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grafX2/src/op_c.c
Thomas Bernard ee11079b86 Count colors when loading True color pictures 
If the count is <= 256 there is no need to execute the expensive
color quantization process
2018-01-16 16:27:30 +01:00

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/* vim:expandtab:ts=2 sw=2:
*/
/* Grafx2 - The Ultimate 256-color bitmap paint program
Copyright 2017 Thomas Bernard
Copyright 2010 Alexander Filyanov
Copyright 2007 Adrien Destugues
Copyright 1996-2001 Sunset Design (Guillaume Dorme & Karl Maritaud)
Grafx2 is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; version 2
of the License.
Grafx2 is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Grafx2; if not, see <http://www.gnu.org/licenses/>
*/
#include <assert.h>
#include <unistd.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <fcntl.h>
#include <sys/stat.h>
#include <math.h>
#include "op_c.h"
#include "errors.h"
#include "colorred.h"
// If GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT is defined,
// the clusters are splitted in two half of equal (pixel) population.
// Otherwise, they are splitted in two half of equal volume.
#define GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT
// If GRAFX2_QUANTIZE_CLUSTER_SORT_BY_VOLUME is defined
// the clusters are sorted by volume. Otherwise, they
// are sorted by length of the diagonal
//#define GRAFX2_QUANTIZE_CLUSTER_SORT_BY_VOLUME
int Convert_24b_bitmap_to_256_fast(T_Bitmap256 dest,T_Bitmap24B source,int width,int height,T_Components * palette);
/// Convert RGB to HSL.
/// Both input and output are in the 0..255 range to use in the palette screen
void RGB_to_HSL(int r,int g,int b,byte * hr,byte * sr,byte* lr)
{
double rd,gd,bd,h,s,l,max,min;
// convert RGB to HSV
rd = r / 255.0; // rd,gd,bd range 0-1 instead of 0-255
gd = g / 255.0;
bd = b / 255.0;
// compute maximum of rd,gd,bd
if (rd>=gd)
{
if (rd>=bd)
max = rd;
else
max = bd;
}
else
{
if (gd>=bd)
max = gd;
else
max = bd;
}
// compute minimum of rd,gd,bd
if (rd<=gd)
{
if (rd<=bd)
min = rd;
else
min = bd;
}
else
{
if (gd<=bd)
min = gd;
else
min = bd;
}
l = (max + min) / 2.0;
if(max==min)
s = h = 0;
else
{
if (l<=0.5)
s = (max - min) / (max + min);
else
s = (max - min) / (2 - (max + min));
if (max == rd)
h = 42.5 * (gd-bd)/(max-min);
else if (max == gd)
h = 42.5 * (bd-rd)/(max-min)+85;
else
h = 42.5 * (rd-gd)/(max-min)+170;
if (h<0) h+=255;
}
*hr = h;
*lr = (l*255.0);
*sr = (s*255.0);
}
/// Convert HSL back to RGB
/// Input and output are all in range 0..255
void HSL_to_RGB(byte h,byte s,byte l, byte* r, byte* g, byte* b)
{
float rf =0 ,gf = 0,bf = 0;
float hf,lf,sf;
float p,q;
if(s==0)
{
*r=*g=*b=l;
return;
}
hf = h / 255.0;
lf = l / 255.0;
sf = s / 255.0;
if (lf<=0.5)
q = lf*(1+sf);
else
q = lf+sf-lf*sf;
p = 2*lf-q;
rf = hf + (1 / 3.0);
gf = hf;
bf = hf - (1 / 3.0);
if (rf < 0) rf+=1;
if (rf > 1) rf-=1;
if (gf < 0) gf+=1;
if (gf > 1) gf-=1;
if (bf < 0) bf+=1;
if (bf > 1) bf-=1;
if (rf < 1/6.0)
rf = p + ((q-p)*6*rf);
else if(rf < 0.5)
rf = q;
else if(rf < 2/3.0)
rf = p + ((q-p)*6*(2/3.0-rf));
else
rf = p;
if (gf < 1/6.0)
gf = p + ((q-p)*6*gf);
else if(gf < 0.5)
gf = q;
else if(gf < 2/3.0)
gf = p + ((q-p)*6*(2/3.0-gf));
else
gf = p;
if (bf < 1/6.0)
bf = p + ((q-p)*6*bf);
else if(bf < 0.5)
bf = q;
else if(bf < 2/3.0)
bf = p + ((q-p)*6*(2/3.0-bf));
else
bf = p;
*r = rf * (255);
*g = gf * (255);
*b = bf * (255);
}
///
/// Returns a value that is high when color is near white,
/// and low when it's darker. Used for sorting.
long Perceptual_lightness(T_Components *color)
{
return 26*color->R*26*color->R +
55*color->G*55*color->G +
19*color->B*19*color->B;
}
// Handlers for the occurences tables
// This table is used to count the occurence of an (RGB) pixel value in the
// source 24bit image. These count are then used by the median cut algorithm to
// decide which cluster to split.
