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Diffstat (limited to 'ext/gd/libgd/gd_topal.c')
| -rw-r--r-- | ext/gd/libgd/gd_topal.c | 1688 |
1 files changed, 1688 insertions, 0 deletions
diff --git a/ext/gd/libgd/gd_topal.c b/ext/gd/libgd/gd_topal.c new file mode 100644 index 0000000000..d2be7508f2 --- /dev/null +++ b/ext/gd/libgd/gd_topal.c @@ -0,0 +1,1688 @@ + + +/* + * gd_topal.c + * + * This code is adapted pretty much entirely from jquant2.c, + * Copyright (C) 1991-1996, Thomas G. Lane. That file is + * part of the Independent JPEG Group's software. Conditions of + * use are compatible with the gd license. See the gd license + * statement and README-JPEG.TXT for additional information. + * + * 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. + * + * SUPPORT FOR ALPHA CHANNELS WAS HACKED IN BY THOMAS BOUTELL, who also + * adapted the code to work within gd rather than within libjpeg, and + * may not have done a great job of either. It's not Thomas G. Lane's fault. + */ + +#include "gd.h" +#include "gdhelpers.h" + +/* + * 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 + * RGBA space, giving G distances more weight than R, and R in turn more than + * B. Alpha is weighted least. 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. + */ + +#ifndef TRUE +#define TRUE 1 +#endif /* TRUE */ + +#ifndef FALSE +#define FALSE 0 +#endif /* FALSE */ + +#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 */ +#define A_SCALE 4 /* and alpha by this much. This really + only scales by 1 because alpha + values are 7-bit to begin with. */ + +/* Channel ordering (fixed in gd) */ +#define C0_SCALE R_SCALE +#define C1_SCALE G_SCALE +#define C2_SCALE B_SCALE +#define C3_SCALE A_SCALE + +/* + * 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 (gdMaxColors) /* maximum size of colormap */ + +#define HIST_C0_BITS 5 /* bits of precision in R histogram */ +#define HIST_C1_BITS 6 /* bits of precision in G histogram */ +#define HIST_C2_BITS 5 /* bits of precision in B histogram */ +#define HIST_C3_BITS 3 /* bits of precision in A histogram */ + +/* Number of elements along histogram axes. */ +#define HIST_C0_ELEMS (1<<HIST_C0_BITS) +#define HIST_C1_ELEMS (1<<HIST_C1_BITS) +#define HIST_C2_ELEMS (1<<HIST_C2_BITS) +#define HIST_C3_ELEMS (1<<HIST_C3_BITS) + +/* These are the amounts to shift an input value to get a histogram index. */ +#define C0_SHIFT (8-HIST_C0_BITS) +#define C1_SHIFT (8-HIST_C1_BITS) +#define C2_SHIFT (8-HIST_C2_BITS) +/* Beware! Alpha is 7 bit to begin with */ +#define C3_SHIFT (7-HIST_C3_BITS) + + +typedef unsigned short histcell; /* histogram cell; prefer an unsigned type */ + +typedef histcell *histptr; /* for pointers to histogram cells */ + +typedef histcell hist1d[HIST_C3_ELEMS]; /* typedefs for the array */ +typedef hist1d *hist2d; /* type for the 2nd-level pointers */ +typedef hist2d *hist3d; /* type for third-level pointer */ +typedef hist3d *hist4d; /* 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. + * + */ + +typedef signed short FSERROR; /* 16 bits should be enough */ +typedef int LOCFSERROR; /* use 'int' for calculation temps */ + +typedef FSERROR *FSERRPTR; /* pointer to error array */ + +/* Private object */ + +typedef struct + { + hist4d histogram; /* pointer to the histogram */ + int needs_zeroed; /* TRUE if next pass must zero histogram */ + + /* Variables for Floyd-Steinberg dithering */ + FSERRPTR fserrors; /* accumulated errors */ + int 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 */ + int transparentIsPresent; /* TBB: for rescaling to ensure that */ + int opaqueIsPresent; /* 100% opacity & transparency are preserved */ + } +my_cquantizer; + +typedef my_cquantizer *my_cquantize_ptr; + +/* + * Prescan the pixel array. + * + * The prescan simply updates the histogram, which has been + * initialized to zeroes by start_pass. + * + */ + +static void +prescan_quantize (gdImagePtr im, my_cquantize_ptr cquantize) +{ + register histptr histp; + register hist4d histogram = cquantize->histogram; + int row; + int col; + int *ptr; + int width = im->sx; + + for (row = 0; row < im->sy; row++) + { + ptr = im->tpixels[row]; + for (col = width; col > 0; col--) + { + /* get pixel value and index into the histogram */ + int r, g, b, a; + r = gdTrueColorGetRed (*ptr) >> C0_SHIFT; + g = gdTrueColorGetGreen (*ptr) >> C1_SHIFT; + b = gdTrueColorGetBlue (*ptr) >> C2_SHIFT; + a = gdTrueColorGetAlpha (*ptr); + /* We must have 100% opacity and transparency available + in the color map to