1 files changed, 225 insertions, 225 deletions
diff --git a/plugins/FreeImage/Source/Quantizers.h b/plugins/FreeImage/Source/Quantizers.h
index bc84f77724..2a671fad1c 100644
--- a/plugins/FreeImage/Source/Quantizers.h
+++ b/plugins/FreeImage/Source/Quantizers.h
@@ -1,225 +1,225 @@
-// =============================================================
-// 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);
-
-};
-
+// =============================================================
+// 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);
+
+};
+