/// Initialize an occurence table
void OT_init(T_Occurrence_table * t)
{
int size;
size=(t->rng_r)*(t->rng_g)*(t->rng_b)*sizeof(int);
memset(t->table,0,size); // Set it to 0
}
/// Allocate an occurence table for given number of bits
T_Occurrence_table * OT_new(int nbb_r,int nbb_g,int nbb_b)
{
T_Occurrence_table * n;
int size;
n=(T_Occurrence_table *)malloc(sizeof(T_Occurrence_table));
if (n!=0)
{
// Copy passed parameters
n->nbb_r=nbb_r;
n->nbb_g=nbb_g;
n->nbb_b=nbb_b;
// Compute others
n->rng_r=(1<<nbb_r);
n->rng_g=(1<<nbb_g);
n->rng_b=(1<<nbb_b);
n->dec_r=nbb_g+nbb_b;
n->dec_g=nbb_b;
n->dec_b=0;
n->red_r=8-nbb_r;
n->red_g=8-nbb_g;
n->red_b=8-nbb_b;
// Allocate the table
size=(n->rng_r)*(n->rng_g)*(n->rng_b);
n->table=(int *)calloc(size, sizeof(int));
if (n->table == NULL)
{
// Not enough memory !
free(n);
n=NULL;
}
}
return n;
}
/// Delete a table and free the memory
void OT_delete(T_Occurrence_table * t)
{
free(t->table);
free(t);
t = NULL;
}
/// Get number of occurences for a given color
int OT_get(T_Occurrence_table * t, byte r, byte g, byte b)
{
int index;
// Drop bits as needed
index=(r<<t->dec_r) | (g<<t->dec_g) | (b<<t->dec_b);
return t->table[index];
}
/// Add 1 to the count for a color
void OT_inc(T_Occurrence_table * t,byte r,byte g,byte b)
{
int index;
// Drop bits as needed
r=(r>>t->red_r);
g=(g>>t->red_g);
b=(b>>t->red_b);
// Compute the address
index=(r<<t->dec_r) | (g<<t->dec_g) | (b<<t->dec_b);
t->table[index]++;
}
/// Count the use of each color in a 24bit picture and fill in the table
void OT_count_occurrences(T_Occurrence_table* t, T_Bitmap24B image, int size)
{
T_Bitmap24B ptr;
int index;
for (index = size, ptr = image; index > 0; index--, ptr++)
OT_inc(t, ptr->R, ptr->G, ptr->B);
}
/// Count the total number of pixels in an occurence table
int OT_count_colors(T_Occurrence_table * t)
{
int val; // Computed return value
int nb; // Number of colors to test
int i; // Loop index
val = 0;
nb=(t->rng_r)*(t->rng_g)*(t->rng_b);
for (i = 0; i < nb; i++)
if (t->table[i]>0)
val++;
return val;
}
// Cluster management
// Clusters are boxes in the RGB spaces, defined by 6 corner coordinates :
// Rmax, Rmin, Vmax (or Gmax), Vmin, Rmax, Rmin
// The median cut algorithm start with a single cluster covering the whole
// colorspace then split it in two smaller clusters on the longest axis until
// there are 256 non-empty clusters (with some tricks if the original image
// actually has less than 256 colors)
// Each cluster also store the number of pixels that are inside and the
// rmin, rmax, vmin, vmax, bmin, bmax values are the first/last values that
// actually are used by a pixel in the cluster
// When you split a big cluster there may be some space between the splitting
// plane and the first pixel actually in a cluster
/// Pack a cluster, ie compute its {r,v,b}{min,max} values
void Cluster_pack(T_Cluster * c,const T_Occurrence_table * const to)
{
int rmin,rmax,vmin,vmax,bmin,bmax;
int r,g,b;
// Find min. and max. values actually used for each component in this cluster
// Pre-shift everything to avoid using OT_Get and be faster.
// GIMP use only 6 bits for G and B components in this table.
rmin=c->rmax << to->dec_r; rmax=c->rmin << to->dec_r;
vmin=c->vmax << to->dec_g; vmax=c->vmin << to->dec_g;
bmin=c->bmax << to->dec_b; bmax=c->bmin << to->dec_b;
c->occurences=0;
// Unoptimized code kept here for documentation purpose because the optimized
// one is unreadable : run over the whole cluster and find the min and max,
// and count the occurences at the same time.
/*
for (r=c->rmin<<to->dec_r;r<=c->rmax<<to->dec_r;r+=1<<to->dec_r)
for (g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
for (b=c->bmin;b<=c->bmax;b++)
{
nbocc=to->table[r + g + b]; // OT_get
if (nbocc)
{
if (r<rmin) rmin=r;
else if (r>rmax) rmax=r;
if (g<vmin) vmin=g;
else if (g>vmax) vmax=g;
if (b<bmin) bmin=b;
else if (b>bmax) bmax=b;
c->occurences+=nbocc;
}
}
*/
// Optimized version : find the extremums one at a time, so we can reduce the
// area to seek for the next one. Start at the edges of the cluster and go to
// the center until we find a pixel.
