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1659 lines
52 KiB
C
1659 lines
52 KiB
C
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/*
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* gd_topal.c
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*
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* This code is adapted pretty much entirely from jquant2.c,
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* Copyright (C) 1991-1996, Thomas G. Lane. That file is
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* part of the Independent JPEG Group's software. Conditions of
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* use are compatible with the gd license. See the gd license
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* statement and README-JPEG.TXT for additional information.
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*
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* This file contains 2-pass color quantization (color mapping) routines.
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* These routines provide selection of a custom color map for an image,
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* followed by mapping of the image to that color map, with optional
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* Floyd-Steinberg dithering.
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*
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* It is also possible to use just the second pass to map to an arbitrary
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* externally-given color map.
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*
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* Note: ordered dithering is not supported, since there isn't any fast
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* way to compute intercolor distances; it's unclear that ordered dither's
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* fundamental assumptions even hold with an irregularly spaced color map.
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*
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* SUPPORT FOR ALPHA CHANNELS WAS HACKED IN BY THOMAS BOUTELL, who also
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* adapted the code to work within gd rather than within libjpeg, and
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* may not have done a great job of either. It's not Thomas G. Lane's fault.
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*/
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#include "gd.h"
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#include "gdhelpers.h"
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#include <string.h>
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#include <stdlib.h>
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/*
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* This module implements the well-known Heckbert paradigm for color
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* quantization. Most of the ideas used here can be traced back to
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* Heckbert's seminal paper
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* Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
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* Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
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*
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* In the first pass over the image, we accumulate a histogram showing the
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* usage count of each possible color. To keep the histogram to a reasonable
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* size, we reduce the precision of the input; typical practice is to retain
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* 5 or 6 bits per color, so that 8 or 4 different input values are counted
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* in the same histogram cell.
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*
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* Next, the color-selection step begins with a box representing the whole
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* color space, and repeatedly splits the "largest" remaining box until we
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* have as many boxes as desired colors. Then the mean color in each
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* remaining box becomes one of the possible output colors.
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*
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* The second pass over the image maps each input pixel to the closest output
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* color (optionally after applying a Floyd-Steinberg dithering correction).
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* This mapping is logically trivial, but making it go fast enough requires
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* considerable care.
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*
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* Heckbert-style quantizers vary a good deal in their policies for choosing
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* the "largest" box and deciding where to cut it. The particular policies
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* used here have proved out well in experimental comparisons, but better ones
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* may yet be found.
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*
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* In earlier versions of the IJG code, this module quantized in YCbCr color
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* space, processing the raw upsampled data without a color conversion step.
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* This allowed the color conversion math to be done only once per colormap
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* entry, not once per pixel. However, that optimization precluded other
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* useful optimizations (such as merging color conversion with upsampling)
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* and it also interfered with desired capabilities such as quantizing to an
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* externally-supplied colormap. We have therefore abandoned that approach.
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* The present code works in the post-conversion color space, typically RGB.
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*
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* To improve the visual quality of the results, we actually work in scaled
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* RGBA space, giving G distances more weight than R, and R in turn more than
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* B. Alpha is weighted least. To do everything in integer math, we must
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* use integer scale factors. The 2/3/1 scale factors used here correspond
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* loosely to the relative weights of the colors in the NTSC grayscale
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* equation.
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*/
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#ifndef TRUE
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#define TRUE 1
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#endif /* TRUE */
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#ifndef FALSE
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#define FALSE 0
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#endif /* FALSE */
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#define R_SCALE 2 /* scale R distances by this much */
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#define G_SCALE 3 /* scale G distances by this much */
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#define B_SCALE 1 /* and B by this much */
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#define A_SCALE 4 /* and alpha by this much. This really
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only scales by 1 because alpha
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values are 7-bit to begin with. */
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/* Channel ordering (fixed in gd) */
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#define C0_SCALE R_SCALE
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#define C1_SCALE G_SCALE
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#define C2_SCALE B_SCALE
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#define C3_SCALE A_SCALE
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/*
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* First we have the histogram data structure and routines for creating it.
