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src/Source/FreeImage/NNQuantizer.cpp  view on Meta::CPAN

			// OLD CODE: network[i][j] >>= netbiasshift; 
			// Fix based on bug report by Juergen Weigert jw@suse.de
			temp = (network[i][j] + (1 << (netbiasshift - 1))) >> netbiasshift;
			if (temp > 255) temp = 255;
			network[i][j] = temp;
		}
		network[i][3] = i;			// record colour no 
	}
}

//////////////////////////////////////////////////////////////////////////////////
// Insertion sort of network and building of netindex[0..255] (to do after unbias)
// -------------------------------------------------------------------------------

void NNQuantizer::inxbuild() {
	int i,j,smallpos,smallval;
	int *p,*q;
	int previouscol,startpos;

	previouscol = 0;
	startpos = 0;
	for (i = 0; i < netsize; i++) {
		p = network[i];
		smallpos = i;
		smallval = p[FI_RGBA_GREEN];			// index on g
		// find smallest in i..netsize-1
		for (j = i+1; j < netsize; j++) {
			q = network[j];
			if (q[FI_RGBA_GREEN] < smallval) {	// index on g
				smallpos = j;
				smallval = q[FI_RGBA_GREEN];	// index on g
			}
		}
		q = network[smallpos];
		// swap p (i) and q (smallpos) entries
		if (i != smallpos) {
			j = q[FI_RGBA_BLUE];  q[FI_RGBA_BLUE]  = p[FI_RGBA_BLUE];  p[FI_RGBA_BLUE]  = j;
			j = q[FI_RGBA_GREEN]; q[FI_RGBA_GREEN] = p[FI_RGBA_GREEN]; p[FI_RGBA_GREEN] = j;
			j = q[FI_RGBA_RED];   q[FI_RGBA_RED]   = p[FI_RGBA_RED];   p[FI_RGBA_RED]   = j;
			j = q[3];   q[3] = p[3];   p[3] = j;
		}
		// smallval entry is now in position i
		if (smallval != previouscol) {
			netindex[previouscol] = (startpos+i)>>1;
			for (j = previouscol+1; j < smallval; j++)
				netindex[j] = i;
			previouscol = smallval;
			startpos = i;
		}
	}
	netindex[previouscol] = (startpos+maxnetpos)>>1;
	for (j = previouscol+1; j < 256; j++)
		netindex[j] = maxnetpos; // really 256
}

///////////////////////////////////////////////////////////////////////////////
// Search for BGR values 0..255 (after net is unbiased) and return colour index
// ----------------------------------------------------------------------------

int NNQuantizer::inxsearch(int b, int g, int r) {
	int i, j, dist, a, bestd;
	int *p;
	int best;

	bestd = 1000;		// biggest possible dist is 256*3
	best = -1;
	i = netindex[g];	// index on g
	j = i-1;			// start at netindex[g] and work outwards

	while ((i < netsize) || (j >= 0)) {
		if (i < netsize) {
			p = network[i];
			dist = p[FI_RGBA_GREEN] - g;				// inx key
			if (dist >= bestd)
				i = netsize;	// stop iter
			else {
				i++;
				if (dist < 0)
					dist = -dist;
				a = p[FI_RGBA_BLUE] - b;
				if (a < 0)
					a = -a;
				dist += a;
				if (dist < bestd) {
					a = p[FI_RGBA_RED] - r;
					if (a<0)
						a = -a;
					dist += a;
					if (dist < bestd) {
						bestd = dist;
						best = p[3];
					}
				}
			}
		}
		if (j >= 0) {
			p = network[j];
			dist = g - p[FI_RGBA_GREEN];			// inx key - reverse dif
			if (dist >= bestd)
				j = -1;	// stop iter
			else {
				j--;
				if (dist < 0)
					dist = -dist;
				a = p[FI_RGBA_BLUE] - b;
				if (a<0)
					a = -a;
				dist += a;
				if (dist < bestd) {
					a = p[FI_RGBA_RED] - r;
					if (a<0)
						a = -a;
					dist += a;
					if (dist < bestd) {
						bestd = dist;
						best = p[3];
					}
				}
			}
		}
	}
	return best;
}

