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Warning fixes continued

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This commit is contained in:
Andrey Kamaev 2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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2
cmake/OpenCVCompilerOptions.cmake
View File

@ -75,7 +75,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
#add_extra_compiler_option(-Wcast-align)
#add_extra_compiler_option(-Wstrict-aliasing=2)
#add_extra_compiler_option(-Wshadow)
add_extra_compiler_option(-Wno-unnamed-type-template-args)
#add_extra_compiler_option(-Wno-unnamed-type-template-args)
# The -Wno-long-long is required in 64bit systems when including sytem headers.
if(X86_64)

4
cmake/OpenCVDetectTBB.cmake
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@ -10,7 +10,7 @@ elseif(UNIX AND NOT APPLE)
if(TBB_FOUND)
set(HAVE_TBB 1)
if(NOT ${TBB_INCLUDE_DIRS} STREQUAL "")
include_directories(SYSTEM ${TBB_INCLUDE_DIRS})
ocv_include_directories(${TBB_INCLUDE_DIRS})
endif()
link_directories(${TBB_LIBRARY_DIRS})
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} ${TBB_LIBRARIES})
@ -63,7 +63,7 @@ if(NOT HAVE_TBB)
set(HAVE_TBB 1)
if(NOT "${TBB_INCLUDE_DIRS}" STREQUAL "")
include_directories(SYSTEM "${TBB_INCLUDE_DIRS}")
ocv_include_directories("${TBB_INCLUDE_DIRS}")
endif()
endif(TBB_INCLUDE_DIRS)
endif(NOT HAVE_TBB)

2
cmake/OpenCVUtils.cmake
View File

@ -19,7 +19,7 @@ function(ocv_include_directories)
if("${__abs_dir}" MATCHES "^${OpenCV_SOURCE_DIR}" OR "${__abs_dir}" MATCHES "^${OpenCV_BINARY_DIR}")
list(APPEND __add_before "${dir}")
else()
include_directories(AFTER "${dir}")
include_directories(AFTER SYSTEM "${dir}")
endif()
endforeach()
include_directories(BEFORE ${__add_before})

89
modules/calib3d/src/calibinit.cpp
View File

@ -230,7 +230,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
int found = 0;
CvCBQuad *quads = 0, **quad_group = 0;
CvCBCorner *corners = 0, **corner_group = 0;
try
{
int k = 0;
@ -252,11 +252,11 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
if( out_corner_count )
*out_corner_count = 0;
IplImage _img;
int check_chessboard_result;
int quad_count = 0, group_idx = 0, i = 0, dilations = 0;
int quad_count = 0, group_idx = 0, dilations = 0;
img = cvGetMat( img, &stub );
//debug_img = img;
@ -316,8 +316,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
for( dilations = min_dilations; dilations <= max_dilations; dilations++ )
{
if (found)
break; // already found it
break; // already found it
cvFree(&quads);
cvFree(&corners);
@ -378,7 +378,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
cvCopy(dbg_img, dbg1_img);
cvNamedWindow("all_quads", 1);
// copy corners to temp array
for( i = 0; i < quad_count; i++ )
for(int i = 0; i < quad_count; i++ )
{
for (int k=0; k<4; k++)
{
@ -432,7 +432,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
cvCopy(dbg_img,dbg2_img);
cvNamedWindow("connected_group", 1);
// copy corners to temp array
for( i = 0; i < quad_count; i++ )
for(int i = 0; i < quad_count; i++ )
{
if (quads[i].group_idx == group_idx)
for (int k=0; k<4; k++)
@ -455,7 +455,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
#endif
if (count == 0)
continue; // haven't found inner quads
continue; // haven't found inner quads
// If count is more than it should be, this will remove those quads
@ -472,7 +472,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
float sum_dist = 0;
int total = 0;
for( i = 0; i < n; i++ )
for(int i = 0; i < n; i++ )
{
int ni = 0;
float avgi = corner_group[i]->meanDist(&ni);
@ -484,7 +484,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
if( count > 0 || (out_corner_count && -count > *out_corner_count) )
{
// copy corners to output array
for( i = 0; i < n; i++ )
for(int i = 0; i < n; i++ )
out_corners[i] = corner_group[i]->pt;
if( out_corner_count )
@ -505,19 +505,19 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
if( found )
found = icvCheckBoardMonotony( out_corners, pattern_size );
// check that none of the found corners is too close to the image boundary
// check that none of the found corners is too close to the image boundary
if( found )
{
const int BORDER = 8;
for( k = 0; k < pattern_size.width*pattern_size.height; k++ )
{
if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER ||
out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER )
break;
}
found = k == pattern_size.width*pattern_size.height;
}
{
const int BORDER = 8;
for( k = 0; k < pattern_size.width*pattern_size.height; k++ )
{
if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER ||
out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER )
break;
}
found = k == pattern_size.width*pattern_size.height;
}
if( found && pattern_size.height % 2 == 0 && pattern_size.width % 2 == 0 )
{
@ -525,8 +525,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
double dy0 = out_corners[last_row].y - out_corners[0].y;
if( dy0 < 0 )
{
int i, n = pattern_size.width*pattern_size.height;
for( i = 0; i < n/2; i++ )
int n = pattern_size.width*pattern_size.height;
for(int i = 0; i < n/2; i++ )
{
CvPoint2D32f temp;
CV_SWAP(out_corners[i], out_corners[n-i-1], temp);
@ -559,7 +559,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
cvFree(&corner_group);
throw;
}
cvFree(&quads);
cvFree(&corners);
cvFree(&quad_group);
@ -582,7 +582,7 @@ static int
icvCheckBoardMonotony( CvPoint2D32f* corners, CvSize pattern_size )
{
int i, j, k;
for( k = 0; k < 2; k++ )
{
for( i = 0; i < (k == 0 ? pattern_size.height : pattern_size.width); i++ )
@ -627,11 +627,10 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
{
cv::Ptr<CvMemStorage> temp_storage = cvCreateChildMemStorage( storage );
CvSeq* stack = cvCreateSeq( 0, sizeof(*stack), sizeof(void*), temp_storage );
int i;
// first find an interior quad
CvCBQuad *start = NULL;
for (i=0; i<quad_count; i++)
for (int i=0; i<quad_count; i++)
{
if (quads[i]->count == 4)
{
@ -682,7 +681,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
case 1:
col += 2; break;
case 2:
row += 2; break;
row += 2; break;
case 3:
col -= 2; break;
}
@ -700,7 +699,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
}
}
for (i=col_min; i<=col_max; i++)
for (int i=col_min; i<=col_max; i++)
PRINTF("HIST[%d] = %d\n", i, col_hist[i]);
// analyze inner quad structure
@ -763,7 +762,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
// if there is an outer quad missing, fill it in
// first order all inner quads
int found = 0;
for (i=0; i<quad_count; i++)
for (int i=0; i<quad_count; i++)
{
if (quads[i]->count == 4)
{ // ok, look at neighbors
@ -778,7 +777,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
case 1:
col += 2; break;
case 2:
row += 2; break;
row += 2; break;
case 3:
col -= 2; break;
}
@ -817,7 +816,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
// final trimming of outer quads
if (dcol == w && drow == h) // found correct inner quads
if (dcol == w && drow == h) // found correct inner quads
{
PRINTF("Inner bounds ok, check outer quads\n");
int rcount = quad_count;
@ -832,7 +831,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads,
if (quads[i]->neighbors[j] && quads[i]->neighbors[j]->ordered)
outer = true;
}
if (!outer) // not an outer quad, eliminate
if (!outer) // not an outer quad, eliminate
{
PRINTF("Removing quad %d\n", i);
icvRemoveQuadFromGroup(quads,rcount,quads[i]);
@ -876,7 +875,7 @@ icvAddOuterQuad( CvCBQuad *quad, CvCBQuad **quads, int quad_count,
quad->count += 1;
q->neighbors[j] = quad;
q->group_idx = quad->group_idx;
q->count = 1; // number of neighbors
q->count = 1; // number of neighbors
q->ordered = false;
q->edge_len = quad->edge_len;
@ -1262,7 +1261,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count,
int width = 0, height = 0;
int hist[5] = {0,0,0,0,0};
CvCBCorner* first = 0, *first2 = 0, *right, *cur, *below, *c;
// build dual graph, which vertices are internal quad corners
// and two vertices are connected iff they lie on the same quad edge
for( i = 0; i < quad_count; i++ )
@ -1485,7 +1484,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count,
result = corner_count;
finalize:
if( result <= 0 )
{
corner_count = MIN( corner_count, pattern_size.width*pattern_size.height );
@ -1697,7 +1696,7 @@ icvGenerateQuads( CvCBQuad **out_quads, CvCBCorner **out_corners,
CV_POLY_APPROX_DP, (float)approx_level );
if( dst_contour->total == 4 )
break;
// we call this again on its own output, because sometimes
// cvApproxPoly() does not simplify as much as it should.
dst_contour = cvApproxPoly( dst_contour, sizeof(CvContour), temp_storage,
@ -2006,17 +2005,17 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize,
#endif
if (isFound)
{
switch(parameters.gridType)
{
switch(parameters.gridType)
{
case CirclesGridFinderParameters::SYMMETRIC_GRID:
boxFinder.getHoles(centers);
break;
case CirclesGridFinderParameters::ASYMMETRIC_GRID:
boxFinder.getAsymmetricHoles(centers);
break;
boxFinder.getAsymmetricHoles(centers);
break;
default:
CV_Error(CV_StsBadArg, "Unkown pattern type");
}
}
if (i != 0)
{
@ -2027,7 +2026,7 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize,
Mat(centers).copyTo(_centers);
return true;
}
boxFinder.getHoles(centers);
if (i != attempts - 1)
{

2
modules/calib3d/src/calibration.cpp
View File

@ -1153,7 +1153,7 @@ CV_IMPL void cvFindExtrinsicCameraParams2( const CvMat* objectPoints,
int useExtrinsicGuess )
{
const int max_iter = 20;
Ptr<CvMat> matM, _Mxy, _m, _mn, matL, matJ;
Ptr<CvMat> matM, _Mxy, _m, _mn, matL;
int i, count;
double a[9], ar[9]={1,0,0,0,1,0,0,0,1}, R[9];

16
modules/calib3d/src/circlesgrid.cpp
View File

@ -65,14 +65,14 @@ void drawPoints(const vector<Point2f> &points, Mat &outImage, int radius = 2, S
}
#endif
void CirclesGridClusterFinder::hierarchicalClustering(const vector<Point2f> points, const Size &patternSize, vector<Point2f> &patternPoints)
void CirclesGridClusterFinder::hierarchicalClustering(const vector<Point2f> points, const Size &patternSz, vector<Point2f> &patternPoints)
{
#ifdef HAVE_TEGRA_OPTIMIZATION
if(tegra::hierarchicalClustering(points, patternSize, patternPoints))
if(tegra::hierarchicalClustering(points, patternSz, patternPoints))
return;
#endif
int i, j, n = (int)points.size();
size_t pn = static_cast<size_t>(patternSize.area());
int j, n = (int)points.size();
size_t pn = static_cast<size_t>(patternSz.area());
patternPoints.clear();
if (pn >= points.size())
@ -84,7 +84,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector<Point2f> poin
Mat dists(n, n, CV_32FC1, Scalar(0));
Mat distsMask(dists.size(), CV_8UC1, Scalar(0));
for(i = 0; i < n; i++)
for(int i = 0; i < n; i++)
{
for(j = i+1; j < n; j++)
{
@ -122,7 +122,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector<Point2f> poin
}
//the largest cluster can have more than pn points -- we need to filter out such situations
if(clusters[patternClusterIdx].size() != static_cast<size_t>(patternSize.area()))
if(clusters[patternClusterIdx].size() != static_cast<size_t>(patternSz.area()))
{
return;
}
@ -505,11 +505,11 @@ void Graph::floydWarshall(cv::Mat &distanceMatrix, int infinity) const
{
for (Vertices::const_iterator it3 = vertices.begin(); it3 != vertices.end(); it3++)
{
int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first;
int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first;
int val1 = distanceMatrix.at<int> (i2, i3);
int val2;
if (distanceMatrix.at<int> (i2, i1) == infinity ||
distanceMatrix.at<int> (i1, i3) == infinity)
distanceMatrix.at<int> (i1, i3) == infinity)
val2 = val1;
else
{

166
modules/calib3d/src/epnp.cpp
View File

@ -8,26 +8,26 @@ epnp::epnp(const cv::Mat& cameraMatrix, const cv::Mat& opoints, const cv::Mat& i
if (cameraMatrix.depth() == CV_32F)
init_camera_parameters<float>(cameraMatrix);
else
init_camera_parameters<double>(cameraMatrix);
init_camera_parameters<double>(cameraMatrix);
number_of_correspondences = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
pws.resize(3 * number_of_correspondences);
us.resize(2 * number_of_correspondences);
us.resize(2 * number_of_correspondences);
if (opoints.depth() == ipoints.depth())
{
if (opoints.depth() == CV_32F)
init_points<cv::Point3f,cv::Point2f>(opoints, ipoints);
else
init_points<cv::Point3d,cv::Point2d>(opoints, ipoints);
if (opoints.depth() == CV_32F)
init_points<cv::Point3f,cv::Point2f>(opoints, ipoints);
else
init_points<cv::Point3d,cv::Point2d>(opoints, ipoints);
}
else if (opoints.depth() == CV_32F)
init_points<cv::Point3f,cv::Point2d>(opoints, ipoints);
init_points<cv::Point3f,cv::Point2d>(opoints, ipoints);
else
init_points<cv::Point3d,cv::Point2f>(opoints, ipoints);
init_points<cv::Point3d,cv::Point2f>(opoints, ipoints);
alphas.resize(4 * number_of_correspondences);
alphas.resize(4 * number_of_correspondences);
pcs.resize(3 * number_of_correspondences);
max_nr = 0;
@ -97,15 +97,15 @@ void epnp::compute_barycentric_coordinates(void)
for(int j = 0; j < 3; j++)
a[1 + j] =
ci[3 * j ] * (pi[0] - cws[0][0]) +
ci[3 * j + 1] * (pi[1] - cws[0][1]) +
ci[3 * j + 2] * (pi[2] - cws[0][2]);
ci[3 * j ] * (pi[0] - cws[0][0]) +
ci[3 * j + 1] * (pi[1] - cws[0][1]) +
ci[3 * j + 2] * (pi[2] - cws[0][2]);
a[0] = 1.0f - a[1] - a[2] - a[3];
}
}
void epnp::fill_M(CvMat * M,
const int row, const double * as, const double u, const double v)
const int row, const double * as, const double u, const double v)
{
double * M1 = M->data.db + row * 12;
double * M2 = M1 + 12;
@ -130,7 +130,7 @@ void epnp::compute_ccs(const double * betas, const double * ut)
const double * v = ut + 12 * (11 - i);
for(int j = 0; j < 4; j++)
for(int k = 0; k < 3; k++)
ccs[j][k] += betas[i] * v[3 * j + k];
ccs[j][k] += betas[i] * v[3 * j + k];
}
}
@ -195,7 +195,7 @@ void epnp::compute_pose(cv::Mat& R, cv::Mat& t)
}
void epnp::copy_R_and_t(const double R_src[3][3], const double t_src[3],
double R_dst[3][3], double t_dst[3])
double R_dst[3][3], double t_dst[3])
{
for(int i = 0; i < 3; i++) {
for(int j = 0; j < 3; j++)
@ -282,7 +282,7 @@ void epnp::solve_for_sign(void)
if (pcs[2] < 0.0) {
for(int i = 0; i < 4; i++)
for(int j = 0; j < 3; j++)
ccs[i][j] = -ccs[i][j];
ccs[i][j] = -ccs[i][j];
for(int i = 0; i < number_of_correspondences; i++) {
pcs[3 * i ] = -pcs[3 * i];
@ -293,7 +293,7 @@ void epnp::solve_for_sign(void)
}
double epnp::compute_R_and_t(const double * ut, const double * betas,
double R[3][3], double t[3])
double R[3][3], double t[3])
{
compute_ccs(betas, ut);
compute_pcs();
@ -322,13 +322,13 @@ double epnp::reprojection_error(const double R[3][3], const double t[3])
}
return sum2 / number_of_correspondences;
}
}
// betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44]
// betas_approx_1 = [B11 B12 B13 B14]
void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
double * betas)
{
double l_6x4[6 * 4], b4[4];
CvMat L_6x4 = cvMat(6, 4, CV_64F, l_6x4);
@ -360,7 +360,7 @@ void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho,
// betas_approx_2 = [B11 B12 B22 ]
void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
double * betas)
{
double l_6x3[6 * 3], b3[3];
CvMat L_6x3 = cvMat(6, 3, CV_64F, l_6x3);
@ -392,7 +392,7 @@ void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho,
// betas_approx_3 = [B11 B12 B22 B13 B23 ]
void epnp::find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
double * betas)
{
double l_6x5[6 * 5], b5[5];
CvMat L_6x5 = cvMat(6, 5, CV_64F, l_6x5);
@ -440,8 +440,8 @@ void epnp::compute_L_6x10(const double * ut, double * l_6x10)
b++;
if (b > 3) {
a++;
b = a + 1;
a++;
b = a + 1;
}
}
}
@ -473,7 +473,7 @@ void epnp::compute_rho(double * rho)
}
void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rho,
const double betas[4], CvMat * A, CvMat * b)
const double betas[4], CvMat * A, CvMat * b)
{
for(int i = 0; i < 6; i++) {
const double * rowL = l_6x10 + i * 10;
@ -485,23 +485,22 @@ void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rh
rowA[3] = rowL[6] * betas[0] + rowL[7] * betas[1] + rowL[8] * betas[2] + 2 * rowL[9] * betas[3];
cvmSet(b, i, 0, rho[i] -
(
rowL[0] * betas[0] * betas[0] +
rowL[1] * betas[0] * betas[1] +
rowL[2] * betas[1] * betas[1] +
rowL[3] * betas[0] * betas[2] +
rowL[4] * betas[1] * betas[2] +
rowL[5] * betas[2] * betas[2] +
rowL[6] * betas[0] * betas[3] +
rowL[7] * betas[1] * betas[3] +
rowL[8] * betas[2] * betas[3] +
rowL[9] * betas[3] * betas[3]
));
(
rowL[0] * betas[0] * betas[0] +
rowL[1] * betas[0] * betas[1] +
rowL[2] * betas[1] * betas[1] +
rowL[3] * betas[0] * betas[2] +
rowL[4] * betas[1] * betas[2] +
rowL[5] * betas[2] * betas[2] +
rowL[6] * betas[0] * betas[3] +
rowL[7] * betas[1] * betas[3] +
rowL[8] * betas[2] * betas[3] +
rowL[9] * betas[3] * betas[3]
));
}
}
void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho,
double betas[4])
void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, double betas[4])
{
const int iterations_number = 5;
@ -510,12 +509,13 @@ void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho,
CvMat B = cvMat(6, 1, CV_64F, b);
CvMat X = cvMat(4, 1, CV_64F, x);
for(int k = 0; k < iterations_number; k++) {
for(int k = 0; k < iterations_number; k++)
{
compute_A_and_b_gauss_newton(L_6x10->data.db, Rho->data.db,
betas, &A, &B);
betas, &A, &B);
qr_solve(&A, &B, &X);
for(int i = 0; i < 4; i++)
betas[i] += x[i];
betas[i] += x[i];
}
}
@ -524,53 +524,64 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X)
const int nr = A->rows;
const int nc = A->cols;
if (max_nr != 0 && max_nr < nr) {
if (max_nr != 0 && max_nr < nr)
{
delete [] A1;
delete [] A2;
}
if (max_nr < nr) {
if (max_nr < nr)
{
max_nr = nr;
A1 = new double[nr];
A2 = new double[nr];
}
double * pA = A->data.db, * ppAkk = pA;
for(int k = 0; k < nc; k++) {
double * ppAik = ppAkk, eta = fabs(*ppAik);
for(int i = k + 1; i < nr; i++) {
double elt = fabs(*ppAik);
for(int k = 0; k < nc; k++)
{
double * ppAik1 = ppAkk, eta = fabs(*ppAik1);
for(int i = k + 1; i < nr; i++)
{
double elt = fabs(*ppAik1);
if (eta < elt) eta = elt;
ppAik += nc;
ppAik1 += nc;
}
if (eta == 0) {
if (eta == 0)
{
A1[k] = A2[k] = 0.0;
//cerr << "God damnit, A is singular, this shouldn't happen." << endl;
return;
} else {
double * ppAik = ppAkk, sum = 0.0, inv_eta = 1. / eta;
for(int i = k; i < nr; i++) {
*ppAik *= inv_eta;
sum += *ppAik * *ppAik;
ppAik += nc;
}
else
{
double * ppAik2 = ppAkk, sum2 = 0.0, inv_eta = 1. / eta;
for(int i = k; i < nr; i++)
{
*ppAik2 *= inv_eta;
sum2 += *ppAik2 * *ppAik2;
ppAik2 += nc;
}
double sigma = sqrt(sum);
double sigma = sqrt(sum2);
if (*ppAkk < 0)
sigma = -sigma;
sigma = -sigma;
*ppAkk += sigma;
A1[k] = sigma * *ppAkk;
A2[k] = -eta * sigma;
for(int j = k + 1; j < nc; j++) {
double * ppAik = ppAkk, sum = 0;
for(int i = k; i < nr; i++) {
sum += *ppAik * ppAik[j - k];
ppAik += nc;
}
double tau = sum / A1[k];
ppAik = ppAkk;
for(int i = k; i < nr; i++) {
ppAik[j - k] -= tau * *ppAik;
ppAik += nc;
}
for(int j = k + 1; j < nc; j++)
{
double * ppAik = ppAkk, sum = 0;
for(int i = k; i < nr; i++)
{
sum += *ppAik * ppAik[j - k];
ppAik += nc;
}
double tau = sum / A1[k];
ppAik = ppAkk;
for(int i = k; i < nr; i++)
{
ppAik[j - k] -= tau * *ppAik;
ppAik += nc;
}
}
}
ppAkk += nc + 1;
@ -578,15 +589,18 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X)
// b <- Qt b
double * ppAjj = pA, * pb = b->data.db;
for(int j = 0; j < nc; j++) {
for(int j = 0; j < nc; j++)
{
double * ppAij = ppAjj, tau = 0;
for(int i = j; i < nr; i++) {
for(int i = j; i < nr; i++)
{
tau += *ppAij * pb[i];
ppAij += nc;
}
tau /= A1[j];
ppAij = ppAjj;
for(int i = j; i < nr; i++) {
for(int i = j; i < nr; i++)
{
pb[i] -= tau * *ppAij;
ppAij += nc;
}
@ -596,10 +610,12 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X)
// X = R-1 b
double * pX = X->data.db;
pX[nc - 1] = pb[nc - 1] / A2[nc - 1];
for(int i = nc - 2; i >= 0; i--) {
for(int i = nc - 2; i >= 0; i--)
{
double * ppAij = pA + i * nc + (i + 1), sum = 0;
for(int j = i + 1; j < nc; j++) {
for(int j = i + 1; j < nc; j++)
{
sum += *ppAij * pX[j];
ppAij++;
}

580
modules/calib3d/src/p3p.cpp
View File

@ -9,151 +9,151 @@ using namespace std;
void p3p::init_inverse_parameters()
{
inv_fx = 1. / fx;
inv_fy = 1. / fy;
cx_fx = cx / fx;
cy_fy = cy / fy;
inv_fx = 1. / fx;
inv_fy = 1. / fy;
cx_fx = cx / fx;
cy_fy = cy / fy;
}
p3p::p3p(cv::Mat cameraMatrix)
{
if (cameraMatrix.depth() == CV_32F)
init_camera_parameters<float>(cameraMatrix);
else
init_camera_parameters<double>(cameraMatrix);
init_inverse_parameters();
if (cameraMatrix.depth() == CV_32F)
init_camera_parameters<float>(cameraMatrix);
else
init_camera_parameters<double>(cameraMatrix);
init_inverse_parameters();
}
p3p::p3p(double _fx, double _fy, double _cx, double _cy)
{
fx = _fx;
fy = _fy;
cx = _cx;
cy = _cy;
init_inverse_parameters();
fx = _fx;
fy = _fy;
cx = _cx;
cy = _cy;
init_inverse_parameters();
}
bool p3p::solve(cv::Mat& R, cv::Mat& tvec, const cv::Mat& opoints, const cv::Mat& ipoints)
{
double rotation_matrix[3][3], translation[3];
std::vector<double> points;
if (opoints.depth() == ipoints.depth())
{
if (opoints.depth() == CV_32F)
extract_points<cv::Point3f,cv::Point2f>(opoints, ipoints, points);
else
extract_points<cv::Point3d,cv::Point2d>(opoints, ipoints, points);
}
else if (opoints.depth() == CV_32F)
extract_points<cv::Point3f,cv::Point2d>(opoints, ipoints, points);
else
extract_points<cv::Point3d,cv::Point2f>(opoints, ipoints, points);
double rotation_matrix[3][3], translation[3];
std::vector<double> points;
if (opoints.depth() == ipoints.depth())
{
if (opoints.depth() == CV_32F)
extract_points<cv::Point3f,cv::Point2f>(opoints, ipoints, points);
else
extract_points<cv::Point3d,cv::Point2d>(opoints, ipoints, points);
}
else if (opoints.depth() == CV_32F)
extract_points<cv::Point3f,cv::Point2d>(opoints, ipoints, points);
else
extract_points<cv::Point3d,cv::Point2f>(opoints, ipoints, points);
bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5],
points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14],
points[15], points[16], points[17], points[18], points[19]);
cv::Mat(3, 1, CV_64F, translation).copyTo(tvec);
bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5],
points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14],
points[15], points[16], points[17], points[18], points[19]);
cv::Mat(3, 1, CV_64F, translation).copyTo(tvec);
cv::Mat(3, 3, CV_64F, rotation_matrix).copyTo(R);
return result;
return result;
}
bool p3p::solve(double R[3][3], double t[3],
double mu0, double mv0, double X0, double Y0, double Z0,
double mu1, double mv1, double X1, double Y1, double Z1,
double mu2, double mv2, double X2, double Y2, double Z2,
double mu3, double mv3, double X3, double Y3, double Z3)
double mu0, double mv0, double X0, double Y0, double Z0,
double mu1, double mv1, double X1, double Y1, double Z1,
double mu2, double mv2, double X2, double Y2, double Z2,
double mu3, double mv3, double X3, double Y3, double Z3)
{
double Rs[4][3][3], ts[4][3];
double Rs[4][3][3], ts[4][3];
int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2);
int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2);
if (n == 0)
return false;
if (n == 0)
return false;
int ns = 0;
double min_reproj = 0;
for(int i = 0; i < n; i++) {
double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0];
double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1];
double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2];
double mu3p = cx + fx * X3p / Z3p;
double mv3p = cy + fy * Y3p / Z3p;
double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3);
if (i == 0 || min_reproj > reproj) {
ns = i;
min_reproj = reproj;
}
}
int ns = 0;
double min_reproj = 0;
for(int i = 0; i < n; i++) {
double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0];
double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1];
double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2];
double mu3p = cx + fx * X3p / Z3p;
double mv3p = cy + fy * Y3p / Z3p;
double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3);
if (i == 0 || min_reproj > reproj) {
ns = i;
min_reproj = reproj;
}
}
for(int i = 0; i < 3; i++) {
for(int j = 0; j < 3; j++)
R[i][j] = Rs[ns][i][j];
t[i] = ts[ns][i];
}
for(int i = 0; i < 3; i++) {
for(int j = 0; j < 3; j++)
R[i][j] = Rs[ns][i][j];
t[i] = ts[ns][i];
}
return true;
return true;
}
int p3p::solve(double R[4][3][3], double t[4][3],
double mu0, double mv0, double X0, double Y0, double Z0,
double mu1, double mv1, double X1, double Y1, double Z1,
double mu2, double mv2, double X2, double Y2, double Z2)
double mu0, double mv0, double X0, double Y0, double Z0,
double mu1, double mv1, double X1, double Y1, double Z1,
double mu2, double mv2, double X2, double Y2, double Z2)
{
double mk0, mk1, mk2;
double norm;
double mk0, mk1, mk2;
double norm;
mu0 = inv_fx * mu0 - cx_fx;
mv0 = inv_fy * mv0 - cy_fy;
norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1);
mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0;
mu0 = inv_fx * mu0 - cx_fx;
mv0 = inv_fy * mv0 - cy_fy;
norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1);
mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0;
mu1 = inv_fx * mu1 - cx_fx;
mv1 = inv_fy * mv1 - cy_fy;
norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1);
mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1;
mu1 = inv_fx * mu1 - cx_fx;
mv1 = inv_fy * mv1 - cy_fy;
norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1);
mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1;
mu2 = inv_fx * mu2 - cx_fx;
mv2 = inv_fy * mv2 - cy_fy;
norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1);
mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2;
mu2 = inv_fx * mu2 - cx_fx;
mv2 = inv_fy * mv2 - cy_fy;
norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1);
mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2;
double distances[3];
distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) );
distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) );
distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) );
double distances[3];
distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) );
distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) );
distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) );
// Calculate angles
double cosines[3];
cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2;
cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2;
cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1;
// Calculate angles
double cosines[3];
cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2;
cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2;
cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1;
double lengths[4][3];
int n = solve_for_lengths(lengths, distances, cosines);
double lengths[4][3];
int n = solve_for_lengths(lengths, distances, cosines);
int nb_solutions = 0;
for(int i = 0; i < n; i++) {
double M_orig[3][3];
int nb_solutions = 0;
for(int i = 0; i < n; i++) {
double M_orig[3][3];
M_orig[0][0] = lengths[i][0] * mu0;
M_orig[0][1] = lengths[i][0] * mv0;
M_orig[0][2] = lengths[i][0] * mk0;
M_orig[0][0] = lengths[i][0] * mu0;
M_orig[0][1] = lengths[i][0] * mv0;
M_orig[0][2] = lengths[i][0] * mk0;
M_orig[1][0] = lengths[i][1] * mu1;
M_orig[1][1] = lengths[i][1] * mv1;
M_orig[1][2] = lengths[i][1] * mk1;
M_orig[1][0] = lengths[i][1] * mu1;
M_orig[1][1] = lengths[i][1] * mv1;
M_orig[1][2] = lengths[i][1] * mk1;
M_orig[2][0] = lengths[i][2] * mu2;
M_orig[2][1] = lengths[i][2] * mv2;
M_orig[2][2] = lengths[i][2] * mk2;
M_orig[2][0] = lengths[i][2] * mu2;
M_orig[2][1] = lengths[i][2] * mv2;
M_orig[2][2] = lengths[i][2] * mk2;
if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions]))
continue;
if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions]))
continue;
nb_solutions++;
}
nb_solutions++;
}
return nb_solutions;
return nb_solutions;
}
/// Given 3D distances between three points and cosines of 3 angles at the apex, calculates
@ -170,247 +170,247 @@ int p3p::solve(double R[4][3][3], double t[4][3],
int p3p::solve_for_lengths(double lengths[4][3], double distances[3], double cosines[3])
{
double p = cosines[0] * 2;
double q = cosines[1] * 2;
double r = cosines[2] * 2;
double p = cosines[0] * 2;
double q = cosines[1] * 2;
double r = cosines[2] * 2;
double inv_d22 = 1. / (distances[2] * distances[2]);
double a = inv_d22 * (distances[0] * distances[0]);
double b = inv_d22 * (distances[1] * distances[1]);
double inv_d22 = 1. / (distances[2] * distances[2]);
double a = inv_d22 * (distances[0] * distances[0]);
double b = inv_d22 * (distances[1] * distances[1]);
double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r;
double pr = p * r, pqr = q * pr;
double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r;
double pr = p * r, pqr = q * pr;
// Check reality condition (the four points should not be coplanar)
if (p2 + q2 + r2 - pqr - 1 == 0)
return 0;
// Check reality condition (the four points should not be coplanar)
if (p2 + q2 + r2 - pqr - 1 == 0)
return 0;
double ab = a * b, a_2 = 2*a;
double ab = a * b, a_2 = 2*a;
double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2;
double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2;
// Check reality condition
if (A == 0) return 0;
// Check reality condition
if (A == 0) return 0;
double a_4 = 4*a;
double a_4 = 4*a;
double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab);
double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2;
double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2);
double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2;
double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab);
double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2;
double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2);
double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2;
double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr);
double b0 = b * temp * temp;
// Check reality condition
if (b0 == 0)
return 0;
double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr);
double b0 = b * temp * temp;
// Check reality condition
if (b0 == 0)
return 0;
double real_roots[4];
int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]);
double real_roots[4];
int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]);
if (n == 0)
return 0;
if (n == 0)
return 0;
int nb_solutions = 0;
double r3 = r2*r, pr2 = p*r2, r3q = r3 * q;
double inv_b0 = 1. / b0;
int nb_solutions = 0;
double r3 = r2*r, pr2 = p*r2, r3q = r3 * q;
double inv_b0 = 1. / b0;
// For each solution of x
for(int i = 0; i < n; i++) {
double x = real_roots[i];
// For each solution of x
for(int i = 0; i < n; i++) {
double x = real_roots[i];
// Check reality condition
if (x <= 0)
continue;
// Check reality condition
if (x <= 0)
continue;
double x2 = x*x;
double x2 = x*x;
double b1 =
((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) *
(((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x +
double b1 =
((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) *
(((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x +
(r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 +
(r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 +
(r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x +
(r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x +
2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) +
p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1)));
2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) +
p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1)));
// Check reality condition
if (b1 <= 0)
continue;
// Check reality condition
if (b1 <= 0)
continue;
double y = inv_b0 * b1;
double v = x2 + y*y - x*y*r;
double y = inv_b0 * b1;
double v = x2 + y*y - x*y*r;
if (v <= 0)
continue;
if (v <= 0)
continue;
double Z = distances[2] / sqrt(v);
double X = x * Z;
double Y = y * Z;
double Z = distances[2] / sqrt(v);
double X = x * Z;
double Y = y * Z;
lengths[nb_solutions][0] = X;
lengths[nb_solutions][1] = Y;
lengths[nb_solutions][2] = Z;
lengths[nb_solutions][0] = X;
lengths[nb_solutions][1] = Y;
lengths[nb_solutions][2] = Z;
nb_solutions++;
}
nb_solutions++;
}
return nb_solutions;
return nb_solutions;
}
bool p3p::align(double M_end[3][3],
double X0, double Y0, double Z0,
double X1, double Y1, double Z1,
double X2, double Y2, double Z2,
double R[3][3], double T[3])
double X0, double Y0, double Z0,
double X1, double Y1, double Z1,
double X2, double Y2, double Z2,
double R[3][3], double T[3])
{
// Centroids:
double C_start[3], C_end[3];
for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3;
C_start[0] = (X0 + X1 + X2) / 3;
C_start[1] = (Y0 + Y1 + Y2) / 3;
C_start[2] = (Z0 + Z1 + Z2) / 3;
// Centroids:
double C_start[3], C_end[3];
for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3;
C_start[0] = (X0 + X1 + X2) / 3;
C_start[1] = (Y0 + Y1 + Y2) / 3;
C_start[2] = (Z0 + Z1 + Z2) / 3;
// Covariance matrix s:
double s[3 * 3];
for(int j = 0; j < 3; j++) {
s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0];
s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1];
s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2];
}
// Covariance matrix s:
double s[3 * 3];
for(int j = 0; j < 3; j++) {
s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0];
s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1];
s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2];
}
double Qs[16], evs[4], U[16];
double Qs[16], evs[4], U[16];
Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2];
Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2];
Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0];
Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1];
Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2];
Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2];
Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0];
Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1];
Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1];
Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2];
Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0];
Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1];
Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2];
Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2];
Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1];
Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2];
Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0];
Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1];
Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2];
Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2];
jacobi_4x4(Qs, evs, U);
jacobi_4x4(Qs, evs, U);
// Looking for the largest eigen value:
int i_ev = 0;
double ev_max = evs[i_ev];
for(int i = 1; i < 4; i++)
if (evs[i] > ev_max)
ev_max = evs[i_ev = i];
// Looking for the largest eigen value:
int i_ev = 0;
double ev_max = evs[i_ev];
for(int i = 1; i < 4; i++)
if (evs[i] > ev_max)
ev_max = evs[i_ev = i];
// Quaternion:
double q[4];
for(int i = 0; i < 4; i++)
q[i] = U[i * 4 + i_ev];
// Quaternion:
double q[4];
for(int i = 0; i < 4; i++)
q[i] = U[i * 4 + i_ev];
double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3];
double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3];
double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3];
double q2_3 = q[2] * q[3];
double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3];
double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3];
double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3];
double q2_3 = q[2] * q[3];
R[0][0] = q02 + q12 - q22 - q32;
R[0][1] = 2. * (q1_2 - q0_3);
R[0][2] = 2. * (q1_3 + q0_2);
R[0][0] = q02 + q12 - q22 - q32;
R[0][1] = 2. * (q1_2 - q0_3);
R[0][2] = 2. * (q1_3 + q0_2);
R[1][0] = 2. * (q1_2 + q0_3);
R[1][1] = q02 + q22 - q12 - q32;
R[1][2] = 2. * (q2_3 - q0_1);
R[1][0] = 2. * (q1_2 + q0_3);
R[1][1] = q02 + q22 - q12 - q32;
R[1][2] = 2. * (q2_3 - q0_1);
R[2][0] = 2. * (q1_3 - q0_2);
R[2][1] = 2. * (q2_3 + q0_1);
R[2][2] = q02 + q32 - q12 - q22;
R[2][0] = 2. * (q1_3 - q0_2);
R[2][1] = 2. * (q2_3 + q0_1);
R[2][2] = q02 + q32 - q12 - q22;
for(int i = 0; i < 3; i++)
T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]);
for(int i = 0; i < 3; i++)
T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]);
return true;
return true;
}
bool p3p::jacobi_4x4(double * A, double * D, double * U)
{
double B[4], Z[4];
double Id[16] = {1., 0., 0., 0.,
0., 1., 0., 0.,
0., 0., 1., 0.,
0., 0., 0., 1.};
double B[4], Z[4];
double Id[16] = {1., 0., 0., 0.,
0., 1., 0., 0.,
0., 0., 1., 0.,
0., 0., 0., 1.};
memcpy(U, Id, 16 * sizeof(double));
memcpy(U, Id, 16 * sizeof(double));
B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15];
memcpy(D, B, 4 * sizeof(double));
memset(Z, 0, 4 * sizeof(double));
B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15];
memcpy(D, B, 4 * sizeof(double));
memset(Z, 0, 4 * sizeof(double));
for(int iter = 0; iter < 50; iter++) {
double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]);
for(int iter = 0; iter < 50; iter++) {
double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]);
if (sum == 0.0)
return true;
if (sum == 0.0)
return true;
double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0;
for(int i = 0; i < 3; i++) {
double * pAij = A + 5 * i + 1;
for(int j = i + 1 ; j < 4; j++) {
double Aij = *pAij;
double eps_machine = 100.0 * fabs(Aij);
double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0;
for(int i = 0; i < 3; i++) {
double * pAij = A + 5 * i + 1;
for(int j = i + 1 ; j < 4; j++) {
double Aij = *pAij;
double eps_machine = 100.0 * fabs(Aij);
if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) )
*pAij = 0.0;
else if (fabs(Aij) > tresh) {
double h = D[j] - D[i], t;
if (fabs(h) + eps_machine == fabs(h))
t = Aij / h;
else {
double theta = 0.5 * h / Aij;
t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta));
if (theta < 0.0) t = -t;
}
if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) )
*pAij = 0.0;
else if (fabs(Aij) > tresh) {
double hh = D[j] - D[i], t;
if (fabs(hh) + eps_machine == fabs(hh))
t = Aij / hh;
else {
double theta = 0.5 * hh / Aij;
t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta));
if (theta < 0.0) t = -t;
}
h = t * Aij;
Z[i] -= h;
Z[j] += h;
D[i] -= h;
D[j] += h;
*pAij = 0.0;
hh = t * Aij;
Z[i] -= hh;
Z[j] += hh;
D[i] -= hh;
D[j] += hh;
*pAij = 0.0;
double c = 1.0 / sqrt(1 + t * t);
double s = t * c;
double tau = s / (1.0 + c);
for(int k = 0; k <= i - 1; k++) {
double g = A[k * 4 + i], h = A[k * 4 + j];
A[k * 4 + i] = g - s * (h + g * tau);
A[k * 4 + j] = h + s * (g - h * tau);
}
for(int k = i + 1; k <= j - 1; k++) {
double g = A[i * 4 + k], h = A[k * 4 + j];
A[i * 4 + k] = g - s * (h + g * tau);
A[k * 4 + j] = h + s * (g - h * tau);
}
for(int k = j + 1; k < 4; k++) {
double g = A[i * 4 + k], h = A[j * 4 + k];
A[i * 4 + k] = g - s * (h + g * tau);
A[j * 4 + k] = h + s * (g - h * tau);
}
for(int k = 0; k < 4; k++) {
double g = U[k * 4 + i], h = U[k * 4 + j];
U[k * 4 + i] = g - s * (h + g * tau);
U[k * 4 + j] = h + s * (g - h * tau);
}
}
pAij++;
}
}
double c = 1.0 / sqrt(1 + t * t);
double s = t * c;
double tau = s / (1.0 + c);
for(int k = 0; k <= i - 1; k++) {
double g = A[k * 4 + i], h = A[k * 4 + j];
A[k * 4 + i] = g - s * (h + g * tau);
A[k * 4 + j] = h + s * (g - h * tau);
}
for(int k = i + 1; k <= j - 1; k++) {
double g = A[i * 4 + k], h = A[k * 4 + j];
A[i * 4 + k] = g - s * (h + g * tau);
A[k * 4 + j] = h + s * (g - h * tau);
}
for(int k = j + 1; k < 4; k++) {
double g = A[i * 4 + k], h = A[j * 4 + k];
A[i * 4 + k] = g - s * (h + g * tau);
A[j * 4 + k] = h + s * (g - h * tau);
}
for(int k = 0; k < 4; k++) {
double g = U[k * 4 + i], h = U[k * 4 + j];
U[k * 4 + i] = g - s * (h + g * tau);
U[k * 4 + j] = h + s * (g - h * tau);
}
}
pAij++;
}
}
for(int i = 0; i < 4; i++) B[i] += Z[i];
memcpy(D, B, 4 * sizeof(double));
memset(Z, 0, 4 * sizeof(double));
}
for(int i = 0; i < 4; i++) B[i] += Z[i];
memcpy(D, B, 4 * sizeof(double));
memset(Z, 0, 4 * sizeof(double));
}
return false;
return false;
}

8
modules/calib3d/src/solvepnp.cpp
View File

@ -253,10 +253,10 @@ namespace cv
}
}
}
PnPSolver(const Mat& objectPoints, const Mat& imagePoints, const Parameters& parameters,
Mat& rvec, Mat& tvec, vector<int>& inliers):
objectPoints(objectPoints), imagePoints(imagePoints), parameters(parameters),
rvec(rvec), tvec(tvec), inliers(inliers)
PnPSolver(const Mat& _objectPoints, const Mat& _imagePoints, const Parameters& _parameters,
Mat& _rvec, Mat& _tvec, vector<int>& _inliers):
objectPoints(_objectPoints), imagePoints(_imagePoints), parameters(_parameters),
rvec(_rvec), tvec(_tvec), inliers(_inliers)
{
rvec.copyTo(initRvec);
tvec.copyTo(initTvec);

