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Delphi-OpenCV/include/imgproc/smooth.pas

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Migration to version 2.4.5 Signed-off-by: Laex <laex@bk.ru>
2013-04-12 21:33:12 +02:00
// --------------------------------- OpenCV license.txt ---------------------------
(* // 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.
// 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. *)
(* / **************************************************************************************************
// Project Delphi-OpenCV
// **************************************************************************************************
// Contributor:
// laentir Valetov
// email:laex@bk.ru
// **************************************************************************************************
// You may retrieve the latest version of this file at the GitHub,
// located at git://github.com/Laex/Delphi-OpenCV.git
// **************************************************************************************************
// License:
// The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
// you may not use this file except in compliance with the License. You may obtain a copy of the
// License at http://www.mozilla.org/MPL/
//
// Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
// ANY KIND, either express or implied. See the License for the specific language governing rights
// and limitations under the License.
//
// Alternatively, the contents of this file may be used under the terms of the
// GNU Lesser General Public License (the "LGPL License"), in which case the
// provisions of the LGPL License are applicable instead of those above.
// If you wish to allow use of your version of this file only under the terms
// of the LGPL License and not to allow others to use your version of this file
// under the MPL, indicate your decision by deleting the provisions above and
// replace them with the notice and other provisions required by the LGPL
// License. If you do not delete the provisions above, a recipient may use
// your version of this file under either the MPL or the LGPL License.
//
// For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
// **************************************************************************************************
// Warning: Using Delphi XE3 syntax!
// **************************************************************************************************
// The Initial Developer of the Original Code:
// OpenCV: open source computer vision library
// Homepage: http://opencv.org
// Online docs: http://docs.opencv.org
// Q&A forum: http://answers.opencv.org
// Dev zone: http://code.opencv.org
// **************************************************************************************************
// Original file:
// opencv\modules\imgproc\src\smooth.cpp
// ************************************************************************************************* *)
{$IFDEF DEBUG}
{$A8,B-,C+,D+,E-,F-,G+,H+,I+,J-,K-,L+,M-,N+,O-,P+,Q+,R+,S-,T-,U-,V+,W+,X+,Y+,Z1}
{$ELSE}
{$A8,B-,C-,D-,E-,F-,G+,H+,I+,J-,K-,L-,M-,N+,O+,P+,Q-,R-,S-,T-,U-,V+,W-,X+,Y-,Z1}
{$ENDIF}
{$WARN SYMBOL_DEPRECATED OFF}
{$WARN SYMBOL_PLATFORM OFF}
{$WARN UNIT_PLATFORM OFF}
{$WARN UNSAFE_TYPE OFF}
{$WARN UNSAFE_CODE OFF}
{$WARN UNSAFE_CAST OFF}
unit smooth;
{$POINTERMATH ON}
interface
Uses Core.types_c;
{$IFDEF CV_SSE2}
const
MEDIAN_HAVE_SIMD = true;
// static inline void histogram_add_simd(const HT x[16], HT y[16])
// {
// const __m128i* rx = (const __m128i*)x;
// __m128i* ry = (__m128i*)y;
// __m128i r0 = _mm_add_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
// __m128i r1 = _mm_add_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
// _mm_store_si128(ry+0, r0);
// _mm_store_si128(ry+1, r1);
// }
//
// static inline void histogram_sub_simd(const HT x[16], HT y[16])
// {
// const __m128i* rx = (const __m128i*)x;
// __m128i* ry = (__m128i*)y;
// __m128i r0 = _mm_sub_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
// __m128i r1 = _mm_sub_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
// _mm_store_si128(ry+0, r0);
// _mm_store_si128(ry+1, r1);
// }
{$ELSE}
const
MEDIAN_HAVE_SIMD = false;
{$ENDIF}
procedure medianBlur(src0: pIplImage; dst: pIplImage; ksize: Integer);
implementation
Uses core_c, imgproc_c, imgproc, Math, System.Generics.Defaults, Windows, imgproc.types_c;
// ****************************************************************************************
// * Median Filter *
// ****************************************************************************************
Type
HT = ushort;
/// **
// * This structure represents a two-tier histogram. The first tier (known as the
// * "coarse" level) is 4 bit wide and the second tier (known as the "fine" level)
// * is 8 bit wide. Pixels inserted in the fine level also get inserted into the
// * coarse bucket designated by the 4 MSBs of the fine bucket value.
// *
// * The structure is aligned on 16 bits, which is a prerequisite for SIMD
// * instructions. Each bucket is 16 bit wide, which means that extra care must be
// * taken to prevent overflow.