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*
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* The number of bits of precision can be adjusted by changing these symbols.
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* We recommend keeping 6 bits for G and 5 each for R and B.
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* If you have plenty of memory and cycles, 6 bits all around gives marginally
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* better results; if you are short of memory, 5 bits all around will save
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* some space but degrade the results.
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* To maintain a fully accurate histogram, we'd need to allocate a "long"
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* (preferably unsigned long) for each cell. In practice this is overkill;
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* we can get by with 16 bits per cell. Few of the cell counts will overflow,
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* and clamping those that do overflow to the maximum value will give close-
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* enough results. This reduces the recommended histogram size from 256Kb
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* to 128Kb, which is a useful savings on PC-class machines.
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* (In the second pass the histogram space is re-used for pixel mapping data;
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* in that capacity, each cell must be able to store zero to the number of
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* desired colors. 16 bits/cell is plenty for that too.)
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* Since the JPEG code is intended to run in small memory model on 80x86
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* machines, we can't just allocate the histogram in one chunk. Instead
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* of a true 3-D array, we use a row of pointers to 2-D arrays. Each
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* pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
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* each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
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* on 80x86 machines, the pointer row is in near memory but the actual
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* arrays are in far memory (same arrangement as we use for image arrays).
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*/
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#define MAXNUMCOLORS (gdMaxColors) /* maximum size of colormap */
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#define HIST_C0_BITS 5 /* bits of precision in R histogram */
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#define HIST_C1_BITS 6 /* bits of precision in G histogram */
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#define HIST_C2_BITS 5 /* bits of precision in B histogram */
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#define HIST_C3_BITS 3 /* bits of precision in A histogram */
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/* Number of elements along histogram axes. */
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#define HIST_C0_ELEMS (1<<HIST_C0_BITS)
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#define HIST_C1_ELEMS (1<<HIST_C1_BITS)
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#define HIST_C2_ELEMS (1<<HIST_C2_BITS)
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#define HIST_C3_ELEMS (1<<HIST_C3_BITS)
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/* These are the amounts to shift an input value to get a histogram index. */
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#define C0_SHIFT (8-HIST_C0_BITS)
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#define C1_SHIFT (8-HIST_C1_BITS)
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#define C2_SHIFT (8-HIST_C2_BITS)
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/* Beware! Alpha is 7 bit to begin with */
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#define C3_SHIFT (7-HIST_C3_BITS)
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typedef unsigned short histcell; /* histogram cell; prefer an unsigned type */
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typedef histcell *histptr; /* for pointers to histogram cells */
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typedef histcell hist1d[HIST_C3_ELEMS]; /* typedefs for the array */
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typedef hist1d *hist2d; /* type for the 2nd-level pointers */
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typedef hist2d *hist3d; /* type for third-level pointer */
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typedef hist3d *hist4d; /* type for top-level pointer */
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/* Declarations for Floyd-Steinberg dithering.
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* Errors are accumulated into the array fserrors[], at a resolution of
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* 1/16th of a pixel count. The error at a given pixel is propagated
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* to its not-yet-processed neighbors using the standard F-S fractions,
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* ... (here) 7/16
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* 3/16 5/16 1/16
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* We work left-to-right on even rows, right-to-left on odd rows.
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*
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* We can get away with a single array (holding one row's worth of errors)
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* by using it to store the current row's errors at pixel columns not yet
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* processed, but the next row's errors at columns already processed. We
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* need only a few extra variables to hold the errors immediately around the
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* current column. (If we are lucky, those variables are in registers, but
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* even if not, they're probably cheaper to access than array elements are.)
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*
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* The fserrors[] array has (#columns + 2) entries; the extra entry at
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* each end saves us from special-casing the first and last pixels.
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* Each entry is three values long, one value for each color component.
