Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

imgproc: dispatch medianBlur

Browse Source
pull/14013/head
Alexander Alekhin 6 years ago
parent 8546ac3ce6
commit 6ec08f268f
3 changed files with 29 additions and 1197 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 1
      modules/imgproc/CMakeLists.txt
  2. 937
      modules/imgproc/src/median_blur.dispatch.cpp
  3. 288
      modules/imgproc/src/median_blur.simd.hpp

1
modules/imgproc/CMakeLists.txt
Unescape View File

@ -4,4 +4,5 @@ ocv_add_dispatched_file(filter SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(color_hsv SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(color_rgb SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(color_yuv SSE2 SSE4_1 AVX2)
ocv_add_dispatched_file(median_blur SSE2 SSE4_1 AVX2)
ocv_define_module(imgproc opencv_core WRAP java python js)

937
modules/imgproc/src/median_blur.dispatch.cpp
Unescape View File

@ -50,895 +50,10 @@
#include "opencv2/core/openvx/ovx_defs.hpp"
/*
* This file includes the code, contributed by Simon Perreault
* (the function icvMedianBlur_8u_O1)
*
* Constant-time median filtering -- http://nomis80.org/ctmf.html
* Copyright (C) 2006 Simon Perreault
*
* Contact:
* Laboratoire de vision et systemes numeriques
* Pavillon Adrien-Pouliot
* Universite Laval
* Sainte-Foy, Quebec, Canada
* G1K 7P4
*
* perreaul@gel.ulaval.ca
*/
#include "median_blur.simd.hpp"
#include "median_blur.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
/****************************************************************************************\
Median Filter
\****************************************************************************************/
namespace cv
{
static void
medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
{
typedef ushort HT;
/**
* This structure represents a two-tier histogram. The first tier (known as the
* "coarse" level) is 4 bit wide and the second tier (known as the "fine" level)
* is 8 bit wide. Pixels inserted in the fine level also get inserted into the
* coarse bucket designated by the 4 MSBs of the fine bucket value.
*
* The structure is aligned on 16 bits, which is a prerequisite for SIMD
* instructions. Each bucket is 16 bit wide, which means that extra care must be
* taken to prevent overflow.
*/
typedef struct
{
HT coarse[16];
HT fine[16][16];
} Histogram;
/**
* HOP is short for Histogram OPeration. This macro makes an operation \a op on
* histogram \a h for pixel value \a x. It takes care of handling both levels.
*/
#define HOP(h,x,op) \
h.coarse[x>>4] op, \
*((HT*)h.fine + x) op
#define COP(c,j,x,op) \
h_coarse[ 16*(n*c+j) + (x>>4) ] op, \
h_fine[ 16 * (n*(16*c+(x>>4)) + j) + (x & 0xF) ] op
int cn = _dst.channels(), m = _dst.rows, r = (ksize-1)/2;
CV_Assert(cn > 0 && cn <= 4);
size_t sstep = _src.step, dstep = _dst.step;
int STRIPE_SIZE = std::min( _dst.cols, 512/cn );
#if defined(CV_SIMD_WIDTH) && CV_SIMD_WIDTH >= 16
# define CV_ALIGNMENT CV_SIMD_WIDTH
#else
# define CV_ALIGNMENT 16
#endif
std::vector<HT> _h_coarse(1 * 16 * (STRIPE_SIZE + 2*r) * cn + CV_ALIGNMENT);
std::vector<HT> _h_fine(16 * 16 * (STRIPE_SIZE + 2*r) * cn + CV_ALIGNMENT);
HT* h_coarse = alignPtr(&_h_coarse[0], CV_ALIGNMENT);
HT* h_fine = alignPtr(&_h_fine[0], CV_ALIGNMENT);
for( int x = 0; x < _dst.cols; x += STRIPE_SIZE )
{
int i, j, k, c, n = std::min(_dst.cols - x, STRIPE_SIZE) + r*2;
const uchar* src = _src.ptr() + x*cn;
uchar* dst = _dst.ptr() + (x - r)*cn;
memset( h_coarse, 0, 16*n*cn*sizeof(h_coarse[0]) );
memset( h_fine, 0, 16*16*n*cn*sizeof(h_fine[0]) );
// First row initialization
for( c = 0; c < cn; c++ )
{
for( j = 0; j < n; j++ )
COP( c, j, src[cn*j+c], += (HT)(r+2) );
for( i = 1; i < r; i++ )
{
const uchar* p = src + sstep*std::min(i, m-1);
for ( j = 0; j < n; j++ )
COP( c, j, p[cn*j+c], ++ );
}
}
for( i = 0; i < m; i++ )
{
const uchar* p0 = src + sstep * std::max( 0, i-r-1 );
const uchar* p1 = src + sstep * std::min( m-1, i+r );
for( c = 0; c < cn; c++ )
{
Histogram CV_DECL_ALIGNED(CV_ALIGNMENT) H;
HT CV_DECL_ALIGNED(CV_ALIGNMENT) luc[16];
memset(&H, 0, sizeof(H));
memset(luc, 0, sizeof(luc));
// Update column histograms for the entire row.