do an acceptable job with alpha + channel, if opacity and transparency are present in the + original, because of the visual properties of large + flat-color border areas (requiring 100% transparency) + and the behavior of poorly implemented browsers + (requiring 100% opacity). Test for the presence of + these here, and rescale the most opaque and transparent + palette entries at the end if so. This avoids the need + to develop a fuller understanding I have not been able + to reach so far in my study of this subject. TBB */ + if (a == gdAlphaTransparent) + { + cquantize->transparentIsPresent = 1; + } + if (a == gdAlphaOpaque) + { + cquantize->opaqueIsPresent = 1; + } + a >>= C3_SHIFT; + histp = &histogram[r][g][b][a]; + /* 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; + int c3min, c3max; + /* The volume (actually 2-norm) of the box */ + int volume; + /* The number of nonzero histogram cells within this box */ + long colorcount; +} +box; + +typedef box *boxptr; + +static 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; +} + + +static 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 int 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; +} + + +static void +update_box (gdImagePtr im, my_cquantize_ptr cquantize, boxptr boxp) +/* Shrink the min/max bounds of a box to enclose only nonzero elements, */ +/* and recompute its volume and population */ +{ + hist4d histogram = cquantize->histogram; + histptr histp; + int c0, c1, c2, c3; + int c0min, c0max, c1min, c1max, c2min, c2max, c3min, c3max; + int dist0, dist1, dist2, dist3; + long ccount; + + c0min = boxp->c0min; + c0max = boxp->c0max; + c1min = boxp->c1min; + c1max = boxp->c1max; + c2min = boxp->c2min; + c2max = boxp->c2max; + c3min = boxp->c3min; + c3max = boxp->c3max; + + if (c0max > c0min) + { + for (c0 = c0min; c0 <= c0max; c0++) + { + for (c1 = c1min; c1 <= c1max; c1++) + { + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++) + { + 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++) + { + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++) + { + 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++) + { + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++) + 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++) + { + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++) + if (*histp++ != 0) + { + boxp->c1max = c1max = c1; + goto have_c1max; + } + } + } +have_c1max: + /* The original version hand-rolled the array lookup a little, but + with four dimensions, I don't even want to think about it. TBB */ + if (c2max > c2min) + for (c2 = c2min; c2 <= c2max; c2++) + for (c0 = c0min; c0 <= c0max; c0++) + for (c1 = c1min; c1 <= c1max; c1++) + for (c3 = c3min; c3 <= c3max; c3++) + if (histogram[c0][c1][c2][c3] != 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++) + for (c1 = c1min; c1 <= c1max; c1++) + for (c3 = c3min; c3 <= c3max; c3++) + if (histogram[c0][c1][c2][c3] != 0) + { + boxp->c2max = c2max = c2; + goto have_c2max; + } +have_c2max: + if (c3max > c3min) + for (c3 = c3min; c3 <= c3max; c3++) + for (c0 = c0min; c0 <= c0max; c0++) + for (c1 = c1min; c1 <= c1max; c1++) + for (c2 = c2min; c2 <= c2max; c2++) + if (histogram[c0][c1][c2][c3] != 0) + { + boxp->c3min = c3min = c3; + goto have_c3min; + } +have_c3min: + if (c3max > c3min) + for (c3 = c3max; c3 >= c3min; c3--) + for (c0 = c0min; c0 <= c0max; c0++) + for (c1 = c1min; c1 <= c1max; c1++) + for (c2 = c2min; c2 <= c2max; c2++) + if (histogram[c0][c1][c2][c3] != 0) + { + boxp->c3max = c3max = c3; + goto have_c3max; + } +have_c3max: + /* 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 8-bit units to get consistent distances; + * after which, we scale according to the selected distance scale factors. + * TBB: alpha shifts back to 7 bit units. That was accounted for in the + * alpha scale factor. + */ + dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; + dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; + dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; + dist3 = ((c3max - c3min) << C3_SHIFT) * C3_SCALE; + boxp->volume = dist0 * dist0 + dist1 * dist1 + dist2 * dist2 + dist3 * dist3; + + /* Now scan remaining volume of box and compute population */ + ccount = 0; + for (c0 = c0min; c0 <= c0max; c0++) + for (c1 = c1min; c1 <= c1max; c1++) + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++, histp++) + if (*histp != 0) + { + ccount++; + } + } + boxp->colorcount = ccount; +} + + +static int +median_cut (gdImagePtr im, 