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refactored video module; use the new-style algorithms now

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pull/680/head
Vadim Pisarevsky 12 years ago
parent 87563c6638
commit 14a0abbfa9
3 changed files with 1238 additions and 86 deletions
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  1. 850
      modules/video/src/affineflow.cpp
  2. 388
      modules/video/src/compat_video.cpp
  3. 86
      modules/video/src/simpleflow.hpp

850
modules/video/src/affineflow.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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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"
// to be moved to legacy
static int icvMinimalPyramidSize( CvSize imgSize )
{
return cvAlign(imgSize.width,8) * imgSize.height / 3;
}
static void
icvInitPyramidalAlgorithm( const CvMat* imgA, const CvMat* imgB,
CvMat* pyrA, CvMat* pyrB,
int level, CvTermCriteria * criteria,
int max_iters, int flags,
uchar *** imgI, uchar *** imgJ,
int **step, CvSize** size,
double **scale, cv::AutoBuffer<uchar>* buffer )
{
const int ALIGN = 8;
int pyrBytes, bufferBytes = 0, elem_size;
int level1 = level + 1;
int i;
CvSize imgSize, levelSize;
*imgI = *imgJ = 0;
*step = 0;
*scale = 0;
*size = 0;
/* check input arguments */
if( ((flags & CV_LKFLOW_PYR_A_READY) != 0 && !pyrA) ||
((flags & CV_LKFLOW_PYR_B_READY) != 0 && !pyrB) )
CV_Error( CV_StsNullPtr, "Some of the precomputed pyramids are missing" );
if( level < 0 )
CV_Error( CV_StsOutOfRange, "The number of pyramid levels is negative" );
switch( criteria->type )
{
case CV_TERMCRIT_ITER:
criteria->epsilon = 0.f;
break;
case CV_TERMCRIT_EPS:
criteria->max_iter = max_iters;
break;
case CV_TERMCRIT_ITER | CV_TERMCRIT_EPS:
break;
default:
assert( 0 );
CV_Error( CV_StsBadArg, "Invalid termination criteria" );
}
/* compare squared values */
criteria->epsilon *= criteria->epsilon;
/* set pointers and step for every level */
pyrBytes = 0;
imgSize = cvGetSize(imgA);
elem_size = CV_ELEM_SIZE(imgA->type);
levelSize = imgSize;
for( i = 1; i < level1; i++ )
{
levelSize.width = (levelSize.width + 1) >> 1;
levelSize.height = (levelSize.height + 1) >> 1;
int tstep = cvAlign(levelSize.width,ALIGN) * elem_size;
pyrBytes += tstep * levelSize.height;
}
assert( pyrBytes <= imgSize.width * imgSize.height * elem_size * 4 / 3 );
/* buffer_size = <size for patches> + <size for pyramids> */
bufferBytes = (int)((level1 >= 0) * ((pyrA->data.ptr == 0) +
(pyrB->data.ptr == 0)) * pyrBytes +
(sizeof(imgI[0][0]) * 2 + sizeof(step[0][0]) +
sizeof(size[0][0]) + sizeof(scale[0][0])) * level1);
buffer->allocate( bufferBytes );
*imgI = (uchar **) (uchar*)(*buffer);
*imgJ = *imgI + level1;
*step = (int *) (*imgJ + level1);
*scale = (double *) (*step + level1);
*size = (CvSize *)(*scale + level1);
imgI[0][0] = imgA->data.ptr;
imgJ[0][0] = imgB->data.ptr;
step[0][0] = imgA->step;
scale[0][0] = 1;
size[0][0] = imgSize;
if( level > 0 )
{
uchar *bufPtr = (uchar *) (*size + level1);
uchar *ptrA = pyrA->data.ptr;
uchar *ptrB = pyrB->data.ptr;
if( !ptrA )
{
ptrA = bufPtr;
bufPtr += pyrBytes;
}
if( !ptrB )
ptrB = bufPtr;
levelSize = imgSize;
/* build pyramids for both frames */
for( i = 1; i <= level; i++ )
{
int levelBytes;
CvMat prev_level, next_level;
levelSize.width = (levelSize.width + 1) >> 1;
levelSize.height = (levelSize.height + 1) >> 1;
size[0][i] = levelSize;
step[0][i] = cvAlign( levelSize.width, ALIGN ) * elem_size;
scale[0][i] = scale[0][i - 1] * 0.5;
levelBytes = step[0][i] * levelSize.height;
imgI[0][i] = (uchar *) ptrA;
ptrA += levelBytes;
if( !(flags & CV_LKFLOW_PYR_A_READY) )
{
prev_level = cvMat( size[0][i-1].height, size[0][i-1].width, CV_8UC1 );