for( j = 0; j < n; j++ )
{
COP( c, j, p0[j*cn + c], -- );
COP( c, j, p1[j*cn + c], ++ );
}
// First column initialization
for (k = 0; k < 16; ++k)
{
#if CV_SIMD256
v_store(H.fine[k], v_mul_wrap(v256_load(h_fine + 16 * n*(16 * c + k)), v256_setall_u16(2 * r + 1)) + v256_load(H.fine[k]));
#elif CV_SIMD128
v_store(H.fine[k], v_mul_wrap(v_load(h_fine + 16 * n*(16 * c + k)), v_setall_u16((ushort)(2 * r + 1))) + v_load(H.fine[k]));
v_store(H.fine[k] + 8, v_mul_wrap(v_load(h_fine + 16 * n*(16 * c + k) + 8), v_setall_u16((ushort)(2 * r + 1))) + v_load(H.fine[k] + 8));
#else
for (int ind = 0; ind < 16; ++ind)
H.fine[k][ind] = (HT)(H.fine[k][ind] + (2 * r + 1) * h_fine[16 * n*(16 * c + k) + ind]);
#endif
}
#if CV_SIMD256
v_uint16x16 v_coarse = v256_load(H.coarse);
#elif CV_SIMD128
v_uint16x8 v_coarsel = v_load(H.coarse);
v_uint16x8 v_coarseh = v_load(H.coarse + 8);
#endif
HT* px = h_coarse + 16 * n*c;
for( j = 0; j < 2*r; ++j, px += 16 )
{
#if CV_SIMD256
v_coarse += v256_load(px);
#elif CV_SIMD128
v_coarsel += v_load(px);
v_coarseh += v_load(px + 8);
#else
for (int ind = 0; ind < 16; ++ind)
H.coarse[ind] += px[ind];
#endif
}
for( j = r; j < n-r; j++ )
{
int t = 2*r*r + 2*r, b, sum = 0;
HT* segment;
px = h_coarse + 16 * (n*c + std::min(j + r, n - 1));
#if CV_SIMD256
v_coarse += v256_load(px);
v_store(H.coarse, v_coarse);
#elif CV_SIMD128
v_coarsel += v_load(px);
v_coarseh += v_load(px + 8);
v_store(H.coarse, v_coarsel);
v_store(H.coarse + 8, v_coarseh);
#else
for (int ind = 0; ind < 16; ++ind)
H.coarse[ind] += px[ind];
#endif
// Find median at coarse level
for ( k = 0; k < 16 ; ++k )
{
sum += H.coarse[k];
if ( sum > t )
{
sum -= H.coarse[k];
break;
}
}
CV_Assert( k < 16 );
/* Update corresponding histogram segment */
#if CV_SIMD256
v_uint16x16 v_fine;
#elif CV_SIMD128
v_uint16x8 v_finel;
v_uint16x8 v_fineh;
#endif
if ( luc[k] <= j-r )
{
#if CV_SIMD256
v_fine = v256_setzero_u16();
#elif CV_SIMD128
v_finel = v_setzero_u16();
v_fineh = v_setzero_u16();
#else
memset(&H.fine[k], 0, 16 * sizeof(HT));
#endif
px = h_fine + 16 * (n*(16 * c + k) + j - r);
for (luc[k] = HT(j - r); luc[k] < MIN(j + r + 1, n); ++luc[k], px += 16)
{
#if CV_SIMD256
v_fine += v256_load(px);
#elif CV_SIMD128
v_finel += v_load(px);
v_fineh += v_load(px + 8);
#else
for (int ind = 0; ind < 16; ++ind)
H.fine[k][ind] += px[ind];
#endif
}
if ( luc[k] < j+r+1 )
{
px = h_fine + 16 * (n*(16 * c + k) + (n - 1));
#if CV_SIMD256
v_fine += v_mul_wrap(v256_load(px), v256_setall_u16(j + r + 1 - n));
#elif CV_SIMD128
v_finel += v_mul_wrap(v_load(px), v_setall_u16((ushort)(j + r + 1 - n)));
v_fineh += v_mul_wrap(v_load(px + 8), v_setall_u16((ushort)(j + r + 1 - n)));
#else
for (int ind = 0; ind < 16; ++ind)
H.fine[k][ind] = (HT)(H.fine[k][ind] + (j + r + 1 - n) * px[ind]);
#endif
luc[k] = (HT)(j+r+1);
}
}
else
{
#if CV_SIMD256
v_fine = v256_load(H.fine[k]);
#elif CV_SIMD128
v_finel = v_load(H.fine[k]);
v_fineh = v_load(H.fine[k] + 8);
#endif
px = h_fine + 16*n*(16 * c + k);
for ( ; luc[k] < j+r+1; ++luc[k] )
{
#if CV_SIMD256
v_fine = v_fine + v256_load(px + 16 * MIN(luc[k], n - 1)) - v256_load(px + 16 * MAX(luc[k] - 2 * r - 1, 0));
#elif CV_SIMD128
v_finel = v_finel + v_load(px + 16 * MIN(luc[k], n - 1) ) - v_load(px + 16 * MAX(luc[k] - 2 * r - 1, 0));
v_fineh = v_fineh + v_load(px + 16 * MIN(luc[k], n - 1) + 8) - v_load(px + 16 * MAX(luc[k] - 2 * r - 1, 0) + 8);
#else
for (int ind = 0; ind < 16; ++ind)
H.fine[k][ind] += px[16 * MIN(luc[k], n - 1) + ind] - px[16 * MAX(luc[k] - 2 * r - 1, 0) + ind];
#endif
}
}
px = h_coarse + 16 * (n*c + MAX(j - r, 0));
#if CV_SIMD256
v_store(H.fine[k], v_fine);
v_coarse -= v256_load(px);
#elif CV_SIMD128
v_store(H.fine[k], v_finel);
v_store(H.fine[k] + 8, v_fineh);
v_coarsel -= v_load(px);
v_coarseh -= v_load(px + 8);
#else
for (int ind = 0; ind < 16; ++ind)
H.coarse[ind] -= px[ind];
#endif
/* Find median in segment */
segment = H.fine[k];
for ( b = 0; b < 16 ; b++ )
{
sum += segment[b];
if ( sum > t )
{
dst[dstep*i+cn*j+c] = (uchar)(16*k + b);
break;
}
}
CV_Assert( b < 16 );
}
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
#undef HOP
#undef COP
}
static void
medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m )
{
#define N 16
int zone0[4][N];
int zone1[4][N*N];