141
modules/calib3d/src/stereobm.cpp
View File

@ -155,7 +155,7 @@ static void prefilterNorm( const Mat& src, Mat& dst, int winsize, int ftzero, uc
val = ((curr[x]*4 + curr[x-1] + curr[x+1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10;
dptr[x] = tab[val + OFS];
}
sum += vsum[x+wsz2] - vsum[x-wsz2-1];
val = ((curr[x]*5 + curr[x-1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10;
dptr[x] = tab[val + OFS];
@ -170,15 +170,15 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero )
const int OFS = 256*4, TABSZ = OFS*2 + 256;
uchar tab[TABSZ];
Size size = src.size();
for( x = 0; x < TABSZ; x++ )
tab[x] = (uchar)(x - OFS < -ftzero ? 0 : x - OFS > ftzero ? ftzero*2 : x - OFS + ftzero);
uchar val0 = tab[0 + OFS];
#if CV_SSE2
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
for( y = 0; y < size.height-1; y += 2 )
{
const uchar* srow1 = src.ptr<uchar>(y);
@ -187,10 +187,10 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero )
const uchar* srow3 = y < size.height-2 ? srow1 + src.step*2 : srow1;
uchar* dptr0 = dst.ptr<uchar>(y);
uchar* dptr1 = dptr0 + dst.step;
dptr0[0] = dptr0[size.width-1] = dptr1[0] = dptr1[size.width-1] = val0;
x = 1;
#if CV_SSE2
if( useSIMD )
{
@ -205,26 +205,26 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero )
d0 = _mm_sub_epi16(d0, c0);
d1 = _mm_sub_epi16(d1, c1);
__m128i c2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z);
__m128i c3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z);
__m128i d2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z);
__m128i d3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z);
d2 = _mm_sub_epi16(d2, c2);
d3 = _mm_sub_epi16(d3, c3);
__m128i v0 = _mm_add_epi16(d0, _mm_add_epi16(d2, _mm_add_epi16(d1, d1)));
__m128i v1 = _mm_add_epi16(d1, _mm_add_epi16(d3, _mm_add_epi16(d2, d2)));
v0 = _mm_packus_epi16(_mm_add_epi16(v0, ftz), _mm_add_epi16(v1, ftz));
v0 = _mm_min_epu8(v0, ftz2);
_mm_storel_epi64((__m128i*)(dptr0 + x), v0);
_mm_storel_epi64((__m128i*)(dptr1 + x), _mm_unpackhi_epi64(v0, v0));
}
}
#endif
for( ; x < size.width-1; x++ )
{
int d0 = srow0[x+1] - srow0[x-1], d1 = srow1[x+1] - srow1[x-1],
@ -235,7 +235,7 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero )
dptr1[x] = (uchar)v1;
}
}
for( ; y < size.height; y++ )
{
uchar* dptr = dst.ptr<uchar>(y);
@ -336,7 +336,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
short* costptr = cost.data ? (short*)cost.data + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
hsad = hsad0 - dy0*ndisp;
lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep;
lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep;
@ -374,7 +374,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
// initialize sums
for( d = 0; d < ndisp; d++ )
sad[d] = (ushort)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0));
hsad = hsad0 + (1 - dy0)*ndisp;
for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp )
for( d = 0; d < ndisp; d += 16 )
@ -405,28 +405,28 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
{
__m128i u0 = _mm_load_si128((__m128i*)(hsad_sub + d));
__m128i u1 = _mm_load_si128((__m128i*)(hsad + d));
__m128i v0 = _mm_load_si128((__m128i*)(hsad_sub + d + 8));
__m128i v1 = _mm_load_si128((__m128i*)(hsad + d + 8));
__m128i usad8 = _mm_load_si128((__m128i*)(sad + d));
__m128i vsad8 = _mm_load_si128((__m128i*)(sad + d + 8));
u1 = _mm_sub_epi16(u1, u0);
v1 = _mm_sub_epi16(v1, v0);
usad8 = _mm_add_epi16(usad8, u1);
vsad8 = _mm_add_epi16(vsad8, v1);
mask = _mm_cmpgt_epi16(minsad8, usad8);
minsad8 = _mm_min_epi16(minsad8, usad8);
mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8));
_mm_store_si128((__m128i*)(sad + d), usad8);
_mm_store_si128((__m128i*)(sad + d + 8), vsad8);
mask = _mm_cmpgt_epi16(minsad8, vsad8);
minsad8 = _mm_min_epi16(minsad8, vsad8);
d8 = _mm_add_epi16(d8, dd_8);
mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8));
d8 = _mm_add_epi16(d8, dd_8);
@ -438,32 +438,33 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
dptr[y*dstep] = FILTERED;
continue;
}
__m128i minsad82 = _mm_unpackhi_epi64(minsad8, minsad8);
__m128i mind82 = _mm_unpackhi_epi64(mind8, mind8);
mask = _mm_cmpgt_epi16(minsad8, minsad82);
mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask));
minsad8 = _mm_min_epi16(minsad8, minsad82);
minsad82 = _mm_shufflelo_epi16(minsad8, _MM_SHUFFLE(3,2,3,2));
mind82 = _mm_shufflelo_epi16(mind8, _MM_SHUFFLE(3,2,3,2));
mask = _mm_cmpgt_epi16(minsad8, minsad82);
mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask));
minsad8 = _mm_min_epi16(minsad8, minsad82);
minsad82 = _mm_shufflelo_epi16(minsad8, 1);
mind82 = _mm_shufflelo_epi16(mind8, 1);
mask = _mm_cmpgt_epi16(minsad8, minsad82);
mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask));
mind = (short)_mm_cvtsi128_si32(mind8);
minsad = sad[mind];
if( uniquenessRatio > 0 )
{
int thresh = minsad + ((minsad * uniquenessRatio) >> 8);
__m128i thresh8 = _mm_set1_epi16((short)(thresh + 1));
__m128i d1 = _mm_set1_epi16((short)(mind-1)), d2 = _mm_set1_epi16((short)(mind+1));
__m128i dd_16 = _mm_add_epi16(dd_8, dd_8), d8 = _mm_sub_epi16(d0_8, dd_16);
__m128i dd_16 = _mm_add_epi16(dd_8, dd_8);
d8 = _mm_sub_epi16(d0_8, dd_16);
for( d = 0; d < ndisp; d += 16 )
{
@ -492,7 +493,8 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
if( 0 < mind && mind < ndisp - 1 )
{
int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n);
int p = sad[mind+1], n = sad[mind-1];
d = p + n - 2*sad[mind] + std::abs(p - n);
dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4);
}
else
@ -567,7 +569,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right,
htext[y] += tab[lval];
}
}
// initialize the left and right borders of the disparity map
for( y = 0; y < height; y++ )
{
@ -583,7 +585,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right,
int* costptr = cost.data ? (int*)cost.data + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
hsad = hsad0 - dy0*ndisp;
lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep;
lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep;
@ -611,7 +613,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right,
// initialize sums
for( d = 0; d < ndisp; d++ )
sad[d] = (int)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0));
hsad = hsad0 + (1 - dy0)*ndisp;
for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp )
for( d = 0; d < ndisp; d++ )
@ -662,7 +664,8 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right,
{
sad[-1] = sad[1];
sad[ndisp] = sad[ndisp-2];
int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n);
int p = sad[mind+1], n = sad[mind-1];
d = p + n - 2*sad[mind] + std::abs(p - n);
dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4);
costptr[y*coststep] = sad[mind];
}
@ -681,16 +684,16 @@ struct PrefilterInvoker
state = _state;
}
void operator()( int ind ) const
void operator()( int ind ) const
{
if( state->preFilterType == CV_STEREO_BM_NORMALIZED_RESPONSE )
prefilterNorm( *imgs0[ind], *imgs[ind], state->preFilterSize, state->preFilterCap, buf[ind] );
else
prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap );
prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap );
}
const Mat* imgs0[2];
Mat* imgs[2];
Mat* imgs[2];
uchar* buf[2];
CvStereoBMState *state;
};
@ -709,21 +712,21 @@ struct FindStereoCorrespInvoker
useShorts = _useShorts;
validDisparityRect = _validDisparityRect;
}
void operator()( const BlockedRange& range ) const
void operator()( const BlockedRange& range ) const
{
int cols = left->cols, rows = left->rows;
int _row0 = min(cvRound(range.begin() * rows / nstripes), rows);
int _row1 = min(cvRound(range.end() * rows / nstripes), rows);
uchar *ptr = state->slidingSumBuf->data.ptr + range.begin() * stripeBufSize;
int FILTERED = (state->minDisparity - 1)*16;
Rect roi = validDisparityRect & Rect(0, _row0, cols, _row1 - _row0);
if( roi.height == 0 )
return;
int row0 = roi.y;
int row1 = roi.y + roi.height;
Mat part;
if( row0 > _row0 )
{
@ -735,22 +738,22 @@ struct FindStereoCorrespInvoker
part = disp->rowRange(row1, _row1);
part = Scalar::all(FILTERED);
}
Mat left_i = left->rowRange(row0, row1);
Mat right_i = right->rowRange(row0, row1);
Mat disp_i = disp->rowRange(row0, row1);
Mat cost_i = state->disp12MaxDiff >= 0 ? Mat(state->cost).rowRange(row0, row1) : Mat();
#if CV_SSE2
#if CV_SSE2
if( useShorts )
findStereoCorrespondenceBM_SSE2( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 );
else
#endif
#endif
findStereoCorrespondenceBM( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 );
if( state->disp12MaxDiff >= 0 )
validateDisparity( disp_i, cost_i, state->minDisparity, state->numberOfDisparities, state->disp12MaxDiff );
if( roi.x > 0 )
{
part = disp_i.colRange(0, roi.x);
@ -767,7 +770,7 @@ protected:
const Mat *left, *right;
Mat* disp;
CvStereoBMState *state;
int nstripes;
int stripeBufSize;
bool useShorts;
@ -775,7 +778,7 @@ protected:
};
static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat& disp0, CvStereoBMState* state)
{
{
if (left0.size() != right0.size() || disp0.size() != left0.size())
CV_Error( CV_StsUnmatchedSizes, "All the images must have the same size" );
@ -783,7 +786,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
CV_Error( CV_StsUnsupportedFormat, "Both input images must have CV_8UC1" );
if (disp0.type() != CV_16SC1 && disp0.type() != CV_32FC1)
CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" );
CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" );
if( !state )
CV_Error( CV_StsNullPtr, "Stereo BM state is NULL." );
@ -809,7 +812,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
if( state->uniquenessRatio < 0 )
CV_Error( CV_StsOutOfRange, "uniqueness ratio must be non-negative" );
if( !state->preFilteredImg0 || state->preFilteredImg0->cols * state->preFilteredImg0->rows < left0.cols * left0.rows )
{
cvReleaseMat( &state->preFilteredImg0 );
@ -822,7 +825,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
}
Mat left(left0.size(), CV_8U, state->preFilteredImg0->data.ptr);
Mat right(right0.size(), CV_8U, state->preFilteredImg1->data.ptr);
int mindisp = state->minDisparity;
int ndisp = state->numberOfDisparities;
@ -832,15 +835,15 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
int rofs = -min(ndisp - 1 + mindisp, 0);
int width1 = width - rofs - ndisp + 1;
int FILTERED = (state->minDisparity - 1) << DISPARITY_SHIFT;
if( lofs >= width || rofs >= width || width1 < 1 )
{
disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) );
disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) );
return;
}
Mat disp = disp0;
if( disp0.type() == CV_32F)
{
if( !state->disp || state->disp->rows != disp0.rows || state->disp->cols != disp0.cols )
@ -850,8 +853,8 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
}
disp = cv::cvarrToMat(state->disp);
}
int wsz = state->SADWindowSize;
int wsz = state->SADWindowSize;
int bufSize0 = (int)((ndisp + 2)*sizeof(int));
bufSize0 += (int)((height+wsz+2)*ndisp*sizeof(int));
bufSize0 += (int)((height + wsz + 2)*sizeof(int));
@ -861,16 +864,16 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
int bufSize2 = 0;
if( state->speckleRange >= 0 && state->speckleWindowSize > 0 )
bufSize2 = width*height*(sizeof(cv::Point_<short>) + sizeof(int) + sizeof(uchar));
#if CV_SSE2
bool useShorts = state->preFilterCap <= 31 && state->SADWindowSize <= 21 && checkHardwareSupport(CV_CPU_SSE2);
#else
const bool useShorts = false;
#endif
#ifdef HAVE_TBB
#ifdef HAVE_TBB
const double SAD_overhead_coeff = 10.0;
double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread
double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread
double maxStripeSize = min(max(N0 / (width * ndisp), (wsz-1) * SAD_overhead_coeff), (double)height);
int nstripes = cvCeil(height / maxStripeSize);
#else
@ -878,27 +881,27 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
#endif
int bufSize = max(bufSize0 * nstripes, max(bufSize1 * 2, bufSize2));
if( !state->slidingSumBuf || state->slidingSumBuf->cols < bufSize )
{
cvReleaseMat( &state->slidingSumBuf );
state->slidingSumBuf = cvCreateMat( 1, bufSize, CV_8U );
}
uchar *_buf = state->slidingSumBuf->data.ptr;
int idx[] = {0,1};
parallel_do(idx, idx+2, PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, state));
Rect validDisparityRect(0, 0, width, height), R1 = state->roi1, R2 = state->roi2;
validDisparityRect = getValidDisparityROI(R1.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect,
R2.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect,
state->minDisparity, state->numberOfDisparities,
state->SADWindowSize);
state->SADWindowSize);
parallel_for(BlockedRange(0, nstripes),
FindStereoCorrespInvoker(left, right, disp, state, nstripes,
bufSize0, useShorts, validDisparityRect));
if( state->speckleRange >= 0 && state->speckleWindowSize > 0 )
{
Mat buf(state->slidingSumBuf);
@ -906,7 +909,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat
}
if (disp0.data != disp.data)
disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0);
disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0);
}
StereoBM::StereoBM()
@ -928,13 +931,13 @@ void StereoBM::operator()( InputArray _left, InputArray _right,
CV_Assert( disptype == CV_16S || disptype == CV_32F );
_disparity.create(left.size(), disptype);
Mat disparity = _disparity.getMat();
findStereoCorrespondenceBM(left, right, disparity, state);
}
template<> void Ptr<CvStereoBMState>::delete_obj()
{ cvReleaseStereoBMState(&obj); }
}
CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* rightarr,
@ -942,7 +945,7 @@ CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* ri
{
cv::Mat left = cv::cvarrToMat(leftarr),
right = cv::cvarrToMat(rightarr),
disp = cv::cvarrToMat(disparr);
disp = cv::cvarrToMat(disparr);
cv::findStereoCorrespondenceBM(left, right, disp, state);
}

336
modules/calib3d/src/stereosgbm.cpp
View File

@ -44,17 +44,17 @@
This is a variation of
"Stereo Processing by Semiglobal Matching and Mutual Information"
by Heiko Hirschmuller.
We match blocks rather than individual pixels, thus the algorithm is called
SGBM (Semi-global block matching)
*/
*/
#include "precomp.hpp"
#include <limits.h>
namespace cv
{
typedef uchar PixType;
typedef short CostType;
typedef short DispType;
@ -105,7 +105,7 @@ StereoSGBM::~StereoSGBM()
row1[x] and row2[x-d]. The subpixel algorithm from
"Depth Discontinuities by Pixel-to-Pixel Stereo" by Stan Birchfield and C. Tomasi
is used, hence the suffix BT.
the temporary buffer should contain width2*2 elements
*/
static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
@ -119,25 +119,25 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
int D = maxD - minD, width1 = maxX1 - minX1, width2 = maxX2 - minX2;
const PixType *row1 = img1.ptr<PixType>(y), *row2 = img2.ptr<PixType>(y);
PixType *prow1 = buffer + width2*2, *prow2 = prow1 + width*cn*2;
tab += tabOfs;
for( c = 0; c < cn*2; c++ )
{
prow1[width*c] = prow1[width*c + width-1] =
prow1[width*c] = prow1[width*c + width-1] =
prow2[width*c] = prow2[width*c + width-1] = tab[0];
}
int n1 = y > 0 ? -(int)img1.step : 0, s1 = y < img1.rows-1 ? (int)img1.step : 0;
int n2 = y > 0 ? -(int)img2.step : 0, s2 = y < img2.rows-1 ? (int)img2.step : 0;
if( cn == 1 )
{
for( x = 1; x < width-1; x++ )
{
prow1[x] = tab[(row1[x+1] - row1[x-1])*2 + row1[x+n1+1] - row1[x+n1-1] + row1[x+s1+1] - row1[x+s1-1]];
prow2[width-1-x] = tab[(row2[x+1] - row2[x-1])*2 + row2[x+n2+1] - row2[x+n2-1] + row2[x+s2+1] - row2[x+s2-1]];
prow1[x+width] = row1[x];
prow2[width-1-x+width] = row2[x];
}
@ -149,35 +149,35 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
prow1[x] = tab[(row1[x*3+3] - row1[x*3-3])*2 + row1[x*3+n1+3] - row1[x*3+n1-3] + row1[x*3+s1+3] - row1[x*3+s1-3]];
prow1[x+width] = tab[(row1[x*3+4] - row1[x*3-2])*2 + row1[x*3+n1+4] - row1[x*3+n1-2] + row1[x*3+s1+4] - row1[x*3+s1-2]];
prow1[x+width*2] = tab[(row1[x*3+5] - row1[x*3-1])*2 + row1[x*3+n1+5] - row1[x*3+n1-1] + row1[x*3+s1+5] - row1[x*3+s1-1]];
prow2[width-1-x] = tab[(row2[x*3+3] - row2[x*3-3])*2 + row2[x*3+n2+3] - row2[x*3+n2-3] + row2[x*3+s2+3] - row2[x*3+s2-3]];
prow2[width-1-x+width] = tab[(row2[x*3+4] - row2[x*3-2])*2 + row2[x*3+n2+4] - row2[x*3+n2-2] + row2[x*3+s2+4] - row2[x*3+s2-2]];
prow2[width-1-x+width*2] = tab[(row2[x*3+5] - row2[x*3-1])*2 + row2[x*3+n2+5] - row2[x*3+n2-1] + row2[x*3+s2+5] - row2[x*3+s2-1]];
prow1[x+width*3] = row1[x*3];
prow1[x+width*4] = row1[x*3+1];
prow1[x+width*5] = row1[x*3+2];
prow2[width-1-x+width*3] = row2[x*3];
prow2[width-1-x+width*4] = row2[x*3+1];
prow2[width-1-x+width*5] = row2[x*3+2];
}
}
memset( cost, 0, width1*D*sizeof(cost[0]) );
buffer -= minX2;
cost -= minX1*D + minD; // simplify the cost indices inside the loop
#if CV_SSE2
#if CV_SSE2
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
#if 1
#if 1
for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width )
{
int diff_scale = c < cn ? 0 : 2;
// precompute
// v0 = min(row2[x-1/2], row2[x], row2[x+1/2]) and
// v1 = max(row2[x-1/2], row2[x], row2[x+1/2]) and
@ -191,7 +191,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
buffer[x] = (PixType)v0;
buffer[x + width2] = (PixType)v1;
}
for( x = minX1; x < maxX1; x++ )
{
int u = prow1[x];
@ -199,14 +199,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
int ur = x < width-1 ? (u + prow1[x+1])/2 : u;
int u0 = min(ul, ur); u0 = min(u0, u);
int u1 = max(ul, ur); u1 = max(u1, u);
#if CV_SSE2
if( useSIMD )
{
__m128i _u = _mm_set1_epi8((char)u), _u0 = _mm_set1_epi8((char)u0);
__m128i _u1 = _mm_set1_epi8((char)u1), z = _mm_setzero_si128();
__m128i ds = _mm_cvtsi32_si128(diff_scale);
for( int d = minD; d < maxD; d += 16 )
{
__m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-x-1 + d));
@ -215,10 +215,10 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
__m128i c0 = _mm_max_epu8(_mm_subs_epu8(_u, _v1), _mm_subs_epu8(_v0, _u));
__m128i c1 = _mm_max_epu8(_mm_subs_epu8(_v, _u1), _mm_subs_epu8(_u0, _v));
__m128i diff = _mm_min_epu8(c0, c1);
c0 = _mm_load_si128((__m128i*)(cost + x*D + d));
c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8));
_mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_srl_epi16(_mm_unpacklo_epi8(diff,z), ds)));
_mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_srl_epi16(_mm_unpackhi_epi8(diff,z), ds)));
}
@ -233,7 +233,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
int v1 = buffer[width-x-1 + d + width2];
int c0 = max(0, u - v1); c0 = max(c0, v0 - u);
int c1 = max(0, v - u1); c1 = max(c1, u0 - v);
cost[x*D + d] = (CostType)(cost[x*D+d] + (min(c0, c1) >> diff_scale));
}
}
@ -249,14 +249,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
if( useSIMD )
{
__m128i _u = _mm_set1_epi8(u), z = _mm_setzero_si128();
for( int d = minD; d < maxD; d += 16 )
{
__m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-1-x + d));
__m128i diff = _mm_adds_epu8(_mm_subs_epu8(_u,_v), _mm_subs_epu8(_v,_u));
__m128i c0 = _mm_load_si128((__m128i*)(cost + x*D + d));
__m128i c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8));
_mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_unpacklo_epi8(diff,z)));
_mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_unpackhi_epi8(diff,z)));
}
@ -282,22 +282,22 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
minD <= d < maxD.
disp2full is the reverse disparity map, that is:
disp2full(x+roi.x,y+roi.y)=d means that img2(x+roi.x, y+roi.y) ~ img1(x+roi.x+d, y+roi.y)
note that disp1buf will have the same size as the roi and
disp2full will have the same size as img1 (or img2).
On exit disp2buf is not the final disparity, it is an intermediate result that becomes
final after all the tiles are processed.
the disparity in disp1buf is written with sub-pixel accuracy
(4 fractional bits, see CvStereoSGBM::DISP_SCALE),
using quadratic interpolation, while the disparity in disp2buf
is written as is, without interpolation.
disp2cost also has the same size as img1 (or img2).
It contains the minimum current cost, used to find the best disparity, corresponding to the minimal cost.
*/
*/
static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
Mat& disp1, const StereoSGBM& params,
Mat& disp1, const StereoSGBM& params,
Mat& buffer )
{
#if CV_SSE2
@ -312,15 +312,15 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
6, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0,
5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0
};
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
#endif
const int ALIGN = 16;
const int DISP_SHIFT = StereoSGBM::DISP_SHIFT;
const int DISP_SCALE = StereoSGBM::DISP_SCALE;
const CostType MAX_COST = SHRT_MAX;
int minD = params.minDisparity, maxD = minD + params.numberOfDisparities;
Size SADWindowSize;
SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5;
@ -336,28 +336,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
int npasses = params.fullDP ? 2 : 1;
const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2;
PixType clipTab[TAB_SIZE];
for( k = 0; k < TAB_SIZE; k++ )
clipTab[k] = (PixType)(min(max(k - TAB_OFS, -ftzero), ftzero) + ftzero);
if( minX1 >= maxX1 )
{
disp1 = Scalar::all(INVALID_DISP_SCALED);
return;
}
CV_Assert( D % 16 == 0 );
// NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8.
// if you change NR, please, modify the loop as well.
int D2 = D+16, NRD2 = NR2*D2;
// the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer:
// for 8-way dynamic programming we need the current row and
// the previous row, i.e. 2 rows in total
const int NLR = 2;
const int LrBorder = NLR - 1;
// for each possible stereo match (img1(x,y) <=> img2(x-d,y))
// we keep pixel difference cost (C) and the summary cost over NR directions (S).
// we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k)
@ -370,29 +370,29 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
CSBufSize*2*sizeof(CostType) + // C, S
width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost
width*(sizeof(CostType) + sizeof(DispType)) + 1024; // disp2cost + disp2
if( !buffer.data || !buffer.isContinuous() ||
buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize )
buffer.create(1, (int)totalBufSize, CV_8U);
// summary cost over different (nDirs) directions
CostType* Cbuf = (CostType*)alignPtr(buffer.data, ALIGN);
CostType* Sbuf = Cbuf + CSBufSize;
CostType* hsumBuf = Sbuf + CSBufSize;
CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows;
CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR;
DispType* disp2ptr = (DispType*)(disp2cost + width);
PixType* tempBuf = (PixType*)(disp2ptr + width);
// add P2 to every C(x,y). it saves a few operations in the inner loops
for( k = 0; k < width1*D; k++ )
Cbuf[k] = (CostType)P2;
for( int pass = 1; pass <= npasses; pass++ )
{
int x1, y1, x2, y2, dx, dy;
if( pass == 1 )
{
y1 = 0; y2 = height; dy = 1;
@ -403,9 +403,9 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
y1 = height-1; y2 = -1; dy = -1;
x1 = width1-1; x2 = -1; dx = -1;
}
CostType *Lr[NLR]={0}, *minLr[NLR]={0};
for( k = 0; k < NLR; k++ )
{
// shift Lr[k] and minLr[k] pointers, because we allocated them with the borders,
@ -418,26 +418,26 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
minLr[k] = pixDiff + costBufSize + LrSize*NLR + minLrSize*k + NR2*2;
memset( minLr[k] - LrBorder*NR2, 0, minLrSize*sizeof(CostType) );
}
for( int y = y1; y != y2; y += dy )
{
int x, d;
DispType* disp1ptr = disp1.ptr<DispType>(y);
CostType* C = Cbuf + (!params.fullDP ? 0 : y*costBufSize);
CostType* S = Sbuf + (!params.fullDP ? 0 : y*costBufSize);
if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any.
{
int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1;
for( k = dy1; k <= dy2; k++ )
{
CostType* hsumAdd = hsumBuf + (min(k, height-1) % hsumBufNRows)*costBufSize;
if( k < height )
{
calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero );
memset(hsumAdd, 0, D*sizeof(CostType));
for( x = 0; x <= SW2*D; x += D )
{
@ -445,17 +445,17 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
for( d = 0; d < D; d++ )
hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale);
}
if( y > 0 )
{
const CostType* hsumSub = hsumBuf + (max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize;
const CostType* Cprev = !params.fullDP || y == 0 ? C : C - costBufSize;
for( x = D; x < width1*D; x += D )
{
const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D);
const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0);
#if CV_SSE2
if( useSIMD )
{
@ -490,13 +490,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
{
const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D);
const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0);
for( d = 0; d < D; d++ )
hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]);
}
}
}
if( y == 0 )
{
int scale = k == 0 ? SH2 + 1 : 1;
@ -504,18 +504,18 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
C[x] = (CostType)(C[x] + hsumAdd[x]*scale);
}
}
// also, clear the S buffer
for( k = 0; k < width1*D; k++ )
S[k] = 0;
}
// clear the left and the right borders
memset( Lr[0] - NRD2*LrBorder - 8, 0, NRD2*LrBorder*sizeof(CostType) );
memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) );
memset( minLr[0] - NR2*LrBorder, 0, NR2*LrBorder*sizeof(CostType) );
memset( minLr[0] + width1*NR2, 0, NR2*LrBorder*sizeof(CostType) );
/*
[formula 13 in the paper]
compute L_r(p, d) = C(p, d) +
@ -537,87 +537,87 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
for( x = x1; x != x2; x += dx )
{
int xm = x*NR2, xd = xm*D2;
int delta0 = minLr[0][xm - dx*NR2] + P2, delta1 = minLr[1][xm - NR2 + 1] + P2;
int delta2 = minLr[1][xm + 2] + P2, delta3 = minLr[1][xm + NR2 + 3] + P2;
CostType* Lr_p0 = Lr[0] + xd - dx*NRD2;
CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2;
CostType* Lr_p2 = Lr[1] + xd + D2*2;
CostType* Lr_p3 = Lr[1] + xd + NRD2 + D2*3;
Lr_p0[-1] = Lr_p0[D] = Lr_p1[-1] = Lr_p1[D] =
Lr_p2[-1] = Lr_p2[D] = Lr_p3[-1] = Lr_p3[D] = MAX_COST;
CostType* Lr_p = Lr[0] + xd;
const CostType* Cp = C + x*D;
CostType* Sp = S + x*D;
#if CV_SSE2
if( useSIMD )
{
__m128i _P1 = _mm_set1_epi16((short)P1);
__m128i _delta0 = _mm_set1_epi16((short)delta0);
__m128i _delta1 = _mm_set1_epi16((short)delta1);
__m128i _delta2 = _mm_set1_epi16((short)delta2);
__m128i _delta3 = _mm_set1_epi16((short)delta3);
__m128i _minL0 = _mm_set1_epi16((short)MAX_COST);
for( d = 0; d < D; d += 8 )
{
__m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d));
__m128i L0, L1, L2, L3;
L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d));
L1 = _mm_load_si128((const __m128i*)(Lr_p1 + d));
L2 = _mm_load_si128((const __m128i*)(Lr_p2 + d));
L3 = _mm_load_si128((const __m128i*)(Lr_p3 + d));
L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1));
L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1));
L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d - 1)), _P1));
L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d + 1)), _P1));
L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d - 1)), _P1));
L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d + 1)), _P1));
L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d - 1)), _P1));
L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d + 1)), _P1));
L0 = _mm_min_epi16(L0, _delta0);
L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd);
L1 = _mm_min_epi16(L1, _delta1);
L1 = _mm_adds_epi16(_mm_subs_epi16(L1, _delta1), Cpd);
L2 = _mm_min_epi16(L2, _delta2);
L2 = _mm_adds_epi16(_mm_subs_epi16(L2, _delta2), Cpd);
L3 = _mm_min_epi16(L3, _delta3);
L3 = _mm_adds_epi16(_mm_subs_epi16(L3, _delta3), Cpd);
_mm_store_si128( (__m128i*)(Lr_p + d), L0);
_mm_store_si128( (__m128i*)(Lr_p + d + D2), L1);
_mm_store_si128( (__m128i*)(Lr_p + d + D2*2), L2);
_mm_store_si128( (__m128i*)(Lr_p + d + D2*3), L3);
__m128i t0 = _mm_min_epi16(_mm_unpacklo_epi16(L0, L2), _mm_unpackhi_epi16(L0, L2));
__m128i t1 = _mm_min_epi16(_mm_unpacklo_epi16(L1, L3), _mm_unpackhi_epi16(L1, L3));
t0 = _mm_min_epi16(_mm_unpacklo_epi16(t0, t1), _mm_unpackhi_epi16(t0, t1));
_minL0 = _mm_min_epi16(_minL0, t0);
__m128i Sval = _mm_load_si128((const __m128i*)(Sp + d));
L0 = _mm_adds_epi16(L0, L1);
L2 = _mm_adds_epi16(L2, L3);
Sval = _mm_adds_epi16(Sval, L0);
Sval = _mm_adds_epi16(Sval, L2);
_mm_store_si128((__m128i*)(Sp + d), Sval);
}
_minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8));
_mm_storel_epi64((__m128i*)&minLr[0][xm], _minL0);
}
@ -625,28 +625,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
#endif
{
int minL0 = MAX_COST, minL1 = MAX_COST, minL2 = MAX_COST, minL3 = MAX_COST;
for( d = 0; d < D; d++ )
{
int Cpd = Cp[d], L0, L1, L2, L3;
L0 = Cpd + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0;
L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1;
L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1;
L2 = Cpd + min((int)Lr_p2[d], min(Lr_p2[d-1] + P1, min(Lr_p2[d+1] + P1, delta2))) - delta2;
L3 = Cpd + min((int)Lr_p3[d], min(Lr_p3[d-1] + P1, min(Lr_p3[d+1] + P1, delta3))) - delta3;
Lr_p[d] = (CostType)L0;
minL0 = min(minL0, L0);
Lr_p[d + D2] = (CostType)L1;
minL1 = min(minL1, L1);
Lr_p[d + D2*2] = (CostType)L2;
minL2 = min(minL2, L2);
Lr_p[d + D2*3] = (CostType)L3;
minL3 = min(minL3, L3);
Sp[d] = saturate_cast<CostType>(Sp[d] + L0 + L1 + L2 + L3);
}
minLr[0][xm] = (CostType)minL0;
@ -655,7 +655,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
minLr[0][xm+3] = (CostType)minL3;
}
}
if( pass == npasses )
{
for( x = 0; x < width; x++ )
@ -663,73 +663,73 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
disp1ptr[x] = disp2ptr[x] = (DispType)INVALID_DISP_SCALED;
disp2cost[x] = MAX_COST;
}
for( x = width1 - 1; x >= 0; x-- )
{
CostType* Sp = S + x*D;
int minS = MAX_COST, bestDisp = -1;
if( npasses == 1 )
{
int xm = x*NR2, xd = xm*D2;
int minL0 = MAX_COST;
int delta0 = minLr[0][xm + NR2] + P2;
CostType* Lr_p0 = Lr[0] + xd + NRD2;
Lr_p0[-1] = Lr_p0[D] = MAX_COST;
CostType* Lr_p = Lr[0] + xd;
const CostType* Cp = C + x*D;
#if CV_SSE2
if( useSIMD )
{
__m128i _P1 = _mm_set1_epi16((short)P1);
__m128i _delta0 = _mm_set1_epi16((short)delta0);
__m128i _minL0 = _mm_set1_epi16((short)minL0);
__m128i _minS = _mm_set1_epi16(MAX_COST), _bestDisp = _mm_set1_epi16(-1);
__m128i _d8 = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7), _8 = _mm_set1_epi16(8);
for( d = 0; d < D; d += 8 )
{
__m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)), L0;
L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d));
L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1));
L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1));
L0 = _mm_min_epi16(L0, _delta0);
L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd);
_mm_store_si128((__m128i*)(Lr_p + d), L0);
_minL0 = _mm_min_epi16(_minL0, L0);
L0 = _mm_adds_epi16(L0, *(__m128i*)(Sp + d));
_mm_store_si128((__m128i*)(Sp + d), L0);
__m128i mask = _mm_cmpgt_epi16(_minS, L0);
_minS = _mm_min_epi16(_minS, L0);
_bestDisp = _mm_xor_si128(_bestDisp, _mm_and_si128(_mm_xor_si128(_bestDisp,_d8), mask));
_d8 = _mm_adds_epi16(_d8, _8);
}
short CV_DECL_ALIGNED(16) bestDispBuf[8];
_mm_store_si128((__m128i*)bestDispBuf, _bestDisp);
_minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8));
_minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 4));
_minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 2));
__m128i qS = _mm_min_epi16(_minS, _mm_srli_si128(_minS, 8));
qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 4));
qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 2));
minLr[0][xm] = (CostType)_mm_cvtsi128_si32(_minL0);
minS = (CostType)_mm_cvtsi128_si32(qS);
qS = _mm_shuffle_epi32(_mm_unpacklo_epi16(qS, qS), 0);
qS = _mm_cmpeq_epi16(_minS, qS);
int idx = _mm_movemask_epi8(_mm_packs_epi16(qS, qS)) & 255;
bestDisp = bestDispBuf[LSBTab[idx]];
}
else
@ -738,10 +738,10 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
for( d = 0; d < D; d++ )
{
int L0 = Cp[d] + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0;
Lr_p[d] = (CostType)L0;
minL0 = min(minL0, L0);
int Sval = Sp[d] = saturate_cast<CostType>(Sp[d] + L0);
if( Sval < minS )
{
@ -764,7 +764,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
}
}
}
for( d = 0; d < D; d++ )
{
if( Sp[d]*(100 - uniquenessRatio) < minS*100 && std::abs(bestDisp - d) > 1 )
@ -773,13 +773,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
if( d < D )
continue;
d = bestDisp;
int x2 = x + minX1 - d - minD;
if( disp2cost[x2] > minS )
int _x2 = x + minX1 - d - minD;
if( disp2cost[_x2] > minS )
{
disp2cost[x2] = (CostType)minS;
disp2ptr[x2] = (DispType)(d + minD);
disp2cost[_x2] = (CostType)minS;
disp2ptr[_x2] = (DispType)(d + minD);
}
if( 0 < d && d < D-1 )
{
// do subpixel quadratic interpolation:
@ -792,24 +792,24 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
d *= DISP_SCALE;
disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE);
}
for( x = minX1; x < maxX1; x++ )
{
// we round the computed disparity both towards -inf and +inf and check
// if either of the corresponding disparities in disp2 is consistent.
// This is to give the computed disparity a chance to look valid if it is.
int d = disp1ptr[x];
if( d == INVALID_DISP_SCALED )
int d1 = disp1ptr[x];
if( d1 == INVALID_DISP_SCALED )
continue;
int _d = d >> DISP_SHIFT;
int d_ = (d + DISP_SCALE-1) >> DISP_SHIFT;
int _d = d1 >> DISP_SHIFT;
int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT;
int _x = x - _d, x_ = x - d_;
if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff &&
0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff )
disp1ptr[x] = (DispType)INVALID_DISP_SCALED;
}
}
// now shift the cyclic buffers
std::swap( Lr[0], Lr[1] );
std::swap( minLr[0], minLr[1] );
@ -825,13 +825,13 @@ void StereoSGBM::operator ()( InputArray _left, InputArray _right,
Mat left = _left.getMat(), right = _right.getMat();
CV_Assert( left.size() == right.size() && left.type() == right.type() &&
left.depth() == DataType<PixType>::depth );
_disp.create( left.size(), CV_16S );
Mat disp = _disp.getMat();
computeDisparitySGBM( left, right, disp, *this, buffer );
medianBlur(disp, disp, 3);
if( speckleWindowSize > 0 )
filterSpeckles(disp, (minDisparity - 1)*DISP_SCALE, speckleWindowSize, DISP_SCALE*speckleRange, buffer);
}
@ -844,33 +844,33 @@ Rect getValidDisparityROI( Rect roi1, Rect roi2,
{
int SW2 = SADWindowSize/2;
int minD = minDisparity, maxD = minDisparity + numberOfDisparities - 1;
int xmin = max(roi1.x, roi2.x + maxD) + SW2;
int xmax = min(roi1.x + roi1.width, roi2.x + roi2.width - minD) - SW2;
int ymin = max(roi1.y, roi2.y) + SW2;
int ymax = min(roi1.y + roi1.height, roi2.y + roi2.height) - SW2;
Rect r(xmin, ymin, xmax - xmin, ymax - ymin);
return r.width > 0 && r.height > 0 ? r : Rect();
}
}
}
void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSize,
double _maxDiff, InputOutputArray __buf )
{
Mat img = _img.getMat();
Mat temp, &_buf = __buf.needed() ? __buf.getMatRef() : temp;
CV_Assert( img.type() == CV_16SC1 );
int newVal = cvRound(_newval);
int maxDiff = cvRound(_maxDiff);
int width = img.cols, height = img.rows, npixels = width*height;
size_t bufSize = npixels*(int)(sizeof(Point2s) + sizeof(int) + sizeof(uchar));
if( !_buf.isContinuous() || !_buf.data || _buf.cols*_buf.rows*_buf.elemSize() < bufSize )
_buf.create(1, (int)bufSize, CV_8U);
uchar* buf = _buf.data;
int i, j, dstep = (int)(img.step/sizeof(short));
int* labels = (int*)buf;
@ -879,33 +879,33 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi
buf += npixels*sizeof(wbuf[0]);
uchar* rtype = (uchar*)buf;
int curlabel = 0;
// clear out label assignments
memset(labels, 0, npixels*sizeof(labels[0]));
for( i = 0; i < height; i++ )
{
short* ds = img.ptr<short>(i);
int* ls = labels + width*i;
for( j = 0; j < width; j++ )
{
if( ds[j] != newVal ) // not a bad disparity
if( ds[j] != newVal ) // not a bad disparity
{
if( ls[j] ) // has a label, check for bad label
{
if( ls[j] ) // has a label, check for bad label
{
if( rtype[ls[j]] ) // small region, zero out disparity
ds[j] = (short)newVal;
}
// no label, assign and propagate
else
{
Point2s* ws = wbuf; // initialize wavefront
Point2s p((short)j, (short)i); // current pixel
curlabel++; // next label
int count = 0; // current region size
Point2s* ws = wbuf; // initialize wavefront
Point2s p((short)j, (short)i); // current pixel
curlabel++; // next label
int count = 0; // current region size
ls[j] = curlabel;
// wavefront propagation
while( ws >= wbuf ) // wavefront not empty
{
@ -914,50 +914,50 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi
short* dpp = &img.at<short>(p.y, p.x);
short dp = *dpp;
int* lpp = labels + width*p.y + p.x;
if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff )
{
lpp[+1] = curlabel;
*ws++ = Point2s(p.x+1, p.y);
}
if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff )
{
lpp[-1] = curlabel;
*ws++ = Point2s(p.x-1, p.y);
}
if( p.y < height-1 && !lpp[+width] && dpp[+dstep] != newVal && std::abs(dp - dpp[+dstep]) <= maxDiff )
{
lpp[+width] = curlabel;
*ws++ = Point2s(p.x, p.y+1);
}
if( p.y > 0 && !lpp[-width] && dpp[-dstep] != newVal && std::abs(dp - dpp[-dstep]) <= maxDiff )
{
lpp[-width] = curlabel;
*ws++ = Point2s(p.x, p.y-1);
}
// pop most recent and propagate
// NB: could try least recent, maybe better convergence
p = *--ws;
}
// assign label type
if( count <= maxSpeckleSize ) // speckle region
if( count <= maxSpeckleSize ) // speckle region
{
rtype[ls[j]] = 1; // small region label
rtype[ls[j]] = 1; // small region label
ds[j] = (short)newVal;
}
else
rtype[ls[j]] = 0; // large region label
rtype[ls[j]] = 0; // large region label
}
}
}
}
}
}
void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDisparity,
int numberOfDisparities, int disp12MaxDiff )
{
@ -971,32 +971,32 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis
const int DISP_SHIFT = 4, DISP_SCALE = 1 << DISP_SHIFT;
int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE;
int costType = cost.type();
disp12MaxDiff *= DISP_SCALE;
CV_Assert( numberOfDisparities > 0 && disp.type() == CV_16S &&
(costType == CV_16S || costType == CV_32S) &&
disp.size() == cost.size() );
for( int y = 0; y < rows; y++ )
{
short* dptr = disp.ptr<short>(y);
for( x = 0; x < cols; x++ )
{
disp2buf[x] = INVALID_DISP_SCALED;
disp2cost[x] = INT_MAX;
}
if( costType == CV_16S )
{
const short* cptr = cost.ptr<short>(y);
for( x = minX1; x < maxX1; x++ )
{
int d = dptr[x], c = cptr[x];
int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT);
if( disp2cost[x2] > c )
{
disp2cost[x2] = c;
@ -1007,12 +1007,12 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis
else
{
const int* cptr = cost.ptr<int>(y);
for( x = minX1; x < maxX1; x++ )
{
int d = dptr[x], c = cptr[x];
int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT);
if( disp2cost[x2] < c )
{
disp2cost[x2] = c;
@ -1020,7 +1020,7 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis
}
}
}
for( x = minX1; x < maxX1; x++ )
{
// we round the computed disparity both towards -inf and +inf and check