// */
pHistogramArray = ^THistogramArray;
THistogramArray = array [0 .. 15] of HT;
THistogram = packed record
coarse: THistogramArray;
fine: array [0 .. 15] of THistogramArray;
end;
{$IFDEF CV_SSE2}
// static inline void histogram_add_simd(const HT x[16], HT y[16])
// {
// const __m128i* rx = (const __m128i*)x;
// __m128i* ry = (__m128i*)y;
// __m128i r0 = _mm_add_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
// __m128i r1 = _mm_add_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
// _mm_store_si128(ry+0, r0);
// _mm_store_si128(ry+1, r1);
// }
//
// static inline void histogram_sub_simd(const HT x[16], HT y[16])
// {
// const __m128i* rx = (const __m128i*)x;
// __m128i* ry = (__m128i*)y;
// __m128i r0 = _mm_sub_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
// __m128i r1 = _mm_sub_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
// _mm_store_si128(ry+0, r0);
// _mm_store_si128(ry+1, r1);
// }
{$ELSE}
procedure histogram_add(const x, y: pHistogramArray); inline;
var
i: Integer;
begin
for i := 0 to 15 do
y^[i] := y^[i] + x^[i];
end;
procedure histogram_sub(const x, y: pHistogramArray); inline;
var
i: Integer;
begin
for i := 0 to 15 do
y^[i] := y^[i] - x^[i];
end;
procedure histogram_muladd(a: Integer; const x, y: pHistogramArray); inline;
var
i: Integer;
begin
for i := 0 to 15 do
y^[i] := y^[i] + a * x^[i];
end;
{$ENDIF}
procedure medianBlur_8u_Om(const _src: pIplImage; _dst: pIplImage; m: Integer);
Const
N = 16;
Var
zone0: array [0 .. 3, 0 .. N - 1] of Integer;
zone1: array [0 .. 3, 0 .. N * N - 1] of Integer;
x, y: Integer;
n2: Integer;
size: TcvSize;
src, dst: PByte;
src_step, dst_step, cn: Integer;
src_max: PByte;
dst_cur, src_top, src_bottom: PByte;
k, c: Integer;
src_step1, dst_step1: Integer;
s: Integer;
t: Integer;
p, q: Integer;
procedure UPDATE_ACC01(pix, cn: Integer; op: Integer);
begin
zone1[cn][pix] := zone1[cn][pix] + op;
zone0[cn][pix shr 4] := zone0[cn][pix shr 4] + op;
end;
begin
n2 := m * m div 2;
size := cvGetSize(_dst);
src := _src^.imagedata;
dst := _dst^.imagedata;
src_step := _src.widthstep;
dst_step := _dst.widthstep;
cn := _src^.nchannels;
src_max := src + size.height * src_step;
// CV_Assert( size.height >= nx and size.width >= nx );
for x := 0 to size.width - 1 do
// src + = cn, dst + = cn
begin
dst_cur := dst;
src_top := src;
src_bottom := src;
src_step1 := src_step;
dst_step1 := dst_step;
if (x mod 2 <> 0) then
begin
src_top := src_top + src_step * (size.height - 1);
src_bottom := src_top;
dst_cur := dst_cur + dst_step * (size.height - 1);
src_step1 := -src_step1;
dst_step1 := -dst_step1;
end;
// init accumulator
FillChar(zone0, sizeof(zone0[0]) * cn, 0);
FillChar(zone1, sizeof(zone1[0]) * cn, 0);
for y := 0 to (m div 2) do
begin
for c := 0 to cn - 1 do
begin
if (y > 0) then
begin
k := 0;
while k < m * cn do
begin
UPDATE_ACC01(src_bottom[k + c], c, 1);
k := k + cn;
end;
end
else
begin
k := 0;
while k < m * cn do
begin
UPDATE_ACC01(src_bottom[k + c], c, m div 2 + 1);
k := k + cn;
end;
end;
end;
if ((src_step1 > 0) and (y < size.height - 1)) or ((src_step1 < 0) and (size.height - y - 1 > 0)) then
src_bottom := src_bottom + src_step1;
end;
for y := 0 to size.height - 1 do
begin
// find median
for c := 0 to cn - 1 do
begin
s := 0;
k := 0;
while true do
begin
t := s + zone0[c][k];
if (t > n2) then
break;
s := t;
Inc(k);
end;
k := k * N;
