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opencv/modules/legacy/src/stereogc.cpp

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//M*//////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace std;
#undef INFINITY
#define INFINITY 10000
#define OCCLUSION_PENALTY 10000
#define OCCLUSION_PENALTY2 1000
#define DENOMINATOR 16
#undef OCCLUDED
#define OCCLUDED CV_STEREO_GC_OCCLUDED
#define CUTOFF 1000
#define IS_BLOCKED(d1, d2) ((d1) > (d2))
typedef struct GCVtx
{
GCVtx *next;
int parent;
int first;
int ts;
int dist;
short weight;
uchar t;
}
GCVtx;
typedef struct GCEdge
{
GCVtx* dst;
int next;
int weight;
}
GCEdge;
typedef struct CvStereoGCState2
{
int Ithreshold, interactionRadius;
int lambda, lambda1, lambda2, K;
int dataCostFuncTab[CUTOFF+1];
int smoothnessR[CUTOFF*2+1];
int smoothnessGrayDiff[512];
GCVtx** orphans;
int maxOrphans;
}
CvStereoGCState2;
// truncTab[x+255] = MAX(x-255,0)
static uchar icvTruncTab[512];
// cutoffSqrTab[x] = MIN(x*x, CUTOFF)
static int icvCutoffSqrTab[256];
static void icvInitStereoConstTabs()
{
static volatile int initialized = 0;
if( !initialized )
{
int i;
for( i = 0; i < 512; i++ )
icvTruncTab[i] = (uchar)MIN(MAX(i-255,0),255);
for( i = 0; i < 256; i++ )
icvCutoffSqrTab[i] = MIN(i*i, CUTOFF);
initialized = 1;
}
}
static void icvInitStereoTabs( CvStereoGCState2* state2 )
{
int i, K = state2->K;
for( i = 0; i <= CUTOFF; i++ )
state2->dataCostFuncTab[i] = MIN(i*DENOMINATOR - K, 0);
for( i = 0; i < CUTOFF*2 + 1; i++ )
state2->smoothnessR[i] = MIN(abs(i-CUTOFF), state2->interactionRadius);
for( i = 0; i < 512; i++ )
{
int diff = abs(i - 255);
state2->smoothnessGrayDiff[i] = diff < state2->Ithreshold ? state2->lambda1 : state2->lambda2;
}
}
static int icvGCResizeOrphansBuf( GCVtx**& orphans, int norphans )
{
int i, newNOrphans = MAX(norphans*3/2, 256);
GCVtx** newOrphans = (GCVtx**)cvAlloc( newNOrphans*sizeof(orphans[0]) );
for( i = 0; i < norphans; i++ )
newOrphans[i] = orphans[i];
cvFree( &orphans );
orphans = newOrphans;
return newNOrphans;
}
static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphans, int& _maxOrphans )
{
const int TERMINAL = -1, ORPHAN = -2;
GCVtx stub, *nilNode = &stub, *first = nilNode, *last = nilNode;
int i, k;
int curr_ts = 0;
int64 flow = 0;
int norphans = 0, maxOrphans = _maxOrphans;
GCVtx** orphans = _orphans;
stub.next = nilNode;
// initialize the active queue and the graph vertices
for( i = 0; i < nvtx; i++ )
{
GCVtx* v = vtx + i;
v->ts = 0;
if( v->weight != 0 )
{
last = last->next = v;
v->dist = 1;
v->parent = TERMINAL;
v->t = v->weight < 0;
}
else
v->parent = 0;
}
first = first->next;
last->next = nilNode;
nilNode->next = 0;
// run the search-path -> augment-graph -> restore-trees loop
for(;;)
{
GCVtx* v, *u;
int e0 = -1, ei = 0, ej = 0, min_weight, weight;
uchar vt;
// grow S & T search trees, find an edge connecting them
while( first != nilNode )
{
v = first;
if( v->parent )
{
vt = v->t;
for( ei = v->first; ei != 0; ei = edges[ei].next )
{