int x, y;
int n2 = m*m/2;
Size size = _dst.size();
const uchar* src = _src.ptr();
uchar* dst = _dst.ptr();
int src_step = (int)_src.step, dst_step = (int)_dst.step;
int cn = _src.channels();
const uchar* src_max = src + size.height*src_step;
CV_Assert(cn > 0 && cn <= 4);
#define UPDATE_ACC01( pix, cn, op ) \
{ \
int p = (pix); \
zone1[cn][p] op; \
zone0[cn][p >> 4] op; \
}
//CV_Assert( size.height >= nx && size.width >= nx );
for( x = 0; x < size.width; x++, src += cn, dst += cn )
{
uchar* dst_cur = dst;
const uchar* src_top = src;
const uchar* src_bottom = src;
int k, c;
int src_step1 = src_step, dst_step1 = dst_step;
if( x % 2 != 0 )
{
src_bottom = src_top += src_step*(size.height-1);
dst_cur += dst_step*(size.height-1);
src_step1 = -src_step1;
dst_step1 = -dst_step1;
}
// init accumulator
memset( zone0, 0, sizeof(zone0[0])*cn );
memset( zone1, 0, sizeof(zone1[0])*cn );
for( y = 0; y <= m/2; y++ )
{
for( c = 0; c < cn; c++ )
{
if( y > 0 )
{
for( k = 0; k < m*cn; k += cn )
UPDATE_ACC01( src_bottom[k+c], c, ++ );
}
else
{
for( k = 0; k < m*cn; k += cn )
UPDATE_ACC01( src_bottom[k+c], c, += m/2+1 );
}
}
if( (src_step1 > 0 && y < size.height-1) ||
(src_step1 < 0 && size.height-y-1 > 0) )
src_bottom += src_step1;
}
for( y = 0; y < size.height; y++, dst_cur += dst_step1 )
{
// find median
for( c = 0; c < cn; c++ )
{
int s = 0;
for( k = 0; ; k++ )
{
int t = s + zone0[c][k];
if( t > n2 ) break;
s = t;
}
for( k *= N; ;k++ )
{
s += zone1[c][k];
if( s > n2 ) break;
}
dst_cur[c] = (uchar)k;
}
if( y+1 == size.height )
break;
if( cn == 1 )
{
for( k = 0; k < m; k++ )
{
int p = src_top[k];
int q = src_bottom[k];
zone1[0][p]--;
zone0[0][p>>4]--;
zone1[0][q]++;
zone0[0][q>>4]++;
}
}
else if( cn == 3 )
{
for( k = 0; k < m*3; k += 3 )
{
UPDATE_ACC01( src_top[k], 0, -- );
UPDATE_ACC01( src_top[k+1], 1, -- );
UPDATE_ACC01( src_top[k+2], 2, -- );
UPDATE_ACC01( src_bottom[k], 0, ++ );
UPDATE_ACC01( src_bottom[k+1], 1, ++ );
UPDATE_ACC01( src_bottom[k+2], 2, ++ );
}
}
else
{
assert( cn == 4 );
for( k = 0; k < m*4; k += 4 )
{
UPDATE_ACC01( src_top[k], 0, -- );
UPDATE_ACC01( src_top[k+1], 1, -- );
UPDATE_ACC01( src_top[k+2], 2, -- );
UPDATE_ACC01( src_top[k+3], 3, -- );
UPDATE_ACC01( src_bottom[k], 0, ++ );
UPDATE_ACC01( src_bottom[k+1], 1, ++ );
UPDATE_ACC01( src_bottom[k+2], 2, ++ );
UPDATE_ACC01( src_bottom[k+3], 3, ++ );
}
}
if( (src_step1 > 0 && src_bottom + src_step1 < src_max) ||
(src_step1 < 0 && src_bottom + src_step1 >= src) )
src_bottom += src_step1;
if( y >= m/2 )
src_top += src_step1;
}
}
#undef N
#undef UPDATE_ACC
}
namespace {
struct MinMax8u
{
typedef uchar value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const uchar* ptr) { return *ptr; }
void store(uchar* ptr, arg_type val) { *ptr = (uchar)val; }
void operator()(arg_type& a, arg_type& b) const
{
int t = CV_FAST_CAST_8U(a - b);
b += t; a -= t;
}
};
struct MinMax16u
{
typedef ushort value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const ushort* ptr) { return *ptr; }
void store(ushort* ptr, arg_type val) { *ptr = (ushort)val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
struct MinMax16s
{
typedef short value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const short* ptr) { return *ptr; }
void store(short* ptr, arg_type val) { *ptr = (short)val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
struct MinMax32f
{
typedef float value_type;
typedef float arg_type;
enum { SIZE = 1 };
arg_type load(const float* ptr) { return *ptr; }
void store(float* ptr, arg_type val) { *ptr = val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
#if CV_SIMD
struct MinMaxVec8u
{
typedef uchar value_type;
typedef v_uint8x16 arg_type;
enum { SIZE = v_uint8x16::nlanes };
arg_type load(const uchar* ptr) { return v_load(ptr); }
void store(uchar* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#if CV_SIMD_WIDTH > 16
typedef v_uint8 warg_type;
enum { WSIZE = v_uint8::nlanes };
warg_type wload(const uchar* ptr) { return vx_load(ptr); }
void store(uchar* ptr, const warg_type &val) { v_store(ptr, val); }
void operator()(warg_type& a, warg_type& b) const
{
warg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#endif
};
struct MinMaxVec16u
{
typedef ushort value_type;
typedef v_uint16x8 arg_type;