for(r=c->rmin<<to->dec_r;r<=c->rmax<<to->dec_r;r+=1<<to->dec_r)
for(g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
for(b=c->bmin;b<=c->bmax;b++)
{
if(to->table[r + g + b]) // OT_get
{
rmin=r;
goto RMAX;
}
}
RMAX:
for(r=c->rmax<<to->dec_r;r>=rmin;r-=1<<to->dec_r)
for(g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
for(b=c->bmin;b<=c->bmax;b++)
{
if(to->table[r + g + b]) // OT_get
{
rmax=r;
goto VMIN;
}
}
VMIN:
for(g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
for(r=rmin;r<=rmax;r+=1<<to->dec_r)
for(b=c->bmin;b<=c->bmax;b++)
{
if(to->table[r + g + b]) // OT_get
{
vmin=g;
goto VMAX;
}
}
VMAX:
for(g=c->vmax<<to->dec_g;g>=vmin;g-=1<<to->dec_g)
for(r=rmin;r<=rmax;r+=1<<to->dec_r)
for(b=c->bmin;b<=c->bmax;b++)
{
if(to->table[r + g + b]) // OT_get
{
vmax=g;
goto BMIN;
}
}
BMIN:
for(b=c->bmin;b<=c->bmax;b++)
for(r=rmin;r<=rmax;r+=1<<to->dec_r)
for(g=vmin;g<=vmax;g+=1<<to->dec_g)
{
if(to->table[r + g + b]) // OT_get
{
bmin=b;
goto BMAX;
}
}
BMAX:
for(b=c->bmax;b>=bmin;b--)
for(r=rmin;r<=rmax;r+=1<<to->dec_r)
for(g=vmin;g<=vmax;g+=1<<to->dec_g)
{
if(to->table[r + g + b]) // OT_get
{
bmax=b;
goto ENDCRUSH;
}
}
ENDCRUSH:
// We still need to seek the internal part of the cluster to count pixels
// inside it
for(r=rmin;r<=rmax;r+=1<<to->dec_r)
for(g=vmin;g<=vmax;g+=1<<to->dec_g)
for(b=bmin;b<=bmax;b++)
{
c->occurences+=to->table[r + g + b]; // OT_get
}
// Unshift the values and put them in the cluster info
c->rmin=rmin>>to->dec_r; c->rmax=rmax>>to->dec_r;
c->vmin=vmin>>to->dec_g; c->vmax=vmax>>to->dec_g;
c->bmin=bmin; c->bmax=bmax;
// Find the longest axis to know which way to split the cluster
r = c->rmax-c->rmin;
g = c->vmax-c->vmin;
b = c->bmax-c->bmin;
c->data.cut.sqdiag = r*r+g*g+b*b;
c->data.cut.volume = (r+1)*(g+1)*(b+1);
if (g>=r)
{
// G>=R
if (g>=b)
{
// G>=R et G>=B
c->data.cut.plus_large=1;
}
else
{
// G>=R et G<B
c->data.cut.plus_large=2;
}
}
else
{
// R>G
if (r>=b)
{
// R>G et R>=B
c->data.cut.plus_large=0;
}
else
{
// R>G et R<B
c->data.cut.plus_large=2;
}
}
}
#ifndef GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT
/// Split a cluster on its longest axis.
/// c = source cluster, c1, c2 = output after split
/// the two output cluster have half volume (and not half population)
void Cluster_split_volume(T_Cluster * c, T_Cluster * c1, T_Cluster * c2, int hue)
{
int r,g,b;
if (hue == 0) // split on red
{
r = (c->rmin + c->rmax) / 2;
c1->Rmin=c->Rmin; c1->Rmax=r;
c1->rmin=c->rmin; c1->rmax=r;
c1->Gmin=c->Gmin; c1->Vmax=c->Vmax;
c1->vmin=c->vmin; c1->vmax=c->vmax;
c1->Bmin=c->Bmin; c1->Bmax=c->Bmax;
c1->bmin=c->bmin; c1->bmax=c->bmax;
c2->Rmin=r+1; c2->Rmax=c->Rmax;
c2->rmin=r+1; c2->rmax=c->rmax;
c2->Gmin=c->Gmin; c2->Vmax=c->Vmax;
c2->vmin=c->vmin; c2->vmax=c->vmax;
c2->Bmin=c->Bmin; c2->Bmax=c->Bmax;
c2->bmin=c->bmin; c2->bmax=c->bmax;
}
else if (hue==1) // split on green
{
g = (c->vmin + c->vmax) / 2;
c1->Rmin=c->Rmin; c1->Rmax=c->Rmax;
c1->rmin=c->rmin; c1->rmax=c->rmax;
c1->Gmin=c->Gmin; c1->Vmax=g;
c1->vmin=c->vmin; c1->vmax=g;
c1->Bmin=c->Bmin; c1->Bmax=c->Bmax;
c1->bmin=c->bmin; c1->bmax=c->bmax;
c2->Rmin=c->Rmin; c2->Rmax=c->Rmax;
c2->rmin=c->rmin; c2->rmax=c->rmax;
c2->Gmin=g+1; c2->Vmax=c->Vmax;
c2->vmin=g+1; c2->vmax=c->vmax;
c2->Bmin=c->Bmin; c2->Bmax=c->Bmax;
c2->bmin=c->bmin; c2->bmax=c->bmax;
}
else
{
b = (c->bmin + c->bmax) / 2;
c1->Rmin=c->Rmin; c1->Rmax=c->Rmax;
c1->rmin=c->rmin; c1->rmax=c->rmax;
c1->Gmin=c->Gmin; c1->Vmax=c->Vmax;
c1->vmin=c->vmin; c1->vmax=c->vmax;
c1->Bmin=c->Bmin; c1->Bmax=b;
c1->bmin=c->bmin; c1->bmax=b;
c2->Rmin=c->Rmin; c2->Rmax=c->Rmax;
c2->rmin=c->rmin; c2->rmax=c->rmax;
c2->Gmin=c->Gmin; c2->Vmax=c->Vmax;
c2->vmin=c->vmin; c2->vmax=c->vmax;
c2->Bmin=b+1; c2->Bmax=c->Bmax;
c2->bmin=b+1; c2->bmax=c->bmax;
}
}
#else // GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT
/// Split a cluster on its longest axis.
