//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 "_cv.h" #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, *nil = &stub, *first = nil, *last = nil; int i, k; int curr_ts = 0; int64 flow = 0; int norphans = 0, maxOrphans = _maxOrphans; GCVtx** orphans = _orphans; stub.next = nil; // 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 = nil; nil->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 != nil ) { 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 = nil; 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 = nil; 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; //CV_FUNCNAME("cvCreateStereoGCState"); __BEGIN__; 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; __END__; 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); int *arr = (int*)cvStackAlloc(k*sizeof(arr[0])), delta, t, sum = 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; CV_FUNCNAME( "cvFindStereoCorrespondenceGC" ); __BEGIN__; 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); int* disp; CvMat _disp; 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 ); disp = (int*)cvStackAlloc( state->numberOfDisparities*sizeof(disp[0]) ); _disp = cvMat( 1, state->numberOfDisparities, CV_32S, disp ); 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 ); __END__; cvFree( &state2.orphans ); }