if( edges[ei^vt].weight == 0 )
continue;
u = edges[ei].dst;
if( !u->parent )
{
u->t = vt;
u->parent = ei ^ 1;
u->ts = v->ts;
u->dist = v->dist + 1;
if( !u->next )
{
u->next = nilNode;
last = last->next = u;
}
continue;
}
if( u->t != vt )
{
e0 = ei ^ vt;
break;
}
if( u->dist > v->dist+1 && u->ts <= v->ts )
{
// reassign the parent
u->parent = ei ^ 1;
u->ts = v->ts;
u->dist = v->dist + 1;
}
}
if( e0 > 0 )
break;
}
// exclude the vertex from the active list
first = first->next;
v->next = 0;
}
if( e0 <= 0 )
break;
// find the minimum edge weight along the path
min_weight = edges[e0].weight;
assert( min_weight > 0 );
// k = 1: source tree, k = 0: destination tree
for( k = 1; k >= 0; k-- )
{
for( v = edges[e0^k].dst;; v = edges[ei].dst )
{
if( (ei = v->parent) < 0 )
break;
weight = edges[ei^k].weight;
min_weight = MIN(min_weight, weight);
assert( min_weight > 0 );
}
weight = abs(v->weight);
min_weight = MIN(min_weight, weight);
assert( min_weight > 0 );
}
// modify weights of the edges along the path and collect orphans
edges[e0].weight -= min_weight;
edges[e0^1].weight += min_weight;
flow += min_weight;
// k = 1: source tree, k = 0: destination tree
for( k = 1; k >= 0; k-- )
{
for( v = edges[e0^k].dst;; v = edges[ei].dst )
{
if( (ei = v->parent) < 0 )
break;
edges[ei^(k^1)].weight += min_weight;
if( (edges[ei^k].weight -= min_weight) == 0 )
{
if( norphans >= maxOrphans )
maxOrphans = icvGCResizeOrphansBuf( orphans, norphans );
orphans[norphans++] = v;
v->parent = ORPHAN;
}
}
v->weight = (short)(v->weight + min_weight*(1-k*2));
if( v->weight == 0 )
{
if( norphans >= maxOrphans )
maxOrphans = icvGCResizeOrphansBuf( orphans, norphans );
orphans[norphans++] = v;
v->parent = ORPHAN;
}
}
// restore the search trees by finding new parents for the orphans
curr_ts++;
while( norphans > 0 )
{
GCVtx* v = orphans[--norphans];
int d, min_dist = INT_MAX;
e0 = 0;
vt = v->t;
for( ei = v->first; ei != 0; ei = edges[ei].next )
{
if( edges[ei^(vt^1)].weight == 0 )
continue;
u = edges[ei].dst;
if( u->t != vt || u->parent == 0 )
continue;
// compute the distance to the tree root
for( d = 0;; )
{
if( u->ts == curr_ts )
{
d += u->dist;
break;
}
ej = u->parent;
d++;
if( ej < 0 )
{
if( ej == ORPHAN )
d = INT_MAX-1;
else
{
u->ts = curr_ts;
u->dist = 1;
}
break;
}
u = edges[ej].dst;
}
// update the distance
if( ++d < INT_MAX )
{
if( d < min_dist )
{
min_dist = d;
e0 = ei;
}
for( u = edges[ei].dst; u->ts != curr_ts; u = edges[u->parent].dst )
{
u->ts = curr_ts;
u->dist = --d;
}
}
}
if( (v->parent = e0) > 0 )
{
v->ts = curr_ts;
v->dist = min_dist;
continue;
}
/* no parent is found */
v->ts = 0;
for( ei = v->first; ei != 0; ei = edges[ei].next )
{
u = edges[ei].dst;
ej = u->parent;
if( u->t != vt || !ej )
continue;
if( edges[ei^(vt^1)].weight && !u->next )
{
u->next = nilNode;
last = last->next = u;
}
if( ej > 0 && edges[ej].dst == v )
{
if( norphans >= maxOrphans )
maxOrphans = icvGCResizeOrphansBuf( orphans, norphans );
orphans[norphans++] = u;
u->parent = ORPHAN;
}
}
}
}
_orphans = orphans;
