From 7029cca974428f4027f2b048a686feef49e22260 Mon Sep 17 00:00:00 2001 From: sam Date: Sat, 11 Oct 2008 14:41:23 +0000 Subject: [PATCH] Merge www/ and trac/ into a single web/ project. git-svn-id: file:///srv/caca.zoy.org/var/lib/svn/web/trunk@2920 92316355-f0b4-4df1-b90c-862c8a59935f --- static/.htaccess | 57 + static/cacacon07/DSC000011.JPG | Bin 0 -> 502210 bytes static/cacacon07/DSC000013.JPG | Bin 0 -> 272055 bytes static/cacacon07/DSC000017.JPG | Bin 0 -> 246770 bytes static/cacacon07/DSC000026.JPG | Bin 0 -> 607515 bytes static/cacacon07/DSC000029.JPG | Bin 0 -> 371267 bytes static/cacacon07/index.html | 117 + static/cacacon07/tn-DSC000011.jpeg | Bin 0 -> 15267 bytes static/cacacon07/tn-DSC000013.jpeg | Bin 0 -> 12657 bytes static/cacacon07/tn-DSC000017.jpeg | Bin 0 -> 25080 bytes static/cacacon07/tn-DSC000026.jpeg | Bin 0 -> 8916 bytes static/cacacon07/tn-DSC000029.jpeg | Bin 0 -> 8494 bytes static/cacatris-sample.png | Bin 0 -> 6485 bytes static/cacatris.html | 37 + 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FOR IMMEDIATE RELEASE

+ +

Report on the First International libcaca Conference

+ +

Wednesday October 24, 2007

+ +

Sam (ZOY) - Paris, France: CacaCon’07, the first international +libcaca conference, just took place on October 19-20, 2007 in Lyon, France. +

+ +

The most eagerly-awaited event of the conference was of course Jylam’s +keynote. The acclaimed libcaca second-in-chief developer covered the +history of the project, gave key figures about its current status, and +of course hinted at future plans.

+ +

The morning of the second day had been busy with uncalled for attacks +directed at the libcaca team by Mozilla Europe Board of Directors +president Tristan Nitot who reportedly managed to say that "all +applications have memory leaks" without immediately dying of maniac, +self-ridiculing laughter. Needless to say, Jylam’s keynote was +immediately refocused on insisting about libcaca’s superior development +model and giving examples of its speed, lightweightness and general lack +of memory leaks.

+ + + + + + + +
+ Jylam
Jylam
+
+ PowerPipo
PowerPipo
+
+ the audience
the audience
+
+ +

The libcaca team eventually announced its new forthcoming product +"neercs", a replacement for the obsolete Unix utility "screen" with +full Unicode support, window thumbnailing, eye-candy transition effects +and textmode window management. The audience also sat in awe as Jylam, +literally knee-shaking in excitement, admitted that the very tool he had +been using during the keynote was not a Microsoft product or a cheap +clone thereof, but a development version of the libcaca team’s own +fully-featured presentation tool, PowerPipo.

+ +

CacaCon’07 ended with a social event at luxury resort Le Chevreul +(Lyon) where hundreds of potential libcaca users and enthusiasts would +have been able to meet main developers Sam and Jylam.

+ + + + + + +
+ Jylam and Sam
Jylam and Sam
+
+ fan art
fan art
+
+ +

About libcaca:
+libcaca is the industry leading colour ASCII art library. It is part +of a global conspiracy aiming at luring basement-dwelling nerds +into thinking that focusing on overtechnical, useless and generally +uninteresting activities is going to help them get laid. Approximately +700,000 Linux users still believe it to be true and frantically +recompile nightly git snapshots of the kernel as a consequence.

+ +

About Firefox:
+Firefox is the Mozilla Corporation’s rebranded version of the famous +Iceweasel web browser, to which they insist on adding a restrictive +trademark license and a suggestive yiff logo. Notwithstanding, we urge +you to donate all your money to the multimillion-dollar company. The +guys look legit.

+ +

About memory leaks:
+All applications called Firefox have memory leaks.

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cacatris

+ +

Cacatris aims to create a graphical Tetris(c) clone, but in text mode. Main concerns focus on gameplay, and graphical quality (althought current screenshot doesn't really show this).

+

It is currently under heavy developement, thus no downloads are available yet.

+ +

Screenshot

+ +

ttyvaders sample 2

+ + + + + diff --git a/static/download.html b/static/download.html new file mode 100644 index 0000000..8ab4cf5 --- /dev/null +++ b/static/download.html @@ -0,0 +1,64 @@ + + + + + + + + + + + + libcaca - downloads + + + + + + + + + +

You can browse old releases of all the libcaca +software.

+ +

Libcaca development versions

+ +

The latest libcaca beta version is libcaca-0.99.beta13b.tar.gz +(November 28th, 2007). Please download and test it. If you are a developer, +please comment on the current API and let us know what is missing.

+ +

Even more recent code can be downloaded using Subversion. See the development page for more information.

+ +

Documentation

+ +

You can download a PDF version of the +libcaca manual.

+ +

Old libcaca stable versions

+ +

Warning: stable +releases of libcaca are terribly outdated. If you want to test libcaca, you +are encouraged to check the development versions.

+ +

Latest stable libcaca release is libcaca-0.9.tar.gz (216KB) / libcaca-0.9.tar.bz2 (172KB).

+ +

A Windows zip archive containing the libcaca demos can be downloaded here: +libcaca-win32-0.9.zip (100KB).

+ +

Older versions (up to 0.9) were under the LGPL license but all +copyright holders agreed to relicence the codebase under the WTFPL (such a +permission is not retroactive and you should consider old versions as still +covered by the LGPL). The logo on this page is copyrighted by Akira Toriyama +so if anyone fancies drawing a new logo it would be much appreciated.

+ + + + + diff --git a/static/export.html b/static/export.html new file mode 100644 index 0000000..db98c03 --- /dev/null +++ b/static/export.html @@ -0,0 +1,48 @@ + +Generated by libcaca 0.99.beta10 + +
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+                                                                                
+                  PiL HQ ■ Syndrome Couriers HQ ■ GODS WHQ                      
+                16.8 Duals ■ 8i8.XxX.XxXx ■ 8 Node Ringdown                     
+                         S: QUAZiMODEM & CiPHER                                 
+                    CO'S:  LORD THiNKER   THE MOZARELLO                         
+                   SLiCE & DiCE ■ HURRiCAN ■ RAZOR BLADE                        
+                                                                                
+▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
+                        ANSi BY   LUC AiM 'NiNE FO' >                         
+                                                                                
+                                                                                
+
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valid XHTML 1.1 & CSS

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libcaca - Colour AsCii Art library

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z>XGWcJ@i2bfdL@`M+EK1qz4(`qy7m(ccDv+ylk?d#w3jxF=EoBN$VNg!%h=@&!*CquuDk9UFkrx}S+fovJjl5q0@F-zWN}M8^X!gre!N4*YXF$r8%F&m3;S0GW$*){%4H3m@2#hbUehqzj89Mu>1TS0yVOS$n`CsJO(8S+D&0&)s|XD(~4_0uzFrKYwoDzCA6+j-^taiA2mWOva3W<&ZrP zfm{UsOF3G2iY$TCA|L_l3heOzSccA!LW`~qW6XA)WHJTHZQ8LzrrN*o$ODD>rX`rK z?}`~m6wWzk94=2TEn5UOkx1wk-jE?f{`}`ZYxHwYx!9;ca=NFHul$*%oqVNkWANrm zDa*37p6aw|(;6BYUVZh|y?gf>mgOiktON>!AwB;17i(+#y+8BKl-((nd-m*^cs$mv zTSaMENkKtA0NJUOZ~c9do>fHC6fb$L)9%5=rNb`a>;D25gylskJTFfG0000 + * All Rights Reserved + * + * Changes: + * Jan 18, 2008: initial release + * Jan 23, 2008: add support for inverse video on attribute change + * improve Floyd-Steinberg coefficient values + * Jun 14, 2008: Win32 version + * Jun 18, 2008: add meaningful error messages + * + * This program is free software. It comes without any warranty, to + * the extent permitted by applicable law. You can redistribute it + * and/or modify it under the terms of the Do What The Fuck You Want + * To Public License, Version 2, as published by Sam Hocevar. See + * http://sam.zoy.org/wtfpl/COPYING for more details. + * + * To build this program on Linux: + * cc -O3 -funroll-loops -W -Wall img2oric.c -o img2oric \ + * $(pkg-config --cflags --libs sdl) -lSDL_image -lm + * To build a Windows executable: + * i586-mingw32msvc-cc -O3 -funroll-loops -W -Wall \ + * img2oric.c -o img2oric.exe -lgdi32 + */ + +#include +#include +#include + +#include + +#if defined _WIN32 +# include +# define uint8_t unsigned char +#else +# include +#endif + +/* + * BMP output name and Oric output name + */ +#define BMPFILE "output.bmp" +#define ORICFILE "OUTPUT" /* ".TAP" will be appended */ + +/* + * Image dimensions and recursion depth. DEPTH = 2 is a reasonable value, + * DEPTH = 3 gives good quality, and higher values may improve the results + * even more but at the cost of significantly longer computation times. + */ +#define WIDTH 240 +#define HEIGHT 200 +#define DEPTH 3 + +/* + * Error diffusion table, similar to Floyd-Steinberg. I choose not to + * propagate 100% of the error, because doing so creates awful artifacts + * (full lines of the same colour, massive colour bleeding) for unclear + * reasons. Atkinson dithering propagates 3/4 of the error, which is even + * less than our 31/32. I also choose to propagate slightly more in the + * X direction to avoid banding effects due to rounding errors. + * It would be interesting, for future versions of this software, to + * propagate the error to the second line, too. But right now I find it far + * too complex to do. + * + * +-------+-------+ + * | error |FS0/FSX| + * +-------+-------+-------+ + * |FS1/FSX|FS2/FSX|FS3/FSX| + * +-------+-------+-------+ + */ +#define FS0 15 +#define FS1 6 +#define FS2 9 +#define FS3 1 +#define FSX 32 + +/* + * The simple Oric RGB palette, made of the 8 Neugebauer primary colours. Each + * colour is repeated 6 times so that we can point to the palette to paste + * whole blocks of 6 pixels. It’s also organised so that palette[7-x] is the + * RGB negative of palette[x], and screen command X uses palette[X & 7]. + */ +#define o 0x0000 +#define X 0xffff +static const int palette[8][6 * 3] = +{ + { o, o, o, o, o, o, o, o, o, o, o, o, o, o, o, o, o, o }, + { X, o, o, X, o, o, X, o, o, X, o, o, X, o, o, X, o, o }, + { o, X, o, o, X, o, o, X, o, o, X, o, o, X, o, o, X, o }, + { X, X, o, X, X, o, X, X, o, X, X, o, X, X, o, X, X, o }, + { o, o, X, o, o, X, o, o, X, o, o, X, o, o, X, o, o, X }, + { X, o, X, X, o, X, X, o, X, X, o, X, X, o, X, X, o, X }, + { o, X, X, o, X, X, o, X, X, o, X, X, o, X, X, o, X, X }, + { X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X }, +}; + +/* + * Gamma correction tables. itoc_table and ctoi_table accept overflow and + * underflow values to a reasonable extent, so that we don’t have to check + * for these cases later in the code. Tests kill performance. + */ +#define PAD 2048 +static int itoc_table_clip[PAD + 256 + PAD], ctoi_table_clip[PAD + 256 + PAD]; +static int *itoc_table = itoc_table_clip + PAD; +static int *ctoi_table = ctoi_table_clip + PAD; + +static void init_tables(void) +{ + int i; + + for(i = 0; i < PAD + 256 + PAD; i++) + { + double f = 1.0 * (i - PAD) / 255.999; + if(f >= 0.) + { + itoc_table_clip[i] = (int)(65535.999 * pow(f, 1./2.2)); + ctoi_table_clip[i] = (int)(65535.999 * pow(f, 2.2)); + } + else + { + itoc_table_clip[i] = - (int)(65535.999 * pow(-f, 1./2.2)); + ctoi_table_clip[i] = - (int)(65535.999 * pow(-f, 2.2)); + } + } +} + +static inline int itoc(int p) { return itoc_table[p / 0x100]; } +static inline int ctoi(int p) { return ctoi_table[p / 0x100]; } + +/* + * Set new background and foreground colours according to the given command. + */ +static inline void domove(uint8_t command, uint8_t *bg, uint8_t *fg) +{ + if((command & 0x78) == 0x00) + *fg = command & 0x7; + else if((command & 0x78) == 0x10) + *bg = command & 0x7; +} + +/* + * Clamp pixel value to avoid colour bleeding. Deactivated because it + * does not give satisfactory results. + */ +#define CLAMP 0x1000 +static inline int clamp(int p) +{ +#if 0 + /* FIXME: doesn’t give terribly good results on eg. eatme.png */ + if(p < - CLAMP) return - CLAMP; + if(p > 0xffff + CLAMP) return 0xffff + CLAMP; +#endif + return p; +} + +/* + * Compute the perceptual error caused by replacing the input pixels "in" + * with the output pixels "out". "inerr" is the diffused error that should + * be applied to "in"’s first pixel. "outerr" will hold the diffused error + * to apply after "in"’s last pixel upon next call. The return value does + * not mean much physically; it is one part of the algorithm where you need + * to play a bit in order to get appealing results. That’s how image + * processing works, dude. + */ +static inline int geterror(int const *in, int const *inerr, + int const *out, int *outerr) +{ + int tmperr[9 * 3]; + int i, c, ret = 0; + + /* 9 cells: 1 for the end of line, 8 for the errors below */ + memcpy(tmperr, inerr, 3 * sizeof(int)); + memset(tmperr + 3, 0, 8 * 3 * sizeof(int)); + + for(i = 0; i < 6; i++) + { + for(c = 0; c < 3; c++) + { + /* Experiment shows that this is important at small depths */ + int a = clamp(in[i * 3 + c] + tmperr[c]); + int b = out[i * 3 + c]; + tmperr[c] = (a - b) * FS0 / FSX; + tmperr[c + (i * 3 + 3)] += (a - b) * FS1 / FSX; + tmperr[c + (i * 3 + 6)] += (a - b) * FS2 / FSX; + tmperr[c + (i * 3 + 9)] += (a - b) * FS3 / FSX; + ret += (a - b) / 256 * (a - b) / 256; + } + } + + for(i = 0; i < 4; i++) + { + for(c = 0; c < 3; c++) + { + /* Experiment shows that this is important at large depths */ + int a = itoc((in[i * 3 + c] + in[i * 3 + 3 + c] + + in[i * 3 + 6 + c]) / 3); + int b = itoc((out[i * 3 + c] + out[i * 3 + 3 + c] + + out[i * 3 + 6 + c]) / 3); + ret += (a - b) / 256 * (a - b) / 256; + } + } + + /* Using the diffused error as a perceptual error component is stupid, + * because that’s not what it is at all, but I found that it helped a + * bit in some cases. */ + for(i = 0; i < 3; i++) + ret += tmperr[i] / 256 * tmperr[i] / 256; + + memcpy(outerr, tmperr, 3 * sizeof(int)); + + return ret; +} + +static uint8_t bestmove(int const *in, uint8_t bg, uint8_t fg, + int const *errvec, int depth, int maxerror, + int *error, int *out) +{ + int voidvec[3], nvoidvec[3], bestrgb[6 * 3], tmprgb[6 * 3], tmpvec[3]; + int const *voidrgb, *nvoidrgb, *vec, *rgb; + int besterror, curerror, suberror, statice, voide, nvoide; + int i, j, c; + uint8_t command, bestcommand; + + /* Precompute error for the case where we change the foreground colour + * and hence only print the background colour or its negative */ + voidrgb = palette[bg]; + voide = geterror(in, errvec, voidrgb, voidvec); + nvoidrgb = palette[7 - bg]; + nvoide = geterror(in, errvec, nvoidrgb, nvoidvec); + + /* Precompute sub-error for the case where we print pixels (and hence + * don’t change the palette). It’s not the exact error because we should + * be propagating the error to the first pixel here. */ + if(depth > 0) + { + int tmp[3] = { 0, 0, 0 }; + bestmove(in + 6 * 3, bg, fg, tmp, depth - 1, maxerror, &statice, NULL); + } + + /* Check every likely command: + * 0-7: change foreground to 0-7 + * 8-15: change foreground to 0-7, print negative background + * 16-23: change background to 0-7 + * 24-31: change background to 0-7, print negative background + * 32: normal stuff + * 33: inverse video stuff */ + besterror = 0x7ffffff; + bestcommand = 0x10; + memcpy(bestrgb, voidrgb, 6 * 3 * sizeof(int)); + for(j = 0; j < 34; j++) + { + static uint8_t const lookup[] = + { + 0x00, 0x04, 0x01, 0x05, 0x02, 0x06, 0x03, 0x07, + 0x80, 0x84, 0x81, 0x85, 0x82, 0x86, 0x83, 0x87, + 0x10, 0x14, 0x11, 0x15, 0x12, 0x16, 0x13, 0x17, + 0x90, 0x94, 0x91, 0x95, 0x92, 0x96, 0x93, 0x97, + 0x40, 0xc0 + }; + + uint8_t newbg = bg, newfg = fg; + + command = lookup[j]; + domove(command, &newbg, &newfg); + + /* Keeping bg and fg is useless, because we could use standard + * pixel printing instead */ + if((command & 0x40) == 0x00 && newbg == bg && newfg == fg) + continue; + + /* I *think* having newfg == newbg is useless, too, but I don’t + * want to miss some corner case where swapping bg and fg may be + * interesting, so we continue anyway. */ + +#if 0 + /* Bit 6 off and bit 5 on seems illegal */ + if((command & 0x60) == 0x20) + continue; + + /* Bits 6 and 5 off and bit 3 on seems illegal */ + if((command & 0x68) == 0x08) + continue; +#endif + + if((command & 0xf8) == 0x00) + { + curerror = voide; + rgb = voidrgb; + vec = voidvec; + } + else if((command & 0xf8) == 0x80) + { + curerror = nvoide; + rgb = nvoidrgb; + vec = nvoidvec; + } + else if((command & 0xf8) == 0x10) + { + rgb = palette[newbg]; + curerror = geterror(in, errvec, rgb, tmpvec); + vec = tmpvec; + } + else if((command & 0xf8) == 0x90) + { + rgb = palette[7 - newbg]; + curerror = geterror(in, errvec, rgb, tmpvec); + vec = tmpvec; + } + else + { + int const *bgcolor, *fgcolor; + + if((command & 0x80) == 0x00) + { + bgcolor = palette[bg]; fgcolor = palette[fg]; + } + else + { + bgcolor = palette[7 - bg]; fgcolor = palette[7 - fg]; + } + + memcpy(tmpvec, errvec, 3 * sizeof(int)); + curerror = 0; + + for(i = 0; i < 6; i++) + { + int vec1[3], vec2[3]; + int smalle1 = 0, smalle2 = 0; + + memcpy(vec1, tmpvec, 3 * sizeof(int)); + memcpy(vec2, tmpvec, 3 * sizeof(int)); + for(c = 0; c < 3; c++) + { + int delta1, delta2; + delta1 = clamp(in[i * 3 + c] + tmpvec[c]) - bgcolor[c]; + vec1[c] = delta1 * FS0 / FSX; + smalle1 += delta1 / 256 * delta1; + delta2 = clamp(in[i * 3 + c] + tmpvec[c]) - fgcolor[c]; + vec2[c] = delta2 * FS0 / FSX; + smalle2 += delta2 / 256 * delta2; + } + + if(smalle1 < smalle2) + { + memcpy(tmpvec, vec1, 3 * sizeof(int)); + memcpy(tmprgb + i * 3, bgcolor, 3 * sizeof(int)); + } + else + { + memcpy(tmpvec, vec2, 3 * sizeof(int)); + memcpy(tmprgb + i * 3, fgcolor, 3 * sizeof(int)); + command |= (1 << (5 - i)); + } + } + + /* Recompute full error */ + curerror += geterror(in, errvec, tmprgb, tmpvec); + + rgb = tmprgb; + vec = tmpvec; + } + + if(curerror > besterror) + continue; + + /* Try to avoid bad decisions now that will have a high cost + * later in the line by making the next error more important than + * the current error. */ + curerror = curerror * 3 / 4; + + if(depth == 0) + suberror = 0; /* It’s the end of the tree */ + else if((command & 0x68) == 0x00) + { + bestmove(in + 6 * 3, newbg, newfg, vec, depth - 1, + besterror - curerror, &suberror, NULL); + +#if 0 + /* Slight penalty for colour changes; they're hard to revert. The + * value of 2 was determined empirically. 1.5 is not enough and + * 3 is too much. */ + if(newbg != bg) + suberror = suberror * 10 / 8; + else if(newfg != fg) + suberror = suberror * 9 / 8; +#endif + } + else + suberror = statice; + + if(curerror + suberror < besterror) + { + besterror = curerror + suberror; + bestcommand = command; + memcpy(bestrgb, rgb, 6 * 3 * sizeof(int)); + } + } + + *error = besterror; + if(out) + memcpy(out, bestrgb, 6 * 3 * sizeof(int)); + + return bestcommand; +} + +int main(int argc, char *argv[]) +{ +#if defined _WIN32 + PBITMAPINFO pbinfo; + BITMAPINFO binfo; + BITMAPFILEHEADER bfheader; + ULONG bisize; + HANDLE hfile; + HBITMAP tmp; + HDC hdc; + DWORD ret; +#else + SDL_Surface *tmp, *surface; +#endif + FILE *f; + uint8_t *pixels; + int *src, *dst, *srcl, *dstl; + int stride, x, y, depth, c; + + if(argc < 2) + { + fprintf(stderr, "Error: missing argument.\n"); + fprintf(stderr, "Usage: img2oric \n"); + return 1; + } + +#if defined _WIN32 + tmp = (HBITMAP)LoadImage(NULL, argv[1], IMAGE_BITMAP, + 0, 0, LR_LOADFROMFILE); +#else + tmp = IMG_Load(argv[1]); +#endif + if(!tmp) + { + fprintf(stderr, "Error: could not load image %s.\n", argv[1]); +#if defined _WIN32 + fprintf(stderr, "Maybe try with an 8-bpp or 24-bpp BMP file?\n"); +#endif + return 2; + } + + f = fopen(ORICFILE ".TAP", "w"); + if(!f) + { + fprintf(stderr, "Error: could not open %s.TAP for writing.\n", + ORICFILE); + return 3; + } + fwrite("\x16\x16\x16\x16\x24", 1, 5, f); + fwrite("\x00\xff\x80\x00\xbf\x3f\xa0\x00\x00", 1, 9, f); + fwrite(ORICFILE, 1, strlen(ORICFILE), f); + fwrite("\x00", 1, 1, f); + + init_tables(); + + /* Load the image into a friendly array of fast integers. We create it + * slightly bigger than the image so that we don’t have to care about + * borders when propagating the error later */ + src = calloc((WIDTH + 1) * (HEIGHT + 1) * 3, sizeof(int)); + dst = calloc((WIDTH + 1) * (HEIGHT + 1) * 3, sizeof(int)); + stride = (WIDTH + 1) * 3; + +#if defined _WIN32 + hdc = CreateCompatibleDC(NULL); + SelectObject(hdc, tmp); + for(y = 0; y < HEIGHT; y++) + for(x = 0; x < WIDTH; x++) + { + COLORREF color = GetPixel(hdc, x, y); + src[y * stride + x * 3] = ctoi(GetRValue(color) * 0x101); + src[y * stride + x * 3 + 1] = ctoi(GetGValue(color) * 0x101); + src[y * stride + x * 3 + 2] = ctoi(GetBValue(color) * 0x101); + for(c = 0; c < 3; c++) + dst[y * stride + x * 3 + c] = 0; + } +#else + /* FIXME: endianness */ + surface = SDL_CreateRGBSurface(SDL_SWSURFACE, WIDTH, HEIGHT, 32, + 0xff0000, 0xff00, 0xff, 0x0); + SDL_BlitSurface(tmp, NULL, surface, NULL); + pixels = (uint8_t *)surface->pixels; + for(y = 0; y < HEIGHT; y++) + for(x = 0; x < WIDTH; x++) + for(c = 0; c < 3; c++) + { + src[y * stride + x * 3 + c] + = ctoi(pixels[y * surface->pitch + x * 4 + 2 - c] * 0x101); + dst[y * stride + x * 3 + c] = 0; + } +#endif + + /* Let the fun begin */ + for(y = 0; y < HEIGHT; y++) + { + uint8_t bg = 0, fg = 7; + + fprintf(stderr, "\rProcessing... %i%%", (y * 100 + 99) / HEIGHT); + + for(x = 0; x < WIDTH; x += 6) + { + int errvec[3] = { 0, 0, 0 }; + int dummy, i; + uint8_t command; + + depth = (x + DEPTH < WIDTH) ? DEPTH : (WIDTH - x) / 6 - 1; + srcl = src + y * stride + x * 3; + dstl = dst + y * stride + x * 3; + + /* Recursively compute and apply best command */ + command = bestmove(srcl, bg, fg, errvec, depth, 0x7fffff, + &dummy, dstl); + /* Propagate error */ + for(c = 0; c < 3; c++) + { + for(i = 0; i < 6; i++) + { + int error = srcl[i * 3 + c] - dstl[i * 3 + c]; + srcl[i * 3 + c + 3] = + clamp(srcl[i * 3 + c + 3] + error * FS0 / FSX); + srcl[i * 3 + c + stride - 3] += error * FS1 / FSX; + srcl[i * 3 + c + stride] += error * FS2 / FSX; + srcl[i * 3 + c + stride + 3] += error * FS3 / FSX; + } + + for(i = -1; i < 7; i++) + srcl[i * 3 + c + stride] = clamp(srcl[i * 3 + c + stride]); + } + /* Iterate */ + domove(command, &bg, &fg); + /* Write byte to file */ + fwrite(&command, 1, 1, f); + } + } + + fclose(f); + + fprintf(stderr, " done.\n"); + + /* Save everything */ +#if defined _WIN32 + for(y = 0; y < HEIGHT; y++) + for(x = 0; x < WIDTH; x++) + { + uint8_t r = dst[y * stride + x * 3] / 0x100; + uint8_t g = dst[y * stride + x * 3 + 1] / 0x100; + uint8_t b = dst[y * stride + x * 3 + 2] / 0x100; + SetPixel(hdc, x, y, RGB(r, g, b)); + } + + binfo.bmiHeader.biSize = sizeof(binfo.bmiHeader); + binfo.bmiHeader.biBitCount = 0; + GetDIBits(hdc, tmp, 0, 0, NULL, &binfo, DIB_RGB_COLORS); + + switch(binfo.bmiHeader.biBitCount) + { + case 24: + bisize = sizeof(BITMAPINFOHEADER); + break; + case 16: + case 32: + bisize = sizeof(BITMAPINFOHEADER) + sizeof(DWORD) * 3; + break; + default: + bisize = sizeof(BITMAPINFOHEADER) + + sizeof(RGBQUAD) * (1 << binfo.bmiHeader.biBitCount); + break; + } + + pbinfo = (PBITMAPINFO)GlobalAlloc(GMEM_FIXED | GMEM_ZEROINIT, bisize); + memcpy(pbinfo, &binfo, sizeof(BITMAPINFOHEADER)); + + bfheader.bfType = 0x4D42; /* "BM" */ + bfheader.bfSize = sizeof(BITMAPFILEHEADER) + + bisize + pbinfo->bmiHeader.biSizeImage; + bfheader.bfReserved1 = 0; + bfheader.bfReserved2 = 0; + bfheader.bfOffBits = sizeof(BITMAPFILEHEADER) + bisize; + + pixels = GlobalAlloc(GMEM_FIXED | GMEM_ZEROINIT, + binfo.bmiHeader.biSizeImage); + GetDIBits(hdc, tmp, 0, pbinfo->bmiHeader.biHeight, + pixels, pbinfo, DIB_RGB_COLORS); + + hfile = CreateFile(BMPFILE, GENERIC_WRITE, 0, NULL, CREATE_ALWAYS, + FILE_ATTRIBUTE_ARCHIVE, NULL); + WriteFile(hfile, &bfheader, sizeof(BITMAPFILEHEADER), &ret, NULL); + WriteFile(hfile, pbinfo, bisize, &ret, NULL); + WriteFile(hfile, pixels, pbinfo->bmiHeader.biSizeImage, &ret, NULL); + CloseHandle(hfile); + + GlobalFree(pbinfo); + GlobalFree(pixels); +#else + for(y = 0; y < HEIGHT; y++) + for(x = 0; x < WIDTH; x++) + for(c = 0; c < 3; c++) + pixels[y * surface->pitch + x * 4 + 2 - c] + = itoc(dst[y * stride + x * 3 + c]) / 0x100; + SDL_SaveBMP(surface, BMPFILE); +#endif + + return 0; +} + diff --git a/static/img2oric/img2oric.exe b/static/img2oric/img2oric.exe new file mode 100755 index 0000000000000000000000000000000000000000..c5e181b52e220577c67db46b4e1380046ecb93ba GIT binary patch literal 39763 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TFe%$smkXeEub8G0OY;8@8ZpT% literal 0 HcmV?d00001 diff --git a/static/img2oric/index.html b/static/img2oric/index.html new file mode 100644 index 0000000..c04c86c --- /dev/null +++ b/static/img2oric/index.html @@ -0,0 +1,139 @@ + + + + + + + + + + + + Caca Labs - img2oric + + + + + + + + + +Oric computer + +

img2oric converts images to the Oric screen format. The Oric +screen format is one of the most twisted standards and very little artwork +exists for this vintage computer. It was therefore the perfect challenge for +the Caca Labs image processing team.