enum { SIZE = v_uint16x8::nlanes };
arg_type load(const ushort* ptr) { return v_load(ptr); }
void store(ushort* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#if CV_SIMD_WIDTH > 16
typedef v_uint16 warg_type;
enum { WSIZE = v_uint16::nlanes };
warg_type wload(const ushort* ptr) { return vx_load(ptr); }
void store(ushort* ptr, const warg_type &val) { v_store(ptr, val); }
void operator()(warg_type& a, warg_type& b) const
{
warg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#endif
};
struct MinMaxVec16s
{
typedef short value_type;
typedef v_int16x8 arg_type;
enum { SIZE = v_int16x8::nlanes };
arg_type load(const short* ptr) { return v_load(ptr); }
void store(short* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#if CV_SIMD_WIDTH > 16
typedef v_int16 warg_type;
enum { WSIZE = v_int16::nlanes };
warg_type wload(const short* ptr) { return vx_load(ptr); }
void store(short* ptr, const warg_type &val) { v_store(ptr, val); }
void operator()(warg_type& a, warg_type& b) const
{
warg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#endif
};
struct MinMaxVec32f
{
typedef float value_type;
typedef v_float32x4 arg_type;
enum { SIZE = v_float32x4::nlanes };
arg_type load(const float* ptr) { return v_load(ptr); }
void store(float* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#if CV_SIMD_WIDTH > 16
typedef v_float32 warg_type;
enum { WSIZE = v_float32::nlanes };
warg_type wload(const float* ptr) { return vx_load(ptr); }
void store(float* ptr, const warg_type &val) { v_store(ptr, val); }
void operator()(warg_type& a, warg_type& b) const
{
warg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
#endif
};
#else
typedef MinMax8u MinMaxVec8u;
typedef MinMax16u MinMaxVec16u;
typedef MinMax16s MinMaxVec16s;
typedef MinMax32f MinMaxVec32f;
#endif
template<class Op, class VecOp>
static void
medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
{
typedef typename Op::value_type T;
typedef typename Op::arg_type WT;
typedef typename VecOp::arg_type VT;
#if CV_SIMD_WIDTH > 16
typedef typename VecOp::warg_type WVT;
#endif
const T* src = _src.ptr<T>();
T* dst = _dst.ptr<T>();
int sstep = (int)(_src.step/sizeof(T));
int dstep = (int)(_dst.step/sizeof(T));
Size size = _dst.size();
int i, j, k, cn = _src.channels();
Op op;
VecOp vop;
if( m == 3 )
{
if( size.width == 1 || size.height == 1 )
{
int len = size.width + size.height - 1;
int sdelta = size.height == 1 ? cn : sstep;
int sdelta0 = size.height == 1 ? 0 : sstep - cn;
int ddelta = size.height == 1 ? cn : dstep;
for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
for( j = 0; j < cn; j++, src++ )
{
WT p0 = src[i > 0 ? -sdelta : 0];
WT p1 = src[0];
WT p2 = src[i < len - 1 ? sdelta : 0];
op(p0, p1); op(p1, p2); op(p0, p1);
dst[j] = (T)p1;
}
return;
}
size.width *= cn;
for( i = 0; i < size.height; i++, dst += dstep )
{
const T* row0 = src + std::max(i - 1, 0)*sstep;
const T* row1 = src + i*sstep;
const T* row2 = src + std::min(i + 1, size.height-1)*sstep;
int limit = cn;
for(j = 0;; )
{
for( ; j < limit; j++ )
{
int j0 = j >= cn ? j - cn : j;
int j2 = j < size.width - cn ? j + cn : j;
WT p0 = row0[j0], p1 = row0[j], p2 = row0[j2];
WT p3 = row1[j0], p4 = row1[j], p5 = row1[j2];
WT p6 = row2[j0], p7 = row2[j], p8 = row2[j2];
op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1);
op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5);
op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7);
op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7);
op(p4, p2); op(p6, p4); op(p4, p2);
dst[j] = (T)p4;
}
if( limit == size.width )
break;
#if CV_SIMD_WIDTH > 16
for( ; j <= size.width - VecOp::WSIZE - cn; j += VecOp::WSIZE )
{
WVT p0 = vop.wload(row0+j-cn), p1 = vop.wload(row0+j), p2 = vop.wload(row0+j+cn);
WVT p3 = vop.wload(row1+j-cn), p4 = vop.wload(row1+j), p5 = vop.wload(row1+j+cn);
WVT p6 = vop.wload(row2+j-cn), p7 = vop.wload(row2+j), p8 = vop.wload(row2+j+cn);
vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p1);
vop(p3, p4); vop(p6, p7); vop(p1, p2); vop(p4, p5);
vop(p7, p8); vop(p0, p3); vop(p5, p8); vop(p4, p7);
vop(p3, p6); vop(p1, p4); vop(p2, p5); vop(p4, p7);
vop(p4, p2); vop(p6, p4); vop(p4, p2);
vop.store(dst+j, p4);
}
#endif
for( ; j <= size.width - VecOp::SIZE - cn; j += VecOp::SIZE )
{