/// c = source cluster, c1, c2 = output after split
/// the two output clusters have half population (and not half volume)
void Cluster_split(T_Cluster * c, T_Cluster * c1, T_Cluster * c2, int hue,
const T_Occurrence_table * const to)
{
int limit;
int cumul;
int r, g, b;
// Split criterion: each of the cluster will have the same number of pixels
limit = c->occurences / 2;
cumul = 0;
if (hue == 0) // split on red
{
// Run over the cluster until we reach the requested number of pixels
for (r = c->rmin<<to->dec_r; r<=c->rmax<<to->dec_r; r+=1<<to->dec_r)
{
for (g = c->vmin<<to->dec_g; g<=c->vmax<<to->dec_g; g+=1<<to->dec_g)
{
for (b = c->bmin; b<=c->bmax; b++)
{
cumul+=to->table[r + g + b];
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
r>>=to->dec_r;
g>>=to->dec_g;
// More than half of the cluster pixel have r = rmin. Ensure we split somewhere anyway.
if (r == c->rmin) r++;
c1->Rmin=c->Rmin; c1->Rmax=r-1;
c1->rmin=c->rmin; c1->rmax=r-1;
c1->Gmin=c->Gmin; c1->Vmax=c->Vmax;
c1->vmin=c->vmin; c1->vmax=c->vmax;
c1->Bmin=c->Bmin; c1->Bmax=c->Bmax;
c1->bmin=c->bmin; c1->bmax=c->bmax;
c2->Rmin=r; c2->Rmax=c->Rmax;
c2->rmin=r; c2->rmax=c->rmax;
c2->Gmin=c->Gmin; c2->Vmax=c->Vmax;
c2->vmin=c->vmin; c2->vmax=c->vmax;
c2->Bmin=c->Bmin; c2->Bmax=c->Bmax;
c2->bmin=c->bmin; c2->bmax=c->bmax;
}
else
if (hue==1) // split on green
{
for (g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
{
for (r=c->rmin<<to->dec_r;r<=c->rmax<<to->dec_r;r+=1<<to->dec_r)
{
for (b=c->bmin;b<=c->bmax;b++)
{
cumul+=to->table[r + g + b];
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
r>>=to->dec_r; g>>=to->dec_g;
if (g == c->vmin) g++;
c1->Rmin=c->Rmin; c1->Rmax=c->Rmax;
c1->rmin=c->rmin; c1->rmax=c->rmax;
c1->Gmin=c->Gmin; c1->Vmax=g-1;
c1->vmin=c->vmin; c1->vmax=g-1;
c1->Bmin=c->Bmin; c1->Bmax=c->Bmax;
c1->bmin=c->bmin; c1->bmax=c->bmax;
c2->Rmin=c->Rmin; c2->Rmax=c->Rmax;
c2->rmin=c->rmin; c2->rmax=c->rmax;
c2->Gmin=g; c2->Vmax=c->Vmax;
c2->vmin=g; c2->vmax=c->vmax;
c2->Bmin=c->Bmin; c2->Bmax=c->Bmax;
c2->bmin=c->bmin; c2->bmax=c->bmax;
}
else // split on blue
{
for (b=c->bmin;b<=c->bmax;b++)
{
for (g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
{
for (r=c->rmin<<to->dec_r;r<=c->rmax<<to->dec_r;r+=1<<to->dec_r)
{
cumul+=to->table[r + g + b];
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
if (cumul>=limit)
break;
}
r>>=to->dec_r; g>>=to->dec_g;
if (b == c->bmin) b++;
c1->Rmin=c->Rmin; c1->Rmax=c->Rmax;
c1->rmin=c->rmin; c1->rmax=c->rmax;
c1->Gmin=c->Gmin; c1->Vmax=c->Vmax;
c1->vmin=c->vmin; c1->vmax=c->vmax;
c1->Bmin=c->Bmin; c1->Bmax=b-1;
c1->bmin=c->bmin; c1->bmax=b-1;
c2->Rmin=c->Rmin; c2->Rmax=c->Rmax;
c2->rmin=c->rmin; c2->rmax=c->rmax;
c2->Gmin=c->Gmin; c2->Vmax=c->Vmax;
c2->vmin=c->vmin; c2->vmax=c->vmax;
c2->Bmin=b; c2->Bmax=c->Bmax;
c2->bmin=b; c2->bmax=c->bmax;
}
}
#endif // GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT
/// Compute the mean R, G, B (for palette generation) and H, L (for palette sorting)
void Cluster_compute_hue(T_Cluster * c,T_Occurrence_table * to)
{
int cumul_r,cumul_g,cumul_b;
int r,g,b;
int nbocc;
byte s=0;
cumul_r=cumul_g=cumul_b=0;
for (r=c->rmin;r<=c->rmax;r++)
for (g=c->vmin;g<=c->vmax;g++)
for (b=c->bmin;b<=c->bmax;b++)
{
nbocc=OT_get(to,r,g,b);
if (nbocc)
{
cumul_r+=r*nbocc;
cumul_g+=g*nbocc;
cumul_b+=b*nbocc;
}
}
c->data.pal.r=(cumul_r<<to->red_r)/c->occurences;
c->data.pal.g=(cumul_g<<to->red_g)/c->occurences;
c->data.pal.b=(cumul_b<<to->red_b)/c->occurences;
RGB_to_HSL(c->data.pal.r, c->data.pal.g, c->data.pal.b, &c->data.pal.h, &s, &c->data.pal.l);
}
// Cluster set management
// A set of clusters in handled as a list, the median cut algorithm pops a
// cluster from the list, split it, and pushes back the two splitted clusters
// until the lit grows to 256 items
// Debug helper : check if a cluster set has the right count value
/*
void CS_Check(T_Cluster_set* cs)
{
int i;
T_Cluster* c = cs->clusters;
for (i = cs->nb; i > 0; i--)
{
assert( c != NULL);
c = c->next;
}
assert(c == NULL);
}
*/
/*
void Cluster_Print(T_Cluster* node)
{
printf("R %d %d\tG %d %d\tB %d %d\n",
node->Rmin, node->Rmax, node->Gmin, node->Vmax,
node->Bmin, node->Bmax);
}
*/
// translate R G B values to 8 8 8
