/* TODO: oim and nim in the lower level functions;

  correct use of stub (sigh). */

/* 2.0.12: a new adaptation from the same original, this time

	by Barend Gehrels. My attempt to incorporate alpha channel

	into the result worked poorly and degraded the quality of

	palette conversion even when the source contained no

	alpha channel data. This version does not attempt to produce

	an output file with transparency in some of the palette

	indexes, which, in practice, doesn't look so hot anyway. TBB */

/*

 * gd_topal, adapted from jquant2.c

 *

 * Copyright (C) 1991-1996, Thomas G. Lane.

 * This file is part of the Independent JPEG Group's software.

 * For conditions of distribution and use, see the accompanying README file.

 *

 * This file contains 2-pass color quantization (color mapping) routines.

 * These routines provide selection of a custom color map for an image,

 * followed by mapping of the image to that color map, with optional

 * Floyd-Steinberg dithering.

 * It is also possible to use just the second pass to map to an arbitrary

 * externally-given color map.

 *

 * Note: ordered dithering is not supported, since there isn't any fast

 * way to compute intercolor distances; it's unclear that ordered dither's

 * fundamental assumptions even hold with an irregularly spaced color map.

 */

/**

 * File: Color Quantization

 *

 * Functions for truecolor to palette conversion

 */

/*

 * THOMAS BOUTELL & BAREND GEHRELS, february 2003

 * adapted the code to work within gd rather than within libjpeg.

 * If it is not working, it's not Thomas G. Lane's fault.

 */

#ifdef HAVE_CONFIG_H

#include "config.h"

#endif

#include <string.h>

#include "gd.h"

#include "gdhelpers.h"

#ifdef HAVE_LIBIMAGEQUANT

#include <libimagequant.h>

#endif

/* (Re)define some defines known by libjpeg */

#define QUANT_2PASS_SUPPORTED

#define RGB_RED		0

#define RGB_GREEN	1

#define RGB_BLUE	2

#define JSAMPLE unsigned char

#define MAXJSAMPLE (gdMaxColors-1)

#define BITS_IN_JSAMPLE 8

#define JSAMPROW int*

#define JDIMENSION int

#define METHODDEF(type) static type

#define LOCAL(type)	static type

/* We assume that right shift corresponds to signed division by 2 with

 * rounding towards minus infinity.  This is correct for typical "arithmetic

 * shift" instructions that shift in copies of the sign bit.  But some

 * C compilers implement >> with an unsigned shift.  For these machines you

 * must define RIGHT_SHIFT_IS_UNSIGNED.

 * RIGHT_SHIFT provides a proper signed right shift of an INT32 quantity.

 * It is only applied with constant shift counts.  SHIFT_TEMPS must be

 * included in the variables of any routine using RIGHT_SHIFT.

 */

#ifdef RIGHT_SHIFT_IS_UNSIGNED

#define SHIFT_TEMPS	INT32 shift_temp;

#define RIGHT_SHIFT(x,shft)  \

	((shift_temp = (x)) < 0 ? \

	 (shift_temp >> (shft)) | ((~((INT32) 0)) << (32-(shft))) : \

	 (shift_temp >> (shft)))

#else

#define SHIFT_TEMPS

#define RIGHT_SHIFT(x,shft)	((x) >> (shft))

#endif

#define range_limit(x) { if(x<0) x=0; if (x>255) x=255; }

#ifndef INT16

#define INT16  short

#endif

#ifndef UINT16

#define UINT16 unsigned short

#endif

#ifndef INT32

#define INT32 int

#endif

#ifndef FAR

#define FAR

#endif

#ifndef boolean

#define boolean int

#endif

#ifndef TRUE

#define TRUE 1

#endif

#ifndef FALSE

#define FALSE 0

#endif

#define input_buf (oim->tpixels)

#define output_buf (nim->pixels)

#ifdef QUANT_2PASS_SUPPORTED

/*

 * This module implements the well-known Heckbert paradigm for color

 * quantization.  Most of the ideas used here can be traced back to

 * Heckbert's seminal paper

 *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",

 *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.

 *

 * In the first pass over the image, we accumulate a histogram showing the

 * usage count of each possible color.  To keep the histogram to a reasonable

 * size, we reduce the precision of the input; typical practice is to retain

 * 5 or 6 bits per color, so that 8 or 4 different input values are counted

 * in the same histogram cell.