VT p0 = vop.load(row0+j-cn), p1 = vop.load(row0+j), p2 = vop.load(row0+j+cn);
VT p3 = vop.load(row1+j-cn), p4 = vop.load(row1+j), p5 = vop.load(row1+j+cn);
VT p6 = vop.load(row2+j-cn), p7 = vop.load(row2+j), p8 = vop.load(row2+j+cn);
vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p1);
vop(p3, p4); vop(p6, p7); vop(p1, p2); vop(p4, p5);
vop(p7, p8); vop(p0, p3); vop(p5, p8); vop(p4, p7);
vop(p3, p6); vop(p1, p4); vop(p2, p5); vop(p4, p7);
vop(p4, p2); vop(p6, p4); vop(p4, p2);
vop.store(dst+j, p4);
}
limit = size.width;
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
else if( m == 5 )
{
if( size.width == 1 || size.height == 1 )
{
int len = size.width + size.height - 1;
int sdelta = size.height == 1 ? cn : sstep;
int sdelta0 = size.height == 1 ? 0 : sstep - cn;
int ddelta = size.height == 1 ? cn : dstep;
for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
for( j = 0; j < cn; j++, src++ )
{
int i1 = i > 0 ? -sdelta : 0;
int i0 = i > 1 ? -sdelta*2 : i1;
int i3 = i < len-1 ? sdelta : 0;
int i4 = i < len-2 ? sdelta*2 : i3;
WT p0 = src[i0], p1 = src[i1], p2 = src[0], p3 = src[i3], p4 = src[i4];
op(p0, p1); op(p3, p4); op(p2, p3); op(p3, p4); op(p0, p2);
op(p2, p4); op(p1, p3); op(p1, p2);
dst[j] = (T)p2;
}
return;
}
size.width *= cn;
for( i = 0; i < size.height; i++, dst += dstep )
{
const T* row[5];
row[0] = src + std::max(i - 2, 0)*sstep;
row[1] = src + std::max(i - 1, 0)*sstep;
row[2] = src + i*sstep;
row[3] = src + std::min(i + 1, size.height-1)*sstep;
row[4] = src + std::min(i + 2, size.height-1)*sstep;
int limit = cn*2;
for(j = 0;; )
{
for( ; j < limit; j++ )
{
WT p[25];
int j1 = j >= cn ? j - cn : j;
int j0 = j >= cn*2 ? j - cn*2 : j1;
int j3 = j < size.width - cn ? j + cn : j;
int j4 = j < size.width - cn*2 ? j + cn*2 : j3;
for( k = 0; k < 5; k++ )
{
const T* rowk = row[k];
p[k*5] = rowk[j0]; p[k*5+1] = rowk[j1];
p[k*5+2] = rowk[j]; p[k*5+3] = rowk[j3];
p[k*5+4] = rowk[j4];
}
op(p[1], p[2]); op(p[0], p[1]); op(p[1], p[2]); op(p[4], p[5]); op(p[3], p[4]);
op(p[4], p[5]); op(p[0], p[3]); op(p[2], p[5]); op(p[2], p[3]); op(p[1], p[4]);
op(p[1], p[2]); op(p[3], p[4]); op(p[7], p[8]); op(p[6], p[7]); op(p[7], p[8]);
op(p[10], p[11]); op(p[9], p[10]); op(p[10], p[11]); op(p[6], p[9]); op(p[8], p[11]);
op(p[8], p[9]); op(p[7], p[10]); op(p[7], p[8]); op(p[9], p[10]); op(p[0], p[6]);
op(p[4], p[10]); op(p[4], p[6]); op(p[2], p[8]); op(p[2], p[4]); op(p[6], p[8]);
op(p[1], p[7]); op(p[5], p[11]); op(p[5], p[7]); op(p[3], p[9]); op(p[3], p[5]);
op(p[7], p[9]); op(p[1], p[2]); op(p[3], p[4]); op(p[5], p[6]); op(p[7], p[8]);
op(p[9], p[10]); op(p[13], p[14]); op(p[12], p[13]); op(p[13], p[14]); op(p[16], p[17]);
op(p[15], p[16]); op(p[16], p[17]); op(p[12], p[15]); op(p[14], p[17]); op(p[14], p[15]);
op(p[13], p[16]); op(p[13], p[14]); op(p[15], p[16]); op(p[19], p[20]); op(p[18], p[19]);
op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[21], p[23]); op(p[22], p[24]);
op(p[22], p[23]); op(p[18], p[21]); op(p[20], p[23]); op(p[20], p[21]); op(p[19], p[22]);
op(p[22], p[24]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[12], p[18]);
op(p[16], p[22]); op(p[16], p[18]); op(p[14], p[20]); op(p[20], p[24]); op(p[14], p[16]);
op(p[18], p[20]); op(p[22], p[24]); op(p[13], p[19]); op(p[17], p[23]); op(p[17], p[19]);
op(p[15], p[21]); op(p[15], p[17]); op(p[19], p[21]); op(p[13], p[14]); op(p[15], p[16]);
op(p[17], p[18]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[0], p[12]);
op(p[8], p[20]); op(p[8], p[12]); op(p[4], p[16]); op(p[16], p[24]); op(p[12], p[16]);
op(p[2], p[14]); op(p[10], p[22]); op(p[10], p[14]); op(p[6], p[18]); op(p[6], p[10]);
op(p[10], p[12]); op(p[1], p[13]); op(p[9], p[21]); op(p[9], p[13]); op(p[5], p[17]);
op(p[13], p[17]); op(p[3], p[15]); op(p[11], p[23]); op(p[11], p[15]); op(p[7], p[19]);
op(p[7], p[11]); op(p[11], p[13]); op(p[11], p[12]);
dst[j] = (T)p[12];
}
if( limit == size.width )
break;
#if CV_SIMD_WIDTH > 16
for( ; j <= size.width - VecOp::WSIZE - cn*2; j += VecOp::WSIZE )
{
WVT p[25];
for( k = 0; k < 5; k++ )
{
const T* rowk = row[k];
p[k*5] = vop.wload(rowk+j-cn*2); p[k*5+1] = vop.wload(rowk+j-cn);
p[k*5+2] = vop.wload(rowk+j); p[k*5+3] = vop.wload(rowk+j+cn);
p[k*5+4] = vop.wload(rowk+j+cn*2);
}
vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]);
vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]);
vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]);
vop(p[10], p[11]); vop(p[9], p[10]); vop(p[10], p[11]); vop(p[6], p[9]); vop(p[8], p[11]);
vop(p[8], p[9]); vop(p[7], p[10]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[0], p[6]);
vop(p[4], p[10]); vop(p[4], p[6]); vop(p[2], p[8]); vop(p[2], p[4]); vop(p[6], p[8]);
vop(p[1], p[7]); vop(p[5], p[11]); vop(p[5], p[7]); vop(p[3], p[9]); vop(p[3], p[5]);
vop(p[7], p[9]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[5], p[6]); vop(p[7], p[8]);
vop(p[9], p[10]); vop(p[13], p[14]); vop(p[12], p[13]); vop(p[13], p[14]); vop(p[16], p[17]);
vop(p[15], p[16]); vop(p[16], p[17]); vop(p[12], p[15]); vop(p[14], p[17]); vop(p[14], p[15]);
vop(p[13], p[16]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[19], p[20]); vop(p[18], p[19]);
vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[21], p[23]); vop(p[22], p[24]);
vop(p[22], p[23]); vop(p[18], p[21]); vop(p[20], p[23]); vop(p[20], p[21]); vop(p[19], p[22]);
vop(p[22], p[24]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[12], p[18]);
vop(p[16], p[22]); vop(p[16], p[18]); vop(p[14], p[20]); vop(p[20], p[24]); vop(p[14], p[16]);
vop(p[18], p[20]); vop(p[22], p[24]); vop(p[13], p[19]); vop(p[17], p[23]); vop(p[17], p[19]);
vop(p[15], p[21]); vop(p[15], p[17]); vop(p[19], p[21]); vop(p[13], p[14]); vop(p[15], p[16]);
vop(p[17], p[18]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[0], p[12]);
vop(p[8], p[20]); vop(p[8], p[12]); vop(p[4], p[16]); vop(p[16], p[24]); vop(p[12], p[16]);
vop(p[2], p[14]); vop(p[10], p[22]); vop(p[10], p[14]); vop(p[6], p[18]); vop(p[6], p[10]);
vop(p[10], p[12]); vop(p[1], p[13]); vop(p[9], p[21]); vop(p[9], p[13]); vop(p[5], p[17]);
vop(p[13], p[17]); vop(p[3], p[15]); vop(p[11], p[23]); vop(p[11], p[15]); vop(p[7], p[19]);
vop(p[7], p[11]); vop(p[11], p[13]); vop(p[11], p[12]);
vop.store(dst+j, p[12]);
}
#endif
for( ; j <= size.width - VecOp::SIZE - cn*2; j += VecOp::SIZE )
{
VT p[25];
for( k = 0; k < 5; k++ )
{
const T* rowk = row[k];
p[k*5] = vop.load(rowk+j-cn*2); p[k*5+1] = vop.load(rowk+j-cn);
p[k*5+2] = vop.load(rowk+j); p[k*5+3] = vop.load(rowk+j+cn);
p[k*5+4] = vop.load(rowk+j+cn*2);
}
vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]);
vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]);
vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]);
vop(p[10], p[11]); vop(p[9], p[10]); vop(p[10], p[11]); vop(p[6], p[9]); vop(p[8], p[11]);
vop(p[8], p[9]); vop(p[7], p[10]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[0], p[6]);
vop(p[4], p[10]); vop(p[4], p[6]); vop(p[2], p[8]); vop(p[2], p[4]); vop(p[6], p[8]);
vop(p[1], p[7]); vop(p[5], p[11]); vop(p[5], p[7]); vop(p[3], p[9]); vop(p[3], p[5]);
vop(p[7], p[9]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[5], p[6]); vop(p[7], p[8]);
vop(p[9], p[10]); vop(p[13], p[14]); vop(p[12], p[13]); vop(p[13], p[14]); vop(p[16], p[17]);
vop(p[15], p[16]); vop(p[16], p[17]); vop(p[12], p[15]); vop(p[14], p[17]); vop(p[14], p[15]);
vop(p[13], p[16]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[19], p[20]); vop(p[18], p[19]);
vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[21], p[23]); vop(p[22], p[24]);
vop(p[22], p[23]); vop(p[18], p[21]); vop(p[20], p[23]); vop(p[20], p[21]); vop(p[19], p[22]);
vop(p[22], p[24]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[12], p[18]);
vop(p[16], p[22]); vop(p[16], p[18]); vop(p[14], p[20]); vop(p[20], p[24]); vop(p[14], p[16]);
vop(p[18], p[20]); vop(p[22], p[24]); vop(p[13], p[19]); vop(p[17], p[23]); vop(p[17], p[19]);
vop(p[15], p[21]); vop(p[15], p[17]); vop(p[19], p[21]); vop(p[13], p[14]); vop(p[15], p[16]);
vop(p[17], p[18]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[0], p[12]);
vop(p[8], p[20]); vop(p[8], p[12]); vop(p[4], p[16]); vop(p[16], p[24]); vop(p[12], p[16]);
vop(p[2], p[14]); vop(p[10], p[22]); vop(p[10], p[14]); vop(p[6], p[18]); vop(p[6], p[10]);
vop(p[10], p[12]); vop(p[1], p[13]); vop(p[9], p[21]); vop(p[9], p[13]); vop(p[5], p[17]);
vop(p[13], p[17]); vop(p[3], p[15]); vop(p[11], p[23]); vop(p[11], p[15]); vop(p[7], p[19]);