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*
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*/
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typedef signed short FSERROR; /* 16 bits should be enough */
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typedef int LOCFSERROR; /* use 'int' for calculation temps */
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typedef FSERROR *FSERRPTR; /* pointer to error array */
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/* Private object */
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typedef struct
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{
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hist4d histogram; /* pointer to the histogram */
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int needs_zeroed; /* TRUE if next pass must zero histogram */
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/* Variables for Floyd-Steinberg dithering */
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FSERRPTR fserrors; /* accumulated errors */
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int on_odd_row; /* flag to remember which row we are on */
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int *error_limiter; /* table for clamping the applied error */
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int *error_limiter_storage; /* gdMalloc'd storage for the above */
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int transparentIsPresent; /* TBB: for rescaling to ensure that */
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int opaqueIsPresent; /* 100% opacity & transparency are preserved */
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}
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my_cquantizer;
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typedef my_cquantizer *my_cquantize_ptr;
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/*
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* Prescan the pixel array.
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*
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* The prescan simply updates the histogram, which has been
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* initialized to zeroes by start_pass.
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*
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*/
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static void
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prescan_quantize (gdImagePtr im, my_cquantize_ptr cquantize)
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{
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register histptr histp;
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register hist4d histogram = cquantize->histogram;
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int row;
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int col;
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int *ptr;
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int width = im->sx;
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for (row = 0; row < im->sy; row++)
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{
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ptr = im->tpixels[row];
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for (col = width; col > 0; col--)
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{
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/* get pixel value and index into the histogram */
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int r, g, b, a;
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r = gdTrueColorGetRed (*ptr) >> C0_SHIFT;
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g = gdTrueColorGetGreen (*ptr) >> C1_SHIFT;
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b = gdTrueColorGetBlue (*ptr) >> C2_SHIFT;
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a = gdTrueColorGetAlpha (*ptr);
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/* We must have 100% opacity and transparency available
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in the color map to do an acceptable job with alpha
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channel, if opacity and transparency are present in the
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original, because of the visual properties of large
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flat-color border areas (requiring 100% transparency)
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and the behavior of poorly implemented browsers
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(requiring 100% opacity). Test for the presence of
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these here, and rescale the most opaque and transparent
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palette entries at the end if so. This avoids the need
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to develop a fuller understanding I have not been able
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to reach so far in my study of this subject. TBB */
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if (a == gdAlphaTransparent)
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{
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cquantize->transparentIsPresent = 1;
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}
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if (a == gdAlphaOpaque)
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{
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cquantize->opaqueIsPresent = 1;
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}
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a >>= C3_SHIFT;
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histp = &histogram[r][g][b][a];
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/* increment, check for overflow and undo increment if so. */
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if (++(*histp) <= 0)
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(*histp)--;
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ptr++;
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}
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}
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}
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/*
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* Next we have the really interesting routines: selection of a colormap
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* given the completed histogram.
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* These routines work with a list of "boxes", each representing a rectangular
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* subset of the input color space (to histogram precision).
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*/
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typedef struct
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{
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/* The bounds of the box (inclusive); expressed as histogram indexes */
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int c0min, c0max;
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int c1min, c1max;