next_level = cvMat( size[0][i].height, size[0][i].width, CV_8UC1 );
cvSetData( &prev_level, imgI[0][i-1], step[0][i-1] );
cvSetData( &next_level, imgI[0][i], step[0][i] );
cvPyrDown( &prev_level, &next_level );
}
imgJ[0][i] = (uchar *) ptrB;
ptrB += levelBytes;
if( !(flags & CV_LKFLOW_PYR_B_READY) )
{
prev_level = cvMat( size[0][i-1].height, size[0][i-1].width, CV_8UC1 );
next_level = cvMat( size[0][i].height, size[0][i].width, CV_8UC1 );
cvSetData( &prev_level, imgJ[0][i-1], step[0][i-1] );
cvSetData( &next_level, imgJ[0][i], step[0][i] );
cvPyrDown( &prev_level, &next_level );
}
}
}
}
/* compute dI/dx and dI/dy */
static void
icvCalcIxIy_32f( const float* src, int src_step, float* dstX, float* dstY, int dst_step,
CvSize src_size, const float* smooth_k, float* buffer0 )
{
int src_width = src_size.width, dst_width = src_size.width-2;
int x, height = src_size.height - 2;
float* buffer1 = buffer0 + src_width;
src_step /= sizeof(src[0]);
dst_step /= sizeof(dstX[0]);
for( ; height--; src += src_step, dstX += dst_step, dstY += dst_step )
{
const float* src2 = src + src_step;
const float* src3 = src + src_step*2;
for( x = 0; x < src_width; x++ )
{
float t0 = (src3[x] + src[x])*smooth_k[0] + src2[x]*smooth_k[1];
float t1 = src3[x] - src[x];
buffer0[x] = t0; buffer1[x] = t1;
}
for( x = 0; x < dst_width; x++ )
{
float t0 = buffer0[x+2] - buffer0[x];
float t1 = (buffer1[x] + buffer1[x+2])*smooth_k[0] + buffer1[x+1]*smooth_k[1];
dstX[x] = t0; dstY[x] = t1;
}
}
}
#undef CV_8TO32F
#define CV_8TO32F(a) (a)
static const void*
icvAdjustRect( const void* srcptr, int src_step, int pix_size,
CvSize src_size, CvSize win_size,
CvPoint ip, CvRect* pRect )
{
CvRect rect;
const char* src = (const char*)srcptr;
if( ip.x >= 0 )
{
src += ip.x*pix_size;
rect.x = 0;
}
else
{
rect.x = -ip.x;
if( rect.x > win_size.width )
rect.x = win_size.width;
}
if( ip.x + win_size.width < src_size.width )
rect.width = win_size.width;
else
{
rect.width = src_size.width - ip.x - 1;
if( rect.width < 0 )
{
src += rect.width*pix_size;
rect.width = 0;
}
assert( rect.width <= win_size.width );
}
if( ip.y >= 0 )
{
src += ip.y * src_step;
rect.y = 0;
}
else
rect.y = -ip.y;
if( ip.y + win_size.height < src_size.height )
rect.height = win_size.height;
else
{
rect.height = src_size.height - ip.y - 1;
if( rect.height < 0 )
{
src += rect.height*src_step;
rect.height = 0;
}
}
*pRect = rect;
return src - rect.x*pix_size;
}
static CvStatus CV_STDCALL icvGetRectSubPix_8u32f_C1R
( const uchar* src, int src_step, CvSize src_size,
float* dst, int dst_step, CvSize win_size, CvPoint2D32f center )
{
CvPoint ip;
float a12, a22, b1, b2;
float a, b;
double s = 0;
int i, j;
center.x -= (win_size.width-1)*0.5f;
center.y -= (win_size.height-1)*0.5f;
ip.x = cvFloor( center.x );
ip.y = cvFloor( center.y );
if( win_size.width <= 0 || win_size.height <= 0 )
return CV_BADRANGE_ERR;
a = center.x - ip.x;
b = center.y - ip.y;
a = MAX(a,0.0001f);
a12 = a*(1.f-b);
a22 = a*b;
b1 = 1.f - b;
b2 = b;
s = (1. - a)/a;
src_step /= sizeof(src[0]);
dst_step /= sizeof(dst[0]);
if( 0 <= ip.x && ip.x + win_size.width < src_size.width &&
0 <= ip.y && ip.y + win_size.height < src_size.height )
{
// extracted rectangle is totally inside the image
src += ip.y * src_step + ip.x;
#if 0
if( icvCopySubpix_8u32f_C1R_p &&
icvCopySubpix_8u32f_C1R_p( src, src_step, dst,
dst_step*sizeof(dst[0]), win_size, a, b ) >= 0 )
return CV_OK;
#endif
for( ; win_size.height--; src += src_step, dst += dst_step )
{
float prev = (1 - a)*(b1*CV_8TO32F(src[0]) + b2*CV_8TO32F(src[src_step]));
for( j = 0; j < win_size.width; j++ )
{
float t = a12*CV_8TO32F(src[j+1]) + a22*CV_8TO32F(src[j+1+src_step]);