_maxOrphans = maxOrphans;
return flow;
}
CvStereoGCState* cvCreateStereoGCState( int numberOfDisparities, int maxIters )
{
CvStereoGCState* state = 0;
state = (CvStereoGCState*)cvAlloc( sizeof(*state) );
memset( state, 0, sizeof(*state) );
state->minDisparity = 0;
state->numberOfDisparities = numberOfDisparities;
state->maxIters = maxIters <= 0 ? 3 : maxIters;
state->Ithreshold = 5;
state->interactionRadius = 1;
state->K = state->lambda = state->lambda1 = state->lambda2 = -1.f;
state->occlusionCost = OCCLUSION_PENALTY;
return state;
}
void cvReleaseStereoGCState( CvStereoGCState** _state )
{
CvStereoGCState* state;
if( !_state && !*_state )
return;
state = *_state;
cvReleaseMat( &state->left );
cvReleaseMat( &state->right );
cvReleaseMat( &state->ptrLeft );
cvReleaseMat( &state->ptrRight );
cvReleaseMat( &state->vtxBuf );
cvReleaseMat( &state->edgeBuf );
cvFree( _state );
}
// ||I(x) - J(x')|| =
// min(CUTOFF,
// min(
// max(
// max(minJ(x') - I(x), 0),
// max(I(x) - maxJ(x'), 0)),
// max(
// max(minI(x) - J(x'), 0),
// max(J(x') - maxI(x), 0)))**2) ==
// min(CUTOFF,
// min(
// max(minJ(x') - I(x), 0) +
// max(I(x) - maxJ(x'), 0),
//
// max(minI(x) - J(x'), 0) +
// max(J(x') - maxI(x), 0)))**2)
// where (I, minI, maxI) and
// (J, minJ, maxJ) are stored as interleaved 3-channel images.
// minI, maxI are computed from I,
// minJ, maxJ are computed from J - see icvInitGraySubPix.
static inline int icvDataCostFuncGraySubpix( const uchar* a, const uchar* b )
{
int va = a[0], vb = b[0];
int da = icvTruncTab[b[1] - va + 255] + icvTruncTab[va - b[2] + 255];
int db = icvTruncTab[a[1] - vb + 255] + icvTruncTab[vb - a[2] + 255];
return icvCutoffSqrTab[MIN(da,db)];
}
static inline int icvSmoothnessCostFunc( int da, int db, int maxR, const int* stabR, int scale )
{
return da == db ? 0 : (da == OCCLUDED || db == OCCLUDED ? maxR : stabR[da - db])*scale;
}
static void icvInitGraySubpix( const CvMat* left, const CvMat* right,
CvMat* left3, CvMat* right3 )
{
int k, x, y, rows = left->rows, cols = left->cols;
for( k = 0; k < 2; k++ )
{
const CvMat* src = k == 0 ? left : right;
CvMat* dst = k == 0 ? left3 : right3;
int sstep = src->step;
for( y = 0; y < rows; y++ )
{
const uchar* sptr = src->data.ptr + sstep*y;
const uchar* sptr_prev = y > 0 ? sptr - sstep : sptr;
const uchar* sptr_next = y < rows-1 ? sptr + sstep : sptr;
uchar* dptr = dst->data.ptr + dst->step*y;
int v_prev = sptr[0];
for( x = 0; x < cols; x++, dptr += 3 )
{
int v = sptr[x], v1, minv = v, maxv = v;
v1 = (v + v_prev)/2;
minv = MIN(minv, v1); maxv = MAX(maxv, v1);
v1 = (v + sptr_prev[x])/2;
minv = MIN(minv, v1); maxv = MAX(maxv, v1);
v1 = (v + sptr_next[x])/2;
minv = MIN(minv, v1); maxv = MAX(maxv, v1);
if( x < cols-1 )
{
v1 = (v + sptr[x+1])/2;
minv = MIN(minv, v1); maxv = MAX(maxv, v1);
}
v_prev = v;
dptr[0] = (uchar)v;
dptr[1] = (uchar)minv;
dptr[2] = (uchar)maxv;
}
}
}
}
// Optimal K is computed as avg_x(k-th-smallest_d(||I(x)-J(x+d)||)),
// where k = number_of_disparities*0.25.