+ +

The graphical part of the Oric screen has 240×200 pixels and can only +display 8 different colours (black, blue, red, green, cyan, magenta, yellow +and white). Each horizontal group of 6 pixels can only use two different +colours: the foreground and the background colours. The background or +the foreground colour may also be changed (one at a time), but while doing +so the next 6 pixels cannot be chosen: they will use the background colour +instead. Finally, a special bit may be activated to display the current group +of pixels in reverse video.

+ +

Downloads

+ +

No real downloads are available yet. However, the file img2oric.c may be compiled on Linux to obtain an +almost functional, yet extremely slow (around 2 minutes to process an image on +a modern computer) program.

+ +

Usage

+ +

img2oric will output to output.bmp, an image file +containing the new version, and to OUTPUT.TAP which contains the +Oric-format file.

+ +

You can download a sample OUTPUT.TAP +file as generated by img2oric. To load it into an Oric +computer, or an emulator such as xeuphoric, just type +HIRES:CLOAD"OUTPUT" and it will be immediately displayed:

+ +

+ +
+ photography courtesy of André Chéramy +

+ +

Screenshots

+ +

These images show how img2oric performs. The results may not seem +terribly clean, but they are probably better than anything that has been seen +so far on an Oric computer.

+ +

These first 4 images show the results on the same images than the ones +on Dbug’s PictConv website, to illustrate the improvements over the +traditional RGB separation technique.

+ +

+ + +

+ +

+ + +

+ +

+ + +

+ +

+ + +

+ +

The following images show img2oric’s results with grayscale +images and cartoon images.

+ +

+ + +

+ +

+ + +

+ +

The following images use real Oric artwork, taken from the Buggy Boy +and the VIP 4 demos: they are almost pixel-perfect.

+ +

+ + +

+ +

+ + +

+ +

And finally, this is an animation whose frames were converted to Oric +colours using img2oric:

+ +

+ + +

+ +

Credits

+ +

img2oric was written by Sam Hocevar. Thanks to Jean-Yves +Lamoureux, Mickaël Pointier, Robert Chéramy, André Chéramy and Fabrice +Frances for their help, their work and the information they provided.

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Introduction

+ +

libcaca is a graphics library that outputs text instead of pixels, +so that it can work on older video cards or text terminals. It is not unlike +the famous AAlib +library, with the following improvements:

+ +
    +
  • Unicode support
  • +
  • 2048 available colours (some devices can only handle 16)
  • +
  • dithering of colour images
  • +
  • advanced text canvas operations (blitting, rotations)
  • +
+ +

Libcaca works in a text terminal (and should thus work on all Unix +systems including Mac OS X) using the S-Lang or ncurses libraries. It also +works natively on DOS and Windows.

+ +

Libcaca was written by Sam Hocevar <sam@zoy.org> and Jean-Yves Lamoureux +<jylam@lnxscene.org>. It is +free software, +and can be used, modified and distributed under the terms of the Do What The Fuck You Want To Public +License.

+ +

+ libcaca Ohloh metrics + toilet Ohloh metrics +

+ +

News

+ + + +libcaca rendering sample + +

Screenshots

+ +

Here are a few libcaca screenshots.

+ + + + + + + + + + + + + + + + +
+ cacaview +
cacaview +
+ cacaball +
cacaball +
+ cacaplas +
cacaplas +
+ truecolor +
TrueColor +
+
+ unicode +
Unicode +
+ swallow +
swallowing applications +
+ rotation +
canvas rotation +
+ +
+ +

See also the various export formats, generated from an old + ANSI file: + PNG (through TGA), + HTML, + SVG, + PostScript. +

+ + + +

Why?

+ +

+Que dites-vous ?... C’est inutile ?... Je le sais ! +
Mais on ne se bat pas dans l’espoir du succès ! +
Non ! non, c’est bien plus beau lorsque c’est inutile ! +
-- Edmond Rostand, Cyrano de Bergerac +

+ +

I am perfectly aware that libcaca is the waste of time it looks to +be. No need to tell me about that. I urge you to read Théophile Gautier’s +preface to Mademoiselle de Maupin, which also gives an excellent +explanation for the name “libcacaâ€:

+ +

+Il n’y a rien de vraiment beau que ce qui ne peut servir à rien ; tout ce +qui est utile est laid ; car c’est l’expression de quelque besoin ; et ceux +de l’homme sont ignobles et dégoûtants, comme sa pauvre et infirme nature. - +L’endroit le plus utile d’une maison, ce sont les latrines. +

+ +

As for the name “libcuculâ€, please refer to the pioneer of modernist +Polish fiction:

+ +

+Mais contre le cucul, il n’y a pas de refuge. Courez après moi +si vous voulez, je m’enfuis la gueule entre les mains. Et voilà, +tralala, Zut à celui qui le lira ! +
-- Witold Gombrowicz, Ferdydurke +

+ + + + + diff --git a/static/lc-mw1.ans b/static/lc-mw1.ans new file mode 100644 index 0000000..ed7b08a --- /dev/null +++ b/static/lc-mw1.ans @@ -0,0 +1,38 @@ + +ÜÜÜÜÜÜ + ÜÜÜßßßÜÜ ÜÜÜÜÜßßßÜÜÜÜÜ + ÜÜßÜܲ²²ÜßÜßßÜÜÜ ÜÜ ÜÜÜßÜÜ ÜÜÜÜÜÜ ÜÜÜÜÜÜÜÜÜÜÜ ÜÜÜÜÜßܲ²²ÜÜÜßßÜÜ +ÜÜßܲ²²±±±²²Ü²²ÜÜßÜÜÜßßÜßÜܲÜßÜßßÜÜßßÜßßÜÜÜÜÜÜÜßßÜÜÜßßÜÜßÝÞ²²±²²²²²ÜßÜÜ +ÞÝÞ²±±°±²ß²±±±±²²²Üßܲ²Üß²²±²Ýܲ²²²²²Üß²²²²²²²²²²Üßܲ²²²²Ü ²²±±±±±²²ÝÞÝÝ +Û ²±±°°±Ý Þ²²ß²±±²²ÝÞ±±±²Üß±±±²±±°ß±°±²²±±±°±±±²²ß²±°±±±²²²±°°±±Û ÛÛ +ÞÝÞ±°°°°±Ü ßÜÝÞ²±±²²°Þ°°±ÝÞ°°±°Û Þ° °±ÝÞ±°° °°±ßßܱ±°ß°°±Û ±°°°°±ÛÛ ÜÜ +ÛÛ °±±°±±²²Üßܲ±°°±²Ý°Ü°°°±°°°°°±±°°°Û °ß Þ° °ÝÞ°°°°°°°° °°ÝÞ°°°°ÛÛÜÜÜßßÜÜ +ÞÞÝÞ²²±±²²ßÜ°±°° °±±²±±±²ÝÞ±±±°°±²²ßßÜßÜÛ ±°°°°±°°±±ß±±° °°°°°ÝÞÛßßÜܲ²ÜßÜÜ + ßßÜß²²²ßܲ²±±°° °°±ÝÞ²²ßßܲ²ß²±±°ÜÜÜßßßÛÝÞ±±°±±±²±±Ý Þ±±°±±Ý±±°±ÜÜ°°°±±±²²ÜßÜÜ ßßÜß ²²²±±°° °°°°ÜÜÜßßÜÜÜßÜß²²±±±°°°ÜÜÜß²²²ßܲ²²²²²²±±±²²²±±±°°°±±±±±²²²ÜßÛßßÜßß²²²±±±°°ÝÞÝßÛÜ ßßß ßßÜÜß²²±±²²²²ßÜßÜßÜÜßßßÜÜÜß²²²²ÝÞ²²²²±±±±²²²²²ßßÜßÜÜßÜÜßßßÜÜßß²²²ßÜßÜÜßßÜÜ ÜÜßßßßÜÜßß²²ßÜÛßßßßÜÜßßßÛÛßßÜÜßßÜÜÜßßß²²²²ßßßÜÜßßß + ÜÜßܲÜßÜÜÜ ßßßÜÜÜÛßßÜܲ²ÜßÜÜßÜ°±²±ÜßÛÛÜÜßÜܲ²²²ÜÜßßÜßßÜÜÛßßßÛÜßÜÜÜ ÜÜßßßßß + ÞÞÝÞ²²²ÜÜßßÜÜÜ Üßܲ²²±±±²²Û°±²²²±Û Ûßܲ²²±±°±²²²²Üß²ÜÜßÜÜÛÛ ²²²²²²ÜßÜÜ + ÛÛ ²²±±±²²ÜÜßßÜÝÞ±±±°°°±±ÛÜß°±²±ßÜÛÝÞ²²±±°°°°±±±²²Û ²²²ÜßÜßÞ²²±±±²²ÝÞÝÝ + ÞÞÝÞ²±±°±±²²²²Üß ±°°°°°°±ÝßÜÛÜÜÜÜßßÜ ±±°°°°°ß±°°°±±ÝÞ²±±²Üܱ±±±°°°±²²ÛÛ + ÛÛ ²²±°°°±±±±ÝßÝÞ±°° °°°²ÜÜßßÜܲ²²Ý °°±±°°ÝÞ°°±°ßܱ°°°±±±°°°° °°°°±ÝÞÝÝ + ÛÛ ±°°°°°°°°Ü°Ü°°° °°°Ý Þ²²±±°±±±²ÜÜÜÜß°±Üß±ßÜ°°°° °°°ßßß°°° °°°°ÝÞÝÝ +ÛÛ °±±±°°°±°²°±±°°°±±±°°°°°°°°Ýܱ±°°°°°±±±Ü ±±°°°°±±Ýܱ±°°°°°±²ÛÛ +ßßÜß²²±±±±²ß²²±±±²²±±±²²²²±±±²²²±±±±±²²²ßÜÛ ²²±±±±²²Þ²±±±±±±²ÝÞÝÝ +ßßÜßß²²ßÜßÜßß²²ß ܲ²²ßßÜÜßß²²²Üßß²²²²²²ßÜßßßßÜß²²²²ßßÜßß²²²²²ßÜßß +ßßßÜÜßß ßßßÜÜßÛÜÜÜÜÛßÛÜÛÛÜÜÜÜßßÜÜÜÜÜÜÛÜÜÜÜ ßßÜÜÜÜßßßßßÜÜÜÜÜßß +ßßÛÛßÜÜÜÜÜÜßßÜÜÜÜÜßÜÜÜÛÛÛÜßÜÜÜÜÜßÜÜ ßßßßßßßßß +ÛÛ  °±²Ü Û  °±²ÛÜßßßßßÛ  °±²Ûßß +ÛÛ  °±²ÛÛ  °±²ßÜ  °±ßÛ  °±²ßÜÜÜ +ÛÛ  °±²ÛÛ  °±²Üß  °±²Û  °±²ÜßÜÜ +ßßÜÜÜÜßßßßÜÜÜÜÜßÛÜÜÜÜÜßßÜÜÜÜÜßß +ßßßß ßßßßß ßßßßßß ßßßßß +M O R T A L W i S H +ÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜ +PiL HQ þ Syndrome Couriers HQ þ GODS WHQ +16.8 Duals þ 8i8.XxX.XxXx þ 8 Node Ringdown +S: QUAZiMODEM & CiPHER +CO'S: LORD THiNKER THE MOZARELLO +SLiCE & DiCE þ HURRiCAN þ RAZOR BLADE + +ßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßßANSi BY LUC < AiM 'NiNE FO' > + + diff --git a/static/list-svn.html b/static/list-svn.html new file mode 100644 index 0000000..b3791f7 --- /dev/null +++ b/static/list-svn.html @@ -0,0 +1,50 @@ + + + + + + + + + + + + libcaca SVN mailing-list + + + + + + + + + + +", "", $data); + $data = eregi_replace(".*", "", $data); + + echo $data; + } + else + { + ?>

Mailing-list error.

+ + + + + diff --git a/static/list-trac.html b/static/list-trac.html new file mode 100644 index 0000000..2dc929c --- /dev/null +++ b/static/list-trac.html @@ -0,0 +1,50 @@ + + + + + + + + + + + + libcaca Trac mailing-list + + + + + + + + + + +", "", $data); + $data = eregi_replace(".*", "", $data); + + echo $data; + } + else + { + ?>

Mailing-list error.

+ + + + + diff --git a/static/list.html b/static/list.html new file mode 100644 index 0000000..7a37c8f --- /dev/null +++ b/static/list.html @@ -0,0 +1,58 @@ + + + + + + + + + + + + libcaca mailing-list + + + + + + + + + + +", "", $data); + $data = eregi_replace(".*", "", $data); + + echo $data; + } + else + { +?> +

There are three libcaca mailing-lists:

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Documentation error.

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neercs

+ +

neercs is a work-in-progress libcaca project. More later.

+ +

Screenshots

+ +

neercs sample

+ + + + + diff --git a/static/robots.txt b/static/robots.txt new file mode 100644 index 0000000..9a846d4 --- /dev/null +++ b/static/robots.txt @@ -0,0 +1,34 @@ +# $Id$ + +# Do not crawl CVS and .svn directories (they are 403 Forbidden anyway) +User-agent: * +Disallow: CVS +Disallow: .svn + +# Prevent excessive search engine hits +Disallow: /cgi-bin/trac.cgi +Disallow: /log + +# "This robot collects content from the Internet for the sole purpose of +# helping educational institutions prevent plagiarism. [...] we compare +# student papers against the content we find on the Internet to see if we +# can find similarities." (http://www.turnitin.com/robot/crawlerinfo.html) +# --> fuck off. +User-Agent: TurnitinBot +Disallow: / + +# "NameProtect engages in crawling activity in search of a wide range of +# brand and other intellectual property violations that may be of interest +# to our clients." (http://www.nameprotect.com/botinfo.html) +# --> fuck off. +User-Agent: NPBot +Disallow: / + +# "iThenticate® is a new service we have developed to combat the piracy +# of intellectual property and ensure the originality of written work for +# publishers, non-profit agencies, corporations, and newspapers." +# (http://www.slysearch.com/) +# --> fuck off. +User-Agent: SlySearch +Disallow: / + diff --git a/static/study/biblio.html b/static/study/biblio.html new file mode 100644 index 0000000..7948d54 --- /dev/null +++ b/static/study/biblio.html @@ -0,0 +1,157 @@ + + + + + + + + + + + + Libcaca study - Bibliography + + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

Bibliography

+ +
    + +
  • [1] Victor Ostromoukhov. A Simple and Efficient Error-Diffusion + Algorithm. In Proceedings of SIGGRAPH 2001, in ACM Computer Graphics, + Annual Conference Series, pp. 567–572, 2001
  • + +
  • [2] Jamie Hutchinson. Culture, Communication, and an Information Age + Madonna. IEEE Professional Communication Society Newsletter, Volume 45, + Number 3, pp. 1, 5–7, 2001
  • + +
  • [3] V. Ostromoukhov, Reproduction couleur par trames irrégulières + et semi-régulières, Ph.D. Thesis No.1330, EPFL, Lausanne, Switzerland, + 1995
  • + +
  • [4] J. Allebach and Q. Lin, FM screen design using DBS algorithm, + in Proc. IEEE International Conference on Image Processing (ICIP ’96), vol. + 1, pp. 549–552, Lausanne, Switzerland, September 1996
  • + +
  • [5] R.W. Floyd, L. Steinberg, An adaptive algorithm for spatial grey + scale. Proceedings of the Society of Information Display 17, 75–77 + (1976)
  • + +
  • [6] P. Stucki, MECCA - a multiple error correcting computation + algorithm for bi-level image hard copy reproduction. Research report + RZ1060, IBM Research Laboratory, Zurich, Switzerland, 1981
  • + +
  • [7] J. F. Jarvis, C. N. Judice and W. H. Ninke, A Survey of + Techniques for the Display of Continuous Tone Pictures on Bi-level + Displays. Computer Graphics and Image Processing, 5 13–40, 1976
  • + +
  • [8] J. Shiau and Z. Fan, Method for Quantization Gray Level Pixel + Data with Extended Distribution Set, U.S. patent 5,353,127, 1994
  • + +
  • [9] R. Silvers. Digital composition of a mosaic image, U.S. + patent 6,137,498, Oct. 2000
  • + +
  • [10] Daniel Burkes, Presentation of the Burkes error filter for use + in preparing continuous-tone images for presentation on bi-level devices, + in LIB 15 (Publications), CIS Graphics Support Forum, September 15, 1988 + (unpublished)
  • + +
  • [11] Frankie Sierra, in LIB 17 (Developer’s Den), CIS Graphics Support + Forum (unpublished)
  • + +
  • [12] Bill Atkinson, private correspondence with John Balestrieri, + January 2003 (unpublished)
  • + +
  • [13] Robert Ulichney, Digital Halftoning, The MIT Press, + Cambridge, Mass. (1987)
  • + +
  • [14] D.E. Knuth, Digital Halftones by Dot Diffusion, ACM + Transcripts on Graphics, 6(4), 1987, 245-273
  • + +
  • [15] T.C. Chan, K.S. Chen (Apple Computer, Inc.), Method and system + for halftoning by tiling subsets of a threshold array over portions of an + image, U.S. patent 5,761,347, 1998
  • + +
  • [16] Éric Brasseur, Gamma error in picture scaling, World Wide + Web, http://www.4p8.com/eric.brasseur/gamma.html, August 2007 +
  • + +
  • [17] B. Bayer, Color imaging array, in U.S. patent 3,971,065, + 1976
  • + +
  • [18] R.A. Ulichney (Digital Equipment Corporation), Void and cluster + apparatus and method for generating dither templates, in U.S. patent + 5,535,020, 1992
  • + +
  • [19] H. Ancin, A. Bhattacharjya and J. Shu (Seiko Epson Corporation), + Void-and-cluster dither-matrix generation for better half-tone + uniformity, in U.S. patent 6,088,512, 1997
  • + +
  • [20] J.S. Arney, P.G. Anderson, S. Ganawan, Error Diffusion and + Edge Enhancement: Raster Versus Omni-Directional Processing, Western NY + Conference on Image Processing, Rochester, NY, September 9, 2001
  • + +
  • [21] Robert Ulichney, Spatial Extent of Void and Cluster Finding + Filters, IS&T’s 11th International Congress on Non-Impact Printing + Technologies, Oct. 29-Nov 3, 1995
  • + +
  • [22] Alejo Hausner, Graph-order dithering, proceedings of SPIE -- + Volume 6058 Color Imaging XI, 1996
  • + +
  • [23] N. Damera-Venkata, B.L. Evans, FM halftoning via block error + diffusion, proceedings of the 2001 International Conference on Image + Processing, 2001
  • + +
  • [24] R.V. Klassen, R. Eschbach, and K. Bharat, Vector diffusion + in a distorted colour space, proceedings of IS&T’s 47th Annual + Conference, 1994, pp. 489–491
  • + +
  • [25] D. Shaked, N. Arad, A. Fitzhugh, and I. Sobel, Ink relocation + for color halftones, proceedings of IS&T Image Processing, Image + Quality, Image Capture Systems Conference, Portland, OR, 1998, pp. 340–343 +
  • + +
  • [26] T. Riemersma, A Balanced Dither Algorithm, C/C++ Users + Journal, volume 16, issue 12, December 1998
  • + +
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Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + +
+ +
+ +

Libcaca study: the science behind colour ASCII art

+ +

This document is an attempt at extending the leverage of skilled +resources by uncovering and addressing the challenges the industry faces +today in the area of colour ASCII art generation.

+ +

Seriously, guys. If you think that what libcaca does is easy, you either +don’t know what you are talking about, or we want you in the team.

+ +

Foreword

+ +

Meet Lena. She will guide us through this document, because the +seriousness of a scientific document in the area of computer graphics can +be measured by the number of times Lena appears in it. She truly is the +Mona Lisa of image processing. [2]

+ +

+ Lena (256×256) + colour gradient (64×256) + Lena (256×256BW) + greyscale gradient (32×256) +

+ +

This document makes a lot of assumptions, such as the fact that input +images are made of pixels that have either one (grey level) or three (red, +green and blue) values uniformly spread between 0 and 1 (with regards to +human contrast perception). Real life is more complicated than that, but +that is beyond the scope of this document for now.

+ +

Table of contents

+ +
    +
  • 1. Colour quantisation +
      +
    • 1.1. Black and white thresholding
    • +
    • 1.2. Greyscale thresholding
    • +
    • 1.3. Dynamic thresholding
    • +
    • 1.4. Random dithering
    • +
    +
  • +
  • 2. Halftoning +
      +
    • 2.1. Halftoning patterns
    • +
    • 2.2. Screen artifacts
    • +
    • 2.3. Ordered dithering
    • +
    • 2.4. Random ordered dithering
    • +
    • 2.5. Non-rectangular dither tiles
    • +
    • 2.6. Supercell dithering
    • +
    • 2.7. Void and cluster method
    • +
    +
  • +
  • 3. Error diffusion +
      +
    • 3.1. Floyd-Steinberg and JaJuNi error diffusion
    • +
    • 3.2. Floyd-Steinberg derivatives
    • +
    • 3.3. Changing image parsing direction
    • +
    • 3.4. Variable coefficients error diffusion
    • +
    • 3.5. Block error diffusion
    • +
    • 3.6. Sub-block error diffusion
    • +
    • 3.7. Direct binary search
    • +
    +
  • +
  • 4. Model-based dithering +
      +
    • 4.1. Gaussian human visual system model
    • +
    • 4.2. Direct binary search
    • +
    • 4.3 Comparing dithering algorithms
    • +
    +
  • +
  • 5. Greyscale dithering +
      +
    • 5.1. Introducing gamma
    • +
    • 5.2. Gamma correction
    • +
    • 5.3. Greyscale sub-block error diffusion
    • +
    +
  • +
  • 6. Colour dithering +
      +
    • 6.1. Separate-space dithering
    • +
    • 6.2. Accounting for other dimensions
    • +
    • 6.3. Reducing visual artifacts
    • +
    • 6.4. Colour sub-block error diffusion
    • +
    +
  • +
  • 7. Photographic mosaics +
      +
    • 7.1. Image classification
    • +
    • 7.2. Error diffusion
    • +
    • 7.3. Colour ASCII art
    • +
    +
  • +
  • Bibliography
  • +
  • Source code
  • +
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Colour quantisation + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + +

1. Colour quantisation

+ +

The process of reducing the number of colours used in an image is called +colour quantisation. It is a very old and common computer graphics +problem. Many methods exist to do the task, and their efficiency depends on +several parameters:

+ +
    +
  • the input image: is it a photograph? a vector drawing? a composition + of both?
  • +
  • the target media: is it a computer screen? if so, what are the size + and the position of the pixels? is it a printed document? if so, + what kind of paper? what kind of ink? or maybe the conversion should + be optimised for both targets?
  • +
  • the quality requirements: for instance, can contrast be raised for + a more appealing result at the expense of accuracy? +
  • the allowed computation time: do we need 50fps or can we afford to + wait 10 seconds for a better result?
  • +
+ +

1.1. Black and white thresholding

+ +

Since a greyscale pixel has a value between 0 and 1, a fast method +to convert the image to black and white is to set all pixels below 0.5 +to black and all pixels above 0.5 to white. This method is called +thresholding and, in our case, results in the following image:

+ +

+ 50% threshold + 50% threshold gradient +

+ +

Not that bad, but we were pretty lucky: the original image’s brightness +was rather well balanced. A lot of detail is lost, though. Different results +can be obtained by choosing “threshold values†other than 0.5, for instance +0.4 or 0.6, resulting in a much brighter or darker image:

+ +

+ 40% threshold + 40% threshold gradient + 60% threshold + 60% threshold gradient +

+ +

Choosing the best thresholding value for a given image is called +average dithering. But even with the best value, the results will +not improve tremendously.

+ +

1.2. Greyscale thresholding

+ +

Better results can be achieved with a slightly bigger palette. Here is +thresholding applied to a 3-colour and to a 5-colour palette:

+ +

+ 3-colour threshold + 3-colour threshold gradient + 5-colour threshold + 5-colour threshold gradient +

+ +

Using this method, shades of grey are evenly used. However, the global +error is far from optimal, as the following graphs show:

+ +

+ mean error 0.138 + mean error 0.075 +

+ +

The following thresholding method minimises the error, at the cost of +underusage of pure black and white colours:

+ +

+ 3-colour threshold, minimal error + 3-colour threshold gradient, minimal error + 5-colour threshold, minimal error + 5-colour threshold gradient, minimal error +

+ +

+ mean error 0.125 + mean error 0.0625 +

+ +

This is a perfect example of a situation where colour accuracy does not +help achieve a better result.

+ +

1.3. Dynamic thresholding

+ +

Dynamic thresholding consists in studying the image before selecting +the threshold values. One strategy, for instance, is to choose the median +pixel value. This is done simply by computing a histogram of the image.

+ +

+ 2-colour dynamic threshold + 2-colour dynamic threshold gradient + 5-colour dynamic threshold + 5-colour dynamic threshold gradient +

+ +

1.4. Random dithering

+ +

Instead of constantly using the same threshold value, one can use a +different random value for each pixel in the image. This technique is simply +called random dithering.

+ +

Here are two simple examples. On the left, threshold values are uniformly +chosen between 0 and 1. On the right, random dithering with threshold values +chosen with a gaussian distribution (mean 0.5, standard deviation 0.15): +

+ +

+ random dithering + random dithering gradient + gaussian (0.5, 0.15) dithering + gaussian (0.5, 0.15) dithering gradient +

+ +

The images look very noisy, but they are arguably an improvement over +standard constant thresholding.

+ +

Finally, this is dynamic thresholding with 4 colours where threshold values +at every pixel are computed as usual, but then perturbated using a gaussian +distribution (mean 0, standard deviation 0.08):

+ +

+ 4-colour dynamic thresholding, gaussian (0, 0.08) + 4-colour dynamic thresholding, gaussian (0, 0.08) gradient +

+ + + + + + + + diff --git a/static/study/part2.html b/static/study/part2.html new file mode 100644 index 0000000..4333d1c --- /dev/null +++ b/static/study/part2.html @@ -0,0 +1,477 @@ + + + + + + + + + + + + Libcaca study - 2. Halftoning + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

2. Halftoning

+ +

2.1. Halftoning patterns

+ +

Observe the following patterns. From a certain distance or assuming small +enough pixels, they look like shades of grey despite being made of only black +and white pixels:

+ +

+ 50% pattern +

+ +

We can do even better using additional patterns such as these 25% and +75% halftone patterns:

+ +

+ 25% and 75% patterns +

+ +

This looks promising. Let’s try immediately on Lena: we will use the +5-colour thresholding picture and replace the 0.25, 0.5 and 0.75 grey values +with the above patterns:

+ +

+ 25%, 50% and 75% halftoning + 25%, 50% and 75% halftoning gradient +

+ +

Not bad for a start. But there is a lot to improve. +By the way, this technique is covered by Apple’s U.S. patent 5761347 +[15].

+ +

2.2. Screen artifacts

+ +

If your screen’s quality is not very good, you might experience slightly +different shades of grey for the following patterns, despite being made of 50% +black and 50% white pixels:

+ +

+ screen imperfections +

+ +

Obviously the middle pattern looks far better to the human eye on a +computer screen. Optimising patterns so that they look good to the human +eye and don't create artifacts is a crucial element of a dithering +algorithm. Here is another example of two patterns that approximate to +the same shade of grey but may look slightly different from a distance:

+ +

+ two different 25% patterns +

+ +

2.3. Ordered dithering

+ +

A generalisation of the dithering technique we just saw that uses a +certain family of patterns is called ordered dithering. It is based on +a dither matrix such as the following one:

+ +

+ 2×2 dither matrix +

+ +

Using the matrix coefficients as threshold values yield the following +results for black, white and three shades of grey (0.25, 0.5 and 0.75):

+ +

+ results of 2×2 dither matrix +

+ +

The dither matrix is therefore repeated all over the image. The first pixel +will be thresholded with a value of 0.2, the second pixel with a value of 0.8, +then the third pixel with a value of 0.2 again, and so on, resulting in an image +very similar to the one previously seen in 2.1:

+ +

+ tiled dither matrix + 2×2 Bayer dithering + 2×2 Bayer dithering gradient +

+ +

For better readability, the matrix is rewritten as following. The dither +coefficients are trivially computed from the matrix cells and the matrix size: +

+ +

+ normalised 2×2 dither matrix +

+ +

Different matrices can give very different results. This is a 4×4 Bayer +ordered dither matrix [17], recursively created from the previous 2×2 +dither matrix:

+ +

+ 4×4 Bayer matrix + 4×4 Bayer dithering + 4×4 Bayer dithering gradient +

+ +

This is an 8×8 Bayer matrix, recursively created from the 4×4 version:

+ +

+ 4×4 Bayer matrix + 4×4 Bayer dithering + 4×4 Bayer dithering gradient +

+ +

This 4×4 cluster dot matrix creates dot patterns:

+ +

+ 4×4 cluster dot matrix + 4×4 cluster dot dithering + 4×4 cluster dot dithering gradient +

+ +

This 8×8 cluster dot matrix mimics the halftoning techniques used by +newspapers:

+ +

+ 4×4 cluster dot matrix + 4×4 cluster dot dithering + 4×4 cluster dot dithering gradient +

+ +

This unusual 5×3 matrix creates artistic vertical line artifacts:

+ +

+ 4×4 cluster dot matrix + 4×4 cluster dot dithering + 4×4 cluster dot dithering gradient +

+ +

There are two major issues with ordered dithering. First, important +visual artifacts may appear. Even Bayer ordered dithering causes +weird cross-hatch pattern artifacts on some images. Second, dithering +matrices do not depend on the original image and thus do not take input +data into account: high frequency features in the image are often missed +and, in some cases, cause even worse artifacts.

