TF2-Source-Code/tf2_src/mathlib/quantize.cpp at master · sr2echa/TF2-Source-Code · GitHub

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// Purpose:

// $NoKeywords: $

//=============================================================================//

#ifndef STDIO_H

#include <stdio.h>

#endif

#ifndef STRING_H

#include <string.h>

#endif

#ifndef QUANTIZE_H

#include <quantize.h>

#endif

#include <stdlib.h>

#include <minmax.h>

#include <math.h>

static int current_ndims;

static struct QuantizedValue *current_root;

static int current_ssize;

static uint8 *current_weights;

double SquaredError;

#define SPLIT_THEN_SORT 1

#define SQ(x) ((x)*(x))

static struct QuantizedValue *AllocQValue(void)

{

struct QuantizedValue *ret=new QuantizedValue;

ret->Samples=0;

ret->Children[0]=ret->Children[1]=0;

ret->NSamples=0;

ret->ErrorMeasure=new double[current_ndims];

ret->Mean=new uint8[current_ndims];

ret->Mins=new uint8[current_ndims];

ret->Maxs=new uint8[current_ndims];

ret->Sums=new int [current_ndims];

memset(ret->Sums,0,sizeof(int)*current_ndims);

ret->NQuant=0;

ret->sortdim=-1;

return ret;

}

void FreeQuantization(struct QuantizedValue *t)

{

if (t)

{

delete[] t->ErrorMeasure;

delete[] t->Mean;

delete[] t->Mins;

delete[] t->Maxs;

FreeQuantization(t->Children[0]);

FreeQuantization(t->Children[1]);

delete[] t->Sums;

delete[] t;

}

static int QNumSort(void const *a, void const *b)

{

int32 as=((struct Sample *) a)->QNum;

int32 bs=((struct Sample *) b)->QNum;

if (as==bs) return 0;

return (as>bs)?1:-1;

}

#if SPLIT_THEN_SORT

#else

static int current_sort_dim;

static int samplesort(void const *a, void const *b)

{

uint8 as=((struct Sample *) a)->Value[current_sort_dim];

uint8 bs=((struct Sample *) b)->Value[current_sort_dim];

if (as==bs) return 0;

return (as>bs)?1:-1;

}

#endif

static int sortlong(void const *a, void const *b)

{

// treat the entire vector of values as a long integer for duplicate removal.

return memcmp(((struct Sample *) a)->Value,

((struct Sample *) b)->Value,current_ndims);

}

#define NEXTSAMPLE(s) ( (struct Sample *) (((uint8 *) s)+current_ssize))

#define SAMPLE(s,i) NthSample(s,i,current_ndims)

static void SetNDims(int n)

{

current_ssize=sizeof(struct Sample)+(n-1);

current_ndims=n;

}

int CompressSamples(struct Sample *s, int nsamples, int ndims)

{

SetNDims(ndims);

qsort(s,nsamples,current_ssize,sortlong);

// now, they are all sorted by treating all dimensions as a large number.

// we may now remove duplicates.

struct Sample *src=s;

struct Sample *dst=s;

struct Sample *lastdst=dst;

dst=NEXTSAMPLE(dst); // copy first sample to get the ball rolling

src=NEXTSAMPLE(src);

int noutput=1;

while(--nsamples) // while some remain

{

if (memcmp(src->Value,lastdst->Value,current_ndims))

{

// yikes, a difference has been found!

memcpy(dst,src,current_ssize);

lastdst=dst;

dst=NEXTSAMPLE(dst);

noutput++;

}

else

lastdst->Count++;

src=NEXTSAMPLE(src);

}

return noutput;

}

void PrintSamples(struct Sample const *s, int nsamples, int ndims)

{

SetNDims(ndims);

int cnt=0;

while(nsamples--)

{

printf("sample #%d, count=%d, values=\n { ",cnt++,s->Count);

for(int d=0;d<ndims;d++)

printf("%02x,",s->Value[d]);

printf("}\n");

s=NEXTSAMPLE(s);

}

void PrintQTree(struct QuantizedValue const *p,int idlevel)

{

int i;

if (p)

{

for(i=0;i<idlevel;i++)

printf(" ");

printf("node=%p NSamples=%d value=%d Mean={",p,p->NSamples,p->value);

for(i=0;i<current_ndims;i++)

printf("%x,",p->Mean[i]);

printf("}\n");

for(i=0;i<idlevel;i++)

printf(" ");

printf("Errors={");

for(i=0;i<current_ndims;i++)

printf("%f,",p->ErrorMeasure[i]);

printf("}\n");

for(i=0;i<idlevel;i++)

printf(" ");

printf("Mins={");

for(i=0;i<current_ndims;i++)

printf("%d,",p->Mins[i]);

printf("} Maxs={");

for(i=0;i<current_ndims;i++)

printf("%d,",p->Maxs[i]);

printf("}\n");

