// ============================================================= // Quantizer objects and functions // // Design and implementation by: // - Hervé Drolon // // 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); };