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, c3, 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->c3max = b1->c3max; + b2->c0min = b1->c0min; + b2->c1min = b1->c1min; + b2->c2min = b1->c2min; + b2->c3min = b1->c3min; + /* 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; + c3 = ((b1->c3max - b1->c3min) << C3_SHIFT) * C3_SCALE; + /* We want to break any ties in favor of green, then red, then blue, + with alpha last. */ + cmax = c1; + n = 1; + if (c0 > cmax) + { + cmax = c0; + n = 0; + } + if (c2 > cmax) + { + cmax = c2; + n = 2; + } + if (c3 > cmax) + { + n = 3; + } + /* 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; + case 3: + lb = (b1->c3max + b1->c3min) / 2; + b1->c3max = lb; + b2->c3min = lb + 1; + break; + } + /* Update stats for boxes */ + update_box (im, cquantize, b1); + update_box (im, cquantize, b2); + numboxes++; + } + return numboxes; +} + + +static void +compute_color (gdImagePtr im, my_cquantize_ptr cquantize, + boxptr boxp, int icolor) +/* + Compute representative color for a box, put it in + palette index icolor */ +{ + /* Current algorithm: mean weighted by pixels (not colors) */ + /* Note it is important to get the rounding correct! */ + hist4d histogram = cquantize->histogram; + histptr histp; + int c0, c1, c2, c3; + int c0min, c0max, c1min, c1max, c2min, c2max, c3min, c3max; + long count; + long total = 0; + long c0total = 0; + long c1total = 0; + long c2total = 0; + long c3total = 0; + + c0min = boxp->c0min; + c0max = boxp->c0max; + c1min = boxp->c1min; + c1max = boxp->c1max; + c2min = boxp->c2min; + c2max = boxp->c2max; + c3min = boxp->c3min; + c3max = boxp->c3max; + + for (c0 = c0min; c0 <= c0max; c0++) + { + for (c1 = c1min; c1 <= c1max; c1++) + { + for (c2 = c2min; c2 <= c2max; c2++) + { + histp = &histogram[c0][c1][c2][c3min]; + for (c3 = c3min; c3 <= c3max; c3++) + { + 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; + c3total += ((c3 << C3_SHIFT) + ((1 << C3_SHIFT) >> 1)) * count; + } + } + } + } + } + im->red[icolor] = (int) ((c0total + (total >> 1)) / total); + im->green[icolor] = (int) ((c1total + (total >> 1)) / total); + im->blue[icolor] = (int) ((c2total + (total >> 1)) / total); + im->alpha[icolor] = (int) ((c3total + (total >> 1)) / total); + im->open[icolor] = 0; + if (im->colorsTotal <= icolor) + { + im->colorsTotal = icolor + 1; + } +} + +static void +select_colors (gdImagePtr im, my_cquantize_ptr cquantize, int desired_colors) +/* Master routine for color selection */ +{ + boxptr boxlist; + int numboxes; + int i; + + /* Allocate workspace for box list */ + boxlist = (boxptr) gdMalloc (desired_colors * sizeof (box)); + /* Initialize one box containing whole space */ + numboxes = 1; + /* Note maxval for alpha is different */ + boxlist[0].c0min = 0; + boxlist[0].c0max = 255 >> C0_SHIFT; + boxlist[0].c1min = 0; + boxlist[0].c1max = 255 >> C1_SHIFT; + boxlist[0].c2min = 0; + boxlist[0].c2max = 255 >> C2_SHIFT; + boxlist[0].c3min = 0; + boxlist[0].c3max = gdAlphaMax >> C3_SHIFT; + /* Shrink it to actually-used volume and set its statistics */ + update_box (im, cquantize, &boxlist[0]); + /* Perform median-cut to produce final box list */ + numboxes = median_cut (im, cquantize, boxlist, numboxes, desired_colors); + /* Compute the representative color for each box, fill colormap */ + for (i = 0; i < numboxes; i++) + compute_color (im, cquantize, &boxlist[i], i); + /* TBB: if the image contains colors at both scaled ends + of the alpha range, rescale slightly to make sure alpha + covers the full spectrum from 100% transparent to 100% + opaque. Even a faint distinct background color is + generally considered failure with regard to alpha. */ + + im->colorsTotal = numboxes; + 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_C3_LOG (HIST_C3_BITS-3) + +#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ +#define BOX_C1_ELEMS (1<<BOX_C1_LOG) +#define BOX_C2_ELEMS (1<<BOX_C2_LOG) +#define BOX_C3_ELEMS (1<<BOX_C3_LOG) + +#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) +#define BOX_C3_SHIFT (C3_SHIFT + BOX_C3_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. + */ + +static int +find_nearby_colors (gdImagePtr im, my_cquantize_ptr cquantize, + int minc0, int minc1, int minc2, int minc3, int 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 = im->colorsTotal; + int maxc0, maxc1, maxc2, maxc3; + int centerc0, centerc1, centerc2, centerc3; + int i, x, ncolors; + int minmaxdist, min_dist, max_dist, tdist; + int mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ + + /* 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; + maxc3 = minc3 + ((1 << BOX_C3_SHIFT) - (1 << C3_SHIFT)); + centerc3 = (minc3 + maxc3) >> 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 three. */ + x = im->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 = im->green[i]; + if (x < minc1) + { + tdist = (x - minc1) * C1_SCALE; + min_dist += tdist * tdist; + tdist = (x - maxc1) * C1_SCALE; + max_dist += tdist * tdist; + } + else if (x > maxc1) + { + tdist = (x - maxc1) * C1_SCALE; + min_dist += tdist * tdist; + tdist = (x - minc1) * C1_SCALE; + max_dist += tdist * tdist; + } + else + { + /* within cell range so no contribution to min_dist */ + if (x <= centerc1) + { + tdist = (x - maxc1) * C1_SCALE; + max_dist += tdist * tdist; + } + else + { + tdist = (x - minc1) * C1_SCALE; + max_dist += tdist * tdist; + } + } + + x = im->blue[i]; + if (x < minc2) + { + tdist = (x - minc2) * C2_SCALE; + min_dist += tdist * tdist; + tdist = (x - maxc2) * C2_SCALE; + max_dist += tdist * tdist; + } + else if (x > maxc2) + { + tdist = (x - maxc2) * C2_SCALE; + min_dist += tdist * tdist; + tdist = (x - minc2) * C2_SCALE; + max_dist += tdist * tdist; + } + else + { + /* within cell range so no contribution to min_dist */ + if (x <= centerc2) + { + tdist = (x - maxc2) * C2_SCALE; + max_dist += tdist * tdist; + } + else + { + tdist = (x - minc2) * C2_SCALE; + max_dist += tdist * tdist; + } + } + + x = im->alpha[i]; + if (x < minc3) + { + tdist = (x - minc3) * C3_SCALE; + min_dist += tdist * tdist; + tdist = (x - maxc3) * C3_SCALE; + max_dist += tdist * tdist; + } + else if (x > maxc3) + { + tdist = (x - maxc3) * C3_SCALE; + min_dist += tdist * tdist; + tdist = (x - minc3) * C3_SCALE; + max_dist += tdist * tdist; + } + else + { + /* within cell range so no contribution to min_dist */ + if (x <= centerc3) + { + tdist = (x - maxc3) * C3_SCALE; + max_dist += tdist * tdist; + } + else + { + tdist = (x - minc3) * C3_SCALE; + max_dist += tdist * tdist; + } + } + + mindist[i] = min_dist; /* save away the results */ + if (max_dist < minmaxdist) + minmaxdist = max_dist; + } + + /* Now we know that no cell in the update box is more than minmaxdist + * away from some colormap entry. Therefore, only colors that are + * within minmaxdist of some part of the box need be considered. + */ + ncolors = 0; + for (i = 0; i < numcolors; i++) + { + if (mindist[i] <= minmaxdist) + colorlist[ncolors++] = i; + } + return ncolors; +} + + +static void +find_best_colors (gdImagePtr im, my_cquantize_ptr cquantize, + int minc0, int minc1, int minc2, int minc3, + int numcolors, int colorlist[], int bestcolor[]) +/* Find the closest colormap entry for each cell in the update box, + * given the list of candidate colors prepared by find_nearby_colors. + * Return the indexes of the closest entries in the bestcolor[] array. + * This routine uses Thomas' incremental distance calculation method to + * find the distance from a colormap entry to successive cells in the box. + */ +{ + int ic0, ic1, ic2, ic3; + int i, icolor; + register int *bptr; /* pointer into bestdist[] array */ + int *cptr; /* pointer into bestcolor[] array */ + int dist0, dist1, dist2; /* initial distance values */ + register int dist3; /* current distance in inner loop */ + int xx0, xx1, xx2; /* distance increments */ + register int xx3; + int inc0, inc1, inc2, inc3; /* initial values for increments */ + /* This array holds the distance to the nearest-so-far color for each cell */ + int bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_ELEMS]; + + /* Initialize best-distance for each cell of the update box */ + bptr = bestdist; + for (i = BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_ELEMS - 1; i >= 0; i--) + *bptr++ = 0x7FFFFFFFL; + + /* For each color selected by find_nearby_colors, + * compute its distance to the center of each cell in the box. + * If that's less than best-so-far, update best distance and color number. + */ + + /* Nominal steps between cell centers ("x" in Thomas article) */ +#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) +#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) +#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) +#define STEP_C3 ((1 << C3_SHIFT) * C3_SCALE) + + for (i = 0; i < numcolors; i++) + { + icolor = colorlist[i]; + /* Compute (square of) distance from minc0/c1/c2 to this color */ + inc0 = (minc0 - (im->red[icolor])) * C0_SCALE; + dist0 = inc0 * inc0; + inc1 = (minc1 - (im->green[icolor])) * C1_SCALE; + dist0 += inc1 * inc1; + inc2 = (minc2 - (im->blue[icolor])) * C2_SCALE; + dist0 += inc2 * inc2; + inc3 = (minc3 - (im->alpha[icolor])) * C3_SCALE; + dist0 += inc3 * inc3; + /* Form the initial difference increments */ + inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; + inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; + inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; + inc3 = inc3 * (2 * STEP_C3) + STEP_C3 * STEP_C3; + /* Now loop over all cells in box, updating distance per Thomas method */ + bptr = bestdist; + cptr = bestcolor; + xx0 = inc0; + for (ic0 = BOX_C0_ELEMS - 1; ic0 >= 0; ic0--) + { + dist1 = dist0; + xx1 = inc1; + for (ic1 = BOX_C1_ELEMS - 1; ic1 >= 0; ic1--) + { + dist2 = dist1; + xx2 = inc2; + for (ic2 = BOX_C2_ELEMS - 1; ic2 >= 0; ic2--) + { + for (ic3 = BOX_C3_ELEMS - 1; ic3 >= 0; ic3--) + { + if (dist3 < *bptr) + { + *bptr = dist3; + *cptr = icolor; + } + dist3 += xx3; + xx3 += 2 * STEP_C3 * STEP_C3; + bptr++; + cptr++; + } + dist2 += xx2; + xx2 += 2 * STEP_C2 * STEP_C2; + } + dist1 += xx1; + xx1 += 2 * STEP_C1 * STEP_C1; + } + dist0 += xx0; + xx0 += 2 * STEP_C0 * STEP_C0; + } + } +} + + +static void +fill_inverse_cmap (gdImagePtr im, my_cquantize_ptr cquantize, + int c0, int c1, int c2, int c3) +/* Fill the inverse-colormap entries in the update box that contains */ +/* histogram cell c0/c1/c2/c3. (Only that one cell MUST be filled, but */ +/* we can fill as many others as we wish.) */ +{ + hist4d histogram = cquantize->histogram; + int minc0, minc1, minc2, minc3; /* lower left corner of update box */ + int ic0, ic1, ic2, ic3; + register int *cptr; /* pointer into bestcolor[] array */ + register histptr cachep; /* pointer into main cache array */ + /* This array lists the candidate colormap indexes. */ + int colorlist[MAXNUMCOLORS]; + int numcolors; /* number of candidate colors */ + /* This array holds the actually closest colormap index for each cell. */ + int bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_ELEMS]; + + /* Convert cell coordinates to update box ID */ + c0 >>= BOX_C0_LOG; + c1 >>= BOX_C1_LOG; + c2 >>= BOX_C2_LOG; + c3 >>= BOX_C3_LOG; + + /* Compute true coordinates of update box's origin corner. + * Actually we compute the coordinates of the center of the corner + * histogram cell, which are the lower bounds of the volume we care about. + */ + minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); + minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); + minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); + minc3 = (c3 << BOX_C3_SHIFT) + ((1 << C3_SHIFT) >> 1); + /* Determine which colormap entries are close enough to be candidates + * for the nearest entry to some cell in the update box. + */ + numcolors = find_nearby_colors (im, cquantize, minc0, minc1, minc2, minc3, colorlist); + + /* Determine the actually nearest colors. */ + find_best_colors (im, cquantize, minc0, minc1, minc2, minc3, numcolors, colorlist, + bestcolor); + + /* Save the best color numbers (plus 1) in the main cache array */ + c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ + c1 <<= BOX_C1_LOG; + c2 <<= BOX_C2_LOG; + c3 <<= BOX_C3_LOG; + cptr = bestcolor; + for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) + { + for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) + { + for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) + { + cachep = &histogram[c0 + ic0][c1 + ic1][c2 + ic2][c3]; + for (ic3 = 0; ic3 < BOX_C3_ELEMS; ic3++) + { + *cachep++ = (histcell) ((*cptr++) + 1); + } + } + } + } +} + + +/* + * Map some rows of pixels to the output colormapped representation. + */ + +void +pass2_no_dither (gdImagePtr im, my_cquantize_ptr cquantize) +/* This version performs no dithering */ +{ + hist4d histogram = cquantize->histogram; + register int *inptr; + register unsigned char *outptr; + register histptr cachep; + register int c0, c1, c2, c3; + int row; + int col; + int width = im->sx; + int num_rows = im->sy; + for (row = 0; row < num_rows; row++) + { + inptr = im->tpixels[row]; + outptr = im->pixels[row]; + for (col = 0; col < width; col++) + { + int r, g, b, a; + /* get pixel value and index into the cache */ + r = gdTrueColorGetRed (*inptr); + g = gdTrueColorGetGreen (*inptr); + b = gdTrueColorGetBlue (*inptr); + a = gdTrueColorGetAlpha (*inptr++); + c0 = r >> C0_SHIFT; + c1 = g >> C1_SHIFT; + c2 = b >> C2_SHIFT; + c3 = a >> C3_SHIFT; + cachep = &histogram[c0][c1][c2][c3]; + /* If we have not seen this color before, find nearest colormap entry */ + /* and