dst[j] = prev + t;
prev = (float)(t*s);
}
}
}
else
{
CvRect r;
src = (const uchar*)icvAdjustRect( src, src_step*sizeof(*src),
sizeof(*src), src_size, win_size,ip, &r);
for( i = 0; i < win_size.height; i++, dst += dst_step )
{
const uchar *src2 = src + src_step;
if( i < r.y || i >= r.height )
src2 -= src_step;
for( j = 0; j < r.x; j++ )
{
float s0 = CV_8TO32F(src[r.x])*b1 +
CV_8TO32F(src2[r.x])*b2;
dst[j] = (float)(s0);
}
if( j < r.width )
{
float prev = (1 - a)*(b1*CV_8TO32F(src[j]) + b2*CV_8TO32F(src2[j]));
for( ; j < r.width; j++ )
{
float t = a12*CV_8TO32F(src[j+1]) + a22*CV_8TO32F(src2[j+1]);
dst[j] = prev + t;
prev = (float)(t*s);
}
}
for( ; j < win_size.width; j++ )
{
float s0 = CV_8TO32F(src[r.width])*b1 +
CV_8TO32F(src2[r.width])*b2;
dst[j] = (float)(s0);
}
if( i < r.height )
src = src2;
}
}
return CV_OK;
}
#define ICV_32F8U(x) ((uchar)cvRound(x))
#define ICV_DEF_GET_QUADRANGLE_SUB_PIX_FUNC( flavor, srctype, dsttype, \
worktype, cast_macro, cvt ) \
static CvStatus CV_STDCALL \
icvGetQuadrangleSubPix_##flavor##_C1R \
( const srctype * src, int src_step, CvSize src_size, \
dsttype *dst, int dst_step, CvSize win_size, const float *matrix ) \
{ \
int x, y; \
double dx = (win_size.width - 1)*0.5; \
double dy = (win_size.height - 1)*0.5; \
double A11 = matrix[0], A12 = matrix[1], A13 = matrix[2]-A11*dx-A12*dy; \
double A21 = matrix[3], A22 = matrix[4], A23 = matrix[5]-A21*dx-A22*dy; \
\
src_step /= sizeof(srctype); \
dst_step /= sizeof(dsttype); \
\
for( y = 0; y < win_size.height; y++, dst += dst_step ) \
{ \
double xs = A12*y + A13; \
double ys = A22*y + A23; \
double xe = A11*(win_size.width-1) + A12*y + A13; \
double ye = A21*(win_size.width-1) + A22*y + A23; \
\
if( (unsigned)(cvFloor(xs)-1) < (unsigned)(src_size.width - 3) && \
(unsigned)(cvFloor(ys)-1) < (unsigned)(src_size.height - 3) && \
(unsigned)(cvFloor(xe)-1) < (unsigned)(src_size.width - 3) && \
(unsigned)(cvFloor(ye)-1) < (unsigned)(src_size.height - 3)) \
{ \
for( x = 0; x < win_size.width; x++ ) \
{ \
int ixs = cvFloor( xs ); \
int iys = cvFloor( ys ); \
const srctype *ptr = src + src_step*iys + ixs; \
double a = xs - ixs, b = ys - iys, a1 = 1.f - a; \
worktype p0 = cvt(ptr[0])*a1 + cvt(ptr[1])*a; \
worktype p1 = cvt(ptr[src_step])*a1 + cvt(ptr[src_step+1])*a;\
xs += A11; \
ys += A21; \
\
dst[x] = cast_macro(p0 + b * (p1 - p0)); \
} \
} \
else \
{ \
for( x = 0; x < win_size.width; x++ ) \
{ \
int ixs = cvFloor( xs ), iys = cvFloor( ys ); \
double a = xs - ixs, b = ys - iys, a1 = 1.f - a; \
const srctype *ptr0, *ptr1; \
worktype p0, p1; \
xs += A11; ys += A21; \
\
if( (unsigned)iys < (unsigned)(src_size.height-1) ) \
ptr0 = src + src_step*iys, ptr1 = ptr0 + src_step; \
else \
ptr0 = ptr1 = src + (iys < 0 ? 0 : src_size.height-1)*src_step; \
\
if( (unsigned)ixs < (unsigned)(src_size.width-1) ) \
{ \
p0 = cvt(ptr0[ixs])*a1 + cvt(ptr0[ixs+1])*a; \
p1 = cvt(ptr1[ixs])*a1 + cvt(ptr1[ixs+1])*a; \
} \
else \
{ \
ixs = ixs < 0 ? 0 : src_size.width - 1; \
p0 = cvt(ptr0[ixs]); p1 = cvt(ptr1[ixs]); \
} \
dst[x] = cast_macro(p0 + b * (p1 - p0)); \
} \
} \
} \
\
return CV_OK; \
}
ICV_DEF_GET_QUADRANGLE_SUB_PIX_FUNC( 8u32f, uchar, float, double, CV_CAST_32F, CV_8TO32F )
/* Affine tracking algorithm */
CV_IMPL void
cvCalcAffineFlowPyrLK( const void* arrA, const void* arrB,
void* pyrarrA, void* pyrarrB,
const CvPoint2D32f * featuresA,
CvPoint2D32f * featuresB,
float *matrices, int count,
CvSize winSize, int level,
char *status, float *error,
CvTermCriteria criteria, int flags )
{
const int MAX_ITERS = 100;
cv::AutoBuffer<char> _status;
cv::AutoBuffer<uchar> buffer;
cv::AutoBuffer<uchar> pyr_buffer;
CvMat stubA, *imgA = (CvMat*)arrA;
CvMat stubB, *imgB = (CvMat*)arrB;