1372
modules/calib3d/test/test_cameracalibration.cpp
View File

File diff suppressed because it is too large Load Diff

140
modules/calib3d/test/test_cameracalibration_artificial.cpp
View File

@ -55,22 +55,22 @@ using namespace cv;
using namespace std;
//template<class T> ostream& operator<<(ostream& out, const Mat_<T>& mat)
//{
//{
// for(Mat_<T>::const_iterator pos = mat.begin(), end = mat.end(); pos != end; ++pos)
// out << *pos << " ";
// return out;
//}
//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_<double>(mat); }
//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_<double>(mat); }
Mat calcRvec(const vector<Point3f>& points, const Size& cornerSize)
{
{
Point3f p00 = points[0];
Point3f p10 = points[1];
Point3f p01 = points[cornerSize.width];
Point3f p01 = points[cornerSize.width];
Vec3d ex(p10.x - p00.x, p10.y - p00.y, p10.z - p00.z);
Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z);
Vec3d ez = ex.cross(ey);
Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z);
Vec3d ez = ex.cross(ey);
Mat rot(3, 3, CV_64F);
*rot.ptr<Vec3d>(0) = ex;
@ -89,7 +89,7 @@ public:
{
}
~CV_CalibrateCameraArtificialTest() {}
protected:
protected:
int r;
const static int JUST_FIND_CORNERS = 0;
@ -111,7 +111,7 @@ protected:
{
ts->printf( cvtest::TS::LOG, "Bad shape of camera matrix returned \n");
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
}
}
double fx_e = camMat_est.at<double>(0, 0), fy_e = camMat_est.at<double>(1, 1);
double cx_e = camMat_est.at<double>(0, 2), cy_e = camMat_est.at<double>(1, 2);
@ -121,19 +121,19 @@ protected:
const double eps = 1e-2;
const double dlt = 1e-5;
bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) ||
checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt);
bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) ||
checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt);
if (fail)
{
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
ts->printf( cvtest::TS::LOG, "%d) Expected [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx, fy, cx, cy);
ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e);
ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e);
}
void compareDistCoeffs(const Mat_<double>& distCoeffs, const Mat& distCoeffs_est)
{
{
const double *dt_e = distCoeffs_est.ptr<double>();
double k1_e = dt_e[0], k2_e = dt_e[1], k3_e = dt_e[4];
@ -143,21 +143,21 @@ protected:
double p1 = distCoeffs(0, 2), p2 = distCoeffs(0, 3);
const double eps = 5e-2;
const double dlt = 1e-3;
const double dlt = 1e-3;
const double eps_k3 = 5;
const double dlt_k3 = 1e-3;
const double dlt_k3 = 1e-3;
bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) ||
checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt);
bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) ||
checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt);
if (fail)
{
// commented according to vp123's recomendation. TODO - improve accuaracy
//ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss
}
//ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss
}
ts->printf( cvtest::TS::LOG, "%d) DistCoeff exp=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1, k2, p1, p2, k3);
ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e);
ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e);
ts->printf( cvtest::TS::LOG, "%d) AbsError = [%.5f %.5f %.5f %.5f %.5f]\n", r, fabs(k1-k1_e), fabs(k2-k2_e), fabs(p1-p1_e), fabs(p2-p2_e), fabs(k3-k3_e));
}
@ -173,20 +173,20 @@ protected:
const Point3d& tvec = *tvecs[i].ptr<Point3d>();
const Point3d& tvec_est = *tvecs_est[i].ptr<Point3d>();
if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt))
if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt))
{
if (err_count++ < errMsgNum)
{
if (err_count == errMsgNum)
ts->printf( cvtest::TS::LOG, "%d) ...\n", r);
else
if (err_count == errMsgNum)
ts->printf( cvtest::TS::LOG, "%d) ...\n", r);
else
{
ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i);
ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i);
ts->printf( cvtest::TS::LOG, "%d) norm(tvec_est - tvec) = %f, norm(tvec_exp) = %f \n", r, norm(tvec_est - tvec), norm(tvec));
}
}
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
}
@ -199,20 +199,20 @@ protected:
int err_count = 0;
const int errMsgNum = 4;
for(size_t i = 0; i < rvecs.size(); ++i)
{
{
Rodrigues(rvecs[i], rmat);
Rodrigues(rvecs_est[i], rmat_est);
Rodrigues(rvecs_est[i], rmat_est);
if (norm(rmat_est, rmat) > eps* (norm(rmat) + dlt))
{
if (err_count++ < errMsgNum)
{
if (err_count == errMsgNum)
ts->printf( cvtest::TS::LOG, "%d) ...\n", r);
ts->printf( cvtest::TS::LOG, "%d) ...\n", r);
else
{
ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i);
ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat));
ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i);
ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat));
}
}
@ -221,19 +221,19 @@ protected:
}
}
double reprojectErrorWithoutIntrinsics(const vector<Point3f>& cb3d, const vector<Mat>& rvecs_exp, const vector<Mat>& tvecs_exp,
double reprojectErrorWithoutIntrinsics(const vector<Point3f>& cb3d, const vector<Mat>& _rvecs_exp, const vector<Mat>& _tvecs_exp,
const vector<Mat>& rvecs_est, const vector<Mat>& tvecs_est)
{
{
const static Mat eye33 = Mat::eye(3, 3, CV_64F);
const static Mat zero15 = Mat::zeros(1, 5, CV_64F);
Mat chessboard3D(cb3d);
Mat _chessboard3D(cb3d);
vector<Point2f> uv_exp, uv_est;
double res = 0;
double res = 0;
for(size_t i = 0; i < rvecs_exp.size(); ++i)
{
projectPoints(chessboard3D, rvecs_exp[i], tvecs_exp[i], eye33, zero15, uv_exp);
projectPoints(chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est);
for(size_t i = 0; i < rvecs_exp.size(); ++i)
{
projectPoints(_chessboard3D, _rvecs_exp[i], _tvecs_exp[i], eye33, zero15, uv_exp);
projectPoints(_chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est);
for(size_t j = 0; j < cb3d.size(); ++j)
res += norm(uv_exp[i] - uv_est[i]);
}
@ -243,7 +243,7 @@ protected:
Size2f sqSile;
vector<Point3f> chessboard3D;
vector<Mat> boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp;
vector<Mat> boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp;
vector< vector<Point3f> > objectPoints;
vector< vector<Point2f> > imagePoints_art;
vector< vector<Point2f> > imagePoints_findCb;
@ -268,29 +268,29 @@ protected:
imagePoints_findCb.clear();
vector<Point2f> corners_art, corners_fcb;
for(size_t i = 0; i < brdsNum; ++i)
{
for(size_t i = 0; i < brdsNum; ++i)
{
for(;;)
{
boards[i] = cbg(bg, camMat, distCoeffs, sqSile, corners_art);
if(findChessboardCorners(boards[i], cornersSize, corners_fcb))
break;
}
if(findChessboardCorners(boards[i], cornersSize, corners_fcb))
break;
}
//cv::namedWindow("CB"); imshow("CB", boards[i]); cv::waitKey();
imagePoints_art.push_back(corners_art);
imagePoints_art.push_back(corners_art);
imagePoints_findCb.push_back(corners_fcb);
tvecs_exp[i].create(1, 3, CV_64F);
*tvecs_exp[i].ptr<Point3d>() = cbg.corners3d[0];
rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize());
rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize());
}
}
void runTest(const Size& imgSize, const Mat_<double>& camMat, const Mat_<double>& distCoeffs, size_t brdsNum, const Size& cornersSize, int flag = 0)
{
{
const TermCriteria tc(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1);
vector< vector<Point2f> > imagePoints;
@ -300,9 +300,9 @@ protected:
case JUST_FIND_CORNERS: imagePoints = imagePoints_findCb; break;
case ARTIFICIAL_CORNERS: imagePoints = imagePoints_art; break;
case USE_CORNERS_SUBPIX:
case USE_CORNERS_SUBPIX:
for(size_t i = 0; i < brdsNum; ++i)
{
{
Mat gray;
cvtColor(boards[i], gray, CV_BGR2GRAY);
vector<Point2f> tmp = imagePoints_findCb[i];
@ -312,9 +312,9 @@ protected:
break;
case USE_4QUAD_CORNERS:
for(size_t i = 0; i < brdsNum; ++i)
{
{
Mat gray;
cvtColor(boards[i], gray, CV_BGR2GRAY);
cvtColor(boards[i], gray, CV_BGR2GRAY);
vector<Point2f> tmp = imagePoints_findCb[i];
find4QuadCornerSubpix(gray, tmp, Size(5, 5));
imagePoints.push_back(tmp);
@ -323,7 +323,7 @@ protected:
default:
throw std::exception();
}
Mat camMat_est = Mat::eye(3, 3, CV_64F), distCoeffs_est = Mat::zeros(1, 5, CV_64F);
vector<Mat> rvecs_est, tvecs_est;
@ -342,9 +342,9 @@ protected:
compareCameraMatrs(camMat, camMat_est);
compareDistCoeffs(distCoeffs, distCoeffs_est);
compareShiftVecs(tvecs_exp, tvecs_est);
compareRotationVecs(rvecs_exp, rvecs_est);
compareRotationVecs(rvecs_exp, rvecs_est);
double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est);
double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est);
rep_errorWOI /= brdsNum * cornersSize.area();
const double thres2 = 0.01;
@ -352,8 +352,8 @@ protected:
{
ts->printf( cvtest::TS::LOG, "%d) Too big reproject error without intrinsics = %f\n", r, rep_errorWOI);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
ts->printf( cvtest::TS::LOG, "%d) Testing solvePnP...\n", r);
rvecs_spnp.resize(brdsNum);
tvecs_spnp.resize(brdsNum);
@ -361,11 +361,11 @@ protected:
solvePnP(Mat(objectPoints[i]), Mat(imagePoints[i]), camMat, distCoeffs, rvecs_spnp[i], tvecs_spnp[i]);
compareShiftVecs(tvecs_exp, tvecs_spnp);
compareRotationVecs(rvecs_exp, rvecs_spnp);
compareRotationVecs(rvecs_exp, rvecs_spnp);
}
void run(int)
{
{
ts->set_failed_test_info(cvtest::TS::OK);
RNG& rng = theRNG();
@ -373,11 +373,11 @@ protected:
int progress = 0;
int repeat_num = 3;
for(r = 0; r < repeat_num; ++r)
{
const int brds_num = 20;
{
const int brds_num = 20;
Mat bg(Size(640, 480), CV_8UC3);
randu(bg, Scalar::all(32), Scalar::all(255));
Mat bg(Size(640, 480), CV_8UC3);
randu(bg, Scalar::all(32), Scalar::all(255));
GaussianBlur(bg, bg, Size(5, 5), 2);
double fx = 300 + (20 * (double)rng - 10);
@ -399,20 +399,20 @@ protected:
Mat_<double> distCoeffs(1, 5, 0.0);
distCoeffs << k1, k2, p1, p2, k3;
ChessBoardGenerator cbg(Size(9, 8));
ChessBoardGenerator cbg(Size(9, 8));
cbg.min_cos = 0.9;
cbg.cov = 0.8;
progress = update_progress(progress, r, repeat_num, 0);
ts->printf( cvtest::TS::LOG, "\n");
ts->printf( cvtest::TS::LOG, "\n");
prepareForTest(bg, camMat, distCoeffs, brds_num, cbg);
ts->printf( cvtest::TS::LOG, "artificial corners\n");
runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS);
ts->printf( cvtest::TS::LOG, "artificial corners\n");
runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS);
progress = update_progress(progress, r, repeat_num, 0);
ts->printf( cvtest::TS::LOG, "findChessboard corners\n");
runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS);
runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS);
progress = update_progress(progress, r, repeat_num, 0);
ts->printf( cvtest::TS::LOG, "cornersSubPix corners\n");
@ -424,6 +424,6 @@ protected:
progress = update_progress(progress, r, repeat_num, 0);
}
}
};
};
TEST(Calib3d_CalibrateCamera_CPP, accuracy_on_artificial_data) { CV_CalibrateCameraArtificialTest test; test.safe_run(); }

159
modules/calib3d/test/test_chesscorners.cpp
View File

@ -54,9 +54,9 @@ void show_points( const Mat& gray, const Mat& u, const vector<Point2f>& v, Size
{
Mat rgb( gray.size(), CV_8U);
merge(vector<Mat>(3, gray), rgb);
for(size_t i = 0; i < v.size(); i++ )
circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED);
circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED);
if( !u.empty() )
{
@ -67,7 +67,7 @@ void show_points( const Mat& gray, const Mat& u, const vector<Point2f>& v, Size
}
if (!v.empty())
{
Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]);
Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]);
drawChessboardCorners( rgb, pattern_size, corners, was_found );
}
//namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0);
@ -122,11 +122,11 @@ double calcError(const vector<Point2f>& v, const Mat& u)
//printf("\n");
err = min(err, err1);
}
#if defined(_L2_ERR)
err = sqrt(err/count_exp);
#endif //_L2_ERR
return err;
}
@ -137,8 +137,7 @@ const double precise_success_error_level = 2;
/* ///////////////////// chess_corner_test ///////////////////////// */
void CV_ChessboardDetectorTest::run( int /*start_from */)
{
cvtest::TS& ts = *this->ts;
ts.set_failed_test_info( cvtest::TS::OK );
ts->set_failed_test_info( cvtest::TS::OK );
/*if (!checkByGenerator())
return;*/
@ -146,23 +145,23 @@ void CV_ChessboardDetectorTest::run( int /*start_from */)
{
case CHESSBOARD:
checkByGenerator();
if (ts.get_err_code() != cvtest::TS::OK)
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("negative_list.dat");
if (ts.get_err_code() != cvtest::TS::OK)
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("chessboard_list.dat");
if (ts.get_err_code() != cvtest::TS::OK)
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("chessboard_list_subpixel.dat");
break;
case CIRCLES_GRID:
@ -176,36 +175,34 @@ void CV_ChessboardDetectorTest::run( int /*start_from */)
void CV_ChessboardDetectorTest::run_batch( const string& filename )
{
cvtest::TS& ts = *this->ts;
ts.printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str());
ts->printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str());
//#define WRITE_POINTS 1
#ifndef WRITE_POINTS
#ifndef WRITE_POINTS
double max_rough_error = 0, max_precise_error = 0;
#endif
string folder;
switch( pattern )
{
case CHESSBOARD:
folder = string(ts.get_data_path()) + "cameracalibration/";
folder = string(ts->get_data_path()) + "cameracalibration/";
break;
case CIRCLES_GRID:
folder = string(ts.get_data_path()) + "cameracalibration/circles/";
folder = string(ts->get_data_path()) + "cameracalibration/circles/";
break;
case ASYMMETRIC_CIRCLES_GRID:
folder = string(ts.get_data_path()) + "cameracalibration/asymmetric_circles/";
folder = string(ts->get_data_path()) + "cameracalibration/asymmetric_circles/";
break;
}
FileStorage fs( folder + filename, FileStorage::READ );
FileNode board_list = fs["boards"];
if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 )
{
ts.printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() );
ts.printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
ts->printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() );
ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2);
ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
@ -216,29 +213,29 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
for(int idx = 0; idx < max_idx; ++idx )
{
ts.update_context( this, idx, true );
ts->update_context( this, idx, true );
/* read the image */
string img_file = board_list[idx * 2];
string img_file = board_list[idx * 2];
Mat gray = imread( folder + img_file, 0);
if( gray.empty() )
{
ts.printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() );
ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
string filename = folder + (string)board_list[idx * 2 + 1];
string _filename = folder + (string)board_list[idx * 2 + 1];
bool doesContatinChessboard;
Mat expected;
{
FileStorage fs(filename, FileStorage::READ);
fs["corners"] >> expected;
fs["isFound"] >> doesContatinChessboard;
fs.release();
}
size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
FileStorage fs1(_filename, FileStorage::READ);
fs1["corners"] >> expected;
fs1["isFound"] >> doesContatinChessboard;
fs1.release();
}
size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
Size pattern_size = expected.size();
vector<Point2f> v;
@ -256,11 +253,11 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
break;
}
show_points( gray, Mat(), v, pattern_size, result );
if( result ^ doesContatinChessboard || v.size() != count_exp )
{
ts.printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() );
ts.set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
ts->printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
@ -291,45 +288,45 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
#if 1
if( err > precise_success_error_level )
{
ts.printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err );
ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
ts->printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
#endif
ts.printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
ts->printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
max_precise_error = MAX( max_precise_error, err );
#endif
#endif
}
#ifdef WRITE_POINTS
Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);
FileStorage fs(filename, FileStorage::WRITE);
FileStorage fs(_filename, FileStorage::WRITE);
fs << "isFound" << result;
fs << "corners" << mat_v;
fs.release();
#endif
progress = update_progress( progress, idx, max_idx, 0 );
}
}
sum_error /= count;
ts.printf(cvtest::TS::LOG, "Average error is %f\n", sum_error);
ts->printf(cvtest::TS::LOG, "Average error is %f\n", sum_error);
}
double calcErrorMinError(const Size& cornSz, const vector<Point2f>& corners_found, const vector<Point2f>& corners_generated)
{
Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]);
Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]);
Mat m2; flip(m1, m2, 0);
Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1);
Mat m4 = m1.t(); flip(m4, m4, 1);
double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2));
double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4));
double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2));
double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4));
return min(min1, min2);
}
bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
const vector<Point2f>& corners_generated)
{
Size cornersSize = cbg.cornersSize();
@ -341,7 +338,7 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
for(int j = 1; j < mat.cols - 2; ++j)
{
const Point2f& cur = mat(i, j);
tmp = norm( cur - mat(i + 1, j + 1) );
if (tmp < minNeibDist)
tmp = minNeibDist;
@ -361,33 +358,33 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
const double threshold = 0.25;
double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist;
int imgsize = min(imgSz.height, imgSz.width);
int imgsize = min(imgSz.height, imgSz.width);
return imgsize * threshold < cbsize;
}
bool CV_ChessboardDetectorTest::checkByGenerator()
{
{
bool res = true;
//theRNG() = 0x58e6e895b9913160;
//cv::DefaultRngAuto dra;
//theRNG() = *ts->get_rng();
Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255));
randu(bg, Scalar::all(0), Scalar::all(255));
GaussianBlur(bg, bg, Size(7,7), 3.0);
Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255));
randu(bg, Scalar::all(0), Scalar::all(255));
GaussianBlur(bg, bg, Size(7,7), 3.0);
Mat_<float> camMat(3, 3);
camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f;
Mat_<float> distCoeffs(1, 5);
distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f;
const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12), Size(5, 4) };
const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]);
const int test_num = 16;
const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]);
const int test_num = 16;
int progress = 0;
for(int i = 0; i < test_num; ++i)
{
{
progress = update_progress( progress, i, test_num, 0 );
ChessBoardGenerator cbg(sizes[i % sizes_num]);
@ -398,37 +395,37 @@ bool CV_ChessboardDetectorTest::checkByGenerator()
if(!validateData(cbg, cb.size(), corners_generated))
{
ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" );
continue;
continue;
}
/*cb = cb * 0.8 + Scalar::all(30);
/*cb = cb * 0.8 + Scalar::all(30);
GaussianBlur(cb, cb, Size(3, 3), 0.8); */
//cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb);
//cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb);
//cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey();
vector<Point2f> corners_found;
int flags = i % 8; // need to check branches for all flags
bool found = findChessboardCorners(cb, cbg.cornersSize(), corners_found, flags);
if (!found)
{
if (!found)
{
ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
res = false;
return res;
return res;
}
double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated);
double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated);
if( err > rough_success_error_level )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
res = false;
return res;
}
}
}
}
/* ***** negative ***** */
{
{
vector<Point2f> corners_found;
bool found = findChessboardCorners(bg, Size(8, 7), corners_found);
if (found)
@ -437,27 +434,27 @@ bool CV_ChessboardDetectorTest::checkByGenerator()
ChessBoardGenerator cbg(Size(8, 7));
vector<Point2f> cg;
Mat cb = cbg(bg, camMat, distCoeffs, cg);
Mat cb = cbg(bg, camMat, distCoeffs, cg);
found = findChessboardCorners(cb, Size(3, 4), corners_found);
if (found)
res = false;
res = false;
Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), plus<Point2f>()) * (1.f/cg.size());
Mat_<double> aff(2, 3);
aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0;
Mat sh;
warpAffine(cb, sh, aff, cb.size());
warpAffine(cb, sh, aff, cb.size());
found = findChessboardCorners(sh, cbg.cornersSize(), corners_found);
if (found)
res = false;
res = false;
vector< vector<Point> > cnts(1);
vector<Point>& cnt = cnts[0];
cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]);
cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]);
cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]);
cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]);
cv::drawContours(cb, cnts, -1, Scalar::all(128), CV_FILLED);
found = findChessboardCorners(cb, cbg.cornersSize(), corners_found);
@ -466,7 +463,7 @@ bool CV_ChessboardDetectorTest::checkByGenerator()
cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found);
}
return res;
}

131
modules/calib3d/test/test_compose_rt.cpp
View File

@ -47,87 +47,87 @@ using namespace std;
class Differential
{
public:
typedef Mat_<double> mat_t;
public:
typedef Mat_<double> mat_t;
Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_)
Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_)
: rv1(rv1_), tv1(tv1_), rv2(rv2_), tv2(tv2_), eps(eps_), ev(3, 1) {}
void dRv1(mat_t& dr3_dr1, mat_t& dt3_dr1)
{
{
dr3_dr1.create(3, 3); dt3_dr1.create(3, 3);
for(int i = 0; i < 3; ++i)
for(int i = 0; i < 3; ++i)
{
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p);
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p);
composeRT( rv1 - ev, tv1, rv2, tv2, rv3_m, tv3_m);
dr3_dr1.col(i) = rv3_p - rv3_m;
dt3_dr1.col(i) = tv3_p - tv3_m;
dr3_dr1.col(i) = rv3_p - rv3_m;
dt3_dr1.col(i) = tv3_p - tv3_m;
}
dr3_dr1 /= 2 * eps; dt3_dr1 /= 2 * eps;
}
void dRv2(mat_t& dr3_dr2, mat_t& dt3_dr2)
{
{
dr3_dr2.create(3, 3); dt3_dr2.create(3, 3);
for(int i = 0; i < 3; ++i)
for(int i = 0; i < 3; ++i)
{
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p);
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p);
composeRT( rv1, tv1, rv2 - ev, tv2, rv3_m, tv3_m);
dr3_dr2.col(i) = rv3_p - rv3_m;
dt3_dr2.col(i) = tv3_p - tv3_m;
dr3_dr2.col(i) = rv3_p - rv3_m;
dt3_dr2.col(i) = tv3_p - tv3_m;
}
dr3_dr2 /= 2 * eps; dt3_dr2 /= 2 * eps;
}
void dTv1(mat_t& drt3_dt1, mat_t& dt3_dt1)
{
{
drt3_dt1.create(3, 3); dt3_dt1.create(3, 3);
for(int i = 0; i < 3; ++i)
for(int i = 0; i < 3; ++i)
{
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p);
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p);
composeRT( rv1, tv1 - ev, rv2, tv2, rv3_m, tv3_m);
drt3_dt1.col(i) = rv3_p - rv3_m;
dt3_dt1.col(i) = tv3_p - tv3_m;
drt3_dt1.col(i) = rv3_p - rv3_m;
dt3_dt1.col(i) = tv3_p - tv3_m;
}
drt3_dt1 /= 2 * eps; dt3_dt1 /= 2 * eps;
}
void dTv2(mat_t& dr3_dt2, mat_t& dt3_dt2)
{
{
dr3_dt2.create(3, 3); dt3_dt2.create(3, 3);
for(int i = 0; i < 3; ++i)
for(int i = 0; i < 3; ++i)
{
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p);
ev.setTo(Scalar(0)); ev(i, 0) = eps;
composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p);
composeRT( rv1, tv1, rv2, tv2 - ev, rv3_m, tv3_m);
dr3_dt2.col(i) = rv3_p - rv3_m;
dt3_dt2.col(i) = tv3_p - tv3_m;
dr3_dt2.col(i) = rv3_p - rv3_m;
dt3_dt2.col(i) = tv3_p - tv3_m;
}
dr3_dt2 /= 2 * eps; dt3_dt2 /= 2 * eps;
}
private:
const mat_t& rv1, tv1, rv2, tv2;
double eps;
Mat_<double> ev;
Differential& operator=(const Differential&);
Mat rv3_m, tv3_m, rv3_p, tv3_p;
Mat rv3_m, tv3_m, rv3_p, tv3_p;
};
class CV_composeRT_Test : public cvtest::BaseTest
@ -135,24 +135,23 @@ class CV_composeRT_Test : public cvtest::BaseTest
public:
CV_composeRT_Test() {}
~CV_composeRT_Test() {}
protected:
protected:
void run(int)
{
cvtest::TS& ts = *this->ts;
ts.set_failed_test_info(cvtest::TS::OK);
Mat_<double> rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1);
ts->set_failed_test_info(cvtest::TS::OK);
Mat_<double> rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1);
randu(rvec1, Scalar(0), Scalar(6.29));
randu(rvec2, Scalar(0), Scalar(6.29));
randu(tvec1, Scalar(-2), Scalar(2));
randu(tvec2, Scalar(-2), Scalar(2));
Mat rvec3, tvec3;
composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3);
Mat rvec3_exp, tvec3_exp;
Mat rmat1, rmat2;
@ -164,53 +163,53 @@ protected:
const double thres = 1e-5;
if (norm(rvec3_exp, rvec3) > thres || norm(tvec3_exp, tvec3) > thres)
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
const double eps = 1e-3;
Differential diff(eps, rvec1, tvec1, rvec2, tvec2);
Mat dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2;
composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3,
composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3,
dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2);
Mat_<double> dr3_dr1, dt3_dr1;
diff.dRv1(dr3_dr1, dt3_dr1);
if (norm(dr3_dr1, dr3dr1) > thres || norm(dt3_dr1, dt3dr1) > thres)
{
ts.printf( cvtest::TS::LOG, "Invalid derivates by r1\n" );
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
{
ts->printf( cvtest::TS::LOG, "Invalid derivates by r1\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
Mat_<double> dr3_dr2, dt3_dr2;
diff.dRv2(dr3_dr2, dt3_dr2);
if (norm(dr3_dr2, dr3dr2) > thres || norm(dt3_dr2, dt3dr2) > thres)
{
ts.printf( cvtest::TS::LOG, "Invalid derivates by r2\n" );
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
{
ts->printf( cvtest::TS::LOG, "Invalid derivates by r2\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
Mat_<double> dr3_dt1, dt3_dt1;
diff.dTv1(dr3_dt1, dt3_dt1);
if (norm(dr3_dt1, dr3dt1) > thres || norm(dt3_dt1, dt3dt1) > thres)
{
ts.printf( cvtest::TS::LOG, "Invalid derivates by t1\n" );
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
{
ts->printf( cvtest::TS::LOG, "Invalid derivates by t1\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
Mat_<double> dr3_dt2, dt3_dt2;
diff.dTv2(dr3_dt2, dt3_dt2);
if (norm(dr3_dt2, dr3dt2) > thres || norm(dt3_dt2, dt3dt2) > thres)
{
ts.printf( cvtest::TS::LOG, "Invalid derivates by t2\n" );
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
{
ts->printf( cvtest::TS::LOG, "Invalid derivates by t2\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
};
}
};
TEST(Calib3d_ComposeRT, accuracy) { CV_composeRT_Test test; test.safe_run(); }

54
modules/calib3d/test/test_homography.cpp
View File

@ -86,20 +86,20 @@ protected:
double sigma;
private:
float max_diff, max_2diff;
bool check_matrix_size(const cv::Mat& H);
bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff);
float max_diff, max_2diff;
bool check_matrix_size(const cv::Mat& H);
bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff);
int check_ransac_mask_1(const Mat& src, const Mat& mask);
int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask);
int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask);
void print_information_1(int j, int N, int method, const Mat& H);
void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff);
void print_information_3(int j, int N, const Mat& mask);
void print_information_4(int method, int j, int N, int k, int l, double diff);
void print_information_5(int method, int j, int N, int l, double diff);
void print_information_6(int j, int N, int k, double diff, bool value);
void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value);
void print_information_8(int j, int N, int k, int l, double diff);
void print_information_1(int j, int N, int method, const Mat& H);
void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff);
void print_information_3(int j, int N, const Mat& mask);
void print_information_4(int method, int j, int N, int k, int l, double diff);
void print_information_5(int method, int j, int N, int l, double diff);
void print_information_6(int j, int N, int k, double diff, bool value);
void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value);
void print_information_8(int j, int N, int k, int l, double diff);
};
CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f)
@ -112,7 +112,7 @@ CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f)
CV_HomographyTest::~CV_HomographyTest() {}
bool CV_HomographyTest::check_matrix_size(const cv::Mat& H)
bool CV_HomographyTest::check_matrix_size(const cv::Mat& H)
{
return (H.rows == 3) && (H.cols == 3);
}
@ -138,25 +138,25 @@ int CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat&
return 0;
}
void CV_HomographyTest::print_information_1(int j, int N, int method, const Mat& H)
void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat& H)
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl;
}
void CV_HomographyTest::print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff)
void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat& H, const Mat& H_res, int k, double diff)
{
cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Original matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Found matrix:" << endl; cout << endl;
@ -178,10 +178,10 @@ void CV_HomographyTest::print_information_3(int j, int N, const Mat& mask)
cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl;
}
void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int l, double diff)
void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, int l, double diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Sigma of normal noise: " << sigma << endl;
@ -192,10 +192,10 @@ void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int
cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_5(int method, int j, int N, int l, double diff)
{
void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Sigma of normal noise: " << sigma << endl;
@ -371,7 +371,7 @@ void CV_HomographyTest::run(int)
if (code)
{
print_information_3(j, N, mask[j]);
switch (code)
{
case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; }
@ -380,7 +380,7 @@ void CV_HomographyTest::run(int)
default: break;
}
return;
}
@ -412,7 +412,7 @@ void CV_HomographyTest::run(int)
{
case 0:
case CV_LMEDS:
{
{
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f),
cv::findHomography(src_mat_2f, dst_vec),
cv::findHomography(src_vec, dst_mat_2f),
@ -465,7 +465,7 @@ void CV_HomographyTest::run(int)
}
continue;
}
}
case CV_RANSAC:
{
cv::Mat mask_res [4];
@ -555,7 +555,7 @@ void CV_HomographyTest::run(int)
}
}
}
continue;
}

17
modules/calib3d/test/test_solvepnp_ransac.cpp
View File

@ -106,7 +106,7 @@ protected:
}
}
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* eps, double& maxError)
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
{
Mat rvec, tvec;
vector<int> inliers;
@ -136,7 +136,7 @@ protected:
bool isTestSuccess = inliers.size() >= points.size()*0.95;
double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
isTestSuccess = isTestSuccess && rvecDiff < eps[method] && tvecDiff < eps[method];
isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
//cout << error << " " << inliers.size() << " " << eps[method] << endl;
if (error > maxError)
@ -147,8 +147,7 @@ protected:
void run(int)
{
cvtest::TS& ts = *this->ts;
ts.set_failed_test_info(cvtest::TS::OK);
ts->set_failed_test_info(cvtest::TS::OK);
vector<Point3f> points;
const int pointsCount = 500;
@ -157,7 +156,7 @@ protected:
const int methodsCount = 3;
RNG rng = ts.get_rng();
RNG rng = ts->get_rng();
for (int mode = 0; mode < 2; mode++)
@ -174,9 +173,9 @@ protected:
//cout << maxError << " " << successfulTestsCount << endl;
if (successfulTestsCount < 0.7*totalTestsCount)
{
ts.printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n",
ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n",
method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode);
ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
@ -198,7 +197,7 @@ public:
~CV_solvePnP_Test() {}
protected:
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* eps, double& maxError)
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
{
Mat rvec, tvec;
Mat trueRvec, trueTvec;
@ -226,7 +225,7 @@ protected:
false, method);
double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
bool isTestSuccess = rvecDiff < eps[method] && tvecDiff < eps[method];
bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
if (error > maxError)

8
modules/calib3d/test/test_stereomatching.cpp
View File

@ -421,7 +421,7 @@ void CV_StereoMatchingTest::run(int)
ts->set_failed_test_info( code );
return;
}
string fullResultFilename = dataPath + ALGORITHMS_DIR + algorithmName + RESULT_FILE;
FileStorage resFS( fullResultFilename, FileStorage::READ );
bool isWrite = true; // write or compare results
@ -593,11 +593,11 @@ int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs )
assert(fn.isSeq());
for( int i = 0; i < (int)fn.size(); i+=3 )
{
string name = fn[i];
string _name = fn[i];
DatasetParams params;
string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str());
string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str());
datasetsParams[name] = params;
datasetsParams[_name] = params;
}
return cvtest::TS::OK;
}
@ -713,7 +713,7 @@ class CV_StereoSGBMTest : public CV_StereoMatchingTest
public:
CV_StereoSGBMTest()
{
name = "stereosgbm";
name = "stereosgbm";
fill(rmsEps.begin(), rmsEps.end(), 0.25f);
fill(fracEps.begin(), fracEps.end(), 0.01f);
}

306
modules/contrib/include/opencv2/contrib/contrib.hpp
View File

@ -63,48 +63,48 @@ private:
GSD_INTENSITY_LT = 15,
GSD_INTENSITY_UT = 250
};
class CV_EXPORTS Histogram
{
private:
enum {
HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1)
};
protected:
int findCoverageIndex(double surfaceToCover, int defaultValue = 0);
public:
CvHistogram *fHistogram;
Histogram();
virtual ~Histogram();
void findCurveThresholds(int &x1, int &x2, double percent = 0.05);
void mergeWith(Histogram *source, double weight);
};
int nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider;
double fHistogramMergeFactor, fHuePercentCovered;
Histogram histogramHueMotion, skinHueHistogram;
IplImage *imgHueFrame, *imgSaturationFrame, *imgLastGrayFrame, *imgMotionFrame, *imgFilteredFrame;
IplImage *imgShrinked, *imgTemp, *imgGrayFrame, *imgHSVFrame;
protected:
void initData(IplImage *src, int widthDivider, int heightDivider);
void adaptiveFilter();
public:
enum {
MORPHING_METHOD_NONE = 0,
MORPHING_METHOD_ERODE = 1,
MORPHING_METHOD_ERODE_ERODE = 2,
MORPHING_METHOD_ERODE_DILATE = 3
};
CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE);
virtual ~CvAdaptiveSkinDetector();
virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask);
};
@ -116,7 +116,7 @@ public:
class CV_EXPORTS CvFuzzyPoint {
public:
double x, y, value;
CvFuzzyPoint(double _x, double _y);
};
@ -124,13 +124,13 @@ class CV_EXPORTS CvFuzzyCurve {
private:
std::vector<CvFuzzyPoint> points;
double value, centre;
bool between(double x, double x1, double x2);
public:
CvFuzzyCurve();
~CvFuzzyCurve();
void setCentre(double _centre);
double getCentre();
void clear();
@ -143,7 +143,7 @@ public:
class CV_EXPORTS CvFuzzyFunction {
public:
std::vector<CvFuzzyCurve> curves;
CvFuzzyFunction();
~CvFuzzyFunction();
void addCurve(CvFuzzyCurve *curve, double value = 0);
@ -186,7 +186,7 @@ private:
FuzzyResizer();
int calcOutput(double edgeDensity, double density);
};
class SearchWindow
{
public:
@ -200,7 +200,7 @@ private:
double density;
unsigned int depthLow, depthHigh;
int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom;
SearchWindow();
~SearchWindow();
void setSize(int _x, int _y, int _width, int _height);
@ -212,7 +212,7 @@ private:
void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth);
};
public:
enum TrackingState
{
@ -222,40 +222,40 @@ public:
tsSetWindow = 3,
tsDisabled = 10
};
enum ResizeMethod {
rmEdgeDensityLinear = 0,
rmEdgeDensityFuzzy = 1,
rmInnerDensity = 2
};
enum {
MinKernelMass = 1000
};
SearchWindow kernel;
int searchMode;
private:
enum
{
MaxMeanShiftIteration = 5,
MaxSetSizeIteration = 5
};
void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth);
public:
CvFuzzyMeanShiftTracker();
~CvFuzzyMeanShiftTracker();
void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass);
};
namespace cv
{
class CV_EXPORTS Octree
{
public:
@ -268,11 +268,11 @@ namespace cv
bool isLeaf;
int children[8];
};
Octree();
Octree( const vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual ~Octree();
virtual void buildTree( const vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual void getPointsWithinSphere( const Point3f& center, float radius,
vector<Point3f>& points ) const;
@ -281,85 +281,85 @@ namespace cv
int minPoints;
vector<Point3f> points;
vector<Node> nodes;
virtual void buildNext(size_t node_ind);
};
class CV_EXPORTS Mesh3D
{
public:
struct EmptyMeshException {};
Mesh3D();
Mesh3D(const vector<Point3f>& vtx);
~Mesh3D();
void buildOctree();
void clearOctree();
float estimateResolution(float tryRatio = 0.1f);
void computeNormals(float normalRadius, int minNeighbors = 20);
void computeNormals(const vector<int>& subset, float normalRadius, int minNeighbors = 20);
void writeAsVrml(const String& file, const vector<Scalar>& colors = vector<Scalar>()) const;
vector<Point3f> vtx;
vector<Point3f> normals;
float resolution;
Octree octree;
const static Point3f allzero;
};
class CV_EXPORTS SpinImageModel
{
public:
/* model parameters, leave unset for default or auto estimate */
float normalRadius;
int minNeighbors;
float binSize;
int imageWidth;
float lambda;
float gamma;
float T_GeometriccConsistency;
float T_GroupingCorespondances;
/* public interface */
SpinImageModel();
explicit SpinImageModel(const Mesh3D& mesh);
~SpinImageModel();
void setLogger(std::ostream* log);
void selectRandomSubset(float ratio);
void setSubset(const vector<int>& subset);
void compute();
void match(const SpinImageModel& scene, vector< vector<Vec2i> >& result);
Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const;
size_t getSpinCount() const { return spinImages.rows; }
Mat getSpinImage(size_t index) const { return spinImages.row((int)index); }
const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; }
const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; }
const Mesh3D& getMesh() const { return mesh; }
Mesh3D& getMesh() { return mesh; }
/* static utility functions */
static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result);
static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal);
static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2);
static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
@ -367,40 +367,40 @@ namespace cv
float gamma);
protected:
void defaultParams();
void matchSpinToModel(const Mat& spin, vector<int>& indeces,
vector<float>& corrCoeffs, bool useExtremeOutliers = true) const;
void repackSpinImages(const vector<uchar>& mask, Mat& spinImages, bool reAlloc = true) const;
vector<int> subset;
Mesh3D mesh;
Mat spinImages;
std::ostream* out;
};
class CV_EXPORTS TickMeter
{
public:
TickMeter();
void start();
void stop();
int64 getTimeTicks() const;
double getTimeMicro() const;
double getTimeMilli() const;
double getTimeSec() const;
int64 getCounter() const;
void reset();
private:
int64 counter;
int64 sumTime;
int64 startTime;
};
CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm);
class CV_EXPORTS SelfSimDescriptor
{
public:
@ -412,29 +412,29 @@ namespace cv
SelfSimDescriptor(const SelfSimDescriptor& ss);
virtual ~SelfSimDescriptor();
SelfSimDescriptor& operator = (const SelfSimDescriptor& ss);
size_t getDescriptorSize() const;
Size getGridSize( Size imgsize, Size winStride ) const;
virtual void compute(const Mat& img, vector<float>& descriptors, Size winStride=Size(),
const vector<Point>& locations=vector<Point>()) const;
virtual void computeLogPolarMapping(Mat& mappingMask) const;
virtual void SSD(const Mat& img, Point pt, Mat& ssd) const;
int smallSize;
int largeSize;
int startDistanceBucket;
int numberOfDistanceBuckets;
int numberOfAngles;
enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41,
DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3,
DEFAULT_NUM_DISTANCE_BUCKETS = 7 };
};
typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data);
class LevMarqSparse {
public:
LevMarqSparse();
@ -447,9 +447,9 @@ namespace cv
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
@ -459,9 +459,9 @@ namespace cv
void* data, // user-specific data passed to the callbacks
BundleAdjustCallback cb, void* user_data
);
virtual ~LevMarqSparse();
virtual void run( int npoints, // number of points
int ncameras, // number of cameras
int nPointParams, // number of params per one point (3 in case of 3D points)
@ -471,9 +471,9 @@ namespace cv
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
@ -482,13 +482,13 @@ namespace cv
Mat& cam_params, Mat& estim, void* data),
void* data // user-specific data passed to the callbacks
);
virtual void clear();
// useful function to do simple bundle adjustment tasks
static void bundleAdjust(vector<Point3d>& points, // positions of points in global coordinate system (input and output)
const vector<vector<Point2d> >& imagePoints, // projections of 3d points for every camera
const vector<vector<int> >& visibility, // visibility of 3d points for every camera
const vector<vector<int> >& visibility, // visibility of 3d points for every camera
vector<Mat>& cameraMatrix, // intrinsic matrices of all cameras (input and output)
vector<Mat>& R, // rotation matrices of all cameras (input and output)
vector<Mat>& T, // translation vector of all cameras (input and output)
@ -496,123 +496,123 @@ namespace cv
const TermCriteria& criteria=
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON),
BundleAdjustCallback cb = 0, void* user_data = 0);
public:
virtual void optimize(CvMat &_vis); //main function that runs minimization
//iteratively asks for measurement for visible camera-point pairs
void ask_for_proj(CvMat &_vis,bool once=false);
//iteratively asks for Jacobians for every camera_point pair
void ask_for_projac(CvMat &_vis);
CvMat* err; //error X-hX
double prevErrNorm, errNorm;
double lambda;
CvTermCriteria criteria;
int iters;
CvMat** U; //size of array is equal to number of cameras
CvMat** V; //size of array is equal to number of points
CvMat** inv_V_star; //inverse of V*
CvMat** A;
CvMat** B;
CvMat** W;
CvMat* X; //measurement
CvMat* hX; //current measurement extimation given new parameter vector
CvMat* prevP; //current already accepted parameter.
CvMat* X; //measurement
CvMat* hX; //current measurement extimation given new parameter vector
CvMat* prevP; //current already accepted parameter.
CvMat* P; // parameters used to evaluate function with new params
// this parameters may be rejected
// this parameters may be rejected
CvMat* deltaP; //computed increase of parameters (result of normal system solution )
CvMat** ea; // sum_i AijT * e_ij , used as right part of normal equation
// length of array is j = number of cameras
// length of array is j = number of cameras
CvMat** eb; // sum_j BijT * e_ij , used as right part of normal equation
// length of array is i = number of points
CvMat** Yj; //length of array is i = num_points
CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params
CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params
CvMat* JtJ_diag; //diagonal of JtJ, used to backup diagonal elements before augmentation
CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j
int num_cams;
int num_points;
int num_err_param;
int num_cam_param;
int num_point_param;
//target function and jacobian pointers, which needs to be initialized
//target function and jacobian pointers, which needs to be initialized
void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data);
void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data);
void* data;
BundleAdjustCallback cb;
void* user_data;
};
};
CV_EXPORTS int chamerMatching( Mat& img, Mat& templ,
vector<vector<Point> >& results, vector<float>& cost,
double templScale=1, int maxMatches = 20,
double minMatchDistance = 1.0, int padX = 3,
int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6,
double orientationWeight = 0.5, double truncate = 20);
class CV_EXPORTS StereoVar
{
public:
// Flags
// Flags
enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16};
enum {CYCLE_O, CYCLE_V};
enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK};
//! the default constructor
CV_WRAP StereoVar();
//! the full constructor taking all the necessary algorithm parameters
CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags);
//! the destructor
virtual ~StereoVar();
//! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair
CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, Mat& disp);
CV_PROP_RW int levels;
CV_PROP_RW double pyrScale;
CV_PROP_RW int nIt;
CV_PROP_RW int minDisp;
CV_PROP_RW int maxDisp;
CV_PROP_RW int poly_n;
CV_PROP_RW double poly_sigma;
CV_PROP_RW float fi;
CV_PROP_RW float lambda;
CV_PROP_RW int penalization;
CV_PROP_RW int cycle;
CV_PROP_RW int flags;
CV_PROP_RW int levels;
CV_PROP_RW double pyrScale;
CV_PROP_RW int nIt;
CV_PROP_RW int minDisp;
CV_PROP_RW int maxDisp;
CV_PROP_RW int poly_n;
CV_PROP_RW double poly_sigma;
CV_PROP_RW float fi;
CV_PROP_RW float lambda;
CV_PROP_RW int penalization;
CV_PROP_RW int cycle;
CV_PROP_RW int flags;
private:
void autoParams();
void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level);
void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level);
void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
};
CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order);
class CV_EXPORTS Directory
class CV_EXPORTS Directory
{
public:
static std::vector<std::string> GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true );
public:
static std::vector<std::string> GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true );
};
/*
@ -654,7 +654,7 @@ namespace cv
class CV_EXPORTS LogPolar_Interp
{
public:
LogPolar_Interp() {}
/**
@ -664,11 +664,11 @@ namespace cv
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0,
@ -689,9 +689,9 @@ namespace cv
*Destructor
*/
~LogPolar_Interp();
protected:
Mat Rsri;
Mat Csri;
@ -716,10 +716,10 @@ namespace cv
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Overlapping
{
{
public:
LogPolar_Overlapping() {}
/**
*Constructor
*\param w the width of the input image
@ -727,11 +727,11 @@ namespace cv
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Overlapping(int w, int h, Point2i center, int R=70,
@ -752,9 +752,9 @@ namespace cv
*Destructor
*/
~LogPolar_Overlapping();
protected:
Mat Rsri;
Mat Csri;
vector<int> Rsr;
@ -793,7 +793,7 @@ namespace cv
{
public:
LogPolar_Adjacent() {}
/**
*Constructor
*\param w the width of the input image
@ -802,13 +802,13 @@ namespace cv
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param smin the size of the subpixel (default value 0.25 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
*/
LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
@ -845,10 +845,10 @@ namespace cv
bool get_uv(double x, double y, int&u, int&v);
void create_map(int M, int N, int R, int S, double ro0, double smin);
};
CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
class CV_EXPORTS LDA
{
public:
@ -908,7 +908,7 @@ namespace cv
// Returns the eigenvalues of this LDA.
Mat eigenvalues() const { return _eigenvalues; }
protected:
bool _dataAsRow;
int _num_components;
@ -917,7 +917,7 @@ namespace cv
void lda(InputArray src, InputArray labels);
};
class CV_EXPORTS FaceRecognizer : public Algorithm
{
public:
@ -941,16 +941,16 @@ namespace cv
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
// Returns eigenvectors (if any)
virtual Mat eigenvectors() const { return Mat(); }
};
CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8);
enum
{
COLORMAP_AUTUMN = 0,
@ -968,9 +968,9 @@ namespace cv
COLORMAP_MKPJ1 = 12,
COLORMAP_MKPJ2 = 13
};
CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap);
CV_EXPORTS bool initModule_contrib();
}

6
modules/contrib/include/opencv2/contrib/hybridtracker.hpp
View File

@ -86,10 +86,10 @@ struct CV_EXPORTS CvMeanShiftTrackerParams
struct CV_EXPORTS CvFeatureTrackerParams
{
enum { SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 };
CvFeatureTrackerParams(int feature_type = 0, int window_size = 0)
CvFeatureTrackerParams(int featureType = 0, int windowSize = 0)
{
feature_type = 0;
window_size = 0;
featureType = 0;
windowSize = 0;
}
int feature_type; // Feature type to use