 *

 * Next, the color-selection step begins with a box representing the whole

 * color space, and repeatedly splits the "largest" remaining box until we

 * have as many boxes as desired colors.  Then the mean color in each

 * remaining box becomes one of the possible output colors.

 *

 * The second pass over the image maps each input pixel to the closest output

 * color (optionally after applying a Floyd-Steinberg dithering correction).

 * This mapping is logically trivial, but making it go fast enough requires

 * considerable care.

 *

 * Heckbert-style quantizers vary a good deal in their policies for choosing

 * the "largest" box and deciding where to cut it.  The particular policies

 * used here have proved out well in experimental comparisons, but better ones

 * may yet be found.

 *

 * In earlier versions of the IJG code, this module quantized in YCbCr color

 * space, processing the raw upsampled data without a color conversion step.

 * This allowed the color conversion math to be done only once per colormap

 * entry, not once per pixel.  However, that optimization precluded other

 * useful optimizations (such as merging color conversion with upsampling)

 * and it also interfered with desired capabilities such as quantizing to an

 * externally-supplied colormap.  We have therefore abandoned that approach.

 * The present code works in the post-conversion color space, typically RGB.

 *

 * To improve the visual quality of the results, we actually work in scaled

 * RGB space, giving G distances more weight than R, and R in turn more than

 * B.  To do everything in integer math, we must use integer scale factors.

 * The 2/3/1 scale factors used here correspond loosely to the relative

 * weights of the colors in the NTSC grayscale equation.

 * If you want to use this code to quantize a non-RGB color space, you'll

 * probably need to change these scale factors.

 */

#define R_SCALE 2		/* scale R distances by this much */

#define G_SCALE 3		/* scale G distances by this much */

#define B_SCALE 1		/* and B by this much */

/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined

 * in jmorecfg.h.  As the code stands, it will do the right thing for R,G,B

 * and B,G,R orders.  If you define some other weird order in jmorecfg.h,

 * you'll get compile errors until you extend this logic.  In that case

 * you'll probably want to tweak the histogram sizes too.

 */

#if RGB_RED == 0

#define C0_SCALE R_SCALE

#endif

#if RGB_BLUE == 0

#define C0_SCALE B_SCALE

#endif

#if RGB_GREEN == 1

#define C1_SCALE G_SCALE

#endif

#if RGB_RED == 2

#define C2_SCALE R_SCALE

#endif

#if RGB_BLUE == 2

#define C2_SCALE B_SCALE

#endif

/*

 * First we have the histogram data structure and routines for creating it.

 *

 * The number of bits of precision can be adjusted by changing these symbols.

 * We recommend keeping 6 bits for G and 5 each for R and B.

 * If you have plenty of memory and cycles, 6 bits all around gives marginally

 * better results; if you are short of memory, 5 bits all around will save

 * some space but degrade the results.

 * To maintain a fully accurate histogram, we'd need to allocate a "long"

 * (preferably unsigned long) for each cell.  In practice this is overkill;

 * we can get by with 16 bits per cell.  Few of the cell counts will overflow,

 * and clamping those that do overflow to the maximum value will give close-

 * enough results.  This reduces the recommended histogram size from 256Kb

 * to 128Kb, which is a useful savings on PC-class machines.

 * (In the second pass the histogram space is re-used for pixel mapping data;

 * in that capacity, each cell must be able to store zero to the number of

 * desired colors.  16 bits/cell is plenty for that too.)

 * Since the JPEG code is intended to run in small memory model on 80x86

 * machines, we can't just allocate the histogram in one chunk.  Instead

 * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each

 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and

 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that

 * on 80x86 machines, the pointer row is in near memory but the actual

 * arrays are in far memory (same arrangement as we use for image arrays).

 */

#define MAXNUMCOLORS  (MAXJSAMPLE+1)	/* maximum size of colormap */

/* These will do the right thing for either R,G,B or B,G,R color order,

 * but you may not like the results for other color orders.

 */

#define HIST_C0_BITS  5		/* bits of precision in R/B histogram */

#define HIST_C1_BITS  6		/* bits of precision in G histogram */

#define HIST_C2_BITS  5		/* bits of precision in B/R histogram */

/* Number of elements along histogram axes. */

#define HIST_C0_ELEMS  (1<

#define HIST_C1_ELEMS  (1<

#define HIST_C2_ELEMS  (1<

/* These are the amounts to shift an input value to get a histogram index. */

#define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)

#define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)

#define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)

typedef UINT16 histcell;	/* histogram cell; prefer an unsigned type */

typedef histcell FAR *histptr;	/* for pointers to histogram cells */

typedef histcell hist1d[HIST_C2_ELEMS];	/* typedefs for the array */

typedef hist1d FAR *hist2d;	/* type for the 2nd-level pointers */

typedef hist2d *hist3d;		/* type for top-level pointer */

/* Declarations for Floyd-Steinberg dithering.