vop(p[7], p[11]); vop(p[11], p[13]); vop(p[11], p[12]);
vop.store(dst+j, p[12]);
}
limit = size.width;
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
}
namespace cv {
#ifdef HAVE_OPENCL
@ -1160,7 +275,6 @@ static bool ipp_medianFilter(Mat &src0, Mat &dst, int ksize)
}
}
#endif
}
void medianBlur( InputArray _src0, OutputArray _dst, int ksize )
{
@ -1194,49 +308,10 @@ void medianBlur( InputArray _src0, OutputArray _dst, int ksize )
return;
#endif
bool useSortNet = ksize == 3 || (ksize == 5
#if !(CV_SIMD)
&& ( src0.depth() > CV_8U || src0.channels() == 2 || src0.channels() > 4 )
#endif
);
Mat src;
if( useSortNet )
{
if( dst.data != src0.data )
src = src0;
else
src0.copyTo(src);
if( src.depth() == CV_8U )
medianBlur_SortNet<MinMax8u, MinMaxVec8u>( src, dst, ksize );
else if( src.depth() == CV_16U )
medianBlur_SortNet<MinMax16u, MinMaxVec16u>( src, dst, ksize );
else if( src.depth() == CV_16S )
medianBlur_SortNet<MinMax16s, MinMaxVec16s>( src, dst, ksize );
else if( src.depth() == CV_32F )
medianBlur_SortNet<MinMax32f, MinMaxVec32f>( src, dst, ksize );
else
CV_Error(CV_StsUnsupportedFormat, "");
return;
}
else
{
cv::copyMakeBorder( src0, src, 0, 0, ksize/2, ksize/2, BORDER_REPLICATE|BORDER_ISOLATED);
int cn = src0.channels();
CV_Assert( src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4) );
double img_size_mp = (double)(src0.total())/(1 << 20);
if( ksize <= 3 + (img_size_mp < 1 ? 12 : img_size_mp < 4 ? 6 : 2)*
(CV_SIMD ? 1 : 3))
medianBlur_8u_Om( src, dst, ksize );
else
medianBlur_8u_O1( src, dst, ksize );
}
CV_CPU_DISPATCH(medianBlur, (src0, dst, ksize),
CV_CPU_DISPATCH_MODES_ALL);
}
}
} // namespace
/* End of file. */

288
modules/imgproc/src/median_blur.simd.hpp
Unescape View File

@ -46,9 +46,11 @@
#include <vector>
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_imgproc.hpp"
#include "opencv2/core/openvx/ovx_defs.hpp"
#ifdef _MSC_VER
#pragma warning(disable: 4244) // warning C4244: 'argument': conversion from 'int' to 'ushort', possible loss of data
// triggered on intrinsic code from medianBlur_8u_O1()
#endif
/*
* This file includes the code, contributed by Simon Perreault
@ -71,12 +73,18 @@
Median Filter
\****************************************************************************************/
namespace cv
{
namespace cv {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
// forward declarations
void medianBlur(const Mat& src0, /*const*/ Mat& dst, int ksize);
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
static void
medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
{
CV_INSTRUMENT_REGION();
typedef ushort HT;
/**
@ -330,9 +338,6 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
}
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
#undef HOP
@ -342,6 +347,8 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
static void
medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m )
{
CV_INSTRUMENT_REGION();
#define N 16
int zone0[4][N];
int zone1[4][N*N];
@ -671,6 +678,8 @@ template<class Op, class VecOp>
static void
medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
{
CV_INSTRUMENT_REGION();
typedef typename Op::value_type T;
typedef typename Op::arg_type WT;
typedef typename VecOp::arg_type VT;
@ -770,9 +779,6 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
limit = size.width;
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
else if( m == 5 )
{
@ -934,266 +940,15 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
limit = size.width;
}
}
#if CV_SIMD
vx_cleanup();
#endif
}
}
#ifdef HAVE_OPENCL
#define DIVUP(total, grain) ((total + grain - 1) / (grain))
static bool ocl_medianFilter(InputArray _src, OutputArray _dst, int m)
{
size_t localsize[2] = { 16, 16 };
size_t globalsize[2];
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if ( !((depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F) && cn <= 4 && (m == 3 || m == 5)) )
return false;
Size imgSize = _src.size();
bool useOptimized = (1 == cn) &&
(size_t)imgSize.width >= localsize[0] * 8 &&
(size_t)imgSize.height >= localsize[1] * 8 &&
imgSize.width % 4 == 0 &&
imgSize.height % 4 == 0 &&
(ocl::Device::getDefault().isIntel());
cv::String kname = format( useOptimized ? "medianFilter%d_u" : "medianFilter%d", m) ;
cv::String kdefs = useOptimized ?