PrintQTree(p->Children[0],idlevel+2);

PrintQTree(p->Children[1],idlevel+2);

}

static void UpdateStats(struct QuantizedValue *v)

{

// first, find mean

int32 Means[MAXDIMS];

double Errors[MAXDIMS];

double WorstError[MAXDIMS];

int i,j;

memset(Means,0,sizeof(Means));

int N=0;

for(i=0;i<v->NSamples;i++)

{

struct Sample *s=SAMPLE(v->Samples,i);

N+=s->Count;

for(j=0;j<current_ndims;j++)

{

uint8 val=s->Value[j];

Means[j]+=val*s->Count;

}

for(j=0;j<current_ndims;j++)

{

if (N) v->Mean[j]=(uint8) (Means[j]/N);

Errors[j]=WorstError[j]=0.;

}

for(i=0;i<v->NSamples;i++)

{

struct Sample *s=SAMPLE(v->Samples,i);

double c=s->Count;

for(j=0;j<current_ndims;j++)

{

double diff=SQ(s->Value[j]-v->Mean[j]);

Errors[j]+=c*diff; // charles uses abs not sq()

if (diff>WorstError[j])

WorstError[j]=diff;

}

v->TotalError=0.;

double ErrorScale=1.; // /sqrt((double) (N));

for(j=0;j<current_ndims;j++)

{

v->ErrorMeasure[j]=(ErrorScale*Errors[j]*current_weights[j]);

v->TotalError+=v->ErrorMeasure[j];

#if SPLIT_THEN_SORT

v->ErrorMeasure[j]*=WorstError[j];

#endif

}

v->TotSamples=N;

}

static int ErrorDim;

static double ErrorVal;

static struct QuantizedValue *ErrorNode;

static void UpdateWorst(struct QuantizedValue *q)

{

if (q->Children[0])

{

// not a leaf node

UpdateWorst(q->Children[0]);

UpdateWorst(q->Children[1]);

}

else

{

if (q->TotalError>ErrorVal)

{

ErrorVal=q->TotalError;

ErrorNode=q;

ErrorDim=0;

for(int d=0;d<current_ndims;d++)

if (q->ErrorMeasure[d]>q->ErrorMeasure[ErrorDim])

ErrorDim=d;

}

static int FindWorst(void)

{

ErrorVal=-1.;

UpdateWorst(current_root);

return (ErrorVal>0);

}

static void SubdivideNode(struct QuantizedValue *n, int whichdim)

{

int NAdded=0;

int i;

#if SPLIT_THEN_SORT

// we will try the "split then sort" method. This works by finding the

// means for all samples above and below the mean along the given axis.

// samples are then split into two groups, with the selection based upon

// which of the n-dimensional means the sample is closest to.

double LocalMean[MAXDIMS][2];

int totsamps[2];

for(i=0;i<current_ndims;i++)

LocalMean[i][0]=LocalMean[i][1]=0.;

totsamps[0]=totsamps[1]=0;

uint8 minv=255;

uint8 maxv=0;

struct Sample *minS=0,*maxS=0;

for(i=0;i<n->NSamples;i++)

{

uint8 v;

int whichside=1;

struct Sample *sl;

sl=SAMPLE(n->Samples,i);

v=sl->Value[whichdim];

if (v<minv) { minv=v; minS=sl; }

if (v>maxv) { maxv=v; maxS=sl; }

if (v<n->Mean[whichdim])

whichside=0;

totsamps[whichside]+=sl->Count;

for(int d=0;d<current_ndims;d++)

LocalMean[d][whichside]+=

sl->Count*sl->Value[d];

}

if (totsamps[0] && totsamps[1])

for(i=0;i<current_ndims;i++)

{

LocalMean[i][0]/=totsamps[0];

LocalMean[i][1]/=totsamps[1];

}

else

{

// it is possible that the clustering failed to split the samples.

// this can happen with a heavily biased sample (i.e. all black

// with a few stars). If this happens, we will cluster around the

// extrema instead. LocalMean[i][0] will be the point with the lowest

// value on the dimension and LocalMean[i][1] the one with the lowest

// value.

for(i=0;i<current_ndims;i++)

{

LocalMean[i][0]=minS->Value[i];

LocalMean[i][1]=maxS->Value[i];

}

// now, we have 2 n-dimensional means. We will label each sample

// for which one it is nearer to by using the QNum field.

for(i=0;i<n->NSamples;i++)