static float
icvComputeK( CvStereoGCState* state )
{
int x, y, x1, d, i, j, rows = state->left->rows, cols = state->left->cols, n = 0;
int mind = state->minDisparity, nd = state->numberOfDisparities, maxd = mind + nd;
int k = MIN(MAX((nd + 2)/4, 3), nd), delta, t, sum = 0;
std::vector<int> _arr(k+1);
int *arr = &_arr[0];
for( y = 0; y < rows; y++ )
{
const uchar* lptr = state->left->data.ptr + state->left->step*y;
const uchar* rptr = state->right->data.ptr + state->right->step*y;
for( x = 0; x < cols; x++ )
{
for( d = maxd-1, i = 0; d >= mind; d-- )
{
x1 = x - d;
if( (unsigned)x1 >= (unsigned)cols )
continue;
delta = icvDataCostFuncGraySubpix( lptr + x*3, rptr + x1*3 );
if( i < k )
arr[i++] = delta;
else
for( i = 0; i < k; i++ )
if( delta < arr[i] )
CV_SWAP( arr[i], delta, t );
}
delta = arr[0];
for( j = 1; j < i; j++ )
delta = MAX(delta, arr[j]);
sum += delta;
n++;
}
}
return (float)sum/n;
}
static int64 icvComputeEnergy( const CvStereoGCState* state, const CvStereoGCState2* state2,
bool allOccluded )
{
int x, y, rows = state->left->rows, cols = state->left->cols;
int64 E = 0;
const int* dtab = state2->dataCostFuncTab;
int maxR = state2->interactionRadius;
const int* stabR = state2->smoothnessR + CUTOFF;
const int* stabI = state2->smoothnessGrayDiff + 255;
const uchar* left = state->left->data.ptr;
const uchar* right = state->right->data.ptr;
short* dleft = state->dispLeft->data.s;
short* dright = state->dispRight->data.s;
int step = state->left->step;
int dstep = (int)(state->dispLeft->step/sizeof(short));
assert( state->left->step == state->right->step &&
state->dispLeft->step == state->dispRight->step );
if( allOccluded )
return (int64)OCCLUSION_PENALTY*rows*cols*2;
for( y = 0; y < rows; y++, left += step, right += step, dleft += dstep, dright += dstep )
{
for( x = 0; x < cols; x++ )
{
int d = dleft[x], x1, d1;
if( d == OCCLUDED )
E += OCCLUSION_PENALTY;
else
{
x1 = x + d;
if( (unsigned)x1 >= (unsigned)cols )
continue;
d1 = dright[x1];
if( d == -d1 )
E += dtab[icvDataCostFuncGraySubpix( left + x*3, right + x1*3 )];
}
if( x < cols-1 )
{
d1 = dleft[x+1];
E += icvSmoothnessCostFunc(d, d1, maxR, stabR, stabI[left[x*3] - left[x*3+3]] );
}
if( y < rows-1 )
{
d1 = dleft[x+dstep];
E += icvSmoothnessCostFunc(d, d1, maxR, stabR, stabI[left[x*3] - left[x*3+step]] );
}
d = dright[x];
if( d == OCCLUDED )
E += OCCLUSION_PENALTY;
if( x < cols-1 )
{
d1 = dright[x+1];
E += icvSmoothnessCostFunc(d, d1, maxR, stabR, stabI[right[x*3] - right[x*3+3]] );
}
if( y < rows-1 )
{
d1 = dright[x+dstep];
E += icvSmoothnessCostFunc(d, d1, maxR, stabR, stabI[right[x*3] - right[x*3+step]] );
}
assert( E >= 0 );
}
}
return E;
}
static inline void icvAddEdge( GCVtx *x, GCVtx* y, GCEdge* edgeBuf, int nedges, int w, int rw )
{
GCEdge *xy = edgeBuf + nedges, *yx = xy + 1;
assert( x != 0 && y != 0 );
xy->dst = y;
xy->next = x->first;
xy->weight = (short)w;
x->first = nedges;
yx->dst = x;
yx->next = y->first;
yx->weight = (short)rw;
y->first = nedges+1;
}
