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48 void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
49 const vector<Point2f>& prevPts,
50 vector<Point2f>& nextPts,
51 vector<uchar>& status, vector<float>& err,
52 Size winSize, int maxLevel,
53 TermCriteria criteria,
57 derivLambda = std::min(std::max(derivLambda, 0.), 1.);
58 double lambda1 = 1. - derivLambda, lambda2 = derivLambda;
59 const int derivKernelSize = 3;
60 const float deriv1Scale = 0.5f/4.f;
61 const float deriv2Scale = 0.25f/4.f;
62 const int derivDepth = CV_32F;
63 Point2f halfWin((winSize.width-1)*0.5f, (winSize.height-1)*0.5f);
65 CV_Assert( maxLevel >= 0 && winSize.width > 2 && winSize.height > 2 );
66 CV_Assert( prevImg.size() == nextImg.size() &&
67 prevImg.type() == nextImg.type() );
69 size_t npoints = prevPts.size();
70 nextPts.resize(npoints);
71 status.resize(npoints);
72 for( size_t i = 0; i < npoints; i++ )
79 vector<Mat> prevPyr, nextPyr;
81 int cn = prevImg.channels();
82 buildPyramid( prevImg, prevPyr, maxLevel );
83 buildPyramid( nextImg, nextPyr, maxLevel );
84 // I, dI/dx ~ Ix, dI/dy ~ Iy, d2I/dx2 ~ Ixx, d2I/dxdy ~ Ixy, d2I/dy2 ~ Iyy
85 Mat derivIBuf((prevImg.rows + winSize.height*2),
86 (prevImg.cols + winSize.width*2),
87 CV_MAKETYPE(derivDepth, cn*6));
88 // J, dJ/dx ~ Jx, dJ/dy ~ Jy
89 Mat derivJBuf((prevImg.rows + winSize.height*2),
90 (prevImg.cols + winSize.width*2),
91 CV_MAKETYPE(derivDepth, cn*3));
92 Mat tempDerivBuf(prevImg.size(), CV_MAKETYPE(derivIBuf.type(), cn));
93 Mat derivIWinBuf(winSize, derivIBuf.type());
95 if( (criteria.type & TermCriteria::COUNT) == 0 )
96 criteria.maxCount = 30;
98 criteria.maxCount = std::min(std::max(criteria.maxCount, 0), 100);
99 if( (criteria.type & TermCriteria::EPS) == 0 )
100 criteria.epsilon = 0.01;
102 criteria.epsilon = std::min(std::max(criteria.epsilon, 0.), 10.);
103 criteria.epsilon *= criteria.epsilon;
105 for( int level = maxLevel; level >= 0; level-- )
108 Size imgSize = prevPyr[level].size();
109 Mat tempDeriv( imgSize, tempDerivBuf.type(), tempDerivBuf.data );
110 Mat _derivI( imgSize.height + winSize.height*2,
111 imgSize.width + winSize.width*2,
112 derivIBuf.type(), derivIBuf.data );
113 Mat _derivJ( imgSize.height + winSize.height*2,
114 imgSize.width + winSize.width*2,
115 derivJBuf.type(), derivJBuf.data );
116 Mat derivI(_derivI, Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
117 Mat derivJ(_derivJ, Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
118 CvMat cvderivI = _derivI;
120 CvMat cvderivJ = _derivJ;
123 vector<int> fromTo(cn*2);
124 for( k = 0; k < cn; k++ )
127 prevPyr[level].convertTo(tempDeriv, derivDepth);
128 for( k = 0; k < cn; k++ )
130 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
132 // compute spatial derivatives and merge them together
133 Sobel(prevPyr[level], tempDeriv, derivDepth, 1, 0, derivKernelSize, deriv1Scale );
134 for( k = 0; k < cn; k++ )
135 fromTo[k*2+1] = k*6 + 1;
136 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
138 Sobel(prevPyr[level], tempDeriv, derivDepth, 0, 1, derivKernelSize, deriv1Scale );
139 for( k = 0; k < cn; k++ )
140 fromTo[k*2+1] = k*6 + 2;
141 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
143 Sobel(prevPyr[level], tempDeriv, derivDepth, 2, 0, derivKernelSize, deriv2Scale );
144 for( k = 0; k < cn; k++ )
145 fromTo[k*2+1] = k*6 + 3;
146 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
148 Sobel(prevPyr[level], tempDeriv, derivDepth, 1, 1, derivKernelSize, deriv2Scale );
149 for( k = 0; k < cn; k++ )
150 fromTo[k*2+1] = k*6 + 4;
151 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
153 Sobel(prevPyr[level], tempDeriv, derivDepth, 0, 2, derivKernelSize, deriv2Scale );
154 for( k = 0; k < cn; k++ )
155 fromTo[k*2+1] = k*6 + 5;
156 mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
158 nextPyr[level].convertTo(tempDeriv, derivDepth);
159 for( k = 0; k < cn; k++ )
161 mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
163 Sobel(nextPyr[level], tempDeriv, derivDepth, 1, 0, derivKernelSize, deriv1Scale );
164 for( k = 0; k < cn; k++ )
165 fromTo[k*2+1] = k*3 + 1;
166 mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
168 Sobel(nextPyr[level], tempDeriv, derivDepth, 0, 1, derivKernelSize, deriv1Scale );
169 for( k = 0; k < cn; k++ )
170 fromTo[k*2+1] = k*3 + 2;
