nr-filter-gaussian.cpp revision 208e5a33acc4a8ad9d8c0488f047c260346f1258
#define __NR_FILTER_GAUSSIAN_CPP__
/*
* Gaussian blur renderer
*
* Authors:
* Niko Kiirala <niko@kiirala.com>
* bulia byak
* Jasper van de Gronde <th.v.d.gronde@hccnet.nl>
*
* Copyright (C) 2006 authors
*
* Released under GNU GPL, read the file 'COPYING' for more information
*/
#include <algorithm>
#include <cmath>
#include <complex>
#include <glib.h>
#include <limits>
#include "isnan.h"
#include "display/nr-filter-primitive.h"
#include "display/nr-filter-gaussian.h"
#include "display/nr-filter-types.h"
#include "libnr/nr-pixblock.h"
#include "libnr/nr-matrix.h"
#include "util/fixed_point.h"
#include "prefs-utils.h"
// IIR filtering method based on:
// L.J. van Vliet, I.T. Young, and P.W. Verbeek, Recursive Gaussian Derivative Filters,
// in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.),
// ICPR'98, Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20),
// IEEE Computer Society Press, Los Alamitos, 1998, 509-514.
//
// Using the backwards-pass initialization procedure from:
// Boundary Conditions for Young - van Vliet Recursive Filtering
// Bill Triggs, Michael Sdika
// IEEE Transactions on Signal Processing, Volume 54, Number 5 - may 2006
// Number of IIR filter coefficients used. Currently only 3 is supported.
// "Recursive Gaussian Derivative Filters" says this is enough though (and
// some testing indeed shows that the quality doesn't improve much if larger
// filters are used).
static size_t const N = 3;
template<typename InIt, typename OutIt, typename Size>
void copy_n(InIt beg_in, Size N, OutIt beg_out) {
std::copy(beg_in, beg_in+N, beg_out);
}
// Type used for IIR filter coefficients (can be 10.21 signed fixed point, see Anisotropic Gaussian Filtering Using Fixed Point Arithmetic, Christoph H. Lampert & Oliver Wirjadi, 2006)
typedef double IIRValue;
// Type used for FIR filter coefficients (can be 16.16 unsigned fixed point, should have 8 or more bits in the fractional part, the integer part should be capable of storing approximately 20*255)
typedef Inkscape::Util::FixedPoint<unsigned int,16> FIRValue;
template<typename T> static inline T sqr(T const& v) { return v*v; }
template<typename T> static inline T clip(T const& v, T const& a, T const& b) {
if ( v < a ) return a;
if ( v > b ) return b;
return v;
}
template<typename Tt, typename Ts>
static inline Tt round_cast(Ts const& v) {
static Ts const rndoffset(.5);
return static_cast<Tt>(v+rndoffset);
}
template<typename Tt, typename Ts>
static inline Tt clip_round_cast(Ts const& v, Tt const minval=std::numeric_limits<Tt>::min(), Tt const maxval=std::numeric_limits<Tt>::max()) {
if ( v < minval ) return minval;
if ( v > maxval ) return maxval;
return round_cast<Tt>(v);
}
namespace NR {
FilterGaussian::FilterGaussian()
{
_deviation_x = _deviation_y = prefs_get_double_attribute("options.filtertest", "value", 1.0);
}
FilterPrimitive *FilterGaussian::create()
{
return new FilterGaussian();
}
FilterGaussian::~FilterGaussian()
{
// Nothing to do here
}
static int
_effect_area_scr(double const deviation)
{
return (int)std::ceil(deviation * 3.0);
}
static void
_make_kernel(FIRValue *const kernel, double const deviation)
{
int const scr_len = _effect_area_scr(deviation);
double const d_sq = sqr(deviation) * 2;
double k[scr_len+1]; // This is only called for small kernel sizes (above approximately 10 coefficients the IIR filter is used)
// Compute kernel and sum of coefficients
// Note that actually only half the kernel is computed, as it is symmetric
double sum = 0;
for ( int i = scr_len; i >= 0 ; i-- ) {
k[i] = std::exp(-sqr(i) / d_sq);
if ( i > 0 ) sum += k[i];
}
// the sum of the complete kernel is twice as large (plus the center element which we skipped above to prevent counting it twice)
sum = 2*sum + k[0];
// Normalize kernel (making sure the sum is exactly 1)
double ksum = 0;
FIRValue kernelsum = 0;
for ( int i = scr_len; i >= 1 ; i-- ) {
ksum += k[i]/sum;
kernel[i] = ksum-static_cast<double>(kernelsum);
kernelsum += kernel[i];
}
kernel[0] = FIRValue(1)-2*kernelsum;
}
// Return value (v) should satisfy:
// 2^(2*v)*255<2^32
// 255<2^(32-2*v)
// 2^8<=2^(32-2*v)
// 8<=32-2*v
// 2*v<=24
// v<=12
static int
_effect_subsample_step_log2(double const deviation, int const quality)
{
// To make sure FIR will always be used (unless the kernel is VERY big):
// deviation/step <= 3
// deviation/3 <= step
// log(deviation/3) <= log(step)
// So when x below is >= 1/3 FIR will almost always be used.