static inline int icvAddTWeights( GCVtx* vtx, int sourceWeight, int sinkWeight )
{
int w = vtx->weight;
if( w > 0 )
sourceWeight += w;
else
sinkWeight -= w;
vtx->weight = (short)(sourceWeight - sinkWeight);
return MIN(sourceWeight, sinkWeight);
}
static inline int icvAddTerm( GCVtx* x, GCVtx* y, int A, int B, int C, int D,
GCEdge* edgeBuf, int& nedges )
{
int dE = 0, w;
assert(B - A + C - D >= 0);
if( B < A )
{
dE += icvAddTWeights(x, D, B);
dE += icvAddTWeights(y, 0, A - B);
if( (w = B - A + C - D) != 0 )
{
icvAddEdge( x, y, edgeBuf, nedges, 0, w );
nedges += 2;
}
}
else if( C < D )
{
dE += icvAddTWeights(x, D, A + D - C);
dE += icvAddTWeights(y, 0, C - D);
if( (w = B - A + C - D) != 0 )
{
icvAddEdge( x, y, edgeBuf, nedges, w, 0 );
nedges += 2;
}
}
else
{
dE += icvAddTWeights(x, D, A);
if( B != A || C != D )
{
icvAddEdge( x, y, edgeBuf, nedges, B - A, C - D );
nedges += 2;
}
}
return dE;
}
static int64 icvAlphaExpand( int64 Eprev, int alpha, CvStereoGCState* state, CvStereoGCState2* state2 )
{
GCVtx *var, *var1;
int64 E = 0;
int delta, E00=0, E0a=0, Ea0=0, Eaa=0;
int k, a, d, d1, x, y, x1, y1, rows = state->left->rows, cols = state->left->cols;
int nvtx = 0, nedges = 2;
GCVtx* vbuf = (GCVtx*)state->vtxBuf->data.ptr;
GCEdge* ebuf = (GCEdge*)state->edgeBuf->data.ptr;
int maxR = state2->interactionRadius;
const int* dtab = state2->dataCostFuncTab;
const int* stabR = state2->smoothnessR + CUTOFF;
const int* stabI = state2->smoothnessGrayDiff + 255;
const uchar* left0 = state->left->data.ptr;
const uchar* right0 = state->right->data.ptr;
short* dleft0 = state->dispLeft->data.s;
short* dright0 = state->dispRight->data.s;
GCVtx** pleft0 = (GCVtx**)state->ptrLeft->data.ptr;
GCVtx** pright0 = (GCVtx**)state->ptrRight->data.ptr;
int step = state->left->step;
int dstep = (int)(state->dispLeft->step/sizeof(short));
int pstep = (int)(state->ptrLeft->step/sizeof(GCVtx*));
int aa[] = { alpha, -alpha };
//double t = (double)cvGetTickCount();
assert( state->left->step == state->right->step &&
state->dispLeft->step == state->dispRight->step &&
state->ptrLeft->step == state->ptrRight->step );
for( k = 0; k < 2; k++ )
{
ebuf[k].dst = 0;
ebuf[k].next = 0;
ebuf[k].weight = 0;
}
for( y = 0; y < rows; y++ )
{
const uchar* left = left0 + step*y;
const uchar* right = right0 + step*y;
const short* dleft = dleft0 + dstep*y;
const short* dright = dright0 + dstep*y;
GCVtx** pleft = pleft0 + pstep*y;
GCVtx** pright = pright0 + pstep*y;
const uchar* lr[] = { left, right };
const short* dlr[] = { dleft, dright };
GCVtx** plr[] = { pleft, pright };
for( k = 0; k < 2; k++ )
{
a = aa[k];
for( y1 = y+(y>0); y1 <= y+(y<rows-1); y1++ )
{
const short* disp = (k == 0 ? dleft0 : dright0) + y1*dstep;
GCVtx** ptr = (k == 0 ? pleft0 : pright0) + y1*pstep;
for( x = 0; x < cols; x++ )
{
GCVtx* v = ptr[x] = &vbuf[nvtx++];
v->first = 0;
v->weight = disp[x] == (short)(OCCLUDED ? -OCCLUSION_PENALTY2 : 0);
}
}
}
for( x = 0; x < cols; x++ )
{
d = dleft[x];
x1 = x + d;
var = pleft[x];
// (left + x, right + x + d)