+ +

2.4. Random ordered dithering

+ +

Random dithering can help reduce the major problem caused by halftoning, +which is the apparition of pattern artifacts. The method is as simple as +slightly perturbating dither matrix coefficients (or pixel values) +during the halftoning step. The difficult part is picking up an adequate +perturbation function: too much perturbation and the result is unrecognisable, +too little and the artifacts stay.

+ +

For instance, this is the result of 8×8 Bayer dithering perturbated by a +gaussian distribution (mean 0.0, standard deviation 0.08):

+ +

+ 4×4 Bayer dithering, gaussian perturbation + 4×4 Bayer dithering, gaussian perturbation gradient +

+ +

Another way to use random number generators to avoid pattern artifacts is +random dither matrix selection [22]. The image space is no longer tiled +with the same matrix over and over again, but with a random selection from a +list of similar dither matrices.

+ +

This example shows random matrix selection from a list of six 3×3 dither +matrices:

+ +

+ four 3×3 dispersed dot matrices + random Bayer matrix dithering + random Bayer matrix dithering gradient +

+ +

2.5. Non-rectangular dither tiles

+ +

Another way to avoid disturbing pattern artifacts is to use non-rectangular +dither tiles. Here are several examples, the first one generating slanted +square patterns, the second one hexagonal patterns, then slanted square +patterns again with a slightly different angle, and hexagonal patterns again. +The artifacts usually seen in Bayer dithering do not appear here:

+ +

+ cross dither tile + cross dithering + cross dithering gradient +

+ +

+ hex dither tile + hex dithering + hex dithering gradient +

+ +

+ square dither tile + square dithering + square dithering gradient +

+ +

+ hex2 dither tile + hex2 dithering + hex2 dithering gradient +

+ +

2.6. Supercell dithering

+ +

Supercell dithering consists in creating bigger dithering tiles +(supercells) from base tiles. One example is Victor Ostromoukhov’s +CombiScreen method [3].

+ +

Just like Bayer matrices, non-rectangular tiles can be used to recursively +create bigger patterns, giving finer results. The amount of shades of grey +that can be rendered using a given tile is the number of cells in the tile +plus one. Here are a few examples using tiles seen previously:

+ +

+ 4-wise cross dither tile + 4-wise cross dithering + 4-wise cross dithering gradient +

+ +

+ 3-wise hex dither tile + 3-wise hex dithering + 3-wise hex dithering gradient +

+ +

+ 4-wise square dither tile + 4-wise square dithering + 4-wise square dithering gradient +

+ +

This example shows a tile resembling a Davis-Knuth dragon curve. Though +the tile itself is beautiful, it is in reality only a reorganisation of an +8×8 Bayer dither matrix. Therefore the resulting image is exactly the same +as for classical Bayer dithering:

+ +

+ twin dragon dither tile + twin dragon dithering + twin dragon dithering gradient +

+ +

Here are two consecutive iterations of the hexagonal tiling shown +above. Since the area of the original tile is 10 cells, the first iteration +could display 11 different shades of grey. These iterations can display +respectively 31 and 91 shades:

+ +

+ 3-way hex2 dither tile + 3-way hex2 dithering + 3-way hex2 dithering gradient +

+ +

+ 9-way hex2 dither tile + 9-way hex2 dithering + 9-way hex2 dithering gradient +

+ +

2.7. Void and cluster method

+ +

Robert A. Ulichney’s void and cluster method [18] is a very generic +method for dither array generation. It mainly targets huge matrices in order +to reduce artifacts caused by tiling.

+ +

The process goes through many steps. First, a working pattern matrix +needs to be created:

+ +
    +
  • Generate an empty w×h matrix full of 0s
  • +
  • Set n cells to 1
  • +
  • The working matrix is uniformly distributed: +
      +
    • Find the 1 with the most neighbours set to 1 and set that cell + to 0
    • +
    • Find the 0 with the fewest neighbours set to 1 and set that cell + to 1
    • +
    • Repeat until the 1 was just put back where it originally was
    • +
    +
  • +
+ +

Usually n is about 10% of w×h. It can be generated +randomly, loaded from a known pattern, or created using a more powerful +algorithm (chapter 3 will introduce error diffusion algorithms that may be +used to generate the working pattern matrix).

+ +

The dither matrix is then generated from the working pattern in three +steps:

+ +
    +
  • Dither indices 0 to n - 1 are set using a first copy of the + working matrix
  • +
      +
    • n = n - 1
    • +
    • Find the 1 with the most neighbours set to 1 and set that cell + to 0
    • +
    • In the dither matrix, set the corresponding cell to n +
    • +
    • Repeat until n = 0
    • +
    + +
  • Dither indices n to w×h/2 are set using a second copy of + the working matrix
  • +
      +
    • Find the 0 with the fewest neighbours set to 1 and set that cell + to 1
    • +
    • In the dither matrix, set the corresponding cell to n +
    • n = n + 1
    • +
    • Repeat until n ≥ w×h/2
    • +
    + +
  • Dither indices w×h/2 to w×h are set using the same copy + of the working matrix
  • +
      +
    • Find the 0 with the fewest neighbours set to 1 and set that cell + to 1
    • +
    • In the dither matrix, set the corresponding cell to n +
    • n = n + 1
    • +
    • Repeat until n = w×h
    • +
    + +
+ +

The key part of the algorithm is the choice of the void finder and the +cluster finder for each step. Best results are achieved using Voronoï +tesselation [19] [22], but simpler methods such as gaussian +convolution [21] give decent results, too.

+ +

The following two matrices show the results of the algorithm using +randomly generated initial matrices of size respectively 14×14 and 25×25. +The void and cluster finder uses a simple 7×7 gaussian convolution filter. +Gray cells show the initial uniformly distributed matrix:

+ +

+ 14×14 void-and-cluster matrix +

+ +

+ 25×25 void-and-cluster matrix +

+ +

Dither matrices generated with the void and cluster method give impressive +results. They are pretty close to the best quality that can be achieved +using standard ordered dithering:

+ +

+ 14×14 void and cluster dithering + 14×14 void and cluster dithering gradient + 25×25 void and cluster dithering + 25×25 void and cluster dithering gradient +

+ +

This technique is covered by Ulichney’s U.S. patent 5535020 +and the specific implementation we showed is partly covered by Epson’s U.S. patent 6088512. +

+ + + + + + + + + diff --git a/static/study/part3.html b/static/study/part3.html new file mode 100644 index 0000000..c2b347f --- /dev/null +++ b/static/study/part3.html @@ -0,0 +1,632 @@ + + + + + + + + + + + + Libcaca study - 3. Error diffusion + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

3. Error diffusion

+ +

The idea behind error diffusion is to compute the error caused by +thresholding a given pixel and propagate it to neighbour pixels, in order to +compensate for the average intensity loss or gain. It is based upon the +assumption that a slightly out-of-place pixel causes little visual harm. +

+ +

The error is computed by simply substracting the source value and the +destination value. Destination value can be chosen by many means but does +not impact the image a lot with most methods in comparison to the crucial +choice of error distribution coefficients.

+ +

This is the simplest error diffusion method. It thresholds the image +to 0.5 and propagates 100% of the error to the next (right) pixel. It is +quite impressive given its simplicity but causes important visual artifacts: +

+ +

+ Simple error diffusion + Simple error diffusion gradient +

+ +

3.1. Floyd-Steinberg and JaJuNi error diffusion

+ +

The most famous error diffusion method is the Floyd-Steinberg +algorithm [5]. It propagates the error to more than one adjacent pixels using +the following coefficients:

+ +

+ Floyd-Steinberg +

+ +

The result of this algorithm is rather impressive even compared to the +best ordered dither results we could achieve:

+ +

+ Floyd-Steinberg error diffusion + Floyd-Steinberg error diffusion gradient +

+ +

Jarvis, Judice and Ninke dithering [7] (sometimes nicknamed +JaJuNi) was published almost at the same time as Floyd-Steinberg. It +uses a much more complex error diffusion matrix:

+ +

+ Jarvis, Judice and Ninke + Jarvis, Judice and Ninke error diffusion + Jarvis, Judice and Ninke error diffusion gradient +

+ +

3.2. Floyd-Steinberg derivatives

+ +

Zhigang Fan came up with several Floyd-Steinberg derivatives. Fan +dithering [8] just moves one coefficient around:

+ +

+ Fan + Fan error diffusion + Fan error diffusion gradient +

+ +

Shiau-Fan dithering use a family of matrices supposed to reduce +the apparition of artifacts usually seen with Floyd-Steinberg:

+ +

+ Shiau-Fan + Shiau-Fan error diffusion + Shiau-Fan error diffusion gradient +

+ +

+ Shiau-Fan 2 + Shiau-Fan 2 error diffusion + Shiau-Fan 2 error diffusion gradient +

+ +

By the way, these matrices are covered by Shiau’s and Fan’s +U.S. patent +5353127.

+ +

Stucki dithering [6] is a slight variation of Jarvis-Judice-Ninke +dithering:

+ +

+ Stucki + Stucki error diffusion + Stucki error diffusion gradient +

+ +

Burkes dithering is yet another variation [10] which improves on +Stucki dithering by removing a line and making the error coefficients fractions +of powers of two:

+ +

+ Burkes + Burkes error diffusion + Burkes error diffusion gradient +

+ +

Frankie Sierra [11] came up with a few error diffusion matrices: Sierra +dithering is a variation of Jarvis that is slightly faster because it +propagates to fewer pixels, Two-row Sierra is a simplified version +thereof, and Filter Lite is one of the simplest Floyd-Steinberg +derivatives:

+ +

+ Sierra + Sierra error diffusion + Sierra error diffusion gradient +

+ +

+ Sierra + Sierra error diffusion + Sierra error diffusion gradient +

+ +

+ Sierra + Sierra error diffusion + Sierra error diffusion gradient +

+ +

Atkinson dithering [12] only propagates 75% of the error, leading +to a loss of contrast around very dark and very light areas (also called +highlights and shadows), but better contrast in the midtones. The +original Macintosh software HyperScan used this dithering algorithm, +still considered superior to other Floyd-Steinberg derivatives by many Mac +zealots:

+ +

+ Atkinson + Atkinson error diffusion + Atkinson error diffusion gradient +

+ + + +

3.3. Changing image parsing direction

+ +

While image parsing order does not matter with ordered dithering, it can +actually be crucial with error diffusion. The reason is that once a pixel has +been processed, standard error diffusion methods do not go back.

+ +

The usual way to parse an image is one pixel after the other, following +their order in memory. When reaching the end of a line, we automatically jump +to the beginning of the next line. Error diffusion methods using this +parsing order are called raster error diffusion:

+ +

+ Regular parsing +

+ +

Changing the parsing order can help prevent the apparition of artifacts in +error diffusion algorithms. This is serpentine parsing, where every odd +line is parsed in reverse order (right to left):

+ +

+ Serpentine parsing +

+ +

The major problem with Floyd-Steinberg is the worm artifacts it +creates. Here is an example of an image made of grey 0.9 dithered with standard +Floyd-Steinberg and with serpentine Floyd-Steinberg [13 pp.266—267]. +Most of the worm artifacts have disappeared or were highly reduced:

+ +

+ Floyd-Steinberg on grey 90% + serpentine Floyd-Steinberg on grey 90% +

+ +

And here are the results of serpentine Floyd-Steinberg on Lena. Only a +very close look will show the differences with standard Floyd-Steinberg, but +a few of the artifacts did disappear:

+ +

+ serpentine Floyd-Steinberg + serpentine Floyd-Steinberg gradient +

+ +

Riemersma dithering [26] parses the image following a plane-filling +Hilbert curve and only propagates the error of the last q pixels, +weighting it with an exponential rule. The method is interesting and inventive, +unfortunately the results are disappointing: structural artifacts are worse +than with other error diffusion methods (shown here with q = 16 and r += 16):

+ +

+ Hilbert curve parsing + Riemersma dither on Hilbert curve + Riemersma dither on Hilbert curve gradient +

+ +

A variation of Riemersma dithering uses a Hilbert 2 curve, giving +slightly better results but still causing random artifacts here and there: +

+ +

+ Hilbert 2 curve parsing + Riemersma dither on Hilbert 2 curve + Riemersma dither on Hilbert 2 curve gradient +

+ +

An inherent problem with plane-filling curves is that distances on the +curve do not mean anything in image space. Riemersma dithering distributes +error to pixels according to their distance on the curve rather than their +distance in the image.

+ +

We introduce spatial Hilbert dithering that addresses this issue +by distributing the error according to spatial coordinates. We also get rid +of the r parameter, choosing to distribute 100% of the error.

+ +

This is spatial Hilbert dithering on a Hilbert curve and on a Hilbert 2 +curve. The results show a clear improvement over the original Riemersma +algorithm, with far less noise and smoother low-gradient areas:

+ +

+ spatial Hilbert dither on Hilbert curve + spatial Hilbert dither on Hilbert curve gradient + spatial Hilbert dither on Hilbert 2 curve + spatial Hilbert dither on Hilbert 2 curve gradient +

+ +

Dot diffusion [14] is an error diffusion method by Donald E. Knuth +that uses tileable matrices just like ordered dithering, except that the cell +value order is taken into account for error propagation. Diagonal cells get +half as much error as directly adjacent cells:

+ +

+ Dot diffusion +

+ +

For instance, in the following example, cell 25’s error is propagated to +cells 44, 36, 30, 34 and 49. Given the diagonal cells rule, cells 44, 30 and +49 each get 1/7 of the error and cells 36 and 34 each get 2/7 of the error. +Similarly, cell 63 gets 100% of cell 61’s error.

+ +

+ Dot diffusion matrix sample + Dot diffusion + Dot diffusion gradient +

+ +

The initial result is not extraordinary. But Knuth suggests applying a +sharpen filter to the original image before applying dot diffusion. He also +introduces a zeta value to deal with the size of laser printer dots, +pretty similar to what we’ll see later as gamma correction. The +following two images had a sharpening value of 0.9 applied to them. The image +on the right shows zeta = 0.2:

+ +

+ Dot diffusion sharpen 0.9 + Dot diffusion sharpen 0.9 gradient + Dot diffusion sharpen 0.9 zeta 0.2 + Dot diffusion sharpen 0.9 zeta 0.2 gradient +

+ +

Do not get fooled by Knuth’s apparent good results. They specifically +target dot printers and do not give terribly good results on a computer +screen. Actually, a sharpening filter makes just any dithering method look +better, even basic Floyd-Steinberg dithering (shown here with a sharpening +value of 0.9, too):

+ +

+ FS with sharpening + FS with sharpening gradient +

+ +

Dot diffusion was reinvented 14 years later by Arney, Anderson and Ganawan +without even citing Knuth. They call their method omni-directional error +diffusion. Instead of using a clustered dot matrix like Knuth recommends +for dot diffusion, they use a dispersed dot matrix, which gives far better +results on a computer display. This is a 16×12 portion of that matrix:

+ +

+ omni-directional ED matrix sample +

+ +

The preferred implementation of omni-directional error diffusion uses +a slightly different propagation matrix, where top and bottom neighbours get +more error than the others:

+ +

+ omni-directional ED + omni-directional ED + omni-directional ED gradient +

+ +

3.4. Variable coefficients error diffusion

+ +

Small error diffusion matrices usually cause artifacts to appear because +the error is not propagated in enough directions. At the same time, such +matrices also reduce the sharpened aspect common in error diffusion +techniques.

+ +

Ostromoukhov suggests error diffusion values that vary according to the +input value. The list of 256 discrete value triplets for d1, d2 +and d3 he provides [1] give pretty good results with serpentine parsing: +

+ +

+ Ostromoukhov ED matrix + Ostromoukhov ED + Ostromoukhov ED gradient +

+ +

3.5. Block error diffusion

+ +

Sometimes, due to physical restrictions of the target media, output +is limited to some combinations of pixel blocks, such as the ones shown +below:

+ +

+ list of 2×2 pixel blocks +

+ +

It is still possible to dither the image, by doing it 4 pixels at a +time and simply choosing the block from the list that minimises the global +error within the 2×2 block:

+ +

+ 2×2 pixel block quantisation + 2×2 pixel block quantisation gradient +

+ +

Damera-Venkata and Evans introduce block error diffusion [23], which +reuses traditional error diffusion methods such as Floyd-Steinberg but applies +the same error value to all pixels of a given block. Only one error value is +propagated, a+b+c+d, which is the global error within the block:

+ +

+ Floyd-Steinberg + ⊗ + 2×2 balanced matrix + = + 2×2-expanded Floyd-Steinberg +

+ +

Here are the results using the previous pixel blocks:

+ +

+ 2×2 block Floyd-Steinberg + 2×2 block Floyd-Steinberg gradient +

+ +

Carefully chosen blocks create constraints on the final picture that may +be of artistic interest:

+ +

+ artistic 3×3 blocks + 3×3 block Floyd-Steinberg + 3×3 block Floyd-Steinberg gradient +

+ +

Using all possible pixel blocks is not equivalent to dithering the image +pixel by pixel. This is due to both the block-choosing method, which only +minimises the difference of mean values within blocks intead of the sum of +local distances, and to the inefficient matrix coefficients, which propagate +the error beyond immediate neighbours, causing the image to look sharpened. +

+ +

This example shows standard block Floyd-Steinberg using all possible 2×2 +blocks:

+ +

+ all possible 2×2 blocks + full 2×2 block Floyd-Steinberg + full 2×2 block Floyd-Steinberg gradient +

+ +

The results on the vertical gradient indicate poor block-choosing. In +order to improve it, we introduce a modified, weighted intra-block error +distribution matrix, still based on the original Floyd-Steinberg matrix:

+ +

+ Floyd-Steinberg + ⊗ + weighted 2×2 matrix + = + weighted 2×2 propagation matrix +

+ +

The result still looks sharpened, but shows considerably less noise:

+ +

+ weighted full 2×2 block Floyd-Steinberg + weighted full 2×2 block Floyd-Steinberg gradient +

+ +

3.6. Sub-block error diffusion

+ +

We introduce sub-block error diffusion, a novel technique improving +on block error diffusion. It addresses the following observations:

+ +
    +
  • it is not a requirement to propagate the error beyond the immediate + neighbours; since it causes a sharpen effect, we decide not to do it. +
  • +
  • the individual subpixels’ error should be propagated, not the + global block error.
  • +
  • subpixel a’s error is harder to compensate than subpixel + d’s because its immediate neighbours are already in the block + being processed, so we weight the sub-block matching in order to + prioritise pixel a’s matching.
  • +
+ +

We use m⋅n error diffusion matrices, one for each of the current +block’s pixels. Here are four error diffusion matrices for 2×2 blocks, +generated from the standard Floyd-Steinberg matrix:

+ +

+ sub-block 0,0 Floyd-Steinberg + sub-block 1,0 Floyd-Steinberg +

+ +

+ sub-block 0,1 Floyd-Steinberg + sub-block 1,1 Floyd-Steinberg +

+ +

The results are far better than with the original block error diffusion +method. On the left, sub-block error diffusion with all possible 2×2 blocks. +On the right, sub-block error diffusion restricted to the tiles seen in +3.5:

+ +

+ full 2×2 sub-block Floyd-Steinberg + full 2×2 sub-block Floyd-Steinberg gradient + 2×2 lines sub-block Floyd-Steinberg + 2×2 lines sub-block Floyd-Steinberg gradient +

+ +

Similar error diffusion matrices can be generated for 3×3 blocks:

+ +

+ sub-block 0,0/3×3 Floyd-Steinberg + sub-block 1,0/3×3 Floyd-Steinberg + sub-block 2,0/3×3 Floyd-Steinberg +

+ +

+ sub-block 0,1/3×3 Floyd-Steinberg + sub-block 1,1/3×3 Floyd-Steinberg + sub-block 2,1/3×3 Floyd-Steinberg +

+ +

+ sub-block 0,2/3×3 Floyd-Steinberg + sub-block 1,2/3×3 Floyd-Steinberg + sub-block 2,2/3×3 Floyd-Steinberg +

+ +

Here are the results with all the possible 3×3 blocks, and with the +artistic 3×3 blocks seen in 3.5:

+ +

+ 3×3 sub-block Floyd-Steinberg + 3×3 sub-block Floyd-Steinberg gradient + 3×3 artistic sub-block Floyd-Steinberg + 3×3 artistic sub-block Floyd-Steinberg gradient +

+ + + + + + + + + diff --git a/static/study/part4.html b/static/study/part4.html new file mode 100644 index 0000000..db447ac --- /dev/null +++ b/static/study/part4.html @@ -0,0 +1,381 @@ + + + + + + + + + + + + Libcaca study - 4. Model-based dithering + + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

4. Model-based dithering

+ +

So far all dithering methods have relied on the assumption that carefully +positioned black and white pixels gave the illusion of greyscales, but we +have not stated exactly how the human eye reacted. Also, we do not have an +automated way to compare the results of two different algorithms: judging that +an algorithm is “better†has mostly been a subjective process so far.

+ +

In order to figure out what the human eye really sees, we need a human +visual system (HVS) model. Countless models exist, and we are only going to +focus on simple ones for now.

+ +

4.1. Gaussian human visual system model

+ +

One of the simplest models is the gaussian HVS model. It states +that the human eye acts like a gaussian convolution filter, slightly blurring +pixel neighbourhoods and therefore simulating what the eye sees as distance +from the image increases. This model, a simplified luminance spatial +frequency response formula, can be expressed using the two-dimensional +e -(x²+y²)/2σ² function. The σ +parameter sort of introduces the viewing distance into the model.

+ +

Here are the results of applying a gaussian HVS to an 8×8 Bayer dithered +image, simply by convoluting the image with a gaussian blur filter. This +process is also known as inverse halftoning. Different values of +σ simulate what the eye sees from different distances. On the left +σ = 1, on the right σ = 2:

+ +

+ 8×8 Bayer dither, gaussian HVS, σ = 1 + 8×8 Bayer dither, gaussian HVS, σ = 1 gradient + 8×8 Bayer dither, gaussian HVS, σ = 2 + 8×8 Bayer dither, gaussian HVS, σ = 2 gradient +

+ +

4.2. Direct binary search

+ +

We have already seen that standard error diffusion methods do not go back +to pixels that have been set. Direct binary search [4] (DBS) is an +iterative method that processes the image a fixed number of times, or until the +error can no longer be minimised:

+ +
    +
  • Generate an initial dithered image
  • +
  • Repeat until no changes can be applied: +
      +
    • Compute the global error between the original and the dithered + images
    • +
    • For each pixel in the dithered image: +
        +
      • Compute the effect on the error of toggling the value of the + current pixel
      • +
      • Compute the effect on the error of swapping the current pixel + with one of its immediate neighbours
      • +
      • If the error can be reduced, perform the corresponding + action
      • +
      +
    • +
    +
  • +
+ +

The efficiency and quality of DBS depend on many implementation details, +starting with the HVS model it uses to compute the error. Also, the initial +image used as iteration zero will give poor results if pattern artifacts are +already present. The order in which pixels are processed is important, too. +Unfortunately, despite its very high-quality results, DBS is usually a very +slow algorithm.

+ +

Below is an example of the algorithm results. The HVS uses a 11×11 +convolution kernel of the e -sqrt(x²+y²) +function. The initial image is randomly thresholded, and pixels are processed +in raster order. Iterations 1, 2 and 5 are shown:

+ +

+ direct binary search, iteration 0 + direct binary search, iteration 0 gradient + direct binary search, iteration 1 + direct binary search, iteration 1 gradient +

+ +

+ direct binary search, iteration 2 + direct binary search, iteration 2 gradient + direct binary search, iteration 5 + direct binary search, iteration 5 gradient +

+ +

Other HVS models can be used, giving very high quality results. Below are +the results of DBS with the following HVS functions:

+ +
    +
  • e -(x²+y²)/2
  • +
  • e -(x²+y²)/4.5
  • +
  • e -(x²+y²)/8
  • +
  • 2e -(x²+y²)/1.5 + e + -(x²+y²)/8
  • +
+ +

The iteration shown is number 5. More iterations would have improved +the results even slightly more, but that would have been at the expense of +performance:

+ +

+ direct binary search, sigma = 1, iteration 5 + direct binary search, sigma = 1, iteration 5 gradient + direct binary search, sigma = 1.5, iteration 5 + direct binary search, sigma = 1.5, iteration 5 gradient +

+ +

+ direct binary search, sigma = 2, iteration 5 + direct binary search, sigma = 2, iteration 5 gradient + direct binary search, best HVS, iteration 5 + direct binary search, best HVS, iteration 5 gradient +

+ +

4.3 Comparing dithering algorithms

+ +

Using an HVS model, it is possible to classify previously seen algorithms +simply by applying the model to both the source and the destination images, +and computing their per-pixel mean squared error. This gives a better +estimation of the dithering algorithm’s quality than simply computing a +local, pixel-bound error.

+ +

The following table shows that different HVS models give very different +error values depending on the value of σ. Also, in the case of +the DBS algorithm, a carefully crafted HVS function such as the last one, +which combines two different HVS models, gives low error values at every +σ, which means the dithered image is close to the original image at all +corresponding viewing distances:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Chapter 1: ThresholdingError %
σ = 1
Error %
σ = 1.5
Error %
σ = 2
0.5 thresholding8.603037.935547.42669
0.4 thresholding9.789909.109948.61878
0.6 thresholding10.7768910.3337310.00462
dynamic thresholding10.315689.607499.09760
uniform random dithering1.578950.697620.39713
gaussian random dithering2.955902.344612.08792
Chapter 2: HalftoningError %
σ = 1
Error %
σ = 1.5
Error %
σ = 2
25/50/75% patterns dithering0.566420.464900.41816
2×2 Bayer dithering0.566940.466220.41956
4×4 Bayer dithering0.205380.080600.05457
8×8 Bayer dithering0.196910.063520.03354
4×4 cluster dot dithering1.200260.148630.07100
8×8 cluster dot dithering3.862360.852360.14443
5×3 artistic line dithering2.755900.494200.11578
perturbated 8×8 Bayer dithering0.405500.169710.09804
random 3×3 dithering matrix selection0.603290.250550.16521
“plus†pattern dithering0.430610.335400.29851
“hex†pattern dithering0.267470.161190.13068
“doubleplus†pattern dithering0.428520.122320.08904
“hex2†pattern dithering0.350570.130460.09533
4-wise supercell “plus†pattern dithering0.219030.074940.04423
3-wise supercell “hex†pattern dithering0.216210.073560.04193
4-wise supercell “doubleplus†pattern dithering0.408830.070510.03609
6th order dragon curve dithering0.205030.067190.03539
3-wise supercell “hex2†pattern dithering0.326430.078610.04061
9-wise supercell “hex2†pattern dithering0.323360.077750.04087
14×14 void-and-cluster dithering0.197260.067480.03663
25×25 void-and-cluster dithering0.192480.062670.03325
Chapter 3: Error diffusionError %
σ = 1
Error %
σ = 1.5
Error %
σ = 2
naïve error diffusion0.333520.063720.01961
Floyd-Steinberg error diffusion0.092490.018470.00859
serpentine Floyd-Steinberg error diffusion0.098910.018490.00749
Jarvis, Judice and Ninke dithering0.271980.062570.03472
Fan dithering0.102200.017280.00705
4-cell Shiau-Fan dithering0.116180.017850.00660
5-cell Shiau-Fan dithering0.115500.019500.00708
Stucki dithering0.197650.053730.03012
Burkes dithering0.158310.040140.02178
Sierra dithering0.234980.054150.03011
Sierra 2 dithering0.222490.051210.02812
Filter Lite0.097400.016050.00638
Atkinson dithering0.472090.310120.27339
Riemersma dithering on a Hilbert curve0.282240.065290.02693
Riemersma dithering on a Hilbert 2 curve0.266220.060030.02492
spatial Hilbert dithering on a Hilbert curve0.272890.061240.02184
spatial Hilbert dithering on a Hilbert 2 curve0.228210.052460.01911
dot diffusion0.383010.080630.01918
dot diffusion with sharpen = 0.90.785460.349410.23479
dot diffusion with sharpen = 0.9 and zeta = 0.22.912872.193981.99858
serpentine Floyd-Steinberg with sharpen = 0.90.626150.308510.22111
omni-directional error diffusion0.238600.068210.03498
Ostromoukhov’s variable error diffusion0.112030.017720.00593
nearest block matching with 2×2 line tiles1.924301.348221.13685
block error diffusion with 2×2 line tiles0.868070.224110.08901
block error diffusion with 3×3 artistic tiles1.786260.569570.23000
block error diffusion with all 2×2 tiles0.564660.159100.06946
block error diffusion with all 2×2 tiles and
weighted intra-block error distribution
0.377380.104340.04643
sub-block error diffusion with all 2×2 tiles0.146970.026050.00894
sub-block error diffusion with 2×2 line tiles0.357590.060810.01855
sub-block error diffusion with all 3×3 tiles0.328020.065160.02165
sub-block error diffusion with 3×3 artistic tiles1.039630.218390.06678
Chapter 4: Model-based ditheringError %
σ = 1
Error %
σ = 1.5
Error %
σ = 2
DBS with HVS e -sqrt(x²+y²), iteration 10.258100.067800.03489
DBS with HVS e -sqrt(x²+y²), iteration 20.137620.032340.01939
DBS with HVS e -sqrt(x²+y²), iteration 50.104520.027450.01918
DBS with HVS e -(x²+y²)/2, iteration 50.103440.038600.02669
DBS with HVS e -(x²+y²)/4.5, iteration 50.180340.009840.00219
DBS with HVS e -(x²+y²)/8, iteration 50.378040.028660.00295
DBS with HVS 2e -(x²+y²)/1.5 + e -(x²+y²)/8, iteration 50.098380.008840.00263
+ + + +

+ + + + + + + + + diff --git a/static/study/part5.html b/static/study/part5.html new file mode 100644 index 0000000..d136e5c --- /dev/null +++ b/static/study/part5.html @@ -0,0 +1,214 @@ + + + + + + + + + + + + Libcaca study - 5. Greyscale dithering + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

5. Greyscale dithering

+ +

At first sight, generalising dithering to three grey scales seems pretty +straightforward: just add grey 0.5 in the middle of the palette and dither +pixels in the [0, 0.5] range with black and grey, and pixels in the [0.5, 1] +range with grey and white. Here are two different results with 8×8 Bayer +ordered dithering and with serpentine Floyd-Steinberg error diffusion:

+ +

+ 8×8 Bayer ordered dithering, 3 colours + 8×8 Bayer ordered dithering gradient, 3 colours + serpentine FS error diffusion, 3 colours + serpentine FS error diffusion gradient, 3 colours +

+ +

These are pretty much the images that imaging software such as The Gimp +would give (using “positioned†and “Floyd-Steinberg†dithering modes).

