gd_topal.c

Linux/Unix下的绘图函数库（Graphic Drawing Library）

  /* If we had a pure transparency color, add it as the last palette entry.
   * Skip incrementing the color count so that the dither / matching phase
   * won't use it on pixels that shouldn't have been transparent.  We'll
   * increment it after all that finishes. */
  if (oim->transparent >= 0)
    {
      /* Save the transparent color. */
      nim->red[nim->colorsTotal] = gdTrueColorGetRed (oim->transparent);
      nim->green[nim->colorsTotal] = gdTrueColorGetGreen (oim->transparent);
      nim->blue[nim->colorsTotal] = gdTrueColorGetBlue (oim->transparent);
      nim->alpha[nim->colorsTotal] = gdAlphaTransparent;
      nim->open[nim->colorsTotal] = 0;
    }

  gdFree (boxlist);
#endif
}


/*
 * These routines are concerned with the time-critical task of mapping input
 * colors to the nearest color in the selected colormap.
 *
 * We re-use the histogram space as an "inverse color map", essentially a
 * cache for the results of nearest-color searches.  All colors within a
 * histogram cell will be mapped to the same colormap entry, namely the one
 * closest to the cell's center.  This may not be quite the closest entry to
 * the actual input color, but it's almost as good.  A zero in the cache
 * indicates we haven't found the nearest color for that cell yet; the array
 * is cleared to zeroes before starting the mapping pass.  When we find the
 * nearest color for a cell, its colormap index plus one is recorded in the
 * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
 * when they need to use an unfilled entry in the cache.
 *
 * Our method of efficiently finding nearest colors is based on the "locally
 * sorted search" idea described by Heckbert and on the incremental distance
 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
 * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
 * the distances from a given colormap entry to each cell of the histogram can
 * be computed quickly using an incremental method: the differences between
 * distances to adjacent cells themselves differ by a constant.  This allows a
 * fairly fast implementation of the "brute force" approach of computing the
 * distance from every colormap entry to every histogram cell.  Unfortunately,
 * it needs a work array to hold the best-distance-so-far for each histogram
 * cell (because the inner loop has to be over cells, not colormap entries).
 * The work array elements have to be INT32s, so the work array would need
 * 256Kb at our recommended precision.  This is not feasible in DOS machines.
 *
 * To get around these problems, we apply Thomas' method to compute the
 * nearest colors for only the cells within a small subbox of the histogram.
 * The work array need be only as big as the subbox, so the memory usage
 * problem is solved.  Furthermore, we need not fill subboxes that are never
 * referenced in pass2; many images use only part of the color gamut, so a
 * fair amount of work is saved.  An additional advantage of this
 * approach is that we can apply Heckbert's locality criterion to quickly
 * eliminate colormap entries that are far away from the subbox; typically
 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
 * and we need not compute their distances to individual cells in the subbox.
 * The speed of this approach is heavily influenced by the subbox size: too
 * small means too much overhead, too big loses because Heckbert's criterion
 * can't eliminate as many colormap entries.  Empirically the best subbox
 * size seems to be about 1/512th of the histogram (1/8th in each direction).
 *
 * Thomas' article also describes a refined method which is asymptotically
 * faster than the brute-force method, but it is also far more complex and
 * cannot efficiently be applied to small subboxes.  It is therefore not
 * useful for programs intended to be portable to DOS machines.  On machines
 * with plenty of memory, filling the whole histogram in one shot with Thomas'
 * refined method might be faster than the present code --- but then again,
 * it might not be any faster, and it's certainly more complicated.
 */


/* log2(histogram cells in update box) for each axis; this can be adjusted */
#define BOX_C0_LOG  (HIST_C0_BITS-3)
#define BOX_C1_LOG  (HIST_C1_BITS-3)
#define BOX_C2_LOG  (HIST_C2_BITS-3)

#define BOX_C0_ELEMS  (1<<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_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
#define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
#define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)


/*
 * The next three routines implement inverse colormap filling.  They could
 * all be folded into one big routine, but splitting them up this way saves
 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
 * and may allow some compilers to produce better code by registerizing more
 * inner-loop variables.
 */

LOCAL (int)
find_nearby_colors (
#ifdef ORIGINAL_LIB_JPEG
		     j_decompress_ptr cinfo,
#else
		     gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,
#endif
		     int minc0, int minc1, int minc2, JSAMPLE colorlist[])
/* Locate the colormap entries close enough to an update box to be candidates
 * for the nearest entry to some cell(s) in the update box.  The update box
 * is specified by the center coordinates of its first cell.  The number of
 * candidate colormap entries is returned, and their colormap indexes are
 * placed in colorlist[].
 * This routine uses Heckbert's "locally sorted search" criterion to select
 * the colors that need further consideration.
 */
{
#ifdef ORIGINAL_LIB_JPEG
  int numcolors = cinfo->actual_number_of_colors;
#else
  int numcolors = nim->colorsTotal;
#endif
  int maxc0, maxc1, maxc2;
  int centerc0, centerc1, centerc2;
  int i, x, ncolors;
  INT32 minmaxdist, min_dist, max_dist, tdist;
  INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */

  /* Compute true coordinates of update box's upper corner and center.
   * Actually we compute the coordinates of the center of the upper-corner
   * histogram cell, which are the upper bounds of the volume we care about.
   * Note that since ">>" rounds down, the "center" values may be closer to
   * min than to max; hence comparisons to them must be "<=", not "<".
   */
  maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
  centerc0 = (minc0 + maxc0) >> 1;
  maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
  centerc1 = (minc1 + maxc1) >> 1;
  maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
  centerc2 = (minc2 + maxc2) >> 1;

  /* For each color in colormap, find:
   *  1. its minimum squared-distance to any point in the update box
   *     (zero if color is within update box);
   *  2. its maximum squared-distance to any point in the update box.
   * Both of these can be found by considering only the corners of the box.
   * We save the minimum distance for each color in mindist[];
   * only the smallest maximum distance is of interest.
   */
  minmaxdist = 0x7FFFFFFFL;

  for (i = 0; i < numcolors; i++)
    {
      /* We compute the squared-c0-distance term, then add in the other two. */
#ifdef ORIGINAL_LIB_JPEG
      x = GETJSAMPLE (cinfo->colormap[0][i]);
#else
      x = nim->red[i];
#endif
      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;
	    }
	}

#ifdef ORIGINAL_LIB_JPEG
      x = GETJSAMPLE (cinfo->colormap[1][i]);
#else
      x = nim->green[i];
#endif
      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;
	    }
	}

#ifdef ORIGINAL_LIB_JPEG
      x = GETJSAMPLE (cinfo->colormap[2][i]);
#else
      x = nim->blue[i];
#endif
      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;
	    }
	}

      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++] = (JSAMPLE) i;
    }
  return ncolors;
}


LOCAL (void) find_best_colors (
#ifdef ORIGINAL_LIB_JPEG
				j_decompress_ptr cinfo,
#else
				gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,
#endif
				int minc0, int minc1, int minc2,
				int numcolors, JSAMPLE colorlist[],
				JSAMPLE 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;
  int i, icolor;
  register INT32 *bptr;		/* pointer into bestdist[] array */
  JSAMPLE *cptr;		/* pointer into bestcolor[] array */
  INT32 dist0, dist1;		/* initial distance values */
  register INT32 dist2;		/* current distance in inner loop */
  INT32 xx0, xx1;		/* distance increments */
  register INT32 xx2;
  INT32 inc0, inc1, inc2;	/* initial values for increments */
  /* This array holds the distance to the nearest-so-far color for each cell */
  INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

  /* Initialize best-distance for each cell of the update box */
  bptr = bestdist;
  for (i = BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_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)

  for (i = 0; i < numcolors; i++)
    {
      int r, g, b;
#ifdef ORIGINAL_LIB_JPEG

      icolor = GETJSAMPLE (colorlist[i]);
      r = GETJSAMPLE (cinfo->colormap[0][icolor]);
      g = GETJSAMPLE (cinfo->colormap[1][icolor]);
      b = GETJSAMPLE (cinfo->colormap[2][icolor]);
#else
      icolor = colorlist[i];
      r = nim->red[icolor];
      g = nim->green[icolor];
      b = nim->blue[icolor];
#endif

      /* Compute (square of) distance from minc0/c1/c2 to this color */
      inc0 = (minc0 - r) * C0_SCALE;
      dist0 = inc0 * inc0;
      inc1 = (minc1 - g) * C1_SCALE;
      dist0 += inc1 * inc1;
      inc2 = (minc2 - b) * C2_SCALE;
      dist0 += inc2 * inc2;
      /* 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;
      /* 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--)
		{
		  if (dist2 < *bptr)
		    {
		      *bptr = dist2;
		      *cptr = (JSAMPLE) icolor;
		    }
		  dist2 += xx2;
		  xx2 += 2 * STEP_C2 * STEP_C2;
		  bptr++;
		  cptr++;
		}
	      dist1 += xx1;
	      xx1 += 2 * STEP_C1 * STEP_C1;
	    }
	  dist0 += xx0;
	  xx0 += 2 * STEP_C0 * STEP_C0;
	}
    }
}


LOCAL (void)
fill_inverse_cmap (
#ifdef ORIGINAL_LIB_JPEG
		    j_decompress_ptr cinfo,
#else
		    gdImagePtr oim, gdImagePtr nim, my_cquantize_ptr cquantize,
#endif
		    int c0, int c1, int c2)
/* Fill the inverse-colormap entries in the update box that contains */
/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
/* we can fill as many others as we wish.) */
{
#ifdef ORIGINAL_LIB_JPEG
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
#endif
  hist3d histogram = cquantize->histogram;
  int minc0, minc1, minc2;	/* lower left corner of update box */
  int ic0, ic1, ic2;
  register JSAMPLE *cptr;	/* pointer into bestcolor[] array */
  register histptr cachep;	/* pointer into main cache array */
  /* This array lists the candidate colormap indexes. */
  JSAMPLE colorlist[MAXNUMCOLORS];
  int numcolors;		/* number of candidate colors */
  /* This array holds the actually closest colormap index for each cell. */
  JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

  /* Convert cell coordinates to update box ID */
  c0 >>= BOX_C0_LOG;
  c1 >>= BOX_C1_LOG;
  c2 >>= BOX_C2_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);

  /* Determine which colormap entries are close enough to be candidates