if( d != alpha && d != OCCLUDED && (unsigned)x1 < (unsigned)cols )
{
var1 = pright[x1];
d1 = dright[x1];
if( d == -d1 )
{
assert( var1 != 0 );
delta = IS_BLOCKED(alpha, d) ? INFINITY : 0;
//add inter edge
E += icvAddTerm( var, var1,
dtab[icvDataCostFuncGraySubpix( left + x*3, right + x1*3 )],
delta, delta, 0, ebuf, nedges );
}
else if( IS_BLOCKED(alpha, d) )
E += icvAddTerm( var, var1, 0, INFINITY, 0, 0, ebuf, nedges );
}
// (left + x, right + x + alpha)
x1 = x + alpha;
if( (unsigned)x1 < (unsigned)cols )
{
var1 = pright[x1];
d1 = dright[x1];
E0a = IS_BLOCKED(d, alpha) ? INFINITY : 0;
Ea0 = IS_BLOCKED(-d1, alpha) ? INFINITY : 0;
Eaa = dtab[icvDataCostFuncGraySubpix( left + x*3, right + x1*3 )];
E += icvAddTerm( var, var1, 0, E0a, Ea0, Eaa, ebuf, nedges );
}
// smoothness
for( k = 0; k < 2; k++ )
{
GCVtx** p = plr[k];
const short* disp = dlr[k];
const uchar* img = lr[k] + x*3;
int scale;
var = p[x];
d = disp[x];
a = aa[k];
if( x < cols - 1 )
{
var1 = p[x+1];
d1 = disp[x+1];
scale = stabI[img[0] - img[3]];
E0a = icvSmoothnessCostFunc( d, a, maxR, stabR, scale );
Ea0 = icvSmoothnessCostFunc( a, d1, maxR, stabR, scale );
E00 = icvSmoothnessCostFunc( d, d1, maxR, stabR, scale );
E += icvAddTerm( var, var1, E00, E0a, Ea0, 0, ebuf, nedges );
}
if( y < rows - 1 )
{
var1 = p[x+pstep];
d1 = disp[x+dstep];
scale = stabI[img[0] - img[step]];
E0a = icvSmoothnessCostFunc( d, a, maxR, stabR, scale );
Ea0 = icvSmoothnessCostFunc( a, d1, maxR, stabR, scale );
E00 = icvSmoothnessCostFunc( d, d1, maxR, stabR, scale );
E += icvAddTerm( var, var1, E00, E0a, Ea0, 0, ebuf, nedges );
}
}
// visibility term
if( d != OCCLUDED && IS_BLOCKED(alpha, -d))
{
x1 = x + d;
if( (unsigned)x1 < (unsigned)cols )
{
if( d != -dleft[x1] )
{
var1 = pleft[x1];
E += icvAddTerm( var, var1, 0, INFINITY, 0, 0, ebuf, nedges );
}
}
}
}
}
//t = (double)cvGetTickCount() - t;
ebuf[0].weight = ebuf[1].weight = 0;
E += icvGCMaxFlow( vbuf, nvtx, ebuf, state2->orphans, state2->maxOrphans );
if( E < Eprev )
{
for( y = 0; y < rows; y++ )
{
short* dleft = dleft0 + dstep*y;
short* dright = dright0 + dstep*y;
GCVtx** pleft = pleft0 + pstep*y;
GCVtx** pright = pright0 + pstep*y;
for( x = 0; x < cols; x++ )
{
GCVtx* var = pleft[x];
if( var && var->parent && var->t )
dleft[x] = (short)alpha;
var = pright[x];
if( var && var->parent && var->t )
dright[x] = (short)-alpha;
}
}
}
return MIN(E, Eprev);
}
CV_IMPL void cvFindStereoCorrespondenceGC( const CvArr* _left, const CvArr* _right,
CvArr* _dispLeft, CvArr* _dispRight, CvStereoGCState* state, int useDisparityGuess )
{
CvStereoGCState2 state2;
state2.orphans = 0;
state2.maxOrphans = 0;
CvMat lstub, *left = cvGetMat( _left, &lstub );
CvMat rstub, *right = cvGetMat( _right, &rstub );
CvMat dlstub, *dispLeft = cvGetMat( _dispLeft, &dlstub );
CvMat drstub, *dispRight = cvGetMat( _dispRight, &drstub );
CvSize size;
int iter, i, nZeroExpansions = 0;
CvRNG rng = cvRNG(-1);
int64 E;
CV_Assert( state != 0 );
CV_Assert( CV_ARE_SIZES_EQ(left, right) && CV_ARE_TYPES_EQ(left, right) &&
CV_MAT_TYPE(left->type) == CV_8UC1 );