+ +

Unfortunately the result is not as good as expected: the white pattern +on Lena’s cheeks is visually disturbing, and there is a lot of 0.5 grey in +the image. Also, the whole image looks darker than with pure black-and-white +dithering, but these previous dithering results looked a lot brighter than +the original image anyway.

+ +

All these issues have to do with the output media’s gamma.

+ +

5.1. Introducing gamma

+ +

If you are reading this document on a computer screen, you may have +noticed that the black and white 50% pattern was closer to a 0.73 greyscale +(left) than to the intuitively expected 0.5 value (right). If you are reading +a printed copy, it might be a different matter.

+ +

+ introducing gamma +

+ +

The mapping linking greyscale steps to intensities is called gamma +correction. An approximate law for gamma correction is given as +I = vγ where v is the coded colour +value (between 0 and 1), I is the perceived colour intensity (between +0% and 100%) and γ is the gamma. A pattern made of even-numbered +0%-intensity pixels and 100%-intensity pixels has an intensity of 50% by +definition. But the corresponding greyscale depends on the gamma value.

+ +

Most modern computer systems use the sRGB gamma model close to the law +with γ = 2.2. As can be seen, it is highly non-linear:

+ +

+ introducing gamma +

+ +

Éric Brasseur wrote a pretty comprehensive +essay [16] about why on a computer screen a 50% black and white pattern +should be scaled down to a grey value of 0.73 instead of 0.5 and how major +computer graphics software totally misses the point. Conversely, it clearly +means that a grey value of 0.5 should not be emulated with a 50% dither +pattern.

+ +

The following figure shows the gamma curve for the naïve three-colour +greyscale gradient we saw above (red curve) compared to the two-colour +gradient (blue curve). Two major observations can be made: the new curve is +far closer to a perfect, linear gradient, but there is a singularity in the +middle of the curve, meaning a break in the gradient’s smoothness.

+ +

+ 3-colour gamma +

+ +

There are three possible ways to reduce the singularity and make the +gradient smoother and/or closer to the original colours:

+ +
    +
  • Choose a different middle grey value, for instance choosing grey 0.73 + will cancel the singularity and match the two-colour gradients we have + been using so far. This is not always possible if the output palette + is fixed.
  • +
  • Don’t place the grey value at the middle of the gradient, for instance + a value of around 25% intensity will again match the previous two-colour + gradients.
  • +
  • Gamma-correct input pixels before assigning them an output + value. This ensures that the resulting gradient is perfectly linear + and has no singularity. +
  • +
+ +

5.2. Gamma correction

+ +

Gamma correction consists in converting pixel values into intensity values +before performing operations on them, then reconverting them to pixel values +before displaying them. The exact same algorithms can be used, they just +operate on slightly different data.

+ +

+ 3-colour gamma coorection +

+ +

Here are the results of gamma-correcting input pixels before doing +any computation on them, then using serpentine Floyd-Steinberg error +diffusion:

+ +

+ serpentine FS, 2 colours, gamma-corrected + serpentine FS, 2 colours, gamma-corrected gradient + serpentine FS, 3 colours, gamma-corrected + serpentine FS, 3 colours, gamma-corrected gradient +

+ +

Two-colour dithering is not visually satisfying: dark areas lack much +detail because the gamma curve is very flat at low intensities. However, +the result itself is far more accurate that previously. The problem, while +still visible, is even less important with three-colour dithering: the image +on the right is superior to what The Gimp or Adobe Photoshop are able to +come up with.

+ +

Finally, this is gamma-corrected 4-colour dithering:

+ +

+ serpentine FS, 4 colours, gamma-corrected + serpentine FS, 4 colours, gamma-corrected gradient +

+ +

5.3. Greyscale sub-block error diffusion

+ +

Support for greyscale and gamma correction is trivially added to our +sub-block error diffusion method. Best-tile choosing is done in contrast +space, while error diffusion is done in intensity space.

+ +

The following picture uses all possible 4-greyscale 2×2 tiles. The +output quality is very close to what standard, pixel-per-pixel error diffusion +achieves:

+ +

+ sub-block FS, full 4-grey tiles + sub-block FS, full 4-grey tiles gradient +

+ +

And finally, this picture only uses 4-greyscale combinations of the +“lines†tiles seen previously:

+ +

+ list of 4-grey 2×2 pixel blocks + sub-block FS, lines 4-grey tiles + sub-block FS, lines 4-grey tiles gradient +

+ + + + + + + + + diff --git a/static/study/part6.html b/static/study/part6.html new file mode 100644 index 0000000..574b43f --- /dev/null +++ b/static/study/part6.html @@ -0,0 +1,256 @@ + + + + + + + + + + + + Libcaca study - 6. Colour dithering + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

6. Colour dithering

+ +

Dithering colour images means dithering three-dimensional elements (RGB +pixels) instead of one-dimensional grey values. It is very complex and +depends on the output media even more than greyscale dithering.

+ +

6.1. Separate-space dithering

+ +

In some cases it is possible to perform three one-dimensional dithering +operations instead of one three-dimensional one. Consider for instance the +following palette: +

+ +

+ 8-colour RGB palette +

+ +

It is made of the eight possible red/green/blue combinations made of 0 +and 1 values:

+ +
+ + + + + + + + + + + + + + +
Red0 0 1 10 0 1 1
Green0 0 0 01 1 1 1
Blue0 1 0 10 1 0 1
+
+ +

One way of dithering an image using this palette is to convert the image +into three greyscale images (separating the red, green and blue channels), +dither each subimage to two colours separately and recombine the images into +three channels. For instance, if at a given pixel the red channel is dithered +to 1 and the green and blue channels are dithered to 0, the final pixel will +be [1 0 0] which is the colour red.

+ +

Separate-space dithering works by splitting the image into three red, +green and blue channels. Each of this channel is treated as a greyscale image +that is then dithered to black and white using any dithering method seen +previously. The resulting images are then treated again as three red, green +and blue channels and recombined into the final image:

+ +

+ separate-space dithering +

+ +

Here are the results with serpentine Floyd-Steinberg dithering applied to +each channel. On the left, no colour correction, as The Gimp or Photoshop would +do; on the right, gamma-corrected dithering:

+ +

+ serpentine FS, 8 colours + serpentine FS, 8 colours gradient + serpentine FS, 8 colours, gamma-corrected + serpentine FS, 8 colours, gamma-corrected gradient +

+ +

6.2. Accounting for other dimensions

+ +

The previous palette was suitable for separate-space dithering. Such a +palette is called separable or orthogonal.

+ +

Here is a palette that cannot be used in the same way:

+ +

+ 8-colour RGB palette +

+ +

As can be seen, it does not have the [0.5 0.5 0.5] grey colour, or the +[1 0.5 0] orange colour, for instance, despite having other combinations of +0, 0.5 and 1 values:

+ +
+ + + + + + + + + + + + + + + + + + + + +
Red0 0 0.5 0.50 0 0.5 0.70.3 0 1 10 0 1 1
Green0 0 0 00.5 0.5 0.5 0.70.3 0 0 01 1 1 1
Blue0 0.5 0 0.50 0.5 0 0.70.3 1 0 10 1 0 1
+
+ +

It is no longer possible to compute the closest colour in each colourspace +and combine them into an RGB colour, since that colour might not be available +in the palette. So we need to determine what the “closest colour†means when +dealing with the whole colour spectrum.

+ +

The following examples show gamma-corrected Floyd-Steinberg using the +above 16-colour palette and two different definitions of distance: the sum +of absolute differences and the euclidian distance. The sum of +absolute differences performs pretty poorly because it does not penalise wide +disparities:

+ +

+ Floyd-Steinberg, sum of absolute differences + Floyd-Steinberg, sum of absolute differences gradient + Floyd-Steinberg, euclidian distance + Floyd-Steinberg, euclidian distance gradient +

+ +

Distances can be computed in another space. For instance, the HSV +space allows to give colour variations a smaller influence than brightness +variations simply by changing the HSV cone’s height. On the left is spatial +Floyd-Steinberg using the euclidian distance in an HSV cone of height 1 and +base radius 1. On the right is the same distance within a cone of height 3 and +base radius 1:

+ +

+ Floyd-Steinberg, 1×1 HSV cone + Floyd-Steinberg, 1×1 HSV cone gradient + Floyd-Steinberg, 3×1 HSV cone + Floyd-Steinberg, 3×1 HSV cone gradient +

+ +

6.3. Reducing visual artifacts

+ +

The following patterns show four ways to dither the same colour using +our 8-colour palette:

+ +
    +
  • 1/2 black, 3/8 blue, 1/8 white
  • +
  • 1/2 blue, 3/8 black, 1/8 yellow
  • +
  • 3/8 black, 3/8 blue, 1/8 red, 1/8 cyan
  • +
  • 1/2 blue, 1/4 black, 1/8 red, 1/8 green
  • +
+ +

All patterns visually blend to the same shade, but the last one is +the most visually appealing:

+ +

+ 3 ways to dither the same colour +

+ +

Shaked, Arad, Fitzhugh and Sobel introduce the minimum brightness +variation criterion (MBVC), stating that in order to reduce halftone noise, +the halftone set which should be used to render the desired colour should be +the one whose brightness variation is minimal [25]. Similarly, Klassen et +al. suggest the selection of low-contrast colour combiation wherever +possible [24].

+ +

6.4. Colour sub-block error diffusion

+ +

Adapting sub-block error diffusion to colour images is almost +straightforward. The major problem is proper weighting in the block-choosing +step. It is a crucial part of the algorithm, and we have yet to find an +efficient method to perform it.

+ +

The images below shows the result using our now well-known “lines†tile +list, using respectively the 8-colour palette and the 16-colour palette:

+ +

+ 8-colour sub-block error diffusion + 8-colour sub-block error diffusion gradient + 16-colour sub-block error diffusion + 16-colour sub-block error diffusion gradient +

+ +

Speed is starting to become a problematic issue. A 16-colour palette can +generate 65,536 unique 2×2 blocks. Exhaustive search to determine the best +tile is no longer realistic, and we need to find better ways to find the +best matching block.

+ + + + + + + + + diff --git a/static/study/part7.html b/static/study/part7.html new file mode 100644 index 0000000..a810502 --- /dev/null +++ b/static/study/part7.html @@ -0,0 +1,140 @@ + + + + + + + + + + + + Libcaca study - 7. Photographic mosaics + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

7. Photographic mosaics

+ +

Photographic mosaics are montages of smaller images creating the illusion +of a bigger image.

+ +

Since we don’t have many images at our disposal, we will simply cut Lena +into small chunks (called tiles) and use these parts to create mosaics. +This is our tile database:

+ +

+ Patterns taken from Lena +

+ +

Generating a photomosaic consists in subdividing the original picture +into x rectangular cells and find x tiles in the database +(with or without duplicates, depending on the set of rules that is decided) +so that when recombined the resulting image resembles the original picture. +By the way, this technique is covered by Runaway Technology Inc.’s +U.S. patent +6137498 [9].

+ +

Picking the right tile for the right cell in the grid is a very +expensive and complicated operation. One of the biggest problems is the +cost of a database lookup: comparing each tile area pixel-by-pixel +is an O(N) operation where N is the size of the database. We can resort +to image classification in order to speed up database lookups.

+ +

7.1. Image classification

+ +

One of the simplest image classification techniques is the storage of +each tile’s average colour into a separate database that is used for +best match lookups. Of course, this computation should be gamma-corrected: +

+ +

+ 1 feature extracted from Lena patterns +

+ +

When creating the mosaic, we then only need to check the average colour +instead of comparing each pixel one by one. Below is the result of the +technique applied on a portion of the Lena picture:

+ +

+ Lena (detail) + Mosaic created from Lena’s detail +

+ +

Better results can be achieved by storing four colour values, one +for each corner of the tile:

+ +

+ 4 features extracted from Lena patterns +

+ +

Having 12 values per tile (4 RGB triplets) is still a lot less than the +original count of 3072 (for 32×32 tiles), and the results show clear +improvement. For instance the feathers-hat frontier is now a lot smoother:

+ +

+ Lena (detail) + Mosaic created from Lena’s detail +

+ +

7.2. Error diffusion

+ +

TODO

+ +

7.3. Colour ASCII art

+ + + +

+ ASCII art tiles +

+ + + + + + + + + diff --git a/static/study/source.html b/static/study/source.html new file mode 100644 index 0000000..8282ad1 --- /dev/null +++ b/static/study/source.html @@ -0,0 +1,101 @@ + + + + + + + + + + + + Libcaca study - Source code + + + + + + + + + + +

Warning: this +document is still work in progress. Feel free to send comments but do not +consider it final material.

+ + + + + +

Source code

+ +

This is the full Python source code to all examples presented in this +document. It can also be downloaded as a standalone study.py Python program.

+ +

Just install the python-gd package on your favourite operating +system and run the script. With the exception of the original colour 512×512 Lena +image, all Lena images and gradients were generated from this program. If +you have installed Inkscape, many graphics such as dither matrices will also +be automatically generated.

+ +

Many algorithms implemented here will seem rather inefficient to people +used to writing image processing software. This is because clarity was almost +always preferred over performance. We believe this document to be of important +value to many researchers in the field, because it implements methods for which +there is no public implementation.

