diff options
Diffstat (limited to 'plugins/FreeImage/src/Quantizers.h')
| -rw-r--r-- | plugins/FreeImage/src/Quantizers.h | 225 |
1 files changed, 0 insertions, 225 deletions
diff --git a/plugins/FreeImage/src/Quantizers.h b/plugins/FreeImage/src/Quantizers.h deleted file mode 100644 index 2a671fad1c..0000000000 --- a/plugins/FreeImage/src/Quantizers.h +++ /dev/null @@ -1,225 +0,0 @@ -// ============================================================= -// Quantizer objects and functions -// -// Design and implementation by: -// - Hervé Drolon <drolon@infonie.fr> -// -// This file is part of FreeImage 3 -// -// COVERED CODE IS PROVIDED UNDER THIS LICENSE ON AN "AS IS" BASIS, WITHOUT WARRANTY -// OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, WITHOUT LIMITATION, WARRANTIES -// THAT THE COVERED CODE IS FREE OF DEFECTS, MERCHANTABLE, FIT FOR A PARTICULAR PURPOSE -// OR NON-INFRINGING. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE COVERED -// CODE IS WITH YOU. SHOULD ANY COVERED CODE PROVE DEFECTIVE IN ANY RESPECT, YOU (NOT -// THE INITIAL DEVELOPER OR ANY OTHER CONTRIBUTOR) ASSUME THE COST OF ANY NECESSARY -// SERVICING, REPAIR OR CORRECTION. THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL -// PART OF THIS LICENSE. NO USE OF ANY COVERED CODE IS AUTHORIZED HEREUNDER EXCEPT UNDER -// THIS DISCLAIMER. -// -// Use at your own risk! -// ============================================================= - -// -//////////////////////////////////////////////////////////////// - -#include "FreeImage.h" - -//////////////////////////////////////////////////////////////// - -/** - Xiaolin Wu color quantization algorithm -*/ -class WuQuantizer -{ -public: - -typedef struct tagBox { - int r0; // min value, exclusive - int r1; // max value, inclusive - int g0; - int g1; - int b0; - int b1; - int vol; -} Box; - -protected: - float *gm2; - LONG *wt, *mr, *mg, *mb; - WORD *Qadd; - - // DIB data - unsigned width, height; - unsigned pitch; - FIBITMAP *m_dib; - -protected: - void Hist3D(LONG *vwt, LONG *vmr, LONG *vmg, LONG *vmb, float *m2, int ReserveSize, RGBQUAD *ReservePalette); - void M3D(LONG *vwt, LONG *vmr, LONG *vmg, LONG *vmb, float *m2); - LONG Vol(Box *cube, LONG *mmt); - LONG Bottom(Box *cube, BYTE dir, LONG *mmt); - LONG Top(Box *cube, BYTE dir, int pos, LONG *mmt); - float Var(Box *cube); - float Maximize(Box *cube, BYTE dir, int first, int last , int *cut, - LONG whole_r, LONG whole_g, LONG whole_b, LONG whole_w); - bool Cut(Box *set1, Box *set2); - void Mark(Box *cube, int label, BYTE *tag); - -public: - // Constructor - Input parameter: DIB 24-bit to be quantized - WuQuantizer(FIBITMAP *dib); - // Destructor - ~WuQuantizer(); - // Quantizer - Return value: quantized 8-bit (color palette) DIB - FIBITMAP* Quantize(int PaletteSize, int ReserveSize, RGBQUAD *ReservePalette); -}; - - -/** - NEUQUANT Neural-Net quantization algorithm by Anthony Dekker -*/ - -// ---------------------------------------------------------------- -// Constant definitions -// ---------------------------------------------------------------- - -/** number of colours used: - for 256 colours, fixed arrays need 8kb, plus space for the image -*/ -//static const int netsize = 256; - -/**@name network definitions */ -//@{ -//static const int maxnetpos = (netsize - 1); -/// bias for colour values -static const int netbiasshift = 4; -/// no. of learning cycles -static const int ncycles = 100; -//@} - -/**@name defs for freq and bias */ -//@{ -/// bias for fractions -static const int intbiasshift = 16; -static const int intbias = (((int)1) << intbiasshift); -/// gamma = 1024 -static const int gammashift = 10; -// static const int gamma = (((int)1) << gammashift); -/// beta = 1 / 1024 -static const int betashift = 10; -static const int beta = (intbias >> betashift); -static const int betagamma = (intbias << (gammashift-betashift)); -//@} - -/**@name defs for decreasing radius factor */ -//@{ -/// for 256 cols, radius starts -//static const int initrad = (netsize >> 3); -/// at 32.0 biased by 6 bits -static const int radiusbiasshift = 6; -static const int radiusbias = (((int)1) << radiusbiasshift); -/// and decreases by a -//static const int initradius = (initrad * radiusbias); -// factor of 1/30 each cycle -static const int radiusdec = 30; -//@} - -/**@name defs for decreasing alpha factor */ -//@{ -/// alpha starts at 1.0 -static const int alphabiasshift = 10; -static const int initalpha = (((int)1) << alphabiasshift); -//@} - -/**@name radbias and alpharadbias used for radpower calculation */ -//@{ -static const int radbiasshift = 8; -static const int radbias = (((int)1) << radbiasshift); -static const int alpharadbshift = (alphabiasshift+radbiasshift); -static const int alpharadbias = (((int)1) << alpharadbshift); -//@} - -class NNQuantizer -{ -protected: - /**@name image parameters */ - //@{ - /// pointer to input dib - FIBITMAP *dib_ptr; - /// image width - int img_width; - /// image height - int img_height; - /// image line length - int img_line; - //@} - - /**@name network parameters */ - //@{ - - int netsize, maxnetpos, initrad, initradius; - - /// BGRc - typedef int pixel[4]; - /// the network itself - pixel *network; - - /// for network lookup - really 256 - int netindex[256]; - - /// bias array for learning - int *bias; - /// freq array for learning - int *freq; - /// radpower for precomputation - int *radpower; - //@} - -protected: - /// Initialise network in range (0,0,0) to (255,255,255) and set parameters - void initnet(); - - /// Unbias network to give byte values 0..255 and record position i to prepare for sort - void unbiasnet(); - - /// Insertion sort of network and building of netindex[0..255] (to do after unbias) - void inxbuild(); - - /// Search for BGR values 0..255 (after net is unbiased) and return colour index - int inxsearch(int b, int g, int r); - - /// Search for biased BGR values - int contest(int b, int g, int r); - - /// Move neuron i towards biased (b,g,r) by factor alpha - void altersingle(int alpha, int i, int b, int g, int r); - - /// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|] - void alterneigh(int rad, int i, int b, int g, int r); - - /** Main Learning Loop - @param sampling_factor sampling factor in [1..30] - */ - void learn(int sampling_factor); - - /// Get a pixel sample at position pos. Handle 4-byte boundary alignment. - void getSample(long pos, int *b, int *g, int *r); - - -public: - /// Constructor - NNQuantizer(int PaletteSize); - - /// Destructor - ~NNQuantizer(); - - /** Quantizer - @param dib input 24-bit dib to be quantized - @param sampling a sampling factor in range 1..30. - 1 => slower (but better), 30 => faster. Default value is 1 - @return returns the quantized 8-bit (color palette) DIB - */ - FIBITMAP* Quantize(FIBITMAP *dib, int ReserveSize, RGBQUAD *ReservePalette, int sampling = 1); - -}; - |