7ec25bb9d03acb929519aa84287da437e3d4d10cCampbell Barton#define __NR_FILTER_GAUSSIAN_CPP__

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include <algorithm>

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include <cmath>

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include <complex>

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include <glib.h>

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include <limits>

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton

096dfde2c1cb7bb1e0a4b76e21f2abf548b900d5Campbell Barton#include "isnan.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton

7a5d553ffd780a7b0e5e7e6935cff061dbdeccadCampbell Barton#include "display/nr-filter-primitive.h"

7a5d553ffd780a7b0e5e7e6935cff061dbdeccadCampbell Barton#include "display/nr-filter-gaussian.h"

7a5d553ffd780a7b0e5e7e6935cff061dbdeccadCampbell Barton#include "display/nr-filter-types.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton#include "libnr/nr-pixblock.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton#include "libnr/nr-matrix.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton#include "util/fixed_point.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton#include "prefs-utils.h"

874cad03a8450ed3464f6dfae2eb16108bec5bbdCampbell Barton

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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);

}

typedef double IIRValue;

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;

}

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;

}

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];

}

}

}

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]);

}

}

}

}

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

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 */