171 mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
173 /*copyMakeBorder( derivI, _derivI, winSize.height, winSize.height,
174 winSize.width, winSize.width, BORDER_CONSTANT );
175 copyMakeBorder( derivJ, _derivJ, winSize.height, winSize.height,
176 winSize.width, winSize.width, BORDER_CONSTANT );*/
178 for( size_t ptidx = 0; ptidx < npoints; ptidx++ )
180 Point2f prevPt = prevPts[ptidx]*(float)(1./(1 << level));
182 if( level == maxLevel )
184 if( flags & OPTFLOW_USE_INITIAL_FLOW )
185 nextPt = nextPts[ptidx]*(float)(1./(1 << level));
190 nextPt = nextPts[ptidx]*2.f;
191 nextPts[ptidx] = nextPt;
193 Point2i iprevPt, inextPt;
195 iprevPt.x = cvFloor(prevPt.x);
196 iprevPt.y = cvFloor(prevPt.y);
198 if( iprevPt.x < -winSize.width || iprevPt.x >= derivI.cols ||
199 iprevPt.y < -winSize.height || iprevPt.y >= derivI.rows )
203 status[ptidx] = false;
204 err[ptidx] = FLT_MAX;
209 float a = prevPt.x - iprevPt.x;
210 float b = prevPt.y - iprevPt.y;
211 float w00 = (1.f - a)*(1.f - b), w01 = a*(1.f - b);
212 float w10 = (1.f - a)*b, w11 = a*b;
213 size_t stepI = derivI.step/derivI.elemSize1();
214 size_t stepJ = derivJ.step/derivJ.elemSize1();
215 int cnI = cn*6, cnJ = cn*3;
216 double A11 = 0, A12 = 0, A22 = 0;
217 double iA11 = 0, iA12 = 0, iA22 = 0;
219 // extract the patch from the first image
221 for( y = 0; y < winSize.height; y++ )
223 const float* src = (const float*)(derivI.data +
224 (y + iprevPt.y)*derivI.step) + iprevPt.x*cnI;
225 float* dst = (float*)(derivIWinBuf.data + y*derivIWinBuf.step);
227 for( x = 0; x < winSize.width*cnI; x += cnI, src += cnI )
229 float I = src[0]*w00 + src[cnI]*w01 + src[stepI]*w10 + src[stepI+cnI]*w11;
232 float Ix = src[1]*w00 + src[cnI+1]*w01 + src[stepI+1]*w10 + src[stepI+cnI+1]*w11;
233 float Iy = src[2]*w00 + src[cnI+2]*w01 + src[stepI+2]*w10 + src[stepI+cnI+2]*w11;
234 dst[x+1] = Ix; dst[x+2] = Iy;
236 float Ixx = src[3]*w00 + src[cnI+3]*w01 + src[stepI+3]*w10 + src[stepI+cnI+3]*w11;
237 float Ixy = src[4]*w00 + src[cnI+4]*w01 + src[stepI+4]*w10 + src[stepI+cnI+4]*w11;
238 float Iyy = src[5]*w00 + src[cnI+5]*w01 + src[stepI+5]*w10 + src[stepI+cnI+5]*w11;
239 dst[x+3] = Ixx; dst[x+4] = Ixy; dst[x+5] = Iyy;
241 iA11 += (double)Ix*Ix;
242 iA12 += (double)Ix*Iy;
243 iA22 += (double)Iy*Iy;
245 A11 += (double)Ixx*Ixx + (double)Ixy*Ixy;
246 A12 += Ixy*((double)Ixx + Iyy);
247 A22 += (double)Ixy*Ixy + (double)Iyy*Iyy;
251 A11 = lambda1*iA11 + lambda2*A11;
252 A12 = lambda1*iA12 + lambda2*A12;
253 A22 = lambda1*iA22 + lambda2*A22;
255 double D = A11*A22 - A12*A12;
256 double minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) +
257 4.*A12*A12))/(2*winSize.width*winSize.height);
258 err[ptidx] = (float)minEig;
260 if( D < DBL_EPSILON )
263 status[ptidx] = false;
272 for( int j = 0; j < criteria.maxCount; j++ )
274 inextPt.x = cvFloor(nextPt.x);
275 inextPt.y = cvFloor(nextPt.y);
277 if( inextPt.x < -winSize.width || inextPt.x >= derivJ.cols ||
278 inextPt.y < -winSize.height || inextPt.y >= derivJ.rows )
281 status[ptidx] = false;
285 a = nextPt.x - inextPt.x;
286 b = nextPt.y - inextPt.y;
287 w00 = (1.f - a)*(1.f - b); w01 = a*(1.f - b);
288 w10 = (1.f - a)*b; w11 = a*b;
290 double b1 = 0, b2 = 0, ib1 = 0, ib2 = 0;
292 for( y = 0; y < winSize.height; y++ )
294 const float* src = (const float*)(derivJ.data +
295 (y + inextPt.y)*derivJ.step) + inextPt.x*cnJ;
296 const float* Ibuf = (float*)(derivIWinBuf.data + y*derivIWinBuf.step);
298 for( x = 0; x < winSize.width; x++, src += cnJ, Ibuf += cnI )
300 double It = src[0]*w00 + src[cnJ]*w01 + src[stepJ]*w10 +
301 src[stepJ+cnJ]*w11 - Ibuf[0];
302 double Ixt = src[1]*w00 + src[cnJ+1]*w01 + src[stepJ+1]*w10 +
303 src[stepJ+cnJ+1]*w11 - Ibuf[1];
304 double Iyt = src[2]*w00 + src[cnJ+2]*w01 + src[stepJ+2]*w10 +
305 src[stepJ+cnJ+2]*w11 - Ibuf[2];
306 b1 += Ixt*Ibuf[3] + Iyt*Ibuf[4];
307 b2 += Ixt*Ibuf[4] + Iyt*Ibuf[5];
313 b1 = lambda1*ib1 + lambda2*b1;
314 b2 = lambda1*ib2 + lambda2*b2;
315 Point2f delta( (float)((A12*b2 - A22*b1) * D),
316 (float)((A12*b1 - A11*b2) * D));
320 nextPts[ptidx] = nextPt + halfWin;
322 if( delta.ddot(delta) <= criteria.epsilon )
325 if( j > 0 && std::abs(delta.x + prevDelta.x) < 0.01 &&
326 std::abs(delta.y + prevDelta.y) < 0.01 )
328 nextPts[ptidx] -= delta*0.5f;
340 intersect( CvPoint2D32f pt, CvSize win_size, CvSize imgSize,