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int c2min, c2max;
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int c3min, c3max;
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/* The volume (actually 2-norm) of the box */
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int volume;
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/* The number of nonzero histogram cells within this box */
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long colorcount;
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}
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box;
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typedef box *boxptr;
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static boxptr
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find_biggest_color_pop (boxptr boxlist, int numboxes)
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/* Find the splittable box with the largest color population */
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/* Returns NULL if no splittable boxes remain */
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{
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register boxptr boxp;
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register int i;
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register long maxc = 0;
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boxptr which = NULL;
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for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++)
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{
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if (boxp->colorcount > maxc && boxp->volume > 0)
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{
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which = boxp;
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maxc = boxp->colorcount;
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}
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}
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return which;
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}
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static boxptr
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find_biggest_volume (boxptr boxlist, int numboxes)
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/* Find the splittable box with the largest (scaled) volume */
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/* Returns NULL if no splittable boxes remain */
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{
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register boxptr boxp;
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register int i;
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register int maxv = 0;
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boxptr which = NULL;
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for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++)
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{
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if (boxp->volume > maxv)
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{
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which = boxp;
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maxv = boxp->volume;
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}
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}
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return which;
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}
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static void
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update_box (gdImagePtr im, my_cquantize_ptr cquantize, boxptr boxp)
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/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
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/* and recompute its volume and population */
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{
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hist4d histogram = cquantize->histogram;
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histptr histp;
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int c0, c1, c2, c3;
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int c0min, c0max, c1min, c1max, c2min, c2max, c3min, c3max;
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int dist0, dist1, dist2, dist3;
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long ccount;
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c0min = boxp->c0min;
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c0max = boxp->c0max;
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c1min = boxp->c1min;
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c1max = boxp->c1max;
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c2min = boxp->c2min;
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c2max = boxp->c2max;
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c3min = boxp->c3min;
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c3max = boxp->c3max;
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if (c0max > c0min)
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{
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for (c0 = c0min; c0 <= c0max; c0++)
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{
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for (c1 = c1min; c1 <= c1max; c1++)
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{
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for (c2 = c2min; c2 <= c2max; c2++)
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{
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histp = &histogram[c0][c1][c2][c3min];
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for (c3 = c3min; c3 <= c3max; c3++)
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{
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if (*histp++ != 0)
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{
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boxp->c0min = c0min = c0;
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goto have_c0min;
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}
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}
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}
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}
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}
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}
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have_c0min:
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if (c0max > c0min)
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{
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for (c0 = c0max; c0 >= c0min; c0--)
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{
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for (c1 = c1min; c1 <= c1max; c1++)
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{
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for (c2 = c2min; c2 <= c2max; c2++)
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{
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histp = &histogram[c0][c1][c2][c3min];
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for (c3 = c3min; c3 <= c3max; c3++)
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{