///////////////////////////////
// Search for biased BGR values
// ----------------------------

int NNQuantizer::contest(int b, int g, int r) {
	// finds closest neuron (min dist) and updates freq
	// finds best neuron (min dist-bias) and returns position
	// for frequently chosen neurons, freq[i] is high and bias[i] is negative
	// bias[i] = gamma*((1/netsize)-freq[i])

	int i,dist,a,biasdist,betafreq;
	int bestpos,bestbiaspos,bestd,bestbiasd;
	int *p,*f, *n;

	bestd = ~(((int) 1)<<31);
	bestbiasd = bestd;
	bestpos = -1;
	bestbiaspos = bestpos;
	p = bias;
	f = freq;

	for (i = 0; i < netsize; i++) {
		n = network[i];
		dist = n[FI_RGBA_BLUE] - b;
		if (dist < 0)
			dist = -dist;
		a = n[FI_RGBA_GREEN] - g;
		if (a < 0)
			a = -a;
		dist += a;
		a = n[FI_RGBA_RED] - r;
		if (a < 0)
			a = -a;
		dist += a;
		if (dist < bestd) {
			bestd = dist;
			bestpos = i;
		}
		biasdist = dist - ((*p)>>(intbiasshift-netbiasshift));
		if (biasdist < bestbiasd) {
			bestbiasd = biasdist;
			bestbiaspos = i;
		}
		betafreq = (*f >> betashift);
		*f++ -= betafreq;
		*p++ += (betafreq << gammashift);
	}
	freq[bestpos] += beta;
	bias[bestpos] -= betagamma;
	return bestbiaspos;
}

///////////////////////////////////////////////////////
// Move neuron i towards biased (b,g,r) by factor alpha
// ---------------------------------------------------- 

void NNQuantizer::altersingle(int alpha, int i, int b, int g, int r) {
	int *n;

	n = network[i];				// alter hit neuron
	n[FI_RGBA_BLUE]	 -= (alpha * (n[FI_RGBA_BLUE]  - b)) / initalpha;
	n[FI_RGBA_GREEN] -= (alpha * (n[FI_RGBA_GREEN] - g)) / initalpha;
	n[FI_RGBA_RED]   -= (alpha * (n[FI_RGBA_RED]   - r)) / initalpha;
}

////////////////////////////////////////////////////////////////////////////////////
// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
// ---------------------------------------------------------------------------------

void NNQuantizer::alterneigh(int rad, int i, int b, int g, int r) {
	int j, k, lo, hi, a;
	int *p, *q;

	lo = i - rad;   if (lo < -1) lo = -1;
	hi = i + rad;   if (hi > netsize) hi = netsize;

	j = i+1;
	k = i-1;
	q = radpower;
	while ((j < hi) || (k > lo)) {
		a = (*(++q));
		if (j < hi) {
			p = network[j];
			p[FI_RGBA_BLUE]  -= (a * (p[FI_RGBA_BLUE] - b)) / alpharadbias;
			p[FI_RGBA_GREEN] -= (a * (p[FI_RGBA_GREEN] - g)) / alpharadbias;
			p[FI_RGBA_RED]   -= (a * (p[FI_RGBA_RED] - r)) / alpharadbias;
			j++;
		}
		if (k > lo) {
			p = network[k];
			p[FI_RGBA_BLUE]  -= (a * (p[FI_RGBA_BLUE] - b)) / alpharadbias;
			p[FI_RGBA_GREEN] -= (a * (p[FI_RGBA_GREEN] - g)) / alpharadbias;
			p[FI_RGBA_RED]   -= (a * (p[FI_RGBA_RED] - r)) / alpharadbias;
			k--;
		}
	}
}

/////////////////////
// Main Learning Loop
// ------------------

/**
 Get a pixel sample at position pos. Handle 4-byte boundary alignment.
 @param pos pixel position in a WxHx3 pixel buffer
 @param b blue pixel component
 @param g green pixel component
 @param r red pixel component
*/
void NNQuantizer::getSample(long pos, int *b, int *g, int *r) {