while true do
begin
s := s + zone1[c][k];
if (s > n2) then
break;
Inc(k);
end;
dst_cur[c] := k;
dst_cur := dst_cur + dst_step1;
end;
if (y + 1) = size.height then
break;
if (cn = 1) then
begin
for k := 0 to m - 1 do
begin
p := src_top[k];
q := src_bottom[k];
Dec(zone1[0][p]);
Dec(zone0[0][p shr 4]);
Inc(zone1[0][q]);
Inc(zone0[0][q shr 4]);
end;
end
else if (cn = 3) then
begin
k := 0;
while k < m * 3 do
begin
UPDATE_ACC01(src_top[k], 0, -1);
UPDATE_ACC01(src_top[k + 1], 1, -1);
UPDATE_ACC01(src_top[k + 2], 2, -1);
UPDATE_ACC01(src_bottom[k], 0, 1);
UPDATE_ACC01(src_bottom[k + 1], 1, 1);
UPDATE_ACC01(src_bottom[k + 2], 2, 1);
k := k + 3;
end;
end
else
begin
assert(cn = 4);
k := 0;
while k < m * 4 do
begin
UPDATE_ACC01(src_top[k], 0, -1);
UPDATE_ACC01(src_top[k + 1], 1, -1);
UPDATE_ACC01(src_top[k + 2], 2, -1);
UPDATE_ACC01(src_top[k + 3], 3, -1);
UPDATE_ACC01(src_bottom[k], 0, 1);
UPDATE_ACC01(src_bottom[k + 1], 1, 1);
UPDATE_ACC01(src_bottom[k + 2], 2, 1);
UPDATE_ACC01(src_bottom[k + 3], 3, 1);
k := k + 4;
end;
end;
if ((src_step1 > 0) and (src_bottom + src_step1 < src_max)) or
((src_step1 < 0) and (src_bottom + src_step1 >= src)) then
src_bottom := src_bottom + src_step1;
if (y >= m div 2) then
src_top := src_top + src_step1;
end;
end;
end;
procedure medianBlur_8u_O1(const _src: pIplImage; _dst: pIplImage; ksize: Integer);
// **
// * 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.
// */
// procedure HOP(h,x,op)
// h.coarse[x shr 4] op,
// *((HT*)h.fine + x) op
var
cn, m, r: Integer;
sstep, dstep: size_t;
H: array [0 .. 3] of THistogram;
luc: array [0 .. 3] of array [0 .. 15] of HT;
STRIPE_SIZE: Integer;
_h_coarse, _h_fine: TArray<HT>;
h_coarse, h_fine: ^HT;
{$IFDEF MEDIAN_HAVE_SIMD}
useSIMD: Boolean;
{$ENDIF}
x: Integer;
i, j, k, c, N: Integer;
src, dst: PByte;
procedure COP(c, j, x, op: Integer);
begin
h_coarse[16 * (N * c + j) + (x shr 4)] := h_coarse[16 * (N * c + j) + (x shr 4)] + op;
h_fine[16 * (N * (16 * c + (x shr 4)) + j) + (x and $F)] :=
h_fine[16 * (N * (16 * c + (x shr 4)) + j) + (x and $F)] + op;
end;
var
p: PByte;
p0, p1: PByte;
t, b, sum: Integer;
segment: ^HT;
begin
cn := _dst^.nchannels;
m := _dst^.height;
r := (ksize - 1) div 2;
sstep := _src.widthstep;
dstep := _dst.widthstep;
STRIPE_SIZE := min(_dst.width, 512 div cn);
SetLength(_h_coarse, 1 * 16 * (STRIPE_SIZE + 2 * r) * cn + 16);
SetLength(_h_fine, 16 * 16 * (STRIPE_SIZE + 2 * r) * cn + 16);
h_coarse := @_h_coarse[0];
h_fine := @_h_fine[0];
{$IFDEF MEDIAN_HAVE_SIMD}
useSIMD := cvCheckHardwareSupport(CV_CPU_SSE2);
{$ENDIF}
x := 0;
while x < _dst^.width do
// x + = STRIPE_SIZE
begin
N := min(_dst.width - x, STRIPE_SIZE) + r * 2;
src := _src.imagedata + x * cn;
dst := _dst.imagedata + (x - r) * cn;
ZeroMemory(h_coarse, 16 * N * cn * sizeof(h_coarse[0]));
ZeroMemory(h_fine, 16 * 16 * N * cn * sizeof(h_fine[0]));
// First row initialization
for c := 0 to cn - 1 do
begin
for j := 0 to N - 1 do
COP(c, j, src[cn * j + c], (r + 2));
for i := 1 to r - 1 do
begin
p := src + sstep * min(i, m - 1);
for j := 0 to N - 1 do
COP(c, j, p[cn * j + c], 1);
end;
end;
for i := 0 to m - 1 do
begin
p0 := src + sstep * max(0, i - r - 1);
p1 := src + sstep * min(m - 1, i + r);
ZeroMemory(@H, cn * sizeof(H[0]));
ZeroMemory(@luc, cn * sizeof(luc[0]));
for c := 0 to cn - 1 do
begin
// Update column histograms for the entire row.