{

double dist[2];

dist[0]=dist[1]=0.;

struct Sample *s=SAMPLE(n->Samples,i);

for(int d=0;d<current_ndims;d++)

for(int w=0;w<2;w++)

dist[w]+=current_weights[d]*SQ(LocalMean[d][w]-s->Value[d]);

s->QNum=(dist[0]<dist[1]);

}

// hey ho! we have now labelled each one with a candidate bin. Let's

// sort the array by moving the 0-labelled ones to the head of the array.

n->sortdim=-1;

qsort(n->Samples,n->NSamples,current_ssize,QNumSort);

for(i=0;i<n->NSamples;i++,NAdded++)

if (SAMPLE(n->Samples,i)->QNum)

break;

#else

if (whichdim != n->sortdim)

{

current_sort_dim=whichdim;

qsort(n->Samples,n->NSamples,current_ssize,samplesort);

n->sortdim=whichdim;

}

// now, the samples are sorted along the proper dimension. we need

// to find the place to cut in order to split the node. this is

// complicated by the fact that each sample entry can represent many

// samples. What we will do is start at the beginning of the array,

// adding samples to the first node, until either the number added

// is >=TotSamples/2, or there is only one left.

int TotAdded=0;

for(;;)

{

if (NAdded==n->NSamples-1)

break;

if (TotAdded>=n->TotSamples/2)

break;

TotAdded+=SAMPLE(n->Samples,NAdded)->Count;

NAdded++;

}

#endif

struct QuantizedValue *a=AllocQValue();

a->sortdim=n->sortdim;

a->Samples=n->Samples;

a->NSamples=NAdded;

n->Children[0]=a;

UpdateStats(a);

a=AllocQValue();

a->Samples=SAMPLE(n->Samples,NAdded);

a->NSamples=n->NSamples-NAdded;

a->sortdim=n->sortdim;

n->Children[1]=a;

UpdateStats(a);

}

static int colorid=0;

static void Label(struct QuantizedValue *q, int updatecolor)

{

// fill in max/min values for tree, etc.

if (q)

{

Label(q->Children[0],updatecolor);

Label(q->Children[1],updatecolor);

if (! q->Children[0]) // leaf node?

{

if (updatecolor)

{

q->value=colorid++;

for(int j=0;j<q->NSamples;j++)

{

SAMPLE(q->Samples,j)->QNum=q->value;

SAMPLE(q->Samples,j)->qptr=q;

}

for(int i=0;i<current_ndims;i++)

{

q->Mins[i]=q->Mean[i];

q->Maxs[i]=q->Mean[i];

}

else

for(int i=0;i<current_ndims;i++)

{

q->Mins[i]=min(q->Children[0]->Mins[i],q->Children[1]->Mins[i]);

q->Maxs[i]=max(q->Children[0]->Maxs[i],q->Children[1]->Maxs[i]);

}

struct QuantizedValue *FindQNode(struct QuantizedValue const *q, int32 code)

{

if (! (q->Children[0]))

if (code==q->value) return (struct QuantizedValue *) q;

else return 0;

else

{

struct QuantizedValue *found=FindQNode(q->Children[0],code);

if (! found) found=FindQNode(q->Children[1],code);

return found;

}

void CheckInRange(struct QuantizedValue *q, uint8 *max, uint8 *min)

{

if (q)

{

if (q->Children[0])

{

// non-leaf node

CheckInRange(q->Children[0],q->Maxs, q->Mins);

CheckInRange(q->Children[1],q->Maxs, q->Mins);

CheckInRange(q->Children[0],max, min);

CheckInRange(q->Children[1],max, min);

}

for (int i=0;i<current_ndims;i++)

{

if (q->Maxs[i]>max[i]) printf("error1\n");

if (q->Mins[i]<min[i]) printf("error2\n");

}

struct QuantizedValue *Quantize(struct Sample *s, int nsamples, int ndims,

int nvalues, uint8 *weights, int firstvalue)

{

SetNDims(ndims);

current_weights=weights;

current_root=AllocQValue();

current_root->Samples=s;

current_root->NSamples=nsamples;

UpdateStats(current_root);

while(--nvalues)

{

if (! FindWorst())

break; // if <n unique ones, stop now

SubdivideNode(ErrorNode,ErrorDim);

}

colorid=firstvalue;

Label(current_root,1);

return current_root;

}

double MinimumError(struct QuantizedValue const *q, uint8 const *sample,

int ndims, uint8 const *weights)

{

double err=0;

for(int i=0;i<ndims;i++)

{

int val1;

int val2=sample[i];

if ((q->Mins[i]<=val2) && (q->Maxs[i]>=val2)) val1=val2;

else

{

val1=(val2<=q->Mins[i])?q->Mins[i]:q->Maxs[i];

}

err+=weights[i]*SQ(val1-val2);

}

return err;