update the cache */ + if (*cachep == 0) + { +#if 0 + /* TBB: quick and dirty approach for use when testing + fill_inverse_cmap for errors */ + int i; + int best = -1; + int mindist = 0x7FFFFFFF; + for (i = 0; (i < im->colorsTotal); i++) + { + int rdist = (im->red[i] >> C0_SHIFT) - c0; + int gdist = (im->green[i] >> C1_SHIFT) - c1; + int bdist = (im->blue[i] >> C2_SHIFT) - c2; + int adist = (im->alpha[i] >> C3_SHIFT) - c3; + int dist = (rdist * rdist) * R_SCALE + + (gdist * gdist) * G_SCALE + + (bdist * bdist) * B_SCALE + + (adist * adist) * A_SCALE; + if (dist < mindist) + { + best = i; + mindist = dist; + } + } + *cachep = best + 1; +#endif + fill_inverse_cmap (im, cquantize, c0, c1, c2, c3); + } + /* Now emit the colormap index for this cell */ + *outptr++ = (*cachep - 1); + } + } +} + +/* 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 + + +void +pass2_fs_dither (gdImagePtr im, my_cquantize_ptr cquantize) + +/* This version performs Floyd-Steinberg dithering */ +{ + hist4d histogram = cquantize->histogram; + register LOCFSERROR cur0, cur1, cur2, cur3; /* current error or pixel value */ + LOCFSERROR belowerr0, belowerr1, belowerr2, belowerr3; /* error for pixel below cur */ + LOCFSERROR bpreverr0, bpreverr1, bpreverr2, bpreverr3; /* error for below/prev col */ + register FSERRPTR errorptr; /* => fserrors[] at column before current */ + int *inptr; /* => current input pixel */ + unsigned char *outptr; /* => current output pixel */ + histptr cachep; + int dir; /* +1 or -1 depending on direction */ + int dir4; /* 4*dir, for advancing errorptr */ + int row; + int col; + int width = im->sx; + int num_rows = im->sy; + int *error_limit = cquantize->error_limiter; + int *colormap0 = im->red; + int *colormap1 = im->green; + int *colormap2 = im->blue; + int *colormap3 = im->alpha; + SHIFT_TEMPS + + for (row = 0; row < num_rows; row++) + { + inptr = im->tpixels[row]; + outptr = im->pixels[row]; + if (cquantize->on_odd_row) + { + /* work right to left in this row */ + inptr += (width - 1); /* so point to rightmost pixel */ + outptr += width - 1; + dir = -1; + dir4 = -4; + errorptr = cquantize->fserrors + (width + 1) * 4; /* => entry after last column */ + cquantize->on_odd_row = FALSE; /* flip for next time */ + } + else + { + /* work left to right in this row */ + dir = 1; + dir4 = 4; + errorptr = cquantize->fserrors; /* => entry before first real column */ + cquantize->on_odd_row = TRUE; /* flip for next time */ + } + /* Preset error values: no error propagated to first pixel from left */ + cur0 = cur1 = cur2 = cur3 = 0; + /* and no error propagated to row below yet */ + belowerr0 = belowerr1 = belowerr2 = belowerr3 = 0; + bpreverr0 = bpreverr1 = bpreverr2 = bpreverr3 = 0; + + for (col = width; col > 0; col--) + { + int a; + /* curN holds the error propagated from the previous pixel on the + * current line. Add the error propagated from the previous line + * to form the complete error correction term for this pixel, and + * round the error term (which is expressed * 16) to an integer. + * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct + * for either sign of the error value. + * Note: errorptr points to *previous* column's array entry. + */ + cur0 = RIGHT_SHIFT (cur0 + errorptr[dir4 + 0] + 8, 4); + cur1 = RIGHT_SHIFT (cur1 + errorptr[dir4 + 1] + 8, 4); + cur2 = RIGHT_SHIFT (cur2 + errorptr[dir4 + 2] + 8, 4); + cur3 = RIGHT_SHIFT (cur3 + errorptr[dir4 + 3] + 8, 4); + /* Limit the error using transfer function set by init_error_limit. + * See comments with init_error_limit for rationale. + */ + cur0 = error_limit[cur0]; + cur1 = error_limit[cur1]; + cur2 = error_limit[cur2]; + cur3 = error_limit[cur3]; + /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. + * The maximum error is +- MAXJSAMPLE (or less with error limiting); + * but we'll be lazy and just clamp this with an if test (TBB). + */ + cur0 += gdTrueColorGetRed (*inptr); + cur1 += gdTrueColorGetGreen (*inptr); + cur2 += gdTrueColorGetBlue (*inptr); + /* Expand to 8 bits for consistency with dithering algorithm -- TBB */ + a = gdTrueColorGetAlpha (*inptr); + cur3 += (a << 1) + (a >> 6); + if (cur0 < 0) + { + cur0 = 0; + } + if (cur0 > 255) + { + cur0 = 255; + } + if (cur1 < 0) + { + cur1 = 0; + } + if (cur1 > 255) + { + cur1 = 255; + } + if (cur2 < 0) + { + cur2 = 0; + } + if (cur2 > 255) + { + cur2 = 255; + } + if (cur3 < 0) + { + cur3 = 0; + } + if (cur3 > 255) + { + cur3 = 255; + } + /* Index into the cache with adjusted pixel value */ + cachep = &histogram + [cur0 >> C0_SHIFT] + [cur1 >> C1_SHIFT] + [cur2 >> C2_SHIFT] + [cur3 >> (C3_SHIFT + 1)]; + /* If we have not seen this color before, find nearest colormap */ + /* entry and update the cache */ + if (*cachep == 0) + fill_inverse_cmap (im, cquantize, + cur0 >> C0_SHIFT, cur1 >> C1_SHIFT, cur2 >> C2_SHIFT, + cur3 >> (C3_SHIFT + 1)); + /* Now emit the colormap index for this cell */ + { + register int pixcode = *cachep - 1; + *outptr = pixcode; + /* Compute representation error for this pixel */ + cur0 -= colormap0[pixcode]; + cur1 -= colormap1[pixcode]; + cur2 -= colormap2[pixcode]; + cur3 -= ((colormap3[pixcode] << 1) + (colormap3[pixcode] >> 6)); + } + /* Compute error fractions to be propagated to adjacent pixels. + * Add these into the running sums, and simultaneously shift the + * next-line error sums left by 1 column. + */ + { + register LOCFSERROR bnexterr, delta; + + bnexterr = cur0; /* Process component 0 */ + delta = cur0 * 2; + cur0 += delta; /* form error * 3 */ + errorptr[0] = (FSERROR) (bpreverr0 + cur0); + cur0 += delta; /* form error * 5 */ + bpreverr0 = belowerr0 + cur0; + belowerr0 = bnexterr; + cur0 += delta; /* form error * 7 */ + bnexterr = cur1; /* Process component 1 */ + delta = cur1 * 2; + cur1 += delta; /* form error * 3 */ + errorptr[1] = (FSERROR) (bpreverr1 + cur1); + cur1 += delta; /* form error * 5 */ + bpreverr1 = belowerr1 + cur1; + belowerr1 = bnexterr; + cur1 += delta; /* form error * 7 */ + bnexterr = cur2; /* Process component 2 */ + delta = cur2 * 2; + cur2 += delta; /* form error * 3 */ + errorptr[2] = (FSERROR) (bpreverr2 + cur2); + cur2 += delta; /* form error * 5 */ + bpreverr2 = belowerr2 + cur2; + belowerr2 = bnexterr; + cur2 += delta; /* form error * 7 */ + bnexterr = cur3; /* Process component 3 */ + delta = cur3 * 2; + cur3 += delta; /* form error * 3 */ + errorptr[3] = (FSERROR) (bpreverr3 + cur3); + cur3 += delta; /* form error * 5 */ + bpreverr3 = belowerr3 + cur3; + belowerr3 = bnexterr; + cur3 += delta; /* form error * 7 */ + } + /* At this point curN contains the 7/16 error value to be propagated + * to the next pixel on the current line, and all the errors for the + * next line have been shifted over. We are therefore ready to move on. + */ + inptr += dir; /* Advance pixel pointers to next column */ + outptr += dir; + errorptr += dir4; /* advance errorptr to current column */ + } + /* Post-loop cleanup: we must unload the final error values into the + * final fserrors[] entry. Note we need not unload belowerrN because + * it is for the dummy column before or after the actual array. + */ + errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ + errorptr[1] = (FSERROR) bpreverr1; + errorptr[2] = (FSERROR) bpreverr2; + errorptr[3] = (FSERROR) bpreverr3; + } +} + + +/* + * Initialize the error-limiting transfer function (lookup table). + * The raw F-S error computation can potentially compute error values of up to + * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be + * much less, otherwise obviously wrong pixels will be created. (Typical + * effects include weird fringes at color-area boundaries, isolated bright + * pixels in a dark area, etc.) The standard advice for avoiding this problem + * is to ensure that the "corners" of the color cube are allocated as output + * colors; then repeated errors in the same direction cannot cause cascading + * error buildup. However, that only prevents the error from getting + * completely out of hand; Aaron Giles reports that error limiting improves + * the results even with corner colors allocated. + * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty + * well, but the smoother transfer function used below is even better. Thanks + * to Aaron Giles for this idea. + */ + +static int +init_error_limit (gdImagePtr im, my_cquantize_ptr cquantize) +/* Allocate and fill in the error_limiter table */ +{ + int *table; + int in, out; + + cquantize->error_limiter_storage = (int *) gdMalloc ((255 * 2 + 1) * sizeof (int)); + if (!cquantize->error_limiter_storage) + { + return 0; + } + /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ + cquantize->error_limiter = cquantize->error_limiter_storage + 255; + table = cquantize->error_limiter; +#define STEPSIZE ((255+1)/16) + /* Map errors 1:1 up to +- MAXJSAMPLE/16 */ + out = 0; + for (in = 0; in < STEPSIZE; in++, out++) + { + table[in] = out; + table[-in] = -out; + } + /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ + for (; in < STEPSIZE * 3; in++, out += (in & 1) ? 