void CT_set_trad(CT_Tree* colorTree, byte Rmin, byte Gmin, byte Bmin,
byte Rmax, byte Gmax, byte Bmax, byte index, const T_Occurrence_table * to)
{
Rmin <<= to->red_r;
Rmax <<= to->red_r;
Rmax += ((1 << to->red_r) - 1);
Gmin <<= to->red_g;
Gmax <<= to->red_g;
Gmax += ((1 << to->red_g) - 1);
Bmin <<= to->red_b;
Bmax <<= to->red_b;
Bmax += ((1 << to->red_b) - 1);
CT_set(colorTree, Rmin, Gmin, Bmin,
Rmax, Gmax, Bmax, index);
}
/// Setup the first cluster before we start the operations
/// This one covers the full palette range
void CS_Init(T_Cluster_set * cs, T_Occurrence_table * to)
{
cs->clusters->Rmin = cs->clusters->rmin = 0;
cs->clusters->Gmin = cs->clusters->vmin = 0;
cs->clusters->Bmin = cs->clusters->bmin = 0;
cs->clusters->Rmax = cs->clusters->rmax = to->rng_r - 1;
cs->clusters->Vmax = cs->clusters->vmax = to->rng_g - 1;
cs->clusters->Bmax = cs->clusters->bmax = to->rng_b - 1;
cs->clusters->next = NULL;
Cluster_pack(cs->clusters, to);
cs->nb = 1;
}
/// Allocate a new cluster set
T_Cluster_set * CS_New(int nbmax, T_Occurrence_table * to)
{
T_Cluster_set * n;
n=(T_Cluster_set *)malloc(sizeof(T_Cluster_set));
if (n != NULL)
{
// Copy requested params
n->nb_max = OT_count_colors(to);
// If the number of colors asked is > 256, we ceil it because we know we
// don't want more
if (n->nb_max > nbmax)
{
n->nb_max = nbmax;
}
// Allocate the first cluster
n->clusters=(T_Cluster *)malloc(sizeof(T_Cluster));
if (n->clusters != NULL)
CS_Init(n, to);
else
{
// No memory free ! Sorry !
free(n);
n = NULL;
}
}
return n;
}
/// Free a cluster set
void CS_Delete(T_Cluster_set * cs)
{
T_Cluster* nxt;
while (cs->clusters != NULL)
{
nxt = cs->clusters->next;
free(cs->clusters);
cs->clusters = nxt;
}
free(cs);
cs = NULL;
}
/// Pop a cluster from the cluster list
void CS_Get(T_Cluster_set * cs, T_Cluster ** c)
{
// Just remove and return the first cluster, which has the biggest volume.
// or the longest diagonal
*c = cs->clusters;
cs->clusters = (*c)->next;
--cs->nb;
}
/// Push a copy of a cluster in the list
/// return -1 in case of error
int CS_Set(T_Cluster_set * cs,T_Cluster * c)
{
T_Cluster* current = cs->clusters;
T_Cluster* prev = NULL;
// Search the first cluster that is smaller than ours
#ifdef GRAFX2_QUANTIZE_CLUSTER_SORT_BY_VOLUME
while (current && current->data.cut.volume > c->data.cut.volume)
#else
while (current && current->data.cut.sqdiag > c->data.cut.sqdiag)
#endif
{
prev = current;
current = current->next;
}
// Now insert our cluster just before the one we found
c -> next = current;
current = malloc(sizeof(T_Cluster));
if(current == NULL)
return -1;
*current = *c ;
if (prev) prev->next = current;
else cs->clusters = current;
cs->nb++;
return 0;
}
/// This is the main median cut algorithm and the function actually called to
/// reduce the palette. We get the number of pixels for each collor in the
/// occurence table and generate the cluster set from it.
// 1) RGB space is a big box
// 2) We seek the pixels with extreme values
// 3) We split the box in 2 parts on its longest axis
// 4) We pack the 2 resulting boxes again to leave no empty space between the box border and the first pixel
// 5) We take the box with the biggest number of pixels inside and we split it again
// 6) Iterate until there are 256 boxes. Associate each of them to its middle color
// At the same time, put the split clusters in the color tree for later palette lookup
int CS_Generate(T_Cluster_set * cs, const T_Occurrence_table * const to, CT_Tree* colorTree)
{
T_Cluster* current;
T_Cluster Nouveau1;
T_Cluster Nouveau2;
// There are less than 256 boxes
while (cs->nb<cs->nb_max)
{
// Get the biggest one
CS_Get(cs,&current);
//Cluster_Print(current);
// We are going to split this cluster, so add it to the color tree. It is a split cluster,
// not a final one. We KNOW its two child will get added later (either because they are split,
// or because they are part of the final cluster set). So, we add thiscluster with a NULL index.
CT_set_trad(colorTree,current->Rmin, current->Gmin, current->Bmin,
current->Rmax, current->Vmax, current->Bmax, 0, to);
// Split it
if (current->data.cut.volume <= 1)
{
// Sorry, but there's nothing more to split !
