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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, 0 insertions, 1688 deletions
diff --git a/ext/gd/libgd/gd_topal.c b/ext/gd/libgd/gd_topal.c deleted file mode 100644 index d2be7508f2..0000000000 --- a/ext/gd/libgd/gd_topal.c +++ /dev/null @@ -1,1688 +0,0 @@ - - -/* - * 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); - } -} |