CvMat pstubA, *pyrA = (CvMat*)pyrarrA;
CvMat pstubB, *pyrB = (CvMat*)pyrarrB;
static const float smoothKernel[] = { 0.09375, 0.3125, 0.09375 }; /* 3/32, 10/32, 3/32 */
int bufferBytes = 0;
uchar **imgI = 0;
uchar **imgJ = 0;
int *step = 0;
double *scale = 0;
CvSize* size = 0;
float *patchI;
float *patchJ;
float *Ix;
float *Iy;
int i, j, k, l;
CvSize patchSize = cvSize( winSize.width * 2 + 1, winSize.height * 2 + 1 );
int patchLen = patchSize.width * patchSize.height;
int patchStep = patchSize.width * sizeof( patchI[0] );
CvSize srcPatchSize = cvSize( patchSize.width + 2, patchSize.height + 2 );
int srcPatchLen = srcPatchSize.width * srcPatchSize.height;
int srcPatchStep = srcPatchSize.width * sizeof( patchI[0] );
CvSize imgSize;
float eps = (float)MIN(winSize.width, winSize.height);
imgA = cvGetMat( imgA, &stubA );
imgB = cvGetMat( imgB, &stubB );
if( CV_MAT_TYPE( imgA->type ) != CV_8UC1 )
CV_Error( CV_StsUnsupportedFormat, "" );
if( !CV_ARE_TYPES_EQ( imgA, imgB ))
CV_Error( CV_StsUnmatchedFormats, "" );
if( !CV_ARE_SIZES_EQ( imgA, imgB ))
CV_Error( CV_StsUnmatchedSizes, "" );
if( imgA->step != imgB->step )
CV_Error( CV_StsUnmatchedSizes, "imgA and imgB must have equal steps" );
if( !matrices )
CV_Error( CV_StsNullPtr, "" );
imgSize = cvGetMatSize( imgA );
if( pyrA )
{
pyrA = cvGetMat( pyrA, &pstubA );
if( pyrA->step*pyrA->height < icvMinimalPyramidSize( imgSize ) )
CV_Error( CV_StsBadArg, "pyramid A has insufficient size" );
}
else
{
pyrA = &pstubA;
pyrA->data.ptr = 0;
}
if( pyrB )
{
pyrB = cvGetMat( pyrB, &pstubB );
if( pyrB->step*pyrB->height < icvMinimalPyramidSize( imgSize ) )
CV_Error( CV_StsBadArg, "pyramid B has insufficient size" );
}
else
{
pyrB = &pstubB;
pyrB->data.ptr = 0;
}
if( count == 0 )
return;
/* check input arguments */
if( !featuresA || !featuresB || !matrices )
CV_Error( CV_StsNullPtr, "" );
if( winSize.width <= 1 || winSize.height <= 1 )
CV_Error( CV_StsOutOfRange, "the search window is too small" );
if( count < 0 )
CV_Error( CV_StsOutOfRange, "" );
icvInitPyramidalAlgorithm( imgA, imgB,
pyrA, pyrB, level, &criteria, MAX_ITERS, flags,
&imgI, &imgJ, &step, &size, &scale, &pyr_buffer );
/* buffer_size = <size for patches> + <size for pyramids> */
bufferBytes = (srcPatchLen + patchLen*3)*sizeof(patchI[0]) + (36*2 + 6)*sizeof(double);
buffer.allocate(bufferBytes);
if( !status )
{
_status.allocate(count);
status = _status;
}
patchI = (float *)(uchar*)buffer;
patchJ = patchI + srcPatchLen;
Ix = patchJ + patchLen;
Iy = Ix + patchLen;
if( status )
memset( status, 1, count );
if( !(flags & CV_LKFLOW_INITIAL_GUESSES) )
{
memcpy( featuresB, featuresA, count * sizeof( featuresA[0] ));
for( i = 0; i < count * 4; i += 4 )
{
matrices[i] = matrices[i + 3] = 1.f;
matrices[i + 1] = matrices[i + 2] = 0.f;
}
}
for( i = 0; i < count; i++ )
{
featuresB[i].x = (float)(featuresB[i].x * scale[level] * 0.5);
featuresB[i].y = (float)(featuresB[i].y * scale[level] * 0.5);
}
/* do processing from top pyramid level (smallest image)
to the bottom (original image) */
for( l = level; l >= 0; l-- )
{
CvSize levelSize = size[l];
int levelStep = step[l];
/* find flow for each given point at the particular level */
for( i = 0; i < count; i++ )
{
CvPoint2D32f u;
float Av[6];
double G[36];
double meanI = 0, meanJ = 0;
int x, y;
int pt_status = status[i];
CvMat mat;
if( !pt_status )
continue;
Av[0] = matrices[i*4];
Av[1] = matrices[i*4+1];
Av[3] = matrices[i*4+2];
Av[4] = matrices[i*4+3];
Av[2] = featuresB[i].x += featuresB[i].x;
Av[5] = featuresB[i].y += featuresB[i].y;
u.x = (float) (featuresA[i].x * scale[l]);
u.y = (float) (featuresA[i].y * scale[l]);
if( u.x < -eps || u.x >= levelSize.width+eps ||