341 CvPoint* min_pt, CvPoint* max_pt )
345 ipt.x = cvFloor( pt.x );
346 ipt.y = cvFloor( pt.y );
348 ipt.x -= win_size.width;
349 ipt.y -= win_size.height;
351 win_size.width = win_size.width * 2 + 1;
352 win_size.height = win_size.height * 2 + 1;
354 min_pt->x = MAX( 0, -ipt.x );
355 min_pt->y = MAX( 0, -ipt.y );
356 max_pt->x = MIN( win_size.width, imgSize.width - ipt.x );
357 max_pt->y = MIN( win_size.height, imgSize.height - ipt.y );
361 static int icvMinimalPyramidSize( CvSize imgSize )
363 return cvAlign(imgSize.width,8) * imgSize.height / 3;
368 icvInitPyramidalAlgorithm( const CvMat* imgA, const CvMat* imgB,
369 CvMat* pyrA, CvMat* pyrB,
370 int level, CvTermCriteria * criteria,
371 int max_iters, int flags,
372 uchar *** imgI, uchar *** imgJ,
373 int **step, CvSize** size,
374 double **scale, uchar ** buffer )
376 CV_FUNCNAME( "icvInitPyramidalAlgorithm" );
381 int pyrBytes, bufferBytes = 0, elem_size;
382 int level1 = level + 1;
385 CvSize imgSize, levelSize;
393 /* check input arguments */
394 if( ((flags & CV_LKFLOW_PYR_A_READY) != 0 && !pyrA) ||
395 ((flags & CV_LKFLOW_PYR_B_READY) != 0 && !pyrB) )
396 CV_ERROR( CV_StsNullPtr, "Some of the precomputed pyramids are missing" );
399 CV_ERROR( CV_StsOutOfRange, "The number of pyramid layers is negative" );
401 switch( criteria->type )
403 case CV_TERMCRIT_ITER:
404 criteria->epsilon = 0.f;
406 case CV_TERMCRIT_EPS:
407 criteria->max_iter = max_iters;
409 case CV_TERMCRIT_ITER | CV_TERMCRIT_EPS:
413 CV_ERROR( CV_StsBadArg, "Invalid termination criteria" );
416 /* compare squared values */
417 criteria->epsilon *= criteria->epsilon;
419 /* set pointers and step for every level */
422 imgSize = cvGetSize(imgA);
423 elem_size = CV_ELEM_SIZE(imgA->type);
426 for( i = 1; i < level1; i++ )
428 levelSize.width = (levelSize.width + 1) >> 1;
429 levelSize.height = (levelSize.height + 1) >> 1;
431 int tstep = cvAlign(levelSize.width,ALIGN) * elem_size;
432 pyrBytes += tstep * levelSize.height;
435 assert( pyrBytes <= imgSize.width * imgSize.height * elem_size * 4 / 3 );
437 /* buffer_size = <size for patches> + <size for pyramids> */
438 bufferBytes = (int)((level1 >= 0) * ((pyrA->data.ptr == 0) +
439 (pyrB->data.ptr == 0)) * pyrBytes +
440 (sizeof(imgI[0][0]) * 2 + sizeof(step[0][0]) +
441 sizeof(size[0][0]) + sizeof(scale[0][0])) * level1);
443 CV_CALL( *buffer = (uchar *)cvAlloc( bufferBytes ));
445 *imgI = (uchar **) buffer[0];
446 *imgJ = *imgI + level1;
447 *step = (int *) (*imgJ + level1);
448 *scale = (double *) (*step + level1);
449 *size = (CvSize *)(*scale + level1);
451 imgI[0][0] = imgA->data.ptr;
452 imgJ[0][0] = imgB->data.ptr;
453 step[0][0] = imgA->step;
455 size[0][0] = imgSize;
459 uchar *bufPtr = (uchar *) (*size + level1);
460 uchar *ptrA = pyrA->data.ptr;
461 uchar *ptrB = pyrB->data.ptr;
474 /* build pyramids for both frames */
475 for( i = 1; i <= level; i++ )
478 CvMat prev_level, next_level;
480 levelSize.width = (levelSize.width + 1) >> 1;
481 levelSize.height = (levelSize.height + 1) >> 1;
483 size[0][i] = levelSize;
484 step[0][i] = cvAlign( levelSize.width, ALIGN ) * elem_size;
485 scale[0][i] = scale[0][i - 1] * 0.5;
487 levelBytes = step[0][i] * levelSize.height;
488 imgI[0][i] = (uchar *) ptrA;
491 if( !(flags & CV_LKFLOW_PYR_A_READY) )
493 prev_level = cvMat( size[0][i-1].height, size[0][i-1].width, CV_8UC1 );
494 next_level = cvMat( size[0][i].height, size[0][i].width, CV_8UC1 );
495 cvSetData( &prev_level, imgI[0][i-1], step[0][i-1] );
496 cvSetData( &next_level, imgI[0][i], step[0][i] );
497 cvPyrDown( &prev_level, &next_level );
500 imgJ[0][i] = (uchar *) ptrB;
503 if( !(flags & CV_LKFLOW_PYR_B_READY) )
505 prev_level = cvMat( size[0][i-1].height, size[0][i-1].width, CV_8UC1 );
506 next_level = cvMat( size[0][i].height, size[0][i].width, CV_8UC1 );
507 cvSetData( &prev_level, imgJ[0][i-1], step[0][i-1] );
508 cvSetData( &next_level, imgJ[0][i], step[0][i] );
509 cvPyrDown( &prev_level, &next_level );
518 /* compute dI/dx and dI/dy */
520 icvCalcIxIy_32f( const float* src, int src_step, float* dstX, float* dstY, int dst_step,
521 CvSize src_size, const float* smooth_k, float* buffer0 )
523 int src_width = src_size.width, dst_width = src_size.width-2;
524 int x, height = src_size.height - 2;
525 float* buffer1 = buffer0 + src_width;
