mm256_broadcastsi128_si256(_mm_set1_epi8(t256c));
for (; j < cols - 32 - 3; j += 32, ptr += 32)
{
__m256i m0, m1;
__m256i v0 = _mm256_loadu_si256((const __m256i*)ptr);
__m256i v1 = _mm256_xor_si256(_mm256_subs_epu8(v0, t256), delta256);
v0 = _mm256_xor_si256(_mm256_adds_epu8(v0, t256), delta256);
__m256i x0 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[0])), delta256);
__m256i x1 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[4])), delta256);
__m256i x2 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[8])), delta256);
__m256i x3 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[12])), delta256);
m0 = _mm256_and_si256(_mm256_cmpgt_epi8(x0, v0), _mm256_cmpgt_epi8(x1, v0));
m1 = _mm256_and_si256(_mm256_cmpgt_epi8(v1, x0), _mm256_cmpgt_epi8(v1, x1));
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x1, v0), _mm256_cmpgt_epi8(x2, v0)));
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x1), _mm256_cmpgt_epi8(v1, x2)));
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x2, v0), _mm256_cmpgt_epi8(x3, v0)));
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x2), _mm256_cmpgt_epi8(v1, x3)));
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x3, v0), _mm256_cmpgt_epi8(x0, v0)));
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x3), _mm256_cmpgt_epi8(v1, x0)));
m0 = _mm256_or_si256(m0, m1);
unsigned int mask = _mm256_movemask_epi8(m0); //unsigned is important!
if (mask == 0){
continue;
}
if ((mask & 0xffff) == 0)
{
j -= 16;
ptr -= 16;
continue;
}
__m256i c0 = _mm256_setzero_si256(), c1 = c0, max0 = c0, max1 = c0;
for (int k = 0; k < 25; k++)
{
__m256i x = _mm256_xor_si256(_mm256_loadu_si256((const __m256i*)(ptr + pixel[k])), delta256);
m0 = _mm256_cmpgt_epi8(x, v0);
m1 = _mm256_cmpgt_epi8(v1, x);
c0 = _mm256_and_si256(_mm256_sub_epi8(c0, m0), m0);
c1 = _mm256_and_si256(_mm256_sub_epi8(c1, m1), m1);
max0 = _mm256_max_epu8(max0, c0);
max1 = _mm256_max_epu8(max1, c1);
}
max0 = _mm256_max_epu8(max0, max1);
unsigned int m = _mm256_movemask_epi8(_mm256_cmpgt_epi8(max0, K16_256));
for (int k = 0; m > 0 && k < 32; k++, m >>= 1)
if (m & 1)
{
cornerpos[ncorners++] = j + k;
if (nonmax_suppression)
{
short d[25];
for (int q = 0; q < 25; q++)
d[q] = (short)(ptr[k] - ptr[k + pixel[q]]);
v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
for (int q = 0; q < 16; q += 8)
{
v_int16x8 v0_ = v_load(d + q + 1);
v_int16x8 v1_ = v_load(d + q + 2);
v_int16x8 a = v_min(v0_, v1_);
v_int16x8 b = v_max(v0_, v1_);
v0_ = v_load(d + q + 3);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q + 4);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q + 5);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q + 6);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q + 7);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q + 8);
a = v_min(a, v0_);
b = v_max(b, v0_);
v0_ = v_load(d + q);
q0 = v_max(q0, v_min(a, v0_));
q1 = v_min(q1, v_max(b, v0_));
v0_ = v_load(d + q + 9);
q0 = v_max(q0, v_min(a, v0_));
q1 = v_min(q1, v_max(b, v0_));
}
q0 = v_max(q0, v_setzero_s16() - q1);
curr[j + k] = (uchar)(v_reduce_max(q0) - 1);
}
}
}
_mm256_zeroupper();
}
virtual ~FAST_t_patternSize16_AVX2_Impl() {};
private:
int cols;
char t256c;
int threshold;
bool nonmax_suppression;
const int* pixel;
};
Ptr<FAST_t_patternSize16_AVX2> FAST_t_patternSize16_AVX2::getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel)
{
return Ptr<FAST_t_patternSize16_AVX2>(new FAST_t_patternSize16_AVX2_Impl(_cols, _threshold, _nonmax_suppression, _pixel));
}
}
}