u.y < -eps || u.y >= levelSize.height+eps ||
icvGetRectSubPix_8u32f_C1R( imgI[l], levelStep,
levelSize, patchI, srcPatchStep, srcPatchSize, u ) < 0 )
{
/* point is outside the image. take the next */
if( l == 0 )
status[i] = 0;
continue;
}
icvCalcIxIy_32f( patchI, srcPatchStep, Ix, Iy,
(srcPatchSize.width-2)*sizeof(patchI[0]), srcPatchSize,
smoothKernel, patchJ );
/* repack patchI (remove borders) */
for( k = 0; k < patchSize.height; k++ )
memcpy( patchI + k * patchSize.width,
patchI + (k + 1) * srcPatchSize.width + 1, patchStep );
memset( G, 0, sizeof( G ));
/* calculate G matrix */
for( y = -winSize.height, k = 0; y <= winSize.height; y++ )
{
for( x = -winSize.width; x <= winSize.width; x++, k++ )
{
double ixix = ((double) Ix[k]) * Ix[k];
double ixiy = ((double) Ix[k]) * Iy[k];
double iyiy = ((double) Iy[k]) * Iy[k];
double xx, xy, yy;
G[0] += ixix;
G[1] += ixiy;
G[2] += x * ixix;
G[3] += y * ixix;
G[4] += x * ixiy;
G[5] += y * ixiy;
// G[6] == G[1]
G[7] += iyiy;
// G[8] == G[4]
// G[9] == G[5]
G[10] += x * iyiy;
G[11] += y * iyiy;
xx = x * x;
xy = x * y;
yy = y * y;
// G[12] == G[2]
// G[13] == G[8] == G[4]
G[14] += xx * ixix;
G[15] += xy * ixix;
G[16] += xx * ixiy;
G[17] += xy * ixiy;
// G[18] == G[3]
// G[19] == G[9]
// G[20] == G[15]
G[21] += yy * ixix;
// G[22] == G[17]
G[23] += yy * ixiy;
// G[24] == G[4]
// G[25] == G[10]
// G[26] == G[16]
// G[27] == G[22]
G[28] += xx * iyiy;
G[29] += xy * iyiy;
// G[30] == G[5]
// G[31] == G[11]
// G[32] == G[17]
// G[33] == G[23]
// G[34] == G[29]
G[35] += yy * iyiy;
meanI += patchI[k];
}
}
meanI /= patchSize.width*patchSize.height;
G[8] = G[4];
G[9] = G[5];
G[22] = G[17];
// fill part of G below its diagonal
for( y = 1; y < 6; y++ )
for( x = 0; x < y; x++ )
G[y * 6 + x] = G[x * 6 + y];
cvInitMatHeader( &mat, 6, 6, CV_64FC1, G );
if( cvInvert( &mat, &mat, CV_SVD ) < 1e-4 )
{
/* bad matrix. take the next point */
if( l == 0 )
status[i] = 0;
continue;
}
for( j = 0; j < criteria.max_iter; j++ )
{
double b[6] = {0,0,0,0,0,0}, eta[6];
double t0, t1, s = 0;
if( Av[2] < -eps || Av[2] >= levelSize.width+eps ||
Av[5] < -eps || Av[5] >= levelSize.height+eps ||
icvGetQuadrangleSubPix_8u32f_C1R( imgJ[l], levelStep,
levelSize, patchJ, patchStep, patchSize, Av ) < 0 )
{
pt_status = 0;
break;
}
for( y = -winSize.height, k = 0, meanJ = 0; y <= winSize.height; y++ )
for( x = -winSize.width; x <= winSize.width; x++, k++ )
meanJ += patchJ[k];
meanJ = meanJ / (patchSize.width * patchSize.height) - meanI;
for( y = -winSize.height, k = 0; y <= winSize.height; y++ )
{
for( x = -winSize.width; x <= winSize.width; x++, k++ )
{
double t = patchI[k] - patchJ[k] + meanJ;
double ixt = Ix[k] * t;
double iyt = Iy[k] * t;
s += t;
b[0] += ixt;
b[1] += iyt;
b[2] += x * ixt;
b[3] += y * ixt;
b[4] += x * iyt;
b[5] += y * iyt;
}
}
for( k = 0; k < 6; k++ )
eta[k] = G[k*6]*b[0] + G[k*6+1]*b[1] + G[k*6+2]*b[2] +
G[k*6+3]*b[3] + G[k*6+4]*b[4] + G[k*6+5]*b[5];
Av[2] = (float)(Av[2] + Av[0] * eta[0] + Av[1] * eta[1]);
Av[5] = (float)(Av[5] + Av[3] * eta[0] + Av[4] * eta[1]);
t0 = Av[0] * (1 + eta[2]) + Av[1] * eta[4];
t1 = Av[0] * eta[3] + Av[1] * (1 + eta[5]);
Av[0] = (float)t0;
Av[1] = (float)t1;
t0 = Av[3] * (1 + eta[2]) + Av[4] * eta[4];
t1 = Av[3] * eta[3] + Av[4] * (1 + eta[5]);
Av[3] = (float)t0;
Av[4] = (float)t1;
if( eta[0] * eta[0] + eta[1] * eta[1] < criteria.epsilon )
break;
}
if( pt_status != 0 || l == 0 )
{
status[i] = (char)pt_status;
featuresB[i].x = Av[2];
featuresB[i].y = Av[5];
matrices[i*4] = Av[0];
matrices[i*4+1] = Av[1];
matrices[i*4+2] = Av[3];
matrices[i*4+3] = Av[4];
}
if( pt_status && l == 0 && error )
{
/* calc error */
double err = 0;
for( y = 0, k = 0; y < patchSize.height; y++ )