// The biggest cluster only has one color...
free(current);
break;
}
#ifndef GRAFX2_QUANTIZE_CLUSTER_POPULATION_SPLIT
Cluster_split_volume(current, &Nouveau1, &Nouveau2, current->data.cut.plus_large);
#else
Cluster_split(current, &Nouveau1, &Nouveau2, current->data.cut.plus_large, to);
#endif
free(current);
// Pack the 2 new clusters (the split may leave some empty space between the
// box border and the first actual pixel)
Cluster_pack(&Nouveau1, to);
Cluster_pack(&Nouveau2, to);
// Put them back in the list
if (Nouveau1.occurences != 0) {
if(CS_Set(cs,&Nouveau1) < 0)
return -1;
}
if (Nouveau2.occurences != 0) {
if(CS_Set(cs,&Nouveau2) < 0)
return -1;
}
}
return 0;
}
/// Compute the color associated to each box in the list
void CS_Compute_colors(T_Cluster_set * cs, T_Occurrence_table * to)
{
T_Cluster * c;
for (c=cs->clusters;c!=NULL;c=c->next) {
Cluster_compute_hue(c,to);
}
}
// We sort the clusters on two criterions to get a somewhat coherent palette.
// TODO : It would be better to do this in one single pass.
/// Sort the clusters by chrominance value
void CS_Sort_by_chrominance(T_Cluster_set * cs)
{
T_Cluster* nc;
T_Cluster* prev = NULL;
T_Cluster* place;
T_Cluster* newlist = NULL;
while (cs->clusters)
{
// Remove the first cluster from the original list
nc = cs->clusters;
cs->clusters = cs->clusters->next;
// Find his position in the new list
for (place = newlist; place != NULL; place = place->next)
{
if (place->data.pal.h > nc->data.pal.h) break;
prev = place;
}
// Chain it there
nc->next = place;
if (prev) prev->next = nc;
else newlist = nc;
prev = NULL;
}
// Put the new list back in place
cs->clusters = newlist;
}
/// Sort the clusters by luminance value
void CS_Sort_by_luminance(T_Cluster_set * cs)
{
T_Cluster* nc;
T_Cluster* prev = NULL;
T_Cluster* place;
T_Cluster* newlist = NULL;
while (cs->clusters)
{
// Remove the first cluster from the original list
nc = cs->clusters;
cs->clusters = cs->clusters->next;
// Find its position in the new list
for (place = newlist; place != NULL; place = place->next)
{
if (place->data.pal.l > nc->data.pal.l) break;
prev = place;
}
// Chain it there
nc->next = place;
if (prev) prev->next = nc;
else newlist = nc;
// reset prev pointer
prev = NULL;
}
// Put the new list back in place
cs->clusters = newlist;
}
/// Generates the palette from the clusters, then the conversion table to map (RGB) to a palette index
void CS_Generate_color_table_and_palette(T_Cluster_set * cs,CT_Tree* tc,T_Components * palette, T_Occurrence_table * to)
{
int index;
T_Cluster* current = cs->clusters;
for (index=0;index<cs->nb;index++)
{
palette[index].R=current->data.pal.r;
palette[index].G=current->data.pal.g;
palette[index].B=current->data.pal.b;
CT_set_trad(tc,current->Rmin, current->Gmin, current->Bmin,
current->Rmax, current->Vmax, current->Bmax,
index, to);
current = current->next;
}
}
/////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////// M<>thodes de gestion des d<>grad<61>s //
/////////////////////////////////////////////////////////////////////////////
void GS_Init(T_Gradient_set * ds,T_Cluster_set * cs)
{
ds->gradients[0].nb_colors=1;
ds->gradients[0].min=cs->clusters->data.pal.h;
ds->gradients[0].max=cs->clusters->data.pal.h;
ds->gradients[0].hue=cs->clusters->data.pal.h;
// Et hop : le 1er ensemble de d<>grad<61>s est initialis<69>
ds->nb=1;
}
T_Gradient_set * GS_New(T_Cluster_set * cs)
{
T_Gradient_set * n;
n=(T_Gradient_set *)malloc(sizeof(T_Gradient_set));
if (n!=NULL)
{
// On recopie les param<61>tres demand<6E>s
n->nb_max=cs->nb_max;
// On tente d'allouer la table
n->gradients=(T_Gradient *)malloc((n->nb_max)*sizeof(T_Gradient));
if (n->gradients!=0)
// C'est bon! On initialise
GS_Init(n,cs);
else
{
// Table impossible <20> allouer
free(n);
n=NULL;
}
}
return n;
}
void GS_Delete(T_Gradient_set * ds)
{
free(ds->gradients);
free(ds);
}
void GS_Generate(T_Gradient_set * ds,T_Cluster_set * cs)
{
int id; // Les indexs de parcours des ensembles
int best_gradient; // Meilleur d<>grad<61>
int best_diff; // Meilleure diff<66>rence de chrominance
int diff; // difference de chrominance courante
T_Cluster * current = cs->clusters;
// Pour chacun des clusters <20> traiter
do
{
// On recherche le d<>grad<61> le plus proche de la chrominance du cluster
best_gradient=-1;
best_diff=99999999;
for (id=0;id<ds->nb;id++)
{
diff=abs(current->data.pal.h - ds->gradients[id].hue);
if ((best_diff>diff) && (diff<16))
{
best_gradient=id;
best_diff=diff;
}
}
// Si on a trouv<75> un d<>grad<61> dans lequel inclure le cluster
if (best_gradient!=-1)
{
// On met <20> jour le d<>grad<61>
if (current->data.pal.h < ds->gradients[best_gradient].min)
ds->gradients[best_gradient].min=current->data.pal.h;
if (current->data.pal.h > ds->gradients[best_gradient].max)
ds->gradients[best_gradient].max=current->data.pal.h;
ds->gradients[best_gradient].hue=((ds->gradients[best_gradient].hue*
ds->gradients[best_gradient].nb_colors)
+current->data.pal.h)
/(ds->gradients[best_gradient].nb_colors+1);
ds->gradients[best_gradient].nb_colors++;
}
else
{
// On cr<63>e un nouveau d<>grad<61>
best_gradient=ds->nb;
ds->gradients[best_gradient].nb_colors=1;
ds->gradients[best_gradient].min=current->data.pal.h;
ds->gradients[best_gradient].max=current->data.pal.h;
ds->gradients[best_gradient].hue=current->data.pal.h;
ds->nb++;
}
current->data.pal.h=best_gradient;
} while((current = current->next));
// On redistribue les valeurs dans les clusters
current = cs -> clusters;
do
current->data.pal.h=ds->gradients[current->data.pal.h].hue;
while((current = current ->next));
}
/// Compute best palette for given picture.