// This means IIR is almost only used with the modes BETTER or BEST.
int stepsize_l2;
switch (quality) {
case BLUR_QUALITY_WORST:
// 2 == log(x*8/3))
// 2^2 == x*2^3/3
// x == 3/2
stepsize_l2 = clip(static_cast<int>(log(deviation*(3./2.))/log(2.)), 0, 12);
break;
case BLUR_QUALITY_WORSE:
// 2 == log(x*16/3))
// 2^2 == x*2^4/3
// x == 3/2^2
stepsize_l2 = clip(static_cast<int>(log(deviation*(3./4.))/log(2.)), 0, 12);
break;
case BLUR_QUALITY_BETTER:
// 2 == log(x*32/3))
// 2 == x*2^5/3
// x == 3/2^4
stepsize_l2 = clip(static_cast<int>(log(deviation*(3./16.))/log(2.)), 0, 12);
break;
case BLUR_QUALITY_BEST:
stepsize_l2 = 0; // no subsampling at all
break;
case BLUR_QUALITY_NORMAL:
default:
// 2 == log(x*16/3))
// 2 == x*2^4/3
// x == 3/2^3
stepsize_l2 = clip(static_cast<int>(log(deviation*(3./8.))/log(2.)), 0, 12);
break;
}
return stepsize_l2;
}
/**
* Sanity check function for indexing pixblocks.
* Catches reading and writing outside the pixblock area.
* When enabled, decreases filter rendering speed massively.
*/
static inline void
_check_index(NRPixBlock const * const pb, int const location, int const line)
{
if (false) {
int max_loc = pb->rs * (pb->area.y1 - pb->area.y0);
if (location < 0 || location >= max_loc)
g_warning("Location %d out of bounds (0 ... %d) at line %d", location, max_loc, line);
}
}
static void calcFilter(double const sigma, double b[N]) {
assert(N==3);
std::complex<double> const d1_org(1.40098, 1.00236);
double const d3_org = 1.85132;
double qbeg = 1; // Don't go lower than sigma==2 (we'd probably want a normal convolution in that case anyway)
double qend = 2*sigma;
double const sigmasqr = sqr(sigma);
double s;
do { // Binary search for right q (a linear interpolation scheme is suggested, but this should work fine as well)
double const q = (qbeg+qend)/2;
// Compute scaled filter coefficients
std::complex<double> const d1 = pow(d1_org, 1.0/q);
double const d3 = pow(d3_org, 1.0/q);
double const absd1sqr = std::norm(d1);
double const re2d1 = 2*d1.real();
double const bscale = 1.0/(absd1sqr*d3);
b[2] = -bscale;
b[1] = bscale*(d3+re2d1);
b[0] = -bscale*(absd1sqr+d3*re2d1);
// Compute actual sigma^2
double const ssqr = 2*(2*(d1/sqr(d1-1.)).real()+d3/sqr(d3-1.));
if ( ssqr < sigmasqr ) {
qbeg = q;
} else {
qend = q;
}
s = sqrt(ssqr);
} while(qend-qbeg>(sigma/(1<<30)));
}
static void calcTriggsSdikaM(double const b[N], double M[N*N]) {
assert(N==3);
double a1=b[0], a2=b[1], a3=b[2];
double const Mscale = 1.0/((1+a1-a2+a3)*(1-a1-a2-a3)*(1+a2+(a1-a3)*a3));
M[0] = 1-a2-a1*a3-sqr(a3);
M[1] = (a1+a3)*(a2+a1*a3);
M[2] = a3*(a1+a2*a3);
M[3] = a1+a2*a3;
M[4] = (1-a2)*(a2+a1*a3);
M[5] = a3*(1-a2-a1*a3-sqr(a3));
M[6] = a1*(a1+a3)+a2*(1-a2);
M[7] = a1*(a2-sqr(a3))+a3*(1+a2*(a2-1)-sqr(a3));
M[8] = a3*(a1+a2*a3);
for(unsigned int i=0; i<9; i++) M[i] *= Mscale;
}
template<unsigned int SIZE>
static void calcTriggsSdikaInitialization(double const M[N*N], IIRValue const uold[N][SIZE], IIRValue const uplus[SIZE], IIRValue const vplus[SIZE], IIRValue const alpha, IIRValue vold[N][SIZE]) {
for(unsigned int c=0; c<SIZE; c++) {
double uminp[N];
for(unsigned int i=0; i<N; i++) uminp[i] = uold[i][c] - uplus[c];
for(unsigned int i=0; i<N; i++) {