{
for( x = 0; x < patchSize.width; x++, k++ )
{
double t = patchI[k] - patchJ[k] + meanJ;
err += t * t;
}
}
error[i] = (float)std::sqrt(err);
}
}
}
}

388
modules/video/src/compat_video.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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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"
/////////////////////////// Meanshift & CAMShift ///////////////////////////
CV_IMPL int
cvMeanShift( const void* imgProb, CvRect windowIn,
CvTermCriteria criteria, CvConnectedComp* comp )
{
cv::Mat img = cv::cvarrToMat(imgProb);
cv::Rect window = windowIn;
int iters = cv::meanShift(img, window, criteria);
if( comp )
{
comp->rect = window;
comp->area = cvRound(cv::sum(img(window))[0]);
}
return iters;
}
CV_IMPL int
cvCamShift( const void* imgProb, CvRect windowIn,
CvTermCriteria criteria,
CvConnectedComp* comp,
CvBox2D* box )
{
cv::Mat img = cv::cvarrToMat(imgProb);
cv::Rect window = windowIn;
cv::RotatedRect rr = cv::CamShift(img, window, criteria);
if( comp )
{
comp->rect = window;
cv::Rect roi = rr.boundingRect() & cv::Rect(0, 0, img.cols, img.rows);
comp->area = cvRound(cv::sum(img(roi))[0]);
}
if( box )
*box = rr;
return rr.size.width*rr.size.height > 0.f ? 1 : -1;
}
///////////////////////// Motion Templates ////////////////////////////
CV_IMPL void
cvUpdateMotionHistory( const void* silhouette, void* mhimg,
double timestamp, double mhi_duration )
{
cv::Mat silh = cv::cvarrToMat(silhouette), mhi = cv::cvarrToMat(mhimg);
cv::updateMotionHistory(silh, mhi, timestamp, mhi_duration);
}
CV_IMPL void
cvCalcMotionGradient( const CvArr* mhimg, CvArr* maskimg,
CvArr* orientation,
double delta1, double delta2,
int aperture_size )
{
cv::Mat mhi = cv::cvarrToMat(mhimg);
const cv::Mat mask = cv::cvarrToMat(maskimg), orient = cv::cvarrToMat(orientation);
cv::calcMotionGradient(mhi, mask, orient, delta1, delta2, aperture_size);
}
CV_IMPL double
cvCalcGlobalOrientation( const void* orientation, const void* maskimg, const void* mhimg,
double curr_mhi_timestamp, double mhi_duration )
{
cv::Mat mhi = cv::cvarrToMat(mhimg);
cv::Mat mask = cv::cvarrToMat(maskimg), orient = cv::cvarrToMat(orientation);
return cv::calcGlobalOrientation(orient, mask, mhi, curr_mhi_timestamp, mhi_duration);
}
CV_IMPL CvSeq*
cvSegmentMotion( const CvArr* mhimg, CvArr* segmaskimg, CvMemStorage* storage,
double timestamp, double segThresh )
{
cv::Mat mhi = cv::cvarrToMat(mhimg);
const cv::Mat segmask = cv::cvarrToMat(segmaskimg);
std::vector<cv::Rect> brs;
cv::segmentMotion(mhi, segmask, brs, timestamp, segThresh);
CvSeq* seq = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvConnectedComp), storage);
CvConnectedComp comp;
memset(&comp, 0, sizeof(comp));
for( size_t i = 0; i < brs.size(); i++ )
{
cv::Rect roi = brs[i];
float compLabel = (float)(i+1);
int x, y, area = 0;
cv::Mat part = segmask(roi);
for( y = 0; y < roi.height; y++ )
{
const float* partptr = part.ptr<float>(y);
for( x = 0; x < roi.width; x++ )
area += partptr[x] == compLabel;
}
comp.value = cv::Scalar(compLabel);
comp.rect = roi;
comp.area = area;
cvSeqPush(seq, &comp);
}
return seq;
}
///////////////////////////////// Kalman ///////////////////////////////
CV_IMPL CvKalman*
cvCreateKalman( int DP, int MP, int CP )
{
CvKalman *kalman = 0;
if( DP <= 0 || MP <= 0 )
CV_Error( CV_StsOutOfRange,
"state and measurement vectors must have positive number of dimensions" );
if( CP < 0 )
CP = DP;
/* allocating memory for the structure */
kalman = (CvKalman *)cvAlloc( sizeof( CvKalman ));
memset( kalman, 0, sizeof(*kalman));
kalman->DP = DP;
kalman->MP = MP;
kalman->CP = CP;
kalman->state_pre = cvCreateMat( DP, 1, CV_32FC1 );
cvZero( kalman->state_pre );