///
/// The picture is first depth-reduced to the given
/// r,g,b resolution, then the median cut algorithm is used to find 256 colors which are suitable
/// for the given picture.
///
/// @returns a conversion tree to be used for converting the picture to indexed with the generated palette (with or without dithering).
///
/// @param image The true-color image for which the palette needs to be optimized
/// @param size in pixels (number of pixels, the height/width doesn't matter)
/// @param palette pointer to the space where the palette will be stored (256 entries at most)
/// @param r Resolution for red
/// @param g Resolution for green
/// @param b Resolution for blue
CT_Tree* Optimize_palette(T_Bitmap24B image, int size,
T_Components * palette, int r, int g, int b)
{
T_Occurrence_table * to;
CT_Tree* tc;
T_Cluster_set * cs;
T_Gradient_set * ds;
// Allocate all the elements
to = 0; tc = 0; cs = 0; ds = 0;
to = OT_new(r, g, b);
if (to == NULL)
return 0;
tc = CT_new();
if (tc == NULL)
{
OT_delete(to);
return NULL;
}
// Count pixels for each color
OT_count_occurrences(to, image, size);
cs = CS_New(256, to);
if (cs == NULL)
{
CT_delete(tc);
OT_delete(to);
return NULL;
}
//CS_Check(cs);
// Ok, everything was allocated
// Generate the cluster set with median cut algorithm
if(CS_Generate(cs, to, tc) < 0) {
CS_Delete(cs);
CT_delete(tc);
OT_delete(to);
return NULL;
}
//CS_Check(cs);
// Compute the color data for each cluster (palette entry + HL)
CS_Compute_colors(cs, to);
//CS_Check(cs);
ds = GS_New(cs);
if (ds!= NULL)
{
GS_Generate(ds, cs);
GS_Delete(ds);
}
// Sort the clusters on L and H to get a nice palette
CS_Sort_by_luminance(cs);
//CS_Check(cs);
CS_Sort_by_chrominance(cs);
//CS_Check(cs);
// And finally generate the conversion table to map RGB > pal. index
CS_Generate_color_table_and_palette(cs, tc, palette, to);
//CS_Check(cs);
CS_Delete(cs);
OT_delete(to);
return tc;
}
/// Change a value with proper ceiling and flooring
int Modified_value(int value,int modif)
{
value+=modif;
if (value<0)
{
value=0;
}
else if (value>255)
{
value=255;
}
return value;
}
/// Convert a 24b image to 256 colors (with a given palette and conversion table)
/// This destroys the 24b picture !
/// Uses floyd steinberg dithering.
void Convert_24b_bitmap_to_256_Floyd_Steinberg(T_Bitmap256 dest,T_Bitmap24B source,int width,int height,T_Components * palette,CT_Tree* tc)
{
T_Bitmap24B current;
T_Bitmap24B c_plus1;
T_Bitmap24B u_minus1;
T_Bitmap24B next;
T_Bitmap24B u_plus1;
T_Bitmap256 d;
int x_pos,y_pos;
int red,green,blue;
float e_red,e_green,e_blue;
// On initialise les variables de parcours:
current =source; // Le pixel dont on s'occupe
next =current+width; // Le pixel en dessous
c_plus1 =current+1; // Le pixel <20> droite
u_minus1=next-1; // Le pixel en bas <20> gauche
u_plus1 =next+1; // Le pixel en bas <20> droite
d =dest;
// On parcours chaque pixel:
for (y_pos=0;y_pos<height;y_pos++)
{
for (x_pos=0;x_pos<width;x_pos++)
{
// On prends la meilleure couleur de la palette qui traduit la couleur
// 24 bits de la source:
red=current->R;
green =current->G;
blue =current->B;
// Cherche la couleur correspondant dans la palette et la range dans l'image de destination
*d=CT_get(tc,red,green,blue);
// Puis on calcule pour chaque composante l'erreur d<>e <20> l'approximation
red-=palette[*d].R;
green -=palette[*d].G;
blue -=palette[*d].B;
// Et dans chaque pixel voisin on propage l'erreur
// A droite:
e_red=(red*7)/16.0;
e_green =(green *7)/16.0;
e_blue =(blue *7)/16.0;
if (x_pos+1<width)
{
// Modified_value fait la somme des 2 params en bornant sur [0,255]
c_plus1->R=Modified_value(c_plus1->R,e_red);
c_plus1->G=Modified_value(c_plus1->G,e_green );
c_plus1->B=Modified_value(c_plus1->B,e_blue );
}
// En bas <20> gauche:
if (y_pos+1<height)
{
e_red=(red*3)/16.0;
e_green =(green *3)/16.0;
e_blue =(blue *3)/16.0;
if (x_pos>0)
{
u_minus1->R=Modified_value(u_minus1->R,e_red);
u_minus1->G=Modified_value(u_minus1->G,e_green );
u_minus1->B=Modified_value(u_minus1->B,e_blue );
}
// En bas:
e_red=(red*5/16.0);
e_green =(green*5 /16.0);
e_blue =(blue*5 /16.0);