double voldf = 0;
for(unsigned int j=0; j<N; j++) {
voldf += uminp[j]*M[i*N+j];
}
// Properly takes care of the scaling coefficient alpha and vplus (which is already appropriately scaled)
// This was arrived at by starting from a version of the blur filter that ignored the scaling coefficient
// (and scaled the final output by alpha^2) and then gradually reintroducing the scaling coefficient.
vold[i][c] = voldf*alpha;
vold[i][c] += vplus[c];
}
}
}
// Filters over 1st dimension
template<typename PT, unsigned int PC, bool PREMULTIPLIED_ALPHA>
static void
filter2D_IIR(PT *const dest, int const dstr1, int const dstr2,
PT const *const src, int const sstr1, int const sstr2,
int const n1, int const n2, IIRValue const b[N+1], double const M[N*N],
IIRValue *const tmpdata)
{
for ( int c2 = 0 ; c2 < n2 ; c2++ ) {
// corresponding line in the source and output buffer
PT const * srcimg = src + c2*sstr2;
PT * dstimg = dest + c2*dstr2 + n1*dstr1;
// Border constants
IIRValue imin[PC]; copy_n(srcimg + (0)*sstr1, PC, imin);
IIRValue iplus[PC]; copy_n(srcimg + (n1-1)*sstr1, PC, iplus);
// Forward pass
IIRValue u[N+1][PC];
for(unsigned int i=0; i<N; i++) copy_n(imin, PC, u[i]);
for ( int c1 = 0 ; c1 < n1 ; c1++ ) {
for(unsigned int i=N; i>0; i--) copy_n(u[i-1], PC, u[i]);
copy_n(srcimg, PC, u[0]);
srcimg += sstr1;
for(unsigned int c=0; c<PC; c++) u[0][c] *= b[0];
for(unsigned int i=1; i<N+1; i++) {
for(unsigned int c=0; c<PC; c++) u[0][c] += u[i][c]*b[i];
}
copy_n(u[0], PC, tmpdata+c1*PC);
}
// Backward pass
IIRValue v[N+1][PC];
calcTriggsSdikaInitialization<PC>(M, u, iplus, iplus, b[0], v);
dstimg -= dstr1;
if ( PREMULTIPLIED_ALPHA ) {
dstimg[PC-1] = clip_round_cast<PT>(v[0][PC-1]);
for(unsigned int c=0; c<PC-1; c++) dstimg[c] = clip_round_cast<PT>(v[0][c], std::numeric_limits<PT>::min(), dstimg[PC-1]);
} else {
for(unsigned int c=0; c<PC; c++) dstimg[c] = clip_round_cast<PT>(v[0][c]);
}
int c1=n1-1;
while(c1-->0) {
for(unsigned int i=N; i>0; i--) copy_n(v[i-1], PC, v[i]);
copy_n(tmpdata+c1*PC, PC, v[0]);
for(unsigned int c=0; c<PC; c++) v[0][c] *= b[0];
for(unsigned int i=1; i<N+1; i++) {
for(unsigned int c=0; c<PC; c++) v[0][c] += v[i][c]*b[i];
}
dstimg -= dstr1;
if ( PREMULTIPLIED_ALPHA ) {
dstimg[PC-1] = clip_round_cast<PT>(v[0][PC-1]);
for(unsigned int c=0; c<PC-1; c++) dstimg[c] = clip_round_cast<PT>(v[0][c], std::numeric_limits<PT>::min(), dstimg[PC-1]);
} else {
for(unsigned int c=0; c<PC; c++) dstimg[c] = clip_round_cast<PT>(v[0][c]);
}
}
}
}
// Filters over 1st dimension
// Assumes kernel is symmetric
// scr_len should be size of kernel - 1
template<typename PT, unsigned int PC>
static void
filter2D_FIR(PT *const dst, int const dstr1, int const dstr2,
PT const *const src, int const sstr1, int const sstr2,
int const n1, int const n2, FIRValue const *const kernel, int const scr_len)
{
// Past pixels seen (to enable in-place operation)
PT history[scr_len+1][PC];
for ( int c2 = 0 ; c2 < n2 ; c2++ ) {
// corresponding line in the source buffer
int const src_line = c2 * sstr2;
// current line in the output buffer
int const dst_line = c2 * dstr2;
int skipbuf[4] = {INT_MIN, INT_MIN, INT_MIN, INT_MIN};
// history initialization