kalman->state_post = cvCreateMat( DP, 1, CV_32FC1 );
cvZero( kalman->state_post );
kalman->transition_matrix = cvCreateMat( DP, DP, CV_32FC1 );
cvSetIdentity( kalman->transition_matrix );
kalman->process_noise_cov = cvCreateMat( DP, DP, CV_32FC1 );
cvSetIdentity( kalman->process_noise_cov );
kalman->measurement_matrix = cvCreateMat( MP, DP, CV_32FC1 );
cvZero( kalman->measurement_matrix );
kalman->measurement_noise_cov = cvCreateMat( MP, MP, CV_32FC1 );
cvSetIdentity( kalman->measurement_noise_cov );
kalman->error_cov_pre = cvCreateMat( DP, DP, CV_32FC1 );
kalman->error_cov_post = cvCreateMat( DP, DP, CV_32FC1 );
cvZero( kalman->error_cov_post );
kalman->gain = cvCreateMat( DP, MP, CV_32FC1 );
if( CP > 0 )
{
kalman->control_matrix = cvCreateMat( DP, CP, CV_32FC1 );
cvZero( kalman->control_matrix );
}
kalman->temp1 = cvCreateMat( DP, DP, CV_32FC1 );
kalman->temp2 = cvCreateMat( MP, DP, CV_32FC1 );
kalman->temp3 = cvCreateMat( MP, MP, CV_32FC1 );
kalman->temp4 = cvCreateMat( MP, DP, CV_32FC1 );
kalman->temp5 = cvCreateMat( MP, 1, CV_32FC1 );
#if 1
kalman->PosterState = kalman->state_pre->data.fl;
kalman->PriorState = kalman->state_post->data.fl;
kalman->DynamMatr = kalman->transition_matrix->data.fl;
kalman->MeasurementMatr = kalman->measurement_matrix->data.fl;
kalman->MNCovariance = kalman->measurement_noise_cov->data.fl;
kalman->PNCovariance = kalman->process_noise_cov->data.fl;
kalman->KalmGainMatr = kalman->gain->data.fl;
kalman->PriorErrorCovariance = kalman->error_cov_pre->data.fl;
kalman->PosterErrorCovariance = kalman->error_cov_post->data.fl;
#endif
return kalman;
}
CV_IMPL void
cvReleaseKalman( CvKalman** _kalman )
{
CvKalman *kalman;
if( !_kalman )
CV_Error( CV_StsNullPtr, "" );
kalman = *_kalman;
if( !kalman )
return;
/* freeing the memory */
cvReleaseMat( &kalman->state_pre );
cvReleaseMat( &kalman->state_post );
cvReleaseMat( &kalman->transition_matrix );
cvReleaseMat( &kalman->control_matrix );
cvReleaseMat( &kalman->measurement_matrix );
cvReleaseMat( &kalman->process_noise_cov );
cvReleaseMat( &kalman->measurement_noise_cov );
cvReleaseMat( &kalman->error_cov_pre );
cvReleaseMat( &kalman->gain );
cvReleaseMat( &kalman->error_cov_post );
cvReleaseMat( &kalman->temp1 );
cvReleaseMat( &kalman->temp2 );
cvReleaseMat( &kalman->temp3 );
cvReleaseMat( &kalman->temp4 );
cvReleaseMat( &kalman->temp5 );
memset( kalman, 0, sizeof(*kalman));
/* deallocating the structure */
cvFree( _kalman );
}
CV_IMPL const CvMat*
cvKalmanPredict( CvKalman* kalman, const CvMat* control )
{
if( !kalman )
CV_Error( CV_StsNullPtr, "" );
/* update the state */
/* x'(k) = A*x(k) */
cvMatMulAdd( kalman->transition_matrix, kalman->state_post, 0, kalman->state_pre );
if( control && kalman->CP > 0 )
/* x'(k) = x'(k) + B*u(k) */
cvMatMulAdd( kalman->control_matrix, control, kalman->state_pre, kalman->state_pre );
/* update error covariance matrices */
/* temp1 = A*P(k) */
cvMatMulAdd( kalman->transition_matrix, kalman->error_cov_post, 0, kalman->temp1 );
/* P'(k) = temp1*At + Q */
cvGEMM( kalman->temp1, kalman->transition_matrix, 1, kalman->process_noise_cov, 1,
kalman->error_cov_pre, CV_GEMM_B_T );
/* handle the case when there will be measurement before the next predict */
cvCopy(kalman->state_pre, kalman->state_post);
return kalman->state_pre;
}
CV_IMPL const CvMat*
cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement )
{
if( !kalman || !measurement )
CV_Error( CV_StsNullPtr, "" );
/* temp2 = H*P'(k) */
cvMatMulAdd( kalman->measurement_matrix, kalman->error_cov_pre, 0, kalman->temp2 );
/* temp3 = temp2*Ht + R */
cvGEMM( kalman->temp2, kalman->measurement_matrix, 1,
kalman->measurement_noise_cov, 1, kalman->temp3, CV_GEMM_B_T );