next->R=Modified_value(next->R,e_red);
next->G=Modified_value(next->G,e_green );
next->B=Modified_value(next->B,e_blue );
// En bas <20> droite:
if (x_pos+1<width)
{
e_red=(red/16.0);
e_green =(green /16.0);
e_blue =(blue /16.0);
u_plus1->R=Modified_value(u_plus1->R,e_red);
u_plus1->G=Modified_value(u_plus1->G,e_green );
u_plus1->B=Modified_value(u_plus1->B,e_blue );
}
}
// On passe au pixel suivant :
current++;
c_plus1++;
u_minus1++;
next++;
u_plus1++;
d++;
}
}
}
/// Converts from 24b to 256c without dithering, using given conversion table
void Convert_24b_bitmap_to_256_nearest_neighbor(T_Bitmap256 dest,
T_Bitmap24B source, int width, int height, T_Components * palette,
CT_Tree* tc)
{
T_Bitmap24B current;
T_Bitmap256 d;
int x_pos, y_pos;
int red, green, blue;
(void)palette; // unused
// On initialise les variables de parcours:
current =source; // Le pixel dont on s'occupe
d =dest;
// On parcours chaque pixel:
for (y_pos = 0; y_pos < height; y_pos++)
{
for (x_pos = 0 ;x_pos < width; x_pos++)
{
// On prends la meilleure couleur de la palette qui traduit la couleur
// 24 bits de la source:
red = current->R;
green = current->G;
blue = current->B;
// Cherche la couleur correspondant dans la palette et la range dans
// l'image de destination
*d = CT_get(tc, red, green, blue);
// On passe au pixel suivant :
current++;
d++;
}
}
}
// Count colors and convert if 256 colors or less are used
// return 0 for success
int Try_Convert_to_256_Without_Loss(T_Bitmap256 dest,T_Bitmap24B source,int width,int height,T_Components * palette)
{
int i;
int n = 0; // number of colors
long index;
T_Bitmap24B ptr;
for (index = width * height, ptr = source; index > 0; index--, ptr++)
{
// look for the color in the table
for (i = 0; i < n; i++) {
if(palette[i].R == ptr->R && palette[i].G == ptr->G && palette[i].B == ptr->B)
break; // found !
}
if (i >= n) {
if (n > 255) {
// there are more than 256 colors
return 1;
}
// need to add the color in the palette
palette[n].R = ptr->R;
palette[n].G = ptr->G;
palette[n].B = ptr->B;
n++;
}
*dest = (byte)i;
dest++;
}
// TODO : Sort the palette ?
return 0;
}
// These are the allowed precisions for all the tables.
// For some of them only the first one may work because of ugly optimizations
static const byte precision_24b[]=
{
8,8,8,
6,6,6,
6,6,5,
5,6,5,
5,5,5,
5,5,4,
4,5,4,
4,4,4,
4,4,3,
3,4,3,
3,3,3,
3,3,2};
// Give this one a 24b source, get back the 256c bitmap and its palette
int Convert_24b_bitmap_to_256(T_Bitmap256 dest,T_Bitmap24B source,int width,int height,T_Components * palette)
{
CT_Tree* table; // table de conversion
int ip; // index de pr<70>cision pour la conversion
if (Try_Convert_to_256_Without_Loss(dest, source, width, height, palette) == 0)
return 0;
#if defined(__GP2X__) || defined(__gp2x__) || defined(__WIZ__) || defined(__CAANOO__)
return Convert_24b_bitmap_to_256_fast(dest, source, width, height, palette);
#else
// On essaye d'obtenir une table de conversion qui loge en m<>moire, avec la
// meilleure pr<70>cision possible
for (ip=0;ip<(10*3);ip+=3)
{
table = Optimize_palette(source,width*height,palette,
precision_24b[ip], precision_24b[ip+1], precision_24b[ip+2]);
if (table != NULL) {
break;
}
}
if (table!=NULL)
{
//Convert_24b_bitmap_to_256_Floyd_Steinberg(dest,source,width,height,palette,table);
Convert_24b_bitmap_to_256_nearest_neighbor(dest,source,width,height,palette,table);
CT_delete(table);
return 0;
}
else
return 1;
#endif
}
//Really small, fast and ugly converter(just for handhelds)
#include "global.h"
#include <limits.h>
#include "engine.h"
#include "windows.h"
extern void Set_palette_fake_24b(T_Palette palette);
/// Really small, fast and dirty convertor(just for handhelds)
int Convert_24b_bitmap_to_256_fast(T_Bitmap256 dest,T_Bitmap24B source,int width,int height,T_Components * palette)
{
int size;
Set_palette_fake_24b(palette);
size = width*height;
while(size--)
{
//Set palette color index to destination bitmap
*dest = ((source->R >> 5) << 5) |
((source->G >> 5) << 2) |
((source->B >> 6));
source++;
dest++;
}
return 0;
}
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