32
modules/contrib/src/ba.cpp
View File

@ -67,15 +67,15 @@ LevMarqSparse::LevMarqSparse(int npoints, // number of points
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X_, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
TermCriteria _criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
void (CV_CDECL * _fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
// callback for estimation of backprojection errors
void (CV_CDECL * func)(int i, int j, Mat& point_params,
void (CV_CDECL * _func)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& estim, void* data),
void* data, // user-specific data passed to the callbacks
void* _data, // user-specific data passed to the callbacks
BundleAdjustCallback _cb, void* _user_data
) {
Vis_index = X = prevP = P = deltaP = err = JtJ_diag = S = hX = NULL;
@ -86,7 +86,7 @@ LevMarqSparse::LevMarqSparse(int npoints, // number of points
user_data = _user_data;
run(npoints, ncameras, nPointParams, nCameraParams, nErrParams, visibility,
P0, X_, criteria, fjac, func, data);
P0, X_, _criteria, _fjac, _func, _data);
}
void LevMarqSparse::clear() {
@ -443,9 +443,11 @@ void LevMarqSparse::optimize(CvMat &_vis) { //main function that runs minimizati
} //U_j and ea_j computed for all j
// if (!(iters%100))
int nviz = X->rows / num_err_param;
double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz;
std::cerr<<"Iteration: "<<iters<<", normError: "<<e2<<" ("<<e2n<<")"<<std::endl;
{
int nviz = X->rows / num_err_param;
double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz;
std::cerr<<"Iteration: "<<iters<<", normError: "<<e2<<" ("<<e2n<<")"<<std::endl;
}
if (cb)
cb(iters, prevErrNorm, user_data);
//compute V_i and eb_i
@ -676,10 +678,12 @@ void LevMarqSparse::optimize(CvMat &_vis) { //main function that runs minimizati
errNorm > prevErrNorm ) { //step was not accepted
//increase lambda and reject change
lambda *= 10;
int nviz = X->rows / num_err_param;
double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm;
double e2n = e2/nviz, e2n_prev = e2_prev/nviz;
std::cerr<<"move failed: lambda = "<<lambda<<", e2 = "<<e2<<" ("<<e2n<<") > "<<e2_prev<<" ("<<e2n_prev<<")"<<std::endl;
{
int nviz = X->rows / num_err_param;
double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm;
double e2n = e2/nviz, e2n_prev = e2_prev/nviz;
std::cerr<<"move failed: lambda = "<<lambda<<", e2 = "<<e2<<" ("<<e2n<<") > "<<e2_prev<<" ("<<e2n_prev<<")"<<std::endl;
}
//restore diagonal from backup
{
@ -886,9 +890,9 @@ static void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, C
double c[4] = { g+2*p1*y_strike+4*p2*x_strike, 2*p1*x_strike,
2*p2*y_strike, g+2*p2*x_strike + 4*p1*y_strike };
CvMat coeffmat = cvMat(2,2,CV_64F, c );
CvMat coeffmat2 = cvMat(2,2,CV_64F, c );
cvMatMul(&coeffmat, dstrike_dbig, dstrike2_dbig );
cvMatMul(&coeffmat2, dstrike_dbig, dstrike2_dbig );
cvGEMM( &strike, dg_dbig, 1, NULL, 0, tmp, CV_GEMM_A_T );
cvAdd( dstrike2_dbig, tmp, dstrike2_dbig );

16
modules/contrib/src/basicretinafilter.cpp
View File

@ -180,13 +180,13 @@ void BasicRetinaFilter::setLPfilterParameters(const float beta, const float tau,
}
float _temp = (1.0f+_beta)/(2.0f*_mu*_alpha);
float _a = _filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[1+tableOffset]=(1.0f-_a)*(1.0f-_a)*(1.0f-_a)*(1.0f-_a)/(1.0f+_beta);
float a = _filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[1+tableOffset]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
_filteringCoeficientsTable[2+tableOffset] =tau;
//std::cout<<"BasicRetinaFilter::normal:"<<(1.0-_a)*(1.0-_a)*(1.0-_a)*(1.0-_a)/(1.0+_beta)<<" -> old:"<<(1-_a)*(1-_a)*(1-_a)*(1-_a)/(1+_beta)<<std::endl;
//std::cout<<"BasicRetinaFilter::normal:"<<(1.0-a)*(1.0-a)*(1.0-a)*(1.0-a)/(1.0+_beta)<<" -> old:"<<(1-a)*(1-a)*(1-a)*(1-a)/(1+_beta)<<std::endl;
//std::cout<<"BasicRetinaFilter::_a="<<_a<<", gain="<<_filteringCoeficientsTable[1+tableOffset]<<", tau="<<tau<<std::endl;
//std::cout<<"BasicRetinaFilter::a="<<a<<", gain="<<_filteringCoeficientsTable[1+tableOffset]<<", tau="<<tau<<std::endl;
}
void BasicRetinaFilter::setProgressiveFilterConstants_CentredAccuracy(const float beta, const float tau, const float alpha0, const unsigned int filterIndex)
@ -210,8 +210,8 @@ void BasicRetinaFilter::setProgressiveFilterConstants_CentredAccuracy(const floa
float _alpha=0.8f;
float _temp = (1.0f+_beta)/(2.0f*_mu*_alpha);
float _a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[tableOffset+1]=(1.0f-_a)*(1.0f-_a)*(1.0f-_a)*(1.0f-_a)/(1.0f+_beta);
float a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[tableOffset+1]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
_filteringCoeficientsTable[tableOffset+2] =tau;
float commonFactor=alpha0/(float)sqrt(_halfNBcolumns*_halfNBcolumns+_halfNBrows*_halfNBrows+1.0f);
@ -266,8 +266,8 @@ void BasicRetinaFilter::setProgressiveFilterConstants_CustomAccuracy(const float
}
unsigned int tableOffset=filterIndex*3;
float _temp = (1.0f+_beta)/(2.0f*_mu*_alpha);
float _a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[tableOffset+1]=(1.0f-_a)*(1.0f-_a)*(1.0f-_a)*(1.0f-_a)/(1.0f+_beta);
float a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
_filteringCoeficientsTable[tableOffset+1]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
_filteringCoeficientsTable[tableOffset+2] =tau;
//memset(_progressiveSpatialConstant, 255, _filterOutput.getNBpixels());

4
modules/contrib/src/colortracker.cpp
View File

@ -68,10 +68,10 @@ void CvMeanShiftTracker::newTrackingWindow(Mat image, Rect selection)
mixChannels(&hsv, 1, &hue, 1, channels, 2);
Mat roi(hue, selection);
Mat maskroi(mask, selection);
Mat mskroi(mask, selection);
int ch[] = {0, 1};
int chsize[] = {32, 32};
calcHist(&roi, 1, ch, maskroi, hist, 1, chsize, ranges);
calcHist(&roi, 1, ch, mskroi, hist, 1, chsize, ranges);
normalize(hist, hist, 0, 255, CV_MINMAX);
prev_trackwindow = selection;

60
modules/contrib/src/facerec.cpp
View File

@ -22,7 +22,7 @@ namespace cv
{
using std::set;
// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
template<typename _Tp>
inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
@ -48,7 +48,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0)
{
// number of samples
@ -67,7 +67,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
}
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
@ -82,7 +82,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
@ -124,10 +124,10 @@ public:
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
AlgorithmInfo* info() const;
};
// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
// faces: Recognition using class specific linear projection.". IEEE
// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
@ -208,11 +208,11 @@ public:
//
// radius, neighbors are used in the local binary patterns creation.
// grid_x, grid_y control the grid size of the spatial histograms.
LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {}
LBPH(int radius_=1, int neighbors_=8, int grid_x_=8, int grid_y_=8) :
_grid_x(grid_x_),
_grid_y(grid_y_),
_radius(radius_),
_neighbors(neighbors_) {}
// Initializes and computes this LBPH Model. The current implementation is
// rather fixed as it uses the Extended Local Binary Patterns per default.
@ -221,12 +221,12 @@ public:
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArray src,
InputArray labels,
int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {
int radius_=1, int neighbors_=8,
int grid_x_=8, int grid_y_=8) :
_grid_x(grid_x_),
_grid_y(grid_y_),
_radius(radius_),
_neighbors(neighbors_) {
train(src, labels);
}
@ -359,9 +359,9 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
// get data
Mat labels = _lbls.getMat();
Mat data = asRowMatrix(src, CV_64FC1);
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
// dimensionality
int N = data.rows; // number of samples
//int D = data.cols; // dimension of samples
@ -369,7 +369,7 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
if(labels.total() != (size_t)N)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// compute the Fisherfaces
vector<int> ll;
labels.copyTo(ll);
int C = (int)remove_dups(ll).size(); // number of unique classes
@ -570,7 +570,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool normed)
{
@ -610,7 +610,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) {
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
@ -684,24 +684,24 @@ int LBPH::predict(InputArray _src) const {
}
return minClass;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components)
{
return new Eigenfaces(num_components);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components)
{
return new Fisherfaces(num_components);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y)
{
return new LBPH(radius, neighbors, grid_x, grid_y);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "projections", obj._projections, true);
@ -716,8 +716,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
obj.info()->addParam(obj, "mean", obj._mean, true));
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
obj.info()->addParam(obj, "neighbors", obj._neighbors);
@ -725,7 +725,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "grid_y", obj._grid_y);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));
bool initModule_contrib()
{
Ptr<Algorithm> efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH();

208
modules/contrib/src/lda.cpp
View File

@ -235,19 +235,19 @@ private:
// Allocates memory.
template<typename _Tp>
_Tp **alloc_2d(int m, int n) {
_Tp **alloc_2d(int m, int _n) {
_Tp **arr = new _Tp*[m];
for (int i = 0; i < m; i++)
arr[i] = new _Tp[n];
arr[i] = new _Tp[_n];
return arr;
}
// Allocates memory.
template<typename _Tp>
_Tp **alloc_2d(int m, int n, _Tp val) {
_Tp **arr = alloc_2d<_Tp> (m, n);
_Tp **alloc_2d(int m, int _n, _Tp val) {
_Tp **arr = alloc_2d<_Tp> (m, _n);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
for (int j = 0; j < _n; j++) {
arr[i][j] = val;
}
}
@ -255,17 +255,17 @@ private:
}
void cdiv(double xr, double xi, double yr, double yi) {
double r, d;
double r, dv;
if (std::abs(yr) > std::abs(yi)) {
r = yi / yr;
d = yr + r * yi;
cdivr = (xr + r * xi) / d;
cdivi = (xi - r * xr) / d;
dv = yr + r * yi;
cdivr = (xr + r * xi) / dv;
cdivi = (xi - r * xr) / dv;
} else {
r = yr / yi;
d = yi + r * yr;
cdivr = (r * xr + xi) / d;
cdivi = (r * xi - xr) / d;
dv = yi + r * yr;
cdivr = (r * xr + xi) / dv;
cdivi = (r * xi - xr) / dv;
}
}
@ -280,7 +280,7 @@ private:
// Initialize
int nn = this->n;
int n = nn - 1;
int n1 = nn - 1;
int low = 0;
int high = nn - 1;
double eps = pow(2.0, -52.0);
@ -302,10 +302,10 @@ private:
// Outer loop over eigenvalue index
int iter = 0;
while (n >= low) {
while (n1 >= low) {
// Look for single small sub-diagonal element
int l = n;
int l = n1;
while (l > low) {
s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]);
if (s == 0.0) {
@ -320,23 +320,23 @@ private:
// Check for convergence
// One root found
if (l == n) {
H[n][n] = H[n][n] + exshift;
d[n] = H[n][n];
e[n] = 0.0;
n--;
if (l == n1) {
H[n1][n1] = H[n1][n1] + exshift;
d[n1] = H[n1][n1];
e[n1] = 0.0;
n1--;
iter = 0;
// Two roots found
} else if (l == n - 1) {
w = H[n][n - 1] * H[n - 1][n];
p = (H[n - 1][n - 1] - H[n][n]) / 2.0;
} else if (l == n1 - 1) {
w = H[n1][n1 - 1] * H[n1 - 1][n1];
p = (H[n1 - 1][n1 - 1] - H[n1][n1]) / 2.0;
q = p * p + w;
z = sqrt(std::abs(q));
H[n][n] = H[n][n] + exshift;
H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
x = H[n][n];
H[n1][n1] = H[n1][n1] + exshift;
H[n1 - 1][n1 - 1] = H[n1 - 1][n1 - 1] + exshift;
x = H[n1][n1];
// Real pair
@ -346,14 +346,14 @@ private:
} else {
z = p - z;
}
d[n - 1] = x + z;
d[n] = d[n - 1];
d[n1 - 1] = x + z;
d[n1] = d[n1 - 1];
if (z != 0.0) {
d[n] = x - w / z;
d[n1] = x - w / z;
}
e[n - 1] = 0.0;
e[n] = 0.0;
x = H[n][n - 1];
e[n1 - 1] = 0.0;
e[n1] = 0.0;
x = H[n1][n1 - 1];
s = std::abs(x) + std::abs(z);
p = x / s;
q = z / s;
@ -363,37 +363,37 @@ private:
// Row modification
for (int j = n - 1; j < nn; j++) {
z = H[n - 1][j];
H[n - 1][j] = q * z + p * H[n][j];
H[n][j] = q * H[n][j] - p * z;
for (int j = n1 - 1; j < nn; j++) {
z = H[n1 - 1][j];
H[n1 - 1][j] = q * z + p * H[n1][j];
H[n1][j] = q * H[n1][j] - p * z;
}
// Column modification
for (int i = 0; i <= n; i++) {
z = H[i][n - 1];
H[i][n - 1] = q * z + p * H[i][n];
H[i][n] = q * H[i][n] - p * z;
for (int i = 0; i <= n1; i++) {
z = H[i][n1 - 1];
H[i][n1 - 1] = q * z + p * H[i][n1];
H[i][n1] = q * H[i][n1] - p * z;
}
// Accumulate transformations
for (int i = low; i <= high; i++) {
z = V[i][n - 1];
V[i][n - 1] = q * z + p * V[i][n];
V[i][n] = q * V[i][n] - p * z;
z = V[i][n1 - 1];
V[i][n1 - 1] = q * z + p * V[i][n1];
V[i][n1] = q * V[i][n1] - p * z;
}
// Complex pair
} else {
d[n - 1] = x + p;
d[n] = x + p;
e[n - 1] = z;
e[n] = -z;
d[n1 - 1] = x + p;
d[n1] = x + p;
e[n1 - 1] = z;
e[n1] = -z;
}
n = n - 2;
n1 = n1 - 2;
iter = 0;
// No convergence yet
@ -402,22 +402,22 @@ private:
// Form shift
x = H[n][n];
x = H[n1][n1];
y = 0.0;
w = 0.0;
if (l < n) {
y = H[n - 1][n - 1];
w = H[n][n - 1] * H[n - 1][n];
if (l < n1) {
y = H[n1 - 1][n1 - 1];
w = H[n1][n1 - 1] * H[n1 - 1][n1];
}
// Wilkinson's original ad hoc shift
if (iter == 10) {
exshift += x;
for (int i = low; i <= n; i++) {
for (int i = low; i <= n1; i++) {
H[i][i] -= x;
}
s = std::abs(H[n][n - 1]) + std::abs(H[n - 1][n - 2]);
s = std::abs(H[n1][n1 - 1]) + std::abs(H[n1 - 1][n1 - 2]);
x = y = 0.75 * s;
w = -0.4375 * s * s;
}
@ -433,7 +433,7 @@ private:
s = -s;
}
s = x - w / ((y - x) / 2.0 + s);
for (int i = low; i <= n; i++) {
for (int i = low; i <= n1; i++) {
H[i][i] -= s;
}
exshift += s;
@ -444,7 +444,7 @@ private:
iter = iter + 1; // (Could check iteration count here.)
// Look for two consecutive small sub-diagonal elements
int m = n - 2;
int m = n1 - 2;
while (m >= l) {
z = H[m][m];
r = x - z;
@ -467,7 +467,7 @@ private:
m--;
}
for (int i = m + 2; i <= n; i++) {
for (int i = m + 2; i <= n1; i++) {
H[i][i - 2] = 0.0;
if (i > m + 2) {
H[i][i - 3] = 0.0;
@ -476,8 +476,8 @@ private:
// Double QR step involving rows l:n and columns m:n
for (int k = m; k <= n - 1; k++) {
bool notlast = (k != n - 1);
for (int k = m; k <= n1 - 1; k++) {
bool notlast = (k != n1 - 1);
if (k != m) {
p = H[k][k - 1];
q = H[k + 1][k - 1];
@ -523,7 +523,7 @@ private:
// Column modification
for (int i = 0; i <= min(n, k + 3); i++) {
for (int i = 0; i <= min(n1, k + 3); i++) {
p = x * H[i][k] + y * H[i][k + 1];
if (notlast) {
p = p + z * H[i][k + 2];
@ -547,7 +547,7 @@ private:
} // (s != 0)
} // k loop
} // check convergence
} // while (n >= low)
} // while (n1 >= low)
// Backsubstitute to find vectors of upper triangular form
@ -555,20 +555,20 @@ private:
return;
}
for (n = nn - 1; n >= 0; n--) {
p = d[n];
q = e[n];
for (n1 = nn - 1; n1 >= 0; n1--) {
p = d[n1];
q = e[n1];
// Real vector
if (q == 0) {
int l = n;
H[n][n] = 1.0;
for (int i = n - 1; i >= 0; i--) {
int l = n1;
H[n1][n1] = 1.0;
for (int i = n1 - 1; i >= 0; i--) {
w = H[i][i] - p;
r = 0.0;
for (int j = l; j <= n; j++) {
r = r + H[i][j] * H[j][n];
for (int j = l; j <= n1; j++) {
r = r + H[i][j] * H[j][n1];
}
if (e[i] < 0.0) {
z = w;
@ -577,9 +577,9 @@ private:
l = i;
if (e[i] == 0.0) {
if (w != 0.0) {
H[i][n] = -r / w;
H[i][n1] = -r / w;
} else {
H[i][n] = -r / (eps * norm);
H[i][n1] = -r / (eps * norm);
}
// Solve real equations
@ -589,20 +589,20 @@ private:
y = H[i + 1][i];
q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
t = (x * s - z * r) / q;
H[i][n] = t;
H[i][n1] = t;
if (std::abs(x) > std::abs(z)) {
H[i + 1][n] = (-r - w * t) / x;
H[i + 1][n1] = (-r - w * t) / x;
} else {
H[i + 1][n] = (-s - y * t) / z;
H[i + 1][n1] = (-s - y * t) / z;
}
}
// Overflow control
t = std::abs(H[i][n]);
t = std::abs(H[i][n1]);
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n] = H[j][n] / t;
for (int j = i; j <= n1; j++) {
H[j][n1] = H[j][n1] / t;
}
}
}
@ -611,27 +611,27 @@ private:
// Complex vector
} else if (q < 0) {
int l = n - 1;
int l = n1 - 1;
// Last vector component imaginary so matrix is triangular
if (std::abs(H[n][n - 1]) > std::abs(H[n - 1][n])) {
H[n - 1][n - 1] = q / H[n][n - 1];
H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
if (std::abs(H[n1][n1 - 1]) > std::abs(H[n1 - 1][n1])) {
H[n1 - 1][n1 - 1] = q / H[n1][n1 - 1];
H[n1 - 1][n1] = -(H[n1][n1] - p) / H[n1][n1 - 1];
} else {
cdiv(0.0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
H[n - 1][n - 1] = cdivr;
H[n - 1][n] = cdivi;
cdiv(0.0, -H[n1 - 1][n1], H[n1 - 1][n1 - 1] - p, q);
H[n1 - 1][n1 - 1] = cdivr;
H[n1 - 1][n1] = cdivi;
}
H[n][n - 1] = 0.0;
H[n][n] = 1.0;
for (int i = n - 2; i >= 0; i--) {
H[n1][n1 - 1] = 0.0;
H[n1][n1] = 1.0;
for (int i = n1 - 2; i >= 0; i--) {
double ra, sa, vr, vi;
ra = 0.0;
sa = 0.0;
for (int j = l; j <= n; j++) {
ra = ra + H[i][j] * H[j][n - 1];
sa = sa + H[i][j] * H[j][n];
for (int j = l; j <= n1; j++) {
ra = ra + H[i][j] * H[j][n1 - 1];
sa = sa + H[i][j] * H[j][n1];
}
w = H[i][i] - p;
@ -643,8 +643,8 @@ private:
l = i;
if (e[i] == 0) {
cdiv(-ra, -sa, w, q);
H[i][n - 1] = cdivr;
H[i][n] = cdivi;
H[i][n1 - 1] = cdivr;
H[i][n1] = cdivi;
} else {
// Solve complex equations
@ -659,28 +659,28 @@ private:
}
cdiv(x * r - z * ra + q * sa,
x * s - z * sa - q * ra, vr, vi);
H[i][n - 1] = cdivr;
H[i][n] = cdivi;
H[i][n1 - 1] = cdivr;
H[i][n1] = cdivi;
if (std::abs(x) > (std::abs(z) + std::abs(q))) {
H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q
* H[i][n]) / x;
H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n
H[i + 1][n1 - 1] = (-ra - w * H[i][n1 - 1] + q
* H[i][n1]) / x;
H[i + 1][n1] = (-sa - w * H[i][n1] - q * H[i][n1
- 1]) / x;
} else {
cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z,
cdiv(-r - y * H[i][n1 - 1], -s - y * H[i][n1], z,
q);
H[i + 1][n - 1] = cdivr;
H[i + 1][n] = cdivi;
H[i + 1][n1 - 1] = cdivr;
H[i + 1][n1] = cdivi;
}
}
// Overflow control
t = max(std::abs(H[i][n - 1]), std::abs(H[i][n]));
t = max(std::abs(H[i][n1 - 1]), std::abs(H[i][n1]));
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n - 1] = H[j][n - 1] / t;
H[j][n] = H[j][n] / t;
for (int j = i; j <= n1; j++) {
H[j][n1 - 1] = H[j][n1 - 1] / t;
H[j][n1] = H[j][n1] / t;
}
}
}

96
modules/contrib/src/logpolar_bsm.cpp
View File

@ -60,9 +60,9 @@ ICVS 2011, Sophia Antipolis, France, September 20-22, 2011
namespace cv
{
//------------------------------------interp-------------------------------------------
LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0, int interp, int full, int S, int sp)
LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int _R, double _ro0, int _interp, int full, int _S, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
@ -97,23 +97,23 @@ LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0
if (sp){
int jc=M/2-1, ic=N/2-1;
int romax=min(ic, jc);
double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(a-1)+0.5);
int _romax=min(ic, jc);
double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
this->interp=interp;
interp=_interp;
create_map(M, N, R, S, ro0);
create_map(M, N, _R, _S, _ro0);
}
void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0)
void LogPolar_Interp::create_map(int _M, int _N, int _R, int _S, double _ro0)
{
this->M=M;
this->N=N;
this->R=R;
this->S=S;
this->ro0=ro0;
M=_M;
N=_N;
R=_R;
S=_S;
ro0=_ro0;
int jc=N/2-1, ic=M/2-1;
romax=min(ic, jc);
@ -130,7 +130,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0)
for(int u=0; u<R; u++)
{
Rsri.at<float>(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc);
Csri.at<float>(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic);
Csri.at<float>(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic);
}
}
@ -158,7 +158,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0)
const Mat LogPolar_Interp::to_cortical(const Mat &source)
{
Mat out(S,R,CV_8UC1,Scalar(0));
Mat source_border;
copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0));
@ -173,7 +173,7 @@ const Mat LogPolar_Interp::to_cartesian(const Mat &source)
Mat out(N,M,CV_8UC1,Scalar(0));
Mat source_border;
if (interp==INTER_NEAREST || interp==INTER_LINEAR){
copyMakeBorder(source,source_border,0,1,0,0,BORDER_CONSTANT,Scalar(0));
Mat rowS0 = source_border.row(S);
@ -208,7 +208,7 @@ LogPolar_Interp::~LogPolar_Interp()
//------------------------------------overlapping----------------------------------
LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R, double ro0, int full, int S, int sp)
LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int _R, double _ro0, int full, int _S, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
@ -244,21 +244,21 @@ LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R,
if (sp){
int jc=M/2-1, ic=N/2-1;
int romax=min(ic, jc);
double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(a-1)+0.5);
int _romax=min(ic, jc);
double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
create_map(M, N, R, S, ro0);
create_map(M, N, _R, _S, _ro0);
}
void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0)
void LogPolar_Overlapping::create_map(int _M, int _N, int _R, int _S, double _ro0)
{
this->M=M;
this->N=N;
this->R=R;
this->S=S;
this->ro0=ro0;
M=_M;
N=_N;
R=_R;
S=_S;
ro0=_ro0;
int jc=N/2-1, ic=M/2-1;
romax=min(ic, jc);
@ -280,14 +280,14 @@ void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0)
for(int u=0; u<R; u++)
{
Rsri.at<float>(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc);
Csri.at<float>(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic);
Csri.at<float>(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic);
Rsr[v*R+u]=(int)floor(Rsri.at<float>(v,u));
Csr[v*R+u]=(int)floor(Csri.at<float>(v,u));
Csr[v*R+u]=(int)floor(Csri.at<float>(v,u));
}
}
bool done=false;
for(int i=0; i<R; i++)
{
Wsr[i]=ro0*(a-1)*pow(a,i-1);
@ -297,7 +297,7 @@ void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0)
done =true;
}
}
for(int j=0; j<N; j++)
{
for(int i=0; i<M; i++)//mdf
@ -312,7 +312,7 @@ void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0)
theta+=2*CV_PI;
ETAyx.at<float>(j,i)=(float)(q*theta);
double ro2=(j-jc)*(j-jc)+(i-ic)*(i-ic);
CSIyx.at<float>(j,i)=(float)(0.5*log(ro2/(ro0*ro0))/log(a));
}
@ -387,7 +387,7 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source)
remap(source_border,out,CSIyx,ETAyx,INTER_LINEAR);
int wm=w_ker_2D[R-1].w;
vector<double> IMG((N+2*wm+1)*(M+2*wm+1), 0.);
vector<double> NOR((N+2*wm+1)*(M+2*wm+1), 0.);
@ -426,14 +426,14 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source)
Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right));
return out_cropped;
}
LogPolar_Overlapping::~LogPolar_Overlapping()
{
}
//----------------------------------------adjacent---------------------------------------
LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double ro0, double smin, int full, int S, int sp)
LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int _R, double _ro0, double smin, int full, int _S, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
@ -468,22 +468,22 @@ LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double
if (sp){
int jc=M/2-1, ic=N/2-1;
int romax=min(ic, jc);
double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(a-1)+0.5);
int _romax=min(ic, jc);
double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
create_map(M, N, R, S, ro0, smin);
create_map(M, N, _R, _S, _ro0, smin);
}
void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, double smin)
void LogPolar_Adjacent::create_map(int _M, int _N, int _R, int _S, double _ro0, double smin)
{
LogPolar_Adjacent::M=M;
LogPolar_Adjacent::N=N;
LogPolar_Adjacent::R=R;
LogPolar_Adjacent::S=S;
LogPolar_Adjacent::ro0=ro0;
M=_M;
N=_N;
R=_R;
S=_S;
ro0=_ro0;
romax=min(M/2.0, N/2.0);
a=exp(log(romax/ro0)/(double)R);
@ -507,7 +507,7 @@ void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, doubl
void LogPolar_Adjacent::subdivide_recursively(double x, double y, int i, int j, double length, double smin)
{
{
if(length<=smin)
{
int u, v;
@ -576,7 +576,7 @@ const Mat LogPolar_Adjacent::to_cortical(const Mat &source)
for(int j=0; j<N; j++)
for(int i=0; i<M; i++)
{
{
for(size_t z=0; z<(L[M*j+i]).size(); z++)
{
map[R*((L[M*j+i])[z].v)+((L[M*j+i])[z].u)]+=((L[M*j+i])[z].a)*(source_border.at<uchar>(j,i));
@ -641,7 +641,7 @@ bool LogPolar_Adjacent::get_uv(double x, double y, int&u, int&v)
else
v= (int) floor(q*(theta+2*CV_PI));
return true;
}
}
}
LogPolar_Adjacent::~LogPolar_Adjacent()

10
modules/contrib/src/octree.cpp
View File

@ -171,9 +171,9 @@ namespace cv
{
}
Octree::Octree(const vector<Point3f>& points3d, int maxLevels, int minPoints)
Octree::Octree(const vector<Point3f>& points3d, int maxLevels, int _minPoints)
{
buildTree(points3d, maxLevels, minPoints);
buildTree(points3d, maxLevels, _minPoints);
}
Octree::~Octree()
@ -256,12 +256,12 @@ namespace cv
}
}
void Octree::buildTree(const vector<Point3f>& points3d, int maxLevels, int minPoints)
void Octree::buildTree(const vector<Point3f>& points3d, int maxLevels, int _minPoints)
{
assert((size_t)maxLevels * 8 < MAX_STACK_SIZE);
points.resize(points3d.size());
std::copy(points3d.begin(), points3d.end(), points.begin());
this->minPoints = minPoints;
minPoints = _minPoints;
nodes.clear();
nodes.push_back(Node());
@ -275,7 +275,7 @@ namespace cv
for (size_t i = 0; i < MAX_LEAFS; i++)
root.children[i] = 0;
if (maxLevels != 1 && (root.end - root.begin) > minPoints)
if (maxLevels != 1 && (root.end - root.begin) > _minPoints)
{
root.isLeaf = false;
buildNext(0);

16
modules/contrib/src/retina.cpp
View File

@ -75,16 +75,16 @@
namespace cv
{
Retina::Retina(const cv::Size inputSize)
Retina::Retina(const cv::Size inputSz)
{
_retinaFilter = 0;
_init(inputSize, true, RETINA_COLOR_BAYER, false);
_init(inputSz, true, RETINA_COLOR_BAYER, false);
}
Retina::Retina(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
Retina::Retina(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
{
_retinaFilter = 0;
_init(inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
_init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
};
Retina::~Retina()
@ -342,20 +342,20 @@ const std::valarray<float> & Retina::getMagno() const {return _retinaFilter->get
const std::valarray<float> & Retina::getParvo() const {if (_retinaFilter->getColorMode())return _retinaFilter->getColorOutput(); /* implicite else */return _retinaFilter->getContours();}
// private method called by constructirs
void Retina::_init(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
void Retina::_init(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
{
// basic error check
if (inputSize.height*inputSize.width <= 0)
if (inputSz.height*inputSz.width <= 0)
throw cv::Exception(-1, "Bad retina size setup : size height and with must be superior to zero", "Retina::setup", "Retina.h", 0);
unsigned int nbPixels=inputSize.height*inputSize.width;
unsigned int nbPixels=inputSz.height*inputSz.width;
// resize buffers if size does not match
_inputBuffer.resize(nbPixels*3); // buffer supports gray images but also 3 channels color buffers... (larger is better...)
// allocate the retina model
if (_retinaFilter)
delete _retinaFilter;
_retinaFilter = new RetinaFilter(inputSize.height, inputSize.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
_retinaFilter = new RetinaFilter(inputSz.height, inputSz.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
// prepare the default parameter XML file with default setup
setup(_retinaParameters);

6
modules/contrib/src/retinacolor.cpp
View File

@ -325,15 +325,15 @@ void RetinaColor::runColorDemultiplexing(const std::valarray<float> &multiplexed
}else
{
register const float *multiplexedColorFramePTR= get_data(multiplexedColorFrame);
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR)
register const float *multiplexedColorFramePTR1= get_data(multiplexedColorFrame);
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR1)
{
// normalize by photoreceptors density
float Cr=*(chrominancePTR)*_colorLocalDensity[indexc];
float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()];
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()];
*luminance=(Cr+Cg+Cb)*_pG;
_demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR - *luminance;
_demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR1 - *luminance;
}

352
modules/contrib/src/spinimages.cpp
View File

@ -60,9 +60,9 @@ namespace cv
using std::min;
using std::sqrt;
}
namespace
namespace
{
const static Scalar colors[] =
const static Scalar colors[] =
{
CV_RGB(255, 0, 0),
CV_RGB( 0, 255, 0),
@ -87,21 +87,21 @@ namespace
template<class FwIt, class T> void iota(FwIt first, FwIt last, T value) { while(first != last) *first++ = value++; }
void computeNormals( const Octree& Octree, const vector<Point3f>& centers, vector<Point3f>& normals,
void computeNormals( const Octree& Octree, const vector<Point3f>& centers, vector<Point3f>& normals,
vector<uchar>& mask, float normalRadius, int minNeighbors = 20)
{
{
size_t normals_size = centers.size();
normals.resize(normals_size);
if (mask.size() != normals_size)
{
size_t m = mask.size();
size_t m = mask.size();
mask.resize(normals_size);
if (normals_size > m)
for(; m < normals_size; ++m)
mask[m] = 1;
}
vector<Point3f> buffer;
buffer.reserve(128);
SVD svd;
@ -132,7 +132,7 @@ void computeNormals( const Octree& Octree, const vector<Point3f>& centers, vecto
mean.x /= buf_size;
mean.y /= buf_size;
mean.z /= buf_size;
double pxpx = 0;
double pypy = 0;
double pzpz = 0;
@ -162,9 +162,9 @@ void computeNormals( const Octree& Octree, const vector<Point3f>& centers, vecto
/*normals[n] = Point3f( (float)((double*)svd.vt.data)[6],
(float)((double*)svd.vt.data)[7],
(float)((double*)svd.vt.data)[8] );*/
normals[n] = reinterpret_cast<Point3d*>(svd.vt.data)[2];
mask[n] = 1;
(float)((double*)svd.vt.data)[8] );*/
normals[n] = reinterpret_cast<Point3d*>(svd.vt.data)[2];
mask[n] = 1;
}
}
@ -213,22 +213,22 @@ inline __m128 transformSSE(const __m128* matrix, const __m128& in)
}
inline __m128i _mm_mullo_epi32_emul(const __m128i& a, __m128i& b)
{
{
__m128i pack = _mm_packs_epi32(a, a);
return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b));
return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b));
}
#endif
void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, const vector<Point3f>& normals,
void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, const vector<Point3f>& normals,
vector<uchar>& mask, Mat& spinImages, int imageWidth, float binSize)
{
{
float pixelsPerMeter = 1.f / binSize;
float support = imageWidth * binSize;
float support = imageWidth * binSize;
assert(normals.size() == points.size());
assert(mask.size() == points.size());
size_t points_size = points.size();
mask.resize(points_size);
@ -257,7 +257,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
int t = cvGetThreadNum();
vector<Point3f>& pointsInSphere = pointsInSpherePool[t];
const Point3f& center = points[i];
Octree.getPointsWithinSphere(center, searchRad, pointsInSphere);
@ -269,7 +269,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
}
const Point3f& normal = normals[i];
float rotmat[9];
initRotationMat(normal, rotmat);
Point3f new_center;
@ -287,7 +287,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
{
__m128 rotmatSSE[3];
convertTransformMatrix(rotmat, (float*)rotmatSSE);
__m128 center_x4 = _mm_set1_ps(new_center.x);
__m128 center_y4 = _mm_set1_ps(new_center.y);
__m128 center_z4 = _mm_set1_ps(new_center.z + halfSuppport);
@ -313,7 +313,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
__m128 z0 = _mm_unpackhi_ps(pt0, pt1); // z0 z1 . .
__m128 z1 = _mm_unpackhi_ps(pt2, pt3); // z2 z3 . .
__m128 beta4 = _mm_sub_ps(center_z4, _mm_movelh_ps(z0, z1)); // b0 b1 b2 b3
__m128 xy0 = _mm_unpacklo_ps(pt0, pt1); // x0 x1 y0 y1
__m128 xy1 = _mm_unpacklo_ps(pt2, pt3); // x2 x3 y2 y3
__m128 x4 = _mm_movelh_ps(xy0, xy1); // x0 x1 x2 x3
@ -322,7 +322,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
x4 = _mm_sub_ps(x4, center_x4);
y4 = _mm_sub_ps(y4, center_y4);
__m128 alpha4 = _mm_sqrt_ps(_mm_add_ps(_mm_mul_ps(x4,x4),_mm_mul_ps(y4,y4)));
__m128 n1f4 = _mm_mul_ps( beta4, ppm4); /* beta4 float */
__m128 n2f4 = _mm_mul_ps(alpha4, ppm4); /* alpha4 float */
@ -333,21 +333,21 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
__m128 f1 = _mm_sub_ps( n1f4, _mm_cvtepi32_ps(n1) ); /* { beta4 } */
__m128 f2 = _mm_sub_ps( n2f4, _mm_cvtepi32_ps(n2) ); /* { alpha4 } */
__m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2
__m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2
__m128 omf1omf2 = _mm_add_ps(_mm_sub_ps(_mm_sub_ps(one4f, f2), f1), f1f2); // (1-f1) * (1-f2)
__m128i mask = _mm_and_si128(
__m128i _mask = _mm_and_si128(
_mm_andnot_si128(_mm_cmpgt_epi32(zero4, n1), _mm_cmpgt_epi32(height4m1, n1)),
_mm_andnot_si128(_mm_cmpgt_epi32(zero4, n2), _mm_cmpgt_epi32(width4m1, n2)));
__m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(mask), zero4f);
__m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(_mask), zero4f);
__m128 v00 = _mm_and_ps( omf1omf2 , maskf); // a00 b00 c00 d00
__m128 v01 = _mm_and_ps( _mm_sub_ps( f2, f1f2 ), maskf); // a01 b01 c01 d01
__m128 v10 = _mm_and_ps( _mm_sub_ps( f1, f1f2 ), maskf); // a10 b10 c10 d10
__m128 v11 = _mm_and_ps( f1f2 , maskf); // a11 b11 c11 d11
__m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), mask);
__m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), _mask);
_mm_store_si128((__m128i*)o, ofs4);
__m128 t0 = _mm_unpacklo_ps(v00, v01); // a00 a01 b00 b01
@ -395,9 +395,9 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
if (beta >= support || beta < 0)
continue;
alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) +
(new_center.y - pt.y) * (new_center.y - pt.y) );
alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) +
(new_center.y - pt.y) * (new_center.y - pt.y) );
float n1f = beta * pixelsPerMeter;
float n2f = alpha * pixelsPerMeter;
@ -407,7 +407,7 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
float f1 = n1f - n1;
float f2 = n2f - n2;
if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) ||
if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) ||
(unsigned)n2 >= (unsigned)(spinImage.cols-1))
continue;
@ -454,27 +454,27 @@ float cv::Mesh3D::estimateResolution(float /*tryRatio*/)
vector<double> dist(tryNum * neighbors);
vector<int> inds(tryNum * neighbors);
vector<Point3f> query;
vector<Point3f> query;
RNG& rng = theRNG();
RNG& rng = theRNG();
for(int i = 0; i < tryNum; ++i)
query.push_back(vtx[rng.next() % vtx.size()]);
CvMat cvinds = cvMat( (int)tryNum, neighbors, CV_32S, &inds[0] );
CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] );
CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] );
CvMat cvquery = cvMat( (int)tryNum, 3, CV_32F, &query[0] );
cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50);
cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50);
cvReleaseFeatureTree(tr);
const int invalid_dist = -2;
const int invalid_dist = -2;
for(int i = 0; i < tryNum; ++i)
if (inds[i] == -1)
dist[i] = invalid_dist;
dist.resize(remove(dist.begin(), dist.end(), invalid_dist) - dist.begin());
sort(dist, less<double>());
return resolution = (float)dist[ dist.size() / 2 ];
#else
CV_Error(CV_StsNotImplemented, "");
@ -494,7 +494,7 @@ void cv::Mesh3D::computeNormals(const vector<int>& subset, float normalRadius, i
{
buildOctree();
vector<uchar> mask(vtx.size(), 0);
for(size_t i = 0; i < subset.size(); ++i)
for(size_t i = 0; i < subset.size(); ++i)
mask[subset[i]] = 1;
::computeNormals(octree, vtx, normals, mask, normalRadius, minNeighbors);
}
@ -504,31 +504,31 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector<Scalar>& _colors)
ofstream ofs(file.c_str());
ofs << "#VRML V2.0 utf8" << endl;
ofs << "Shape" << std::endl << "{" << endl;
ofs << "geometry PointSet" << endl << "{" << endl;
ofs << "coord Coordinate" << endl << "{" << endl;
ofs << "point[" << endl;
ofs << "Shape" << std::endl << "{" << endl;
ofs << "geometry PointSet" << endl << "{" << endl;
ofs << "coord Coordinate" << endl << "{" << endl;
ofs << "point[" << endl;
for(size_t i = 0; i < vtx.size(); ++i)
ofs << vtx[i].x << " " << vtx[i].y << " " << vtx[i].z << endl;
ofs << "]" << endl; //point[
ofs << "}" << endl; //Coordinate{
ofs << "]" << endl; //point[
ofs << "}" << endl; //Coordinate{
if (vtx.size() == _colors.size())
{
ofs << "color Color" << endl << "{" << endl;
ofs << "color[" << endl;
for(size_t i = 0; i < _colors.size(); ++i)
ofs << (float)_colors[i][2] << " " << (float)_colors[i][1] << " " << (float)_colors[i][0] << endl;
ofs << "]" << endl; //color[
ofs << "}" << endl; //color Color{
ofs << "}" << endl; //color Color{
}
ofs << "}" << endl; //PointSet{
ofs << "}" << endl; //Shape{
ofs << "}" << endl; //PointSet{
ofs << "}" << endl; //Shape{
}
@ -538,45 +538,45 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector<Scalar>& _colors)
bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result)
{
struct Math { static double atanh(double x) { return 0.5 * std::log( (1 + x) / (1 - x) ); } };
const float* s1 = spin1.ptr<float>();
const float* s2 = spin2.ptr<float>();
int spin_sz = spin1.cols * spin1.rows;
int spin_sz = spin1.cols * spin1.rows;
double sum1 = 0.0, sum2 = 0.0, sum12 = 0.0, sum11 = 0.0, sum22 = 0.0;
int N = 0;
int i = 0;
#if CV_SSE2//____________TEMPORARY_DISABLED_____________
float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4];
float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4];
__m128 zerof4 = _mm_setzero_ps();
__m128 onef4 = _mm_set1_ps(1.f);
__m128 Nf4 = zerof4;
__m128 Nf4 = zerof4;
__m128 sum1f4 = zerof4;
__m128 sum2f4 = zerof4;
__m128 sum11f4 = zerof4;
__m128 sum22f4 = zerof4;
__m128 sum12f4 = zerof4;
__m128 sum12f4 = zerof4;
for(; i < spin_sz - 5; i += 4)
{
__m128 v1f4 = _mm_loadu_ps(s1 + i);
__m128 v2f4 = _mm_loadu_ps(s2 + i);
__m128 v1f4 = _mm_loadu_ps(s1 + i);
__m128 v2f4 = _mm_loadu_ps(s2 + i);
__m128 mskf4 = _mm_and_ps(_mm_cmpneq_ps(v1f4, zerof4), _mm_cmpneq_ps(v2f4, zerof4));
if( !_mm_movemask_ps(mskf4) )
if( !_mm_movemask_ps(mskf4) )
continue;
Nf4 = _mm_add_ps(Nf4, _mm_and_ps(onef4, mskf4));
v1f4 = _mm_and_ps(v1f4, mskf4);
v2f4 = _mm_and_ps(v2f4, mskf4);
sum1f4 = _mm_add_ps(sum1f4, v1f4);
sum2f4 = _mm_add_ps(sum2f4, v2f4);
sum11f4 = _mm_add_ps(sum11f4, _mm_mul_ps(v1f4, v1f4));
sum22f4 = _mm_add_ps(sum22f4, _mm_mul_ps(v2f4, v2f4));
sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4));
sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4));
}
_mm_store_ps( su1, sum1f4 );
_mm_store_ps( su2, sum2f4 );
@ -601,11 +601,11 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo
if( !v1 || !v2 )
continue;
N++;
sum1 += v1;
sum2 += v2;
sum11 += v1 * v1;
sum22 += v2 * v2;
sum1 += v1;
sum2 += v2;
sum11 += v1 * v1;
sum22 += v2 * v2;
sum12 += v1 * v2;
}
if( N < 4 )
@ -624,13 +624,13 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo
double corr = (Nsum12 - sum1 * sum2) / sqrt( (Nsum11 - sum1sum1) * (Nsum22 - sum2sum2) );
double atanh = Math::atanh(corr);
result = (float)( atanh * atanh - lambda * ( 1.0 / (N - 3) ) );
return true;
return true;
}
inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3f& v, const Point3f& n)
{
/*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z);
float normalNorm = (float)norm(n);
{
/*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z);
float normalNorm = (float)norm(n);
float beta = PmV.dot(n) / normalNorm;
float pmcNorm = (float)norm(PmV);
float alpha = sqrt( pmcNorm * pmcNorm - beta * beta);
@ -639,23 +639,23 @@ inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3
float pmv_x = p.x - v.x, pmv_y = p.y - v.y, pmv_z = p.z - v.z;
float beta = (pmv_x * n.x + pmv_y + n.y + pmv_z * n.z) / sqrt(n.x * n.x + n.y * n.y + n.z * n.z);
float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta);
float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta);
return Point2f(alpha, beta);
}
inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2)
{
{
Point2f Sm2_to_m1, Ss2_to_s1;
Point2f Sm1_to_m2, Ss1_to_s2;
double n_Sm2_to_m1 = norm(Sm2_to_m1 = calcSpinMapCoo(pointModel2, pointModel1, normalModel1));
double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1));
double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1));
double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 ) ;
double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2));
double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2));
@ -666,10 +666,10 @@ inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1
inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2,
float gamma)
{
{
Point2f Sm2_to_m1, Ss2_to_s1;
Point2f Sm1_to_m2, Ss1_to_s2;
@ -680,7 +680,7 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co
double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 );
double wgc21 = gc21 / (1 - exp( -(n_Sm2_to_m1 + n_Ss2_to_s1) * gamma05_inv ) );
double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2));
double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2));
@ -692,10 +692,10 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co
cv::SpinImageModel::SpinImageModel(const Mesh3D& _mesh) : mesh(_mesh) , out(0)
{
{
if (mesh.vtx.empty())
throw Mesh3D::EmptyMeshException();
defaultParams();
defaultParams();
}
cv::SpinImageModel::SpinImageModel() : out(0) { defaultParams(); }
cv::SpinImageModel::~SpinImageModel() {}
@ -708,8 +708,8 @@ void cv::SpinImageModel::defaultParams()
minNeighbors = 20;
binSize = 0.f; /* autodetect according to mesh resolution */
imageWidth = 32;
imageWidth = 32;
lambda = 0.f; /* autodetect according to medan non zero images bin */
gamma = 0.f; /* autodetect according to mesh resolution */
@ -725,28 +725,28 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount,
if (num == 0)
return Mat();
RNG& rng = theRNG();
RNG& rng = theRNG();
vector<Mat> spins;
for(int i = 0; i < num; ++i)
spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth));
spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth));
if (separateScale)
for(int i = 0; i < num; ++i)
{
double max;
Mat spin8u;
minMaxLoc(spins[i], 0, &max);
minMaxLoc(spins[i], 0, &max);
spins[i].convertTo(spin8u, CV_8U, -255.0/max, 255.0);
spins[i] = spin8u;
}
else
{
{
double totalMax = 0;
for(int i = 0; i < num; ++i)
{
double m;
minMaxLoc(spins[i], 0, &m);
minMaxLoc(spins[i], 0, &m);
totalMax = max(m, totalMax);
}
@ -760,12 +760,12 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount,
int sz = spins.front().cols;
Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3);
Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3);
result = colors[(static_cast<int64>(cvGetTickCount()/cvGetTickFrequency())/1000) % colors_mum];
int pos = 0;
for(int y = 0; y < (int)yCount; ++y)
for(int x = 0; x < (int)xCount; ++x)
for(int x = 0; x < (int)xCount; ++x)
if (pos < num)
{
int starty = (y + 0) * sz + y;
@ -778,7 +778,7 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount,
cvtColor(spins[pos++], color, CV_GRAY2BGR);
Mat roi = result(Range(starty, endy), Range(startx, endx));
color.copyTo(roi);
}
}
return result;
}
@ -811,8 +811,8 @@ void cv::SpinImageModel::selectRandomSubset(float ratio)
int pos = rnd.next() % left.size();
subset[i] = (int)left[pos];
left[pos] = left.back();
left.resize(left.size() - 1);
left[pos] = left.back();
left.resize(left.size() - 1);
}
sort(subset, less<int>());
}
@ -823,21 +823,21 @@ void cv::SpinImageModel::setSubset(const vector<int>& ss)
subset = ss;
}
void cv::SpinImageModel::repackSpinImages(const vector<uchar>& mask, Mat& spinImages, bool reAlloc) const
{
void cv::SpinImageModel::repackSpinImages(const vector<uchar>& mask, Mat& _spinImages, bool reAlloc) const
{
if (reAlloc)
{
size_t spinCount = mask.size() - count(mask.begin(), mask.end(), (uchar)0);
Mat newImgs((int)spinCount, spinImages.cols, spinImages.type());
Mat newImgs((int)spinCount, _spinImages.cols, _spinImages.type());
int pos = 0;
for(size_t t = 0; t < mask.size(); ++t)
if (mask[t])
{
Mat row = newImgs.row(pos++);
spinImages.row((int)t).copyTo(row);
_spinImages.row((int)t).copyTo(row);
}
spinImages = newImgs;
_spinImages = newImgs;
}
else
{
@ -849,13 +849,13 @@ void cv::SpinImageModel::repackSpinImages(const vector<uchar>& mask, Mat& spinIm
int first = dest + 1;
for (; first != last; ++first)
if (mask[first] != 0)
if (mask[first] != 0)
{
Mat row = spinImages.row(dest);
spinImages.row(first).copyTo(row);
Mat row = _spinImages.row(dest);
_spinImages.row(first).copyTo(row);
++dest;
}
spinImages = spinImages.rowRange(0, dest);
_spinImages = _spinImages.rowRange(0, dest);
}
}
@ -865,13 +865,13 @@ void cv::SpinImageModel::compute()
if (binSize == 0.f)
{
if (mesh.resolution == -1.f)
mesh.estimateResolution();
mesh.estimateResolution();
binSize = mesh.resolution;
}
/* estimate normalRadius */
normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2;
/* estimate normalRadius */
normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2;
mesh.buildOctree();
mesh.buildOctree();
if (subset.empty())
{
mesh.computeNormals(normalRadius, minNeighbors);
@ -881,16 +881,16 @@ void cv::SpinImageModel::compute()
else
mesh.computeNormals(subset, normalRadius, minNeighbors);
vector<uchar> mask(mesh.vtx.size(), 0);
vector<uchar> mask(mesh.vtx.size(), 0);
for(size_t i = 0; i < subset.size(); ++i)
if (mesh.normals[subset[i]] == Mesh3D::allzero)
subset[i] = -1;
if (mesh.normals[subset[i]] == Mesh3D::allzero)
subset[i] = -1;
else
mask[subset[i]] = 1;
subset.resize( remove(subset.begin(), subset.end(), -1) - subset.begin() );
vector<Point3f> vtx;
vector<Point3f> normals;
vector<Point3f> normals;
for(size_t i = 0; i < mask.size(); ++i)
if(mask[i])
{
@ -906,7 +906,7 @@ void cv::SpinImageModel::compute()
for(size_t i = 0; i < mask.size(); ++i)
if(mask[i])
if (spinMask[mask_pos++] == 0)
subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() );
subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() );
}
void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector<int>& indeces, vector<float>& corrCoeffs, bool useExtremeOutliers) const
@ -920,46 +920,46 @@ void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector<int>& indeces,
vector<uchar> masks(model.spinImages.rows);
vector<float> cleanCorrs;
cleanCorrs.reserve(model.spinImages.rows);
for(int i = 0; i < model.spinImages.rows; ++i)
{
masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]);
masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]);
if (masks[i])
cleanCorrs.push_back(corrs[i]);
}
/* Filtering by measure histogram */
size_t total = cleanCorrs.size();
if(total < 5)
return;
sort(cleanCorrs, less<float>());
float lower_fourth = cleanCorrs[(1 * total) / 4 - 1];
float upper_fourth = cleanCorrs[(3 * total) / 4 - 0];
float fourth_spread = upper_fourth - lower_fourth;
//extreme or moderate?
float coef = useExtremeOutliers ? 3.0f : 1.5f;
float coef = useExtremeOutliers ? 3.0f : 1.5f;
float histThresHi = upper_fourth + coef * fourth_spread;
//float histThresLo = lower_fourth - coef * fourth_spread;
float histThresHi = upper_fourth + coef * fourth_spread;
//float histThresLo = lower_fourth - coef * fourth_spread;
for(size_t i = 0; i < corrs.size(); ++i)
if (masks[i])
if (/* corrs[i] < histThresLo || */ corrs[i] > histThresHi)
{
indeces.push_back((int)i);
corrCoeffs.push_back(corrs[i]);
corrCoeffs.push_back(corrs[i]);
}
}
}
namespace
namespace
{
struct Match
{
int sceneInd;
int sceneInd;
int modelInd;
float measure;
@ -984,7 +984,7 @@ struct WgcHelper
{
const float* wgcLine = mat.ptr<float>((int)corespInd);
float maximum = numeric_limits<float>::min();
for(citer pos = group.begin(); pos != group.end(); ++pos)
maximum = max(wgcLine[*pos], maximum);
@ -997,7 +997,7 @@ private:
}
void cv::SpinImageModel::match(const SpinImageModel& scene, vector< vector<Vec2i> >& result)
{
{
if (mesh.vtx.empty())
throw Mesh3D::EmptyMeshException();
@ -1006,25 +1006,25 @@ private:
SpinImageModel& model = *this;
const float infinity = numeric_limits<float>::infinity();
const float float_max = numeric_limits<float>::max();
/* estimate gamma */
if (model.gamma == 0.f)
{
if (model.mesh.resolution == -1.f)
model.mesh.estimateResolution();
model.mesh.estimateResolution();
model.gamma = 4 * model.mesh.resolution;
}
/* estimate lambda */
if (model.lambda == 0.f)
{
vector<int> nonzero(model.spinImages.rows);
vector<int> nonzero(model.spinImages.rows);
for(int i = 0; i < model.spinImages.rows; ++i)
nonzero[i] = countNonZero(model.spinImages.row(i));
sort(nonzero, less<int>());
model.lambda = static_cast<float>( nonzero[ nonzero.size()/2 ] ) / 2;
}
}
TickMeter corr_timer;
corr_timer.start();
vector<Match> allMatches;
@ -1032,37 +1032,37 @@ private:
{
vector<int> indeces;
vector<float> coeffs;
matchSpinToModel(scene.spinImages.row(i), indeces, coeffs);
matchSpinToModel(scene.spinImages.row(i), indeces, coeffs);
for(size_t t = 0; t < indeces.size(); ++t)
allMatches.push_back(Match(i, indeces[t], coeffs[t]));
allMatches.push_back(Match(i, indeces[t], coeffs[t]));
if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl;
if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl;
}
corr_timer.stop();
if (out) *out << "Spin correlation time = " << corr_timer << endl;
if (out) *out << "Matches number = " << allMatches.size() << endl;
if(allMatches.empty())
if(allMatches.empty())
return;
/* filtering by similarity measure */
const float fraction = 0.5f;
float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less<float>())->measure;
float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less<float>())->measure;
allMatches.erase(
remove_if(allMatches.begin(), allMatches.end(), bind2nd(less<float>(), maxMeasure * fraction)),
remove_if(allMatches.begin(), allMatches.end(), bind2nd(less<float>(), maxMeasure * fraction)),
allMatches.end());
if (out) *out << "Matches number [filtered by similarity measure] = " << allMatches.size() << endl;
int matchesSize = (int)allMatches.size();
if(matchesSize == 0)
return;
/* filtering by geometric consistency */
/* filtering by geometric consistency */
for(int i = 0; i < matchesSize; ++i)
{
int consistNum = 1;
float gc = float_max;
for(int j = 0; j < matchesSize; ++j)
if (i != j)
{
@ -1075,31 +1075,31 @@ private:
{
const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd);
const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd);
const Point3f& pointModelI = model.getSpinVertex(mi.modelInd);
const Point3f& normalModelI = model.getSpinNormal(mi.modelInd);
const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd);
const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd);
const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd);
const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd);
gc = geometricConsistency(pointSceneI, normalSceneI, pointModelI, normalModelI,
pointSceneJ, normalSceneJ, pointModelJ, normalModelJ);
pointSceneJ, normalSceneJ, pointModelJ, normalModelJ);
}
if (gc < model.T_GeometriccConsistency)
++consistNum;
}
if (consistNum < matchesSize / 4) /* failed consistensy test */
allMatches[i].measure = infinity;
allMatches[i].measure = infinity;
}
allMatches.erase(
remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to<float>(), infinity)),
allMatches.end());
remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to<float>(), infinity)),
allMatches.end());
if (out) *out << "Matches number [filtered by geometric consistency] = " << allMatches.size() << endl;
@ -1110,11 +1110,11 @@ private:
if (out) *out << "grouping ..." << endl;
Mat groupingMat((int)matchesSize, (int)matchesSize, CV_32F);
groupingMat = Scalar(0);
groupingMat = Scalar(0);
/* grouping */
for(int j = 0; j < matchesSize; ++j)
for(int i = j + 1; i < matchesSize; ++i)
for(int i = j + 1; i < matchesSize; ++i)
{
const Match& mi = allMatches[i];
const Match& mj = allMatches[j];
@ -1128,20 +1128,20 @@ private:
const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd);
const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd);
const Point3f& pointModelI = model.getSpinVertex(mi.modelInd);
const Point3f& normalModelI = model.getSpinNormal(mi.modelInd);
const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd);
const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd);
const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd);
const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd);
float wgc = groupingCreteria(pointSceneI, normalSceneI, pointModelI, normalModelI,
pointSceneJ, normalSceneJ, pointModelJ, normalModelJ,
model.gamma);
model.gamma);
groupingMat.ptr<float>(i)[j] = wgc;
groupingMat.ptr<float>(j)[i] = wgc;
}
@ -1149,35 +1149,35 @@ private:
group_t allMatchesInds;
for(int i = 0; i < matchesSize; ++i)
allMatchesInds.insert(i);
vector<float> buf(matchesSize);
float *buf_beg = &buf[0];
vector<group_t> groups;
for(int g = 0; g < matchesSize; ++g)
{
{
if (out) if (g % 100 == 0) *out << "G = " << g << endl;
group_t left = allMatchesInds;
group_t group;
left.erase(g);
group.insert(g);
for(;;)
{
size_t left_size = left.size();
if (left_size == 0)
break;
std::transform(left.begin(), left.end(), buf_beg, WgcHelper(group, groupingMat));
size_t minInd = min_element(buf_beg, buf_beg + left_size) - buf_beg;
if (buf[minInd] < model.T_GroupingCorespondances) /* can add corespondance to group */
{
iter pos = left.begin();
advance(pos, minInd);
group.insert(*pos);
left.erase(pos);
}
@ -1199,16 +1199,16 @@ private:
{
const Match& m = allMatches[*pos];
outgrp.push_back(Vec2i(subset[m.modelInd], scene.subset[m.sceneInd]));
}
}
result.push_back(outgrp);
}
}
}
cv::TickMeter::TickMeter() { reset(); }
int64 cv::TickMeter::getTimeTicks() const { return sumTime; }
double cv::TickMeter::getTimeMicro() const { return (double)getTimeTicks()/cvGetTickFrequency(); }
double cv::TickMeter::getTimeMilli() const { return getTimeMicro()*1e-3; }
double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; }
double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; }
int64 cv::TickMeter::getCounter() const { return counter; }
void cv::TickMeter::reset() {startTime = 0; sumTime = 0; counter = 0; }