/* temp4 = inv(temp3)*temp2 = Kt(k) */
cvSolve( kalman->temp3, kalman->temp2, kalman->temp4, CV_SVD );
/* K(k) */
cvTranspose( kalman->temp4, kalman->gain );
/* temp5 = z(k) - H*x'(k) */
cvGEMM( kalman->measurement_matrix, kalman->state_pre, -1, measurement, 1, kalman->temp5 );
/* x(k) = x'(k) + K(k)*temp5 */
cvMatMulAdd( kalman->gain, kalman->temp5, kalman->state_pre, kalman->state_post );
/* P(k) = P'(k) - K(k)*temp2 */
cvGEMM( kalman->gain, kalman->temp2, -1, kalman->error_cov_pre, 1,
kalman->error_cov_post, 0 );
return kalman->state_post;
}
///////////////////////////////////// Optical Flow ////////////////////////////////
CV_IMPL void
cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB,
void* /*pyrarrA*/, void* /*pyrarrB*/,
const CvPoint2D32f * featuresA,
CvPoint2D32f * featuresB,
int count, CvSize winSize, int level,
char *status, float *error,
CvTermCriteria criteria, int flags )
{
if( count <= 0 )
return;
CV_Assert( featuresA && featuresB );
cv::Mat A = cv::cvarrToMat(arrA), B = cv::cvarrToMat(arrB);
cv::Mat ptA(count, 1, CV_32FC2, (void*)featuresA);
cv::Mat ptB(count, 1, CV_32FC2, (void*)featuresB);
cv::Mat st, err;
if( status )
st = cv::Mat(count, 1, CV_8U, (void*)status);
if( error )
err = cv::Mat(count, 1, CV_32F, (void*)error);
cv::calcOpticalFlowPyrLK( A, B, ptA, ptB, st,
error ? cv::_OutputArray(err) : cv::noArray(),
winSize, level, criteria, flags);
}
CV_IMPL void cvCalcOpticalFlowFarneback(
const CvArr* _prev, const CvArr* _next,
CvArr* _flow, double pyr_scale, int levels,
int winsize, int iterations, int poly_n,
double poly_sigma, int flags )
{
cv::Mat prev = cv::cvarrToMat(_prev), next = cv::cvarrToMat(_next);
cv::Mat flow = cv::cvarrToMat(_flow);
CV_Assert( flow.size() == prev.size() && flow.type() == CV_32FC2 );
cv::calcOpticalFlowFarneback( prev, next, flow, pyr_scale, levels,
winsize, iterations, poly_n, poly_sigma, flags );
}
CV_IMPL int
cvEstimateRigidTransform( const CvArr* arrA, const CvArr* arrB, CvMat* arrM, int full_affine )
{
cv::Mat matA = cv::cvarrToMat(arrA), matB = cv::cvarrToMat(arrB);
const cv::Mat matM0 = cv::cvarrToMat(arrM);
cv::Mat matM = cv::estimateRigidTransform(matA, matB, full_affine != 0);
if( matM.empty() )
{
matM = cv::cvarrToMat(arrM);
matM.setTo(cv::Scalar::all(0));
return 0;
}
matM.convertTo(matM0, matM0.type());
return 1;
}

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modules/video/src/simpleflow.hpp
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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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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*/
#ifndef __OPENCV_SIMPLEFLOW_H__
#define __OPENCV_SIMPLEFLOW_H__
#include <vector>
#define MASK_TRUE_VALUE 255
#define UNKNOWN_FLOW_THRESH 1e9
namespace cv {
inline static float dist(const Vec3b& p1, const Vec3b& p2) {
return (float)((p1[0] - p2[0]) * (p1[0] - p2[0]) +
(p1[1] - p2[1]) * (p1[1] - p2[1]) +
(p1[2] - p2[2]) * (p1[2] - p2[2]));
}
inline static float dist(const Vec2f& p1, const Vec2f& p2) {
return (p1[0] - p2[0]) * (p1[0] - p2[0]) +
(p1[1] - p2[1]) * (p1[1] - p2[1]);
}
inline static float dist(const Point2f& p1, const Point2f& p2) {
return (p1.x - p2.x) * (p1.x - p2.x) +
(p1.y - p2.y) * (p1.y - p2.y);
}
inline static float dist(float x1, float y1, float x2, float y2) {
return (x1 - x2) * (x1 - x2) +
(y1 - y2) * (y1 - y2);
}
inline static int dist(int x1, int y1, int x2, int y2) {
return (x1 - x2) * (x1 - x2) +
(y1 - y2) * (y1 - y2);
}
template<class T>
inline static T min(T t1, T t2, T t3) {
return (t1 <= t2 && t1 <= t3) ? t1 : min(t2, t3);
}
}
#endif
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