for j := 0 to N - 1 do
begin
COP(c, j, p0[j * cn + c], -1);
COP(c, j, p1[j * cn + c], 1);
end;
// First column initialization
for k := 1 to 16 do
histogram_muladd(2 * r + 1, @h_fine[16 * N * (16 * c + k)], @H[c].fine[k][0]);
{$IFDEF MEDIAN_HAVE_SIMD}
// if (useSIMD) then
// begin
// for j := 0 to 2 * r-1 do
// histogram_add_simd(&h_coarse[16 * (N * c + j)], H[c].coarse);
//
// for (j := r; j < N - r; j + +)begin int t := 2 * r * r + 2 * r, b, sum := 0;
// HT * segment;
//
// histogram_add_simd(&h_coarse[16 * (N * c + std: : min(j + r, N - 1))], H[c].coarse);
//
// // Find median at coarse level
// for (k := 0; k < 16; + + k)begin sum + = H[c].coarse[k];
// if (sum > t)begin sum - = H[c].coarse[k];
// break;
// end;
// end;
// assert(k < 16);
//
// / * Update corresponding Histogram segment * /
// if (luc[c][k] <= j - r)begin memset(&H[c].fine[k], 0, 16 * sizeof(HT));
// for (luc[c][k] := cv: : HT(j - r); luc[c][k] < min(j + r + 1, N); + + luc[c][k])
// histogram_add_simd(&h_fine[16 * (N * (16 * c + k) + luc[c][k])], H[c].fine[k]);
//
// if (luc[c][k] < j + r + 1)begin histogram_muladd(j + r + 1 - N, &h_fine[16 * (N * (16 * c + k) + (N - 1))],
// &H[c].fine[k][0]);
// luc[c][k] := (HT)(j + r + 1);
// end;
// end;
// else
// begin
// for (; luc[c][k] < j + r + 1; + + luc[c][k])begin histogram_sub_simd
// (&h_fine[16 * (N * (16 * c + k) + max(luc[c][k] - 2 * r - 1, 0))], H[c].fine[k]);
// histogram_add_simd(&h_fine[16 * (N * (16 * c + k) + min(luc[c][k], N - 1))], H[c].fine[k]);
// end;
// end;
//
// histogram_sub_simd(&h_coarse[16 * (N * c + max(j - r, 0))], H[c].coarse);
//
/// * Find median in segment * / segment := H[c].fine[k];
// for (b := 0; b < 16; b + +)begin sum + = segment[b];
// if (sum > t)begin dst[dstep * i + cn * j + c] := (uchar)(16 * k + b);
// break;
// end;
// end;
// assert(b < 16);
// end;
// end;
// else
{$ENDIF}
begin
for j := 0 to 2 * r - 1 do
histogram_add(@h_coarse[16 * (N * c + j)], @H[c].coarse);
for j := r to (N - r) - 1 do
begin
t := 2 * r * r + 2 * r;
sum := 0;
histogram_add(@h_coarse[16 * (N * c + min(j + r, N - 1))], @H[c].coarse);
// Find median at coarse level
for k := 0 to 15 do
begin
sum := sum + H[c].coarse[k];
if (sum > t) then
begin
sum := sum - H[c].coarse[k];
break;
end;
end;
assert(k < 16);
// * Update corresponding Histogram segment * /
if (luc[c][k] <= j - r) then
begin
ZeroMemory(@H[c].fine[k], 16 * sizeof(HT));
luc[c][k] := (j - r);
while luc[c][k] < min(j + r + 1, N) do
begin
histogram_add(@h_fine[16 * (N * (16 * c + k) + luc[c][k])], @H[c].fine[k]);
Inc(luc[c][k]);
end;
if (luc[c][k] < j + r + 1) then
begin
histogram_muladd(j + r + 1 - N, @h_fine[16 * (N * (16 * c + k) + (N - 1))], @H[c].fine[k][0]);
luc[c][k] := j + r + 1;
end;
end
else
begin
while luc[c][k] < j + r + 1 do
begin
histogram_sub(@h_fine[16 * (N * (16 * c + k) + max(luc[c][k] - 2 * r - 1, 0))], @H[c].fine[k]);
histogram_add(@h_fine[16 * (N * (16 * c + k) + min(luc[c][k], N - 1))], @H[c].fine[k]);