 *

 * Errors are accumulated into the array fserrors[], at a resolution of

 * 1/16th of a pixel count.  The error at a given pixel is propagated

 * to its not-yet-processed neighbors using the standard F-S fractions,

 *		...	(here)	7/16

 *		3/16	5/16	1/16

 * We work left-to-right on even rows, right-to-left on odd rows.

 *

 * We can get away with a single array (holding one row's worth of errors)

 * by using it to store the current row's errors at pixel columns not yet

 * processed, but the next row's errors at columns already processed.  We

 * need only a few extra variables to hold the errors immediately around the

 * current column.  (If we are lucky, those variables are in registers, but

 * even if not, they're probably cheaper to access than array elements are.)

 *

 * The fserrors[] array has (#columns + 2) entries; the extra entry at

 * each end saves us from special-casing the first and last pixels.

 * Each entry is three values long, one value for each color component.

 *

 * Note: on a wide image, we might not have enough room in a PC's near data

 * segment to hold the error array; so it is allocated with alloc_large.

 */

#if BITS_IN_JSAMPLE == 8

typedef INT16 FSERROR;		/* 16 bits should be enough */

typedef int LOCFSERROR;		/* use 'int' for calculation temps */

#else

typedef INT32 FSERROR;		/* may need more than 16 bits */

typedef INT32 LOCFSERROR;	/* be sure calculation temps are big enough */

#endif

typedef FSERROR FAR *FSERRPTR;	/* pointer to error array (in FAR storage!) */

/* Private subobject */

typedef struct {

	/* Variables for accumulating image statistics */

	hist3d histogram;		/* pointer to the histogram */

	/* Variables for Floyd-Steinberg dithering */

	FSERRPTR fserrors;		/* accumulated errors */

	boolean on_odd_row;		/* flag to remember which row we are on */

	int *error_limiter;		/* table for clamping the applied error */

	int *error_limiter_storage;	/* gdMalloc'd storage for the above */

}

my_cquantizer;

typedef my_cquantizer *my_cquantize_ptr;

/*

 * Prescan some rows of pixels.

 * In this module the prescan simply updates the histogram, which has been

 * initialized to zeroes by start_pass.

 * An output_buf parameter is required by the method signature, but no data

 * is actually output (in fact the buffer controller is probably passing a

 * NULL pointer).

 */

METHODDEF (void)

prescan_quantize (gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize)

{

	register JSAMPROW ptr;

	register histptr histp;

	register hist3d histogram = cquantize->histogram;

	int row;

	JDIMENSION col;

	int width = oim->sx;

	int num_rows = oim->sy;

	(void)nim;

	for (row = 0; row < num_rows; row++) {

		ptr = input_buf[row];

		for (col = width; col > 0; col--) {

			int r = gdTrueColorGetRed (*ptr) >> C0_SHIFT;

			int g = gdTrueColorGetGreen (*ptr) >> C1_SHIFT;

			int b = gdTrueColorGetBlue (*ptr) >> C2_SHIFT;

			/* 2.0.12: Steven Brown: support a single totally transparent

			   color in the original. */

			if ((oim->transparent >= 0) && (*ptr == oim->transparent)) {

				ptr++;

				continue;

			}

			/* get pixel value and index into the histogram */

			histp = &histogram[r][g][b];

			/* increment, check for overflow and undo increment if so. */

			if (++(*histp) == 0)

				(*histp)--;

			ptr++;

		}

	}

}

/*

 * Next we have the really interesting routines: selection of a colormap

 * given the completed histogram.

 * These routines work with a list of "boxes", each representing a rectangular

 * subset of the input color space (to histogram precision).