CV_Assert( !dispLeft ||
(CV_ARE_SIZES_EQ(dispLeft, left) && CV_MAT_CN(dispLeft->type) == 1) );
CV_Assert( !dispRight ||
(CV_ARE_SIZES_EQ(dispRight, left) && CV_MAT_CN(dispRight->type) == 1) );
size = cvGetSize(left);
if( !state->left || state->left->width != size.width || state->left->height != size.height )
{
int pcn = (int)(sizeof(GCVtx*)/sizeof(int));
int vcn = (int)(sizeof(GCVtx)/sizeof(int));
int ecn = (int)(sizeof(GCEdge)/sizeof(int));
cvReleaseMat( &state->left );
cvReleaseMat( &state->right );
cvReleaseMat( &state->ptrLeft );
cvReleaseMat( &state->ptrRight );
cvReleaseMat( &state->dispLeft );
cvReleaseMat( &state->dispRight );
state->left = cvCreateMat( size.height, size.width, CV_8UC3 );
state->right = cvCreateMat( size.height, size.width, CV_8UC3 );
state->dispLeft = cvCreateMat( size.height, size.width, CV_16SC1 );
state->dispRight = cvCreateMat( size.height, size.width, CV_16SC1 );
state->ptrLeft = cvCreateMat( size.height, size.width, CV_32SC(pcn) );
state->ptrRight = cvCreateMat( size.height, size.width, CV_32SC(pcn) );
state->vtxBuf = cvCreateMat( 1, size.height*size.width*2, CV_32SC(vcn) );
state->edgeBuf = cvCreateMat( 1, size.height*size.width*12 + 16, CV_32SC(ecn) );
}
if( !useDisparityGuess )
{
cvSet( state->dispLeft, cvScalarAll(OCCLUDED));
cvSet( state->dispRight, cvScalarAll(OCCLUDED));
}
else
{
CV_Assert( dispLeft && dispRight );
cvConvert( dispLeft, state->dispLeft );
cvConvert( dispRight, state->dispRight );
}
state2.Ithreshold = state->Ithreshold;
state2.interactionRadius = state->interactionRadius;
state2.lambda = cvRound(state->lambda*DENOMINATOR);
state2.lambda1 = cvRound(state->lambda1*DENOMINATOR);
state2.lambda2 = cvRound(state->lambda2*DENOMINATOR);
state2.K = cvRound(state->K*DENOMINATOR);
icvInitStereoConstTabs();
icvInitGraySubpix( left, right, state->left, state->right );
std::vector<int> disp(state->numberOfDisparities);
CvMat _disp = cvMat( 1, (int)disp.size(), CV_32S, &disp[0] );
cvRange( &_disp, state->minDisparity, state->minDisparity + state->numberOfDisparities );
cvRandShuffle( &_disp, &rng );
if( state2.lambda < 0 && (state2.K < 0 || state2.lambda1 < 0 || state2.lambda2 < 0) )
{
float L = icvComputeK(state)*0.2f;
state2.lambda = cvRound(L*DENOMINATOR);
}
if( state2.K < 0 )
state2.K = state2.lambda*5;
if( state2.lambda1 < 0 )
state2.lambda1 = state2.lambda*3;
if( state2.lambda2 < 0 )
state2.lambda2 = state2.lambda;
icvInitStereoTabs( &state2 );
E = icvComputeEnergy( state, &state2, !useDisparityGuess );
for( iter = 0; iter < state->maxIters; iter++ )
{
for( i = 0; i < state->numberOfDisparities; i++ )
{
int alpha = disp[i];
int64 Enew = icvAlphaExpand( E, -alpha, state, &state2 );
if( Enew < E )
{
nZeroExpansions = 0;
E = Enew;
}
else if( ++nZeroExpansions >= state->numberOfDisparities )
break;
}
}
if( dispLeft )
cvConvert( state->dispLeft, dispLeft );
if( dispRight )
cvConvert( state->dispRight, dispRight );
cvFree( &state2.orphans );
}
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