}

double MaximumError(struct QuantizedValue const *q, uint8 const *sample,

int ndims, uint8 const *weights)

{

double err=0;

for(int i=0;i<ndims;i++)

{

int val2=sample[i];

int val1=(abs(val2-q->Mins[i])>abs(val2-q->Maxs[i]))?

q->Mins[i]:

q->Maxs[i];

err+=weights[i]*SQ(val2-val1);

}

return err;

}

// heap (priority queue) routines used for nearest-neghbor searches

struct FHeap {

int heap_n;

double *heap[MAXQUANT];

};

void InitHeap(struct FHeap *h)

{

h->heap_n=0;

}

void UpHeap(int k, struct FHeap *h)

{

double *tmpk=h->heap[k];

double tmpkn=*tmpk;

while((k>1) && (tmpkn <= *(h->heap[k/2])))

{

h->heap[k]=h->heap[k/2];

k/=2;

}

h->heap[k]=tmpk;

}

void HeapInsert(struct FHeap *h,double *elem)

{

h->heap_n++;

h->heap[h->heap_n]=elem;

UpHeap(h->heap_n,h);

}

void DownHeap(int k, struct FHeap *h)

{

double *v=h->heap[k];

while(k<=h->heap_n/2)

{

int j=2*k;

if (j<h->heap_n)

if (*(h->heap[j]) >= *(h->heap[j+1]))

j++;

if (*v < *(h->heap[j]))

{

h->heap[k]=v;

return;

}

h->heap[k]=h->heap[j]; k=j;

}

h->heap[k]=v;

}

void *RemoveHeapItem(struct FHeap *h)

{

void *ret=0;

if (h->heap_n!=0)

{

ret=h->heap[1];

h->heap[1]=h->heap[h->heap_n];

h->heap_n--;

DownHeap(1,h);

}

return ret;

}

// now, nearest neighbor finder. Use a heap to traverse the tree, stopping

// when there are no nodes with a minimum error < the current error.

struct FHeap TheQueue;

#define PUSHNODE(a) { \

(a)->MinError=MinimumError(a,sample,ndims,weights); \

if ((a)->MinError < besterror) HeapInsert(&TheQueue,&(a)->MinError); \

}

struct QuantizedValue *FindMatch(uint8 const *sample, int ndims,

uint8 *weights, struct QuantizedValue *q)

{

InitHeap(&TheQueue);

struct QuantizedValue *bestmatch=0;

double besterror=1.0e63;

PUSHNODE(q);

for(;;)

{

struct QuantizedValue *test=(struct QuantizedValue *)

RemoveHeapItem(&TheQueue);

if (! test) break; // heap empty

// printf("got pop node =%p minerror=%f\n",test,test->MinError);

if (test->MinError>besterror) break;

if (test->Children[0])

{

// it's a parent node. put the children on the queue

struct QuantizedValue *c1=test->Children[0];

struct QuantizedValue *c2=test->Children[1];

c1->MinError=MinimumError(c1,sample,ndims,weights);

if (c1->MinError < besterror)

HeapInsert(&TheQueue,&(c1->MinError));

c2->MinError=MinimumError(c2,sample,ndims,weights);

if (c2->MinError < besterror)

HeapInsert(&TheQueue,&(c2->MinError));

}

else

{

// it's a leaf node. This must be a new minimum or the MinError

// test would have failed.

if (test->MinError < besterror)

{

bestmatch=test;

besterror=test->MinError;

}

if (bestmatch)

{

SquaredError+=besterror;

bestmatch->NQuant++;

for(int i=0;i<ndims;i++)

bestmatch->Sums[i]+=sample[i];

}

return bestmatch;

}

static void RecalcMeans(struct QuantizedValue *q)

{

if (q)

{

if (q->Children[0])

{

// not a leaf, invoke recursively.

RecalcMeans(q->Children[0]);

}

else

{

// it's a leaf. Set the means

if (q->NQuant)

{

for(int i=0;i<current_ndims;i++)

{

q->Mean[i]=(uint8) (q->Sums[i]/q->NQuant);

q->Sums[i]=0;

}

q->NQuant=0;

}

void OptimizeQuantizer(struct QuantizedValue *q, int ndims)

{

SetNDims(ndims);

RecalcMeans(q); // reset q values

Label(q,0); // update max/mins

}

static void RecalcStats(struct QuantizedValue *q)

{

if (q)

{

UpdateStats(q);

RecalcStats(q->Children[0]);

RecalcStats(q->Children[1]);

}

void RecalculateValues(struct QuantizedValue *q, int ndims)

{

SetNDims(ndims);

RecalcStats(q);

Label(q,0);

}

TF2-Source-Code/tf2_src/mathlib/quantize.cpp at master · sr2echa/TF2-Source-Code · GitHub

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