 */

typedef struct {

	/* The bounds of the box (inclusive); expressed as histogram indexes */

	int c0min, c0max;

	int c1min, c1max;

	int c2min, c2max;

	/* The volume (actually 2-norm) of the box */

	INT32 volume;

	/* The number of nonzero histogram cells within this box */

	long colorcount;

}

box;

typedef box *boxptr;

LOCAL (boxptr) find_biggest_color_pop (boxptr boxlist, int numboxes)

/* Find the splittable box with the largest color population */

/* Returns NULL if no splittable boxes remain */

{

	register boxptr boxp;

	register int i;

	register long maxc = 0;

	boxptr which = NULL;

	for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {

		if (boxp->colorcount > maxc && boxp->volume > 0) {

			which = boxp;

			maxc = boxp->colorcount;

		}

	}

	return which;

}

LOCAL (boxptr) find_biggest_volume (boxptr boxlist, int numboxes)

/* Find the splittable box with the largest (scaled) volume */

/* Returns NULL if no splittable boxes remain */

{

	register boxptr boxp;

	register int i;

	register INT32 maxv = 0;

	boxptr which = NULL;

	for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {

		if (boxp->volume > maxv) {

			which = boxp;

			maxv = boxp->volume;

		}

	}

	return which;

}

LOCAL (void)

update_box (gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize, boxptr boxp)

{

	hist3d histogram = cquantize->histogram;

	histptr histp;

	int c0, c1, c2;

	int c0min, c0max, c1min, c1max, c2min, c2max;

	INT32 dist0, dist1, dist2;

	long ccount;

	(void)oim;

	(void)nim;

	c0min = boxp->c0min;

	c0max = boxp->c0max;

	c1min = boxp->c1min;

	c1max = boxp->c1max;

	c2min = boxp->c2min;

	c2max = boxp->c2max;

	if (c0max > c0min)

		for (c0 = c0min; c0 <= c0max; c0++)

			for (c1 = c1min; c1 <= c1max; c1++) {

				histp = &histogram[c0][c1][c2min];

				for (c2 = c2min; c2 <= c2max; c2++)

					if (*histp++ != 0) {

						boxp->c0min = c0min = c0;

						goto have_c0min;

					}

			}

have_c0min:

	if (c0max > c0min)

		for (c0 = c0max; c0 >= c0min; c0--)

			for (c1 = c1min; c1 <= c1max; c1++) {

				histp = &histogram[c0][c1][c2min];

				for (c2 = c2min; c2 <= c2max; c2++)

					if (*histp++ != 0) {

						boxp->c0max = c0max = c0;

						goto have_c0max;

					}

			}

have_c0max:

	if (c1max > c1min)

		for (c1 = c1min; c1 <= c1max; c1++)

			for (c0 = c0min; c0 <= c0max; c0++) {

				histp = &histogram[c0][c1][c2min];

				for (c2 = c2min; c2 <= c2max; c2++)

					if (*histp++ != 0) {

						boxp->c1min = c1min = c1;

						goto have_c1min;

					}

			}

have_c1min:

	if (c1max > c1min)

		for (c1 = c1max; c1 >= c1min; c1--)

			for (c0 = c0min; c0 <= c0max; c0++) {

				histp = &histogram[c0][c1][c2min];

				for (c2 = c2min; c2 <= c2max; c2++)

					if (*histp++ != 0) {

						boxp->c1max = c1max = c1;

						goto have_c1max;

					}

			}

have_c1max:

	if (c2max > c2min)

		for (c2 = c2min; c2 <= c2max; c2++)

			for (c0 = c0min; c0 <= c0max; c0++) {

				histp = &histogram[c0][c1min][c2];

				for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)

					if (*histp != 0) {

						boxp->c2min = c2min = c2;

						goto have_c2min;

					}

			}

have_c2min:

	if (c2max > c2min)

		for (c2 = c2max; c2 >= c2min; c2--)

			for (c0 = c0min; c0 <= c0max; c0++) {

				histp = &histogram[c0][c1min][c2];

				for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)

					if (*histp != 0) {

						boxp->c2max = c2max = c2;

						goto have_c2max;

					}

			}

have_c2max:

	/* Update box volume.

	 * We use 2-norm rather than real volume here; this biases the method

	 * against making long narrow boxes, and it has the side benefit that

	 * a box is splittable iff norm > 0.

	 * Since the differences are expressed in histogram-cell units,

	 * we have to shift back to JSAMPLE units to get consistent distances;

	 * after which, we scale according to the selected distance scale factors.