530
modules/contrib/src/stereovar.cpp
View File

@ -46,14 +46,14 @@
Proceedings of the 5th International Symposium on Visual Computing, Vegas, USA
This code is written by Sergey G. Kosov for "Visir PX" application as part of Project X (www.project-10.de)
*/
*/
#include "precomp.hpp"
#include <limits.h>
namespace cv
namespace cv
{
StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS)
StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS)
{
}
@ -67,9 +67,9 @@ StereoVar::~StereoVar()
static Mat diffX(Mat &src)
{
register int x, y, cols = src.cols - 1;
Mat dst(src.size(), src.type());
for(y = 0; y < src.rows; y++){
register int x, y, cols = src.cols - 1;
Mat dst(src.size(), src.type());
for(y = 0; y < src.rows; y++){
const float* pSrc = src.ptr<float>(y);
float* pDst = dst.ptr<float>(y);
#if CV_SSE2
@ -92,319 +92,319 @@ static Mat diffX(Mat &src)
static Mat getGradient(Mat &src)
{
register int x, y;
Mat dst(src.size(), src.type());
dst.setTo(0);
for (y = 0; y < src.rows - 1; y++) {
float *pSrc = src.ptr<float>(y);
float *pSrcF = src.ptr<float>(y + 1);
float *pDst = dst.ptr<float>(y);
for (x = 0; x < src.cols - 1; x++)
pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]);
}
return dst;
register int x, y;
Mat dst(src.size(), src.type());
dst.setTo(0);
for (y = 0; y < src.rows - 1; y++) {
float *pSrc = src.ptr<float>(y);
float *pSrcF = src.ptr<float>(y + 1);
float *pDst = dst.ptr<float>(y);
for (x = 0; x < src.cols - 1; x++)
pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]);
}
return dst;
}
static Mat getG_c(Mat &src, float l)
{
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]);
}
return dst;
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]);
}
return dst;
}
static Mat getG_p(Mat &src, float l)
{
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]);
}
return dst;
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]);
}
return dst;
}
void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
{
register int n, x, y;
float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4;
Mat g_c, g_p;
Mat U;
u.copyTo(U);
register int n, x, y;
float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4;
Mat g_c, g_p;
Mat U;
u.copyTo(U);
int N = nIt;
float l = lambda;
float Fi = fi;
int N = nIt;
float l = lambda;
float Fi = fi;
if (flags & USE_SMART_ID) {
double scale = pow(pyrScale, (double) level) * (1 + pyrScale);
N = (int) (N / scale);
}
double scale = pow(pyrScale, (double) level);
Fi /= (float) scale;
l *= (float) scale;
if (flags & USE_SMART_ID) {
double scale = pow(pyrScale, (double) level) * (1 + pyrScale);
N = (int) (N / scale);
}
int width = u.cols - 1;
int height = u.rows - 1;
for (n = 0; n < N; n++) {
if (penalization != PENALIZATION_TICHONOV) {
Mat gradient = getGradient(U);
switch (penalization) {
case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break;
case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break;
}
gradient.release();
}
for (y = 1 ; y < height; y++) {
float *pU = U.ptr<float>(y);
float *pUu = U.ptr<float>(y + 1);
float *pUd = U.ptr<float>(y - 1);
float *pu = u.ptr<float>(y);
float *pI1 = I1.ptr<float>(y);
float *pI2 = I2.ptr<float>(y);
float *pI2x = I2x.ptr<float>(y);
float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL;
float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL;
switch (penalization) {
case PENALIZATION_CHARBONNIER:
pG_c = g_c.ptr<float>(y);
pG_cu = g_c.ptr<float>(y + 1);
pG_cd = g_c.ptr<float>(y - 1);
break;
case PENALIZATION_PERONA_MALIK:
pG_p = g_p.ptr<float>(y);
pG_pu = g_p.ptr<float>(y + 1);
pG_pd = g_p.ptr<float>(y - 1);
break;
}
for (x = 1; x < width; x++) {
switch (penalization) {
case PENALIZATION_CHARBONNIER:
gc = pG_c[x];
gl = gc + pG_c[x - 1];
gr = gc + pG_c[x + 1];
gu = gc + pG_cu[x];
gd = gc + pG_cd[x];
gc = gl + gr + gu + gd;
break;
case PENALIZATION_PERONA_MALIK:
gc = pG_p[x];
gl = gc + pG_p[x - 1];
gr = gc + pG_p[x + 1];
gu = gc + pG_pu[x];
gd = gc + pG_pd[x];
gc = gl + gr + gu + gd;
break;
}
double scale = pow(pyrScale, (double) level);
Fi /= (float) scale;
l *= (float) scale;
float fi = Fi;
if (maxDisp > minDisp) {
if (pU[x] > maxDisp * scale) {fi *= 1000; pU[x] = static_cast<float>(maxDisp * scale);}
if (pU[x] < minDisp * scale) {fi *= 1000; pU[x] = static_cast<float>(minDisp * scale);}
}
int width = u.cols - 1;
int height = u.rows - 1;
for (n = 0; n < N; n++) {
if (penalization != PENALIZATION_TICHONOV) {
Mat gradient = getGradient(U);
switch (penalization) {
case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break;
case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break;
}
gradient.release();
}
for (y = 1 ; y < height; y++) {
float *pU = U.ptr<float>(y);
float *pUu = U.ptr<float>(y + 1);
float *pUd = U.ptr<float>(y - 1);
float *pu = u.ptr<float>(y);
float *pI1 = I1.ptr<float>(y);
float *pI2 = I2.ptr<float>(y);
float *pI2x = I2x.ptr<float>(y);
float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL;
float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL;
switch (penalization) {
case PENALIZATION_CHARBONNIER:
pG_c = g_c.ptr<float>(y);
pG_cu = g_c.ptr<float>(y + 1);
pG_cd = g_c.ptr<float>(y - 1);
break;
case PENALIZATION_PERONA_MALIK:
pG_p = g_p.ptr<float>(y);
pG_pu = g_p.ptr<float>(y + 1);
pG_pd = g_p.ptr<float>(y - 1);
break;
}
for (x = 1; x < width; x++) {
switch (penalization) {
case PENALIZATION_CHARBONNIER:
gc = pG_c[x];
gl = gc + pG_c[x - 1];
gr = gc + pG_c[x + 1];
gu = gc + pG_cu[x];
gd = gc + pG_cd[x];
gc = gl + gr + gu + gd;
break;
case PENALIZATION_PERONA_MALIK:
gc = pG_p[x];
gl = gc + pG_p[x - 1];
gr = gc + pG_p[x + 1];
gu = gc + pG_pu[x];
gd = gc + pG_pd[x];
gc = gl + gr + gu + gd;
break;
}
int A = static_cast<int>(pU[x]);
int neg = 0; if (pU[x] <= 0) neg = -1;
float _fi = Fi;
if (maxDisp > minDisp) {
if (pU[x] > maxDisp * scale) {_fi *= 1000; pU[x] = static_cast<float>(maxDisp * scale);}
if (pU[x] < minDisp * scale) {_fi *= 1000; pU[x] = static_cast<float>(minDisp * scale);}
}
if (x + A > width)
pu[x] = pU[width - A];
else if (x + A + neg < 0)
pu[x] = pU[- A + 2];
else {
pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A])
+ fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A))
/ (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * fi) ;
}
}// x
pu[0] = pu[1];
pu[width] = pu[width - 1];
}// y
for (x = 0; x <= width; x++) {
u.at<float>(0, x) = u.at<float>(1, x);
u.at<float>(height, x) = u.at<float>(height - 1, x);
}
u.copyTo(U);
if (!g_c.empty()) g_c.release();
if (!g_p.empty()) g_p.release();
}//n
int A = static_cast<int>(pU[x]);
int neg = 0; if (pU[x] <= 0) neg = -1;
if (x + A > width)
pu[x] = pU[width - A];
else if (x + A + neg < 0)
pu[x] = pU[- A + 2];
else {
pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A])
+ _fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A))
/ (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * _fi) ;
}
}// x
pu[0] = pu[1];
pu[width] = pu[width - 1];
}// y
for (x = 0; x <= width; x++) {
u.at<float>(0, x) = u.at<float>(1, x);
u.at<float>(height, x) = u.at<float>(height - 1, x);
}
u.copyTo(U);
if (!g_c.empty()) g_c.release();
if (!g_p.empty()) g_p.release();
}//n
}
void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level)
{
CvSize imgSize = _u.size();
CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h, U, U_h;
CvSize imgSize = _u.size();
CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h, U, U_h;
//PRE relaxation
VariationalSolver(I1, I2, I2x, _u, level);
//PRE relaxation
VariationalSolver(I1, I2, I2x, _u, level);
if (level >= levels - 1) return;
level ++;
if (level >= levels - 1) return;
level ++;
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(_u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), pyrScale);
I2x_h = diffX(I2_h);
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(_u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), pyrScale);
I2x_h = diffX(I2_h);
//Next level
U_h = u_h.clone();
VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level);
//Next level
U_h = u_h.clone();
VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level);
subtract(U_h, u_h, U_h);
U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale);
subtract(U_h, u_h, U_h);
U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale);
//scaling UP
resize(U_h, U, imgSize);
//scaling UP
resize(U_h, U, imgSize);
//correcting the solution
add(_u, U, _u);
//correcting the solution
add(_u, U, _u);
//POST relaxation
VariationalSolver(I1, I2, I2x, _u, level - 1);
//POST relaxation
VariationalSolver(I1, I2, I2x, _u, level - 1);
if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3);
if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3);
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
U.release();
U_h.release();
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
U.release();
U_h.release();
}
void StereoVar::FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
{
double scale = pow(pyrScale, (double) level);
CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h;
double scale = pow(pyrScale, (double) level);
CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h;
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), scale);
I2x_h = diffX(I2_h);
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), scale);
I2x_h = diffX(I2_h);
switch (cycle) {
case CYCLE_O:
VariationalSolver(I1_h, I2_h, I2x_h, u_h, level);
break;
case CYCLE_V:
VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level);
break;
}
switch (cycle) {
case CYCLE_O:
VariationalSolver(I1_h, I2_h, I2x_h, u_h, level);
break;
case CYCLE_V:
VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level);
break;
}
u_h.convertTo(u_h, u_h.type(), 1.0 / scale);
u_h.convertTo(u_h, u_h.type(), 1.0 / scale);
//scaling UP
resize(u_h, u, u.size(), 0, 0, INTER_CUBIC);
//scaling UP
resize(u_h, u, u.size(), 0, 0, INTER_CUBIC);
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
level--;
if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) {
penalization = PENALIZATION_PERONA_MALIK;
fi *= 100;
flags -= USE_AUTO_PARAMS;
autoParams();
}
if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3);
if (level >= 0) FMG(I1, I2, I2x, u, level);
level--;
if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) {
penalization = PENALIZATION_PERONA_MALIK;
fi *= 100;
flags -= USE_AUTO_PARAMS;
autoParams();
}
if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3);
if (level >= 0) FMG(I1, I2, I2x, u, level);
}
void StereoVar::autoParams()
{
int maxD = MAX(labs(maxDisp), labs(minDisp));
if (!maxD) pyrScale = 0.85;
else if (maxD < 8) pyrScale = 0.5;
else if (maxD < 64) pyrScale = 0.5 + static_cast<double>(maxD - 8) * 0.00625;
else pyrScale = 0.85;
if (maxD) {
levels = 0;
while ( pow(pyrScale, levels) * maxD > 1.5) levels ++;
levels++;
}
{
int maxD = MAX(labs(maxDisp), labs(minDisp));
switch(penalization) {
case PENALIZATION_TICHONOV: cycle = CYCLE_V; break;
case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break;
case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break;
}
if (!maxD) pyrScale = 0.85;
else if (maxD < 8) pyrScale = 0.5;
else if (maxD < 64) pyrScale = 0.5 + static_cast<double>(maxD - 8) * 0.00625;
else pyrScale = 0.85;
if (maxD) {
levels = 0;
while ( pow(pyrScale, levels) * maxD > 1.5) levels ++;
levels++;
}
switch(penalization) {
case PENALIZATION_TICHONOV: cycle = CYCLE_V; break;
case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break;
case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break;
}
}
void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp )
{
CV_Assert(left.size() == right.size() && left.type() == right.type());
CvSize imgSize = left.size();
int MaxD = MAX(labs(minDisp), labs(maxDisp));
int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1;
if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;}
Mat u;
if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) {
CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1);
disp.convertTo(u, CV_32FC1, static_cast<double>(SignD * MaxD) / 256);
} else {
u.create(imgSize, CV_32FC1);
u.setTo(0);
}
CV_Assert(left.size() == right.size() && left.type() == right.type());
CvSize imgSize = left.size();
int MaxD = MAX(labs(minDisp), labs(maxDisp));
int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1;
if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;}
// Preprocessing
Mat leftgray, rightgray;
if (left.type() != CV_8UC1) {
cvtColor(left, leftgray, CV_BGR2GRAY);
cvtColor(right, rightgray, CV_BGR2GRAY);
} else {
left.copyTo(leftgray);
right.copyTo(rightgray);
}
if (flags & USE_EQUALIZE_HIST) {
equalizeHist(leftgray, leftgray);
equalizeHist(rightgray, rightgray);
}
if (poly_sigma > 0.0001) {
GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma);
GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma);
}
if (flags & USE_AUTO_PARAMS) {
penalization = PENALIZATION_TICHONOV;
autoParams();
}
Mat u;
if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) {
CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1);
disp.convertTo(u, CV_32FC1, static_cast<double>(SignD * MaxD) / 256);
} else {
u.create(imgSize, CV_32FC1);
u.setTo(0);
}
Mat I1, I2;
leftgray.convertTo(I1, CV_32FC1);
rightgray.convertTo(I2, CV_32FC1);
leftgray.release();
rightgray.release();
// Preprocessing
Mat leftgray, rightgray;
if (left.type() != CV_8UC1) {
cvtColor(left, leftgray, CV_BGR2GRAY);
cvtColor(right, rightgray, CV_BGR2GRAY);
} else {
left.copyTo(leftgray);
right.copyTo(rightgray);
}
if (flags & USE_EQUALIZE_HIST) {
equalizeHist(leftgray, leftgray);
equalizeHist(rightgray, rightgray);
}
if (poly_sigma > 0.0001) {
GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma);
GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma);
}
Mat I2x = diffX(I2);
FMG(I1, I2, I2x, u, levels - 1);
I1.release();
I2.release();
I2x.release();
disp.create( left.size(), CV_8UC1 );
u = abs(u);
u.convertTo(disp, disp.type(), 256 / MaxD, 0);
if (flags & USE_AUTO_PARAMS) {
penalization = PENALIZATION_TICHONOV;
autoParams();
}
u.release();
Mat I1, I2;
leftgray.convertTo(I1, CV_32FC1);
rightgray.convertTo(I2, CV_32FC1);
leftgray.release();
rightgray.release();
Mat I2x = diffX(I2);
FMG(I1, I2, I2x, u, levels - 1);
I1.release();
I2.release();
I2x.release();
disp.create( left.size(), CV_8UC1 );
u = abs(u);
u.convertTo(disp, disp.type(), 256 / MaxD, 0);
u.release();
}
} // namespace

6
modules/core/CMakeLists.txt
View File

@ -5,13 +5,11 @@ ocv_module_include_directories(${ZLIB_INCLUDE_DIR})
if(HAVE_CUDA)
file(GLOB lib_cuda "src/cuda/*.cu")
source_group("Cuda" FILES "${lib_cuda}")
include_directories(AFTER SYSTEM ${CUDA_INCLUDE_DIRS})
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda")
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda" ${CUDA_INCLUDE_DIRS})
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
ocv_cuda_compile(cuda_objs ${lib_cuda})
OCV_CUDA_COMPILE(cuda_objs ${lib_cuda})
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
else()
set(lib_cuda "")

10
modules/core/include/opencv2/core/gpumat.hpp
View File

@ -366,12 +366,12 @@ namespace cv { namespace gpu
return m;
}
inline void GpuMat::assignTo(GpuMat& m, int type) const
inline void GpuMat::assignTo(GpuMat& m, int _type) const
{
if (type < 0)
if (_type < 0)
m = *this;
else
convertTo(m, type);
convertTo(m, _type);
}
inline size_t GpuMat::step1() const
@ -434,9 +434,9 @@ namespace cv { namespace gpu
create(size_.height, size_.width, type_);
}
inline GpuMat GpuMat::operator()(Range rowRange, Range colRange) const
inline GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const
{
return GpuMat(*this, rowRange, colRange);
return GpuMat(*this, _rowRange, _colRange);
}
inline GpuMat GpuMat::operator()(Rect roi) const

358
modules/core/include/opencv2/core/mat.hpp
View File

File diff suppressed because it is too large Load Diff

529
modules/core/include/opencv2/core/opengl_interop.hpp
View File

@ -47,282 +47,287 @@
#include "opencv2/core/core.hpp"
namespace cv
namespace cv
{
//! Smart pointer for OpenGL buffer memory with reference counting.
class CV_EXPORTS GlBuffer
//! Smart pointer for OpenGL buffer memory with reference counting.
class CV_EXPORTS GlBuffer
{
public:
enum Usage
{
public:
enum Usage
{
ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc)
TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures
};
//! create empty buffer
explicit GlBuffer(Usage usage);
//! create buffer
GlBuffer(int rows, int cols, int type, Usage usage);
GlBuffer(Size size, int type, Usage usage);
//! copy from host/device memory
GlBuffer(InputArray mat, Usage usage);
void create(int rows, int cols, int type, Usage usage);
inline void create(Size size, int type, Usage usage) { create(size.height, size.width, type, usage); }
inline void create(int rows, int cols, int type) { create(rows, cols, type, usage()); }
inline void create(Size size, int type) { create(size.height, size.width, type, usage()); }
void release();
//! copy from host/device memory
void copyFrom(InputArray mat);
void bind() const;
void unbind() const;
//! map to host memory
Mat mapHost();
void unmapHost();
//! map to device memory
gpu::GpuMat mapDevice();
void unmapDevice();
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
inline Usage usage() const { return usage_; }
class Impl;
private:
int rows_;
int cols_;
int type_;
Usage usage_;
Ptr<Impl> impl_;
ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc)
TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures
};
template <> CV_EXPORTS void Ptr<GlBuffer::Impl>::delete_obj();
//! create empty buffer
explicit GlBuffer(Usage usage);
//! Smart pointer for OpenGL 2d texture memory with reference counting.
class CV_EXPORTS GlTexture
//! create buffer
GlBuffer(int rows, int cols, int type, Usage usage);
GlBuffer(Size size, int type, Usage usage);
//! copy from host/device memory
GlBuffer(InputArray mat, Usage usage);
void create(int rows, int cols, int type, Usage usage);
void create(Size size, int type, Usage usage);
void create(int rows, int cols, int type);
void create(Size size, int type);
void release();
//! copy from host/device memory
void copyFrom(InputArray mat);
void bind() const;
void unbind() const;
//! map to host memory
Mat mapHost();
void unmapHost();
//! map to device memory
gpu::GpuMat mapDevice();
void unmapDevice();
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
inline Usage usage() const { return usage_; }
class Impl;
private:
int rows_;
int cols_;
int type_;
Usage usage_;
Ptr<Impl> impl_;
};
template <> CV_EXPORTS void Ptr<GlBuffer::Impl>::delete_obj();
//! Smart pointer for OpenGL 2d texture memory with reference counting.
class CV_EXPORTS GlTexture
{
public:
//! create empty texture
GlTexture();
//! create texture
GlTexture(int rows, int cols, int type);
GlTexture(Size size, int type);
//! copy from host/device memory
explicit GlTexture(InputArray mat, bool bgra = true);
void create(int rows, int cols, int type);
void create(Size size, int type);
void release();
//! copy from host/device memory
void copyFrom(InputArray mat, bool bgra = true);
void bind() const;
void unbind() const;
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
class Impl;
private:
int rows_;
int cols_;
int type_;
Ptr<Impl> impl_;
GlBuffer buf_;
};
template <> CV_EXPORTS void Ptr<GlTexture::Impl>::delete_obj();
//! OpenGL Arrays
class CV_EXPORTS GlArrays
{
public:
inline GlArrays()
: vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER)
{
public:
//! create empty texture
GlTexture();
//! create texture
GlTexture(int rows, int cols, int type);
GlTexture(Size size, int type);
//! copy from host/device memory
explicit GlTexture(InputArray mat, bool bgra = true);
void create(int rows, int cols, int type);
inline void create(Size size, int type) { create(size.height, size.width, type); }
void release();
//! copy from host/device memory
void copyFrom(InputArray mat, bool bgra = true);
void bind() const;
void unbind() const;
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
class Impl;
private:
int rows_;
int cols_;
int type_;
Ptr<Impl> impl_;
GlBuffer buf_;
};
template <> CV_EXPORTS void Ptr<GlTexture::Impl>::delete_obj();
//! OpenGL Arrays
class CV_EXPORTS GlArrays
{
public:
inline GlArrays()
: vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER)
{
}
void setVertexArray(InputArray vertex);
inline void resetVertexArray() { vertex_.release(); }
void setColorArray(InputArray color, bool bgra = true);
inline void resetColorArray() { color_.release(); }
void setNormalArray(InputArray normal);
inline void resetNormalArray() { normal_.release(); }
void setTexCoordArray(InputArray texCoord);
inline void resetTexCoordArray() { texCoord_.release(); }
void bind() const;
void unbind() const;
inline int rows() const { return vertex_.rows(); }
inline int cols() const { return vertex_.cols(); }
inline Size size() const { return vertex_.size(); }
inline bool empty() const { return vertex_.empty(); }
private:
GlBuffer vertex_;
GlBuffer color_;
bool bgra_;
GlBuffer normal_;
GlBuffer texCoord_;
};
//! OpenGL Font
class CV_EXPORTS GlFont
{
public:
enum Weight
{
WEIGHT_LIGHT = 300,
WEIGHT_NORMAL = 400,
WEIGHT_SEMIBOLD = 600,
WEIGHT_BOLD = 700,
WEIGHT_BLACK = 900
};
enum Style
{
STYLE_NORMAL = 0,
STYLE_ITALIC = 1,
STYLE_UNDERLINE = 2
};
static Ptr<GlFont> get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL);
void draw(const char* str, size_t len) const;
inline const std::string& family() const { return family_; }
inline int height() const { return height_; }
inline Weight weight() const { return weight_; }
inline Style style() const { return style_; }
private:
GlFont(const std::string& family, int height, Weight weight, Style style);
std::string family_;
int height_;
Weight weight_;
Style style_;
unsigned int base_;
GlFont(const GlFont&);
GlFont& operator =(const GlFont&);
};
//! render functions
//! render texture rectangle in window
CV_EXPORTS void render(const GlTexture& tex,
Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
//! render mode
namespace RenderMode {
enum {
POINTS = 0x0000,
LINES = 0x0001,
LINE_LOOP = 0x0002,
LINE_STRIP = 0x0003,
TRIANGLES = 0x0004,
TRIANGLE_STRIP = 0x0005,
TRIANGLE_FAN = 0x0006,
QUADS = 0x0007,
QUAD_STRIP = 0x0008,
POLYGON = 0x0009
};
}
//! render OpenGL arrays
CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255));
void setVertexArray(InputArray vertex);
inline void resetVertexArray() { vertex_.release(); }
CV_EXPORTS void render(const std::string& str, const Ptr<GlFont>& font, Scalar color, Point2d pos);
void setColorArray(InputArray color, bool bgra = true);
inline void resetColorArray() { color_.release(); }
//! OpenGL camera
class CV_EXPORTS GlCamera
void setNormalArray(InputArray normal);
inline void resetNormalArray() { normal_.release(); }
void setTexCoordArray(InputArray texCoord);
inline void resetTexCoordArray() { texCoord_.release(); }
void bind() const;
void unbind() const;
inline int rows() const { return vertex_.rows(); }
inline int cols() const { return vertex_.cols(); }
inline Size size() const { return vertex_.size(); }
inline bool empty() const { return vertex_.empty(); }
private:
GlBuffer vertex_;
GlBuffer color_;
bool bgra_;
GlBuffer normal_;
GlBuffer texCoord_;
};
//! OpenGL Font
class CV_EXPORTS GlFont
{
public:
enum Weight
{
public:
GlCamera();
void lookAt(Point3d eye, Point3d center, Point3d up);
void setCameraPos(Point3d pos, double yaw, double pitch, double roll);
void setScale(Point3d scale);
void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true);
void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar);
void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar);
void setupProjectionMatrix() const;
void setupModelViewMatrix() const;
private:
Point3d eye_;
Point3d center_;
Point3d up_;
Point3d pos_;
double yaw_;
double pitch_;
double roll_;
bool useLookAtParams_;
Point3d scale_;
Mat projectionMatrix_;
double fov_;
double aspect_;
double left_;
double right_;
double bottom_;
double top_;
double zNear_;
double zFar_;
bool perspectiveProjection_;
WEIGHT_LIGHT = 300,
WEIGHT_NORMAL = 400,
WEIGHT_SEMIBOLD = 600,
WEIGHT_BOLD = 700,
WEIGHT_BLACK = 900
};
namespace gpu
enum Style
{
//! set a CUDA device to use OpenGL interoperability
CV_EXPORTS void setGlDevice(int device = 0);
}
STYLE_NORMAL = 0,
STYLE_ITALIC = 1,
STYLE_UNDERLINE = 2
};
static Ptr<GlFont> get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL);
void draw(const char* str, size_t len) const;
inline const std::string& family() const { return family_; }
inline int height() const { return height_; }
inline Weight weight() const { return weight_; }
inline Style style() const { return style_; }
private:
GlFont(const std::string& family, int height, Weight weight, Style style);
std::string family_;
int height_;
Weight weight_;
Style style_;
unsigned int base_;
GlFont(const GlFont&);
GlFont& operator =(const GlFont&);
};
//! render functions
//! render texture rectangle in window
CV_EXPORTS void render(const GlTexture& tex,
Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
//! render mode
namespace RenderMode {
enum {
POINTS = 0x0000,
LINES = 0x0001,
LINE_LOOP = 0x0002,
LINE_STRIP = 0x0003,
TRIANGLES = 0x0004,
TRIANGLE_STRIP = 0x0005,
TRIANGLE_FAN = 0x0006,
QUADS = 0x0007,
QUAD_STRIP = 0x0008,
POLYGON = 0x0009
};
}
//! render OpenGL arrays
CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255));
CV_EXPORTS void render(const std::string& str, const Ptr<GlFont>& font, Scalar color, Point2d pos);
//! OpenGL camera
class CV_EXPORTS GlCamera
{
public:
GlCamera();
void lookAt(Point3d eye, Point3d center, Point3d up);
void setCameraPos(Point3d pos, double yaw, double pitch, double roll);
void setScale(Point3d scale);
void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true);
void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar);
void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar);
void setupProjectionMatrix() const;
void setupModelViewMatrix() const;
private:
Point3d eye_;
Point3d center_;
Point3d up_;
Point3d pos_;
double yaw_;
double pitch_;
double roll_;
bool useLookAtParams_;
Point3d scale_;
Mat projectionMatrix_;
double fov_;
double aspect_;
double left_;
double right_;
double bottom_;
double top_;
double zNear_;
double zFar_;
bool perspectiveProjection_;
};
inline void GlBuffer::create(Size _size, int _type, Usage _usage) { create(_size.height, _size.width, _type, _usage); }
inline void GlBuffer::create(int _rows, int _cols, int _type) { create(_rows, _cols, _type, usage()); }
inline void GlBuffer::create(Size _size, int _type) { create(_size.height, _size.width, _type, usage()); }
inline void GlTexture::create(Size _size, int _type) { create(_size.height, _size.width, _type); }
namespace gpu
{
//! set a CUDA device to use OpenGL interoperability
CV_EXPORTS void setGlDevice(int device = 0);
}
} // namespace cv
#endif // __cplusplus

26
modules/core/include/opencv2/core/operations.hpp
View File

@ -2616,20 +2616,20 @@ template<typename _Tp> inline void Ptr<_Tp>::delete_obj()
template<typename _Tp> inline Ptr<_Tp>::~Ptr() { release(); }
template<typename _Tp> inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& ptr)
template<typename _Tp> inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& _ptr)
{
obj = ptr.obj;
refcount = ptr.refcount;
obj = _ptr.obj;
refcount = _ptr.refcount;
addref();
}
template<typename _Tp> inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& ptr)
template<typename _Tp> inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& _ptr)
{
int* _refcount = ptr.refcount;
int* _refcount = _ptr.refcount;
if( _refcount )
CV_XADD(_refcount, 1);
release();
obj = ptr.obj;
obj = _ptr.obj;
refcount = _refcount;
return *this;
}
@ -3593,10 +3593,10 @@ template<typename _Tp> inline Seq<_Tp>::operator vector<_Tp>() const
template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator()
{ memset(this, 0, sizeof(*this)); }
template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& seq, bool seekEnd)
template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd)
{
cvStartReadSeq(seq.seq, this);
index = seekEnd ? seq.seq->total : 0;
cvStartReadSeq(_seq.seq, this);
index = seekEnd ? _seq.seq->total : 0;
}
template<typename _Tp> inline void SeqIterator<_Tp>::seek(size_t pos)
@ -3842,17 +3842,17 @@ template<typename _Tp> inline Ptr<_Tp> Algorithm::create(const string& name)
return _create(name).ptr<_Tp>();
}
template<typename _Tp> inline typename ParamType<_Tp>::member_type Algorithm::get(const string& name) const
template<typename _Tp> inline typename ParamType<_Tp>::member_type Algorithm::get(const string& _name) const
{
typename ParamType<_Tp>::member_type value;
info()->get(this, name.c_str(), ParamType<_Tp>::type, &value);
info()->get(this, _name.c_str(), ParamType<_Tp>::type, &value);
return value;
}
template<typename _Tp> inline typename ParamType<_Tp>::member_type Algorithm::get(const char* name) const
template<typename _Tp> inline typename ParamType<_Tp>::member_type Algorithm::get(const char* _name) const
{
typename ParamType<_Tp>::member_type value;
info()->get(this, name, ParamType<_Tp>::type, &value);
info()->get(this, _name, ParamType<_Tp>::type, &value);
return value;
}