527 src_step /= sizeof(src[0]);
528 dst_step /= sizeof(dstX[0]);
530 for( ; height--; src += src_step, dstX += dst_step, dstY += dst_step )
532 const float* src2 = src + src_step;
533 const float* src3 = src + src_step*2;
535 for( x = 0; x < src_width; x++ )
537 float t0 = (src3[x] + src[x])*smooth_k[0] + src2[x]*smooth_k[1];
538 float t1 = src3[x] - src[x];
539 buffer0[x] = t0; buffer1[x] = t1;
542 for( x = 0; x < dst_width; x++ )
544 float t0 = buffer0[x+2] - buffer0[x];
545 float t1 = (buffer1[x] + buffer1[x+2])*smooth_k[0] + buffer1[x+1]*smooth_k[1];
546 dstX[x] = t0; dstY[x] = t1;
553 cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB,
554 void* pyrarrA, void* pyrarrB,
555 const CvPoint2D32f * featuresA,
556 CvPoint2D32f * featuresB,
557 int count, CvSize winSize, int level,
558 char *status, float *error,
559 CvTermCriteria criteria, int flags )
561 uchar *pyrBuffer = 0;
566 CV_FUNCNAME( "cvCalcOpticalFlowPyrLK" );
570 const int MAX_ITERS = 100;
572 CvMat stubA, *imgA = (CvMat*)arrA;
573 CvMat stubB, *imgB = (CvMat*)arrB;
574 CvMat pstubA, *pyrA = (CvMat*)pyrarrA;
575 CvMat pstubB, *pyrB = (CvMat*)pyrarrB;
577 static const float smoothKernel[] = { 0.09375, 0.3125, 0.09375 }; /* 3/32, 10/32, 3/32 */
586 int threadCount = cvGetNumThreads();
587 float* _patchI[CV_MAX_THREADS];
588 float* _patchJ[CV_MAX_THREADS];
589 float* _Ix[CV_MAX_THREADS];
590 float* _Iy[CV_MAX_THREADS];
594 CvSize patchSize = cvSize( winSize.width * 2 + 1, winSize.height * 2 + 1 );
595 int patchLen = patchSize.width * patchSize.height;
596 int srcPatchLen = (patchSize.width + 2)*(patchSize.height + 2);
598 CV_CALL( imgA = cvGetMat( imgA, &stubA ));
599 CV_CALL( imgB = cvGetMat( imgB, &stubB ));
601 if( CV_MAT_TYPE( imgA->type ) != CV_8UC1 )
602 CV_ERROR( CV_StsUnsupportedFormat, "" );
604 if( !CV_ARE_TYPES_EQ( imgA, imgB ))
605 CV_ERROR( CV_StsUnmatchedFormats, "" );
607 if( !CV_ARE_SIZES_EQ( imgA, imgB ))
608 CV_ERROR( CV_StsUnmatchedSizes, "" );
610 if( imgA->step != imgB->step )
611 CV_ERROR( CV_StsUnmatchedSizes, "imgA and imgB must have equal steps" );
613 imgSize = cvGetMatSize( imgA );
617 CV_CALL( pyrA = cvGetMat( pyrA, &pstubA ));
619 if( pyrA->step*pyrA->height < icvMinimalPyramidSize( imgSize ) )
620 CV_ERROR( CV_StsBadArg, "pyramid A has insufficient size" );
630 CV_CALL( pyrB = cvGetMat( pyrB, &pstubB ));
632 if( pyrB->step*pyrB->height < icvMinimalPyramidSize( imgSize ) )
633 CV_ERROR( CV_StsBadArg, "pyramid B has insufficient size" );
644 if( !featuresA || !featuresB )
645 CV_ERROR( CV_StsNullPtr, "Some of arrays of point coordinates are missing" );
648 CV_ERROR( CV_StsOutOfRange, "The number of tracked points is negative or zero" );
650 if( winSize.width <= 1 || winSize.height <= 1 )
651 CV_ERROR( CV_StsBadSize, "Invalid search window size" );
653 for( i = 0; i < threadCount; i++ )
654 _patchI[i] = _patchJ[i] = _Ix[i] = _Iy[i] = 0;
656 CV_CALL( icvInitPyramidalAlgorithm( imgA, imgB, pyrA, pyrB,
657 level, &criteria, MAX_ITERS, flags,
658 &imgI, &imgJ, &step, &size, &scale, &pyrBuffer ));
661 CV_CALL( status = _status = (char*)cvAlloc( count*sizeof(_status[0]) ));
663 /* buffer_size = <size for patches> + <size for pyramids> */
664 bufferBytes = (srcPatchLen + patchLen * 3) * sizeof( _patchI[0][0] ) * threadCount;
665 CV_CALL( buffer = (uchar*)cvAlloc( bufferBytes ));
667 for( i = 0; i < threadCount; i++ )
669 _patchI[i] = i == 0 ? (float*)buffer : _Iy[i-1] + patchLen;
670 _patchJ[i] = _patchI[i] + srcPatchLen;
671 _Ix[i] = _patchJ[i] + patchLen;
672 _Iy[i] = _Ix[i] + patchLen;
675 memset( status, 1, count );
677 memset( error, 0, count*sizeof(error[0]) );
679 if( !(flags & CV_LKFLOW_INITIAL_GUESSES) )
680 memcpy( featuresB, featuresA, count*sizeof(featuresA[0]));
682 /* do processing from top pyramid level (smallest image)
683 to the bottom (original image) */
684 for( l = level; l >= 0; l-- )
686 CvSize levelSize = size[l];
687 int levelStep = step[l];
691 #pragma omp parallel for num_threads(threadCount) schedule(dynamic)
693 /* find flow for each given point */
694 for( i = 0; i < count; i++ )
697 CvPoint minI, maxI, minJ, maxJ;
701 CvPoint prev_minJ = { -1, -1 }, prev_maxJ = { -1, -1 };
702 double Gxx = 0, Gxy = 0, Gyy = 0, D = 0, minEig = 0;
703 float prev_mx = 0, prev_my = 0;
705 int threadIdx = cvGetThreadNum();