	 */

	dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;

	dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;

	dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;

	boxp->volume = dist0 * dist0 + dist1 * dist1 + dist2 * dist2;

	/* Now scan remaining volume of box and compute population */

	ccount = 0;

	for (c0 = c0min; c0 <= c0max; c0++)

		for (c1 = c1min; c1 <= c1max; c1++) {

			histp = &histogram[c0][c1][c2min];

			for (c2 = c2min; c2 <= c2max; c2++, histp++)

				if (*histp != 0) {

					ccount++;

				}

		}

	boxp->colorcount = ccount;

}

LOCAL (int)

median_cut (gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,

	    boxptr boxlist, int numboxes, int desired_colors)

/* Repeatedly select and split the largest box until we have enough boxes */

{

	int n, lb;

	int c0, c1, c2, cmax;

	register boxptr b1, b2;

	while (numboxes < desired_colors) {

		/* Select box to split.

		 * Current algorithm: by population for first half, then by volume.

		 */

		if (numboxes * 2 <= desired_colors) {

			b1 = find_biggest_color_pop (boxlist, numboxes);

		} else {

			b1 = find_biggest_volume (boxlist, numboxes);

		}

		if (b1 == NULL)		/* no splittable boxes left! */

			break;

		b2 = &boxlist[numboxes];	/* where new box will go */

		/* Copy the color bounds to the new box. */

		b2->c0max = b1->c0max;

		b2->c1max = b1->c1max;

		b2->c2max = b1->c2max;

		b2->c0min = b1->c0min;

		b2->c1min = b1->c1min;

		b2->c2min = b1->c2min;

		/* Choose which axis to split the box on.

		 * Current algorithm: longest scaled axis.

		 * See notes in update_box about scaling distances.

		 */

		c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;

		c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;

		c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;

		/* We want to break any ties in favor of green, then red, blue last.

		 * This code does the right thing for R,G,B or B,G,R color orders only.

		 */

#if RGB_RED == 0

		cmax = c1;

		n = 1;

		if (c0 > cmax) {

			cmax = c0;

			n = 0;

		}

		if (c2 > cmax) {

			n = 2;

		}

#else

		cmax = c1;

		n = 1;

		if (c2 > cmax) {

			cmax = c2;

			n = 2;

		}

		if (c0 > cmax) {

			n = 0;

		}

#endif

		/* Choose split point along selected axis, and update box bounds.

		 * Current algorithm: split at halfway point.

		 * (Since the box has been shrunk to minimum volume,

		 * any split will produce two nonempty subboxes.)

		 * Note that lb value is max for lower box, so must be < old max.

		 */

		switch (n) {

		case 0:

			lb = (b1->c0max + b1->c0min) / 2;

			b1->c0max = lb;

			b2->c0min = lb + 1;

			break;

		case 1:

			lb = (b1->c1max + b1->c1min) / 2;

			b1->c1max = lb;

			b2->c1min = lb + 1;

			break;

		case 2:

			lb = (b1->c2max + b1->c2min) / 2;

			b1->c2max = lb;

			b2->c2min = lb + 1;

			break;

		}

		/* Update stats for boxes */

		update_box (oim, nim, cquantize, b1);

		update_box (oim, nim, cquantize, b2);

		numboxes++;

	}

	return numboxes;

}

LOCAL (void)

compute_color (gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,

	       boxptr boxp, int icolor)

{

	hist3d histogram = cquantize->histogram;

	histptr histp;

	int c0, c1, c2;

	int c0min, c0max, c1min, c1max, c2min, c2max;

	long count = 0; /* 2.0.28: = 0 */

	long total = 0;

	long c0total = 0;

	long c1total = 0;

	long c2total = 0;

	(void)oim;

	c0min = boxp->c0min;

	c0max = boxp->c0max;

	c1min = boxp->c1min;

	c1max = boxp->c1max;

	c2min = boxp->c2min;

	c2max = boxp->c2max;

	for (c0 = c0min; c0 <= c0max; c0++)

		for (c1 = c1min; c1 <= c1max; c1++) {

			histp = &histogram[c0][c1][c2min];

			for (c2 = c2min; c2 <= c2max; c2++) {

				if ((count = *histp++) != 0) {

					total += count;

					c0total +=

					    ((c0 << C0_SHIFT) + ((1 << C0_SHIFT) >> 1)) * count;

					c1total +=

					    ((c1 << C1_SHIFT) + ((1 << C1_SHIFT) >> 1)) * count;

					c2total +=

					    ((c2 << C2_SHIFT) + ((1 << C2_SHIFT) >> 1)) * count;