Inc(luc[c][k]);
end;
end;
histogram_sub(@h_coarse[16 * (N * c + max(j - r, 0))], @H[c].coarse);
// * Find median in segment * / segment := H[c].fine[k];
for b := 0 to 15 do
begin
sum := sum + segment[b];
if (sum > t) then
begin
dst[dstep * i + cn * j + c] := 16 * k + b;
break;
end;
end;
assert(b < 16);
end;
end;
end;
end;
end;
end;
Type
MinMax < t: record >= record
const
_SIZE = 1;
function load(const ptr: Pointer): t;
procedure store(ptr: Pointer; val: t);
procedure op(var a, b: t);
end;
mm8u = Byte; // CV_8U
mm16u = Word; // CV_16U
mm16s = SmallInt; // CV_16S
mm32f = Single; // CV_32F
TmedianBlur<t: record > = class public class
procedure SortNet(const _src: pIplImage; _dst: pIplImage; m: Integer);
end;
procedure medianBlur(src0: pIplImage; dst: pIplImage; ksize: Integer);
Var
useSortNet: Boolean;
src: pIplImage;
cn: Integer;
img_size_mp: double;
begin
if (ksize <= 1) then
begin
cvCopy(src0, dst);
Exit;
end;
assert((ksize mod 2 = 1));
{$IFDEF HAVE_TEGRA_OPTIMIZATION}
if (tegra_medianBlur(src0, dst, ksize)) then
Exit;
{$ENDIF}
useSortNet := (ksize = 3) or (ksize = 5
{$IFNDEF CV_SSE2}
) and (src0^.depth > CV_8U
{$ENDIF}
);
if useSortNet then
begin
if (dst^.imagedata <> src0^.imagedata) then
src := src0
else
cvCopy(src0, src);
if (src^.depth = CV_8U) then
TmedianBlur<mm8u>.SortNet(src, dst, ksize)
else if (src^.depth = CV_16U) then
TmedianBlur<mm16u>.SortNet(src, dst, ksize)
else if (src^.depth = CV_16S) then
TmedianBlur<mm16s>.SortNet(src, dst, ksize)
else if (src^.depth = CV_32F) then
TmedianBlur<mm32f>.SortNet(src, dst, ksize)
else
assert(false, 'CV_StsUnsupportedFormat');
Exit;
end
else
begin
cvCopyMakeBorder(src0, src, CvPoint(0, 0), { ksize / 2, ksize / 2, } BORDER_REPLICATE, cvScalarAll(0));
cn := src0^.nchannels;
assert((src^.depth = CV_8U) and ((cn = 1) or (cn = 3) or (cn = 4)));
img_size_mp := (src0^.width * src0^.height) / (1 shl 20);
if ksize <= (3 + (iif(img_size_mp < 1, 12, iif(img_size_mp < 4, 6, 2)) *
(iif(MEDIAN_HAVE_SIMD and (cvCheckHardwareSupport(CV_CPU_SSE2) <> 0), 1, 3)))) then
medianBlur_8u_Om(src, dst, ksize)
else
medianBlur_8u_O1(src, dst, ksize);
end;
end;
{ MinMax<T> }
function MinMax<t>.load(const ptr: Pointer): t;
begin
Result := t(ptr^);
end;
procedure MinMax<t>.op(var a, b: t);
var
_t: t;
Comparer: IComparer<t>;
begin
_t := a;
Comparer := TComparer<t>.Default;
if Comparer.Compare(a, b) > 0 then // min
a := b;
if Comparer.Compare(b, _t) < 0 then // max
b := _t;
end;
procedure MinMax<t>.store(ptr: Pointer; val: t);
begin
t(ptr^) := val;
end;
{ TmedianBlur<T> }
class procedure TmedianBlur<t>.SortNet(const _src: pIplImage; _dst: pIplImage; m: Integer);
type
pT = ^t;
Var
MM: MinMax<t>;
src, dst: pT;
sstep, dstep: Integer;
size: TcvSize;
i, j, k, cn: Integer;
useSIMD: Boolean;
len, sdelta, sdelta0, ddelta: Integer;
p0, p1, p2, p3, p4, p5, p6, p7, p8: t;
row0, row1, row2: pT;
limit: Integer;
j0, j1, j2, j3, j4: Integer;