+ +
 $l) {
+    if(ereg('temporary cruft', $l))
+      break;
+    $l = htmlspecialchars($l);
+    $c = 0;
+    $l = preg_replace('/#[^0-9a-f].*/', '\\0',
+                      $l, -1, $c);
+    if($c <= 0) {
+      $l = preg_replace('/(".*"|\\\\$)/',
+                        '\\0', $l);
+      $l = ereg_replace('[A-Z][A-Z][A-Z0-9_]*',
+                        '\\0', $l);
+      $l = preg_replace('/\b(for|def|in|return|while|if|elif|else|class |True|False|None|break|continue|and|or|not|print|global|import|try|catch|yield)\b/',
+                        '\\0', $l);
+      $l = preg_replace('/\b([0-9]+[.][0-9]*|[0-9]*[.][0-9]+|[0-9]+)\b/',
+                        '\\0', $l);
+    }
+    echo $l;
+  }
+?>
+ + + + + + + + + diff --git a/static/study/study.py b/static/study/study.py new file mode 100755 index 0000000..af886aa --- /dev/null +++ b/static/study/study.py @@ -0,0 +1,2775 @@ +#!/usr/bin/env python + +import math, gd, random, sys, os + +# Select which chapters to run +def chapter(n): + if len(sys.argv) == 1: + return True + return str(n) in sys.argv + +############################################################################## + +# Tiny image class to make examples short and readable +class Image(gd.image): + gd.gdMaxColors = 256 * 256 * 256 + def __init__(self, *args): + if args[0].__class__ == str: + print "[LOAD] %s" % (args[0],) + gd.image.__init__(self, *args) + def save(self, name): + print "[PNG] %s" % (name,) + self.writePng(name) + def getGray(self, x, y): + p = self.getPixel((x, y)) + c = self.colorComponents(p)[0] / 255.0 + return c + def getRgb(self, x, y): + p = self.getPixel((x, y)) + rgb = self.colorComponents(p) + return [rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0] + def setGray(self, x, y, t): + p = (int)(t * 255.999) + c = self.colorResolve((p, p, p)) + self.setPixel((x, y), c) + def setRgb(self, x, y, r, g, b): + r = (int)(r * 255.999) + g = (int)(g * 255.999) + b = (int)(b * 255.999) + c = self.colorResolve((r, g, b)) + self.setPixel((x, y), c) + def getRegion(self, x, y, w, h): + dest = Image((w, h), True) + self.copyTo(dest, (-x, -y)) + return dest + def getZoom(self, z): + (w, h) = self.size() + dest = Image((w * z, h * z), True) + for y in range(h): + for x in range(w): + rgb = self.getRgb(x, y) + for j in range(z): + for i in range(z): + dest.setRgb(x * z + i, y * z + j, *rgb) + return dest + +# Manipulate gamma values +class Gamma: + def CtoI(x): + if x < 0: + return - math.pow(-x, 2.2) + return math.pow(x, 2.2) + def ItoC(x): + if x < 0: + return - math.pow(-x, 1 / 2.2) + return math.pow(x, 1 / 2.2) + CtoI = staticmethod(CtoI) + ItoC = staticmethod(ItoC) + def CtoI3(x): + return [Gamma.CtoI(x[0]), Gamma.CtoI(x[1]), Gamma.CtoI(x[2])] + def ItoC3(x): + return [Gamma.ItoC(x[0]), Gamma.ItoC(x[1]), Gamma.ItoC(x[2])] + CtoI3 = staticmethod(CtoI3) + ItoC3 = staticmethod(ItoC3) + def Cto2(x): + if x < Gamma.CtoI(0.50): + return 0. + return 1. + def Cto3(x): + if x < Gamma.CtoI(0.25): + return 0. + elif x < Gamma.CtoI(0.75): + return Gamma.CtoI(0.5) + return 1. + def Cto4(x): + if x < Gamma.CtoI(0.17): + return 0. + elif x < Gamma.CtoI(0.50): + return Gamma.CtoI(0.3333) + elif x < Gamma.CtoI(0.83): + return Gamma.CtoI(0.6666) + return 1. + Cto2 = staticmethod(Cto2) + Cto3 = staticmethod(Cto3) + Cto4 = staticmethod(Cto4) + +# Create matrices +def Matrix(w, h, val = 0): + return [[val] * w for n in range(h)] + +# Iterate in 2D space +def rangexy(w, h): + for y in range(h): + for x in range(w): + yield (x, y) + +############################################################################## + +# Create SVG files from matrix data +class Svg: + _data = '' + _w, _h = 0, 0 + def _colorise(val): + return '#fff' + def _reduce(this, val): + if type(val) == float: + for x in range(1, 1000): + if abs(val * x - round(val * x)) < 0.001: + return (int)(round(val * x)), x + return val, 1 + def __init__(self, mat, colorise = _colorise): + # Check whether it is an error diffusion matrix + ed = False + for l in mat: + for x in l: + if type(x) == str or math.floor(x) != x: + ed = True + # Generate SVG file + (w, h) = (len(mat[0]), len(mat)) + s = \ + '\n' \ + '\n' \ + ' \n' \ + ' \n' % (64 * w + 2, 64 * h + 2) + line = \ + ' \n' + box = \ + ' \n' + text = \ + ' %s\n' + for x, y in rangexy(w, h): + val = mat[y][x] + if not ed and val == -1: + continue + if ed and val == 0: + continue + # Put box + (ix, iy) = (64. * x + 1, 64. * y + 1) + if ed and val == -1: + val = '' + c = '#fbb' + else: + c = colorise(val) + s += box % (ix, iy, ix + 64, iy, ix + 64, iy + 64, ix, \ + iy + 64, c) + # Put value + a, b = self._reduce(val) + extra = '' + if b == 1: + (tx, ty) = (ix + 32, iy + 44) + n = len(str(val)) + if n > 3 and type(val) == str: + extra = ' transform="scale(0.7,0.7)"' + tx = tx / 0.7 + ty = ty / 0.7 + elif n > 3: + extra = ' transform="scale(0.7,1.)"' + tx = tx / 0.7 + elif n > 2: + extra = ' transform="scale(0.8,1.)"' + tx = tx / 0.8 + s += text % (32, tx, ty, extra, val) + else: + s += line % (ix + 8, iy + 32, ix + 56, iy + 32) + (tx, ty) = (ix + 32, iy + 26) + s += text % (24, tx, ty, extra, a) + (tx, ty) = (ix + 32, iy + 56) + s += text % (24, tx, ty, extra, b) + s += \ + ' \n' \ + '\n' + self._w = 64 * w + 2 + self._h = 64 * h + 2 + self._data = s + def save(self, name, size): + svgname = name + ".tmp.svg" + f = open(svgname, 'w') + f.write(self._data) + f.close() + f = os.popen("inkscape %s -a %u:%u:%u:%u -w%u -h%u -e %s >/dev/null 2>&1" % (svgname, 0, 0, self._w, self._h, size * self._w / 64., size * self._h / 64., name)) + print "[SVG] %s" % (name,) + f.close() + os.unlink(svgname) + +############################################################################## +print "Initialisation" + +# Load the original Lena image +lena512 = Image("lena512.png") +(w, h) = lena512.size() + +# Image 1: greyscale conversion +# Read the compression FAQ [55] for the rationale behind using the green +# channel (http://www.faqs.org/faqs/compression-faq/part1/section-30.html) +if chapter(0): + (w, h) = lena512.size() + lena512bw = Image((w, h)) + for x, y in rangexy(w, h): + rgb = lena512.getRgb(x, y) + c = rgb[1] + lena512bw.setGray(x, y, c) + lena512bw.save("lena512bw.png") +else: + lena512bw = Image("lena512bw.png") + +def gammascale(src, scale): + (w, h) = src.size() + count = src.colorsTotal() + dest = Image((w / scale, h / scale), count == 0 or count > 256) + for x, y in rangexy(w / scale, h / scale): + r = g = b = 0. + for i, j in rangexy(scale, scale): + rgb = src.getRgb(x * scale + i, y * scale + j) + r += Gamma.CtoI(rgb[0]) + g += Gamma.CtoI(rgb[1]) + b += Gamma.CtoI(rgb[2]) + r = Gamma.ItoC(r / (scale * scale)) + g = Gamma.ItoC(g / (scale * scale)) + b = Gamma.ItoC(b / (scale * scale)) + dest.setRgb(x, y, r, g, b) + return dest + +# Image 2: 50% greyscale +# Image 3: 50% scaling +if chapter(0): + lena256bw = gammascale(lena512bw, 2) + lena256bw.save("lena256bw.png") + lena256 = gammascale(lena512, 2) + lena256.save("lena256.png") +else: + lena256bw = Image("lena256bw.png") + lena256 = Image("lena256.png") + +# Create a 32x256 greyscale gradient +if chapter(0): + grad256bw = Image((32, 256)) + for x, y in rangexy(32, 256): + grad256bw.setGray(x, 255 - y, y / 255.) + grad256bw.save("gradient256bw.png") +else: + grad256bw = Image("gradient256bw.png") + +# Create a 64x256 colour gradient +if chapter(0): + grad256 = Image((64, 256), True) + for x, y in rangexy(64, 256): + grad256.setRgb(x, y, x / 63., (255. - y) / 255, x / 63.) + grad256.save("gradient256.png") +else: + grad256 = Image("gradient256.png") + +############################################################################## +if chapter(1): + print "Chapter 1. Colour quantisation" + +# Output 1.1.1: 50% threshold +# Output 1.1.2: 40% threshold +# Output 1.1.3: 60% threshold +def test11x(src, threshold): + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) > threshold + dest.setGray(x, y, c) + return dest + +if chapter(1): + test11x(grad256bw, 0.5).save("out/grad1-1-1.png") + test11x(lena256bw, 0.5).save("out/lena1-1-1.png") + test11x(grad256bw, 0.4).save("out/grad1-1-2.png") + test11x(lena256bw, 0.4).save("out/lena1-1-2.png") + test11x(grad256bw, 0.6).save("out/grad1-1-3.png") + test11x(lena256bw, 0.6).save("out/lena1-1-3.png") + +# Output 1.2.1: 3-colour threshold +# Output 1.2.2: 5-colour threshold +def test12x(src, colors): + (w, h) = src.size() + dest = Image((w, h)) + q = colors - 1 + p = -.00001 + colors + for x, y in rangexy(w, h): + c = src.getGray(x, y) + c = math.floor(c * p) / q + dest.setGray(x, y, c) + return dest + +if chapter(1): + test12x(grad256bw, 3).save("out/grad1-2-1.png") + test12x(lena256bw, 3).save("out/lena1-2-1.png") + test12x(grad256bw, 5).save("out/grad1-2-2.png") + test12x(lena256bw, 5).save("out/lena1-2-2.png") + +# Output 1.2.3: 3-colour threshold, minimal error +# Output 1.2.4: 5-colour threshold, minimal error +def test12y(src, colors): + (w, h) = src.size() + dest = Image((w, h)) + q = colors - 1 + p = -.00001 + colors - 1 + for x, y in rangexy(w, h): + c = src.getGray(x, y) + c = math.floor((c + 0.5 / p) * p) / q + dest.setGray(x, y, c) + return dest + +if chapter(1): + test12y(grad256bw, 3).save("out/grad1-2-3.png") + test12y(lena256bw, 3).save("out/lena1-2-3.png") + test12y(grad256bw, 5).save("out/grad1-2-4.png") + test12y(lena256bw, 5).save("out/lena1-2-4.png") + +# Output 1.3.1: 2-colour threshold, dynamic thresholding +# Output 1.3.2: 5-colour threshold, dynamic thresholding +def test13x(src, n, fuzzy = None): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + # Compute histogram + histo = [0] * 256 + for x, y in rangexy(w, h): + histo[(int)(src.getGray(x, y) * 255.9999)] += 1 + thresholds = [1. * (1. + i) / n for i in range(n - 1)] + values = [i / (n - 1.) for i in range(n)] + # Parse histogram + total = 0 + t = 0 + for i in range(256): + total += histo[i] + if total > thresholds[t] * w * h: + thresholds[t] = i / 255.0 + t += 1 + if t + 1 > n - 1: + break + # Compute image + for x, y in rangexy(w, h): + c = src.getGray(x, y) + for (i, t) in enumerate(thresholds): + if fuzzy: + t += fuzzy() + if c < t: + dest.setGray(x, y, values[i]) + break + else: + dest.setGray(x, y, values[n - 1]) + return dest + +if chapter(1): + test13x(grad256bw, 2).save("out/grad1-3-1.png") + test13x(lena256bw, 2).save("out/lena1-3-1.png") + test13x(grad256bw, 5).save("out/grad1-3-2.png") + test13x(lena256bw, 5).save("out/lena1-3-2.png") + +# Output 1.4.1: uniform random dithering +def test141(src): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + d = c > random.random() + dest.setGray(x, y, d) + return dest + +if chapter(1): + test141(grad256bw).save("out/grad1-4-1.png") + test141(lena256bw).save("out/lena1-4-1.png") + +# Output 1.4.2: gaussian random dithering +def test142(src): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + d = c > random.gauss(0.5, 0.15) + dest.setGray(x, y, d) + return dest + +if chapter(1): + test142(grad256bw).save("out/grad1-4-2.png") + test142(lena256bw).save("out/lena1-4-2.png") + +# Output 1.4.3: 3-colour threshold, dynamic thresholding, gaussian perturbation +if chapter(1): + fuzzy = lambda : random.gauss(0., 0.08) + test13x(grad256bw, 4, fuzzy).save("out/grad1-4-3.png") + test13x(lena256bw, 4, fuzzy).save("out/lena1-4-3.png") + +############################################################################## +if chapter(2): + print "Chapter 2. Halftoning patterns" + +# Pattern 2.1.1: a 50% halftone pattern with various block sizes +# Pattern 2.1.2: 25% and 75% halftone patterns with various block sizes +if chapter(2): + dest = Image((320, 80)) + for x in range(320): + d = 8 >> (x / 80) + for y in range(80): + c = (x / d + y / d) & 1 + dest.setGray(x, y, c) + dest.save("out/pat2-1-1.png") + + dest = Image((320, 80)) + for x in range(320): + d = 8 >> (x / 80) + for y in range(40): + c = ((x / d + y / d) & 1) or (y / d & 1) + dest.setGray(x, y, c) + for y in range(40, 80): + c = ((x / d + y / d) & 1) and (y / d & 1) + dest.setGray(x, y, c) + dest.save("out/pat2-1-2.png") + +# Output 2.1.1: 20/40/60/80% threshold with 25/50/75% patterns inbetween: +def test211(src): + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + if c < 0.2: + c = 0. + elif c < 0.4: + c = ((x + y) & 1) and (y & 1) + elif c < 0.6: + c = (x + y) & 1 + elif c < 0.8: + c = ((x + y) & 1) or (y & 1) + else: + c = 1. + dest.setGray(x, y, c) + return dest + +if chapter(2): + test211(grad256bw).save("out/grad2-1-1.png") + test211(lena256bw).save("out/lena2-1-1.png") + +# Pattern 2.2.1: vertical, mixed and horizontal black-white halftones +# Pattern 2.2.2: two different 25% patterns +if chapter(2): + dest = Image((240, 80)) + for y in range(80): + for x in range(80): + c = x & 1 + dest.setGray(x, y, c) + for x in range(80, 160): + c = (x / d + y / d) & 1 + dest.setGray(x, y, c) + for x in range(160, 240): + c = y & 1 + dest.setGray(x, y, c) + dest.save("out/pat2-2-1.png") + + dest = Image((320, 80)) + for y in range(80): + for x in range(80): + c = (x / 2 & 1) and (y / 2 & 1) + dest.setGray(x, y, c) + for x in range(80, 160): + c = (x & 1) and (y & 1) + dest.setGray(x, y, c) + for x in range(160, 240): + c = (x & 1) and ((y + x / 2) & 1) + dest.setGray(x, y, c) + for x in range(240, 320): + c = (x / 2 & 1) and ((y / 2 + x / 4) & 1) + dest.setGray(x, y, c) + dest.save("out/pat2-2-2.png") + +# Output 2.3.0: 2x2 Bayer dithering +# Output 2.3.1: 4x4 Bayer dithering +# Output 2.3.1b: 8x8 Bayer dithering +# Output 2.3.2: 4x4 cluster dot +# Output 2.3.2b: 8x8 cluster dot +# Output 2.3.3: 5x3 line dithering +def ordereddither(src, mat): + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mat[0]) + dy = len(mat) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + threshold = (1. + mat[y % dy][x % dx]) / (dx * dy + 1) + c = c > threshold + dest.setGray(x, y, c) + return dest + +def makebayer(rank, mat = False): + if not mat: + mat = Matrix(1, 1) + if not rank: + return mat + n = len(mat) + newmat = Matrix(n * 2, n * 2) + for i, j in rangexy(n, n): + x = mat[j][i] + newmat[j * 2][i * 2] = x + newmat[j * 2][i * 2 + 1] = x + n * n * 3 + newmat[j * 2 + 1][i * 2] = x + n * n * 2 + newmat[j * 2 + 1][i * 2 + 1] = x + n * n + return makebayer(rank - 1, newmat) + +DITHER_BAYER22 = makebayer(1) +DITHER_BAYER44 = makebayer(2) +DITHER_BAYER88 = makebayer(3) + +DITHER_CLUSTER44 = \ + [[ 12, 5, 6, 13], + [ 4, 0, 1, 7], + [ 11, 3, 2, 8], + [ 15, 10, 9, 14]] +DITHER_CLUSTER88 = \ + [[ 24, 10, 12, 26, 35, 47, 49, 37], + [ 8, 0, 2, 14, 45, 59, 61, 51], + [ 22, 6, 4, 16, 43, 57, 63, 53], + [ 30, 20, 18, 28, 33, 41, 55, 39], + [ 34, 46, 48, 36, 25, 11, 13, 27], + [ 44, 58, 60, 50, 9, 1, 3, 15], + [ 42, 56, 62, 52, 23, 7, 5, 17], + [ 32, 40, 54, 38, 31, 21, 19, 29]] +DITHER_LINE53 = \ + [[ 9, 3, 0, 6, 12], + [ 10, 4, 1, 7, 13], + [ 11, 5, 2, 8, 14]] + +def bayercol(val): return ['#fdf', '#dfd', '#ffd', '#dff'][val % 4] + +if chapter(2): + ordereddither(grad256bw, DITHER_BAYER22).save("out/grad2-3-0.png") + ordereddither(lena256bw, DITHER_BAYER22).save("out/lena2-3-0.png") + + Svg(DITHER_BAYER44, bayercol).save("out/fig2-3-2.png", 40) + ordereddither(grad256bw, DITHER_BAYER44).save("out/grad2-3-1.png") + ordereddither(lena256bw, DITHER_BAYER44).save("out/lena2-3-1.png") + + Svg(DITHER_BAYER88, bayercol).save("out/fig2-3-2b.png", 30) + ordereddither(grad256bw, DITHER_BAYER88).save("out/grad2-3-1b.png") + ordereddither(lena256bw, DITHER_BAYER88).save("out/lena2-3-1b.png") + + Svg(DITHER_CLUSTER44).save("out/fig2-3-3.png", 40) + ordereddither(grad256bw, DITHER_CLUSTER44).save("out/grad2-3-2.png") + ordereddither(lena256bw, DITHER_CLUSTER44).save("out/lena2-3-2.png") + + Svg(DITHER_CLUSTER88).save("out/fig2-3-3b.png", 30) + ordereddither(grad256bw, DITHER_CLUSTER88).save("out/grad2-3-2b.png") + ordereddither(lena256bw, DITHER_CLUSTER88).save("out/lena2-3-2b.png") + + Svg(DITHER_LINE53).save("out/fig2-3-4.png", 40) + ordereddither(grad256bw, DITHER_LINE53).save("out/grad2-3-3.png") + ordereddither(lena256bw, DITHER_LINE53).save("out/lena2-3-3.png") + +# Output 2.4.1: 4x4 Bayer dithering with gaussian perturbation +def test241(src, mat): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mat[0]) + dy = len(mat) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + threshold = (1. + mat[y % dy][x % dx]) / (dx * dy + 1) + threshold += random.gauss(0, 0.08) + c = c > threshold + dest.setGray(x, y, c) + return dest + +if chapter(2): + test241(grad256bw, DITHER_BAYER88).save("out/grad2-4-1.png") + test241(lena256bw, DITHER_BAYER88).save("out/lena2-4-1.png") + +# Output 2.4.2: random dither matrice selection +def test242(src, mlist): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mlist[0][0]) + dy = len(mlist[0]) + for x, y in rangexy(w / dx, h / dy): + mat = random.choice(mlist) + for i, j in rangexy(dx, dy): + c = src.getGray(x * dx + i, y * dy + j) + threshold = (1. + mat[j][i]) / (dx * dy + 1) + d = c > threshold + dest.setGray(x * dx + i, y * dy + j, d) + return dest + +if chapter(2): + m1 = [[1, 4, 7], + [6, 0, 2], + [3, 8, 5]] + m2 = [[4, 6, 3], + [8, 1, 5], + [0, 3, 7]] + m3 = [[5, 0, 3], + [2, 8, 6], + [7, 4, 1]] + m4 = [[8, 2, 5], + [6, 4, 0], + [1, 7, 3]] + m5 = [[2, 5, 8], + [0, 7, 3], + [4, 1, 6]] + m6 = [[7, 4, 1], + [3, 6, 8], + [2, 0, 5]] + mlist = [m1, m2, m3, m4, m5, m6] + test242(grad256bw, mlist).save("out/grad2-4-2.png") + test242(lena256bw, mlist).save("out/lena2-4-2.png") + +# Output 2.5.1: cross pattern +# Output 2.5.2: hex pattern +# Output 2.5.3: square pattern +def test25x(src, mat, vec): + # 1. count positive cells + n = 0 + for line in mat: + for x in line: + if x >= 0: + n += 1 + # 2. create list of vectors + l = [] + x = y = 0 + while (x, y) not in l: + l.append((x, y)) + (x, y) = ((x + vec[0][0]) % n, (y + vec[0][1]) % n) + if (x, y) in l: + (x, y) = ((x + vec[1][0]) % n, (y + vec[1][1]) % n) + # 3. create big matrix + m = Matrix(n, n) + for v in l: + for x, y in rangexy(len(mat[0]), len(mat)): + if mat[y][x] < 0: + continue + m[(v[1] + y + n) % n][(v[0] + x + n) % n] = mat[y][x] + # 4. dither image + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + threshold = (1. + m[y % n][x % n]) / (1. + n) + d = c > threshold + dest.setGray(x, y, d) + return dest + +if chapter(2): + mat = [[-1, 4, -1], + [ 3, 0, 1], + [-1, 2, -1]] + vec = [(2, -1), (1, 2)] + test25x(grad256bw, mat, vec).save("out/grad2-5-1.png") + test25x(lena256bw, mat, vec).save("out/lena2-5-1.png") + mat = [[-1, 4, 2, -1], + [ 6, 0, 5, 3], + [-1, 7, 1, -1]] + vec = [(2, -2), (3, 1)] + test25x(grad256bw, mat, vec).save("out/grad2-5-2.png") + test25x(lena256bw, mat, vec).save("out/lena2-5-2.png") + mat = [[-1, -1, -1, 7, -1], + [-1, 2, 6, 9, 8], + [ 3, 0, 1, 5, -1], + [-1, 4, -1, -1, -1]] + vec = [(2, 4), (3, 1)] + test25x(grad256bw, mat, vec).save("out/grad2-5-3.png") + test25x(lena256bw, mat, vec).save("out/lena2-5-3.png") + mat = [[-1, -1, 2, -1], + [ 0, 1, 3, 8], + [ 5, 4, 7, 6], + [-1, 9, -1, -1]] + vec = [(2, 2), (0, 5)] + test25x(grad256bw, mat, vec).save("out/grad2-5-4.png") + test25x(lena256bw, mat, vec).save("out/lena2-5-4.png") + +# Output 2.6.1: 4-wise cross pattern +# Output 2.6.2: 3-wise hex pattern +# Output 2.6.3: 4-wise square pattern +if chapter(2): + mat = [[-1, -1, -1, 18, -1, -1], + [-1, 16, 14, 2, 6, -1], + [12, 0, 4, 10, 17, -1], + [-1, 8, 19, 13, 1, 5], + [-1, 15, 3, 7, 9, -1], + [-1, -1, 11, -1, -1, -1]] + vec = [(4, -2), (2, 4)] + test25x(grad256bw, mat, vec).save("out/grad2-6-1.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-1.png") + + mat = [[-1, 12, 6, -1, -1, -1, -1], + [18, 0, 15, 9, 14, 8, -1], + [-1, 21, 3, 20, 2, 17, 11], + [-1, -1, 13, 7, 23, 5, -1], + [-1, 19, 1, 16, 10, -1, -1], + [-1, -1, 22, 4, -1, -1, -1]] + vec = [(5, -1), (-1, 5)] + test25x(grad256bw, mat, vec).save("out/grad2-6-2.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-2.png") + + mat = [[-1, -1, -1, -1, 30, -1, -1, -1, -1], + [-1, -1, 10, 26, 38, 34, -1, 29, -1], + [-1, 14, 2, 6, 22, 9, 25, 37, 33], + [-1, -1, 18, 28, 13, 1, 5, 21, -1], + [-1, 8, 24, 36, 32, 17, 31, -1, -1], + [12, 0, 4, 20, 11, 27, 39, 35, -1], + [-1, 16, -1, 15, 3, 7, 23, -1, -1], + [-1, -1, -1, -1, 19, -1, -1, -1, -1]] + vec = [(6, 2), (-2, 6)] + test25x(grad256bw, mat, vec).save("out/grad2-6-3.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-3.png") + + mat = [[-1, -1, 3, 35, -1, -1, 1, 33, -1, -1, -1, -1, -1], + [-1, -1, -1, 19, 51, -1, -1, 17, 49, -1, -1, -1, -1], + [ 7, 39, 11, 43, 5, 37, 9, 41, -1, -1, -1, -1, -1], + [-1, 23, 55, 27, 59, 21, 53, 25, 57, -1, -1, -1, -1], + [15, 47, -1, -1, 13, 45, 2, 34, -1, -1, 0, 32, -1], + [-1, 31, 63, -1, -1, 29, 61, 18, 50, -1, -1, 16, 48], + [-1, -1, -1, -1, 6, 38, 10, 42, 4, 36, 8, 40, -1], + [-1, -1, -1, -1, -1, 22, 54, 26, 58, 20, 52, 24, 56], + [-1, -1, -1, -1, 14, 46, -1, -1, 12, 44, -1, -1, -1], + [-1, -1, -1, -1, -1, 30, 62, -1, -1, 28, 60, -1, -1]] + vec = [(8, 0), (0, 8)] + def colorise(val): + if val == 0: return '#fff' + if (val % 64) == 32: return '#ddf' + if (val % 32) == 16: return '#fdd' + if (val % 16) == 8: return '#dff' + if (val % 8) == 4: return '#ffd' + if (val % 4) == 2: return '#fdf' + return '#dfd' + Svg(mat, colorise).save("out/fig2-6-4.png", 25) + test25x(grad256bw, mat, vec).save("out/grad2-6-4.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-4.png") + + mat = [[ -1, -1, -1, -1, -1, -1, 8, -1], + [ -1, -1, 6, -1, 2, 5, 11, 26], + [ 0, 3, 9, 24, 17, 14, 23, 20], + [ 15, 12, 21, 18, 7, 29, -1, -1], + [ -1, 27, 1, 4, 10, 25, -1, -1], + [ -1, -1, 16, 13, 22, 19, -1, -1], + [ -1, -1, -1, 28, -1, -1, -1, -1]] + vec = [(6, 1), (0, 5)] + def colorise(val): return ['#dff', '#ffd', '#fdf'][val % 3] + #Svg(mat, colorise).save("out/fig2-6-5.png", 30) + test25x(grad256bw, mat, vec).save("out/grad2-6-5.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-5.png") + + mat = [[ -1, -1, -1, -1, -1, -1, 24, -1, -1, -1, -1, -1, -1, -1], + [ -1, -1, 18, -1, 6, 15, 33, 78, -1, -1, -1, -1, 26, -1], + [ 0, 9, 27, 72, 51, 42, 69, 60, 20, -1, 8, 17, 35, 80], + [ 45, 36, 63, 54, 21, 87, 2, 11, 29, 74, 53, 44, 71, 62], + [ -1, 81, 3, 12, 30, 75, 47, 38, 65, 56, 23, 89, -1, -1], + [ -1, -1, 48, 39, 66, 57, 25, 83, 5, 14, 32, 77, -1, -1], + [ -1, -1, 19, 84, 7, 16, 34, 79, 50, 41, 68, 59, -1, -1], + [ 1, 10, 28, 73, 52, 43, 70, 61, -1, 86, -1, -1, -1, -1], + [ 46, 37, 64, 55, 22, 88, -1, -1, -1, -1, -1, -1, -1, -1], + [ -1, 82, 4, 13, 31, 76, -1, -1, -1, -1, -1, -1, -1, -1], + [ -1, -1, 49, 40, 67, 58, -1, -1, -1, -1, -1, -1, -1, -1], + [ -1, -1, -1, 85, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]] + test25x(grad256bw, mat, vec).save("out/grad2-6-6.png") + test25x(lena256bw, mat, vec).save("out/lena2-6-6.png") + +# Output 2.7.1: void and cluster matrix generation +def makegauss(n): + c = (-1. + n) / 2 + mat = Matrix(n, n) + for x, y in rangexy(n, n): + mat[y][x] = math.exp(- ((c - x) * (c - x) + (c - y) * (c - y)) / (0.05 * n * n)) + return mat + +def countones(mat): + total = 0 + for l in mat: + for x in l: + if x: + total += 1 + return total + +GAUSS77 = makegauss(7) +GAUSS99 = makegauss(9) + +def getminmax(mat, c): + min = 9999. + max = 0. + h = len(mat) + w = len(mat[0]) + for x, y in rangexy(w, h): + if mat[y][x] != c: + continue + total = 0. + for i, j in rangexy(7, 7): + total += mat[(y + j - 3 + h) % h][(x + i - 3 + w) % w] \ + * GAUSS77[j][i] + if total > max: + (max, max_x, max_y) = (total, x, y) + if total < min: + (min, min_x, min_y) = (total, x, y) + return (min_x, min_y, max_x, max_y) + +def makeuniform(n): + random.seed(0) + mat = Matrix(n, n) + for t in range(n * n / 10): + x = (int)(random.random() * n) + y = (int)(random.random() * n) + mat[y][x] = 1 + while True: + (dummy0, dummy1, x, y) = getminmax(mat, 1.) + mat[y][x] = 0. + (x2, y2, dummy0, dummy1) = getminmax(mat, 0.) + mat[y2][x2] = 1. + if x2 == x and y2 == y: + break + return mat + +def makevoidandcluster(n): + vnc = Matrix(n, n) + # Step 1: step down to zero + mat = makeuniform(n) + rank = countones(mat) + while rank > 0: + rank -= 1 + (dummy0, dummy1, x, y) = getminmax(mat, 1.) + mat[y][x] = 0. + vnc[y][x] = rank + # Step 2: step up to n * n + mat = makeuniform(n) + rank = countones(mat) + while rank < n * n: + (x, y, dummy0, dummy1) = getminmax(mat, 0.) + mat[y][x] = 1. + vnc[y][x] = rank + rank += 1 + return vnc + +def vnccol(n): + return lambda val : ['#fff', '#ddd'][val < n * n / 10] + +if chapter(2): + tmp = makevoidandcluster(14) + Svg(tmp, vnccol(14)).save("out/fig2-7-1.png", 25) + ordereddither(grad256bw, tmp).save("out/grad2-7-1.png") + ordereddither(lena256bw, tmp).save("out/lena2-7-1.png") + tmp = makevoidandcluster(25) + Svg(tmp, vnccol(25)).save("out/fig2-7-2.png", 20) + ordereddither(grad256bw, tmp).save("out/grad2-7-2.png") + ordereddither(lena256bw, tmp).save("out/lena2-7-2.png") + +############################################################################## +if chapter(3): + print "Chapter 3. Error diffusion" + +# Output 3.0.1: naive error diffusion +# Output 3.1.1: standard Floyd-Steinberg +# Output 3.1.2: serpentine Floyd-Steinberg +# FIXME: serpentine only works if rows == offset * 2 + 1 +# Output 3.2.1: Fan (modified Floyd-Steinberg) +# Output 3.2.1b: Shiau-Fan 1 +# Output 3.2.1c: Shiau-Fan 2 +# Output 3.2.2: Jarvis, Judice and Ninke +# Output 3.2.3: Stucki +# Output 3.2.4: Burkes +# Output 3.2.5: Sierra +# Output 3.2.6: Two-line Sierra +# Output 3.2.7: Sierra's Filter Lite +# Output 3.2.8: Atkinson +def test3xx(src, mat, serpentine): + (w, h) = src.size() + dest = Image((w, h)) + lines = len(mat) + rows = len(mat[0]) + offset = mat[0].index(-1) + ey = Matrix(w + rows - 1, lines, 0.) + for y in range(h): + ex = [0.] * (rows - offset) + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Set pixel + c = src.getGray(x, y) + ex[0] + ey[0][x + offset] + d = c > 0.5 + dest.setGray(x, y, d) + error = c - d + # Propagate first line of error + for dx in range(rows - offset - 2): + ex[dx] = ex[dx + 1] + error * mat[0][offset + 1 + dx] + ex[rows - offset - 2] = error * mat[0][rows - 1] + # Propagate next lines + if serpentine and y & 1: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][rows - 1 - dx] + else: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][dx] + for dy in range(lines - 1): + ey[dy] = ey[dy + 1] + ey[lines - 1] = [0.] * (w + rows - 1) + return dest + +ERROR_NAIVE = \ + [[ -1, 1]] +ERROR_FSTEIN = \ + [[ 0., -1, 7./16], + [ 3./16, 5./16, 1./16]] +ERROR_JAJUNI = \ + [[ 0., 0., -1, 7./48, 5./48], + [ 3./48, 5./48, 7./48, 5./48, 3./48], + [ 1./48, 3./48, 5./48, 3./48, 1./48]] +ERROR_FAN = \ + [[ 0., 0., -1, 7./16], + [ 1./16, 3./16, 5./16, 0.]] +ERROR_SHIAUFAN = \ + [[ 0., 0., -1, 8./16], + [ 2./16, 2./16, 4./16, 0.]] +ERROR_SHIAUFAN2 = \ + [[ 0., 0., 0., -1, 8./16], + [ 1./16, 1./16, 2./16, 4./16, 0.]] +ERROR_STUCKI = \ + [[ 0., 0., -1, 8./42, 4./42], + [ 2./42, 4./42, 8./42, 4./42, 2./42], + [ 1./42, 2./42, 4./42, 2./42, 1./42]] +ERROR_BURKES = \ + [[ 0., 0., -1, 8./32, 4./32], + [ 2./32, 4./32, 8./32, 4./32, 2./32]] +ERROR_SIERRA = \ + [[ 0., 0., -1, 5./32, 3./32], + [ 2./32, 4./32, 5./32, 4./32, 2./32], + [ 0., 2./32, 3./32, 2./32, 0.]] +ERROR_SIERRA2 = \ + [[ 0., 0., -1, 4./16, 3./16], + [ 1./16, 2./16, 3./16, 2./16, 1./16]] +ERROR_FILTERLITE = \ + [[ 0., -1, 2./4], + [ 1./4, 1./4, 0.]] +ERROR_ATKINSON = \ + [[ 0., -1, 1./8, 1./8], + [ 1./8, 1./8, 1./8, 0.], + [ 0., 1./8, 0., 0.]] +## This is Stevenson-Arce in hex lattice +#ERROR_STAR = \ +# [[ 0., 0., 0., -1, 0., 32./200, 0.], +# [ 12./200, 0., 26./200, 0., 30./200, 0., 16./200], +# [ 0., 12./200, 0., 26./200, 0., 12./200, 0.], +# [ 5./200, 0., 12./200, 0., 12./200, 0., 5./200]] +## This is an attempt at implementing Stevenson-Arce in square lattice +#ERROR_STAR = \ +# [[ 0., 0., -1, 32./200, 0.], +# [ 6./200, 19./200, 28./200, 23./200, 8./200], +# [ 0., 12./200, 26./200, 12./200, 0.], +# [ 2./200, 9./200, 12./200, 9./200, 