				}

			}

		}

	/* 2.0.16: Paul den Dulk found an occasion where total can be 0 */

	if (total) {

		nim->red[icolor] = (int) ((c0total + (total >> 1)) / total);

		nim->green[icolor] = (int) ((c1total + (total >> 1)) / total);

		nim->blue[icolor] = (int) ((c2total + (total >> 1)) / total);

	} else {

		nim->red[icolor] = 255;

		nim->green[icolor] = 255;

		nim->blue[icolor] = 255;

	}

	nim->open[icolor] = 0;

}

LOCAL (void)

select_colors (gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize, int desired_colors)

/* Master routine for color selection */

{

	boxptr boxlist;

	int numboxes;

	int i;

	/* Allocate workspace for box list */

	/* This can't happen because we clamp desired_colors at gdMaxColors,

	  but anyway */

	if (overflow2(desired_colors, sizeof (box))) {

		return;

	}

	boxlist = (boxptr) gdMalloc (desired_colors * sizeof (box));

	if (!boxlist) {

		return;

	}

	/* Initialize one box containing whole space */

	numboxes = 1;

	boxlist[0].c0min = 0;

	boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;

	boxlist[0].c1min = 0;

	boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;

	boxlist[0].c2min = 0;

	boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;

	/* Shrink it to actually-used volume and set its statistics */

	update_box (oim, nim, cquantize, &boxlist[0]);

	/* Perform median-cut to produce final box list */

	numboxes = median_cut (oim, nim, cquantize, boxlist, numboxes, desired_colors);

	/* Compute the representative color for each box, fill colormap */

	for (i = 0; i < numboxes; i++)

		compute_color (oim, nim, cquantize, &boxlist[i], i);

	nim->colorsTotal = numboxes;

	/* If we had a pure transparency color, add it as the last palette entry.

	 * Skip incrementing the color count so that the dither / matching phase

	 * won't use it on pixels that shouldn't have been transparent.  We'll

	 * increment it after all that finishes. */

	if (oim->transparent >= 0) {

		/* Save the transparent color. */

		nim->red[nim->colorsTotal] = gdTrueColorGetRed (oim->transparent);

		nim->green[nim->colorsTotal] = gdTrueColorGetGreen (oim->transparent);

		nim->blue[nim->colorsTotal] = gdTrueColorGetBlue (oim->transparent);

		nim->alpha[nim->colorsTotal] = gdAlphaTransparent;

		nim->open[nim->colorsTotal] = 0;

	}

	gdFree (boxlist);

}

/*

 * These routines are concerned with the time-critical task of mapping input

 * colors to the nearest color in the selected colormap.

 *

 * We re-use the histogram space as an "inverse color map", essentially a

 * cache for the results of nearest-color searches.  All colors within a

 * histogram cell will be mapped to the same colormap entry, namely the one

 * closest to the cell's center.  This may not be quite the closest entry to

 * the actual input color, but it's almost as good.  A zero in the cache

 * indicates we haven't found the nearest color for that cell yet; the array

 * is cleared to zeroes before starting the mapping pass.  When we find the

 * nearest color for a cell, its colormap index plus one is recorded in the

 * cache for future use.  The pass2 scanning routines call fill_inverse_cmap

 * when they need to use an unfilled entry in the cache.

 *

 * Our method of efficiently finding nearest colors is based on the "locally

 * sorted search" idea described by Heckbert and on the incremental distance

 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics

 * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that

 * the distances from a given colormap entry to each cell of the histogram can

 * be computed quickly using an incremental method: the differences between

 * distances to adjacent cells themselves differ by a constant.  This allows a

 * fairly fast implementation of the "brute force" approach of computing the

 * distance from every colormap entry to every histogram cell.  Unfortunately,

 * it needs a work array to hold the best-distance-so-far for each histogram

 * cell (because the inner loop has to be over cells, not colormap entries).

 * The work array elements have to be INT32s, so the work array would need

 * 256Kb at our recommended precision.  This is not feasible in DOS machines.

 *

 * To get around these problems, we apply Thomas' method to compute the

 * nearest colors for only the cells within a small subbox of the histogram.

 * The work array need be only as big as the subbox, so the memory usage

 * problem is solved.  Furthermore, we need not fill subboxes that are never

 * referenced in pass2; many images use only part of the color gamut, so a

 * fair amount of work is saved.  An additional advantage of this

 * approach is that we can apply Heckbert's locality criterion to quickly

 * eliminate colormap entries that are far away from the subbox; typically

 * three-fourths of the colormap entries are rejected by Heckbert's criterion,

 * and we need not compute their distances to individual cells in the subbox.