706 float* patchI = _patchI[threadIdx];
707 float* patchJ = _patchJ[threadIdx];
708 float* Ix = _Ix[threadIdx];
709 float* Iy = _Iy[threadIdx];
711 v.x = featuresB[i].x;
712 v.y = featuresB[i].y;
720 v.x = (float)(v.x * scale[l]);
721 v.y = (float)(v.y * scale[l]);
724 pt_status = status[i];
728 minI = maxI = minJ = maxJ = cvPoint( 0, 0 );
730 u.x = (float) (featuresA[i].x * scale[l]);
731 u.y = (float) (featuresA[i].y * scale[l]);
733 intersect( u, winSize, levelSize, &minI, &maxI );
734 isz = jsz = cvSize(maxI.x - minI.x + 2, maxI.y - minI.y + 2);
735 u.x += (minI.x - (patchSize.width - maxI.x + 1))*0.5f;
736 u.y += (minI.y - (patchSize.height - maxI.y + 1))*0.5f;
738 if( isz.width < 3 || isz.height < 3 ||
739 icvGetRectSubPix_8u32f_C1R( imgI[l], levelStep, levelSize,
740 patchI, isz.width*sizeof(patchI[0]), isz, u ) < 0 )
742 /* point is outside the image. take the next */
747 icvCalcIxIy_32f( patchI, isz.width*sizeof(patchI[0]), Ix, Iy,
748 (isz.width-2)*sizeof(patchI[0]), isz, smoothKernel, patchJ );
750 for( j = 0; j < criteria.max_iter; j++ )
752 double bx = 0, by = 0;
756 intersect( v, winSize, levelSize, &minJ, &maxJ );
758 minJ.x = MAX( minJ.x, minI.x );
759 minJ.y = MAX( minJ.y, minI.y );
761 maxJ.x = MIN( maxJ.x, maxI.x );
762 maxJ.y = MIN( maxJ.y, maxI.y );
764 jsz = cvSize(maxJ.x - minJ.x, maxJ.y - minJ.y);
766 _v.x = v.x + (minJ.x - (patchSize.width - maxJ.x + 1))*0.5f;
767 _v.y = v.y + (minJ.y - (patchSize.height - maxJ.y + 1))*0.5f;
769 if( jsz.width < 1 || jsz.height < 1 ||
770 icvGetRectSubPix_8u32f_C1R( imgJ[l], levelStep, levelSize, patchJ,
771 jsz.width*sizeof(patchJ[0]), jsz, _v ) < 0 )
773 /* point is outside image. take the next */
778 if( maxJ.x == prev_maxJ.x && maxJ.y == prev_maxJ.y &&
779 minJ.x == prev_minJ.x && minJ.y == prev_minJ.y )
781 for( y = 0; y < jsz.height; y++ )
783 const float* pi = patchI +
784 (y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
785 const float* pj = patchJ + y*jsz.width;
786 const float* ix = Ix +
787 (y + minJ.y - minI.y)*(isz.width-2) + minJ.x - minI.x;
788 const float* iy = Iy + (ix - Ix);
790 for( x = 0; x < jsz.width; x++ )
792 double t0 = pi[x] - pj[x];
801 for( y = 0; y < jsz.height; y++ )
803 const float* pi = patchI +
804 (y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
805 const float* pj = patchJ + y*jsz.width;
806 const float* ix = Ix +
807 (y + minJ.y - minI.y)*(isz.width-2) + minJ.x - minI.x;
808 const float* iy = Iy + (ix - Ix);
810 for( x = 0; x < jsz.width; x++ )
812 double t = pi[x] - pj[x];
813 bx += (double) (t * ix[x]);
814 by += (double) (t * iy[x]);
815 Gxx += ix[x] * ix[x];
816 Gxy += ix[x] * iy[x];
817 Gyy += iy[x] * iy[x];
821 D = Gxx * Gyy - Gxy * Gxy;
822 if( D < DBL_EPSILON )
828 // Adi Shavit - 2008.05
829 if( flags & CV_LKFLOW_GET_MIN_EIGENVALS )
830 minEig = (Gyy + Gxx - sqrt((Gxx-Gyy)*(Gxx-Gyy) + 4.*Gxy*Gxy))/(2*jsz.height*jsz.width);
838 mx = (float) ((Gyy * bx - Gxy * by) * D);
839 my = (float) ((Gxx * by - Gxy * bx) * D);
844 if( mx * mx + my * my < criteria.epsilon )
847 if( j > 0 && fabs(mx + prev_mx) < 0.01 && fabs(my + prev_my) < 0.01 )
858 status[i] = (char)pt_status;
859 if( l == 0 && error && pt_status )
863 if( flags & CV_LKFLOW_GET_MIN_EIGENVALS )
867 for( y = 0; y < jsz.height; y++ )
869 const float* pi = patchI +
870 (y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
871 const float* pj = patchJ + y*jsz.width;
873 for( x = 0; x < jsz.width; x++ )
875 double t = pi[x] - pj[x];
881 error[i] = (float)err;
883 } // end of point processing loop (i)
885 } // end of pyramid levels loop (l)
889 cvFree( &pyrBuffer );
896 /* Affine tracking algorithm */
899 cvCalcAffineFlowPyrLK( const void* arrA, const void* arrB,
900 void* pyrarrA, void* pyrarrB,
901 const CvPoint2D32f * featuresA,
902 CvPoint2D32f * featuresB,
903 float *matrices, int count,
904 CvSize winSize, int level,
905 char *status, float *error,
906 CvTermCriteria criteria, int flags )
908 const int MAX_ITERS = 100;
912 uchar *pyr_buffer = 0;
914 CV_FUNCNAME( "cvCalcAffineFlowPyrLK" );
918 CvMat stubA, *imgA = (CvMat*)arrA;
919 CvMat stubB, *imgB = (CvMat*)arrB;
920 CvMat pstubA, *pyrA = (CvMat*)pyrarrA;
921 CvMat pstubB, *pyrB = (CvMat*)pyrarrB;
923 static const float smoothKernel[] = { 0.09375, 0.3125, 0.09375 }; /* 3/32, 10/32, 3/32 */