2./200]] + +if chapter(3): + test3xx(grad256bw, ERROR_NAIVE, False).save("out/grad3-0-1.png") + test3xx(lena256bw, ERROR_NAIVE, False).save("out/lena3-0-1.png") + + Svg(ERROR_FSTEIN).save("out/fig3-1-1.png", 40) + test3xx(grad256bw, ERROR_FSTEIN, False).save("out/grad3-1-1.png") + test3xx(lena256bw, ERROR_FSTEIN, False).save("out/lena3-1-1.png") + test3xx(grad256bw, ERROR_FSTEIN, True).save("out/grad3-1-2.png") + test3xx(lena256bw, ERROR_FSTEIN, True).save("out/lena3-1-2.png") + + Svg(ERROR_JAJUNI).save("out/fig3-1-3.png", 40) + test3xx(grad256bw, ERROR_JAJUNI, False).save("out/grad3-1-3.png") + test3xx(lena256bw, ERROR_JAJUNI, False).save("out/lena3-1-3.png") + + Svg(ERROR_FAN).save("out/fig3-2-1.png", 40) + test3xx(grad256bw, ERROR_FAN, False).save("out/grad3-2-1.png") + test3xx(lena256bw, ERROR_FAN, False).save("out/lena3-2-1.png") + Svg(ERROR_SHIAUFAN).save("out/fig3-2-1b.png", 40) + test3xx(grad256bw, ERROR_SHIAUFAN, False).save("out/grad3-2-1b.png") + test3xx(lena256bw, ERROR_SHIAUFAN, False).save("out/lena3-2-1b.png") + Svg(ERROR_SHIAUFAN2).save("out/fig3-2-1c.png", 40) + test3xx(grad256bw, ERROR_SHIAUFAN2, False).save("out/grad3-2-1c.png") + test3xx(lena256bw, ERROR_SHIAUFAN2, False).save("out/lena3-2-1c.png") + + Svg(ERROR_STUCKI).save("out/fig3-2-3.png", 40) + test3xx(grad256bw, ERROR_STUCKI, False).save("out/grad3-2-3.png") + test3xx(lena256bw, ERROR_STUCKI, False).save("out/lena3-2-3.png") + + Svg(ERROR_BURKES).save("out/fig3-2-4.png", 40) + test3xx(grad256bw, ERROR_BURKES, False).save("out/grad3-2-4.png") + test3xx(lena256bw, ERROR_BURKES, False).save("out/lena3-2-4.png") + + Svg(ERROR_SIERRA).save("out/fig3-2-5.png", 40) + test3xx(grad256bw, ERROR_SIERRA, False).save("out/grad3-2-5.png") + test3xx(lena256bw, ERROR_SIERRA, False).save("out/lena3-2-5.png") + + Svg(ERROR_SIERRA2).save("out/fig3-2-6.png", 40) + test3xx(grad256bw, ERROR_SIERRA2, False).save("out/grad3-2-6.png") + test3xx(lena256bw, ERROR_SIERRA2, False).save("out/lena3-2-6.png") + + Svg(ERROR_FILTERLITE).save("out/fig3-2-7.png", 40) + test3xx(grad256bw, ERROR_FILTERLITE, False).save("out/grad3-2-7.png") + test3xx(lena256bw, ERROR_FILTERLITE, False).save("out/lena3-2-7.png") + + Svg(ERROR_ATKINSON).save("out/fig3-2-8.png", 40) + test3xx(grad256bw, ERROR_ATKINSON, False).save("out/grad3-2-8.png") + test3xx(lena256bw, ERROR_ATKINSON, False).save("out/lena3-2-8.png") + + #test3xx(grad256bw, ERROR_STAR, False).save("out/grad3-2-9.png") + #test3xx(lena256bw, ERROR_STAR, False).save("out/lena3-2-9.png") + + #test3xx(grad256bw, ERROR_STAR, False).save("out/grad3-2-9.png") + #test3xx(lena256bw, ERROR_STAR, False).save("out/lena3-2-9.png") + +# Output 3.3.1: Floyd-Steinberg on grey 90% +# Output 3.3.2: serpentine Floyd-Steinberg on grey 90% +if chapter(3): + tmp = Image((128, 128)) + for x, y in rangexy(128, 128): + tmp.setGray(x, y, 0.90) + test3xx(tmp, ERROR_FSTEIN, True).getZoom(2).save("out/lena3-3-2.png") + test3xx(tmp, ERROR_FSTEIN, False).getZoom(2).save("out/lena3-3-1.png") + +# Output 3.3.3: Riemersma dither on a Hilbert curve +# Output 3.3.4: Riemersma dither on a Hilbert 2 curve +def hilbert(x, y, n): + d1 = [0, 4, 3, 2, 1] + d2 = [0, 3, 4, 1, 2] + m = n/2 + + if x == n - 1 and y == 0: return 0 + if x == 0 and y == m - 1: return 1 + if x == m - 1 and y == m: return 4 + if x == n - 1 and y == m: return 2 + + if y >= m: return hilbert(x % m, y - m, m) + if x < m: return d1[hilbert(y, x, m)] + else: return d2[hilbert(m - 1 - y, n - 1 - x, m)] + +def hilbert2(x, y, n): + d1 = [0, 2, 1, 3, 4] + d2 = [0, 1, 2, 4, 3] + d3 = [0, 2, 1, 4, 3] + m = n/3 + + if x == n - 1 and y == n - 1: return 0 + if x == m - 1 and y == m - 1: return 4 + if x == 2 * m - 1 and y == 0: return 4 + if x == n - 1 and y == m - 1: return 1 + if x == 0 and y == 2 * m - 1: return 1 + if x == m and y == m: return 3 + if x == m * 2 and y == m * 2 - 1: return 3 + if x == m - 1 and y == n - 1: return 4 + if x == 2 * m - 1 and y == 2 * m: return 4 + + if (x < m or x >= m * 2) and (y < m or y >= m * 2): + return hilbert2(x % m, y % m, m) + if x >= m and x < m * 2 and y >= m and y < m * 2: + return d3[hilbert2(2 * m - 1 - x, 2 * m - 1 - y, m)] + if x >= m and x < m * 2: + return d1[hilbert2(x - m, m - 1 - (y % m), m)] + else: # if y >= m and y < m * 2 + return d2[hilbert2(m - 1 - (x % m), y - m, m)] + +def riemersma(src, iterator, order): + (w, h) = src.size() + dest = Image((w, h)) + q = 16 + r = 16 + size = 1 + while size < w or size < h: size *= order + coord = [(0, 0), (0, 1), (0, -1), (-1, 0), (1, 0)] + err = [0] * q + list = [math.exp(math.log(r) * i / (q - 1)) / r for i in range(q)] + (x, y) = (0, 0) + out = False + while True: + if not out: + a = c = src.getGray(x, y) + for i in range(q): + a += err[i] * list[i] + d = a > 0.5 + dest.setGray(x, y, d) + for i in range(q - 1): + err[i] = err[i + 1] + err[q - 1] = c - d + t = iterator(x, y, size) + if t == 0: + break + dx, dy = coord[t] + x += dx + y += dy + if out: + if x < w and y < h: + err = [0] * q + out = False + continue + # Did we fall off the screen? + out = (x > w + q or y > h + q) + return dest + +if chapter(3): + riemersma(grad256bw, hilbert, 2).save("out/grad3-3-3.png") + riemersma(lena256bw, hilbert, 2).save("out/lena3-3-3.png") + riemersma(grad256bw, hilbert2, 3).save("out/grad3-3-4.png") + riemersma(lena256bw, hilbert2, 3).save("out/lena3-3-4.png") + +# Output 3.3.5: spatial Hilbert dither on a Hilbert curve +# Output 3.3.6: spatial Hilbert dither on a Hilbert 2 curve +def spatialhilbert(src, iterator, order): + (w, h) = src.size() + dest = Image((w, h)) + q = 16 + size = 1 + while size < w or size < h: size *= order + coord = [(0, 0), (0, 1), (0, -1), (-1, 0), (1, 0)] + err = [0] * q + dx = [0] * q + dy = [0] * q + dist = [0] * q + (x, y) = (0, 0) + out = False + while True: + if not out: + c = src.getGray(x, y) + err[0] + d = c > 0.5 + dest.setGray(x, y, d) + t = iterator(x, y, size) + if t == 0: + break + dx[0], dy[0] = coord[t] + if not out: + error = c - d + errdiv = 0. + for i in range(q - 1): + t = coord[iterator(x + dx[i], y + dy[i], size)] + dx[i + 1] = dx[i] + t[0] + dy[i + 1] = dy[i] + t[1] + for i in range(q): + dist[i] = dx[i] * dx[i] + dy[i] * dy[i] + errdiv += 1. / dist[i] + error /= errdiv + for i in range(q - 1): + err[i] = err[i + 1] + error / dist[i] + err[q - 1] = error / dist[q - 1] + x += dx[0] + y += dy[0] + if out: + if x < w and y < h: + err = [0] * q + out = False + continue + # Did we fall off the screen? + out = (x > w + q or y > h + q) + return dest + +if chapter(3): + spatialhilbert(grad256bw, hilbert, 2).save("out/grad3-3-5.png") + spatialhilbert(lena256bw, hilbert, 2).save("out/lena3-3-5.png") + spatialhilbert(grad256bw, hilbert2, 3).save("out/grad3-3-6.png") + spatialhilbert(lena256bw, hilbert2, 3).save("out/lena3-3-6.png") + +# Output 3.3.7: Knuth's dot diffusion +# Output 3.3.8: Knuth's dot diffusion, sharpen = 0.9 +# Output 3.3.9: Knuth's dot diffusion, sharpen = 0.9, zeta = 0.2 +# Output 3.3.10: serpentine Floyd-Steinberg, sharpen = 0.9 +def sharpen(src, sharpening): + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + total = 0. + for j in range(-1, 2): + for i in range(-1, 2): + total += src.getGray(x + i, y + j) + total /= 9. + d = (c - sharpening * total) / (1.0 - sharpening) + if d < 0.: + d = 0. + elif d > 1.: + d = 1. + dest.setGray(x, y, d) + return dest + +def test337(src, mat, propagate, zeta): + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mat[0]) + dy = len(mat) + # 0. analyse diffusion matrix to speed up things later + diff = [] + cx, cy = -1, -1 + for x, y in rangexy(len(propagate[0]), len(propagate)): + if propagate[y][x] == -1: + cx, cy = x, y + for x, y in rangexy(len(propagate[0]), len(propagate)): + diff.append((x - cx, y - cy, propagate[y][x])) + # 1. analyse order matrix to get equivalence classes + nclasses = 0 + for l in mat: + for v in l: + if v + 1 > nclasses: + nclasses = v + 1 + classes = [[] for n in range(nclasses)] + for x, y in rangexy(w, h): + classes[mat[y % dy][x % dx]].append((x, y)) + # 2. copy image + img = [[src.getGray(x, y) for x in range(w)] for y in range(h)] + aa = Matrix(w, h, 1.) + # 3. parse all classes + for l in classes: + for x, y in l: + c = img[y][x] + if aa[y][x] == 1.: + error = c + 4. * zeta + else: + error = c - zeta + if x > 0 and aa[y][x-1] == 1.: error += zeta + if y > 0 and aa[y-1][x] == 1.: error += zeta + if x < w-1 and aa[y][x+1] == 1.: error += zeta + if y < h-1 and aa[y+1][x] == 1.: error += zeta + if c + error < 1.: + aa[y][x] = 0. + if x > 0 and aa[y][x-1] == 1.: aa[y][x-1] = .5 + if y > 0 and aa[y-1][x] == 1.: aa[y-1][x] = .5 + if x < w-1 and aa[y][x+1] == 1.: aa[y][x+1] = .5 + if y < h-1 and aa[y+1][x] == 1.: aa[y+1][x] = .5 + else: + error = c - 1. + # Propagate first line of error + total = 0 + err = [] + for (i, j, e) in diff: + if x + i < 0 or x + i >= w \ + or y + j < 0 or y + j >= h: + continue + n = mat[(y + j) % dy][(x + i) % dx] - mat[y % dy][x % dx] + if n <= 0: + continue + err.append((i, j, e)) + total += e + for (i, j, e) in err: + img[y + j][x + i] += error * e / total + # 4. copy image, replacing grey with white + for x, y in rangexy(w, h): + dest.setGray(x, y, aa[y][x] > 0.) + return dest + +ERROR_DOT = \ + [[1./12, 1./6, 1./12], + [ 1./6, -1, 1./6], + [1./12, 1./6, 1./12]] + +if chapter(3): + Svg(ERROR_DOT).save("out/fig3-3-7b.png", 40) + test337(grad256bw, DITHER_CLUSTER88, ERROR_DOT, 0.).save("out/grad3-3-7.png") + test337(lena256bw, DITHER_CLUSTER88, ERROR_DOT, 0.).save("out/lena3-3-7.png") + tmp = sharpen(grad256bw, 0.9) + test337(tmp, DITHER_CLUSTER88, ERROR_DOT, 0.).save("out/grad3-3-8.png") + test337(tmp, DITHER_CLUSTER88, ERROR_DOT, 0.2).save("out/grad3-3-9.png") + test3xx(tmp, ERROR_FSTEIN, True).save("out/grad3-3-10.png") + tmp = sharpen(lena256bw, 0.9) + test337(tmp, DITHER_CLUSTER88, ERROR_DOT, 0.).save("out/lena3-3-8.png") + test337(tmp, DITHER_CLUSTER88, ERROR_DOT, 0.2).save("out/lena3-3-9.png") + test3xx(tmp, ERROR_FSTEIN, True).save("out/lena3-3-10.png") + +# Output 3.3.11: omni-directional error diffusion +def test3311(src): + w, h = src.size() + g = { 0: 0, 1: 1, 2: 1 } + g[-1] = g[2] - g[1] + g[-2] = g[1] - g[0] + n = 2 + while g[n] < w and g[n] < h: + n += 1 + g[n] = g[n - 1] + g[n - 3] + g[-n] = g[-n + 3] - g[-n + 2] + u = g[n - 2] + v = g[n - 1] + a = [[(i * u + j * v) % g[n] for i in range(g[n])] for j in range(g[n])] + return a + +ERROR_OMNI = \ + [[0.1, 0.2, 0.1], + [0.1, -1, 0.1], + [0.1, 0.2, 0.1]] + +if chapter(3): + Svg(ERROR_OMNI).save("out/fig3-3-11.png", 40) + + mat = test3311(grad256bw) + test337(grad256bw, mat, ERROR_OMNI, 0.).save("out/grad3-3-11.png") + + mat = test3311(lena256bw) + tmp = [[str(mat[y][x]) for x in range(16)] for y in range(12)] + for x in range(16): tmp[11][x] = "..." + for y in range(12): tmp[y][15] = "..." + Svg(tmp).save("out/fig3-3-11b.png", 20) + test337(lena256bw, mat, ERROR_OMNI, 0.).save("out/lena3-3-11.png") + +# Output 3.4.1: Ostromoukhov's variable error diffusion +def test341(src, serpentine): + m = [[13, 0, 5], [13, 0, 5], [21, 0, 10], [7, 0, 4], + [8, 0, 5], [47, 3, 28], [23, 3, 13], [15, 3, 8], + [22, 6, 11], [43, 15, 20], [7, 3, 3], [501, 224, 211], + [249, 116, 103], [165, 80, 67], [123, 62, 49], [489, 256, 191], + [81, 44, 31], [483, 272, 181], [60, 35, 22], [53, 32, 19], + [237, 148, 83], [471, 304, 161], [3, 2, 1], [481, 314, 185], + [354, 226, 155], [1389, 866, 685], [227, 138, 125], [267, 158, 163], + [327, 188, 220], [61, 34, 45], [627, 338, 505], [1227, 638, 1075], + [20, 10, 19], [1937, 1000, 1767], [977, 520, 855], [657, 360, 551], + [71, 40, 57], [2005, 1160, 1539], [337, 200, 247], [2039, 1240, 1425], + [257, 160, 171], [691, 440, 437], [1045, 680, 627], [301, 200, 171], + [177, 120, 95], [2141, 1480, 1083], [1079, 760, 513], [725, 520, 323], + [137, 100, 57], [2209, 1640, 855], [53, 40, 19], [2243, 1720, 741], + [565, 440, 171], [759, 600, 209], [1147, 920, 285], [2311, 1880, 513], + [97, 80, 19], [335, 280, 57], [1181, 1000, 171], [793, 680, 95], + [599, 520, 57], [2413, 2120, 171], [405, 360, 19], [2447, 2200, 57], + [11, 10, 0], [158, 151, 3], [178, 179, 7], [1030, 1091, 63], + [248, 277, 21], [318, 375, 35], [458, 571, 63], [878, 1159, 147], + [5, 7, 1], [172, 181, 37], [97, 76, 22], [72, 41, 17], + [119, 47, 29], [4, 1, 1], [4, 1, 1], [4, 1, 1], + [4, 1, 1], [4, 1, 1], [4, 1, 1], [4, 1, 1], + [4, 1, 1], [4, 1, 1], [65, 18, 17], [95, 29, 26], + [185, 62, 53], [30, 11, 9], [35, 14, 11], [85, 37, 28], + [55, 26, 19], [80, 41, 29], [155, 86, 59], [5, 3, 2], + [5, 3, 2], [5, 3, 2], [5, 3, 2], [5, 3, 2], + [5, 3, 2], [5, 3, 2], [5, 3, 2], [5, 3, 2], + [5, 3, 2], [5, 3, 2], [5, 3, 2], [5, 3, 2], + [305, 176, 119], [155, 86, 59], [105, 56, 39], [80, 41, 29], + [65, 32, 23], [55, 26, 19], [335, 152, 113], [85, 37, 28], + [115, 48, 37], [35, 14, 11], [355, 136, 109], [30, 11, 9], + [365, 128, 107], [185, 62, 53], [25, 8, 7], [95, 29, 26], + [385, 112, 103], [65, 18, 17], [395, 104, 101], [4, 1, 1]] + (w, h) = src.size() + dest = Image((w, h)) + ey = [0.] * (w + 2) + for y in range(h): + ex = 0 + newey = [0.] * (w + 2) + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Set pixel + c = src.getGray(x, y) + ex + ey[x + 1] + d = c > 0.5 + dest.setGray(x, y, d) + error = c - d + i = (int)(c * 255.9999) + if i > 127: + i = 255 - i + (d1, d2, d3) = m[i] + t = d1 + d2 + d3 + # Propagate error + ex = error * d1 / t + if serpentine and y & 1: + newey[x + 2] += error * d3 / t + newey[x + 1] += error * d2 / t + else: + newey[x] += error * d2 / t + newey[x + 1] += error * d3 / t + ey = newey + return dest + +if chapter(3): + mat = [[0, -1, + 'd1(i)'], + ['d2(i)', + 'd3(i)', 0]] + Svg(mat).save("out/fig3-4-1.png", 40) + test341(grad256bw, True).save("out/grad3-4-1.png") + test341(lena256bw, True).save("out/lena3-4-1.png") + +def test351(src, mat, tiles, diff, serpentine, glob): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + ntiles = len(tiles) + ty = len(tiles[0]) + tx = len(tiles[0][0]) + cur = Matrix(tx, ty, 0.) + w, h = w / tx, h / ty + lines = len(mat) + rows = len(mat[0]) + offset = mat[0].index(-1) + ey = Matrix(w + rows - 1, lines, 0.) + for y in range(h): + ex = [0.] * (rows - offset) + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Get block value + for i, j in rangexy(tx, ty): + e = ex[0] + ey[0][x + offset] + cur[j][i] = src.getGray(x * tx + i, y * ty + j) + diff[j][i] * e + # Compute closest block and associated error + dist = tx * ty + for n in range(ntiles): + e = 0. + d1 = 0. + d2 = random.random() / 1000. + for i, j in rangexy(tx, ty): + e += cur[j][i] - tiles[n][j][i] + d1 += diff[j][i] * (cur[j][i] - tiles[n][j][i]) + d2 += diff[j][i] * abs(cur[j][i] - tiles[n][j][i]) + if glob: + d = abs(d1) + d2 / 1000. + else: + d = abs(d1) / (tx * ty) + d2 + if d < dist: + dist = d + error = e + best = n + # Set pixel + for i, j in rangexy(tx, ty): + dest.setGray(x * tx + i, y * ty + j, tiles[best][j][i]) + # Propagate first line of error + for dx in range(rows - offset - 2): + ex[dx] = ex[dx + 1] + error * mat[0][offset + 1 + dx] + ex[rows - offset - 2] = error * mat[0][rows - 1] + # Propagate next lines + if serpentine and y & 1: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][rows - 1 - dx] + else: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][dx] + for dy in range(lines - 1): + ey[dy] = ey[dy + 1] + ey[lines - 1] = [0.] * (w + rows - 1) + return dest + +LINES22 = \ + [[[0., 0.], [0., 0.]], + [[0., 1.], [0., 1.]], + [[1., 0.], [1., 0.]], + [[1., 1.], [0., 0.]], + [[0., 0.], [1., 1.]], + [[1., 1.], [1., 1.]]] + +SQUARES33 = \ + [[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], + [[1., 0., 0.], [0., 0., 0.], [0., 0., 0.]], + [[0., 1., 0.], [0., 0., 0.], [0., 0., 0.]], + [[0., 0., 1.], [0., 0., 0.], [0., 0., 0.]], + [[0., 0., 0.], [1., 0., 0.], [0., 0., 0.]], + [[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]], + [[0., 0., 0.], [0., 0., 1.], [0., 0., 0.]], + [[0., 0., 0.], [0., 0., 0.], [1., 0., 0.]], + [[0., 0., 0.], [0., 0., 0.], [0., 1., 0.]], + [[0., 0., 0.], [0., 0., 0.], [0., 0., 1.]], + [[1., 1., 0.], [1., 1., 0.], [0., 0., 0.]], + [[0., 1., 1.], [0., 1., 1.], [0., 0., 0.]], + [[0., 0., 0.], [1., 1., 0.], [1., 1., 0.]], + [[0., 0., 0.], [0., 1., 1.], [0., 1., 1.]], + [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]] + +TILES22 = [] +for n in range(1 << 4): + mat = Matrix(2, 2) + for x, y in rangexy(2, 2): + if n & (1 << (y * 2 + x)): + mat[y][x] = 1. + TILES22.append(mat) + +DIFF_EVEN22 = \ + [[1./4, 1./4], + [1./4, 1./4]] + +DIFF_EVEN33 = \ + [[1./9, 1./9, 1./9], + [1./9, 1./9, 1./9], + [1./9, 1./9, 1./9]] + +if chapter(3): + def colorise(val): + if type(val) == str: + return '#fbb' + return '#fff' + + mat = [[.25, .25], + [.25, .25]] + Svg(mat).save("out/fig3-5-2b.png", 40) + mat = [[0, 0, 'a', 'b', 7./64, 7./64], + [0, 0, 'c', 'd', 7./64, 7./64], + [3./64, 3./64, 5./64, 5./64, 1./64, 1./64], + [3./64, 3./64, 5./64, 5./64, 1./64, 1./64]] + Svg(mat, colorise).save("out/fig3-5-2.png", 40) + + test351(grad256bw, [[-1, 0]], + LINES22, DIFF_EVEN22, True, True).save("out/grad3-5-1.png") + test351(lena256bw, [[-1, 0]], + LINES22, DIFF_EVEN22, True, True).save("out/lena3-5-1.png") + test351(grad256bw, ERROR_FSTEIN, + LINES22, DIFF_EVEN22, True, True).save("out/grad3-5-2.png") + test351(lena256bw, ERROR_FSTEIN, + LINES22, DIFF_EVEN22, True, True).save("out/lena3-5-2.png") + + test351(grad256bw, ERROR_FSTEIN, + SQUARES33, DIFF_EVEN33, True, True).save("out/grad3-5-3.png") + test351(lena256bw, ERROR_FSTEIN, + SQUARES33, DIFF_EVEN33, True, True).save("out/lena3-5-3.png") + + test351(grad256bw, ERROR_FSTEIN, + TILES22, DIFF_EVEN22, True, True).save("out/grad3-5-4.png") + test351(lena256bw, ERROR_FSTEIN, + TILES22, DIFF_EVEN22, True, True).save("out/lena3-5-4.png") + +DIFF_WEIGHTED22 = \ + [[51./128, 33./128], + [25./128, 19./128]] + +if chapter(3): + mat = [[6./16, 5./16], + [3./16, 2./16]] + Svg(mat).save("out/fig3-5-5b.png", 40) + mat = [[0, 0, 'a', 'b', 6*7./256, 5*7./256], + [0, 0, 'c', 'd', 3*7./256, 2*7./256], + [6*3./256, 5*3./256, 6*5./256, 5*5./256, 6*1./256, 5*1./256], + [3*3./256, 2*3./256, 3*5./256, 2*5./256, 3*1./256, 2*1./256]] + Svg(mat, colorise).save("out/fig3-5-5.png", 40) + + test351(grad256bw, ERROR_FSTEIN, + TILES22, DIFF_WEIGHTED22, True, False).save("out/grad3-5-5.png") + test351(lena256bw, ERROR_FSTEIN, + TILES22, DIFF_WEIGHTED22, True, False).save("out/lena3-5-5.png") + +# Output 3.6.1: sub-block error diffusion +def subblock(src, tiles, propagate, diff, gamma): + (w, h) = src.size() + # Gamma correction + if gamma: + ctoi = Gamma.CtoI + itoc = Gamma.ItoC + else: + ctoi = itoc = lambda x : x + # Propagating the error to a temporary buffer is becoming more and + # more complicated. We decide to use an intermediate matrix instead. + tmp = Matrix(w, h, 0.) + for x, y in rangexy(w, h): + tmp[y][x] = ctoi(src.getGray(x, y)) + dest = Image((w, h)) + # Analyse tile list + ntiles = len(tiles) + ty = len(tiles[0]) + tx = len(tiles[0][0]) + cur = Matrix(tx, ty, 0.) + w, h = w / tx, h / ty + # Analyse error propagate list + for x, y in rangexy(w, h): + # Get block value + for i, j in rangexy(tx, ty): + cur[j][i] = itoc(tmp[y * ty + j][x * tx + i]) + # Select closest block + dist = tx * ty + for n in range(ntiles): + d = 0. + e = 0. + for i, j in rangexy(tx, ty): + d += cur[j][i] - tiles[n][j][i] + e += diff[j][i] * abs(cur[j][i] - tiles[n][j][i]) + if abs(d) / (tx * ty) + e < dist: + dist = abs(d) / (tx * ty) + e + best = n + # Set pixel + for i, j in rangexy(tx, ty): + dest.setGray(x * tx + i, y * ty + j, tiles[best][j][i]) + # Propagate error + for i, j in rangexy(tx, ty): + e = ctoi(cur[j][i]) - ctoi(tiles[best][j][i]) + m = propagate[j][i] + for px, py in rangexy(len(m[0]), len(m)): + if m[py][px] == 0: + continue + if m[py][px] == -1: + cx, cy = px, py + continue + tmpx = x * tx + i + px - cx + tmpy = y * ty + j + py - cy + if tmpx > w * tx - 1 or tmpy > h * ty - 1: + continue + tmp[tmpy][tmpx] += m[py][px] * e + return dest + +ERROR_SUBFS22 = \ + [[[[0, -1, 0, 8./64], + [0, 0, 0, 10./64], + [7./64, 22./64, 15./64, 2./64]], + [[0, 0, -1, 20./64], + [0, 0, 0, 14./64], + [2./64, 11./64, 15./64, 2./64]]], + [[[0, 0, 0, 0./64], + [0, -1, 0, 6./64], + [12./64, 32./64, 13./64, 1./64]], + [[0, 0, 0, 0./64], + [0, 0, -1, 20./64], + [0./64, 12./64, 28./64, 4./64]]]] + +if chapter(3): + subblock(grad256bw, TILES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, False).save("out/grad3-6-1.png") + subblock(lena256bw, TILES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, False).save("out/lena3-6-1.png") + + subblock(grad256bw, LINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, False).save("out/grad3-6-2.png") + subblock(lena256bw, LINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, False).save("out/lena3-6-2.png") + + def colorise(val): + if val == '': + return '#ccc' + if type(val) == str: + return '#fbb' + return '#fff' + + # XXX: hack, we modify ERROR_SUBFS22 because it's so much more convenient + for x, y in rangexy(2, 2): + for j in range(2): + for i in range(1, 3): + ERROR_SUBFS22[y][x][j][i] = '' + ERROR_SUBFS22[y][x][y][1 + x] = chr(ord('a') + 2 * y + x) + Svg(ERROR_SUBFS22[0][0], colorise).save("out/fig3-6-1a.png", 40) + Svg(ERROR_SUBFS22[0][1], colorise).save("out/fig3-6-1b.png", 40) + Svg(ERROR_SUBFS22[1][0], colorise).save("out/fig3-6-1c.png", 40) + Svg(ERROR_SUBFS22[1][1], colorise).save("out/fig3-6-1d.png", 40) + for x, y in rangexy(2, 2): + for j in range(2): + for i in range(1, 3): + ERROR_SUBFS22[y][x][j][i] = 0 + ERROR_SUBFS22[y][x][y][1 + x] = -1 + +ERROR_SUBFS33 = \ + [[[[ 0, -1, 0, 0, 2./64], + [ 0, 0, 0, 0, 5./64], + [ 0, 0, 0, 0, 6./64], + [ 5./64, 17./64, 17./64, 9./64, 1./64]], + [[ 0, 0, -1, 0, 6./64], + [ 0, 0, 0, 0, 9./64], + [ 0, 0, 0, 0, 8./64], + [ 2./64, 11./64, 16./64, 11./64, 1./64]], + [[ 0, 0, 0, -1, 20./64], + [ 0, 0, 0, 0, 14./64], + [ 0, 0, 0, 0, 8./64], + [ 0, 3./64, 9./64, 9./64, 1./64]]], + [[[ 0, 0, 0, 0, 0], + [ 0, -1, 0, 0, 2./64], + [ 0, 0, 0, 0, 5./64], + [ 7./64, 23./64, 18./64, 8./64, 1./64]], + [[ 0, 0, 0, 0, 0], + [ 0, 0, -1, 0, 6./64], + [ 0, 0, 0, 0, 9./64], + [ 2./64, 12./64, 21./64, 13./64, 1./64]], + [[ 0, 0, 0, 0, 0], + [ 0, 0, 0, -1, 20./64], + [ 0, 0, 0, 0, 14./64], + [ 0, 2./64, 11./64, 15./64, 2./64]]], + [[[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, -1, 0, 0, 2./64], + [12./64, 32./64, 14./64, 4./64, 0]], + [[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, -1, 0, 6./64], + [ 0, 12./64, 32./64, 13./64, 1./64]], + [[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, -1, 20./64], + [ 0, 0, 12./64, 28./64, 4./64]]]] + +TILES33 = [] +for n in range(1 << 9): + mat = Matrix(3, 3) + for x, y in rangexy(3, 3): + if n & (1 << (y * 3 + x)): + mat[y][x] = 1. + TILES33.append(mat) + +DIFF_WEIGHTED33 = \ + [[15./64, 10./64, 6./64], + [10./64, 6./64, 4./64], + [ 6./64, 4./64, 3./64]] + +if chapter(3): + subblock(grad256bw, TILES33, + ERROR_SUBFS33, DIFF_WEIGHTED33, False).save("out/grad3-6-3.png") + subblock(lena256bw, TILES33, + ERROR_SUBFS33, DIFF_WEIGHTED33, False).save("out/lena3-6-3.png") + subblock(grad256bw, SQUARES33, + ERROR_SUBFS33, DIFF_WEIGHTED33, False).save("out/grad3-6-4.png") + subblock(lena256bw, SQUARES33, + ERROR_SUBFS33, DIFF_WEIGHTED33, False).save("out/lena3-6-4.png") + + # XXX: hack, we modify ERROR_SUBFS33 because it's so much more convenient + for x, y in rangexy(3, 3): + for j in range(3): + for i in range(1, 4): + ERROR_SUBFS33[y][x][j][i] = '' + ERROR_SUBFS33[y][x][y][1 + x] = chr(ord('a') + 3 * y + x) + Svg(ERROR_SUBFS33[0][0], colorise).save("out/fig3-6-3a.png", 30) + Svg(ERROR_SUBFS33[0][1], colorise).save("out/fig3-6-3b.png", 30) + Svg(ERROR_SUBFS33[0][2], colorise).save("out/fig3-6-3c.png", 30) + Svg(ERROR_SUBFS33[1][0], colorise).save("out/fig3-6-3d.png", 30) + Svg(ERROR_SUBFS33[1][1], colorise).save("out/fig3-6-3e.png", 30) + Svg(ERROR_SUBFS33[1][2], colorise).save("out/fig3-6-3f.png", 30) + Svg(ERROR_SUBFS33[2][0], colorise).save("out/fig3-6-3g.png", 30) + Svg(ERROR_SUBFS33[2][1], colorise).save("out/fig3-6-3h.png", 30) + Svg(ERROR_SUBFS33[2][2], colorise).save("out/fig3-6-3i.png", 30) + for x, y in rangexy(3, 3): + for j in range(3): + for i in range(1, 4): + ERROR_SUBFS33[y][x][j][i] = 0 + ERROR_SUBFS33[y][x][y][1 + x] = -1 + +############################################################################## +if chapter(4): + print "Chapter 4. Model-based dithering" + +# Output 4.1.1: gaussian HVS applied to 8x8 Bayer dither, sigma = 1 +# Output 4.1.2: gaussian HVS applied to 8x8 Bayer dither, sigma = 2 +def gaussian(n, sigma): + m = Matrix(n, n, 0.) + t = 0. + for x, y in rangexy(n, n): + i = x - (float)(n - 1.) / 2. + j = y - (float)(n - 1.) / 2. + v = math.pow(math.e, - (i * i + j * j) / (2. * sigma * sigma)) + m[y][x] = v + t += v + for x, y in rangexy(n, n): + m[y][x] /= t + return m + +def convolution(src, m): + (w, h) = src.size() + dest = Image((w, h)) + dy = len(m) + dx = len(m[0]) + srcmat = [[src.getGray(x, y) for x in range(w)] for y in range(h)] + for x, y in rangexy(w, h): + c = t = 0. + for i, j in rangexy(dx, dy): + u = i - (dx - 1) / 2 + v = j - (dy - 1) / 2 + if x + u >= w or y + v >= h or x + u < 0 or y + v < 0: + continue + c += srcmat[y + v][x + u] * m[j][i] + t += m[j][i] + dest.setGray(x, y, c / t) + return dest + +if chapter(4): + tmp = Image("out/grad2-3-1b.png") + convolution(tmp, gaussian(11, 1.)).save("out/grad4-1-1.png") + convolution(tmp, gaussian(11, 2.)).save("out/grad4-1-2.png") + tmp = Image("out/lena2-3-1b.png") + convolution(tmp, gaussian(11, 1.)).save("out/lena4-1-1.png") + convolution(tmp, gaussian(11, 2.)).save("out/lena4-1-2.png") + +# Output 4.2.1: direct binary search, iteration 0 +# Output 4.2.2: direct binary search, iteration 1 +# Output 4.2.3: direct binary search, iteration 2 +# Output 4.2.4: direct binary search, iteration 5 +def test42x(src): + random.seed(0) + (w, h) = src.size() + dest = Image((w, h)) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + random.random() - 0.5 + d = c > 0.5 + dest.setGray(x, y, d) + return dest + +def test42y(src, dest, hvs): + threshold = 0.4 + kernel = Matrix(8, 8, 0.) # have two borders of zeroes + for i, j in rangexy(6, 6): + kernel[j][i] = hvs(i * i + j * j) + (w, h) = src.size() + # Build fast pixel lookup tables + srcmat = Matrix(w, h, 0.) + destmat = Matrix(w, h, 0.) + for x, y in rangexy(w, h): + srcmat[y][x] = src.getGray(x, y) + destmat[y][x] = dest.getGray(x, y) + # Build human perception model for both source and destination + srchvs = Matrix(w, h, 0.) + desthvs = Matrix(w, h, 0.) + for x, y in rangexy(w, h): + srcp = destp = 0. + for j in range(-5, 6): + if y + j < 0 or y + j >= h: + continue + for i in range(-3, 4): + if x + i < 0 or x + i >= w: + continue + m = kernel[abs(j)][abs(i)] + srcp += m * srcmat[y + j][x + i] + destp += m * destmat[y + j][x + i] + srchvs[y][x] = srcp + desthvs[y][x] = destp + swaps = toggles = 0 + for x, y in rangexy(w, h): + d = destmat[y][x] + best = 0. + # Compute the effect of a toggle + e = 0. + for j in range(-5, 6): + if y + j < 0 or y + j >= h: + continue + for i in range(-5, 6): + if x + i < 0 or x + i >= w: + continue + m = kernel[abs(j)][abs(i)] + p = srchvs[y + j][x + i] + q1 = desthvs[y + j][x + i] + q2 = q1 - m * d + m * (1. - d) + e += abs(q1 - p) - abs(q2 - p) + if e > best: + best = e + op = False + # Compute the effect of swaps + for dx, dy in [(0, 1), (0, -1), (-1, 0), (1, 0)]: + if y + dy < 0 or y + dy >= h or x + dx < 0 or x + dx >= w: + continue + d2 = destmat[y + dy][x + dx] + if d2 == d: + continue + e = 0. + for j in range(-6, 7): + for i in range(-6, 7): + if y + j < 0 or y + j >= h or x + i < 0 or x + i >= w: + continue + ma = kernel[abs(j)][abs(i)] + mb = kernel[abs(j - dy)][abs(i - dx)] + p = srchvs[y + j][x + i] + q1 = desthvs[y + j][x + i] + q2 = q1 - ma * d + ma * d2 - mb * d2 + mb * d + e += abs(q1 - p) - abs(q2 - p) + if e > best: + best = e + op = (dx, dy) + # Apply the change if interesting + if best <= 0.: + continue + if op: + dx, dy = op + d2 = destmat[y + dy][x + dx] + destmat[y + dy][x + dx] = d + else: + d2 = 1. - d + destmat[y][x] = d2 + for j in range(-5, 6): + for i in range(-5, 6): + m = kernel[abs(j)][abs(i)] + if y + j >= 0 and y + j < h and x + i >= 0 and x + i < w: + desthvs[y + j][x + i] -= m * d + desthvs[y + j][x + i] += m * d2 + if op and y + dy + j >= 0 and y + dy + j < h \ + and x + dx + i >= 0 and x + dx + i < w: + desthvs[y + dy + j][x + dx + i] -= m * d2 + desthvs[y + dy + j][x + dx + i] += m * d + for x, y in rangexy(w, h): + dest.setGray(x, y, destmat[y][x]) + return dest + +if chapter(4): + hvs = lambda x: math.pow(math.e, - math.sqrt(x)) + + tmp = test42x(grad256bw) + tmp.save("out/grad4-2-1.png") + tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-2.png") + tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-3.png") + for n in range(3): tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-4.png") + + tmp = test42x(lena256bw) + tmp.save("out/lena4-2-1.png") + tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-2.png") + tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-3.png") + for n in range(3): tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-4.png") + +if chapter(4): + hvs = lambda x: math.pow(math.e, -x / 2.) + tmp = test42x(grad256bw) + for n in range(5): tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-5.png") + tmp = test42x(lena256bw) + for n in range(5): tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-5.png") + +if chapter(4): + hvs = lambda x: math.pow(math.e, -x / 4.5) + tmp = test42x(grad256bw) + for n in range(5): tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-6.png") + tmp = test42x(lena256bw) + for n in range(5): tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-6.png") + +if chapter(4): + hvs = lambda x: math.pow(math.e, -x / 8.) + tmp = test42x(grad256bw) + for n in range(5): tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-7.png") + tmp = test42x(lena256bw) + for n in range(5): tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-7.png") + +if chapter(4): + hvs = lambda x: math.pow(math.e, -x / 8.) + 2. * math.pow(math.e, -x / 1.5) + tmp = test42x(grad256bw) + for n in range(5): tmp = test42y(grad256bw, tmp, hvs) + tmp.save("out/grad4-2-8.png") + tmp = test42x(lena256bw) + for n in range(5): tmp = test42y(lena256bw, tmp, hvs) + tmp.save("out/lena4-2-8.png") + +# Chapter 4.3: RMSE computations +def rmse(gray, dither): + (w, h) = gray.size() + error = 0. + for y in range(5, h - 5): + for x in range(5, w - 5): + c = gray.getGray(x, y) + d = dither.getGray(x, y) + error += (c - d) * (c - d) + return error / ((w - 10) * (h - 10)) + +if chapter(4): + sigmas = [1., 1.5, 2.] + tmp = [convolution(lena256bw, gaussian(11, s)) for s in sigmas] + for name in ["1-1-1", "1-1-2", "1-1-3", "1-3-1", "1-4-1", "1-4-2", + "2-1-1", "2-3-0", "2-3-1", "2-3-1b", "2-3-2", "2-3-2b", + "2-3-3", "2-4-1", "2-4-2", "2-5-1", "2-5-2", "2-5-3", + "2-5-4", "2-6-1", "2-6-2", "2-6-3", "2-6-4", "2-6-5", + "2-6-6", "2-7-1", "2-7-2", + "3-0-1", "3-1-1", "3-1-2", "3-1-3", "3-2-1", "3-2-1b", + "3-2-1c", "3-2-3", "3-2-4", "3-2-5", "3-2-6", "3-2-7", + "3-2-8", "3-3-3", "3-3-4", "3-3-5", "3-3-6", "3-3-7", + "3-3-8", "3-3-9", "3-3-10", "3-3-11", "3-4-1", "3-5-1", + "3-5-2", "3-5-3", "3-5-4", "3-5-5", "3-6-1", "3-6-2", + "3-6-3", "3-6-4", + "4-2-2", "4-2-3", "4-2-4", "4-2-5", "4-2-6", "4-2-7", + "4-2-8"]: + img = Image("out/lena" + name + ".png") + for i in range(3): + tmp2 = convolution(img, gaussian(11, sigmas[i])) + err = 100. * rmse(tmp[i], tmp2) + print "[ERR] %.5f" % (err,) + +############################################################################## +if chapter(5): + print "Chapter 5. Greyscale dithering" + +# Output 5.0.1: 4x4 Bayer dithering, 3 colours +def test501(src, mat): + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mat[0]) + dy = len(mat) + for x, y in rangexy(w, h): + c = src.getGray(x, y) + threshold = (1. + mat[y % dy][x % dx]) / (dx * dy + 1) + if c < 0.5: + c = 0.5 * (c > threshold / 2) + else: + c = 0.5 + 0.5 * (c > 0.5 + threshold / 2) + dest.setGray(x, y, c) + return dest + +if chapter(5): + test501(grad256bw, DITHER_BAYER88).save("out/grad5-0-1.png") + test501(lena256bw, DITHER_BAYER88).save("out/lena5-0-1.png") + +# Output 5.0.2: standard Floyd-Steinberg, 3 colours +def test502(src, mat, serpentine): + (w, h) = src.size() + dest = Image((w, h)) + lines = len(mat) + rows = len(mat[0]) + offset = mat[0].index(-1) + ey = Matrix(w + rows - 1, lines, 0.) + for y in range(h): + ex = [0.] * (rows - offset) + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Set pixel + c = src.getGray(x, y) + ex[0] + ey[0][x + offset] + d = 0.5 * (c > 0.25) + 0.5 * (c > 0.75) + dest.setGray(x, y, d) + error = c - d + # Propagate first line of error + for dx in range(rows - offset - 2): + ex[dx] = ex[dx + 1] + error * mat[0][offset + 1 + dx] + ex[rows - offset - 2] = error * mat[0][rows - 1] + # Propagate next lines + if serpentine and y & 1: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][rows - 1 - dx] + else: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][dx] + for dy in range(lines - 1): + ey[dy] = ey[dy + 1] + ey[lines - 1] = [0.] * (w + rows - 1) + return dest + +if chapter(5): + test502(grad256bw, ERROR_FSTEIN, True).save("out/grad5-0-2.png") + test502(lena256bw, ERROR_FSTEIN, True).save("out/lena5-0-2.png") + +# Pattern 5.1.1: gamma-corrected 50% grey, black-white halftone, 50% grey +if chapter(5): + dest = Image((240, 80)) + for y in range(80): + for x in range(80): + dest.setGray(x, y, Gamma.ItoC(0.5)) + for x in range(80, 160): + c = (x + y) & 1 + dest.setGray(x, y, c) + for x in range(160, 240): + dest.setGray(x, y, 0.5) + dest.save("out/pat5-1-1.png") + +# Output 5.2.1: gamma-corrected 2-colour Floyd-Steinberg +# Output 5.2.2: gamma-corrected 3-colour Floyd-Steinberg +# Output 5.2.3: gamma-corrected 4-colour Floyd-Steinberg +def test52x(src, mat, serpentine, threshold): + (w, h) = src.size() + dest = Image((w, h)) + lines = len(mat) + rows = len(mat[0]) + offset = mat[0].index(-1) + ey = Matrix(w + rows - 1, lines, 0.) + for y in range(h): + ex = [0.] * (rows - offset) + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Set pixel + c = Gamma.CtoI(src.getGray(x, y)) + ex[0] + ey[0][x + offset] + d = threshold(c) + dest.setGray(x, y, Gamma.ItoC(d)) + error = c - d + # Propagate first line of error + for dx in range(rows - offset - 2): + ex[dx] = ex[dx + 1] + error * mat[0][offset + 1 + dx] + ex[rows - offset - 2] = error * mat[0][rows - 1] + # Propagate next lines + if serpentine and y & 1: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][rows - 1 - dx] + else: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx] += error * mat[dy][dx] + for dy in range(lines - 1): + ey[dy] = ey[dy + 1] + ey[lines - 1] = [0.] * (w + rows - 1) + return dest + +if chapter(5): + test52x(grad256bw, ERROR_FSTEIN, True, Gamma.Cto2).save("out/grad5-2-1.png") + test52x(lena256bw, ERROR_FSTEIN, True, Gamma.Cto2).save("out/lena5-2-1.png") + test52x(grad256bw, ERROR_FSTEIN, True, Gamma.Cto3).save("out/grad5-2-2.png") + test52x(lena256bw, ERROR_FSTEIN, True, Gamma.Cto3).save("out/lena5-2-2.png") + test52x(grad256bw, ERROR_FSTEIN, True, Gamma.Cto4).save("out/grad5-2-3.png") + test52x(lena256bw, ERROR_FSTEIN, True, Gamma.Cto4).save("out/lena5-2-3.png") + +# Output 5.3.1: full 4-colour block error diffusion +GREY22 = [] +for n in range(4*4*4*4): + vals = [0., 0.333, 0.666, 1.] + a, b, c, d = n & 3, (n >> 2) & 3, (n >> 4) & 3, (n >> 6) & 3 + GREY22.append([[vals[a], vals[b]], [vals[c], vals[d]]]) + +if chapter(5): + subblock(grad256bw, GREY22, + ERROR_SUBFS22, DIFF_WEIGHTED22, True).save("out/grad5-3-1.png") + subblock(lena256bw, GREY22, + ERROR_SUBFS22, DIFF_WEIGHTED22, True).save("out/lena5-3-1.png") + +# Output 5.3.2: 4-colour block error diffusion with only line tiles +GREYLINES22 = [] +for n in range(4*4*4*4): + vals = [0., 0.333, 0.666, 1.] + a, b, c, d = n & 3, (n >> 2) & 3, (n >> 4) & 3, (n >> 6) & 3 + if (a != b or c != d) and (a != c or b != d): + continue + GREYLINES22.append([[vals[a], vals[b]], [vals[c], vals[d]]]) + +if chapter(5): + subblock(grad256bw, GREYLINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, True).save("out/grad5-3-2.png") + subblock(lena256bw, GREYLINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22, True).save("out/lena5-3-2.png") + +############################################################################## +if chapter(6): + print "Chapter 6. Colour dithering" + +# Pattern 6.1.1: 8-colour palette +if chapter(6): + dest = Image((512, 64)) + for x in range(512): + d = x / 64 + r = (d & 2) >> 1 + g = (d & 4) >> 2 + b = d & 1 + for y in range(64): + dest.setRgb(x, y, r, g, b) + dest.save("out/pat6-1-1.png") + +# Figure 6.1.1: 128x128 Lena +# Figure 6.1.2a: red channel +# Figure 6.1.2b: green channel +# Figure 6.1.2c: blue channel +# Figure 6.1.3a: red channel promoted to greyscale +# Figure 6.1.3b: green channel promoted to greyscale +# Figure 6.1.3c: blue channel promoted to greyscale +# Figure 6.1.4a: dithered red channel +# Figure 6.1.4b: dithered green channel +# Figure 6.1.4c: dithered blue channel +# Figure 6.1.5: combined dithered channels +if chapter(6): + tmp = gammascale(lena256, 2) + tmp.save("out/fig6-1-1.png") + (w, h) = tmp.size() + dst = [Image((w, h), True) for i in range(3)] + for x, y in rangexy(w, h): + rgb = tmp.getRgb(x, y) + dst[0].setRgb(x, y, rgb[0], 0, 0) + dst[1].setRgb(x, y, 0, rgb[1], 0) + dst[2].setRgb(x, y, 0, 0, rgb[2]) + dst[0].save("out/fig6-1-2a.png") + dst[1].save("out/fig6-1-2b.png") + dst[2].save("out/fig6-1-2c.png") + for x, y in rangexy(w, h): + for i in range(3): + rgb = dst[i].getRgb(x, y) + dst[i].setRgb(x, y, rgb[i], rgb[i], rgb[i]) + dst[0].save("out/fig6-1-3a.png") + dst[1].save("out/fig6-1-3b.png") + dst[2].save("out/fig6-1-3c.png") + for i in range(3): + dst[i] = test52x(dst[i], ERROR_FSTEIN, True, Gamma.Cto2) + dst[0].save("out/fig6-1-4a.png") + dst[1].save("out/fig6-1-4b.png") + dst[2].save("out/fig6-1-4c.png") + for x, y in rangexy(w, h): + for i in range(3): + rgb = [0., 0., 0.] + rgb[i] = (dst[i].getRgb(x, y))[i] + dst[i].setRgb(x, y, *rgb) + dst[0].save("out/fig6-1-5a.png") + dst[1].save("out/fig6-1-5b.png") + dst[2].save("out/fig6-1-5c.png") + for x, y in rangexy(w, h): + rgb = [0., 0., 0.] + for i in range(3): + rgb[i] = (dst[i].getRgb(x, y))[i] + tmp.setRgb(x, y, *rgb) + tmp.save("out/fig6-1-6.png") + +# Output 6.1.1: 8-colour Floyd-Steinberg RGB dithering +# Output 6.1.2: 8-colour gamma-corrected Floyd-Steinberg RGB dithering +def test61x(src, mat, func): + (w, h) = src.size() + dest = Image((w, h)) + tmp = [Image((w, h)), Image((w, h)), Image((w, h))] + for x, y in rangexy(w, h): + rgb = src.getRgb(x, y) + for i in range(3): + tmp[i].setGray(x, y, rgb[i]) + for i in range(3): + tmp[i] = func(tmp[i], mat, True, Gamma.Cto2) + for x, y in rangexy(w, h): + (r, g, b) = [tmp[i].getGray(x, y) for i in range(3)] + dest.setRgb(x, y, r, g, b) + return dest + +def test61y(src, mat, serpentine, threshold): + return test3xx(src, mat, serpentine) + +if chapter(6): + test61x(grad256, ERROR_FSTEIN, test61y).save("out/grad6-1-1.png") + test61x(lena256, ERROR_FSTEIN, test61y).save("out/lena6-1-1.png") + test61x(grad256, ERROR_FSTEIN, test52x).save("out/grad6-1-2.png") + test61x(lena256, ERROR_FSTEIN, test52x).save("out/lena6-1-2.png") + +# Pattern 6.2.1: different colours give the same result +if chapter(6): + dest = Image((320, 160)) + for x in range(80): + for y in range(80): + r = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 7 + g = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + b = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + dest.setRgb(x, y, b, g, r) + for y in range(80, 160): + r = DITHER_BAYER44[y % 4][x % 4] > 7 + g = DITHER_BAYER44[y % 4][x % 4] > 13 + b = DITHER_BAYER44[y % 4][x % 4] > 13 + dest.setRgb(x, y, b, g, r) + for x in range(80, 160): + for y in range(80): + r = DITHER_BAYER44[(y / 8) % 4][(x / 8 - 1) % 4] > 7 + g = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + b = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + dest.setRgb(x, y, b, g, r) + for y in range(80, 160): + r = DITHER_BAYER44[y % 4][(x - 1) % 4] > 7 + g = DITHER_BAYER44[y % 4][x % 4] > 13 + b = DITHER_BAYER44[y % 4][x % 4] > 13 + dest.setRgb(x, y, b, g, r) + for x in range(160, 240): + for y in range(80): + r = DITHER_BAYER44[(y / 8 + 1) % 4][(x / 8 + 1) % 4] > 7 + g = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + b = DITHER_BAYER44[(y / 8 + 1) % 4][(x / 8) % 4] > 13 + dest.setRgb(x, y, b, g, r) + for y in range(80, 160): + r = DITHER_BAYER44[(y + 1) % 4][(x + 1) % 4] > 7 + g = DITHER_BAYER44[y % 4][x % 4] > 13 + b = DITHER_BAYER44[(y + 1) % 4][x % 4] > 13 + dest.setRgb(x, y, b, g, r) + for x in range(240, 320): + for y in range(80): + r = DITHER_BAYER44[(y / 8 + 1) % 4][(x / 8) % 4] > 7 + g = DITHER_BAYER44[(y / 8) % 4][(x / 8) % 4] > 13 + b = DITHER_BAYER44[(y / 8) % 4][(x / 8 + 2) % 4] > 13 + dest.setRgb(x, y, b, g, r) + for y in range(80, 160): + r = DITHER_BAYER44[(y + 1) % 4][x % 4] > 7 + g = DITHER_BAYER44[y % 4][x % 4] > 13 + b = DITHER_BAYER44[y % 4][(x + 2) % 4] > 13 + dest.setRgb(x, y, b, g, r) + dest.save("out/pat6-2-1.png") + +# Pattern 6.2.2: 16-colour palette +if chapter(6): + dest = Image((512, 128)) + for x, y in rangexy(64, 64): + dest.setGray(x, y, 0) + dest.setGray(448 + x, y, 0.7) + dest.setGray(x, 64 + y, 0.3) + dest.setGray(448 + x, 64 + y, 1.) + for x in range(64, 448): + d = x / 64 + r = (d & 2) >> 1 + g = (d & 4) >> 2 + b = d & 1 + for y in range(64, 128): + dest.setRgb(x, y, r, g, b) + r /= 2. + g /= 2. + b /= 2. + for y in range(64): + dest.setRgb(x, y, r, g, b) + dest.save("out/pat6-2-2.png") + +# Output 6.2.1: gamma-corrected Floyd-Steinberg with ANSI palette (sigma-abs) +# Output 6.2.2: gamma-corrected Floyd-Steinberg with ANSI palette (euclidian) +def test62x(src, mat, cpal, distance, serpentine): + (w, h) = src.size() + ipal = [[Gamma.CtoI(c[i]) for i in range(3)] for c in cpal] + dest = Image((w, h)) + lines = len(mat) + rows = len(mat[0]) + offset = mat[0].index(-1) + ey = [[[0.] * 3 for n in range(w + rows - 1)] for n in range(lines)] + for y in range(h): + ex = [[0.] * 3 for n in range(rows - offset)] + if serpentine and y & 1: + xrange = range(w - 1, -1, -1) + else: + xrange = range(w) + for x in xrange: + # Get pixel, add error, set pixel + crgb = src.getRgb(x, y) + irgb = [Gamma.CtoI(crgb[i]) + ex[0][i] + ey[0][x + offset][i] \ + for i in range(3)] + crgb = [Gamma.ItoC(irgb[i]) for i in range(3)] + max = 999999. + for n in range(len(cpal)): + d = distance(crgb, cpal[n]) + if d < max: + max = d + cbest = cpal[n] + ibest = ipal[n] + dest.setRgb(x, y, *cbest) + # Compute error and propagate it + for i in range(3): + # Propagate first line of error + error = irgb[i] - ibest[i] + for dx in range(rows - offset - 2): + ex[dx][i] = ex[dx + 1][i] + error * mat[0][offset + 1 + dx] + ex[rows - offset - 2][i] = error * mat[0][rows - 1] + # Propagate next lines + if serpentine and y & 1: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx][i] += error * mat[dy][rows - 1 - dx] + else: + for dy in range(1, lines): + for dx in range(rows): + ey[dy][x + dx][i] += error * mat[dy][dx] + for dy in range(lines - 1): + ey[dy] = ey[dy + 1] + ey[lines - 1] = [[0.] * 3 for n in range(w + rows - 1)] + return dest + +RGB_PALETTE = \ + [[0, 0, 0], + [0, 0, 1], + [0, 1, 0], + [0, 1, 1], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + [1, 1, 1]] + +ANSI_PALETTE = \ + [[0, 0, 0], + [0, 0, 0.5], + [0, 0.5, 0], + [0, 0.5, 0.5], + [0.5, 0, 0], + [0.5, 0, 0.5], + [0.5, 0.5, 0], + [0.7, 0.7, 0.7], + [0.3, 0.3, 0.3], + [0, 0, 1], + [0, 1, 0], + [0, 1, 1], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + [1, 1, 1]] + +def distmax(u, v): + r, g, b = [abs(u[i] - v[i]) for i in range(3)] + return r + g + b + +def disteuclidian(u, v): + r, g, b = [u[i] - v[i] for i in range(3)] + return r*r + g*g + b*b + +if chapter(6): + test62x(grad256, ERROR_FSTEIN, + ANSI_PALETTE, distmax, True).save("out/grad6-2-1.png") + test62x(lena256, ERROR_FSTEIN, + ANSI_PALETTE, distmax, True).save("out/lena6-2-1.png") + test62x(grad256, ERROR_FSTEIN, + ANSI_PALETTE, disteuclidian, True).save("out/grad6-2-2.png") + test62x(lena256, ERROR_FSTEIN, + ANSI_PALETTE, disteuclidian, True).save("out/lena6-2-2.png") + +def rgb2hsv(r, g, b): + m = (float)(min(r, g, b)) + v = (float)(max(r, g, b)) + if v == m or v == 0: + return 0., 0., v + s = (v - m) / v + if r == v: + h = (g - b) / (v - m) + if h < 0.: + h += 6 + elif g == v: + h = 2. + (b - r) / (v - m) + elif b == v: + h = 4. + (r - g) / (v - m) + return math.pi * h / 3, s, v + +def disthsv(u, v): + u = rgb2hsv(*u) + v = rgb2hsv(*v) + d1 = u[2] - v[2] + d2 = u[2] * u[1] * math.cos(u[0]) - v[2] * v[1] * math.cos(v[0]) + d3 = u[2] * u[1] * math.sin(u[0]) - v[2] * v[1] * math.sin(v[0]) + return d1*d1 + d2*d2 + d3*d3 + +def disthsv3(u, v): + u = rgb2hsv(*u) + v = rgb2hsv(*v) + d1 = u[2] - v[2] + d2 = u[2] * u[1] * math.cos(u[0]) - v[2] * v[1] * math.cos(v[0]) + d3 = u[2] * u[1] * math.sin(u[0]) - v[2] * v[1] * math.sin(v[0]) + return 9*d1*d1 + d2*d2 + d3*d3 + +if chapter(6): + test62x(grad256, ERROR_FSTEIN, + ANSI_PALETTE, disthsv, True).save("out/grad6-2-3.png") + test62x(lena256, ERROR_FSTEIN, + ANSI_PALETTE, disthsv, True).save("out/lena6-2-3.png") + test62x(grad256, ERROR_FSTEIN, + ANSI_PALETTE, disthsv3, True).save("out/grad6-2-4.png") + test62x(lena256, ERROR_FSTEIN, + ANSI_PALETTE, disthsv3, True).save("out/lena6-2-4.png") + +# Output 6.4.1: colour sub-block error diffusion +def colorsubblock(src, tiles, propagate, diff): + (w, h) = src.size() + # Propagating the error to a temporary buffer is becoming more and + # more complicated. We decide to use an intermediate matrix instead. + tmp = Matrix(w, h, None) + for x, y in rangexy(w, h): + tmp[y][x] = Gamma.CtoI3(src.getRgb(x, y)) + dest = Image((w, h)) + # Analyse tile list + ntiles = len(tiles) + ty = len(tiles[0]) + tx = len(tiles[0][0]) + cur = Matrix(tx, ty, None) + w, h = w / tx, h / ty + # Analyse error propagate list + for x, y in rangexy(w, h): + # Get block value + for i, j in rangexy(tx, ty): + cur[j][i] = Gamma.ItoC3(tmp[y * ty + j][x * tx + i]) + # Select closest block + dist = tx * ty + for n in range(ntiles): + d = [0., 0., 0.] + e = 0. + for i, j in rangexy(tx, ty): + tmpe = 0. + for k in range(3): + delta = cur[j][i][k] - tiles[n][j][i][k] + d[k] += delta + tmpe += delta * delta + e += diff[j][i] * math.sqrt(tmpe) + # Without / 3. ugly colour bleeding artifacts appear + absd = (abs(d[0]) + abs(d[1]) + abs(d[2])) / 3. + if absd / (tx * ty) + e < dist: + dist = absd / (tx * ty) + e + best = n + # Set pixel + for i, j in rangexy(tx, ty): + dest.setRgb(x * tx + i, y * ty + j, *(tiles[best][j][i])) + # Propagate error + for i, j in rangexy(tx, ty): + curp = Gamma.CtoI3(cur[j][i]) + bestp = Gamma.CtoI3(tiles[best][j][i]) + m = propagate[j][i] + for k in range(3): + e = curp[k] - bestp[k] + for px, py in rangexy(len(m[0]), len(m)): + if m[py][px] == 0: + continue + if m[py][px] == -1: + cx, cy = px, py + continue + tmpx = x * tx + i + px - cx + tmpy = y * ty + j + py - cy + if tmpx > w * tx - 1 or tmpy > h * ty - 1: + continue + tmp[tmpy][tmpx][k] += m[py][px] * e + return dest + +RGBLINES22 = [] +for n in range(8*8*8*8): + a, b, c, d = n & 7, (n >> 3) & 7, (n >> 6) & 7, (n >> 9) & 7 + if (a != b or c != d) and (a != c or b != d): + continue + RGBLINES22.append([[RGB_PALETTE[a], RGB_PALETTE[b]], + [RGB_PALETTE[c], RGB_PALETTE[d]]]) + +if chapter(6): + colorsubblock(grad256, RGBLINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22).save("out/grad6-4-1.png") + colorsubblock(lena256, RGBLINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22).save("out/lena6-4-1.png") + +ANSILINES22 = [] +for n in range(16*16*16*16): + a, b, c, d = n & 15, (n >> 4) & 15, (n >> 8) & 15, (n >> 12) & 15 + if (a != b or c != d) and (a != c or b != d): + continue + ANSILINES22.append([[ANSI_PALETTE[a], ANSI_PALETTE[b]], + [ANSI_PALETTE[c], ANSI_PALETTE[d]]]) + +if chapter(6): + colorsubblock(grad256, ANSILINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22).save("out/grad6-4-2.png") + colorsubblock(lena256, ANSILINES22, + ERROR_SUBFS22, DIFF_WEIGHTED22).save("out/lena6-4-2.png") + +############################################################################## +if chapter(7): + print "Chapter 7. Photographic mosaics" + +# Output 7.0.1: create a mosaic from Lena +def mosaic_split(src, tnw, tnh): + random.seed(0) + thumbs = [] + (w, h) = src.size() + sw = w / tnw + sh = h / tnh + for x, y in rangexy(sw, sh): + thumbs.append(src.getRegion(x * tnw, y * tnh, tnw, tnh)) + random.shuffle(thumbs) + return thumbs + +def mosaic_analyse(tnlist, dx, dy): + coeffs = [] + for (n, img) in enumerate(tnlist): + tmp = [[[0] * 3 for x in range(dx)] for y in range(dy)] + (w, h) = img.size() + for x, y in rangexy(w, h): + my = y * dy / h + mx = x * dx / w + (r, g, b) = img.getRgb(x, y) + tmp[my][mx][0] += Gamma.CtoI(r) / (w / dx * h / dy) + tmp[my][mx][1] += Gamma.CtoI(g) / (w / dx * h / dy) + tmp[my][mx][2] += Gamma.CtoI(b) / (w / dx * h / dy) + coeffs.append(tmp) + return coeffs + +def test701(tnlist, cols): + (tnw, tnh) = tnlist[0].size() + dw = cols + dh = (len(tnlist) + cols - 1) / cols + dest = Image((dw * tnw + 8 * (dw + 1), dh * tnh + 8 * (dh + 1)), True) + for (n, img) in enumerate(tnlist): + di = 8 + (n % dw) * (tnw + 8) + dj = 8 + (n / dw) * (tnh + 8) + img.copyTo(dest, (di, dj)) + return dest + +if chapter(7): + tnlist = mosaic_split(lena256, 32, 32) + test701(tnlist, 10).save("out/lena7-0-1.png") + +# Output 7.1.1: extract 1 colour feature from mosaic tiles +# Output 7.1.2: crop Lena +# Output 7.1.3: generate a mosaic from the 1-feature database +# Output 7.1.4: extract 4 colour features from mosaic tiles +# Output 7.1.5: generate a mosaic from the 4-feature database +def test71x(coeffs, cols, tnw, tnh): + dx = len(coeffs[0][0]) + dy = len(coeffs[0]) + dw = cols + dh = (len(coeffs) + cols - 1) / cols + dest = Image((dw * tnw + 8 * (dw + 1), dh * tnh + 8 * (dh + 1)), True) + for (n, tab) in enumerate(coeffs): + di = 8 + (n % dw) * (tnw + 8) + dj = 8 + (n / dw) * (tnh + 8) + for x, y in rangexy(tnw, tnh): + (r, g, b) = tab[y * dy / tnh][x * dx / tnw] + dest.setRgb(di + x, dj + y, Gamma.ItoC(r), Gamma.ItoC(g), Gamma.ItoC(b)) + return dest + +def test71y(src, sqw, sqh, tnlist, coeffs): + (w, h) = src.size() + (tnw, tnh) = tnlist[0].size() + dx = len(coeffs[0][0]) + dy = len(coeffs[0]) + nx = w / sqw + ny = h / sqh + dest = Image((nx * tnw, ny * tnh), True) + for X, Y in rangexy(nx, ny): + # 1. create statistics about the current square + cur = [[[0] * 3 for x in range(dx)] for y in range(dy)] + for x, y in rangexy(sqw, sqh): + my = y * dy / sqh + mx = x * dx / sqw + (r, g, b) = src.getRgb(X * sqw + x, Y * sqh + y) + cur[my][mx][0] += Gamma.CtoI(r) / (sqw / dx * sqh / dy) + cur[my][mx][1] += Gamma.CtoI(g) / (sqw / dx * sqh / dy) + cur[my][mx][2] += Gamma.CtoI(b) / (sqw / dx * sqh / dy) + # 2. find the best mosaic part + best = -1 + dist = 5. + for (n, tmp) in enumerate(coeffs): + d = 0 + for i, j in rangexy(dx, dy): + for c in range(3): + t = cur[j][i][c] - tmp[j][i][c] + d += t * t + if d < dist: + dist = d + best = n + # 3. blit mosaic chunk + tnlist[best].copyTo(dest, (X * tnw, Y * tnh)) + return dest + +if chapter(7): + coeffs1x1 = mosaic_analyse(tnlist, 1, 1) + test71x(coeffs1x1, 10, 8, 8).save("out/lena7-1-1.png") + out712 = lena256.getRegion(100, 90, 80, 80) + out712.save("out/lena7-1-2.png") + test71y(out712, 6, 6, tnlist, coeffs1x1).save("out/lena7-1-3.png") + + coeffs2x2 = mosaic_analyse(tnlist, 2, 2) + test71x(coeffs2x2, 10, 16, 16).save("out/lena7-1-4.png") + test71y(out712, 6, 6, tnlist, coeffs2x2).save("out/lena7-1-5.png") + +############################################################################## +print "Finished" + +# Place temporary cruft below this +sys.exit(0) + +def simusubblock(src): + (w, h) = src.size() + dest = Image((w, h)) + tmp = Matrix(w, h, 0.) + for x, y in rangexy(w, h): + tmp[y][x] = src.getRgb(x, y) + dest = Image((w, h)) + # Analyse tile list + ntiles = len(tiles) + ty = len(tiles[0]) + tx = len(tiles[0][0]) + cur = Matrix(tx, ty, None) + w, h = w / tx, h / ty + # Analyse error propagate list + for x, y in rangexy(w, h): + # Get block value + for i, j in rangexy(tx, ty): + cur[j][i] = Gamma.ItoC3(tmp[y * ty + j][x * tx + i]) + # Select closest block + dist = tx * ty + for n in range(ntiles): + d = [0., 0., 0.] + e = 0. + for i, j in rangexy(tx, ty): + tmpe = 0. + for k in range(3): + delta = cur[j][i][k] - tiles[n][j][i][k] + d[k] += delta + tmpe += delta * delta + e += diff[j][i] * math.sqrt(tmpe) + # Without / 3. ugly colour bleeding artifacts appear + absd = (abs(d[0]) + abs(d[1]) + abs(d[2])) / 3. + if absd / (tx * ty) + e < dist: + dist = absd / (tx * ty) + e + best = n + # Set pixel + for i, j in rangexy(tx, ty): + dest.setRgb(x * tx + i, y * ty + j, *(tiles[best][j][i])) + # Propagate error + for i, j in rangexy(tx, ty): + curp = Gamma.CtoI3(cur[j][i]) + bestp = Gamma.CtoI3(tiles[best][j][i]) + m = propagate[j][i] + for k in range(3): + e = curp[k] - bestp[k] + for px, py in rangexy(len(m[0]), len(m)): + if m[py][px] == 0: + continue + if m[py][px] == -1: + cx, cy = px, py + continue + tmpx = x * tx + i + px - cx + tmpy = y * ty + j + py - cy + if tmpx > w * tx - 1 or tmpy > h * ty - 1: + continue + tmp[tmpy][tmpx][k] += m[py][px] * e + return dest + + +# XXX: test -- ranked dither -- it SUCKS +def test26x(src, mat): + (w, h) = src.size() + dest = Image((w, h)) + dx = len(mat[0]) + dy = len(mat) + for x, y in rangexy(w / dx, h / dy): + # Step 1: get the pixels and count groups + groups = {} + for i, j in rangexy(dx, dy): + p = src.getGray(x * dx + i, y * dy + j) + if groups.has_key(p): + groups[p].append((i, j)) + else: + groups[p] = [(i, j)] + # Step 2: create the ranked dither + ranked = Matrix(dx, dy) + for p, g in groups.items(): + n = (int)(round(p * len(g))) + if not n: + continue + v = [(mat[j][i], (i, j)) for (i, j) in g] + v.sort() + v = v[0 : n - 1] + for (k, (i, j)) in v: + ranked[j][i] = 1 + # Step 3: blit the ranked dither + for j in range(dy): + for i in range(dx): + dest.setGray(x * dx + i, y * dy + j, ranked[j][i]) + return dest + +if chapter(2): + #test26x(lena256bw, DITHER_BAYER88).save("out/lena2-6-1.png") + #test26x(grad256bw, DITHER_BAYER88).save("out/grad2-6-1.png") + test26x(lena256bw, DITHER_CLUSTER88).save("out/lena2-6-1.png") + test26x(grad256bw, DITHER_CLUSTER88).save("out/grad2-6-1.png") + +##################### +#CIE-L*a*b* transformation -- euclidian distance doesn't seem to work great +def rgb2lab(r, g, b): + Xw50 = 0.9642; + Yw50 = 1.0000; + Zw50 = 0.8249; + # RGB to sRGB + r = Gamma.CtoI(r) + g = Gamma.CtoI(g) + b = Gamma.CtoI(b) + # sRGB to XYZ(D65) + x65 = 0.4124*r + 0.3576*g + 0.1805*b + y65 = 0.2126*r + 0.7152*g + 0.0722*b + z65 = 0.0193*r + 0.1192*g + 0.9505*b + # XYZ(D65) to XYZ(D50) + x50 = 1.0282015*x65 + 0.0500707*y65 - 0.0579688*z65 + y50 = 0.0197032*x65 + 0.9871848*y65 - 0.0054285*z65 + z50 = -0.0002329*x65 + 0.0006862*y65 + 0.7573070*z65 + # XYZ(D50) to Lab(D50) + if x50 / Xw50 > 0.008856: + xx50 = math.pow(x50 / Xw50, 1. / 3.) + else: + xx50 = 7.78 * (x50 / Xw50) + 16. / 116. + if y50 / Yw50 > 0.008856: + yy50 = math.pow(y50 / Yw50, 1. / 3.) + else: + yy50 = 7.78 * (y50 / Yw50) + 16. / 116. + if z50 / Zw50 > 0.008856: + zz50 = math.pow(z50 / Zw50, 1. / 3.) + else: + zz50 = 7.78 * (z50 / Zw50) + 16. / 116. + lo = 116. * yy50 - 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    TOIlet