i1, i0, i3, i4: Integer;
row: array [0 .. 4] of pT;
p: array [0 .. 24] of t;
rowk: pT;
begin
src := pT(_src.imagedata);
dst := pT(_dst.imagedata);
sstep := _src^.widthstep div sizeof(t);
dstep := _dst.widthstep div sizeof(t);
size := cvGetSize(_dst);
cn := _src^.nchannels;
useSIMD := cvCheckHardwareSupport(CV_CPU_SSE2) <> 0;
if (m = 3) then
begin
if (size.width = 1) or (size.height = 1) then
begin
len := size.width + size.height - 1;
sdelta := iif(size.height = 1, cn, sstep);
sdelta0 := iif(size.height = 1, 0, sstep - cn);
ddelta := iif(size.height = 1, cn, dstep);
for i := 0 to len - 1 do
begin
for j := 0 to cn - 1 do
begin
p0 := src[Integer(iif(i > 0, -sdelta, 0))];
p1 := src[0];
p2 := src[Integer(iif(i < len - 1, sdelta, 0))];
MM.op(p0, p1);
MM.op(p1, p2);
MM.op(p0, p1);
dst[j] := p1;
src := src + sizeof(t);
end;
src := src + sdelta0;
dst := dst + ddelta;
end;
Exit;
end;
size.width := size.width * cn;
for i := 0 to size.height - 1 do
begin
row0 := src + max(i - 1, 0) * sstep;
row1 := src + i * sstep;
row2 := src + min(i + 1, size.height - 1) * sstep;
limit := iif(useSIMD, cn, size.width);
j := 0;
while true do
begin
while j < limit do
begin
j0 := iif(j >= cn, j - cn, j);
j2 := iif(j < size.width - cn, j + cn, j);
p0 := row0[j0];
p1 := row0[j];
p2 := row0[j2];
p3 := row1[j0];
p4 := row1[j];
p5 := row1[j2];
p6 := row2[j0];
p7 := row2[j];
p8 := row2[j2];
MM.op(p1, p2);
MM.op(p4, p5);
MM.op(p7, p8);
MM.op(p0, p1);
MM.op(p3, p4);
MM.op(p6, p7);
MM.op(p1, p2);
MM.op(p4, p5);
MM.op(p7, p8);
MM.op(p0, p3);
MM.op(p5, p8);
MM.op(p4, p7);
MM.op(p3, p6);
MM.op(p1, p4);
MM.op(p2, p5);
MM.op(p4, p7);
MM.op(p4, p2);
MM.op(p6, p4);
MM.op(p4, p2);
dst[j] := p4;
Inc(j);
end;
if (limit = size.width) then
break;
While j <= size.width - MM._SIZE - cn do
begin
p0 := MM.load(row0 + j - cn);
p1 := MM.load(row0 + j);
p2 := MM.load(row0 + j + cn);
p3 := MM.load(row1 + j - cn);
p4 := MM.load(row1 + j);
p5 := MM.load(row1 + j + cn);
p6 := MM.load(row2 + j - cn);
p7 := MM.load(row2 + j);
p8 := MM.load(row2 + j + cn);
MM.op(p1, p2);
MM.op(p4, p5);
MM.op(p7, p8);
MM.op(p0, p1);
MM.op(p3, p4);
MM.op(p6, p7);
MM.op(p1, p2);
MM.op(p4, p5);
MM.op(p7, p8);
MM.op(p0, p3);
MM.op(p5, p8);
MM.op(p4, p7);
MM.op(p3, p6);
MM.op(p1, p4);
MM.op(p2, p5);
MM.op(p4, p7);
MM.op(p4, p2);
MM.op(p6, p4);
MM.op(p4, p2);
MM.store(dst + j, p4);
j := j + MM._SIZE
end;
limit := size.width;
end;
dst := dst + dstep;
end;
end
else if (m = 5) then
begin
if (size.width = 1) or (size.height = 1) then
begin
len := size.width + size.height - 1;
sdelta := iif(size.height = 1, cn, sstep);
sdelta0 := iif(size.height = 1, 0, sstep - cn);
ddelta := iif(size.height = 1, cn, dstep);
for i := 0 to len - 1 do
begin
for j := 0 to cn - 1 do
begin
i1 := iif(i > 0, -sdelta, 0);
i0 := iif(i > 1, -sdelta * 2, i1);
i3 := iif(i < len - 1, sdelta, 0);
i4 := iif(i < len - 2, sdelta * 2, i3);
p0 := src[i0];
p1 := src[i1];
p2 := src[0];
p3 := src[i3];
p4 := src[i4];