0 : 1) + { + table[in] = out; + table[-in] = -out; + } + /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ + for (; in <= 255; in++) + { + table[in] = out; + table[-in] = -out; + } +#undef STEPSIZE + return 1; +} + +static void +zeroHistogram (hist4d histogram) +{ + int i; + int j; + /* Zero the histogram or inverse color map */ + for (i = 0; i < HIST_C0_ELEMS; i++) + { + for (j = 0; j < HIST_C1_ELEMS; j++) + { + memset (histogram[i][j], + 0, + HIST_C2_ELEMS * HIST_C3_ELEMS * sizeof (histcell)); + } + } +} + +/* Here we go at last. */ +void +gdImageTrueColorToPalette (gdImagePtr im, int dither, int colorsWanted) +{ + my_cquantize_ptr cquantize = 0; + int i; + size_t arraysize; + if (!im->trueColor) + { + /* Nothing to do! */ + return; + } + if (colorsWanted > gdMaxColors) + { + colorsWanted = gdMaxColors; + } + im->pixels = gdCalloc (sizeof (unsigned char *), im->sy); + if (!im->pixels) + { + /* No can do */ + goto outOfMemory; + } + for (i = 0; (i < im->sy); i++) + { + im->pixels[i] = gdCalloc (sizeof (unsigned char *), im->sx); + if (!im->pixels[i]) + { + goto outOfMemory; + } + } + cquantize = (my_cquantize_ptr) gdCalloc (sizeof (my_cquantizer), 1); + if (!cquantize) + { + /* No can do */ + goto outOfMemory; + } + /* Allocate the histogram/inverse colormap storage */ + cquantize->histogram = (hist4d) gdMalloc (HIST_C0_ELEMS * sizeof (hist3d)); + for (i = 0; i < HIST_C0_ELEMS; i++) + { + int j; + cquantize->histogram[i] = (hist3d) gdCalloc (HIST_C1_ELEMS, + sizeof (hist2d)); + if (!cquantize->histogram[i]) + { + goto outOfMemory; + } + for (j = 0; (j < HIST_C1_ELEMS); j++) + { + cquantize->histogram[i][j] = (hist2d) gdCalloc (HIST_C2_ELEMS * HIST_C3_ELEMS, + sizeof (histcell)); + if (!cquantize->histogram[i][j]) + { + goto outOfMemory; + } + } + } + cquantize->fserrors = (FSERRPTR) gdMalloc (4 * sizeof (FSERROR)); + init_error_limit (im, cquantize); + arraysize = (size_t) ((im->sx + 2) * + (4 * sizeof (FSERROR))); + /* Allocate Floyd-Steinberg workspace. */ + cquantize->fserrors = gdCalloc (arraysize, 1); + if (!cquantize->fserrors) + { + goto outOfMemory; + } + cquantize->on_odd_row = FALSE; + + /* Do the work! */ + zeroHistogram (cquantize->histogram); + prescan_quantize (im, cquantize); + select_colors (im, cquantize, 256); + /* TBB HACK REMOVE */ + { + FILE *out = fopen ("palettemap.png", "wb"); + int i; + gdImagePtr im2 = gdImageCreateTrueColor (256, 256); + for (i = 0; (i < 256); i++) + { + gdImageFilledRectangle (im2, (i % 16) * 16, (i / 16) * 16, + (i % 16) * 16 + 15, (i / 16) * 16 + 15, + gdTrueColorAlpha (im->red[i], im->green[i], + im->blue[i], im->alpha[i])); + } + gdImagePng (im2, out); + fclose (out); + gdImageDestroy (im2); + } + zeroHistogram (cquantize->histogram); + if (dither) + { + pass2_fs_dither (im, cquantize); + } + else + { + pass2_no_dither (im, cquantize); + } + if (cquantize->transparentIsPresent) + { + int mt = -1; + int mtIndex = -1; + for (i = 0; (i < im->colorsTotal); i++) + { + if (im->alpha[i] > mt) + { + mtIndex = i; + mt = im->alpha[i]; + } + } + for (i = 0; (i < im->colorsTotal); i++) + { + if (im->alpha[i] == mt) + { + im->alpha[i] = gdAlphaTransparent; + } + } + } + if (cquantize->opaqueIsPresent) + { + int mo = 128; + int moIndex = -1; + for (i = 0; (i < im->colorsTotal); i++) + { + if (im->alpha[i] < mo) + { + moIndex = i; + mo = im->alpha[i]; + } + } + for (i = 0; (i < im->colorsTotal); i++) + { + if (im->alpha[i] == mo) + { + im->alpha[i] = gdAlphaOpaque; + } + } + } + /* Success! Get rid of the truecolor image data. */ + im->trueColor = 0; + /* Junk the truecolor pixels */ + for (i = 0; i < im->sy; i++) + { + gdFree (im->tpixels[i]); + } + gdFree (im->tpixels); + im->tpixels = 0; + /* Tediously free stuff. */ +outOfMemory: + if (im->trueColor) + { + /* On failure only */ + for (i = 0; i < im->sy; i++) + { + if (im->pixels[i]) + { + gdFree (im->pixels[i]); + } + } + if (im->pixels) + { + gdFree (im->pixels); + } + im->pixels = 0; + } + for (i = 0; i < HIST_C0_ELEMS; i++) + { + if (cquantize->histogram[i]) + { + int j; + for (j = 0; j < HIST_C1_ELEMS; j++) + { + if (cquantize->histogram[i][j]) + { + gdFree (cquantize->histogram[i][j]); + } + } + gdFree (cquantize->histogram[i]); + } + } + if (cquantize->histogram) + { + gdFree (cquantize->histogram); + } + if (cquantize->fserrors) + { + gdFree (cquantize->fserrors); + } + if (cquantize->error_limiter_storage) + { + gdFree (cquantize->error_limiter_storage); + } + if (cquantize) + { + gdFree (cquantize); + } +} |