    + +

    The TOIlet project attempts to create a free replacement for the FIGlet utility. TOIlet stands for “The +Other Implementation’s lettersâ€, coined after FIGlet’s “Frank, Ian and +Glen’s lettersâ€.

    + +

    TOIlet is in its very early development phase. It uses the powerful +libcucul library to achieve various text-based effects. TOIlet +implements or plans to implement the following features:

    + +
      +
    • The ability to load FIGlet fonts
    • +
    • Support for Unicode input and output
    • +
    • Support for colour output
    • +
    • Support for various output formats: HTML, IRC, ANSI...
    • +
    + +

    TOIlet also aims for full FIGlet compatibility. It is currently able +to load FIGlet fonts and perform horizontal smushing.

    + +

    Live test

    + + 'Emboss', + 'emboss2' => 'Emboss 2', + 'future' => 'Future', + 'smblock' => 'Small Block', + 'smbraille' => 'Small Braille', + 'circle' => 'Circle', + 'term' => 'Terminal'); + if(!isset($fontlist[$font])) $font = 'smblock'; + $filter = $_POST['filter']; + $filterlist = array('""' => 'No filter', + 'flip' => 'Flip', + 'flop' => 'Flop', + 'rotate' => 'Rotate'); + if(!isset($filterlist[$filter])) $filter = '""'; ?> + +
    +

    + + + + +

    +
    + + +
    +

    +
    + + +

    Download

    + +

    The latest TOIlet version is toilet-0.1.tar.gz.

    + +

    Screenshot

    + +

    TOIlet sample

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z6cK)h>0D&lJ({SG)&gptHE7VIY5{z%Y!EJu9Wpdo`r>w>DIft9FMS#Ri58U>l%Nsx z4sFPzhn?T^_X`o0otAL7Gf_;Ml1Kn>>2#j^;QaR!`@o4)mzXkoy6^n3q3lo6--f^9210sxc;c&`B~GHU#+v^@6^`@|P~O z?pGi6JT&U!0i51=z9&ZMfO;X`J9;(NjPif z##GOpqkwteTzK4{ckcL<7BJ8MQcf5G;u?~Fh;=bggF2~ujU%oF#kn$0`|x=HZyL8< z&0o-DD8S`=Iol*6H^+eC8^T*tM6u`Qao25&$z%7t9qQ~o5dC2Ha?*I zrjg?*=;tZ=etGiVUyt$==(Jt8D(K-Uv6t9Mp^5yAqlp02I0%DDl|Bo|-iuO>b{!JL zdkLwMQwJ_kA-}ukD|`W@%sIBy_u`W7q26cqqvSDv1M%POW_zYEb0GV)lFvFWAGZU# zp0okcWwHbUj`(op7{CR3TF!vfuWi_xkajXt= z)D=*H5^nL&HvOQA)G$2ODg{(y0rsbYZc2Ex4)ne005wzLf*VE2|Gq%Phj!p(H{XWM zk86K-r6BnG(q91+fE54k_&$4>7dS5avuxRL={8^!Q1j)4lojXx5Lde+5~+@h?DT@> z1b0yJt08*g`|?>I5c zA-kTy7YXzj#~^!RGbzj~+Jzb=AKYS3f__cub;YCALEJRCuu`vR9A0n6vERo$ODM~v z@omUkq*;9RG8aNJ*tSWK#DbKlm~}eG + + + + + + + + + + + libcaca - ttyvaders + + + + + + + + + +

    ttyvaders

    + +

    ttyvaders is a work-in-progress libcaca project. More later.

    + +

    Screenshots

    + +

    ttyvaders sample 2

    + +

    ttyvaders sample 1

    + + + + + diff --git a/trac/README b/trac/README new file mode 100644 index 0000000..7e97d25 --- /dev/null +++ b/trac/README @@ -0,0 +1,2 @@ +This directory contains a Trac environment. +Visit http://trac.edgewall.org/ for more information. diff --git a/trac/VERSION b/trac/VERSION new file mode 100644 index 0000000..44d5dcc --- /dev/null +++ b/trac/VERSION @@ -0,0 +1 @@ +Trac Environment Version 1 diff --git a/trac/cgi-bin/trac.cgi b/trac/cgi-bin/trac.cgi new file mode 100755 index 0000000..ba51ec5 --- /dev/null +++ b/trac/cgi-bin/trac.cgi @@ -0,0 +1,44 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2003-2008 Edgewall Software +# Copyright (C) 2003-2004 Jonas Borgström +# All rights reserved. +# +# This software is licensed as described in the file COPYING, which +# you should have received as part of this distribution. The terms +# are also available at http://trac.edgewall.org/wiki/TracLicense. +# +# This software consists of voluntary contributions made by many +# individuals. For the exact contribution history, see the revision +# history and logs, available at http://trac.edgewall.org/log/. +# +# Author: Jonas Borgström + +try: + import os + import tempfile + if 'TRAC_ENV' not in os.environ and \ + 'TRAC_ENV_PARENT_DIR' not in os.environ: + os.environ['TRAC_ENV'] = '/srv/caca.zoy.org/var/lib/trac' + if 'PYTHON_EGG_CACHE' not in os.environ: + os.environ['PYTHON_EGG_CACHE'] = tempfile.gettempdir() + from trac.web import cgi_frontend + cgi_frontend.run() +except SystemExit: + raise +except Exception, e: + import sys + import traceback + + print>>sys.stderr, e + traceback.print_exc(file=sys.stderr) + + print 'Status: 500 Internal Server Error' + print 'Content-Type: text/plain' + print + print 'Oops...' + print + print 'Trac detected an internal error:', e + print + traceback.print_exc(file=sys.stdout) diff --git a/trac/cgi-bin/trac.fcgi b/trac/cgi-bin/trac.fcgi new file mode 100644 index 0000000..dcc0449 --- /dev/null +++ b/trac/cgi-bin/trac.fcgi @@ -0,0 +1,42 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2003-2008 Edgewall Software +# Copyright (C) 2003-2004 Jonas Borgström +# All rights reserved. +# +# This software is licensed as described in the file COPYING, which +# you should have received as part of this distribution. The terms +# are also available at http://trac.edgewall.org/wiki/TracLicense. +# +# This software consists of voluntary contributions made by many +# individuals. For the exact contribution history, see the revision +# history and logs, available at http://trac.edgewall.org/log/. +# +# Author: Jonas Borgström + +try: + import os + import tempfile + if 'TRAC_ENV' not in os.environ and \ + 'TRAC_ENV_PARENT_DIR' not in os.environ: + os.environ['TRAC_ENV'] = '/srv/caca.zoy.org/var/lib/trac' + if 'PYTHON_EGG_CACHE' not in os.environ: + os.environ['PYTHON_EGG_CACHE'] = tempfile.gettempdir() + from trac.web import fcgi_frontend + fcgi_frontend.run() +except SystemExit: + raise +except Exception, e: + print 'Content-Type: text/plain\r\n\r\n', + print 'Oops...' + print + print 'Trac detected an internal error:' + print + print e + print + import traceback + import StringIO + tb = StringIO.StringIO() + traceback.print_exc(file=tb) + print tb.getvalue() diff --git a/trac/cgi-bin/trac.wsgi b/trac/cgi-bin/trac.wsgi new file mode 100644 index 0000000..ef071ef --- /dev/null +++ b/trac/cgi-bin/trac.wsgi @@ -0,0 +1,27 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2008 Edgewall Software +# Copyright (C) 2008 Noah Kantrowitz +# All rights reserved. +# +# This software is licensed as described in the file COPYING, which +# you should have received as part of this distribution. The terms +# are also available at http://trac.edgewall.org/wiki/TracLicense. +# +# This software consists of voluntary contributions made by many +# individuals. For the exact contribution history, see the revision +# history and logs, available at http://trac.edgewall.org/log/. +# +# Author: Noah Kantrowitz +import os +import tempfile + +from trac.web.main import dispatch_request +import pkg_resources + +def application(environ, start_request): + environ['trac.env_path'] = '/srv/caca.zoy.org/var/lib/trac' + if 'PYTHON_EGG_CACHE' not in os.environ: + pkg_resources.set_extraction_path(tempfile.gettempdir()) + return dispatch_request(environ, start_request) diff --git a/trac/conf/trac.ini b/trac/conf/trac.ini new file mode 100644 index 0000000..9df5337 --- /dev/null +++ b/trac/conf/trac.ini @@ -0,0 +1,125 @@ +# -*- coding: utf-8 -*- + +[attachment] +max_size = 26214400 +render_unsafe_content = false + +[browser] +downloadable_paths = /trunk, /branches/*, /tags/* +hide_properties = svk:merge +render_unsafe_content = false + +[changeset] +max_diff_bytes = 10000000 +max_diff_files = 0 +wiki_format_messages = true + +[header_logo] +alt = +height = -1 +link = http://caca.zoy.org/ +src = /browser/www/img/trac_banner.png?format=raw +width = -1 + +[logging] +log_file = trac.log +log_level = DEBUG +log_type = none + +[mimeviewer] +enscript_modes = text/x-dylan:dylan:4 +enscript_path = enscript +max_preview_size = 2621440 +mime_map = text/x-dylan:dylan,text/x-idl:ice,text/x-ada:ads:adb +php_path = php +silvercity_modes = +tab_width = 8 + +[notification] +always_notify_owner = false +always_notify_reporter = false +always_notify_updater = false +mime_encoding = qp +smtp_always_bcc = +smtp_always_cc = libcaca-trac@lists.zoy.org +smtp_default_domain = +smtp_enabled = true +smtp_from = sam+trac@zoy.org +smtp_from_name = Caca Labs Trac +smtp_password = +smtp_port = 25 +smtp_server = localhost +smtp_subject_prefix = +smtp_user = +use_public_cc = true +use_short_addr = false +use_tls = false + +[project] +descr = Caca Labs +footer = Visit the Caca Labs at
    http://caca.zoy.org/ +icon = /favicon.ico +name = caca labs +url = http://caca.zoy.org/ + +[search] +min_query_length = 3 + +[ticket] +default_component = libcaca +default_milestone = 1.0 +default_priority = major +default_type = defect +default_version = SVN +restrict_owner = false + +[ticket-workflow] +accept = new -> assigned +accept.operations = set_owner_to_self +accept.permissions = TICKET_MODIFY +leave = * -> * +leave.default = 1 +leave.operations = leave_status +reassign = new,assigned,reopened -> new +reassign.operations = set_owner +reassign.permissions = TICKET_MODIFY +reopen = closed -> reopened +reopen.operations = del_resolution +reopen.permissions = TICKET_CREATE +resolve = new,assigned,reopened -> closed +resolve.operations = set_resolution +resolve.permissions = TICKET_MODIFY + +[timeline] +changeset_long_messages = false +changeset_show_files = 0 +default_daysback = 30 +ticket_show_details = false + +[trac] +authz_file = +authz_module_name = +check_auth_ip = true +database = sqlite:db/trac.db +default_charset = utf-8 +default_handler = WikiModule +htdocs_location = +ignore_auth_case = false +mainnav = wiki,timeline,tickets,newticket,browser,search +metanav = login,logout,settings,help,about +permission_store = DefaultPermissionStore +repository_dir = /srv/caca.zoy.org/var/lib/svn +repository_type = svn +timeout = 20 +show_email_addresses = true + +[mainnav] +wiki.label = Home +tickets.label = Bug reports +tickets.href = /report/1 +newticket.label = Report a bug + +[wiki] +ignore_missing_pages = false +split_page_names = false + diff --git a/trac/conf/trac.ini.sample b/trac/conf/trac.ini.sample new file mode 100644 index 0000000..7c3c806 --- /dev/null +++ b/trac/conf/trac.ini.sample @@ -0,0 +1,156 @@ +# -*- coding: utf-8 -*- + +[attachment] +max_size = 262144 +render_unsafe_content = false + +[browser] +color_scale = True +downloadable_paths = /trunk, /branches/*, /tags/* +hide_properties = svk:merge +intermediate_color = +intermediate_point = +newest_color = (255, 136, 136) +oldest_color = (136, 136, 255) +oneliner_properties = trac:summary +render_unsafe_content = false +wiki_properties = trac:description + +[changeset] +max_diff_bytes = 10000000 +max_diff_files = 0 +wiki_format_messages = true + +[header_logo] +alt = (please configure the [header_logo] section in trac.ini) +height = -1 +link = +src = site/your_project_logo.png +width = -1 + +[inherit] +plugins_dir = +templates_dir = + +[logging] +log_file = trac.log +# log_format = +log_level = DEBUG +log_type = none + +[milestone] +stats_provider = DefaultTicketGroupStatsProvider + +[mimeviewer] +enscript_modes = text/x-dylan:dylan:4 +enscript_path = enscript +max_preview_size = 262144 +mime_map = text/x-dylan:dylan,text/x-idl:ice,text/x-ada:ads:adb +php_path = php +silvercity_modes = +tab_width = 8 + +[notification] +admit_domains = +always_notify_owner = false +always_notify_reporter = false +always_notify_updater = true +ignore_domains = +mime_encoding = base64 +smtp_always_bcc = +smtp_always_cc = +smtp_default_domain = +smtp_enabled = false +smtp_from = trac@localhost +smtp_from_name = +smtp_password = +smtp_port = 25 +smtp_replyto = trac@localhost +smtp_server = localhost +smtp_subject_prefix = __default__ +smtp_user = +ticket_subject_template = $prefix #$ticket.id: $summary +use_public_cc = false +use_short_addr = false +use_tls = false + +[project] +admin = +descr = My example project +footer = Visit the Trac open source project at
    http://trac.edgewall.org/ +icon = common/trac.ico +name = My Project +url = + +[query] +default_anonymous_query = status!=closed&cc~=$USER +default_query = status!=closed&owner=$USER +items_per_page = 100 + +[report] +items_per_page = 100 +items_per_page_rss = 0 + +[revisionlog] +default_log_limit = 100 + +[roadmap] +stats_provider = DefaultTicketGroupStatsProvider + +[search] +min_query_length = 3 + +[svn] +branches = trunk,branches/* +tags = tags/* + +[ticket] +default_component = +default_milestone = +default_priority = major +default_resolution = fixed +default_type = defect +default_version = +max_description_size = 262144 +preserve_newlines = default +restrict_owner = false +workflow = ConfigurableTicketWorkflow + +[timeline] +abbreviated_messages = true +changeset_collapse_events = false +changeset_long_messages = false +changeset_show_files = 0 +default_daysback = 30 +max_daysback = 90 +newticket_formatter = oneliner +ticket_show_details = false + +[trac] +authz_file = +authz_module_name = +auto_reload = False +base_url = +check_auth_ip = true +database = sqlite:db/trac.db +default_charset = iso-8859-15 +default_handler = WikiModule +htdocs_location = +ignore_auth_case = false +mainnav = wiki,timeline,roadmap,browser,tickets,newticket,search +metanav = login,logout,prefs,help,about +permission_policies = DefaultPermissionPolicy, LegacyAttachmentPolicy +permission_store = DefaultPermissionStore +repository_dir = +repository_type = svn +request_filters = None +show_email_addresses = false +templates_dir = /usr/share/trac/templates +timeout = 20 +use_base_url_for_redirect = False + +[wiki] +ignore_missing_pages = false +render_unsafe_content = false +split_page_names = false + diff --git a/trac/htdocs/common/README b/trac/htdocs/common/README new file mode 100644 index 0000000..96c5745 --- /dev/null +++ b/trac/htdocs/common/README @@ -0,0 +1,5 @@ +This directory contains files used by Trac's default clearsilver templates. + +Local modifications to these files might be lost during the installation of +a new Trac version. This can be avoided by making a copy of this entire +directory before beginning modifications. diff --git a/trac/htdocs/common/asc.png b/trac/htdocs/common/asc.png new file mode 100644 index 0000000000000000000000000000000000000000..486b7315902f8bd4d7c953f4b6e65e56cb171c50 GIT binary patch literal 222 zcmeAS@N?(olHy`uVBq!ia0vp^96-#@!VDyjmkZ_sDdu7)&kzm{j@u9Y9{{=h0X`wF zuV25udiCm=GiUbi-@kI@%BvF}%mYfXmIV0)GyDevnG0$lvp5SpB8wRqxITa|qthCb z89+hB64!{5;QX|b^2DN42H(WwqWs*{l*~j0x6Go{^8BLgVg+M8gZG(+KwS(Po-U3d z6?2jk5`q#E5(Elagw)RQ@bu{EG4Y(y(_`b&($n+f@p52bFt%pi>BpeA4ycF0)78&q Iol`;+0Q%iTa{vGU literal 0 HcmV?d00001 diff --git a/trac/htdocs/common/attachment.png b/trac/htdocs/common/attachment.png new file mode 100644 index 0000000000000000000000000000000000000000..a4d212439158aa08cbf451a557a5f987c8f9f32a GIT binary patch literal 280 zcmeAS@N?(olHy`uVBq!ia0vp^JRr=$3?vg*uel1OSkfJR9T^zbpD<_bdI{u9mbgZg z1m~xflqVLYGB~E>C#5QQ<|d}62BjvZR2H60wE-$J2=EDU_3`lu3JQvdh=`4i&B@6r zD=X{j>Y6ZN!tB|zckbMI^5n^j7cbtrb?e!)XYb#?2Wt9c9la7r36uo+1^-6{3=foJ zC4q85o-U3d6}NoOy%us1~z``8;h%n3^2IkhduF<%B(nCd+l_ z8BIL1SLy72C-HC1@v1R1mYqp&3=!6>U2O2HTqI!GSKc-TW`-;FZ2S>7J>~#SXYh3O Kb6Mw<&;$Szcyo3D literal 0 HcmV?d00001 diff --git a/trac/htdocs/common/changeset.png b/trac/htdocs/common/changeset.png new file mode 100644 index 0000000000000000000000000000000000000000..31c0356a3eaf6530f6b0a619de10c0435f063fff GIT binary patch literal 294 zcmeAS@N?(olHy`uVBq!ia0vp^JRr=%3?!FCJ6-`&%*9TgAsieWw;%dH0CIH$d_r9R z|NsB}`}fbEKfiwc`u_dCQJ%$lJ@YOqh8GisJ_kT-2&KSz^iDzCfqFQ+$H@l{dS%G?wQ)VhpiNUHw{cfmfcHzTAeo1w0HN XdU@A;Uma-!G?T&8)z4*}Q$iB}7AA96 literal 0 HcmV?d00001 diff --git a/trac/htdocs/common/closedticket.png b/trac/htdocs/common/closedticket.png new file mode 100644 index 0000000000000000000000000000000000000000..43f7a844739b0875c3ea281d8c4720585d6ead4f GIT binary patch literal 297 zcmeAS@N?(olHy`uVBq!ia0vp^JRr=%3?!FCJ6-`&%*9TgAsieWw;%dH0CFV)d_r6q z(ilEGtvtQJ`Tmiv|NsAMPBma?VEFm9RrsysT%a0`k|4iep!k0VAl7XOQ3Z-|7I;J! zGca%qfiUBxyLEqnf{G=s5hcO-X(i=}MX3zFiOEIzxv43ci3)C+MXBZaMcKs)CVB?1 z)YMCXip4;Ror_WvOEUBG6hbm{QyB~m^$pGS4NNZIvWx<%aP@R?45^rtT)@c8mTZt< zpwHeU#8#}jRH-!4g5QPJc*bhBmDAM>W=vO`(r(B+m1Tum!;?cdZuBh($ literal 0 HcmV?d00001 diff --git a/trac/htdocs/common/collapsed.png b/trac/htdocs/common/collapsed.png new file mode 100644 index 0000000000000000000000000000000000000000..4a2ac198ca926247cafe5781160345335e6ecb9b GIT binary patch literal 218 zcmeAS@N?(olHy`uVBq!ia0vp^+#t-s3?%0jwTl2LmUKs7M+Sy#H?H_c7y{LCMyhNC3NFBr%L0fu|cdn18jCY~;iAr-fh64KM!8QIF*+{`Ry zH#W9T

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