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Diffstat (limited to 'plugins/freeimage/Source/Quantizers.h')
| -rw-r--r-- | plugins/freeimage/Source/Quantizers.h | 225 |
1 files changed, 0 insertions, 225 deletions
diff --git a/plugins/freeimage/Source/Quantizers.h b/plugins/freeimage/Source/Quantizers.h deleted file mode 100644 index bc84f77724..0000000000 --- a/plugins/freeimage/Source/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);
-
-};
-
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