308
modules/core/src/algorithm.cpp
View File

@ -7,7 +7,7 @@
// copy or use the software.
//
//
// License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
@ -46,7 +46,7 @@ namespace cv
{
using std::pair;
template<typename _KeyTp, typename _ValueTp> struct sorted_vector
{
sorted_vector() {}
@ -54,7 +54,7 @@ template<typename _KeyTp, typename _ValueTp> struct sorted_vector
size_t size() const { return vec.size(); }
_ValueTp& operator [](size_t idx) { return vec[idx]; }
const _ValueTp& operator [](size_t idx) const { return vec[idx]; }
void add(const _KeyTp& k, const _ValueTp& val)
{
pair<_KeyTp, _ValueTp> p(k, val);
@ -64,7 +64,7 @@ template<typename _KeyTp, typename _ValueTp> struct sorted_vector
std::swap(vec[i-1], vec[i]);
CV_Assert( i == 0 || vec[i].first != vec[i-1].first );
}
bool find(const _KeyTp& key, _ValueTp& value) const
{
size_t a = 0, b = vec.size();
@ -76,7 +76,7 @@ template<typename _KeyTp, typename _ValueTp> struct sorted_vector
else
b = c;
}
if( a < vec.size() && vec[a].first == key )
{
value = vec[a].second;
@ -84,26 +84,26 @@ template<typename _KeyTp, typename _ValueTp> struct sorted_vector
}
return false;
}
void get_keys(vector<_KeyTp>& keys) const
{
size_t i = 0, n = vec.size();
keys.resize(n);
for( i = 0; i < n; i++ )
keys[i] = vec[i].first;
}
vector<pair<_KeyTp, _ValueTp> > vec;
};
template<typename _ValueTp> inline const _ValueTp* findstr(const sorted_vector<string, _ValueTp>& vec,
const char* key)
{
if( !key )
return 0;
size_t a = 0, b = vec.vec.size();
while( b > a )
{
@ -113,13 +113,13 @@ template<typename _ValueTp> inline const _ValueTp* findstr(const sorted_vector<s
else
b = c;
}
if( strcmp(vec.vec[a].first.c_str(), key) == 0 )
return &vec.vec[a].second;
return 0;
}
Param::Param()
{
type = 0;
@ -129,7 +129,7 @@ Param::Param()
setter = 0;
}
Param::Param(int _type, bool _readonly, int _offset,
Algorithm::Getter _getter, Algorithm::Setter _setter,
const string& _help)
@ -148,7 +148,7 @@ struct CV_EXPORTS AlgorithmInfoData
string _name;
};
static sorted_vector<string, Algorithm::Constructor>& alglist()
{
static sorted_vector<string, Algorithm::Constructor> alglist_var;
@ -171,152 +171,152 @@ Ptr<Algorithm> Algorithm::_create(const string& name)
Algorithm::Algorithm()
{
}
Algorithm::~Algorithm()
{
}
string Algorithm::name() const
{
return info()->name();
}
void Algorithm::set(const string& name, int value)
void Algorithm::set(const string& parameter, int value)
{
info()->set(this, name.c_str(), ParamType<int>::type, &value);
info()->set(this, parameter.c_str(), ParamType<int>::type, &value);
}
void Algorithm::set(const string& name, double value)
void Algorithm::set(const string& parameter, double value)
{
info()->set(this, name.c_str(), ParamType<double>::type, &value);
info()->set(this, parameter.c_str(), ParamType<double>::type, &value);
}
void Algorithm::set(const string& name, bool value)
void Algorithm::set(const string& parameter, bool value)
{
info()->set(this, name.c_str(), ParamType<bool>::type, &value);
info()->set(this, parameter.c_str(), ParamType<bool>::type, &value);
}
void Algorithm::set(const string& name, const string& value)
void Algorithm::set(const string& parameter, const string& value)
{
info()->set(this, name.c_str(), ParamType<string>::type, &value);
info()->set(this, parameter.c_str(), ParamType<string>::type, &value);
}
void Algorithm::set(const string& name, const Mat& value)
void Algorithm::set(const string& parameter, const Mat& value)
{
info()->set(this, name.c_str(), ParamType<Mat>::type, &value);
info()->set(this, parameter.c_str(), ParamType<Mat>::type, &value);
}
void Algorithm::set(const string& name, const vector<Mat>& value)
void Algorithm::set(const string& parameter, const vector<Mat>& value)
{
info()->set(this, name.c_str(), ParamType<vector<Mat> >::type, &value);
}
void Algorithm::set(const string& name, const Ptr<Algorithm>& value)
{
info()->set(this, name.c_str(), ParamType<Algorithm>::type, &value);
info()->set(this, parameter.c_str(), ParamType<vector<Mat> >::type, &value);
}
void Algorithm::set(const char* name, int value)
void Algorithm::set(const string& parameter, const Ptr<Algorithm>& value)
{
info()->set(this, name, ParamType<int>::type, &value);
info()->set(this, parameter.c_str(), ParamType<Algorithm>::type, &value);
}
void Algorithm::set(const char* name, double value)
void Algorithm::set(const char* parameter, int value)
{
info()->set(this, name, ParamType<double>::type, &value);
info()->set(this, parameter, ParamType<int>::type, &value);
}
void Algorithm::set(const char* name, bool value)
void Algorithm::set(const char* parameter, double value)
{
info()->set(this, name, ParamType<bool>::type, &value);
info()->set(this, parameter, ParamType<double>::type, &value);
}
void Algorithm::set(const char* name, const string& value)
void Algorithm::set(const char* parameter, bool value)
{
info()->set(this, name, ParamType<string>::type, &value);
info()->set(this, parameter, ParamType<bool>::type, &value);
}
void Algorithm::set(const char* name, const Mat& value)
void Algorithm::set(const char* parameter, const string& value)
{
info()->set(this, name, ParamType<Mat>::type, &value);
info()->set(this, parameter, ParamType<string>::type, &value);
}
void Algorithm::set(const char* name, const vector<Mat>& value)
void Algorithm::set(const char* parameter, const Mat& value)
{
info()->set(this, name, ParamType<vector<Mat> >::type, &value);
}
void Algorithm::set(const char* name, const Ptr<Algorithm>& value)
{
info()->set(this, name, ParamType<Algorithm>::type, &value);
}
int Algorithm::getInt(const string& name) const
{
return get<int>(name);
}
double Algorithm::getDouble(const string& name) const
{
return get<double>(name);
info()->set(this, parameter, ParamType<Mat>::type, &value);
}
bool Algorithm::getBool(const string& name) const
void Algorithm::set(const char* parameter, const vector<Mat>& value)
{
return get<bool>(name);
info()->set(this, parameter, ParamType<vector<Mat> >::type, &value);
}
string Algorithm::getString(const string& name) const
void Algorithm::set(const char* parameter, const Ptr<Algorithm>& value)
{
return get<string>(name);
info()->set(this, parameter, ParamType<Algorithm>::type, &value);
}
Mat Algorithm::getMat(const string& name) const
int Algorithm::getInt(const string& parameter) const
{
return get<Mat>(name);
return get<int>(parameter);
}
vector<Mat> Algorithm::getMatVector(const string& name) const
double Algorithm::getDouble(const string& parameter) const
{
return get<vector<Mat> >(name);
return get<double>(parameter);
}
Ptr<Algorithm> Algorithm::getAlgorithm(const string& name) const
bool Algorithm::getBool(const string& parameter) const
{
return get<Algorithm>(name);
}
string Algorithm::paramHelp(const string& name) const
{
return info()->paramHelp(name.c_str());
}
int Algorithm::paramType(const string& name) const
{
return info()->paramType(name.c_str());
return get<bool>(parameter);
}
int Algorithm::paramType(const char* name) const
string Algorithm::getString(const string& parameter) const
{
return info()->paramType(name);
}
return get<string>(parameter);
}
Mat Algorithm::getMat(const string& parameter) const
{
return get<Mat>(parameter);
}
vector<Mat> Algorithm::getMatVector(const string& parameter) const
{
return get<vector<Mat> >(parameter);
}
Ptr<Algorithm> Algorithm::getAlgorithm(const string& parameter) const
{
return get<Algorithm>(parameter);
}
string Algorithm::paramHelp(const string& parameter) const
{
return info()->paramHelp(parameter.c_str());
}
int Algorithm::paramType(const string& parameter) const
{
return info()->paramType(parameter.c_str());
}
int Algorithm::paramType(const char* parameter) const
{
return info()->paramType(parameter);
}
void Algorithm::getParams(vector<string>& names) const
{
info()->getParams(names);
}
void Algorithm::write(FileStorage& fs) const
{
info()->write(this, fs);
}
void Algorithm::read(const FileNode& fn)
{
info()->read(this, fn);
}
}
AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create)
{
data = new AlgorithmInfoData;
@ -327,8 +327,8 @@ AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create)
AlgorithmInfo::~AlgorithmInfo()
{
delete data;
}
}
void AlgorithmInfo::write(const Algorithm* algo, FileStorage& fs) const
{
size_t i = 0, nparams = data->params.vec.size();
@ -364,7 +364,7 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const
{
size_t i = 0, nparams = data->params.vec.size();
AlgorithmInfo* info = algo->info();
for( i = 0; i < nparams; i++ )
{
const Param& p = data->params.vec[i].second;
@ -414,13 +414,13 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const
else
CV_Error( CV_StsUnsupportedFormat, "unknown/unsupported parameter type");
}
}
}
string AlgorithmInfo::name() const
{
return data->_name;
}
union GetSetParam
{
int (Algorithm::*get_int)() const;
@ -430,7 +430,7 @@ union GetSetParam
Mat (Algorithm::*get_mat)() const;
vector<Mat> (Algorithm::*get_mat_vector)() const;
Ptr<Algorithm> (Algorithm::*get_algo)() const;
void (Algorithm::*set_int)(int);
void (Algorithm::*set_bool)(bool);
void (Algorithm::*set_double)(double);
@ -440,15 +440,15 @@ union GetSetParam
void (Algorithm::*set_algo)(const Ptr<Algorithm>&);
};
void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const void* value, bool force) const
void AlgorithmInfo::set(Algorithm* algo, const char* parameter, int argType, const void* value, bool force) const
{
const Param* p = findstr(data->params, name);
const Param* p = findstr(data->params, parameter);
if( !p )
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "<NULL>") );
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "<NULL>") );
if( !force && p->readonly )
CV_Error_( CV_StsError, ("Parameter '%s' is readonly", name));
CV_Error_( CV_StsError, ("Parameter '%s' is readonly", parameter));
GetSetParam f;
f.set_int = p->setter;
@ -531,23 +531,23 @@ void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const vo
else
CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type");
}
void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, void* value) const
void AlgorithmInfo::get(const Algorithm* algo, const char* parameter, int argType, void* value) const
{
const Param* p = findstr(data->params, name);
const Param* p = findstr(data->params, parameter);
if( !p )
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "<NULL>") );
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "<NULL>") );
GetSetParam f;
f.get_int = p->getter;
if( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL )
{
if( p->type == Param::INT )
{
CV_Assert( argType == Param::INT || argType == Param::REAL );
int val = p->getter ? (algo->*f.get_int)() : *(int*)((uchar*)algo + p->offset);
if( argType == Param::INT )
*(int*)value = val;
else
@ -557,7 +557,7 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo
{
CV_Assert( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL );
bool val = p->getter ? (algo->*f.get_bool)() : *(bool*)((uchar*)algo + p->offset);
if( argType == Param::INT )
*(int*)value = (int)val;
else if( argType == Param::BOOLEAN )
@ -569,35 +569,35 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo
{
CV_Assert( argType == Param::REAL );
double val = p->getter ? (algo->*f.get_double)() : *(double*)((uchar*)algo + p->offset);
*(double*)value = val;
}
}
else if( argType == Param::STRING )
{
CV_Assert( p->type == Param::STRING );
*(string*)value = p->getter ? (algo->*f.get_string)() :
*(string*)((uchar*)algo + p->offset);
}
else if( argType == Param::MAT )
{
CV_Assert( p->type == Param::MAT );
*(Mat*)value = p->getter ? (algo->*f.get_mat)() :
*(Mat*)((uchar*)algo + p->offset);
}
else if( argType == Param::MAT_VECTOR )
{
CV_Assert( p->type == Param::MAT_VECTOR );
*(vector<Mat>*)value = p->getter ? (algo->*f.get_mat_vector)() :
*(vector<Mat>*)((uchar*)algo + p->offset);
}
else if( argType == Param::ALGORITHM )
{
CV_Assert( p->type == Param::ALGORITHM );
*(Ptr<Algorithm>*)value = p->getter ? (algo->*f.get_algo)() :
*(Ptr<Algorithm>*)((uchar*)algo + p->offset);
}
@ -605,21 +605,21 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo
CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type");
}
int AlgorithmInfo::paramType(const char* name) const
int AlgorithmInfo::paramType(const char* parameter) const
{
const Param* p = findstr(data->params, name);
const Param* p = findstr(data->params, parameter);
if( !p )
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "<NULL>") );
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "<NULL>") );
return p->type;
}
string AlgorithmInfo::paramHelp(const char* name) const
string AlgorithmInfo::paramHelp(const char* parameter) const
{
const Param* p = findstr(data->params, name);
const Param* p = findstr(data->params, parameter);
if( !p )
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "<NULL>") );
CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "<NULL>") );
return p->help;
}
@ -628,10 +628,10 @@ void AlgorithmInfo::getParams(vector<string>& names) const
{
data->params.get_keys(names);
}
void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType,
void* value, bool readOnly,
void AlgorithmInfo::addParam_(Algorithm& algo, const char* parameter, int argType,
void* value, bool readOnly,
Algorithm::Getter getter, Algorithm::Setter setter,
const string& help)
{
@ -639,82 +639,82 @@ void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType,
argType == Param::REAL || argType == Param::STRING ||
argType == Param::MAT || argType == Param::MAT_VECTOR ||
argType == Param::ALGORITHM );
data->params.add(string(name), Param(argType, readOnly,
data->params.add(string(parameter), Param(argType, readOnly,
(int)((size_t)value - (size_t)(void*)&algo),
getter, setter, help));
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
int& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
int& value, bool readOnly,
int (Algorithm::*getter)(),
void (Algorithm::*setter)(int),
const string& help)
{
addParam_(algo, name, ParamType<int>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<int>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
bool& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
bool& value, bool readOnly,
int (Algorithm::*getter)(),
void (Algorithm::*setter)(int),
const string& help)
{
addParam_(algo, name, ParamType<bool>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<bool>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
double& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
double& value, bool readOnly,
double (Algorithm::*getter)(),
void (Algorithm::*setter)(double),
const string& help)
{
addParam_(algo, name, ParamType<double>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<double>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
string& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
string& value, bool readOnly,
string (Algorithm::*getter)(),
void (Algorithm::*setter)(const string&),
const string& help)
{
addParam_(algo, name, ParamType<string>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<string>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
Mat& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
Mat& value, bool readOnly,
Mat (Algorithm::*getter)(),
void (Algorithm::*setter)(const Mat&),
const string& help)
{
addParam_(algo, name, ParamType<Mat>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<Mat>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
vector<Mat>& value, bool readOnly,
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
vector<Mat>& value, bool readOnly,
vector<Mat> (Algorithm::*getter)(),
void (Algorithm::*setter)(const vector<Mat>&),
const string& help)
{
addParam_(algo, name, ParamType<vector<Mat> >::type, &value, readOnly,
addParam_(algo, parameter, ParamType<vector<Mat> >::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* name,
Ptr<Algorithm>& value, bool readOnly,
}
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter,
Ptr<Algorithm>& value, bool readOnly,
Ptr<Algorithm> (Algorithm::*getter)(),
void (Algorithm::*setter)(const Ptr<Algorithm>&),
const string& help)
{
addParam_(algo, name, ParamType<Algorithm>::type, &value, readOnly,
addParam_(algo, parameter, ParamType<Algorithm>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
}
}
/* End of file. */

246
modules/core/src/convert.cpp
View File

@ -88,7 +88,7 @@ split_( const T* src, T** dst, int len, int cn )
dst2[i] = src[j+2]; dst3[i] = src[j+3];
}
}
for( ; k < cn; k += 4 )
{
T *dst0 = dst[k], *dst1 = dst[k+1], *dst2 = dst[k+2], *dst3 = dst[k+3];
@ -99,7 +99,7 @@ split_( const T* src, T** dst, int len, int cn )
}
}
}
template<typename T> static void
merge_( const T** src, T* dst, int len, int cn )
{
@ -139,7 +139,7 @@ merge_( const T** src, T* dst, int len, int cn )
dst[j+2] = src2[i]; dst[j+3] = src3[i];
}
}
for( ; k < cn; k += 4 )
{
const T *src0 = src[k], *src1 = src[k+1], *src2 = src[k+2], *src3 = src[k+3];
@ -165,7 +165,7 @@ static void split32s(const int* src, int** dst, int len, int cn )
{
split_(src, dst, len, cn);
}
static void split64s(const int64* src, int64** dst, int len, int cn )
{
split_(src, dst, len, cn);
@ -189,7 +189,7 @@ static void merge32s(const int** src, int* dst, int len, int cn )
static void merge64s(const int64** src, int64* dst, int len, int cn )
{
merge_(src, dst, len, cn);
}
}
typedef void (*SplitFunc)(const uchar* src, uchar** dst, int len, int cn);
typedef void (*MergeFunc)(const uchar** src, uchar* dst, int len, int cn);
@ -205,9 +205,9 @@ static MergeFunc mergeTab[] =
(MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge16u),
(MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge64s), 0
};
}
void cv::split(const Mat& src, Mat* mv)
{
int k, depth = src.depth(), cn = src.channels();
@ -219,30 +219,30 @@ void cv::split(const Mat& src, Mat* mv)
SplitFunc func = splitTab[depth];
CV_Assert( func != 0 );
int esz = (int)src.elemSize(), esz1 = (int)src.elemSize1();
int blocksize0 = (BLOCK_SIZE + esz-1)/esz;
AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
const Mat** arrays = (const Mat**)(uchar*)_buf;
uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
arrays[0] = &src;
for( k = 0; k < cn; k++ )
{
mv[k].create(src.dims, src.size, depth);
arrays[k+1] = &mv[k];
}
NAryMatIterator it(arrays, ptrs, cn+1);
int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( int j = 0; j < total; j += blocksize )
{
int bsz = std::min(total - j, blocksize);
func( ptrs[0], &ptrs[1], bsz, cn );
if( j + blocksize < total )
{
ptrs[0] += bsz*esz;
@ -252,7 +252,7 @@ void cv::split(const Mat& src, Mat* mv)
}
}
}
void cv::split(InputArray _m, OutputArrayOfArrays _mv)
{
Mat m = _m.getMat();
@ -266,38 +266,38 @@ void cv::split(InputArray _m, OutputArrayOfArrays _mv)
Mat* dst = &_mv.getMatRef(0);
split(m, dst);
}
void cv::merge(const Mat* mv, size_t n, OutputArray _dst)
{
CV_Assert( mv && n > 0 );
int depth = mv[0].depth();
bool allch1 = true;
int k, cn = 0;
size_t i;
for( i = 0; i < n; i++ )
{
CV_Assert(mv[i].size == mv[0].size && mv[i].depth() == depth);
allch1 = allch1 && mv[i].channels() == 1;
cn += mv[i].channels();
}
CV_Assert( 0 < cn && cn <= CV_CN_MAX );
_dst.create(mv[0].dims, mv[0].size, CV_MAKETYPE(depth, cn));
Mat dst = _dst.getMat();
if( n == 1 )
{
mv[0].copyTo(dst);
return;
}
if( !allch1 )
{
AutoBuffer<int> pairs(cn*2);
int j, ni=0;
for( i = 0, j = 0; i < n; i++, j += ni )
{
ni = mv[i].channels();
@ -310,33 +310,33 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst)
mixChannels( mv, n, &dst, 1, &pairs[0], cn );
return;
}
size_t esz = dst.elemSize(), esz1 = dst.elemSize1();
int blocksize0 = (int)((BLOCK_SIZE + esz-1)/esz);
AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
const Mat** arrays = (const Mat**)(uchar*)_buf;
uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
arrays[0] = &dst;
for( k = 0; k < cn; k++ )
arrays[k+1] = &mv[k];
NAryMatIterator it(arrays, ptrs, cn+1);
int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0);
MergeFunc func = mergeTab[depth];
for( i = 0; i < it.nplanes; i++, ++it )
{
for( int j = 0; j < total; j += blocksize )
{
int bsz = std::min(total - j, blocksize);
func( (const uchar**)&ptrs[1], ptrs[0], bsz, cn );
if( j + blocksize < total )
{
ptrs[0] += bsz*esz;
for( int k = 0; k < cn; k++ )
ptrs[k+1] += bsz*esz1;
for( int t = 0; t < cn; t++ )
ptrs[t+1] += bsz*esz1;
}
}
}
@ -347,7 +347,7 @@ void cv::merge(InputArrayOfArrays _mv, OutputArray _dst)
vector<Mat> mv;
_mv.getMatVector(mv);
merge(!mv.empty() ? &mv[0] : 0, mv.size(), _dst);
}
}
/****************************************************************************************\
* Generalized split/merge: mixing channels *
@ -387,7 +387,7 @@ mixChannels_( const T** src, const int* sdelta,
}
}
static void mixChannels8u( const uchar** src, const int* sdelta,
uchar** dst, const int* ddelta,
int len, int npairs )
@ -408,14 +408,14 @@ static void mixChannels32s( const int** src, const int* sdelta,
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
static void mixChannels64s( const int64** src, const int* sdelta,
int64** dst, const int* ddelta,
int len, int npairs )
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
typedef void (*MixChannelsFunc)( const uchar** src, const int* sdelta,
uchar** dst, const int* ddelta, int len, int npairs );
@ -423,17 +423,17 @@ static MixChannelsFunc mixchTab[] =
{
(MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels16u,
(MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels32s, (MixChannelsFunc)mixChannels32s,
(MixChannelsFunc)mixChannels64s, 0
(MixChannelsFunc)mixChannels64s, 0
};
}
void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, const int* fromTo, size_t npairs )
{
if( npairs == 0 )
return;
CV_Assert( src && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0 );
size_t i, j, k, esz1 = dst[0].elemSize1();
int depth = dst[0].depth();
@ -444,13 +444,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
uchar** dsts = (uchar**)(srcs + npairs);
int* tab = (int*)(dsts + npairs);
int *sdelta = (int*)(tab + npairs*4), *ddelta = sdelta + npairs;
for( i = 0; i < nsrcs; i++ )
arrays[i] = &src[i];
for( i = 0; i < ndsts; i++ )
arrays[i + nsrcs] = &dst[i];
ptrs[nsrcs + ndsts] = 0;
for( i = 0; i < npairs; i++ )
{
int i0 = fromTo[i*2], i1 = fromTo[i*2+1];
@ -468,7 +468,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
tab[i*4] = (int)(nsrcs + ndsts); tab[i*4+1] = 0;
sdelta[i] = 0;
}
for( j = 0; j < ndsts; i1 -= dst[j].channels(), j++ )
if( i1 < dst[j].channels() )
break;
@ -480,7 +480,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
NAryMatIterator it(arrays, ptrs, (int)(nsrcs + ndsts));
int total = (int)it.size, blocksize = std::min(total, (int)((BLOCK_SIZE + esz1-1)/esz1));
MixChannelsFunc func = mixchTab[depth];
for( i = 0; i < it.nplanes; i++, ++it )
{
for( k = 0; k < npairs; k++ )
@ -488,13 +488,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
srcs[k] = ptrs[tab[k*4]] + tab[k*4+1];
dsts[k] = ptrs[tab[k*4+2]] + tab[k*4+3];
}
for( int j = 0; j < total; j += blocksize )
for( int t = 0; t < total; t += blocksize )
{
int bsz = std::min(total - j, blocksize);
int bsz = std::min(total - t, blocksize);
func( srcs, sdelta, dsts, ddelta, bsz, (int)npairs );
if( j + blocksize < total )
if( t + blocksize < total )
for( k = 0; k < npairs; k++ )
{
srcs[k] += blocksize*sdelta[k]*esz1;
@ -524,7 +524,7 @@ void cv::mixChannels(InputArrayOfArrays src, InputArrayOfArrays dst,
int i;
int nsrc = src_is_mat ? 1 : (int)src.total();
int ndst = dst_is_mat ? 1 : (int)dst.total();
CV_Assert(fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0);
cv::AutoBuffer<Mat> _buf(nsrc + ndst);
Mat* buf = _buf;
@ -568,7 +568,7 @@ cvtScaleAbs_( const T* src, size_t sstep,
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
@ -583,11 +583,11 @@ cvtScaleAbs_( const T* src, size_t sstep,
t1 = saturate_cast<DT>(std::abs(src[x+3]*scale + shift));
dst[x+2] = t0; dst[x+3] = t1;
}
#endif
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<DT>(std::abs(src[x]*scale + shift));
}
}
}
template<typename T, typename DT, typename WT> static void
@ -597,7 +597,7 @@ cvtScale_( const T* src, size_t sstep,
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
@ -623,38 +623,38 @@ cvtScale_( const T* src, size_t sstep,
template<> void
cvtScale_<short, short, float>( const short* src, size_t sstep,
short* dst, size_t dstep, Size size,
float scale, float shift )
float scale, float shift )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_SSE2
if(USE_SSE2)
#if CV_SSE2
if(USE_SSE2)
{
__m128 scale128 = _mm_set1_ps (scale);
__m128 shift128 = _mm_set1_ps (shift);
for(; x <= size.width - 8; x += 8 )
{
__m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x));
for(; x <= size.width - 8; x += 8 )
{
__m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x));
__m128i r1 = _mm_loadl_epi64((const __m128i*)(src + x + 4));
__m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16));
__m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16));
__m128 rf1 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r1, r1), 16));
rf0 = _mm_add_ps(_mm_mul_ps(rf0, scale128), shift128);
rf1 = _mm_add_ps(_mm_mul_ps(rf1, scale128), shift128);
r0 = _mm_cvtps_epi32(rf0);
r1 = _mm_cvtps_epi32(rf1);
r0 = _mm_packs_epi32(r0, r1);
_mm_storeu_si128((__m128i*)(dst + x), r0);
}
}
r0 = _mm_cvtps_epi32(rf0);
r1 = _mm_cvtps_epi32(rf1);
r0 = _mm_packs_epi32(r0, r1);
_mm_storeu_si128((__m128i*)(dst + x), r0);
}
}
#endif
for(; x < size.width; x++ )
dst[x] = saturate_cast<short>(src[x]*scale + shift);
}
}
}
@ -664,21 +664,21 @@ cvt_( const T* src, size_t sstep,
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
DT t0, t1;
t0 = saturate_cast<DT>(src[x]);
t1 = saturate_cast<DT>(src[x+1]);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<DT>(src[x+2]);
t1 = saturate_cast<DT>(src[x+3]);
dst[x+2] = t0; dst[x+3] = t1;
}
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
DT t0, t1;
t0 = saturate_cast<DT>(src[x]);
t1 = saturate_cast<DT>(src[x+1]);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<DT>(src[x+2]);
t1 = saturate_cast<DT>(src[x+3]);
dst[x+2] = t0; dst[x+3] = t1;
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<DT>(src[x]);
@ -692,24 +692,24 @@ cvt_<float, short>( const float* src, size_t sstep,
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_SSE2
if(USE_SSE2){
for( ; x <= size.width - 8; x += 8 )
{
__m128 src128 = _mm_loadu_ps (src + x);
__m128i src_int128 = _mm_cvtps_epi32 (src128);
src128 = _mm_loadu_ps (src + x + 4);
__m128i src1_int128 = _mm_cvtps_epi32 (src128);
src1_int128 = _mm_packs_epi32(src_int128, src1_int128);
_mm_storeu_si128((__m128i*)(dst + x),src1_int128);
}
}
#if CV_SSE2
if(USE_SSE2){
for( ; x <= size.width - 8; x += 8 )
{
__m128 src128 = _mm_loadu_ps (src + x);
__m128i src_int128 = _mm_cvtps_epi32 (src128);
src128 = _mm_loadu_ps (src + x + 4);
__m128i src1_int128 = _mm_cvtps_epi32 (src128);
src1_int128 = _mm_packs_epi32(src_int128, src1_int128);
_mm_storeu_si128((__m128i*)(dst + x),src1_int128);
}
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<short>(src[x]);
@ -723,11 +723,11 @@ cpy_( const T* src, size_t sstep, T* dst, size_t dstep, Size size )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
memcpy(dst, src, size.width*sizeof(src[0]));
}
#define DEF_CVT_SCALE_ABS_FUNC(suffix, tfunc, stype, dtype, wtype) \
static void cvtScaleAbs##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double* scale) \
@ -741,8 +741,8 @@ dtype* dst, size_t dstep, Size size, double* scale) \
{ \
cvtScale_(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \
}
#define DEF_CVT_FUNC(suffix, stype, dtype) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double*) \
@ -756,15 +756,15 @@ stype* dst, size_t dstep, Size size, double*) \
{ \
cpy_(src, sstep, dst, dstep, size); \
}
DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float);
DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float);
DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float);
@ -772,7 +772,7 @@ DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float);
DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float);
DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float);
DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float);
DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float);
DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float);
DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float);
DEF_CVT_SCALE_FUNC(8s, schar, schar, float);
@ -780,7 +780,7 @@ DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float);
DEF_CVT_SCALE_FUNC(16s8s, short, schar, float);
DEF_CVT_SCALE_FUNC(32s8s, int, schar, float);
DEF_CVT_SCALE_FUNC(32f8s, float, schar, float);
DEF_CVT_SCALE_FUNC(64f8s, double, schar, float);
DEF_CVT_SCALE_FUNC(64f8s, double, schar, float);
DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float);
DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float);
@ -788,7 +788,7 @@ DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float);
DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float);
DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float);
DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float);
DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float);
DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float);
DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float);
DEF_CVT_SCALE_FUNC(8s16s, schar, short, float);
@ -797,7 +797,7 @@ DEF_CVT_SCALE_FUNC(16s, short, short, float);
DEF_CVT_SCALE_FUNC(32s16s, int, short, float);
DEF_CVT_SCALE_FUNC(32f16s, float, short, float);
DEF_CVT_SCALE_FUNC(64f16s, double, short, float);
DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float);
DEF_CVT_SCALE_FUNC(8s32s, schar, int, float);
DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float);
@ -874,7 +874,7 @@ DEF_CVT_FUNC(16s64f, short, double);
DEF_CVT_FUNC(32s64f, int, double);
DEF_CVT_FUNC(32f64f, float, double);
DEF_CPY_FUNC(64s, int64);
static BinaryFunc cvtScaleAbsTab[] =
{
(BinaryFunc)cvtScaleAbs8u, (BinaryFunc)cvtScaleAbs8s8u, (BinaryFunc)cvtScaleAbs16u8u,
@ -965,7 +965,7 @@ static BinaryFunc cvtTab[][8] =
0, 0, 0, 0, 0, 0, 0, 0
}
};
BinaryFunc getConvertFunc(int sdepth, int ddepth)
{
return cvtTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)];
@ -974,10 +974,10 @@ BinaryFunc getConvertFunc(int sdepth, int ddepth)
BinaryFunc getConvertScaleFunc(int sdepth, int ddepth)
{
return cvtScaleTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)];
}
}
}
void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta )
{
Mat src = _src.getMat();
@ -987,7 +987,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl
Mat dst = _dst.getMat();
BinaryFunc func = cvtScaleAbsTab[src.depth()];
CV_Assert( func != 0 );
if( src.dims <= 2 )
{
Size sz = getContinuousSize(src, dst, cn);
@ -999,7 +999,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
Size sz((int)it.size*cn, 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale );
}
@ -1022,12 +1022,12 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta)
}
Mat src = *this;
BinaryFunc func = noScale ? getConvertFunc(sdepth, ddepth) : getConvertScaleFunc(sdepth, ddepth);
double scale[] = {alpha, beta};
int cn = channels();
CV_Assert( func != 0 );
if( dims <= 2 )
{
_dst.create( size(), _type );
@ -1043,7 +1043,7 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta)
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
Size sz((int)(it.size*cn), 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
func(ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale);
}
@ -1105,10 +1105,10 @@ static void LUT8u_32f( const uchar* src, const float* lut, float* dst, int len,
static void LUT8u_64f( const uchar* src, const double* lut, double* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
}
typedef void (*LUTFunc)( const uchar* src, const uchar* lut, uchar* dst, int len, int cn, int lutcn );
static LUTFunc lutTab[] =
{
(LUTFunc)LUT8u_8u, (LUTFunc)LUT8u_8s, (LUTFunc)LUT8u_16u, (LUTFunc)LUT8u_16s,
@ -1116,7 +1116,7 @@ static LUTFunc lutTab[] =
};
}
void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolation )
{
Mat src = _src.getMat(), lut = _lut.getMat();
@ -1132,12 +1132,12 @@ void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolat
LUTFunc func = lutTab[lut.depth()];
CV_Assert( func != 0 );
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
int len = (int)it.size;
for( size_t i = 0; i < it.nplanes; i++, ++it )
func(ptrs[0], lut.data, ptrs[1], len, cn, lutcn);
}
@ -1147,7 +1147,7 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b,
int norm_type, int rtype, InputArray _mask )
{
Mat src = _src.getMat(), mask = _mask.getMat();
double scale = 1, shift = 0;
if( norm_type == CV_MINMAX )
{
@ -1165,13 +1165,13 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b,
}
else
CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
if( rtype < 0 )
rtype = _dst.fixedType() ? _dst.depth() : src.depth();
_dst.create(src.dims, src.size, CV_MAKETYPE(rtype, src.channels()));
Mat dst = _dst.getMat();
if( !mask.data )
src.convertTo( dst, rtype, scale, shift );
else
@ -1282,7 +1282,7 @@ cvConvertScale( const void* srcarr, void* dstarr,
double scale, double shift )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.channels() == dst.channels() );
src.convertTo(dst, dst.type(), scale, shift);
}

108
modules/core/src/copy.cpp
View File

@ -59,7 +59,7 @@ copyMask_(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep, ucha
const T* src = (const T*)_src;
T* dst = (T*)_dst;
int x = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
if( mask[x] )
@ -96,16 +96,16 @@ copyMaskGeneric(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep
}
}
}
#define DEF_COPY_MASK(suffix, type) \
static void copyMask##suffix(const uchar* src, size_t sstep, const uchar* mask, size_t mstep, \
uchar* dst, size_t dstep, Size size, void*) \
{ \
copyMask_<type>(src, sstep, mask, mstep, dst, dstep, size); \
}
DEF_COPY_MASK(8u, uchar);
DEF_COPY_MASK(16u, ushort);
DEF_COPY_MASK(8uC3, Vec3b);
@ -116,7 +116,7 @@ DEF_COPY_MASK(32sC3, Vec3i);
DEF_COPY_MASK(32sC4, Vec4i);
DEF_COPY_MASK(32sC6, Vec6i);
DEF_COPY_MASK(32sC8, Vec8i);
BinaryFunc copyMaskTab[] =
{
0,
@ -137,7 +137,7 @@ BinaryFunc copyMaskTab[] =
0, 0, 0, 0, 0, 0, 0,
copyMask32sC8
};
BinaryFunc getCopyMaskFunc(size_t esz)
{
return esz <= 32 && copyMaskTab[esz] ? copyMaskTab[esz] : copyMaskGeneric;
@ -152,51 +152,51 @@ void Mat::copyTo( OutputArray _dst ) const
convertTo( _dst, dtype );
return;
}
if( empty() )
{
_dst.release();
return;
}
if( dims <= 2 )
{
_dst.create( rows, cols, type() );
Mat dst = _dst.getMat();
if( data == dst.data )
return;
if( rows > 0 && cols > 0 )
{
const uchar* sptr = data;
uchar* dptr = dst.data;
// to handle the copying 1xn matrix => nx1 std vector.
Size sz = size() == dst.size() ?
getContinuousSize(*this, dst) :
getContinuousSize(*this);
size_t len = sz.width*elemSize();
for( ; sz.height--; sptr += step, dptr += dst.step )
memcpy( dptr, sptr, len );
}
return;
}
_dst.create( dims, size, type() );
Mat dst = _dst.getMat();
if( data == dst.data )
return;
if( total() != 0 )
{
const Mat* arrays[] = { this, &dst };
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs, 2);
size_t size = it.size*elemSize();
size_t sz = it.size*elemSize();
for( size_t i = 0; i < it.nplanes; i++, ++it )
memcpy(ptrs[1], ptrs[0], size);
memcpy(ptrs[1], ptrs[0], sz);
}
}
@ -208,33 +208,33 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
copyTo(_dst);
return;
}
int cn = channels(), mcn = mask.channels();
CV_Assert( mask.depth() == CV_8U && (mcn == 1 || mcn == cn) );
bool colorMask = mcn > 1;
size_t esz = colorMask ? elemSize1() : elemSize();
BinaryFunc copymask = getCopyMaskFunc(esz);
uchar* data0 = _dst.getMat().data;
_dst.create( dims, size, type() );
Mat dst = _dst.getMat();
if( dst.data != data0 ) // do not leave dst uninitialized
dst = Scalar(0);
if( dims <= 2 )
{
Size sz = getContinuousSize(*this, dst, mask, mcn);
copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz);
return;
}
const Mat* arrays[] = { this, &dst, &mask, 0 };
uchar* ptrs[3];
NAryMatIterator it(arrays, ptrs);
Size sz((int)(it.size*mcn), 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
copymask(ptrs[0], 0, ptrs[2], 0, ptrs[1], 0, sz, &esz);
}
@ -242,14 +242,14 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
Mat& Mat::operator = (const Scalar& s)
{
const Mat* arrays[] = { this };
uchar* ptr;
NAryMatIterator it(arrays, &ptr, 1);
size_t size = it.size*elemSize();
uchar* dptr;
NAryMatIterator it(arrays, &dptr, 1);
size_t elsize = it.size*elemSize();
if( s[0] == 0 && s[1] == 0 && s[2] == 0 && s[3] == 0 )
{
for( size_t i = 0; i < it.nplanes; i++, ++it )
memset( ptr, 0, size );
memset( dptr, 0, elsize );
}
else
{
@ -258,50 +258,50 @@ Mat& Mat::operator = (const Scalar& s)
double scalar[12];
scalarToRawData(s, scalar, type(), 12);
size_t blockSize = 12*elemSize1();
for( size_t j = 0; j < size; j += blockSize )
for( size_t j = 0; j < elsize; j += blockSize )
{
size_t sz = MIN(blockSize, size - j);
memcpy( ptr + j, scalar, sz );
size_t sz = MIN(blockSize, elsize - j);
memcpy( dptr + j, scalar, sz );
}
}
for( size_t i = 1; i < it.nplanes; i++ )
{
++it;
memcpy( ptr, data, size );
memcpy( dptr, data, elsize );
}
}
return *this;
}
Mat& Mat::setTo(InputArray _value, InputArray _mask)
{
if( !data )
return *this;
Mat value = _value.getMat(), mask = _mask.getMat();
CV_Assert( checkScalar(value, type(), _value.kind(), _InputArray::MAT ));
CV_Assert( mask.empty() || mask.type() == CV_8U );
size_t esz = elemSize();
BinaryFunc copymask = getCopyMaskFunc(esz);
const Mat* arrays[] = { this, !mask.empty() ? &mask : 0, 0 };
uchar* ptrs[2]={0,0};
NAryMatIterator it(arrays, ptrs);
int total = (int)it.size, blockSize0 = std::min(total, (int)((BLOCK_SIZE + esz-1)/esz));
int totalsz = (int)it.size, blockSize0 = std::min(totalsz, (int)((BLOCK_SIZE + esz-1)/esz));
AutoBuffer<uchar> _scbuf(blockSize0*esz + 32);
uchar* scbuf = alignPtr((uchar*)_scbuf, (int)sizeof(double));
convertAndUnrollScalar( value, type(), scbuf, blockSize0 );
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( int j = 0; j < total; j += blockSize0 )
for( int j = 0; j < totalsz; j += blockSize0 )
{
Size sz(std::min(blockSize0, total - j), 1);
Size sz(std::min(blockSize0, totalsz - j), 1);
size_t blockSize = sz.width*esz;
if( ptrs[1] )
{
@ -323,7 +323,7 @@ flipHoriz( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size size,
int i, j, limit = (int)(((size.width + 1)/2)*esz);
AutoBuffer<int> _tab(size.width*esz);
int* tab = _tab;
for( i = 0; i < size.width; i++ )
for( size_t k = 0; k < esz; k++ )
tab[i*esz + k] = (int)((size.width - i - 1)*esz + k);
@ -403,7 +403,7 @@ flipVert( const uchar* src0, size_t sstep, uchar* dst0, size_t dstep, Size size,
void flip( InputArray _src, OutputArray _dst, int flip_mode )
{
Mat src = _src.getMat();
CV_Assert( src.dims <= 2 );
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
@ -413,7 +413,7 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz );
else
flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz );
if( flip_mode < 0 )
flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz );
}
@ -423,7 +423,7 @@ void repeat(InputArray _src, int ny, int nx, OutputArray _dst)
{
Mat src = _src.getMat();
CV_Assert( src.dims <= 2 );
_dst.create(src.rows*ny, src.cols*nx, src.type());
Mat dst = _dst.getMat();
Size ssize = src.size(), dsize = dst.size();
@ -493,25 +493,25 @@ cvCopy( const void* srcarr, void* dstarr, const void* maskarr )
}
cv::Mat src = cv::cvarrToMat(srcarr, false, true, 1), dst = cv::cvarrToMat(dstarr, false, true, 1);
CV_Assert( src.depth() == dst.depth() && src.size == dst.size );
int coi1 = 0, coi2 = 0;
if( CV_IS_IMAGE(srcarr) )
coi1 = cvGetImageCOI((const IplImage*)srcarr);
if( CV_IS_IMAGE(dstarr) )
coi2 = cvGetImageCOI((const IplImage*)dstarr);
if( coi1 || coi2 )
{
CV_Assert( (coi1 != 0 || src.channels() == 1) &&
(coi2 != 0 || dst.channels() == 1) );
int pair[] = { std::max(coi1-1, 0), std::max(coi2-1, 0) };
cv::mixChannels( &src, 1, &dst, 1, pair, 1 );
return;
}
else
CV_Assert( src.channels() == dst.channels() );
if( !maskarr )
src.copyTo(dst);
else
@ -548,12 +548,12 @@ cvFlip( const CvArr* srcarr, CvArr* dstarr, int flip_mode )
{
cv::Mat src = cv::cvarrToMat(srcarr);
cv::Mat dst;
if (!dstarr)
dst = src;
else
dst = cv::cvarrToMat(dstarr);
CV_Assert( src.type() == dst.type() && src.size() == dst.size() );
cv::flip( src, dst, flip_mode );
}