PT imin[PC]; copy_n(src + src_line, PC, imin);
for(int i=0; i<scr_len; i++) copy_n(imin, PC, history[i]);
for ( int c1 = 0 ; c1 < n1 ; c1++ ) {
int const src_disp = src_line + c1 * sstr1;
int const dst_disp = dst_line + c1 * sstr1;
// update history
for(int i=scr_len; i>0; i--) copy_n(history[i-1], PC, history[i]);
copy_n(src + src_disp, PC, history[0]);
// for all bytes of the pixel
for ( unsigned int byte = 0 ; byte < PC ; byte++) {
if(skipbuf[byte] > c1) continue;
FIRValue sum = 0;
int last_in = -1;
int different_count = 0;
// go over our point's neighbours in the history
for ( int i = 0 ; i <= scr_len ; i++ ) {
// value at the pixel
PT in_byte = history[i][byte];
// is it the same as last one we saw?
if(in_byte != last_in) different_count++;
last_in = in_byte;
// sum pixels weighted by the kernel
sum += in_byte * kernel[i];
}
// go over our point's neighborhood on x axis in the in buffer
int nb_src_disp = src_disp + byte;
for ( int i = 1 ; i <= scr_len ; i++ ) {
// the pixel we're looking at
int c1_in = c1 + i;
if (c1_in >= n1) {
c1_in = n1 - 1;
} else {
nb_src_disp += sstr1;
}
// value at the pixel
PT in_byte = src[nb_src_disp];
// is it the same as last one we saw?
if(in_byte != last_in) different_count++;
last_in = in_byte;
// sum pixels weighted by the kernel
sum += in_byte * kernel[i];
}
// store the result in bufx
dst[dst_disp + byte] = round_cast<PT>(sum);
// optimization: if there was no variation within this point's neighborhood,
// skip ahead while we keep seeing the same last_in byte:
// blurring flat color would not change it anyway
if (different_count <= 1) {
int pos = c1 + 1;
int nb_src_disp = src_disp + (1+scr_len)*sstr1 + byte; // src_line + (pos+scr_len) * sstr1 + byte
int nb_dst_disp = dst_disp + (1) *dstr1 + byte; // dst_line + (pos) * sstr1 + byte
while(pos + scr_len < n1 && src[nb_src_disp] == last_in) {
dst[nb_dst_disp] = last_in;
pos++;
nb_src_disp += sstr1;
nb_dst_disp += sstr1;
}
skipbuf[byte] = pos;
}
}
}
}
}
template<typename PT, unsigned int PC>
static void
downsample(PT *const dst, int const dstr1, int const dstr2, int const dn1, int const dn2,
PT const *const src, int const sstr1, int const sstr2, int const sn1, int const sn2,
int const step1_l2, int const step2_l2)
{
unsigned int const divisor_l2 = step1_l2+step2_l2; // step1*step2=2^(step1_l2+step2_l2)
unsigned int const round_offset = (1<<divisor_l2)/2;
int const step1 = 1<<step1_l2;
int const step2 = 1<<step2_l2;
int const step1_2 = step1/2;
int const step2_2 = step2/2;
for(int dc2 = 0 ; dc2 < dn2 ; dc2++) {
int const sc2_begin = (dc2<<step2_l2)-step2_2;
int const sc2_end = sc2_begin+step2;
for(int dc1 = 0 ; dc1 < dn1 ; dc1++) {
int const sc1_begin = (dc1<<step1_l2)-step1_2;
int const sc1_end = sc1_begin+step1;
unsigned int sum[PC];
std::fill_n(sum, PC, 0);
for(int sc2 = sc2_begin ; sc2 < sc2_end ; sc2++) {
for(int sc1 = sc1_begin ; sc1 < sc1_end ; sc1++) {
for(unsigned int ch = 0 ; ch < PC ; ch++) {
sum[ch] += src[clip(sc2,0,sn2-1)*sstr2+clip(sc1,0,sn1-1)*sstr1+ch];
}
}
}
for(unsigned int ch = 0 ; ch < PC ; ch++) {
dst[dc2*dstr2+dc1*dstr1+ch] = static_cast<PT>((sum[ch]+round_offset)>>divisor_l2);
}
}
}
}
template<typename PT, unsigned int PC>