125
modules/features2d/src/fast.cpp
View File

@ -42,6 +42,7 @@ The references are:
*/
#include "precomp.hpp"
#include "fast.hpp"
#include "fast_score.hpp"
#include "opencl_kernels_features2d.hpp"
#include "opencv2/core/hal/intrin.hpp"
@ -59,13 +60,20 @@ void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bo
{
Mat img = _img.getMat();
const int K = patternSize/2, N = patternSize + K + 1;
int i, j, k, pixel[25];
makeOffsets(pixel, (int)img.step, patternSize);
#if CV_SIMD128
const int quarterPatternSize = patternSize/4;
v_uint8x16 delta = v_setall_u8(0x80), t = v_setall_u8((char)threshold), K16 = v_setall_u8((char)K);
bool hasSimd = hasSIMD128();
#if CV_TRY_AVX2
Ptr<opt_AVX2::FAST_t_patternSize16_AVX2> fast_t_impl_avx2;
if(CV_CPU_HAS_SUPPORT_AVX2)
fast_t_impl_avx2 = opt_AVX2::FAST_t_patternSize16_AVX2::getImpl(img.cols, threshold, nonmax_suppression, pixel);
#endif
#endif
int i, j, k, pixel[25];
makeOffsets(pixel, (int)img.step, patternSize);
keypoints.clear();
@ -100,65 +108,72 @@ void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bo
{
if( patternSize == 16 )
{
for(; j < img.cols - 16 - 3; j += 16, ptr += 16)
#if CV_TRY_AVX2
if (fast_t_impl_avx2)
fast_t_impl_avx2->process(j, ptr, curr, cornerpos, ncorners);
#endif
//vz if (j <= (img.cols - 27)) //it doesn't make sense using vectors for less than 8 elements
{
v_uint8x16 v = v_load(ptr);
v_int8x16 v0 = v_reinterpret_as_s8((v + t) ^ delta);
v_int8x16 v1 = v_reinterpret_as_s8((v - t) ^ delta);
v_int8x16 x0 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[0]), delta));
v_int8x16 x1 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[quarterPatternSize]), delta));
v_int8x16 x2 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[2*quarterPatternSize]), delta));
v_int8x16 x3 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[3*quarterPatternSize]), delta));
v_int8x16 m0, m1;
m0 = (v0 < x0) & (v0 < x1);
m1 = (x0 < v1) & (x1 < v1);
m0 = m0 | ((v0 < x1) & (v0 < x2));
m1 = m1 | ((x1 < v1) & (x2 < v1));
m0 = m0 | ((v0 < x2) & (v0 < x3));
m1 = m1 | ((x2 < v1) & (x3 < v1));
m0 = m0 | ((v0 < x3) & (v0 < x0));
m1 = m1 | ((x3 < v1) & (x0 < v1));
m0 = m0 | m1;
int mask = v_signmask(m0);
if( mask == 0 )
continue;
if( (mask & 255) == 0 )
for (; j < img.cols - 16 - 3; j += 16, ptr += 16)
{
j -= 8;
ptr -= 8;
continue;
}
v_uint8x16 v = v_load(ptr);
v_int8x16 v0 = v_reinterpret_as_s8((v + t) ^ delta);
v_int8x16 v1 = v_reinterpret_as_s8((v - t) ^ delta);
v_int8x16 c0 = v_setzero_s8();
v_int8x16 c1 = v_setzero_s8();
v_uint8x16 max0 = v_setzero_u8();
v_uint8x16 max1 = v_setzero_u8();
for( k = 0; k < N; k++ )
{
v_int8x16 x = v_reinterpret_as_s8(v_load((ptr + pixel[k])) ^ delta);
m0 = v0 < x;
m1 = x < v1;
v_int8x16 x0 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[0]), delta));
v_int8x16 x1 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[quarterPatternSize]), delta));
v_int8x16 x2 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[2*quarterPatternSize]), delta));
v_int8x16 x3 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[3*quarterPatternSize]), delta));
c0 = v_sub_wrap(c0, m0) & m0;
c1 = v_sub_wrap(c1, m1) & m1;
v_int8x16 m0, m1;
m0 = (v0 < x0) & (v0 < x1);
m1 = (x0 < v1) & (x1 < v1);
m0 = m0 | ((v0 < x1) & (v0 < x2));
m1 = m1 | ((x1 < v1) & (x2 < v1));
m0 = m0 | ((v0 < x2) & (v0 < x3));
m1 = m1 | ((x2 < v1) & (x3 < v1));
m0 = m0 | ((v0 < x3) & (v0 < x0));
m1 = m1 | ((x3 < v1) & (x0 < v1));
m0 = m0 | m1;
max0 = v_max(max0, v_reinterpret_as_u8(c0));
max1 = v_max(max1, v_reinterpret_as_u8(c1));
}
max0 = v_max(max0, max1);
int m = v_signmask(K16 < max0);
for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
{
if(m & 1)
int mask = v_signmask(m0);
if( mask == 0 )
continue;
if( (mask & 255) == 0 )
{
cornerpos[ncorners++] = j+k;
if(nonmax_suppression)
curr[j+k] = (uchar)cornerScore<patternSize>(ptr+k, pixel, threshold);
j -= 8;
ptr -= 8;
continue;
}
v_int8x16 c0 = v_setzero_s8();
v_int8x16 c1 = v_setzero_s8();
v_uint8x16 max0 = v_setzero_u8();
v_uint8x16 max1 = v_setzero_u8();
for( k = 0; k < N; k++ )
{
v_int8x16 x = v_reinterpret_as_s8(v_load((ptr + pixel[k])) ^ delta);
m0 = v0 < x;
m1 = x < v1;
c0 = v_sub_wrap(c0, m0) & m0;
c1 = v_sub_wrap(c1, m1) & m1;
max0 = v_max(max0, v_reinterpret_as_u8(c0));
max1 = v_max(max1, v_reinterpret_as_u8(c1));
}
max0 = v_max(max0, max1);
int m = v_signmask(K16 < max0);
for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
{
if(m & 1)
{
cornerpos[ncorners++] = j+k;
if(nonmax_suppression)
curr[j+k] = (uchar)cornerScore<patternSize>(ptr+k, pixel, threshold);
}
}
}
}

62
modules/features2d/src/fast.hpp Normal file
View File

@ -0,0 +1,62 @@
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
Below is the original copyright and the references */
/*
Copyright (c) 2006, 2008 Edward Rosten
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
*Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
*Redistributions 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.
*Neither the name of the University of Cambridge nor the names of
its contributors may 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 COPYRIGHT OWNER 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.
*/
/*
The references are:
* Machine learning for high-speed corner detection,
E. Rosten and T. Drummond, ECCV 2006
* Faster and better: A machine learning approach to corner detection
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
*/
#ifndef OPENCV_FEATURES2D_FAST_HPP
#define OPENCV_FEATURES2D_FAST_HPP
namespace cv
{
namespace opt_AVX2
{
#if CV_TRY_AVX2
class FAST_t_patternSize16_AVX2
{
public:
static Ptr<FAST_t_patternSize16_AVX2> getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel);
virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners) = 0;
virtual ~FAST_t_patternSize16_AVX2() {};
};
#endif
}
}
#endif