92
modules/core/src/datastructs.cpp
View File

@ -3349,7 +3349,7 @@ cvTreeToNodeSeq( const void* first, int header_size, CvMemStorage* storage )
}
}
return allseq;
}
@ -3531,9 +3531,9 @@ namespace cv
// both cv (CvFeatureTree) and ml (kNN).
// The algorithm is taken from:
// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search
// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog.,
// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html
// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search
// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog.,
// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html
const int MAX_TREE_DEPTH = 32;
@ -3555,8 +3555,8 @@ KDTree::KDTree(InputArray _points, InputArray _labels, bool _copyData)
maxDepth = -1;
normType = NORM_L2;
build(_points, _labels, _copyData);
}
}
struct SubTree
{
SubTree() : first(0), last(0), nodeIdx(0), depth(0) {}
@ -3596,7 +3596,7 @@ medianPartition( size_t* ofs, int a, int b, const float* vals )
else
a = i0;
}
float pivot = vals[ofs[middle]];
int less = 0, more = 0;
for( k = a0; k < middle; k++ )
@ -3632,7 +3632,7 @@ computeSums( const Mat& points, const size_t* ofs, int a, int b, double* sums )
}
}
void KDTree::build(InputArray _points, bool _copyData)
{
build(_points, noArray(), _copyData);
@ -3652,8 +3652,8 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
points.release();
points.create(_points.size(), _points.type());
}
int i, j, n = _points.rows, dims = _points.cols, top = 0;
int i, j, n = _points.rows, ptdims = _points.cols, top = 0;
const float* data = _points.ptr<float>(0);
float* dstdata = points.ptr<float>(0);
size_t step = _points.step1();
@ -3661,7 +3661,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
int ptpos = 0;
labels.resize(n);
const int* _labels_data = 0;
if( !_labels.empty() )
{
int nlabels = _labels.checkVector(1, CV_32S, true);
@ -3669,9 +3669,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
_labels_data = (const int*)_labels.data;
}
Mat sumstack(MAX_TREE_DEPTH*2, dims*2, CV_64F);
Mat sumstack(MAX_TREE_DEPTH*2, ptdims*2, CV_64F);
SubTree stack[MAX_TREE_DEPTH*2];
vector<size_t> _ptofs(n);
size_t* ptofs = &_ptofs[0];
@ -3682,7 +3682,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
computeSums(points, ptofs, 0, n-1, sumstack.ptr<double>(top));
stack[top++] = SubTree(0, n-1, 0, 0);
int _maxDepth = 0;
while( --top >= 0 )
{
int first = stack[top].first, last = stack[top].last;
@ -3700,16 +3700,16 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
{
const float* src = data + ptofs[first];
float* dst = dstdata + idx*dstep;
for( j = 0; j < dims; j++ )
for( j = 0; j < ptdims; j++ )
dst[j] = src[j];
}
labels[idx] = _labels_data ? _labels_data[idx0] : idx0;
labels[idx] = _labels_data ? _labels_data[idx0] : idx0;
_maxDepth = std::max(_maxDepth, depth);
continue;
}
// find the dimensionality with the biggest variance
for( j = 0; j < dims; j++ )
for( j = 0; j < ptdims; j++ )
{
double m = sums[j*2]*invCount;
double varj = sums[j*2+1]*invCount - m*m;
@ -3729,9 +3729,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
nodes[nidx].boundary = medianPartition(ptofs, first, last, data + dim);
int middle = (first + last)/2;
double *lsums = (double*)sums, *rsums = lsums + dims*2;
double *lsums = (double*)sums, *rsums = lsums + ptdims*2;
computeSums(points, ptofs, middle+1, last, rsums);
for( j = 0; j < dims*2; j++ )
for( j = 0; j < ptdims*2; j++ )
lsums[j] = sums[j] - rsums[j];
stack[top++] = SubTree(first, middle, left, depth+1);
stack[top++] = SubTree(middle+1, last, right, depth+1);
@ -3752,13 +3752,13 @@ struct PQueueElem
int KDTree::findNearest(InputArray _vec, int K, int emax,
OutputArray _neighborsIdx, OutputArray _neighbors,
OutputArray _dist, OutputArray _labels) const
{
Mat vecmat = _vec.getMat();
CV_Assert( vecmat.isContinuous() && vecmat.type() == CV_32F && vecmat.total() == (size_t)points.cols );
const float* vec = vecmat.ptr<float>();
K = std::min(K, points.rows);
int dims = points.cols;
int ptdims = points.cols;
CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1));
@ -3776,7 +3776,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
{
float d, alt_d = 0.f;
int nidx;
if( e == 0 )
nidx = 0;
else
@ -3803,7 +3803,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
i = left;
}
}
if( ncount == K && alt_d > dist[ncount-1] )
continue;
}
@ -3813,21 +3813,21 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
if( nidx < 0 )
break;
const Node& n = nodes[nidx];
if( n.idx < 0 )
{
i = ~n.idx;
const float* row = points.ptr<float>(i);
if( normType == NORM_L2 )
for( j = 0, d = 0.f; j < dims; j++ )
for( j = 0, d = 0.f; j < ptdims; j++ )
{
float t = vec[j] - row[j];
d += t*t;
}
else
for( j = 0, d = 0.f; j < dims; j++ )
for( j = 0, d = 0.f; j < ptdims; j++ )
d += std::abs(vec[j] - row[j]);
dist[ncount] = d;
idx[ncount] = i;
for( i = ncount-1; i >= 0; i-- )
@ -3839,9 +3839,9 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
}
ncount += ncount < K;
e++;
break;
break;
}
int alt;
if( vec[n.idx] <= n.boundary )
{
@ -3853,7 +3853,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
nidx = n.right;
alt = n.left;
}
d = vec[n.idx] - n.boundary;
if( normType == NORM_L2 )
d = d*d + alt_d;
@ -3898,22 +3898,22 @@ void KDTree::findOrthoRange(InputArray _lowerBound,
OutputArray _neighbors,
OutputArray _labels ) const
{
int dims = points.cols;
int ptdims = points.cols;
Mat lowerBound = _lowerBound.getMat(), upperBound = _upperBound.getMat();
CV_Assert( lowerBound.size == upperBound.size &&
lowerBound.isContinuous() &&
upperBound.isContinuous() &&
lowerBound.type() == upperBound.type() &&
lowerBound.type() == CV_32F &&
lowerBound.total() == (size_t)dims );
lowerBound.total() == (size_t)ptdims );
const float* L = lowerBound.ptr<float>();
const float* R = upperBound.ptr<float>();
vector<int> idx;
AutoBuffer<int> _stack(MAX_TREE_DEPTH*2 + 1);
int* stack = _stack;
int top = 0;
stack[top++] = 0;
while( --top >= 0 )
@ -3926,10 +3926,10 @@ void KDTree::findOrthoRange(InputArray _lowerBound,
{
int j, i = ~n.idx;
const float* row = points.ptr<float>(i);
for( j = 0; j < dims; j++ )
for( j = 0; j < ptdims; j++ )
if( row[j] < L[j] || row[j] >= R[j] )
break;
if( j == dims )
if( j == ptdims )
idx.push_back(i);
continue;
}
@ -3948,7 +3948,7 @@ void KDTree::findOrthoRange(InputArray _lowerBound,
getPoints( idx, _neighbors, _labels );
}
void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const
{
Mat idxmat = _idx.getMat(), pts, labelsmat;
@ -3956,8 +3956,8 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
(idxmat.cols == 1 || idxmat.rows == 1) );
const int* idx = idxmat.ptr<int>();
int* dstlabels = 0;
int dims = points.cols;
int ptdims = points.cols;
int i, nidx = (int)idxmat.total();
if( nidx == 0 )
{
@ -3965,13 +3965,13 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
_labels.release();
return;
}
if( _pts.needed() )
{
_pts.create( nidx, dims, points.type());
_pts.create( nidx, ptdims, points.type());
pts = _pts.getMat();
}
if(_labels.needed())
{
_labels.create(nidx, 1, CV_32S, -1, true);
@ -3980,14 +3980,14 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
dstlabels = labelsmat.ptr<int>();
}
const int* srclabels = !labels.empty() ? &labels[0] : 0;
for( i = 0; i < nidx; i++ )
{
int k = idx[i];
CV_Assert( (unsigned)k < (unsigned)points.rows );
const float* src = points.ptr<float>(k);
if( pts.data )
std::copy(src, src + dims, pts.ptr<float>(i));
std::copy(src, src + ptdims, pts.ptr<float>(i));
if( dstlabels )
dstlabels[i] = srclabels ? srclabels[k] : k;
}
@ -4007,9 +4007,9 @@ int KDTree::dims() const
{
return !points.empty() ? points.cols : 0;
}
////////////////////////////////////////////////////////////////////////////////
schar* seqPush( CvSeq* seq, const void* element )
{
return cvSeqPush(seq, element);

16
modules/core/src/drawing.cpp
View File

@ -169,7 +169,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
}
int bt_pix0 = (int)img.elemSize(), bt_pix = bt_pix0;
size_t step = img.step;
size_t istep = img.step;
int dx = pt2.x - pt1.x;
int dy = pt2.y - pt1.y;
@ -188,11 +188,11 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
bt_pix = (bt_pix ^ s) - s;
}
ptr = (uchar*)(img.data + pt1.y * step + pt1.x * bt_pix0);
ptr = (uchar*)(img.data + pt1.y * istep + pt1.x * bt_pix0);
s = dy < 0 ? -1 : 0;
dy = (dy ^ s) - s;
step = (step ^ s) - s;
istep = (istep ^ s) - s;
s = dy > dx ? -1 : 0;
@ -201,9 +201,9 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
dy ^= dx & s;
dx ^= dy & s;
bt_pix ^= step & s;
step ^= bt_pix & s;
bt_pix ^= step & s;
bt_pix ^= istep & s;
istep ^= bt_pix & s;
bt_pix ^= istep & s;
if( connectivity == 8 )
{
@ -212,7 +212,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
err = dx - (dy + dy);
plusDelta = dx + dx;
minusDelta = -(dy + dy);
plusStep = (int)step;
plusStep = (int)istep;
minusStep = bt_pix;
count = dx + 1;
}
@ -223,7 +223,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
err = 0;
plusDelta = (dx + dx) + (dy + dy);
minusDelta = -(dy + dy);
plusStep = (int)step - bt_pix;
plusStep = (int)istep - bt_pix;
minusStep = bt_pix;
count = dx + dy + 1;
}

18
modules/core/src/gpumat.cpp
View File

@ -524,30 +524,30 @@ cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
dataend += step * (rows - 1) + minstep;
}
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange, Range colRange)
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range _rowRange, Range _colRange)
{
flags = m.flags;
step = m.step; refcount = m.refcount;
data = m.data; datastart = m.datastart; dataend = m.dataend;
if (rowRange == Range::all())
if (_rowRange == Range::all())
rows = m.rows;
else
{
CV_Assert(0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows);
CV_Assert(0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows);
rows = rowRange.size();
data += step*rowRange.start;
rows = _rowRange.size();
data += step*_rowRange.start;
}
if (colRange == Range::all())
if (_colRange == Range::all())
cols = m.cols;
else
{
CV_Assert(0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols);
CV_Assert(0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols);
cols = colRange.size();
data += colRange.start*elemSize();
cols = _colRange.size();
data += _colRange.start*elemSize();
flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1;
}

58
modules/core/src/matmul.cpp
View File

@ -63,7 +63,7 @@ GEMM_CopyBlock( const uchar* src, size_t src_step,
for( ; size.height--; src += src_step, dst += dst_step )
{
j=0;
j=0;
#if CV_ENABLE_UNROLLED
for( ; j <= size.width - 4; j += 4 )
{
@ -345,7 +345,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
for( k = 0; k < n; k++, b_data += b_step )
{
WT al(a_data[k]);
j=0;
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= m - 4; j += 4 )
{
@ -513,8 +513,8 @@ GEMMStore( const T* c_data, size_t c_step,
if( _c_data )
{
c_data = _c_data;
j=0;
#if CV_ENABLE_UNROLLED
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 )
{
WT t0 = alpha*d_buf[j];
@ -539,8 +539,8 @@ GEMMStore( const T* c_data, size_t c_step,
}
else
{
j = 0;
#if CV_ENABLE_UNROLLED
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= d_size.width - 4; j += 4 )
{
WT t0 = alpha*d_buf[j];
@ -552,7 +552,7 @@ GEMMStore( const T* c_data, size_t c_step,
d_data[j+2] = T(t0);
d_data[j+3] = T(t1);
}
#endif
#endif
for( ; j < d_size.width; j++ )
d_data[j] = T(alpha*d_buf[j]);
}
@ -597,7 +597,7 @@ static void GEMMSingleMul_64f( const double* a_data, size_t a_step,
alpha, beta, flags);
}
static void GEMMSingleMul_32fc( const Complexf* a_data, size_t a_step,
const Complexf* b_data, size_t b_step,
const Complexf* c_data, size_t c_step,
@ -620,7 +620,7 @@ static void GEMMSingleMul_64fc( const Complexd* a_data, size_t a_step,
GEMMSingleMul<Complexd,Complexd>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
}
static void GEMMBlockMul_32f( const float* a_data, size_t a_step,
const float* b_data, size_t b_step,
@ -696,7 +696,7 @@ static void GEMMStore_64fc( const Complexd* c_data, size_t c_step,
}
void cv::gemm( InputArray matA, InputArray matB, double alpha,
InputArray matC, double beta, OutputArray matD, int flags )
InputArray matC, double beta, OutputArray _matD, int flags )
{
const int block_lin_size = 128;
const int block_size = block_lin_size * block_lin_size;
@ -741,8 +741,8 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height)));
}
matD.create( d_size.height, d_size.width, type );
Mat D = matD.getMat();
_matD.create( d_size.height, d_size.width, type );
Mat D = _matD.getMat();
if( (flags & GEMM_3_T) != 0 && C.data == D.data )
{
transpose( C, C );
@ -2008,7 +2008,7 @@ static void scaleAdd_32f(const float* src1, const float* src2, float* dst,
t1 = src1[i+3]*alpha + src2[i+3];
dst[i+2] = t0; dst[i+3] = t1;
}
for(; i < len; i++ )
for(; i < len; i++ )
dst[i] = src1[i]*alpha + src2[i];
}
@ -2035,7 +2035,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst,
}
else
#endif
//vz why do we need unroll here?
//vz why do we need unroll here?
for( ; i <= len - 4; i += 4 )
{
double t0, t1;
@ -2046,7 +2046,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst,
t1 = src1[i+3]*alpha + src2[i+3];
dst[i+2] = t0; dst[i+3] = t1;
}
for(; i < len; i++ )
for(; i < len; i++ )
dst[i] = src1[i]*alpha + src2[i];
}
@ -2072,7 +2072,7 @@ void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray
float falpha = (float)alpha;
void* palpha = depth == CV_32F ? (void*)&falpha : (void*)&alpha;
ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f;
ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f;
if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )
{
@ -2134,12 +2134,12 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
_mean = mean.reshape(1, size.height);
}
void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
{
if(_data.kind() == _InputArray::STD_VECTOR_MAT)
if(_src.kind() == _InputArray::STD_VECTOR_MAT)
{
std::vector<cv::Mat> src;
_data.getMatVector(src);
_src.getMatVector(src);
CV_Assert( src.size() > 0 );
@ -2185,7 +2185,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
return;
}
Mat data = _data.getMat(), mean;
Mat data = _src.getMat(), mean;
CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) );
bool takeRows = (flags & CV_COVAR_ROWS) != 0;
int type = data.type();
@ -2209,7 +2209,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
else
{
ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F);
reduce( _data, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
mean = _mean.getMat();
}
@ -2223,7 +2223,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat();
Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat();
int type = v1.type(), depth = v1.depth();
Size sz = v1.size();
int i, j, len = sz.width*sz.height*v1.channels();
@ -2261,7 +2261,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
@ -2292,7 +2292,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
@ -2642,7 +2642,7 @@ dotProd_(const T* src1, const T* src2, int len)
{
int i = 0;
double result = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; i <= len - 4; i += 4 )
result += (double)src1[i]*src2[i] + (double)src1[i+1]*src2[i+1] +
(double)src1[i+2]*src2[i+2] + (double)src1[i+3]*src2[i+3];
@ -2674,7 +2674,7 @@ static double dotProd_8u(const uchar* src1, const uchar* src2, int len)
{
blockSize = std::min(len0 - i, blockSize0);
__m128i s = _mm_setzero_si128();
j = 0;
j = 0;
for( ; j <= blockSize - 16; j += 16 )
{
__m128i b0 = _mm_loadu_si128((const __m128i*)(src1 + j));
@ -2806,9 +2806,9 @@ double Mat::dot(InputArray _mat) const
PCA::PCA() {}
PCA::PCA(InputArray data, InputArray mean, int flags, int maxComponents)
PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents)
{
operator()(data, mean, flags, maxComponents);
operator()(data, _mean, flags, maxComponents);
}
PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
@ -2964,7 +2964,7 @@ void cv::PCACompute(InputArray data, InputOutputArray mean,
pca.mean.copyTo(mean);
pca.eigenvectors.copyTo(eigenvectors);
}
void cv::PCAProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result)
{

386
modules/core/src/matop.cpp
View File

File diff suppressed because it is too large Load Diff

122
modules/core/src/matrix.cpp
View File

@ -210,9 +210,9 @@ void Mat::create(int d, const int* _sizes, int _type)
#endif
if( !allocator )
{
size_t total = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount));
data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount));
refcount = (int*)(data + total);
size_t totalsize = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount));
data = datastart = (uchar*)fastMalloc(totalsize + (int)sizeof(*refcount));
refcount = (int*)(data + totalsize);
*refcount = 1;
}
else
@ -262,15 +262,15 @@ void Mat::deallocate()
}
Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&rows)
Mat::Mat(const Mat& m, const Range& _rowRange, const Range& _colRange) : size(&rows)
{
initEmpty();
CV_Assert( m.dims >= 2 );
if( m.dims > 2 )
{
AutoBuffer<Range> rs(m.dims);
rs[0] = rowRange;
rs[1] = colRange;
rs[0] = _rowRange;
rs[1] = _colRange;
for( int i = 2; i < m.dims; i++ )
rs[i] = Range::all();
*this = m(rs);
@ -278,19 +278,19 @@ Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&row
}
*this = m;
if( rowRange != Range::all() && rowRange != Range(0,rows) )
if( _rowRange != Range::all() && _rowRange != Range(0,rows) )
{
CV_Assert( 0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows );
rows = rowRange.size();
data += step*rowRange.start;
CV_Assert( 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows );
rows = _rowRange.size();
data += step*_rowRange.start;
flags |= SUBMATRIX_FLAG;
}
if( colRange != Range::all() && colRange != Range(0,cols) )
if( _colRange != Range::all() && _colRange != Range(0,cols) )
{
CV_Assert( 0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols );
cols = colRange.size();
data += colRange.start*elemSize();
CV_Assert( 0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols );
cols = _colRange.size();
data += _colRange.start*elemSize();
flags &= cols < m.cols ? ~CONTINUOUS_FLAG : -1;
flags |= SUBMATRIX_FLAG;
}
@ -473,14 +473,14 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows)
dims = 2;
CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
int depth = IPL2CV_DEPTH(img->depth);
int imgdepth = IPL2CV_DEPTH(img->depth);
size_t esz;
step[0] = img->widthStep;
if(!img->roi)
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL);
flags = MAGIC_VAL + CV_MAKETYPE(depth, img->nChannels);
flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels);
rows = img->height; cols = img->width;
datastart = data = (uchar*)img->imageData;
esz = CV_ELEM_SIZE(flags);
@ -489,7 +489,7 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows)
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0);
bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE;
flags = MAGIC_VAL + CV_MAKETYPE(depth, selectedPlane ? 1 : img->nChannels);
flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels);
rows = img->roi->height; cols = img->roi->width;
esz = CV_ELEM_SIZE(flags);
data = datastart = (uchar*)img->imageData +
@ -1299,38 +1299,38 @@ bool _OutputArray::fixedType() const
return (flags & FIXED_TYPE) == FIXED_TYPE;
}
void _OutputArray::create(Size _sz, int type, int i, bool allowTransposed, int fixedDepthMask) const
void _OutputArray::create(Size _sz, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == _sz);
CV_Assert(!fixedType() || ((Mat*)obj)->type() == type);
((Mat*)obj)->create(_sz, type);
CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
((Mat*)obj)->create(_sz, mtype);
return;
}
int sz[] = {_sz.height, _sz.width};
create(2, sz, type, i, allowTransposed, fixedDepthMask);
int sizes[] = {_sz.height, _sz.width};
create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
}
void _OutputArray::create(int rows, int cols, int type, int i, bool allowTransposed, int fixedDepthMask) const
void _OutputArray::create(int rows, int cols, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == Size(cols, rows));
CV_Assert(!fixedType() || ((Mat*)obj)->type() == type);
((Mat*)obj)->create(rows, cols, type);
CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
((Mat*)obj)->create(rows, cols, mtype);
return;
}
int sz[] = {rows, cols};
create(2, sz, type, i, allowTransposed, fixedDepthMask);
int sizes[] = {rows, cols};
create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
}
void _OutputArray::create(int dims, const int* size, int type, int i, bool allowTransposed, int fixedDepthMask) const
void _OutputArray::create(int dims, const int* sizes, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
type = CV_MAT_TYPE(type);
mtype = CV_MAT_TYPE(mtype);
if( k == MAT )
{
@ -1345,24 +1345,24 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow
}
if( dims == 2 && m.dims == 2 && m.data &&
m.type() == type && m.rows == size[1] && m.cols == size[0] )
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
return;
}
if(fixedType())
{
if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
type = m.type();
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_Assert(CV_MAT_TYPE(type) == m.type());
CV_Assert(CV_MAT_TYPE(mtype) == m.type());
}
if(fixedSize())
{
CV_Assert(m.dims == dims);
for(int j = 0; j < dims; ++j)
CV_Assert(m.size[j] == size[j]);
CV_Assert(m.size[j] == sizes[j]);
}
m.create(dims, size, type);
m.create(dims, sizes, mtype);
return;
}
@ -1370,16 +1370,16 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow
{
CV_Assert( i < 0 );
int type0 = CV_MAT_TYPE(flags);
CV_Assert( type == type0 || (CV_MAT_CN(type) == 1 && ((1 << type0) & fixedDepthMask) != 0) );
CV_Assert( dims == 2 && ((size[0] == sz.height && size[1] == sz.width) ||
(allowTransposed && size[0] == sz.width && size[1] == sz.height)));
CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) );
CV_Assert( dims == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) ||
(allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height)));
return;
}
if( k == STD_VECTOR || k == STD_VECTOR_VECTOR )
{
CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) );
size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0;
CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0;
vector<uchar>* v = (vector<uchar>*)obj;
if( k == STD_VECTOR_VECTOR )
@ -1398,7 +1398,7 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow
CV_Assert( i < 0 );
int type0 = CV_MAT_TYPE(flags);
CV_Assert( type == type0 || (CV_MAT_CN(type) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) );
CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) );
int esz = CV_ELEM_SIZE(type0);
CV_Assert(!fixedSize() || len == ((vector<uchar>*)v)->size() / esz);
@ -1471,20 +1471,20 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow
if( i < 0 )
{
CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) );
size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0, len0 = v.size();
CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size();
CV_Assert(!fixedSize() || len == len0);
v.resize(len);
if( fixedType() )
{
int type = CV_MAT_TYPE(flags);
int _type = CV_MAT_TYPE(flags);
for( size_t j = len0; j < len; j++ )
{
if( v[i].type() == type )
if( v[i].type() == _type )
continue;
CV_Assert( v[i].empty() );
v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | type;
v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | _type;
}
}
return;
@ -1502,25 +1502,25 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow
}
if( dims == 2 && m.dims == 2 && m.data &&
m.type() == type && m.rows == size[1] && m.cols == size[0] )
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
return;
}
if(fixedType())
{
if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
type = m.type();
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_Assert(!fixedType() || (CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0));
CV_Assert(!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0));
}
if(fixedSize())
{
CV_Assert(m.dims == dims);
for(int j = 0; j < dims; ++j)
CV_Assert(m.size[j] == size[j]);
CV_Assert(m.size[j] == sizes[j]);
}
m.create(dims, size, type);
m.create(dims, sizes, mtype);
}
}
@ -1929,10 +1929,10 @@ void cv::completeSymm( InputOutputArray _m, bool LtoR )
cv::Mat cv::Mat::cross(InputArray _m) const
{
Mat m = _m.getMat();
int t = type(), d = CV_MAT_DEPTH(t);
CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && t == m.type() &&
int tp = type(), d = CV_MAT_DEPTH(tp);
CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() &&
((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1)));
Mat result(rows, cols, t);
Mat result(rows, cols, tp);
if( d == CV_32F )
{
@ -2845,7 +2845,7 @@ cvRange( CvArr* arr, double start, double end )
CV_IMPL void
cvSort( const CvArr* _src, CvArr* _dst, CvArr* _idx, int flags )
{
cv::Mat src = cv::cvarrToMat(_src), dst, idx;
cv::Mat src = cv::cvarrToMat(_src);
if( _idx )
{
@ -3410,22 +3410,22 @@ SparseMat::SparseMat(const Mat& m)
int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1];
size_t esz = m.elemSize();
uchar* ptr = m.data;
uchar* dptr = m.data;
for(;;)
{
for( i = 0; i < lastSize; i++, ptr += esz )
for( i = 0; i < lastSize; i++, dptr += esz )
{
if( isZeroElem(ptr, esz) )
if( isZeroElem(dptr, esz) )
continue;
idx[d-1] = i;
uchar* to = newNode(idx, hash(idx));
copyElem( ptr, to, esz );
copyElem( dptr, to, esz );
}
for( i = d - 2; i >= 0; i-- )
{
ptr += m.step[i] - m.size[i+1]*m.step[i+1];
dptr += m.step[i] - m.size[i+1]*m.step[i+1];
if( ++idx[i] < m.size[i] )
break;
idx[i] = 0;

214
modules/core/src/opengl_interop.cpp
View File

@ -163,11 +163,11 @@ void icvSetOpenGlFuncTab(const CvOpenGlFuncTab* tab)
void cv::gpu::setGlDevice(int device)
{
#ifndef HAVE_CUDA
(void)device;
(void)device;
throw_nocuda;
#else
#ifndef HAVE_OPENGL
(void)device;
(void)device;
throw_nogl;
#else
if (!glFuncTab()->isGlContextInitialized())
@ -287,7 +287,7 @@ class cv::GlBuffer::Impl
{
public:
static const Ptr<Impl>& empty();
Impl(int rows, int cols, int type, unsigned int target);
Impl(const Mat& m, unsigned int target);
~Impl();
@ -311,7 +311,7 @@ public:
private:
Impl();
unsigned int buffer_;
#ifdef HAVE_CUDA
@ -484,57 +484,57 @@ inline void cv::GlBuffer::Impl::unmapDevice(cudaStream_t stream)
#endif // HAVE_OPENGL
cv::GlBuffer::GlBuffer(Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage)
cv::GlBuffer::GlBuffer(Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage)
{
#ifndef HAVE_OPENGL
(void)usage;
(void)_usage;
throw_nogl;
#else
impl_ = Impl::empty();
#endif
}
cv::GlBuffer::GlBuffer(int rows, int cols, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage)
cv::GlBuffer::GlBuffer(int _rows, int _cols, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage)
{
#ifndef HAVE_OPENGL
(void)rows;
(void)cols;
(void)type;
(void)usage;
(void)_rows;
(void)_cols;
(void)_type;
(void)_usage;
throw_nogl;
#else
impl_ = new Impl(rows, cols, type, usage);
rows_ = rows;
cols_ = cols;
type_ = type;
impl_ = new Impl(_rows, _cols, _type, _usage);
rows_ = _rows;
cols_ = _cols;
type_ = _type;
#endif
}
cv::GlBuffer::GlBuffer(Size size, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage)
cv::GlBuffer::GlBuffer(Size _size, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage)
{
#ifndef HAVE_OPENGL
(void)size;
(void)type;
(void)usage;
(void)_size;
(void)_type;
(void)_usage;
throw_nogl;
#else
impl_ = new Impl(size.height, size.width, type, usage);
rows_ = size.height;
cols_ = size.width;
type_ = type;
impl_ = new Impl(_size.height, _size.width, _type, _usage);
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage)
cv::GlBuffer::GlBuffer(InputArray mat_, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)usage;
(void)mat_;
(void)_usage;
throw_nogl;
#else
int kind = mat_.kind();
Size size = mat_.size();
int type = mat_.type();
Size _size = mat_.size();
int _type = mat_.type();
if (kind == _InputArray::GPU_MAT)
{
@ -542,38 +542,38 @@ cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_
throw_nocuda;
#else
GpuMat d_mat = mat_.getGpuMat();
impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), usage);
impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), _usage);
impl_->copyFrom(d_mat);
#endif
}
else
{
Mat mat = mat_.getMat();
impl_ = new Impl(mat, usage);
impl_ = new Impl(mat, _usage);
}
rows_ = size.height;
cols_ = size.width;
type_ = type;
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
void cv::GlBuffer::create(int rows, int cols, int type, Usage usage)
void cv::GlBuffer::create(int _rows, int _cols, int _type, Usage _usage)
{
#ifndef HAVE_OPENGL
(void)rows;
(void)cols;
(void)type;
(void)usage;
(void)_rows;
(void)_cols;
(void)_type;
(void)_usage;
throw_nogl;
#else
if (rows_ != rows || cols_ != cols || type_ != type || usage_ != usage)
if (rows_ != _rows || cols_ != _cols || type_ != _type || usage_ != _usage)
{
impl_ = new Impl(rows, cols, type, usage);
rows_ = rows;
cols_ = cols;
type_ = type;
usage_ = usage;
impl_ = new Impl(_rows, _cols, _type, _usage);
rows_ = _rows;
cols_ = _cols;
type_ = _type;
usage_ = _usage;
}
#endif
}
@ -590,14 +590,14 @@ void cv::GlBuffer::release()
void cv::GlBuffer::copyFrom(InputArray mat_)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)mat_;
throw_nogl;
#else
int kind = mat_.kind();
Size size = mat_.size();
int type = mat_.type();
Size _size = mat_.size();
int _type = mat_.type();
create(size, type);
create(_size, _type);
switch (kind)
{
@ -728,7 +728,7 @@ public:
private:
Impl();
GLuint tex_;
};
@ -926,45 +926,45 @@ cv::GlTexture::GlTexture() : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTUR
#endif
}
cv::GlTexture::GlTexture(int rows, int cols, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
cv::GlTexture::GlTexture(int _rows, int _cols, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
{
#ifndef HAVE_OPENGL
(void)rows;
(void)cols;
(void)type;
(void)_rows;
(void)_cols;
(void)_type;
throw_nogl;
#else
impl_ = new Impl(rows, cols, type);
rows_ = rows;
cols_ = cols;
type_ = type;
impl_ = new Impl(_rows, _cols, _type);
rows_ = _rows;
cols_ = _cols;
type_ = _type;
#endif
}
cv::GlTexture::GlTexture(Size size, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
cv::GlTexture::GlTexture(Size _size, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
{
#ifndef HAVE_OPENGL
(void)size;
(void)type;
(void)_size;
(void)_type;
throw_nogl;
#else
impl_ = new Impl(size.height, size.width, type);
rows_ = size.height;
cols_ = size.width;
type_ = type;
impl_ = new Impl(_size.height, _size.width, _type);
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)bgra;
(void)mat_;
(void)bgra;
throw_nogl;
#else
#else
int kind = mat_.kind();
Size size = mat_.size();
int type = mat_.type();
Size _size = mat_.size();
int _type = mat_.type();
switch (kind)
{
@ -994,26 +994,26 @@ cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_
}
}
rows_ = size.height;
cols_ = size.width;
type_ = type;
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
void cv::GlTexture::create(int rows, int cols, int type)
void cv::GlTexture::create(int _rows, int _cols, int _type)
{
#ifndef HAVE_OPENGL
(void)rows;
(void)cols;
(void)type;
(void)_rows;
(void)_cols;
(void)_type;
throw_nogl;
#else
if (rows_ != rows || cols_ != cols || type_ != type)
if (rows_ != _rows || cols_ != _cols || type_ != _type)
{
impl_ = new Impl(rows, cols, type);
rows_ = rows;
cols_ = cols;
type_ = type;
impl_ = new Impl(_rows, _cols, _type);
rows_ = _rows;
cols_ = _cols;
type_ = _type;
}
#endif
}
@ -1030,15 +1030,15 @@ void cv::GlTexture::release()
void cv::GlTexture::copyFrom(InputArray mat_, bool bgra)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)bgra;
(void)mat_;
(void)bgra;
throw_nogl;
#else
int kind = mat_.kind();
Size size = mat_.size();
int type = mat_.type();
Size _size = mat_.size();
int _type = mat_.type();
create(size, type);
create(_size, _type);
switch(kind)
{
@ -1244,8 +1244,8 @@ void cv::GlArrays::unbind() const
////////////////////////////////////////////////////////////////////////
// GlFont
cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style)
: family_(family), height_(height), weight_(weight), style_(style), base_(0)
cv::GlFont::GlFont(const string& _family, int _height, Weight _weight, Style _style)
: family_(_family), height_(_height), weight_(_weight), style_(_style), base_(0)
{
#ifndef HAVE_OPENGL
throw_nogl;
@ -1253,7 +1253,7 @@ cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style)
base_ = glGenLists(256);
CV_CheckGlError();
glFuncTab()->generateBitmapFont(family, height, weight, (style & STYLE_ITALIC) != 0, (style & STYLE_UNDERLINE) != 0, 0, 256, base_);
glFuncTab()->generateBitmapFont(family_, height_, weight_, (style_ & STYLE_ITALIC) != 0, (style_ & STYLE_UNDERLINE) != 0, 0, 256, base_);
#endif
}
@ -1283,7 +1283,7 @@ namespace
class FontCompare : public unary_function<Ptr<GlFont>, bool>
{
public:
inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style)
inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style)
: family_(family), height_(height), weight_(weight), style_(style)
{
}
@ -1304,10 +1304,10 @@ namespace
Ptr<GlFont> cv::GlFont::get(const std::string& family, int height, Weight weight, Style style)
{
#ifndef HAVE_OPENGL
(void)family;
(void)height;
(void)weight;
(void)style;
(void)family;
(void)height;
(void)weight;
(void)style;
throw_nogl;
return Ptr<GlFont>();
#else
@ -1333,9 +1333,9 @@ Ptr<GlFont> cv::GlFont::get(const std::string& family, int height, Weight weight
void cv::render(const GlTexture& tex, Rect_<double> wndRect, Rect_<double> texRect)
{
#ifndef HAVE_OPENGL
(void)tex;
(void)wndRect;
(void)texRect;
(void)tex;
(void)wndRect;
(void)texRect;
throw_nogl;
#else
if (!tex.empty())
@ -1368,9 +1368,9 @@ void cv::render(const GlTexture& tex, Rect_<double> wndRect, Rect_<double> texRe
void cv::render(const GlArrays& arr, int mode, Scalar color)
{
#ifndef HAVE_OPENGL
(void)arr;
(void)mode;
(void)color;
(void)arr;
(void)mode;
(void)color;
throw_nogl;
#else
glColor3d(color[0] / 255.0, color[1] / 255.0, color[2] / 255.0);
@ -1386,10 +1386,10 @@ void cv::render(const GlArrays& arr, int mode, Scalar color)
void cv::render(const string& str, const Ptr<GlFont>& font, Scalar color, Point2d pos)
{
#ifndef HAVE_OPENGL
(void)str;
(void)font;
(void)color;
(void)pos;
(void)str;
(void)font;
(void)color;
(void)pos;
throw_nogl;
#else
glPushAttrib(GL_DEPTH_BUFFER_BIT);
@ -1544,9 +1544,9 @@ void cv::GlCamera::setupModelViewMatrix() const
bool icvCheckGlError(const char* file, const int line, const char* func)
{
#ifndef HAVE_OPENGL
(void)file;
(void)line;
(void)func;
(void)file;
(void)line;
(void)func;
return true;
#else
GLenum err = glGetError();

8
modules/core/test/test_ds.cpp
View File

@ -1262,7 +1262,7 @@ int Core_SetTest::test_set_ops( int iters )
if( iter > iters/10 && cvtest::randInt(rng)%200 == 0 ) // clear set
{
int prev_count = cvset->total;
prev_count = cvset->total;
cvClearSet( cvset );
cvTsClearSimpleSet( sset );
@ -1482,19 +1482,19 @@ int Core_GraphTest::test_graph_ops( int iters )
if( cvtest::randInt(rng) % 200 == 0 ) // clear graph
{
int prev_vtx_count = graph->total, prev_edge_count = graph->edges->total;
int prev_vtx_count2 = graph->total, prev_edge_count2 = graph->edges->total;
cvClearGraph( graph );
cvTsClearSimpleGraph( sgraph );
CV_TS_SEQ_CHECK_CONDITION( graph->active_count == 0 && graph->total == 0 &&
graph->first == 0 && graph->free_elems == 0 &&
(graph->free_blocks != 0 || prev_vtx_count == 0),
(graph->free_blocks != 0 || prev_vtx_count2 == 0),
"The graph is not empty after clearing" );
CV_TS_SEQ_CHECK_CONDITION( edges->active_count == 0 && edges->total == 0 &&
edges->first == 0 && edges->free_elems == 0 &&
(edges->free_blocks != 0 || prev_edge_count == 0),
(edges->free_blocks != 0 || prev_edge_count2 == 0),
"The graph is not empty after clearing" );
}
else if( op == 0 ) // add vertex

166
modules/core/test/test_mat.cpp
View File

@ -22,25 +22,25 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim
assert( src.channels() == 1 );
if( dim == 0 ) // row
{
sum.create( 1, src.cols, CV_64FC1 );
sum.create( 1, src.cols, CV_64FC1 );
max.create( 1, src.cols, CV_64FC1 );
min.create( 1, src.cols, CV_64FC1 );
}
else
{
sum.create( src.rows, 1, CV_64FC1 );
sum.create( src.rows, 1, CV_64FC1 );
max.create( src.rows, 1, CV_64FC1 );
min.create( src.rows, 1, CV_64FC1 );
}
sum.setTo(Scalar(0));
max.setTo(Scalar(-DBL_MAX));
min.setTo(Scalar(DBL_MAX));
const Mat_<Type>& src_ = src;
Mat_<double>& sum_ = (Mat_<double>&)sum;
Mat_<double>& min_ = (Mat_<double>&)min;
Mat_<double>& max_ = (Mat_<double>&)max;
if( dim == 0 )
{
for( int ri = 0; ri < src.rows; ri++ )
@ -128,7 +128,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
else if ( dstType == CV_32S )
eps = 0.6;
}
assert( opRes.type() == CV_64FC1 );
Mat _dst, dst, diff;
reduce( src, _dst, dim, opType, dstType );
@ -151,7 +151,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
getMatTypeStr( src.type(), srcTypeStr );
getMatTypeStr( dstType, dstTypeStr );
const char* dimStr = dim == 0 ? "ROWS" : "COLS";
sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s",
srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr );
ts->printf( cvtest::TS::LOG, msg );
@ -164,10 +164,10 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz )
{
int code = cvtest::TS::OK, tempCode;
Mat src, sum, avg, max, min;
src.create( sz, srcType );
randu( src, Scalar(0), Scalar(100) );
if( srcType == CV_8UC1 )
testReduce<uchar>( src, sum, avg, max, min, dim );
else if( srcType == CV_8SC1 )
@ -182,110 +182,108 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz )
testReduce<float>( src, sum, avg, max, min, dim );
else if( srcType == CV_64FC1 )
testReduce<double>( src, sum, avg, max, min, dim );
else
else
assert( 0 );
// 1. sum
tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 2. avg
tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 3. max
tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 4. min
tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
int Core_ReduceTest::checkDim( int dim, Size sz )
{
int code = cvtest::TS::OK, tempCode;
// CV_8UC1
tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_16UC1
tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_16SC1
tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_32FC1
tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_64FC1
tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
int Core_ReduceTest::checkSize( Size sz )
{
int code = cvtest::TS::OK, tempCode;
tempCode = checkDim( 0, sz ); // rows
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkDim( 1, sz ); // cols
tempCode = checkDim( 1, sz ); // cols
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
void Core_ReduceTest::run( int )
{
int code = cvtest::TS::OK, tempCode;
tempCode = checkSize( Size(1,1) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(1,100) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(100,1) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(1000,500) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
ts->set_failed_test_info( code );
}
#define CHECK_C
Size sz(200, 500);
class Core_PCATest : public cvtest::BaseTest
{
public:
@ -293,41 +291,43 @@ public:
protected:
void run(int)
{
const Size sz(200, 500);
double diffPrjEps, diffBackPrjEps,
prjEps, backPrjEps,
evalEps, evecEps;
int maxComponents = 100;
Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
RNG& rng = ts->get_rng();
RNG& rng = ts->get_rng();
rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
// 1. check C++ PCA & ROW
Mat rPrjTestPoints = rPCA.project( rTestPoints );
Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
Mat avg(1, sz.width, CV_32FC1 );
reduce( rPoints, avg, 0, CV_REDUCE_AVG );
Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
Q = Qt * Q;
Q = Q /(float)rPoints.rows;
eigen( Q, eval, evec );
/*SVD svd(Q);
evec = svd.vt;
eval = svd.w;*/
Mat subEval( maxComponents, 1, eval.type(), eval.data ),
subEvec( maxComponents, evec.cols, evec.type(), evec.data );
#ifdef CHECK_C
Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
#endif
// check eigen()
double eigenEps = 1e-6;
double err;
@ -335,7 +335,7 @@ protected:
{
Mat v = evec.row(i).t();
Mat Qv = Q * v;
Mat lv = eval.at<float>(i,0) * v;
err = norm( Qv, lv );
if( err > eigenEps )
@ -370,7 +370,7 @@ protected:
absdiff(rPCA.eigenvectors, subEvec, tmp);
double mval = 0; Point mloc;
minMaxLoc(tmp, 0, &mval, 0, &mloc);
ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
@ -380,7 +380,7 @@ protected:
}
}
}
prjEps = 1.265, backPrjEps = 1.265;
for( int i = 0; i < rTestPoints.rows; i++ )
{
@ -404,7 +404,7 @@ protected:
return;
}
}
// 2. check C++ PCA & COL
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
diffPrjEps = 1, diffBackPrjEps = 1;
@ -423,7 +423,7 @@ protected:
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
#ifdef CHECK_C
// 3. check C PCA & ROW
_points = rPoints;
@ -435,11 +435,11 @@ protected:
backPrjTestPoints.create(rPoints.size(), rPoints.type() );
_prjTestPoints = prjTestPoints;
_backPrjTestPoints = backPrjTestPoints;
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2);
if( err > diffPrjEps )
{
@ -454,7 +454,7 @@ protected:
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// 3. check C PCA & COL
_points = cPoints;
_testPoints = cTestPoints;
@ -463,11 +463,11 @@ protected:
evec = evec.t(); _evec = evec;
prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints;
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints;
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL );
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
if( err > diffPrjEps )
{
@ -490,9 +490,9 @@ class Core_ArrayOpTest : public cvtest::BaseTest
{
public:
Core_ArrayOpTest();
~Core_ArrayOpTest();
~Core_ArrayOpTest();
protected:
void run(int);
void run(int);
};
@ -536,7 +536,7 @@ static double getValue(SparseMat& M, const int* idx, RNG& rng)
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
phv = &hv;
}
const uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], false, phv) :
d == 3 ? M.ptr(idx[0], idx[1], idx[2], false, phv) :
M.ptr(idx, false, phv);
@ -560,7 +560,7 @@ static void eraseValue(SparseMat& M, const int* idx, RNG& rng)
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
phv = &hv;
}
if( d == 2 )
M.erase(idx[0], idx[1], phv);
else if( d == 3 )
@ -584,7 +584,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng)
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx);
phv = &hv;
}
uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], true, phv) :
d == 3 ? M.ptr(idx[0], idx[1], idx[2], true, phv) :
M.ptr(idx, true, phv);
@ -599,7 +599,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng)
void Core_ArrayOpTest::run( int /* start_from */)
{
int errcount = 0;
// dense matrix operations
{
int sz3[] = {5, 10, 15};
@ -608,7 +608,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
RNG rng;
rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
int idx0[] = {3,4,5}, idx1[] = {0, 9, 7};
float val0 = 130;
Scalar val1(-1000, 30, 3, 8);
@ -617,12 +617,12 @@ void Core_ArrayOpTest::run( int /* start_from */)
cvSetND(&matB, idx0, val1);
cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1);
Ptr<CvMatND> matC = cvCloneMatND(&matB);
if( A.at<float>(idx0[0], idx0[1], idx0[2]) != val0 ||
A.at<float>(idx1[0], idx1[1], idx1[2]) != -val0 ||
cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 ||
cvGetRealND(&matA, idx1) != -val0 ||
Scalar(B.at<Vec4s>(idx0[0], idx0[1], idx0[2])) != val1 ||
Scalar(B.at<Vec4s>(idx1[0], idx1[1], idx1[2])) != -val1 ||
Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 ||
@ -633,7 +633,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
errcount++;
}
}
RNG rng;
const int MAX_DIM = 5, MAX_DIM_SZ = 10;
// sparse matrix operations
@ -647,7 +647,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
vector<double> all_vals2;
string sidx, min_sidx, max_sidx;
double min_val=0, max_val=0;
int p = 1;
for( k = 0; k < dims; k++ )
{
@ -656,7 +656,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
}
SparseMat M( dims, size, depth );
map<string, double> M0;
int nz0 = (unsigned)rng % max(p/5,10);
nz0 = min(max(nz0, 1), p);
all_vals.resize(nz0);
@ -676,12 +676,12 @@ void Core_ArrayOpTest::run( int /* start_from */)
_all_vals2.convertTo(_all_vals2_f, CV_32F);
_all_vals2_f.convertTo(_all_vals2, CV_64F);
}
minMaxLoc(_all_vals, &min_val, &max_val);
double _norm0 = norm(_all_vals, CV_C);
double _norm1 = norm(_all_vals, CV_L1);
double _norm2 = norm(_all_vals, CV_L2);
for( i = 0; i < nz0; i++ )
{
for(;;)
@ -708,18 +708,18 @@ void Core_ArrayOpTest::run( int /* start_from */)
break;
}
}
Ptr<CvSparseMat> M2 = (CvSparseMat*)M;
MatND Md;
M.copyTo(Md);
SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2);
int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount();
double norm0 = norm(M, CV_C);
double norm1 = norm(M, CV_L1);
double norm2 = norm(M, CV_L2);
double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000;
if( nz1 != nz0 || nz2 != nz0)
{
errcount++;
@ -727,7 +727,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
si, nz1, nz2, nz0 );
break;
}
if( fabs(norm0 - _norm0) > fabs(_norm0)*eps ||
fabs(norm1 - _norm1) > fabs(_norm1)*eps ||
fabs(norm2 - _norm2) > fabs(_norm2)*eps )
@ -737,10 +737,10 @@ void Core_ArrayOpTest::run( int /* start_from */)
si, norm0, norm1, norm2, _norm0, _norm1, _norm2 );
break;
}
int n = (unsigned)rng % max(p/5,10);
n = min(max(n, 1), p) + nz0;
for( i = 0; i < n; i++ )
{
double val1, val2, val3, val0;
@ -760,7 +760,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
val1 = getValue(M, idx, rng);
val2 = getValue(M2, idx);
val3 = getValue(M3, idx, rng);
if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON )
{
errcount++;
@ -768,7 +768,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
break;
}
}
for( i = 0; i < n; i++ )
{
double val1, val2;
@ -792,9 +792,9 @@ void Core_ArrayOpTest::run( int /* start_from */)
errcount++;
ts->printf(cvtest::TS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 );
break;
}
}
}
int nz = (int)M.nzcount();
if( nz != 0 )
{
@ -802,7 +802,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
ts->printf(cvtest::TS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz );
break;
}
int idx1[MAX_DIM], idx2[MAX_DIM];
double val1 = 0, val2 = 0;
M3 = SparseMat(Md);
@ -816,7 +816,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
min_val, max_val, min_sidx.c_str(), max_sidx.c_str());
break;
}
minMaxIdx(Md, &val1, &val2, idx1, idx2);
s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims);
if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) ||
@ -829,7 +829,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
break;
}
}
ts->set_failed_test_info(errcount == 0 ? cvtest::TS::OK : cvtest::TS::FAIL_INVALID_OUTPUT);
}