940 CvSize patchSize = cvSize( winSize.width * 2 + 1, winSize.height * 2 + 1 );
941 int patchLen = patchSize.width * patchSize.height;
942 int patchStep = patchSize.width * sizeof( patchI[0] );
944 CvSize srcPatchSize = cvSize( patchSize.width + 2, patchSize.height + 2 );
945 int srcPatchLen = srcPatchSize.width * srcPatchSize.height;
946 int srcPatchStep = srcPatchSize.width * sizeof( patchI[0] );
948 float eps = (float)MIN(winSize.width, winSize.height);
950 CV_CALL( imgA = cvGetMat( imgA, &stubA ));
951 CV_CALL( imgB = cvGetMat( imgB, &stubB ));
953 if( CV_MAT_TYPE( imgA->type ) != CV_8UC1 )
954 CV_ERROR( CV_StsUnsupportedFormat, "" );
956 if( !CV_ARE_TYPES_EQ( imgA, imgB ))
957 CV_ERROR( CV_StsUnmatchedFormats, "" );
959 if( !CV_ARE_SIZES_EQ( imgA, imgB ))
960 CV_ERROR( CV_StsUnmatchedSizes, "" );
962 if( imgA->step != imgB->step )
963 CV_ERROR( CV_StsUnmatchedSizes, "imgA and imgB must have equal steps" );
966 CV_ERROR( CV_StsNullPtr, "" );
968 imgSize = cvGetMatSize( imgA );
972 CV_CALL( pyrA = cvGetMat( pyrA, &pstubA ));
974 if( pyrA->step*pyrA->height < icvMinimalPyramidSize( imgSize ) )
975 CV_ERROR( CV_StsBadArg, "pyramid A has insufficient size" );
985 CV_CALL( pyrB = cvGetMat( pyrB, &pstubB ));
987 if( pyrB->step*pyrB->height < icvMinimalPyramidSize( imgSize ) )
988 CV_ERROR( CV_StsBadArg, "pyramid B has insufficient size" );
999 /* check input arguments */
1000 if( !featuresA || !featuresB || !matrices )
1001 CV_ERROR( CV_StsNullPtr, "" );
1003 if( winSize.width <= 1 || winSize.height <= 1 )
1004 CV_ERROR( CV_StsOutOfRange, "the search window is too small" );
1007 CV_ERROR( CV_StsOutOfRange, "" );
1009 CV_CALL( icvInitPyramidalAlgorithm( imgA, imgB,
1010 pyrA, pyrB, level, &criteria, MAX_ITERS, flags,
1011 &imgI, &imgJ, &step, &size, &scale, &pyr_buffer ));
1013 /* buffer_size = <size for patches> + <size for pyramids> */
1014 bufferBytes = (srcPatchLen + patchLen*3)*sizeof(patchI[0]) + (36*2 + 6)*sizeof(double);
1016 CV_CALL( buffer = (uchar*)cvAlloc(bufferBytes));
1019 CV_CALL( status = _status = (char*)cvAlloc(count) );
1021 patchI = (float *) buffer;
1022 patchJ = patchI + srcPatchLen;
1023 Ix = patchJ + patchLen;
1027 memset( status, 1, count );
1029 if( !(flags & CV_LKFLOW_INITIAL_GUESSES) )
1031 memcpy( featuresB, featuresA, count * sizeof( featuresA[0] ));
1032 for( i = 0; i < count * 4; i += 4 )
1034 matrices[i] = matrices[i + 3] = 1.f;
1035 matrices[i + 1] = matrices[i + 2] = 0.f;
1039 for( i = 0; i < count; i++ )
1041 featuresB[i].x = (float)(featuresB[i].x * scale[level] * 0.5);
1042 featuresB[i].y = (float)(featuresB[i].y * scale[level] * 0.5);
1045 /* do processing from top pyramid level (smallest image)
1046 to the bottom (original image) */
1047 for( l = level; l >= 0; l-- )
1049 CvSize levelSize = size[l];
1050 int levelStep = step[l];
1052 /* find flow for each given point at the particular level */
1053 for( i = 0; i < count; i++ )
1058 double meanI = 0, meanJ = 0;
1060 int pt_status = status[i];
1066 Av[0] = matrices[i*4];
1067 Av[1] = matrices[i*4+1];
1068 Av[3] = matrices[i*4+2];
1069 Av[4] = matrices[i*4+3];
1071 Av[2] = featuresB[i].x += featuresB[i].x;
1072 Av[5] = featuresB[i].y += featuresB[i].y;
1074 u.x = (float) (featuresA[i].x * scale[l]);
1075 u.y = (float) (featuresA[i].y * scale[l]);
1077 if( u.x < -eps || u.x >= levelSize.width+eps ||
1078 u.y < -eps || u.y >= levelSize.height+eps ||
1079 icvGetRectSubPix_8u32f_C1R( imgI[l], levelStep,
1080 levelSize, patchI, srcPatchStep, srcPatchSize, u ) < 0 )
1082 /* point is outside the image. take the next */
1088 icvCalcIxIy_32f( patchI, srcPatchStep, Ix, Iy,
1089 (srcPatchSize.width-2)*sizeof(patchI[0]), srcPatchSize,
1090 smoothKernel, patchJ );
1092 /* repack patchI (remove borders) */
1093 for( k = 0; k < patchSize.height; k++ )
1094 memcpy( patchI + k * patchSize.width,
1095 patchI + (k + 1) * srcPatchSize.width + 1, patchStep );
1097 memset( G, 0, sizeof( G ));
1099 /* calculate G matrix */
1100 for( y = -winSize.height, k = 0; y <= winSize.height; y++ )
1102 for( x = -winSize.width; x <= winSize.width; x++, k++ )
1104 double ixix = ((double) Ix[k]) * Ix[k];
1105 double ixiy = ((double) Ix[k]) * Iy[k];
1106 double iyiy = ((double) Iy[k]) * Iy[k];
1129 // G[13] == G[8] == G[4]
1160 meanI /= patchSize.width*patchSize.height;
1166 // fill part of G below its diagonal