static void
upsample(PT *const dst, int const dstr1, int const dstr2, unsigned int const dn1, unsigned int const dn2,
PT const *const src, int const sstr1, int const sstr2, unsigned int const sn1, unsigned int const sn2,
unsigned int const step1_l2, unsigned int const step2_l2)
{
assert(((sn1-1)<<step1_l2)>=dn1 && ((sn2-1)<<step2_l2)>=dn2); // The last pixel of the source image should fall outside the destination image
unsigned int const divisor_l2 = step1_l2+step2_l2; // step1*step2=2^(step1_l2+step2_l2)
unsigned int const round_offset = (1<<divisor_l2)/2;
unsigned int const step1 = 1<<step1_l2;
unsigned int const step2 = 1<<step2_l2;
for ( unsigned int sc2 = 0 ; sc2 < sn2-1 ; sc2++ ) {
unsigned int const dc2_begin = (sc2 << step2_l2);
unsigned int const dc2_end = std::min(dn2, dc2_begin+step2);
for ( unsigned int sc1 = 0 ; sc1 < sn1-1 ; sc1++ ) {
unsigned int const dc1_begin = (sc1 << step1_l2);
unsigned int const dc1_end = std::min(dn1, dc1_begin+step1);
for ( unsigned int byte = 0 ; byte < PC ; byte++) {
// get 4 values at the corners of the pixel from src
PT a00 = src[sstr2* sc2 + sstr1* sc1 + byte];
PT a10 = src[sstr2* sc2 + sstr1*(sc1+1) + byte];
PT a01 = src[sstr2*(sc2+1) + sstr1* sc1 + byte];
PT a11 = src[sstr2*(sc2+1) + sstr1*(sc1+1) + byte];
// initialize values for linear interpolation
unsigned int a0 = a00*step2/*+a01*0*/;
unsigned int a1 = a10*step2/*+a11*0*/;
// iterate over the rectangle to be interpolated
for ( unsigned int dc2 = dc2_begin ; dc2 < dc2_end ; dc2++ ) {
// prepare linear interpolation for this row
unsigned int a = a0*step1/*+a1*0*/+round_offset;
for ( unsigned int dc1 = dc1_begin ; dc1 < dc1_end ; dc1++ ) {
// simple linear interpolation
dst[dstr2*dc2 + dstr1*dc1 + byte] = static_cast<PT>(a>>divisor_l2);
// compute a = a0*(ix-1)+a1*(xi+1)+round_offset
a = a - a0 + a1;
}
// compute a0 = a00*(iy-1)+a01*(yi+1) and similar for a1
a0 = a0 - a00 + a01;
a1 = a1 - a10 + a11;
}
}
}
}
}
int FilterGaussian::render(FilterSlot &slot, Matrix const &trans)
{
/* in holds the input pixblock */
NRPixBlock *in = slot.get(_input);
/* If to either direction, the standard deviation is zero or
* input image is not defined,
* a transparent black image should be returned. */
if (_deviation_x <= 0 || _deviation_y <= 0 || in == NULL) {
NRPixBlock *out = new NRPixBlock;
if (in == NULL) {
// A bit guessing here, but source graphic is likely to be of
// right size
in = slot.get(NR_FILTER_SOURCEGRAPHIC);
}
nr_pixblock_setup_fast(out, in->mode, in->area.x0, in->area.y0,
in->area.x1, in->area.y1, true);
if (out->data.px != NULL) {
out->empty = false;
slot.set(_output, out);
}
return 0;
}
// Some common constants
int const width_org = in->area.x1-in->area.x0, height_org = in->area.y1-in->area.y0;
double const deviation_x_org = _deviation_x * trans.expansionX();
double const deviation_y_org = _deviation_y * trans.expansionY();
int const PC = NR_PIXBLOCK_BPP(in);
// Subsampling constants
int const quality = prefs_get_int_attribute("options.blurquality", "value", 0);
int const x_step_l2 = _effect_subsample_step_log2(deviation_x_org, quality);
int const y_step_l2 = _effect_subsample_step_log2(deviation_y_org, quality);