format("-D T=%s -D T1=%s -D T4=%s%d -D cn=%d -D USE_4OPT", ocl::typeToStr(type),
ocl::typeToStr(depth), ocl::typeToStr(depth), cn*4, cn)
:
format("-D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn) ;
ocl::Kernel k(kname.c_str(), ocl::imgproc::medianFilter_oclsrc, kdefs.c_str() );
if (k.empty())
return false;
UMat src = _src.getUMat();
_dst.create(src.size(), type);
UMat dst = _dst.getUMat();
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst));
if( useOptimized )
{
globalsize[0] = DIVUP(src.cols / 4, localsize[0]) * localsize[0];
globalsize[1] = DIVUP(src.rows / 4, localsize[1]) * localsize[1];
}
else
{
globalsize[0] = (src.cols + localsize[0] + 2) / localsize[0] * localsize[0];
globalsize[1] = (src.rows + localsize[1] - 1) / localsize[1] * localsize[1];
}
return k.run(2, globalsize, localsize, false);
}
#undef DIVUP
#endif
#ifdef HAVE_OPENVX
namespace ovx {
template <> inline bool skipSmallImages<VX_KERNEL_MEDIAN_3x3>(int w, int h) { return w*h < 1280 * 720; }
}
static bool openvx_medianFilter(InputArray _src, OutputArray _dst, int ksize)
{
if (_src.type() != CV_8UC1 || _dst.type() != CV_8U
#ifndef VX_VERSION_1_1
|| ksize != 3
#endif
)
return false;
Mat src = _src.getMat();
Mat dst = _dst.getMat();
if (
#ifdef VX_VERSION_1_1
ksize != 3 ? ovx::skipSmallImages<VX_KERNEL_NON_LINEAR_FILTER>(src.cols, src.rows) :
#endif
ovx::skipSmallImages<VX_KERNEL_MEDIAN_3x3>(src.cols, src.rows)
)
return false;
try
{
ivx::Context ctx = ovx::getOpenVXContext();
#ifdef VX_VERSION_1_1
if ((vx_size)ksize > ctx.nonlinearMaxDimension())
return false;
#endif
Mat a;
if (dst.data != src.data)
a = src;
else
src.copyTo(a);
ivx::Image
ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),
ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols, dst.rows, 1, (vx_int32)(dst.step)), dst.data);
//ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments
//since OpenVX standard says nothing about thread-safety for now
ivx::border_t prevBorder = ctx.immediateBorder();
ctx.setImmediateBorder(VX_BORDER_REPLICATE);
#ifdef VX_VERSION_1_1
if (ksize == 3)
#endif
{
ivx::IVX_CHECK_STATUS(vxuMedian3x3(ctx, ia, ib));
}
#ifdef VX_VERSION_1_1
else
{
ivx::Matrix mtx;
if(ksize == 5)
mtx = ivx::Matrix::createFromPattern(ctx, VX_PATTERN_BOX, ksize, ksize);
else
{
vx_size supportedSize;
ivx::IVX_CHECK_STATUS(vxQueryContext(ctx, VX_CONTEXT_NONLINEAR_MAX_DIMENSION, &supportedSize, sizeof(supportedSize)));
if ((vx_size)ksize > supportedSize)
{
ctx.setImmediateBorder(prevBorder);
return false;
}
Mat mask(ksize, ksize, CV_8UC1, Scalar(255));
mtx = ivx::Matrix::create(ctx, VX_TYPE_UINT8, ksize, ksize);
mtx.copyFrom(mask);
}
ivx::IVX_CHECK_STATUS(vxuNonLinearFilter(ctx, VX_NONLINEAR_FILTER_MEDIAN, ia, mtx, ib));
}
#endif
ctx.setImmediateBorder(prevBorder);
}
catch (const ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (const ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
#endif
#ifdef HAVE_IPP
static bool ipp_medianFilter(Mat &src0, Mat &dst, int ksize)
{
CV_INSTRUMENT_REGION_IPP();
#if IPP_VERSION_X100 < 201801
// Degradations for big kernel
if(ksize > 7)
return false;
#endif
{
int bufSize;
IppiSize dstRoiSize = ippiSize(dst.cols, dst.rows), maskSize = ippiSize(ksize, ksize);
IppDataType ippType = ippiGetDataType(src0.type());
int channels = src0.channels();
IppAutoBuffer<Ipp8u> buffer;
if(src0.isSubmatrix())
return false;
Mat src;
if(dst.data != src0.data)
src = src0;
else
src0.copyTo(src);
if(ippiFilterMedianBorderGetBufferSize(dstRoiSize, maskSize, ippType, channels, &bufSize) < 0)
return false;
buffer.allocate(bufSize);
} // namespace anon
switch(ippType)
{
case ipp8u:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C1R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C3R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C4R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp16u:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C1R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C3R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C4R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp16s:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C1R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C3R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C4R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp32f:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_32f_C1R, src.ptr<Ipp32f>(), (int)src.step, dst.ptr<Ipp32f>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
default:
return false;
}
}
}
#endif
}
void medianBlur( InputArray _src0, OutputArray _dst, int ksize )
void medianBlur(const Mat& src0, /*const*/ Mat& dst, int ksize)
{
CV_INSTRUMENT_REGION();
CV_Assert( (ksize % 2 == 1) && (_src0.dims() <= 2 ));
if( ksize <= 1 || _src0.empty() )
{
_src0.copyTo(_dst);
return;
}
CV_OCL_RUN(_dst.isUMat(),
ocl_medianFilter(_src0,_dst, ksize))
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
CALL_HAL(medianBlur, cv_hal_medianBlur, src0.data, src0.step, dst.data, dst.step, src0.cols, src0.rows, src0.depth(),
src0.channels(), ksize);
CV_OVX_RUN(true,
openvx_medianFilter(_src0, _dst, ksize))
CV_IPP_RUN_FAST(ipp_medianFilter(src0, dst, ksize));
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra() && tegra::medianBlur(src0, dst, ksize))
return;
#endif
bool useSortNet = ksize == 3 || (ksize == 5
#if !(CV_SIMD)
&& ( src0.depth() > CV_8U || src0.channels() == 2 || src0.channels() > 4 )
@ -1223,6 +978,7 @@ void medianBlur( InputArray _src0, OutputArray _dst, int ksize )
}
else
{
// TODO AVX guard (external call)
cv::copyMakeBorder( src0, src, 0, 0, ksize/2, ksize/2, BORDER_REPLICATE|BORDER_ISOLATED);
int cn = src0.channels();
@ -1237,6 +993,6 @@ void medianBlur( InputArray _src0, OutputArray _dst, int ksize )
}
}
}
/* End of file. */
#endif
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace

Write Preview
Loading…
Cancel
Save