MM.op(p0, p1);
MM.op(p3, p4);
MM.op(p2, p3);
MM.op(p3, p4);
MM.op(p0, p2);
MM.op(p2, p4);
MM.op(p1, p3);
MM.op(p1, p2);
dst[j] := p2;
src := src + sizeof(t);
end;
src := src + sdelta0;
dst := dst + ddelta;
end;
Exit;
end;
size.width := size.width * cn;
for i := 0 to size.height - 1 do
begin
row[0] := src + max(i - 2, 0) * sstep;
row[1] := src + max(i - 1, 0) * sstep;
row[2] := src + i * sstep;
row[3] := src + min(i + 1, size.height - 1) * sstep;
row[4] := src + min(i + 2, size.height - 1) * sstep;
limit := iif(useSIMD, cn * 2, size.width);
j := 0;
while true do
begin
while j < limit do
begin
j1 := iif(j >= cn, j - cn, j);
j0 := iif(j >= cn * 2, j - cn * 2, j1);
j3 := iif(j < size.width - cn, j + cn, j);
j4 := iif(j < size.width - cn * 2, j + cn * 2, j3);
for k := 0 to 4 do
begin
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];
end;
MM.op(p[1], p[2]);
MM.op(p[0], p[1]);
MM.op(p[1], p[2]);
MM.op(p[4], p[5]);
MM.op(p[3], p[4]);
MM.op(p[4], p[5]);
MM.op(p[0], p[3]);
MM.op(p[2], p[5]);
MM.op(p[2], p[3]);
MM.op(p[1], p[4]);
MM.op(p[1], p[2]);
MM.op(p[3], p[4]);
MM.op(p[7], p[8]);
MM.op(p[6], p[7]);
MM.op(p[7], p[8]);
MM.op(p[10], p[11]);
MM.op(p[9], p[10]);
MM.op(p[10], p[11]);
MM.op(p[6], p[9]);
MM.op(p[8], p[11]);
MM.op(p[8], p[9]);
MM.op(p[7], p[10]);
MM.op(p[7], p[8]);
MM.op(p[9], p[10]);
MM.op(p[0], p[6]);
MM.op(p[4], p[10]);
MM.op(p[4], p[6]);
MM.op(p[2], p[8]);
MM.op(p[2], p[4]);
MM.op(p[6], p[8]);
MM.op(p[1], p[7]);
MM.op(p[5], p[11]);
MM.op(p[5], p[7]);
MM.op(p[3], p[9]);
MM.op(p[3], p[5]);
MM.op(p[7], p[9]);
MM.op(p[1], p[2]);
MM.op(p[3], p[4]);
MM.op(p[5], p[6]);
MM.op(p[7], p[8]);
MM.op(p[9], p[10]);
MM.op(p[13], p[14]);
MM.op(p[12], p[13]);
MM.op(p[13], p[14]);
MM.op(p[16], p[17]);
MM.op(p[15], p[16]);
MM.op(p[16], p[17]);
MM.op(p[12], p[15]);
MM.op(p[14], p[17]);
MM.op(p[14], p[15]);
MM.op(p[13], p[16]);
MM.op(p[13], p[14]);
MM.op(p[15], p[16]);
MM.op(p[19], p[20]);
MM.op(p[18], p[19]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[21], p[23]);
MM.op(p[22], p[24]);
MM.op(p[22], p[23]);
MM.op(p[18], p[21]);
MM.op(p[20], p[23]);
MM.op(p[20], p[21]);
MM.op(p[19], p[22]);
MM.op(p[22], p[24]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[12], p[18]);
MM.op(p[16], p[22]);
MM.op(p[16], p[18]);
MM.op(p[14], p[20]);
MM.op(p[20], p[24]);
MM.op(p[14], p[16]);
MM.op(p[18], p[20]);
MM.op(p[22], p[24]);
MM.op(p[13], p[19]);
MM.op(p[17], p[23]);
MM.op(p[17], p[19]);
MM.op(p[15], p[21]);
MM.op(p[15], p[17]);
MM.op(p[19], p[21]);
MM.op(p[13], p[14]);
MM.op(p[15], p[16]);
MM.op(p[17], p[18]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[0], p[12]);
MM.op(p[8], p[20]);
MM.op(p[8], p[12]);
MM.op(p[4], p[16]);
MM.op(p[16], p[24]);
MM.op(p[12], p[16]);
MM.op(p[2], p[14]);
MM.op(p[10], p[22]);
MM.op(p[10], p[14]);
MM.op(p[6], p[18]);
MM.op(p[6], p[10]);
MM.op(p[10], p[12]);
MM.op(p[1], p[13]);
MM.op(p[9], p[21]);
MM.op(p[9], p[13]);
MM.op(p[5], p[17]);
MM.op(p[13], p[17]);