 * The speed of this approach is heavily influenced by the subbox size: too

 * small means too much overhead, too big loses because Heckbert's criterion

 * can't eliminate as many colormap entries.  Empirically the best subbox

 * size seems to be about 1/512th of the histogram (1/8th in each direction).

 *

 * Thomas' article also describes a refined method which is asymptotically

 * faster than the brute-force method, but it is also far more complex and

 * cannot efficiently be applied to small subboxes.  It is therefore not

 * useful for programs intended to be portable to DOS machines.  On machines

 * with plenty of memory, filling the whole histogram in one shot with Thomas'

 * refined method might be faster than the present code --- but then again,

 * it might not be any faster, and it's certainly more complicated.

 */

/* log2(histogram cells in update box) for each axis; this can be adjusted */

#define BOX_C0_LOG  (HIST_C0_BITS-3)

#define BOX_C1_LOG  (HIST_C1_BITS-3)

#define BOX_C2_LOG  (HIST_C2_BITS-3)

#define BOX_C0_ELEMS  (1<

#define BOX_C1_ELEMS  (1<

#define BOX_C2_ELEMS  (1<

#define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)

#define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)

#define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)

/*

 * The next three routines implement inverse colormap filling.  They could

 * all be folded into one big routine, but splitting them up this way saves

 * some stack space (the mindist[] and bestdist[] arrays need not coexist)

 * and may allow some compilers to produce better code by registerizing more

 * inner-loop variables.

 */

LOCAL (int)

find_nearby_colors (

    gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,

    int minc0, int minc1, int minc2, JSAMPLE colorlist[])

/* Locate the colormap entries close enough to an update box to be candidates

 * for the nearest entry to some cell(s) in the update box.  The update box

 * is specified by the center coordinates of its first cell.  The number of

 * candidate colormap entries is returned, and their colormap indexes are

 * placed in colorlist[].

 * This routine uses Heckbert's "locally sorted search" criterion to select

 * the colors that need further consideration.

 */

{

	int numcolors = nim->colorsTotal;

	int maxc0, maxc1, maxc2;

	int centerc0, centerc1, centerc2;

	int i, x, ncolors;

	INT32 minmaxdist, min_dist, max_dist, tdist;

	INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */

	(void)oim;

	(void)cquantize;

	/* Compute true coordinates of update box's upper corner and center.

	 * Actually we compute the coordinates of the center of the upper-corner

	 * histogram cell, which are the upper bounds of the volume we care about.

	 * Note that since ">>" rounds down, the "center" values may be closer to

	 * min than to max; hence comparisons to them must be "<=", not "<".

	 */

	maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));

	centerc0 = (minc0 + maxc0) >> 1;

	maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));

	centerc1 = (minc1 + maxc1) >> 1;

	maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));

	centerc2 = (minc2 + maxc2) >> 1;

	/* For each color in colormap, find:

	 *  1. its minimum squared-distance to any point in the update box

	 *     (zero if color is within update box);

	 *  2. its maximum squared-distance to any point in the update box.

	 * Both of these can be found by considering only the corners of the box.

	 * We save the minimum distance for each color in mindist[];

	 * only the smallest maximum distance is of interest.

	 */

	minmaxdist = 0x7FFFFFFFL;

	for (i = 0; i < numcolors; i++) {

		/* We compute the squared-c0-distance term, then add in the other two. */

		x = nim->red[i];

		if (x < minc0) {

			tdist = (x - minc0) * C0_SCALE;

			min_dist = tdist * tdist;

			tdist = (x - maxc0) * C0_SCALE;

			max_dist = tdist * tdist;

		} else if (x > maxc0) {

			tdist = (x - maxc0) * C0_SCALE;

			min_dist = tdist * tdist;

			tdist = (x - minc0) * C0_SCALE;

			max_dist = tdist * tdist;

		} else {

			/* within cell range so no contribution to min_dist */

			min_dist = 0;

			if (x <= centerc0) {

				tdist = (x - maxc0) * C0_SCALE;

				max_dist = tdist * tdist;

			} else {

				tdist = (x - minc0) * C0_SCALE;

				max_dist = tdist * tdist;

			}

		}

		x = nim->green[i];

		if (x < minc1) {

			tdist = (x - minc1) * C1_SCALE;