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if (*histp++ != 0)
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{
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boxp->c0max = c0max = c0;
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goto have_c0max;
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}
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}
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}
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}
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}
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}
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have_c0max:
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if (c1max > c1min)
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for (c1 = c1min; c1 <= c1max; c1++)
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for (c0 = c0min; c0 <= c0max; c0++)
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{
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for (c2 = c2min; c2 <= c2max; c2++)
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{
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histp = &histogram[c0][c1][c2][c3min];
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for (c3 = c3min; c3 <= c3max; c3++)
|
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if (*histp++ != 0)
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{
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boxp->c1min = c1min = c1;
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goto have_c1min;
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}
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}
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}
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have_c1min:
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if (c1max > c1min)
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for (c1 = c1max; c1 >= c1min; c1--)
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for (c0 = c0min; c0 <= c0max; c0++)
|
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{
|
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for (c2 = c2min; c2 <= c2max; c2++)
|
|
{
|
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histp = &histogram[c0][c1][c2][c3min];
|
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for (c3 = c3min; c3 <= c3max; c3++)
|
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if (*histp++ != 0)
|
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{
|
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boxp->c1max = c1max = c1;
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goto have_c1max;
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}
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}
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}
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have_c1max:
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|
/* The original version hand-rolled the array lookup a little, but
|
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with four dimensions, I don't even want to think about it. TBB */
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if (c2max > c2min)
|
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for (c2 = c2min; c2 <= c2max; c2++)
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for (c0 = c0min; c0 <= c0max; c0++)
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for (c1 = c1min; c1 <= c1max; c1++)
|
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for (c3 = c3min; c3 <= c3max; c3++)
|
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if (histogram[c0][c1][c2][c3] != 0)
|
|
{
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boxp->c2min = c2min = c2;
|
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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++)
|
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if (histogram[c0][c1][c2][c3] != 0)
|
|
{
|
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boxp->c2max = c2max = c2;
|
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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;
|
|
int i, icolor;
|
|
register int *bptr; /* pointer into bestdist[] array */
|
|
int *cptr; /* pointer into bestcolor[] array */
|
|
int dist0, dist1, dist2; /* initial distance values */
|
|
int xx0, xx1, xx2; /* distance increments */
|
|
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--) {
|
|
register int dist3 = dist2; /* current distance in inner loop */
|
|
register int xx3 = inc3;
|
|
register int ic3;
|
|
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 || colorsWanted <= 0) {
|
|
/* Nothing to do! */
|
|
return;
|
|
}
|
|
|
|
if (colorsWanted > gdMaxColors) {
|
|
colorsWanted = gdMaxColors;
|
|
}
|
|
|
|
im->pixels = gdCalloc (sizeof (unsigned char *), im->sy);
|
|
|
|
for (i = 0; i < im->sy; i++) {
|
|
im->pixels[i] = gdCalloc (sizeof (unsigned char *), im->sx);
|
|
}
|
|
cquantize = (my_cquantize_ptr) gdCalloc (sizeof (my_cquantizer), 1);
|
|
|
|
/* 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));
|
|
for (j = 0; j < HIST_C1_ELEMS; j++) {
|
|
cquantize->histogram[i][j] = (hist2d) gdCalloc (HIST_C2_ELEMS * HIST_C3_ELEMS, sizeof (histcell));
|
|
}
|
|
}
|
|
|
|
cquantize->fserrors = (FSERRPTR) gdMalloc (4 * sizeof (FSERROR));
|
|
init_error_limit (im, cquantize);
|
|
arraysize = (size_t) ((im->sx + 2) * (4 * sizeof (FSERROR)));
|
|
gdFree(cquantize->fserrors);
|
|
/* Allocate Floyd-Steinberg workspace. */
|
|
cquantize->fserrors = gdCalloc (arraysize, 1);
|
|
cquantize->on_odd_row = FALSE;
|
|
|
|
/* Do the work! */
|
|
zeroHistogram (cquantize->histogram);
|
|
prescan_quantize (im, cquantize);
|
|
select_colors (im, cquantize, colorsWanted);
|
|
|
|
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. */
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
/* bring the palette colors in im2 to be closer to im1
|
|
*
|
|
*/
|
|
int
|
|
gdImageColorMatch (gdImagePtr im1, gdImagePtr im2)
|
|
{
|
|
unsigned long *buf; /* stores our calculations */
|
|
unsigned long *bp; /* buf ptr */
|
|
int color, rgb;
|
|
int x,y;
|
|
int count;
|
|
|
|
if( !im1->trueColor ) {
|
|
return -1; /* im1 must be True Color */
|
|
}
|
|
if( im2->trueColor ) {
|
|
return -2; /* im2 must be indexed */
|
|
}
|
|
if( (im1->sx != im2->sx) || (im1->sy != im2->sy) ) {
|
|
return -3; /* the images are meant to be the same dimensions */
|
|
}
|
|
|
|
buf = (unsigned long *)gdMalloc( sizeof(unsigned long) * 5 * im2->colorsTotal );
|
|
memset( buf, 0, sizeof(unsigned long) * 5 * im2->colorsTotal );
|
|
|
|
for( x=0; x<im1->sx; x++ ) {
|
|
for( y=0; y<im1->sy; y++ ) {
|
|
color = im2->pixels[y][x];
|
|
rgb = im1->tpixels[y][x];
|
|
bp = buf + (color * 5);
|
|
(*(bp++))++;
|
|
*(bp++) += gdTrueColorGetRed(rgb);
|
|
*(bp++) += gdTrueColorGetGreen(rgb);
|
|
*(bp++) += gdTrueColorGetBlue(rgb);
|
|
*(bp++) += gdTrueColorGetAlpha(rgb);
|
|
}
|
|
}
|
|
bp = buf;
|
|
for( color=0; color<im2->colorsTotal; color++ ) {
|
|
count = *(bp++);
|
|
if( count > 0 ) {
|
|
im2->red[color] = *(bp++) / count;
|
|
im2->green[color] = *(bp++) / count;
|
|
im2->blue[color] = *(bp++) / count;
|
|
im2->alpha[color] = *(bp++) / count;
|
|
} else {
|
|
bp += 4;
|
|
}
|
|
}
|
|
gdFree(buf);
|
|
return 0;
|
|
}
|