MM.op(p[3], p[15]);
MM.op(p[11], p[23]);
MM.op(p[11], p[15]);
MM.op(p[7], p[19]);
MM.op(p[7], p[11]);
MM.op(p[11], p[13]);
MM.op(p[11], p[12]);
dst[j] := p[12];
Inc(j);
end;
if (limit = size.width) then
break;
While j <= size.width - MM._SIZE - cn * 2 do
begin
for k := 0 to 4 do
begin
rowk := row[k];
p[k * 5] := MM.load(rowk + j - cn * 2);
p[k * 5 + 1] := MM.load(rowk + j - cn);
p[k * 5 + 2] := MM.load(rowk + j);
p[k * 5 + 3] := MM.load(rowk + j + cn);
p[k * 5 + 4] := MM.load(rowk + j + cn * 2);
end;
MM.op(p[1], p[2]);
MM.op(p[0], p[1]);
MM.op(p[1], p[2]);
MM.op(p[4], p[5]);
MM.op(p[3], p[4]);
MM.op(p[4], p[5]);
MM.op(p[0], p[3]);
MM.op(p[2], p[5]);
MM.op(p[2], p[3]);
MM.op(p[1], p[4]);
MM.op(p[1], p[2]);
MM.op(p[3], p[4]);
MM.op(p[7], p[8]);
MM.op(p[6], p[7]);
MM.op(p[7], p[8]);
MM.op(p[10], p[11]);
MM.op(p[9], p[10]);
MM.op(p[10], p[11]);
MM.op(p[6], p[9]);
MM.op(p[8], p[11]);
MM.op(p[8], p[9]);
MM.op(p[7], p[10]);
MM.op(p[7], p[8]);
MM.op(p[9], p[10]);
MM.op(p[0], p[6]);
MM.op(p[4], p[10]);
MM.op(p[4], p[6]);
MM.op(p[2], p[8]);
MM.op(p[2], p[4]);
MM.op(p[6], p[8]);
MM.op(p[1], p[7]);
MM.op(p[5], p[11]);
MM.op(p[5], p[7]);
MM.op(p[3], p[9]);
MM.op(p[3], p[5]);
MM.op(p[7], p[9]);
MM.op(p[1], p[2]);
MM.op(p[3], p[4]);
MM.op(p[5], p[6]);
MM.op(p[7], p[8]);
MM.op(p[9], p[10]);
MM.op(p[13], p[14]);
MM.op(p[12], p[13]);
MM.op(p[13], p[14]);
MM.op(p[16], p[17]);
MM.op(p[15], p[16]);
MM.op(p[16], p[17]);
MM.op(p[12], p[15]);
MM.op(p[14], p[17]);
MM.op(p[14], p[15]);
MM.op(p[13], p[16]);
MM.op(p[13], p[14]);
MM.op(p[15], p[16]);
MM.op(p[19], p[20]);
MM.op(p[18], p[19]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[21], p[23]);
MM.op(p[22], p[24]);
MM.op(p[22], p[23]);
MM.op(p[18], p[21]);
MM.op(p[20], p[23]);
MM.op(p[20], p[21]);
MM.op(p[19], p[22]);
MM.op(p[22], p[24]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[12], p[18]);
MM.op(p[16], p[22]);
MM.op(p[16], p[18]);
MM.op(p[14], p[20]);
MM.op(p[20], p[24]);
MM.op(p[14], p[16]);
MM.op(p[18], p[20]);
MM.op(p[22], p[24]);
MM.op(p[13], p[19]);
MM.op(p[17], p[23]);
MM.op(p[17], p[19]);
MM.op(p[15], p[21]);
MM.op(p[15], p[17]);
MM.op(p[19], p[21]);
MM.op(p[13], p[14]);
MM.op(p[15], p[16]);
MM.op(p[17], p[18]);
MM.op(p[19], p[20]);
MM.op(p[21], p[22]);
MM.op(p[23], p[24]);
MM.op(p[0], p[12]);
MM.op(p[8], p[20]);
MM.op(p[8], p[12]);
MM.op(p[4], p[16]);
MM.op(p[16], p[24]);
MM.op(p[12], p[16]);
MM.op(p[2], p[14]);
MM.op(p[10], p[22]);
MM.op(p[10], p[14]);
MM.op(p[6], p[18]);
MM.op(p[6], p[10]);
MM.op(p[10], p[12]);
MM.op(p[1], p[13]);
MM.op(p[9], p[21]);
MM.op(p[9], p[13]);
MM.op(p[5], p[17]);
MM.op(p[13], p[17]);
MM.op(p[3], p[15]);
MM.op(p[11], p[23]);
MM.op(p[11], p[15]);
MM.op(p[7], p[19]);
MM.op(p[7], p[11]);
MM.op(p[11], p[13]);
MM.op(p[11], p[12]);
MM.store(dst + j, p[12]);
j := j + MM._SIZE
end;
limit := size.width;
end;
dst := dst + dstep;
end;
end;
end;
end.
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