1167 for( y = 1; y < 6; y++ )
1168 for( x = 0; x < y; x++ )
1169 G[y * 6 + x] = G[x * 6 + y];
1171 cvInitMatHeader( &mat, 6, 6, CV_64FC1, G );
1173 if( cvInvert( &mat, &mat, CV_SVD ) < 1e-4 )
1175 /* bad matrix. take the next point */
1181 for( j = 0; j < criteria.max_iter; j++ )
1183 double b[6] = {0,0,0,0,0,0}, eta[6];
1184 double t0, t1, s = 0;
1186 if( Av[2] < -eps || Av[2] >= levelSize.width+eps ||
1187 Av[5] < -eps || Av[5] >= levelSize.height+eps ||
1188 icvGetQuadrangleSubPix_8u32f_C1R( imgJ[l], levelStep,
1189 levelSize, patchJ, patchStep, patchSize, Av ) < 0 )
1195 for( y = -winSize.height, k = 0, meanJ = 0; y <= winSize.height; y++ )
1196 for( x = -winSize.width; x <= winSize.width; x++, k++ )
1199 meanJ = meanJ / (patchSize.width * patchSize.height) - meanI;
1201 for( y = -winSize.height, k = 0; y <= winSize.height; y++ )
1203 for( x = -winSize.width; x <= winSize.width; x++, k++ )
1205 double t = patchI[k] - patchJ[k] + meanJ;
1206 double ixt = Ix[k] * t;
1207 double iyt = Iy[k] * t;
1220 icvTransformVector_64d( G, b, eta, 6, 6 );
1222 Av[2] = (float)(Av[2] + Av[0] * eta[0] + Av[1] * eta[1]);
1223 Av[5] = (float)(Av[5] + Av[3] * eta[0] + Av[4] * eta[1]);
1225 t0 = Av[0] * (1 + eta[2]) + Av[1] * eta[4];
1226 t1 = Av[0] * eta[3] + Av[1] * (1 + eta[5]);
1230 t0 = Av[3] * (1 + eta[2]) + Av[4] * eta[4];
1231 t1 = Av[3] * eta[3] + Av[4] * (1 + eta[5]);
1235 if( eta[0] * eta[0] + eta[1] * eta[1] < criteria.epsilon )
1239 if( pt_status != 0 || l == 0 )
1241 status[i] = (char)pt_status;
1242 featuresB[i].x = Av[2];
1243 featuresB[i].y = Av[5];
1245 matrices[i*4] = Av[0];
1246 matrices[i*4+1] = Av[1];
1247 matrices[i*4+2] = Av[3];
1248 matrices[i*4+3] = Av[4];
1251 if( pt_status && l == 0 && error )
1256 for( y = 0, k = 0; y < patchSize.height; y++ )
1258 for( x = 0; x < patchSize.width; x++, k++ )
1260 double t = patchI[k] - patchJ[k] + meanJ;
1264 error[i] = (float)sqrt(err);
1271 cvFree( &pyr_buffer );
1279 icvGetRTMatrix( const CvPoint2D32f* a, const CvPoint2D32f* b,
1280 int count, CvMat* M, int full_affine )
1284 double sa[36], sb[6];
1285 CvMat A = cvMat( 6, 6, CV_64F, sa ), B = cvMat( 6, 1, CV_64F, sb );
1286 CvMat MM = cvMat( 6, 1, CV_64F, M->data.db );
1290 memset( sa, 0, sizeof(sa) );
1291 memset( sb, 0, sizeof(sb) );
1293 for( i = 0; i < count; i++ )
1295 sa[0] += a[i].x*a[i].x;
1296 sa[1] += a[i].y*a[i].x;
1299 sa[6] += a[i].x*a[i].y;
1300 sa[7] += a[i].y*a[i].y;
1307 sb[0] += a[i].x*b[i].x;
1308 sb[1] += a[i].y*b[i].x;
1310 sb[3] += a[i].x*b[i].y;
1311 sb[4] += a[i].y*b[i].y;
1325 cvSolve( &A, &B, &MM, CV_SVD );
1329 double sa[16], sb[4], m[4], *om = M->data.db;
1330 CvMat A = cvMat( 4, 4, CV_64F, sa ), B = cvMat( 4, 1, CV_64F, sb );
1331 CvMat MM = cvMat( 4, 1, CV_64F, m );
1335 memset( sa, 0, sizeof(sa) );
1336 memset( sb, 0, sizeof(sb) );
1338 for( i = 0; i < count; i++ )
1340 sa[0] += a[i].x*a[i].x + a[i].y*a[i].y;
1346 sa[5] += a[i].x*a[i].x + a[i].y*a[i].y;
1360 sb[0] += a[i].x*b[i].x + a[i].y*b[i].y;
1361 sb[1] += a[i].x*b[i].y - a[i].y*b[i].x;
1366 cvSolve( &A, &B, &MM, CV_SVD );
1368 om[0] = om[4] = m[0];
1378 cvEstimateRigidTransform( const CvArr* _A, const CvArr* _B, CvMat* _M, int full_affine )
1382 const int COUNT = 15;
1383 const int WIDTH = 160, HEIGHT = 120;
1384 const int RANSAC_MAX_ITERS = 100;
1385 const int RANSAC_SIZE0 = 3;
1386 const double MIN_TRIANGLE_SIDE = 20;
1387 const double RANSAC_GOOD_RATIO = 0.5;
1390 CvMat *sA = 0, *sB = 0;
1391 CvPoint2D32f *pA = 0, *pB = 0;
1396 CV_FUNCNAME( "cvEstimateRigidTransform" );
1403 int cn, equal_sizes;
1405 int count_x, count_y, count;
1407 CvRNG rng = cvRNG(-1);
1409 CvMat M = cvMat( 2, 3, CV_64F, m );
1412 CV_CALL( A = cvGetMat( _A, &stubA ));
1413 CV_CALL( B = cvGetMat( _B, &stubB ));
1415 if( !CV_IS_MAT(_M) )
1416 CV_ERROR( _M ? CV_StsBadArg : CV_StsNullPtr, "Output parameter M is not a valid matrix" );
1418 if( !CV_ARE_SIZES_EQ( A, B ) )
1419 CV_ERROR( CV_StsUnmatchedSizes, "Both input images must have the same size" );
1421 if( !CV_ARE_TYPES_EQ( A, B ) )
1422 CV_ERROR( CV_StsUnmatchedFormats, "Both input images must have the same data type" );
1424 if( CV_MAT_TYPE(A->type) == CV_8UC1 || CV_MAT_TYPE(A->type) == CV_8UC3 )
1426 cn = CV_MAT_CN(A->type);
1428 sz1 = cvSize(WIDTH, HEIGHT);
1430 scale = MAX( (double)sz1.width/sz0.width, (double)sz1.height/sz0.height );