int const x_step = 1<<x_step_l2;
int const y_step = 1<<y_step_l2;
bool const resampling = x_step > 1 || y_step > 1;
int const width = resampling ? static_cast<int>(ceil(static_cast<double>(width_org)/x_step))+1 : width_org;
int const height = resampling ? static_cast<int>(ceil(static_cast<double>(height_org)/y_step))+1 : height_org;
double const deviation_x = deviation_x_org / x_step;
double const deviation_y = deviation_y_org / y_step;
int const scr_len_x = _effect_area_scr(deviation_x);
int const scr_len_y = _effect_area_scr(deviation_y);
// Decide which filter to use for X and Y
// This threshold was determined by trial-and-error for one specific machine,
// so there's a good chance that it's not optimal.
// Whatever you do, don't go below 1 (and preferrably not even below 2), as
// the IIR filter gets unstable there.
bool const use_IIR_x = deviation_x > 3;
bool const use_IIR_y = deviation_y > 3;
// new buffer for the subsampled output
NRPixBlock *out = new NRPixBlock;
nr_pixblock_setup_fast(out, in->mode, in->area.x0/x_step, in->area.y0/y_step,
in->area.x0/x_step+width, in->area.y0/y_step+height, true);
if (out->size != NR_PIXBLOCK_SIZE_TINY && out->data.px == NULL) {
// alas, we've accomplished a lot, but ran out of memory - so abort
return 0;
}
// Temporary storage for IIR filter
// NOTE: This can be eliminated, but it reduces the precision a bit
IIRValue * tmpdata = 0;
if ( use_IIR_x || use_IIR_y ) {
tmpdata = new IIRValue[std::max(width,height)*PC];
if (tmpdata == NULL) {
nr_pixblock_release(out);
delete out;
return 0;
}
}
NRPixBlock *ssin = in;
if ( resampling ) {
ssin = out;
// Downsample
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
downsample<unsigned char,1>(NR_PIXBLOCK_PX(out), 1, out->rs, width, height, NR_PIXBLOCK_PX(in), 1, in->rs, width_org, height_org, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
downsample<unsigned char,3>(NR_PIXBLOCK_PX(out), 3, out->rs, width, height, NR_PIXBLOCK_PX(in), 3, in->rs, width_org, height_org, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
downsample<unsigned char,4>(NR_PIXBLOCK_PX(out), 4, out->rs, width, height, NR_PIXBLOCK_PX(in), 4, in->rs, width_org, height_org, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
downsample<unsigned char,4>(NR_PIXBLOCK_PX(out), 4, out->rs, width, height, NR_PIXBLOCK_PX(in), 4, in->rs, width_org, height_org, x_step_l2, y_step_l2);
break;
default:
assert(false);
};
}
if (use_IIR_x) {
// Filter variables
IIRValue b[N+1]; // scaling coefficient + filter coefficients (can be 10.21 fixed point)
double bf[N]; // computed filter coefficients
double M[N*N]; // matrix used for initialization procedure (has to be double)
// Compute filter (x)
calcFilter(deviation_x, bf);
for(size_t i=0; i<N; i++) bf[i] = -bf[i];
b[0] = 1; // b[0] == alpha (scaling coefficient)
for(size_t i=0; i<N; i++) {
b[i+1] = bf[i];
b[0] -= b[i+1];
}
// Compute initialization matrix (x)
calcTriggsSdikaM(bf, M);
// Filter (x)
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
filter2D_IIR<unsigned char,1,false>(NR_PIXBLOCK_PX(out), 1, out->rs, NR_PIXBLOCK_PX(ssin), 1, ssin->rs, width, height, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
filter2D_IIR<unsigned char,3,false>(NR_PIXBLOCK_PX(out), 3, out->rs, NR_PIXBLOCK_PX(ssin), 3, ssin->rs, width, height, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
filter2D_IIR<unsigned char,4,false>(NR_PIXBLOCK_PX(out), 4, out->rs, NR_PIXBLOCK_PX(ssin), 4, ssin->rs, width, height, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
filter2D_IIR<unsigned char,4,true >(NR_PIXBLOCK_PX(out), 4, out->rs, NR_PIXBLOCK_PX(ssin), 4, ssin->rs, width, height, b, M, tmpdata);
break;
default:
assert(false);
};
} else { // !use_IIR_x
// Filter kernel for x direction
FIRValue kernel[scr_len_x];
_make_kernel(kernel, deviation_x);
// Filter (x)
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
filter2D_FIR<unsigned char,1>(NR_PIXBLOCK_PX(out), 1, out->rs, NR_PIXBLOCK_PX(ssin), 1, ssin->rs, width, height, kernel, scr_len_x);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
filter2D_FIR<unsigned char,3>(NR_PIXBLOCK_PX(out), 3, out->rs, NR_PIXBLOCK_PX(ssin), 3, ssin->rs, width, height, kernel, scr_len_x);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
filter2D_FIR<unsigned char,4>(NR_PIXBLOCK_PX(out), 4, out->rs, NR_PIXBLOCK_PX(ssin), 4, ssin->rs, width, height, kernel, scr_len_x);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
filter2D_FIR<unsigned char,4>(NR_PIXBLOCK_PX(out), 4, out->rs, NR_PIXBLOCK_PX(ssin), 4, ssin->rs, width, height, kernel, scr_len_x);
break;
default:
assert(false);
};
}
if (use_IIR_y) {
// Filter variables
IIRValue b[N+1]; // scaling coefficient + filter coefficients (can be 10.21 fixed point)
double bf[N]; // computed filter coefficients
double M[N*N]; // matrix used for initialization procedure (has to be double)
// Compute filter (y)
calcFilter(deviation_y, bf);
for(size_t i=0; i<N; i++) bf[i] = -bf[i];
b[0] = 1; // b[0] == alpha (scaling coefficient)
for(size_t i=0; i<N; i++) {
b[i+1] = bf[i];
b[0] -= b[i+1];
}
// Compute initialization matrix (y)
calcTriggsSdikaM(bf, M);
// Filter (y)
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
filter2D_IIR<unsigned char,1,false>(NR_PIXBLOCK_PX(out), out->rs, 1, NR_PIXBLOCK_PX(out), out->rs, 1, height, width, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
filter2D_IIR<unsigned char,3,false>(NR_PIXBLOCK_PX(out), out->rs, 3, NR_PIXBLOCK_PX(out), out->rs, 3, height, width, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
filter2D_IIR<unsigned char,4,false>(NR_PIXBLOCK_PX(out), out->rs, 4, NR_PIXBLOCK_PX(out), out->rs, 4, height, width, b, M, tmpdata);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
filter2D_IIR<unsigned char,4,true >(NR_PIXBLOCK_PX(out), out->rs, 4, NR_PIXBLOCK_PX(out), out->rs, 4, height, width, b, M, tmpdata);
break;
default:
assert(false);
};
} else { // !use_IIR_y
// Filter kernel for y direction
FIRValue kernel[scr_len_y];
_make_kernel(kernel, deviation_y);
// Filter (y)
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
filter2D_FIR<unsigned char,1>(NR_PIXBLOCK_PX(out), out->rs, 1, NR_PIXBLOCK_PX(out), out->rs, 1, height, width, kernel, scr_len_y);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
filter2D_FIR<unsigned char,3>(NR_PIXBLOCK_PX(out), out->rs, 3, NR_PIXBLOCK_PX(out), out->rs, 3, height, width, kernel, scr_len_y);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