1431 scale = MIN( scale, 1. );
1432 sz1.width = cvRound( sz0.width * scale );
1433 sz1.height = cvRound( sz0.height * scale );
1435 equal_sizes = sz1.width == sz0.width && sz1.height == sz0.height;
1437 if( !equal_sizes || cn != 1 )
1439 CV_CALL( sA = cvCreateMat( sz1.height, sz1.width, CV_8UC1 ));
1440 CV_CALL( sB = cvCreateMat( sz1.height, sz1.width, CV_8UC1 ));
1442 if( !equal_sizes && cn != 1 )
1443 CV_CALL( gray = cvCreateMat( sz0.height, sz0.width, CV_8UC1 ));
1447 cvCvtColor( A, gray, CV_BGR2GRAY );
1448 cvResize( gray, sA, CV_INTER_AREA );
1449 cvCvtColor( B, gray, CV_BGR2GRAY );
1450 cvResize( gray, sB, CV_INTER_AREA );
1454 cvResize( gray, sA, CV_INTER_AREA );
1455 cvResize( gray, sB, CV_INTER_AREA );
1459 cvCvtColor( A, gray, CV_BGR2GRAY );
1460 cvResize( gray, sA, CV_INTER_AREA );
1461 cvCvtColor( B, gray, CV_BGR2GRAY );
1464 cvReleaseMat( &gray );
1470 count_x = cvRound((double)COUNT*sz1.width/sz1.height);
1471 count = count_x * count_y;
1473 CV_CALL( pA = (CvPoint2D32f*)cvAlloc( count*sizeof(pA[0]) ));
1474 CV_CALL( pB = (CvPoint2D32f*)cvAlloc( count*sizeof(pB[0]) ));
1475 CV_CALL( status = (char*)cvAlloc( count*sizeof(status[0]) ));
1477 for( i = 0, k = 0; i < count_y; i++ )
1478 for( j = 0; j < count_x; j++, k++ )
1480 pA[k].x = (j+0.5f)*sz1.width/count_x;
1481 pA[k].y = (i+0.5f)*sz1.height/count_y;
1484 // find the corresponding points in B
1485 cvCalcOpticalFlowPyrLK( A, B, 0, 0, pA, pB, count, cvSize(10,10), 3,
1486 status, 0, cvTermCriteria(CV_TERMCRIT_ITER,40,0.1), 0 );
1488 // repack the remained points
1489 for( i = 0, k = 0; i < count; i++ )
1502 else if( CV_MAT_TYPE(A->type) == CV_32FC2 || CV_MAT_TYPE(A->type) == CV_32SC2 )
1504 count = A->cols*A->rows;
1506 if( CV_IS_MAT_CONT(A->type & B->type) && CV_MAT_TYPE(A->type) == CV_32FC2 )
1508 pA = (CvPoint2D32f*)A->data.ptr;
1509 pB = (CvPoint2D32f*)B->data.ptr;
1516 CV_CALL( pA = (CvPoint2D32f*)cvAlloc( count*sizeof(pA[0]) ));
1517 CV_CALL( pB = (CvPoint2D32f*)cvAlloc( count*sizeof(pB[0]) ));
1518 _pA = cvMat( A->rows, A->cols, CV_32FC2, pA );
1519 _pB = cvMat( B->rows, B->cols, CV_32FC2, pB );
1520 cvConvert( A, &_pA );
1521 cvConvert( B, &_pB );
1525 CV_ERROR( CV_StsUnsupportedFormat, "Both input images must have either 8uC1 or 8uC3 type" );
1527 CV_CALL( good_idx = (int*)cvAlloc( count*sizeof(good_idx[0]) ));
1529 if( count < RANSAC_SIZE0 )
1533 // 1. find the consensus
1534 for( k = 0; k < RANSAC_MAX_ITERS; k++ )
1536 int idx[RANSAC_SIZE0];
1540 memset( a, 0, sizeof(a) );
1541 memset( b, 0, sizeof(b) );
1543 // choose random 3 non-complanar points from A & B
1544 for( i = 0; i < RANSAC_SIZE0; i++ )
1546 for( k1 = 0; k1 < RANSAC_MAX_ITERS; k1++ )
1548 idx[i] = cvRandInt(&rng) % count;
1550 for( j = 0; j < i; j++ )
1552 if( idx[j] == idx[i] )
1554 // check that the points are not very close one each other
1555 if( fabs(pA[idx[i]].x - pA[idx[j]].x) +
1556 fabs(pA[idx[i]].y - pA[idx[j]].y) < MIN_TRIANGLE_SIDE )
1558 if( fabs(pB[idx[i]].x - pB[idx[j]].x) +
1559 fabs(pB[idx[i]].y - pB[idx[j]].y) < MIN_TRIANGLE_SIDE )
1566 if( i+1 == RANSAC_SIZE0 )
1568 // additional check for non-complanar vectors
1577 if( fabs((a[1].x - a[0].x)*(a[2].y - a[0].y) - (a[1].y - a[0].y)*(a[2].x - a[0].x)) < 1 ||
1578 fabs((b[1].x - b[0].x)*(b[2].y - b[0].y) - (b[1].y - b[0].y)*(b[2].x - b[0].x)) < 1 )
1584 if( k1 >= RANSAC_MAX_ITERS )
1588 if( i < RANSAC_SIZE0 )
1591 // estimate the transformation using 3 points
1592 icvGetRTMatrix( a, b, 3, &M, full_affine );
1594 for( i = 0, good_count = 0; i < count; i++ )
1596 if( fabs( m[0]*pA[i].x + m[1]*pA[i].y + m[2] - pB[i].x ) +
1597 fabs( m[3]*pA[i].x + m[4]*pA[i].y + m[5] - pB[i].y ) < 8 )
1598 good_idx[good_count++] = i;
1601 if( good_count >= count*RANSAC_GOOD_RATIO )
1605 if( k >= RANSAC_MAX_ITERS )
1608 if( good_count < count )
1610 for( i = 0; i < good_count; i++ )
1618 icvGetRTMatrix( pA, pB, good_count, &M, full_affine );
1621 CV_CALL( cvConvert( &M, _M ));
1626 cvReleaseMat( &sA );
1627 cvReleaseMat( &sB );
1631 cvFree( &good_idx );
1632 cvReleaseMat( &gray );
1639 Mat estimateRigidTransform( const vector<Point2f>& A,
1640 const vector<Point2f>& B,
1643 Mat M(2, 3, CV_64F);
1644 CvMat _A = Mat_<Point2f>(A), _B = Mat_<Point2f>(B), _M = M;
1645 cvEstimateRigidTransform(&_A, &_B, &_M, fullAffine);