filter2D_FIR<unsigned char,4>(NR_PIXBLOCK_PX(out), out->rs, 4, NR_PIXBLOCK_PX(out), out->rs, 4, height, width, kernel, scr_len_y);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
filter2D_FIR<unsigned char,4>(NR_PIXBLOCK_PX(out), out->rs, 4, NR_PIXBLOCK_PX(out), out->rs, 4, height, width, kernel, scr_len_y);
break;
default:
assert(false);
};
}
delete[] tmpdata; // deleting a nullptr has no effect, so this is save
if ( !resampling ) {
// No upsampling needed
out->empty = FALSE;
slot.set(_output, out);
} else {
// New buffer for the final output, same resolution as the in buffer
NRPixBlock *finalout = new NRPixBlock;
nr_pixblock_setup_fast(finalout, in->mode, in->area.x0, in->area.y0,
in->area.x1, in->area.y1, true);
if (finalout->size != NR_PIXBLOCK_SIZE_TINY && finalout->data.px == NULL) {
// alas, we've accomplished a lot, but ran out of memory - so abort
nr_pixblock_release(out);
delete out;
return 0;
}
// Upsample
switch(in->mode) {
case NR_PIXBLOCK_MODE_A8: ///< Grayscale
upsample<unsigned char,1>(NR_PIXBLOCK_PX(finalout), 1, finalout->rs, width_org, height_org, NR_PIXBLOCK_PX(out), 1, out->rs, width, height, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8: ///< 8 bit RGB
upsample<unsigned char,3>(NR_PIXBLOCK_PX(finalout), 3, finalout->rs, width_org, height_org, NR_PIXBLOCK_PX(out), 3, out->rs, width, height, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8N: ///< Normal 8 bit RGBA
upsample<unsigned char,4>(NR_PIXBLOCK_PX(finalout), 4, finalout->rs, width_org, height_org, NR_PIXBLOCK_PX(out), 4, out->rs, width, height, x_step_l2, y_step_l2);
break;
case NR_PIXBLOCK_MODE_R8G8B8A8P: ///< Premultiplied 8 bit RGBA
upsample<unsigned char,4>(NR_PIXBLOCK_PX(finalout), 4, finalout->rs, width_org, height_org, NR_PIXBLOCK_PX(out), 4, out->rs, width, height, x_step_l2, y_step_l2);
break;
default:
assert(false);
};
// We don't need the out buffer anymore
nr_pixblock_release(out);
delete out;
// The final out buffer gets returned
finalout->empty = FALSE;
slot.set(_output, finalout);
}
return 0;
}
void FilterGaussian::area_enlarge(NRRectL &area, Matrix const &trans)
{
int area_x = _effect_area_scr(_deviation_x * trans.expansionX());
int area_y = _effect_area_scr(_deviation_y * trans.expansionY());
// maximum is used because rotations can mix up these directions
// TODO: calculate a more tight-fitting rendering area
int area_max = std::max(area_x, area_y);
area.x0 -= area_max;
area.x1 += area_max;
area.y0 -= area_max;
area.y1 += area_max;
}
FilterTraits FilterGaussian::get_input_traits() {
return TRAIT_PARALLER;
}
void FilterGaussian::set_deviation(double deviation)
{
if(isFinite(deviation) && deviation >= 0) {
_deviation_x = _deviation_y = deviation;
}
}
void FilterGaussian::set_deviation(double x, double y)
{
if(isFinite(x) && x >= 0 && isFinite(y) && y >= 0) {
_deviation_x = x;
_deviation_y = y;
}
}
} /* namespace NR */
/*
Local Variables:
mode:c++
c-file-style:"stroustrup"
c-file-offsets:((innamespace . 0)(inline-open . 0)(case-label . +))
indent-tabs-mode:nil
fill-column:99
End:
*/
// vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=99 :