72
modules/core/test/test_rand.cpp
View File

@ -27,7 +27,7 @@ static double chi2_p95(int n)
36.42f, 37.65f, 38.89f, 40.11f, 41.34f, 42.56f, 43.77f };
static const double xp = 1.64;
CV_Assert(n >= 1);
if( n <= 30 )
return chi2_tab95[n-1];
return n + sqrt((double)2*n)*xp + 0.6666666666666*(xp*xp - 1);
@ -40,12 +40,12 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale,
const int* H = (const int*)hist.data;
float* H0 = ((float*)hist0.data);
int i, hsz = hist.cols;
double sum = 0;
for( i = 0; i < hsz; i++ )
sum += H[i];
CV_Assert( fabs(1./sum - scale) < FLT_EPSILON );
if( dist_type == CV_RAND_UNI )
{
float scale0 = (float)(1./hsz);
@ -54,19 +54,19 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale,
}
else
{
double sum = 0, r = (hsz-1.)/2;
double sum2 = 0, r = (hsz-1.)/2;
double alpha = 2*sqrt(2.)/r, beta = -alpha*r;
for( i = 0; i < hsz; i++ )
{
double x = i*alpha + beta;
H0[i] = (float)exp(-x*x);
sum += H0[i];
sum2 += H0[i];
}
sum = 1./sum;
sum2 = 1./sum2;
for( i = 0; i < hsz; i++ )
H0[i] = (float)(H0[i]*sum);
H0[i] = (float)(H0[i]*sum2);
}
double chi2 = 0;
for( i = 0; i < hsz; i++ )
{
@ -76,7 +76,7 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale,
chi2 += (a - b)*(a - b)/(a + b);
}
realval = chi2;
double chi2_pval = chi2_p95(hsz - 1 - (dist_type == CV_RAND_NORMAL ? 2 : 0));
refval = chi2_pval*0.01;
return realval <= refval;
@ -87,22 +87,22 @@ void Core_RandTest::run( int )
static int _ranges[][2] =
{{ 0, 256 }, { -128, 128 }, { 0, 65536 }, { -32768, 32768 },
{ -1000000, 1000000 }, { -1000, 1000 }, { -1000, 1000 }};
const int MAX_SDIM = 10;
const int N = 2000000;
const int maxSlice = 1000;
const int MAX_HIST_SIZE = 1000;
int progress = 0;
RNG& rng = ts->get_rng();
RNG tested_rng = theRNG();
test_case_count = 200;
for( int idx = 0; idx < test_case_count; idx++ )
{
progress = update_progress( progress, idx, test_case_count, 0 );
ts->update_context( this, idx, false );
int depth = cvtest::randInt(rng) % (CV_64F+1);
int c, cn = (cvtest::randInt(rng) % 4) + 1;
int type = CV_MAKETYPE(depth, cn);
@ -113,15 +113,15 @@ void Core_RandTest::run( int )
double eps = 1.e-4;
if (depth == CV_64F)
eps = 1.e-7;
bool do_sphere_test = dist_type == CV_RAND_UNI;
Mat arr[2], hist[4];
int W[] = {0,0,0,0};
arr[0].create(1, SZ, type);
arr[1].create(1, SZ, type);
bool fast_algo = dist_type == CV_RAND_UNI && depth < CV_32F;
for( c = 0; c < cn; c++ )
{
int a, b, hsz;
@ -137,7 +137,7 @@ void Core_RandTest::run( int )
while( abs(a-b) <= 1 );
if( a > b )
std::swap(a, b);
unsigned r = (unsigned)(b - a);
fast_algo = fast_algo && r <= 256 && (r & (r-1)) == 0;
hsz = min((unsigned)(b - a), (unsigned)MAX_HIST_SIZE);
@ -149,7 +149,7 @@ void Core_RandTest::run( int )
int meanrange = vrange/16;
int mindiv = MAX(vrange/20, 5);
int maxdiv = MIN(vrange/8, 10000);
a = cvtest::randInt(rng) % meanrange - meanrange/2 +
(_ranges[depth][0] + _ranges[depth][1])/2;
b = cvtest::randInt(rng) % (maxdiv - mindiv) + mindiv;
@ -157,9 +157,9 @@ void Core_RandTest::run( int )
}
A[c] = a;
B[c] = b;
hist[c].create(1, hsz, CV_32S);
hist[c].create(1, hsz, CV_32S);
}
cv::RNG saved_rng = tested_rng;
int maxk = fast_algo ? 0 : 1;
for( k = 0; k <= maxk; k++ )
@ -173,14 +173,14 @@ void Core_RandTest::run( int )
tested_rng.fill(aslice, dist_type, A, B);
}
}
if( maxk >= 1 && norm(arr[0], arr[1], NORM_INF) > eps)
{
ts->printf( cvtest::TS::LOG, "RNG output depends on the array lengths (some generated numbers get lost?)" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
for( c = 0; c < cn; c++ )
{
const uchar* data = arr[0].data;
@ -190,9 +190,9 @@ void Core_RandTest::run( int )
double maxVal = dist_type == CV_RAND_UNI ? B[c] : A[c] + B[c]*4;
double scale = HSZ/(maxVal - minVal);
double delta = -minVal*scale;
hist[c] = Scalar::all(0);
for( i = c; i < SZ*cn; i += cn )
{
double val = depth == CV_8U ? ((const uchar*)data)[i] :
@ -221,7 +221,7 @@ void Core_RandTest::run( int )
}
}
}
if( dist_type == CV_RAND_UNI && W[c] != SZ )
{
ts->printf( cvtest::TS::LOG, "Uniform RNG gave values out of the range [%g,%g) on channel %d/%d\n",
@ -237,7 +237,7 @@ void Core_RandTest::run( int )
return;
}
double refval = 0, realval = 0;
if( !check_pdf(hist[c], 1./W[c], dist_type, refval, realval) )
{
ts->printf( cvtest::TS::LOG, "RNG failed Chi-square test "
@ -247,13 +247,13 @@ void Core_RandTest::run( int )
return;
}
}
// Monte-Carlo test. Compute volume of SDIM-dimensional sphere
// inscribed in [-1,1]^SDIM cube.
if( do_sphere_test )
{
int SDIM = cvtest::randInt(rng) % (MAX_SDIM-1) + 2;
int N0 = (SZ*cn/SDIM), N = 0;
int N0 = (SZ*cn/SDIM), n = 0;
double r2 = 0;
const uchar* data = arr[0].data;
double scale[4], delta[4];
@ -262,7 +262,7 @@ void Core_RandTest::run( int )
scale[c] = 2./(B[c] - A[c]);
delta[c] = -A[c]*scale[c] - 1;
}
for( i = k = c = 0; i <= SZ*cn - SDIM; i++, k++, c++ )
{
double val = depth == CV_8U ? ((const uchar*)data)[i] :
@ -276,20 +276,20 @@ void Core_RandTest::run( int )
r2 += val*val;
if( k == SDIM-1 )
{
N += r2 <= 1;
n += r2 <= 1;
r2 = 0;
k = -1;
}
}
double V = ((double)N/N0)*(1 << SDIM);
double V = ((double)n/N0)*(1 << SDIM);
// the theoretically computed volume
int sdim = SDIM % 2;
double V0 = sdim + 1;
for( sdim += 2; sdim <= SDIM; sdim += 2 )
V0 *= 2*CV_PI/sdim;
if( fabs(V - V0) > 0.3*fabs(V0) )
{
ts->printf( cvtest::TS::LOG, "RNG failed %d-dim sphere volume test (got %g instead of %g)\n",
@ -309,7 +309,7 @@ class Core_RandRangeTest : public cvtest::BaseTest
{
public:
Core_RandRangeTest() {}
~Core_RandRangeTest() {}
~Core_RandRangeTest() {}
protected:
void run(int)
{
@ -319,7 +319,7 @@ protected:
theRNG().fill(af, RNG::UNIFORM, -DBL_MAX, DBL_MAX);
int n0 = 0, n255 = 0, nx = 0;
int nfmin = 0, nfmax = 0, nfx = 0;
for( int i = 0; i < a.rows; i++ )
for( int j = 0; j < a.cols; j++ )
{

4
modules/features2d/src/bagofwords.cpp
View File

@ -110,10 +110,10 @@ Mat BOWKMeansTrainer::cluster() const
BOWKMeansTrainer::~BOWKMeansTrainer()
{}
Mat BOWKMeansTrainer::cluster( const Mat& descriptors ) const
Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const
{
Mat labels, vocabulary;
kmeans( descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary );
kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary );
return vocabulary;
}

6
modules/features2d/src/brief.cpp
View File

@ -127,8 +127,8 @@ int BriefDescriptorExtractor::descriptorType() const
void BriefDescriptorExtractor::read( const FileNode& fn)
{
int descriptorSize = fn["descriptorSize"];
switch (descriptorSize)
int dSize = fn["descriptorSize"];
switch (dSize)
{
case 16:
test_fn_ = pixelTests16;
@ -142,7 +142,7 @@ void BriefDescriptorExtractor::read( const FileNode& fn)
default:
CV_Error(CV_StsBadArg, "descriptorSize must be 16, 32, or 64");
}
bytes_ = descriptorSize;
bytes_ = dSize;
}
void BriefDescriptorExtractor::write( FileStorage& fs) const

6
modules/features2d/src/descriptors.cpp
View File

@ -223,8 +223,8 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector<
vector<KeyPoint> outKeypoints;
outKeypoints.reserve( keypoints.size() );
int descriptorSize = descriptorExtractor->descriptorSize();
Mat mergedDescriptors( maxKeypointsCount, 3*descriptorSize, descriptorExtractor->descriptorType() );
int dSize = descriptorExtractor->descriptorSize();
Mat mergedDescriptors( maxKeypointsCount, 3*dSize, descriptorExtractor->descriptorType() );
int mergedCount = 0;
// cp - current channel position
size_t cp[] = {0, 0, 0};
@ -250,7 +250,7 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector<
// merge descriptors
for( int ci = 0; ci < N; ci++ )
{
Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*descriptorSize, (ci+1)*descriptorSize));
Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*dSize, (ci+1)*dSize));
channelDescriptors[ci].row( idxs[ci][cp[ci]] ).copyTo( dst );
cp[ci]++;
}

14
modules/features2d/src/draw.cpp
View File

@ -156,11 +156,11 @@ static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector<KeyPoint>
// draw keypoints
if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) )
{
Mat outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
drawKeypoints( outImg1, keypoints1, outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
Mat _outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
drawKeypoints( _outImg1, keypoints1, _outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
Mat outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
drawKeypoints( outImg2, keypoints2, outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
Mat _outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
drawKeypoints( _outImg2, keypoints2, _outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG );
}
}
@ -178,9 +178,9 @@ static inline void _drawMatch( Mat& outImg, Mat& outImg1, Mat& outImg2 ,
pt2 = kp2.pt,
dpt2 = Point2f( std::min(pt2.x+outImg1.cols, float(outImg.cols-1)), pt2.y );
line( outImg,
Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
line( outImg,
Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
color, 1, CV_AA, draw_shift_bits );
}

7
modules/features2d/src/features2d_init.cpp
View File

@ -109,11 +109,14 @@ class CV_EXPORTS HarrisDetector : public GFTTDetector
{
public:
HarrisDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
int blockSize=3, bool useHarrisDetector=true, double k=0.04 )
: GFTTDetector( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k ) {}
int blockSize=3, bool useHarrisDetector=true, double k=0.04 );
AlgorithmInfo* info() const;
};
inline HarrisDetector::HarrisDetector( int _maxCorners, double _qualityLevel, double _minDistance,
int _blockSize, bool _useHarrisDetector, double _k )
: GFTTDetector( _maxCorners, _qualityLevel, _minDistance, _blockSize, _useHarrisDetector, _k ) {}
CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
obj.info()->addParam(obj, "nfeatures", obj.nfeatures);
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);

24
modules/features2d/src/matchers.cpp
View File

@ -539,7 +539,7 @@ void FlannBasedMatcher::read( const FileNode& fn)
for(int i = 0; i < (int)ip.size(); ++i)
{
CV_Assert(ip[i].type() == FileNode::MAP);
std::string name = (std::string)ip[i]["name"];
std::string _name = (std::string)ip[i]["name"];
int type = (int)ip[i]["type"];
switch(type)
@ -549,19 +549,19 @@ void FlannBasedMatcher::read( const FileNode& fn)
case CV_16U:
case CV_16S:
case CV_32S:
indexParams->setInt(name, (int) ip[i]["value"]);
indexParams->setInt(_name, (int) ip[i]["value"]);
break;
case CV_32F:
indexParams->setFloat(name, (float) ip[i]["value"]);
indexParams->setFloat(_name, (float) ip[i]["value"]);
break;
case CV_64F:
indexParams->setDouble(name, (double) ip[i]["value"]);
indexParams->setDouble(_name, (double) ip[i]["value"]);
break;
case CV_USRTYPE1:
indexParams->setString(name, (std::string) ip[i]["value"]);
indexParams->setString(_name, (std::string) ip[i]["value"]);
break;
case CV_MAKETYPE(CV_USRTYPE1,2):
indexParams->setBool(name, (int) ip[i]["value"] != 0);
indexParams->setBool(_name, (int) ip[i]["value"] != 0);
break;
case CV_MAKETYPE(CV_USRTYPE1,3):
indexParams->setAlgorithm((int) ip[i]["value"]);
@ -578,7 +578,7 @@ void FlannBasedMatcher::read( const FileNode& fn)
for(int i = 0; i < (int)sp.size(); ++i)
{
CV_Assert(sp[i].type() == FileNode::MAP);
std::string name = (std::string)sp[i]["name"];
std::string _name = (std::string)sp[i]["name"];
int type = (int)sp[i]["type"];
switch(type)
@ -588,19 +588,19 @@ void FlannBasedMatcher::read( const FileNode& fn)
case CV_16U:
case CV_16S:
case CV_32S:
searchParams->setInt(name, (int) sp[i]["value"]);
searchParams->setInt(_name, (int) sp[i]["value"]);
break;
case CV_32F:
searchParams->setFloat(name, (float) ip[i]["value"]);
searchParams->setFloat(_name, (float) ip[i]["value"]);
break;
case CV_64F:
searchParams->setDouble(name, (double) ip[i]["value"]);
searchParams->setDouble(_name, (double) ip[i]["value"]);
break;
case CV_USRTYPE1:
searchParams->setString(name, (std::string) ip[i]["value"]);
searchParams->setString(_name, (std::string) ip[i]["value"]);
break;
case CV_MAKETYPE(CV_USRTYPE1,2):
searchParams->setBool(name, (int) ip[i]["value"] != 0);
searchParams->setBool(_name, (int) ip[i]["value"] != 0);
break;
case CV_MAKETYPE(CV_USRTYPE1,3):
searchParams->setAlgorithm((int) ip[i]["value"]);

4
modules/features2d/src/mser.cpp
View File

@ -539,8 +539,8 @@ static void extractMSER_8UC1_Pass( int* ioptr,
}
*imgptr += 0x10000;
}
int i = (int)(imgptr-ioptr);
ptsptr->pt = cvPoint( i&stepmask, i>>stepgap );
int imsk = (int)(imgptr-ioptr);
ptsptr->pt = cvPoint( imsk&stepmask, imsk>>stepgap );
// get the current location
accumulateMSERComp( comptr, ptsptr );
ptsptr++;

32
modules/features2d/src/orb.cpp
View File

@ -555,9 +555,9 @@ static inline float getScale(int level, int firstLevel, double scaleFactor)
* @param detector_params parameters to use
*/
ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
int _firstLevel, int WTA_K, int _scoreType, int _patchSize) :
int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) :
nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(WTA_K),
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
scoreType(_scoreType), patchSize(_patchSize)
{}
@ -653,8 +653,8 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
for (int level = 0; level < nlevels; ++level)
{
int nfeatures = nfeaturesPerLevel[level];
allKeypoints[level].reserve(nfeatures*2);
int featuresNum = nfeaturesPerLevel[level];
allKeypoints[level].reserve(featuresNum*2);
vector<KeyPoint> & keypoints = allKeypoints[level];
@ -668,14 +668,14 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
if( scoreType == ORB::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, 2 * nfeatures);
KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
KeyPointsFilter::retainBest(keypoints, nfeatures);
KeyPointsFilter::retainBest(keypoints, featuresNum);
float sf = getScale(level, firstLevel, scaleFactor);
@ -738,7 +738,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
if( image.type() != CV_8UC1 )
cvtColor(_image, image, CV_BGR2GRAY);
int nlevels = this->nlevels;
int levelsNum = this->nlevels;
if( !do_keypoints )
{
@ -751,15 +751,15 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
//
// In short, ultimately the descriptor should
// ignore octave parameter and deal only with the keypoint size.
nlevels = 0;
levelsNum = 0;
for( size_t i = 0; i < _keypoints.size(); i++ )
nlevels = std::max(nlevels, std::max(_keypoints[i].octave, 0));
nlevels++;
levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
levelsNum++;
}
// Pre-compute the scale pyramids
vector<Mat> imagePyramid(nlevels), maskPyramid(nlevels);
for (int level = 0; level < nlevels; ++level)
vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
for (int level = 0; level < levelsNum; ++level)
{
float scale = 1/getScale(level, firstLevel, scaleFactor);
Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
@ -839,13 +839,13 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
// Cluster the input keypoints depending on the level they were computed at
allKeypoints.resize(nlevels);
allKeypoints.resize(levelsNum);
for (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
allKeypoints[keypoint->octave].push_back(*keypoint);
// Make sure we rescale the coordinates
for (int level = 0; level < nlevels; ++level)
for (int level = 0; level < levelsNum; ++level)
{
if (level == firstLevel)
continue;
@ -864,7 +864,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
if( do_descriptors )
{
int nkeypoints = 0;
for (int level = 0; level < nlevels; ++level)
for (int level = 0; level < levelsNum; ++level)
nkeypoints += (int)allKeypoints[level].size();
if( nkeypoints == 0 )
_descriptors.release();
@ -897,7 +897,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
_keypoints.clear();
int offset = 0;
for (int level = 0; level < nlevels; ++level)
for (int level = 0; level < levelsNum; ++level)
{
// Get the features and compute their orientation
vector<KeyPoint>& keypoints = allKeypoints[level];

61
modules/features2d/src/stardetector.cpp
View File

@ -48,13 +48,13 @@ static void
computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT )
{
CV_Assert( matI.type() == CV_8U );
int x, y, rows = matI.rows, cols = matI.cols;
matS.create(rows + 1, cols + 1, CV_32S);
matT.create(rows + 1, cols + 1, CV_32S);
_FT.create(rows + 1, cols + 1, CV_32S);
const uchar* I = matI.ptr<uchar>();
int *S = matS.ptr<int>(), *T = matT.ptr<int>(), *FT = _FT.ptr<int>();
int istep = (int)matI.step, step = (int)(matS.step/sizeof(S[0]));
@ -121,29 +121,28 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
StarFeature f[MAX_PATTERN];
Mat sum, tilted, flatTilted;
int y, i=0, rows = img.rows, cols = img.cols;
int y, rows = img.rows, cols = img.cols;
int border, npatterns=0, maxIdx=0;
CV_Assert( img.type() == CV_8UC1 );
responses.create( img.size(), CV_32F );
sizes.create( img.size(), CV_16S );
while( pairs[i][0] >= 0 && !
( sizes0[pairs[i][0]] >= maxSize
|| sizes0[pairs[i+1][0]] + sizes0[pairs[i+1][0]]/2 >= std::min(rows, cols) ) )
while( pairs[npatterns][0] >= 0 && !
( sizes0[pairs[npatterns][0]] >= maxSize
|| sizes0[pairs[npatterns+1][0]] + sizes0[pairs[npatterns+1][0]]/2 >= std::min(rows, cols) ) )
{
++i;
++npatterns;
}
npatterns = i;
npatterns += (pairs[npatterns-1][0] >= 0);
maxIdx = pairs[npatterns-1][0];
computeIntegralImages( img, sum, tilted, flatTilted );
int step = (int)(sum.step/sum.elemSize());
for( i = 0; i <= maxIdx; i++ )
for(int i = 0; i <= maxIdx; i++ )
{
int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2;
int ur_area = (2*ur_size + 1)*(2*ur_size + 1);
@ -169,24 +168,24 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
sizes1[maxIdx] = -sizes1[maxIdx];
border = sizes0[maxIdx] + sizes0[maxIdx]/2;
for( i = 0; i < npatterns; i++ )
for(int i = 0; i < npatterns; i++ )
{
int innerArea = f[pairs[i][1]].area;
int outerArea = f[pairs[i][0]].area - innerArea;
invSizes[i][0] = 1.f/outerArea;
invSizes[i][1] = 1.f/innerArea;
}
#if CV_SSE2
if( useSIMD )
{
for( i = 0; i < npatterns; i++ )
for(int i = 0; i < npatterns; i++ )
{
_mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0]));
_mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1]));
}
for( i = 0; i <= maxIdx; i++ )
for(int i = 0; i <= maxIdx; i++ )
_mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i]));
}
#endif
@ -197,7 +196,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
float* r_ptr2 = responses.ptr<float>(rows - 1 - y);
short* s_ptr = sizes.ptr<short>(y);
short* s_ptr2 = sizes.ptr<short>(rows - 1 - y);
memset( r_ptr, 0, cols*sizeof(r_ptr[0]));
memset( r_ptr2, 0, cols*sizeof(r_ptr2[0]));
memset( s_ptr, 0, cols*sizeof(s_ptr[0]));
@ -206,10 +205,10 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
for( y = border; y < rows - border; y++ )
{
int x = border, i;
int x = border;
float* r_ptr = responses.ptr<float>(y);
short* s_ptr = sizes.ptr<short>(y);
memset( r_ptr, 0, border*sizeof(r_ptr[0]));
memset( s_ptr, 0, border*sizeof(s_ptr[0]));
memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0]));
@ -226,7 +225,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
__m128 bestResponse = _mm_setzero_ps();
__m128 bestSize = _mm_setzero_ps();
for( i = 0; i <= maxIdx; i++ )
for(int i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
__m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)),
@ -241,7 +240,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
_mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0));
}
for( i = 0; i < npatterns; i++ )
for(int i = 0; i < npatterns; i++ )
{
__m128 inner_sum = vals[pairs[i][1]];
__m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum);
@ -260,7 +259,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
_mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128()));
}
}
#endif
#endif
for( ; x < cols - border; x++ )
{
int ofs = y*step + x;
@ -268,13 +267,13 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
float bestResponse = 0;
int bestSize = 0;
for( i = 0; i <= maxIdx; i++ )
for(int i = 0; i <= maxIdx; i++ )
{
const int** p = (const int**)&f[i].p[0];
vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] +
p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs];
}
for( i = 0; i < npatterns; i++ )
for(int i = 0; i < npatterns; i++ )
{
int inner_sum = vals[pairs[i][1]];
int outer_sum = vals[pairs[i][0]] - inner_sum;
@ -306,7 +305,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P
int x, y, delta = sz/4, radius = delta*4;
float Lxx = 0, Lyy = 0, Lxy = 0;
int Lxxb = 0, Lyyb = 0, Lxyb = 0;
for( y = pt.y - radius; y <= pt.y + radius; y += delta )
for( x = pt.x - radius; x <= pt.x + radius; x += delta )
{
@ -314,7 +313,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P
float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x];
Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly;
}
if( (Lxx + Lyy)*(Lxx + Lyy) >= lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) )
return true;
@ -415,7 +414,7 @@ StarDetectorSuppressNonmax( const Mat& responses, const Mat& sizes,
;
}
}
StarDetector::StarDetector(int _maxSize, int _responseThreshold,
int _lineThresholdProjected,
int _lineThresholdBinarized,
@ -431,10 +430,10 @@ void StarDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, co
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
(*this)(grayImage, keypoints);
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
}
void StarDetector::operator()(const Mat& img, vector<KeyPoint>& keypoints) const
{
@ -446,5 +445,5 @@ void StarDetector::operator()(const Mat& img, vector<KeyPoint>& keypoints) const
responseThreshold, lineThresholdProjected,
lineThresholdBinarized, suppressNonmaxSize );
}
}

12
modules/flann/include/opencv2/flann/allocator.h
View File

@ -92,9 +92,9 @@ public:
/**
Default constructor. Initializes a new pool.
*/
PooledAllocator(int blocksize = BLOCKSIZE)
PooledAllocator(int blockSize = BLOCKSIZE)
{
this->blocksize = blocksize;
blocksize = blockSize;
remaining = 0;
base = NULL;
@ -122,7 +122,7 @@ public:
*/
void* allocateMemory(int size)
{
int blocksize;
int blockSize;
/* Round size up to a multiple of wordsize. The following expression
only works for WORDSIZE that is a power of 2, by masking last bits of
@ -138,11 +138,11 @@ public:
wastedMemory += remaining;
/* Allocate new storage. */
blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
// use the standard C malloc to allocate memory
void* m = ::malloc(blocksize);
void* m = ::malloc(blockSize);
if (!m) {
fprintf(stderr,"Failed to allocate memory.\n");
return NULL;
@ -155,7 +155,7 @@ public:
int shift = 0;
//int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
remaining = blocksize - sizeof(void*) - shift;
remaining = blockSize - sizeof(void*) - shift;
loc = ((char*)m + sizeof(void*) + shift);
}
void* rloc = loc;

10
modules/flann/include/opencv2/flann/dynamic_bitset.h
View File

@ -66,9 +66,9 @@ public:
/** @param only constructor we use in our code
* @param the size of the bitset (in bits)
*/
DynamicBitset(size_t size)
DynamicBitset(size_t sz)
{
resize(size);
resize(sz);
reset();
}
@ -116,10 +116,10 @@ public:
/** @param resize the bitset so that it contains at least size bits
* @param size
*/
void resize(size_t size)
void resize(size_t sz)
{
size_ = size;
bitset_.resize(size / cell_bit_size_ + 1);
size_ = sz;
bitset_.resize(sz / cell_bit_size_ + 1);
}
/** @param set a bit to true

6
modules/flann/include/opencv2/flann/heap.h
View File

@ -67,12 +67,12 @@ public:
* Constructor.
*
* Params:
* size = heap size
* sz = heap size
*/
Heap(int size)
Heap(int sz)
{
length = size;
length = sz;
heap.reserve(length);
count = 0;
}

44
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
View File

@ -106,7 +106,7 @@ private:
* indices_length = length of indices vector
*
*/
void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
UniqueRandom r(indices_length);
@ -122,7 +122,7 @@ private:
return;
}
centers[index] = indices[rnd];
centers[index] = dsindices[rnd];
for (int j=0; j<index; ++j) {
DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols);
@ -147,14 +147,14 @@ private:
* indices = indices in the dataset
* Returns:
*/
void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersGonzales(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
int rnd = rand_int(n);
assert(rnd >=0 && rnd < n);
centers[0] = indices[rnd];
centers[0] = dsindices[rnd];
int index;
for (index=1; index<k; ++index) {
@ -162,9 +162,9 @@ private:
int best_index = -1;
DistanceType best_val = 0;
for (int j=0; j<n; ++j) {
DistanceType dist = distance(dataset[centers[0]],dataset[indices[j]],dataset.cols);
DistanceType dist = distance(dataset[centers[0]],dataset[dsindices[j]],dataset.cols);
for (int i=1; i<index; ++i) {
DistanceType tmp_dist = distance(dataset[centers[i]],dataset[indices[j]],dataset.cols);
DistanceType tmp_dist = distance(dataset[centers[i]],dataset[dsindices[j]],dataset.cols);
if (tmp_dist<dist) {
dist = tmp_dist;
}
@ -175,7 +175,7 @@ private:
}
}
if (best_index!=-1) {
centers[index] = indices[best_index];
centers[index] = dsindices[best_index];
}
else {
break;
@ -198,7 +198,7 @@ private:
* indices = indices in the dataset
* Returns:
*/
void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
@ -208,10 +208,10 @@ private:
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
centers[0] = indices[index];
centers[0] = dsindices[index];
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[indices[i]], dataset[indices[index]], dataset.cols);
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
currentPot += closestDistSq[i];
}
@ -237,7 +237,7 @@ private:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[indices[i]], dataset[indices[index]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@ -247,9 +247,9 @@ private:
}
// Add the appropriate center
centers[centerCount] = indices[bestNewIndex];
centers[centerCount] = dsindices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[indices[i]], dataset[indices[bestNewIndex]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
}
centers_length = centerCount;
@ -518,11 +518,11 @@ private:
void computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost)
void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost)
{
cost = 0;
for (int i=0; i<indices_length; ++i) {
ElementType* point = dataset[indices[i]];
ElementType* point = dataset[dsindices[i]];
DistanceType dist = distance(point, dataset[centers[0]], veclen_);
labels[i] = 0;
for (int j=1; j<centers_length; ++j) {
@ -547,13 +547,13 @@ private:
*
* TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
*/
void computeClustering(NodePtr node, int* indices, int indices_length, int branching, int level)
void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level)
{
node->size = indices_length;
node->level = level;
if (indices_length < leaf_size_) { // leaf node
node->indices = indices;
node->indices = dsindices;
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
@ -563,10 +563,10 @@ private:
std::vector<int> labels(indices_length);
int centers_length;
(this->*chooseCenters)(branching, indices, indices_length, &centers[0], centers_length);
(this->*chooseCenters)(branching, dsindices, indices_length, &centers[0], centers_length);
if (centers_length<branching) {
node->indices = indices;
node->indices = dsindices;
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
@ -575,7 +575,7 @@ private:
// assign points to clusters
DistanceType cost;
computeLabels(indices, indices_length, &centers[0], centers_length, &labels[0], cost);
computeLabels(dsindices, indices_length, &centers[0], centers_length, &labels[0], cost);
node->childs = pool.allocate<NodePtr>(branching);
int start = 0;
@ -583,7 +583,7 @@ private:
for (int i=0; i<branching; ++i) {
for (int j=0; j<indices_length; ++j) {
if (labels[j]==i) {
std::swap(indices[j],indices[end]);
std::swap(dsindices[j],dsindices[end]);
std::swap(labels[j],labels[end]);
end++;
}
@ -592,7 +592,7 @@ private:
node->childs[i] = pool.allocate<Node>();
node->childs[i]->pivot = centers[i];
node->childs[i]->indices = NULL;
computeClustering(node->childs[i],indices+start, end-start, branching, level+1);
computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1);
start=end;
}
}

17
modules/gpu/CMakeLists.txt
View File

@ -5,9 +5,7 @@ endif()
set(the_description "GPU-accelerated Computer Vision")
ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_nonfree opencv_legacy)
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda")
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../highgui/src")
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda" "${CMAKE_CURRENT_SOURCE_DIR}/../highgui/src")
file(GLOB lib_hdrs "include/opencv2/${name}/*.hpp" "include/opencv2/${name}/*.h")
file(GLOB lib_int_hdrs "src/*.hpp" "src/*.h")
@ -30,17 +28,14 @@ if (HAVE_CUDA)
set(ncv_files ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda})
source_group("Src\\NVidia" FILES ${ncv_files})
include_directories(AFTER SYSTEM ${CUDA_INCLUDE_DIRS})
ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS})
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef /wd4211 /wd4201 /wd4100 /wd4505 /wd4408)
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep")
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;")
if(MSVC)
if(NOT ENABLE_NOISY_WARNINGS)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4211 /wd4201 /wd4100 /wd4505 /wd4408")
foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
string(REPLACE "/W4" "/W3" ${var} "${${var}}")
endforeach()
@ -52,10 +47,8 @@ if (HAVE_CUDA)
ocv_cuda_compile(cuda_objs ${lib_cuda} ${ncv_cuda})
#CUDA_BUILD_CLEAN_TARGET()
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
if(NOT APPLE)
unset(CUDA_nvcuvid_LIBRARY CACHE)
find_cuda_helper_libs(nvcuvid)
@ -106,11 +99,11 @@ ocv_add_precompiled_headers(${the_module})
################################################################################################################
file(GLOB test_srcs "test/*.cpp")
file(GLOB test_hdrs "test/*.hpp" "test/*.h")
set(nvidia "")
if(HAVE_CUDA)
file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.hpp" "test/nvidia/*.h")
set(nvidia FILES "Src\\\\\\\\NVidia" ${nvidia}) # 8 ugly backslashes :'(
else()
set(nvidia "")
endif()
ocv_add_accuracy_tests(FILES "Include" ${test_hdrs}

2
modules/highgui/CMakeLists.txt
View File

@ -134,7 +134,7 @@ endif()
if(HAVE_OPENNI)
list(APPEND highgui_srcs src/cap_openni.cpp)
include_directories(AFTER SYSTEM ${OPENNI_INCLUDE_DIR})
ocv_include_directories(${OPENNI_INCLUDE_DIR})
list(APPEND HIGHGUI_LIBRARIES ${OPENNI_LIBRARY})
endif(HAVE_OPENNI)

24
modules/highgui/src/cap_gstreamer.cpp
View File

@ -283,7 +283,7 @@ void CvCapture_GStreamer::newPad(GstElement *uridecodebin,
sinkpad = gst_element_get_static_pad (color, "sink");
// printf("linking dynamic pad to colourconverter %p %p\n", uridecodebin, pad);
gst_pad_link (pad, sinkpad);
@ -357,13 +357,13 @@ bool CvCapture_GStreamer::open( int type, const char* filename )
if(manualpipeline) {
GstIterator *it = gst_bin_iterate_sinks(GST_BIN(uridecodebin));
if(gst_iterator_next(it, (gpointer *)&sink) != GST_ITERATOR_OK) {
CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n");
return false;
CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n");
return false;
}
pipeline = uridecodebin;
pipeline = uridecodebin;
} else {
pipeline = gst_pipeline_new (NULL);
pipeline = gst_pipeline_new (NULL);
color = gst_element_factory_make("ffmpegcolorspace", NULL);
sink = gst_element_factory_make("appsink", NULL);
@ -381,16 +381,12 @@ bool CvCapture_GStreamer::open( int type, const char* filename )
gst_app_sink_set_max_buffers (GST_APP_SINK(sink), 1);
gst_app_sink_set_drop (GST_APP_SINK(sink), stream);
{
GstCaps* caps;
caps = gst_caps_new_simple("video/x-raw-rgb",
"red_mask", G_TYPE_INT, 0x0000FF,
"green_mask", G_TYPE_INT, 0x00FF00,
"blue_mask", G_TYPE_INT, 0xFF0000,
NULL);
gst_app_sink_set_caps(GST_APP_SINK(sink), caps);
gst_app_sink_set_caps(GST_APP_SINK(sink), gst_caps_new_simple("video/x-raw-rgb",
"red_mask", G_TYPE_INT, 0x0000FF,
"green_mask", G_TYPE_INT, 0x00FF00,
"blue_mask", G_TYPE_INT, 0xFF0000,
NULL));
gst_caps_unref(caps);
}
if(gst_element_set_state(GST_ELEMENT(pipeline), GST_STATE_READY) ==
GST_STATE_CHANGE_FAILURE) {

38
modules/highgui/src/cap_libv4l.cpp
View File

@ -779,9 +779,9 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName)
return -1;
} else {
buffer_number--;
fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName);
fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName);
goto try_again;
goto try_again;
}
}
@ -824,8 +824,8 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName)
if (capture->buffers[MAX_V4L_BUFFERS].start) {
free(capture->buffers[MAX_V4L_BUFFERS].start);
capture->buffers[MAX_V4L_BUFFERS].start = NULL;
}
}
capture->buffers[MAX_V4L_BUFFERS].start = malloc(buf.length);
capture->buffers[MAX_V4L_BUFFERS].length = buf.length;
};
@ -1080,11 +1080,11 @@ static int read_frame_v4l2(CvCaptureCAM_V4L* capture) {
#ifdef USE_TEMP_BUFFER
memcpy(capture->buffers[MAX_V4L_BUFFERS].start,
capture->buffers[buf.index].start,
capture->buffers[MAX_V4L_BUFFERS].length );
capture->buffers[buf.index].start,
capture->buffers[MAX_V4L_BUFFERS].length );
capture->bufferIndex = MAX_V4L_BUFFERS;
//printf("got data in buff %d, len=%d, flags=0x%X, seq=%d, used=%d)\n",
// buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused);
// buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused);
#else
capture->bufferIndex = buf.index;
#endif
@ -1211,9 +1211,9 @@ static int icvGrabFrameCAM_V4L(CvCaptureCAM_V4L* capture) {
capture->mmaps[capture->bufferIndex].format = capture->imageProperties.palette;
if (v4l1_ioctl (capture->deviceHandle, VIDIOCMCAPTURE,
&capture->mmaps[capture->bufferIndex]) == -1) {
/* capture is on the way, so just exit */
return 1;
&capture->mmaps[capture->bufferIndex]) == -1) {
/* capture is on the way, so just exit */
return 1;
}
++capture->bufferIndex;
@ -1273,11 +1273,11 @@ static IplImage* icvRetrieveFrameCAM_V4L( CvCaptureCAM_V4L* capture, int) {
if (capture->is_v4l2_device == 1)
{
if(capture->buffers[capture->bufferIndex].start){
memcpy((char *)capture->frame.imageData,
(char *)capture->buffers[capture->bufferIndex].start,
capture->frame.imageSize);
}
if(capture->buffers[capture->bufferIndex].start){
memcpy((char *)capture->frame.imageData,
(char *)capture->buffers[capture->bufferIndex].start,
capture->frame.imageSize);
}
} else
#endif /* HAVE_CAMV4L2 */
@ -1353,7 +1353,7 @@ static double icvGetPropertyCAM_V4L (CvCaptureCAM_V4L* capture,
sprintf(name, "<unknown property string>");
capture->control.id = property_id;
}
if(v4l2_ioctl(capture->deviceHandle, VIDIOC_G_CTRL, &capture->control) == 0) {
/* all went well */
is_v4l2_device = 1;
@ -1519,7 +1519,7 @@ static int icvSetControl (CvCaptureCAM_V4L* capture, int property_id, double val
CLEAR (capture->control);
CLEAR (capture->queryctrl);
/* get current values */
switch (property_id) {
case CV_CAP_PROP_BRIGHTNESS:
@ -1688,8 +1688,8 @@ static void icvCloseCAM_V4L( CvCaptureCAM_V4L* capture ){
if (xioctl(capture->deviceHandle, VIDIOC_STREAMOFF, &capture->type) < 0) {
perror ("Unable to stop the stream.");
}
for (unsigned int n_buffers = 0; n_buffers < capture->req.count; ++n_buffers) {
if (-1 == v4l2_munmap (capture->buffers[n_buffers].start, capture->buffers[n_buffers].length)) {
for (unsigned int n_buffers2 = 0; n_buffers2 < capture->req.count; ++n_buffers2) {
if (-1 == v4l2_munmap (capture->buffers[n_buffers2].start, capture->buffers[n_buffers2].length)) {
perror ("munmap");
}
}

16
modules/highgui/src/grfmt_png.cpp
View File

@ -157,22 +157,22 @@ bool PngDecoder::readHeader()
if( !m_buf.empty() || m_f )
{
png_uint_32 width, height;
png_uint_32 wdth, hght;
int bit_depth, color_type;
png_read_info( png_ptr, info_ptr );
png_get_IHDR( png_ptr, info_ptr, &width, &height,
png_get_IHDR( png_ptr, info_ptr, &wdth, &hght,
&bit_depth, &color_type, 0, 0, 0 );
m_width = (int)width;
m_height = (int)height;
m_width = (int)wdth;
m_height = (int)hght;
m_color_type = color_type;
m_bit_depth = bit_depth;
if( bit_depth <= 8 || bit_depth == 16 )
{
switch(color_type)
switch(color_type)
{
case PNG_COLOR_TYPE_RGB:
case PNG_COLOR_TYPE_PALETTE:
@ -224,7 +224,7 @@ bool PngDecoder::readData( Mat& img )
else if( !isBigEndian() )
png_set_swap( png_ptr );
if(img.channels() < 4)
if(img.channels() < 4)
{
/* observation: png_read_image() writes 400 bytes beyond
* end of data when reading a 400x118 color png
@ -247,7 +247,7 @@ bool PngDecoder::readData( Mat& img )
#else
png_set_gray_1_2_4_to_8( png_ptr );
#endif
if( CV_MAT_CN(m_type) > 1 && color )
png_set_bgr( png_ptr ); // convert RGB to BGR
else if( color )
@ -330,7 +330,7 @@ bool PngEncoder::write( const Mat& img, const vector<int>& params )
if( params[i] == CV_IMWRITE_PNG_STRATEGY )
{
compression_strategy = params[i+1];
compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED);
compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED);
}
}

24
modules/highgui/src/grfmt_tiff.cpp
View File

@ -115,19 +115,19 @@ bool TiffDecoder::readHeader()
if( tif )
{
int width = 0, height = 0, photometric = 0;
int wdth = 0, hght = 0, photometric = 0;
m_tif = tif;
if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &width ) &&
TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &height ) &&
if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &wdth ) &&
TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &hght ) &&
TIFFGetField( tif, TIFFTAG_PHOTOMETRIC, &photometric ))
{
int bpp=8, ncn = photometric > 1 ? 3 : 1;
TIFFGetField( tif, TIFFTAG_BITSPERSAMPLE, &bpp );
TIFFGetField( tif, TIFFTAG_SAMPLESPERPIXEL, &ncn );
m_width = width;
m_height = height;
m_width = wdth;
m_height = hght;
if( bpp > 8 &&
((photometric != 2 && photometric != 1) ||
(ncn != 1 && ncn != 3 && ncn != 4)))
@ -169,7 +169,7 @@ bool TiffDecoder::readData( Mat& img )
bool color = img.channels() > 1;
uchar* data = img.data;
int step = (int)img.step;
if( img.depth() != CV_8U && img.depth() != CV_16U && img.depth() != CV_32F && img.depth() != CV_64F )
return false;
@ -422,9 +422,9 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector<int>& /*params*/)
default:
{
return false;
}
}
}
const int bitsPerByte = 8;
size_t fileStep = (width * channels * bitsPerChannel) / bitsPerByte;
int rowsPerStrip = (int)((1 << 13)/fileStep);
@ -443,7 +443,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector<int>& /*params*/)
{
return false;
}
// defaults for now, maybe base them on params in the future
int compression = COMPRESSION_LZW;
int predictor = PREDICTOR_HORIZONTAL;
@ -516,7 +516,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector<int>& /*params*/)
return false;
}
}
TIFFClose(pTiffHandle);
return true;
}
@ -546,7 +546,7 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& /*params*/)
if( !strm.open(*m_buf) )
return false;
}
else
else
{
#ifdef HAVE_TIFF
return writeLibTiff(img, params);

16
modules/highgui/test/test_drawing.cpp
View File

@ -60,9 +60,9 @@ protected:
void CV_DrawingTest::run( int )
{
Mat testImg, valImg;
const string name = "drawing/image.jpg";
const string fname = "drawing/image.jpg";
string path = ts->get_data_path(), filename;
filename = path + name;
filename = path + fname;
draw( testImg );
@ -415,30 +415,30 @@ class CV_FillConvexPolyTest : public cvtest::BaseTest
{
public:
CV_FillConvexPolyTest() {}
~CV_FillConvexPolyTest() {}
~CV_FillConvexPolyTest() {}
protected:
void run(int)
{
vector<Point> line1;
vector<Point> line2;
line1.push_back(Point(1, 1));
line1.push_back(Point(5, 1));
line1.push_back(Point(5, 8));
line1.push_back(Point(1, 8));
line2.push_back(Point(2, 2));
line2.push_back(Point(10, 2));
line2.push_back(Point(10, 16));
line2.push_back(Point(2, 16));
Mat gray0(10,10,CV_8U, Scalar(0));
fillConvexPoly(gray0, line1, Scalar(255), 8, 0);
int nz1 = countNonZero(gray0);
fillConvexPoly(gray0, line2, Scalar(0), 8, 1);
int nz2 = countNonZero(gray0)/255;
CV_Assert( nz1 == 40 && nz2 == 0 );
}
};

16
modules/highgui/test/test_video_io.cpp
View File

@ -53,7 +53,7 @@ string fourccToString(int fourcc)
{
return format("%c%c%c%c", fourcc & 255, (fourcc >> 8) & 255, (fourcc >> 16) & 255, (fourcc >> 24) & 255);
}
const VideoFormat g_specific_fmt_list[] =
{
VideoFormat("avi", CV_FOURCC('X', 'V', 'I', 'D')),
@ -63,11 +63,11 @@ const VideoFormat g_specific_fmt_list[] =
VideoFormat("mkv", CV_FOURCC('X', 'V', 'I', 'D')),
VideoFormat("mkv", CV_FOURCC('M', 'P', 'E', 'G')),
VideoFormat("mkv", CV_FOURCC('M', 'J', 'P', 'G')),
VideoFormat("mov", CV_FOURCC('m', 'p', '4', 'v')),
VideoFormat()
};
}
class CV_HighGuiTest : public cvtest::BaseTest
@ -246,7 +246,7 @@ void CV_HighGuiTest::VideoTest(const string& dir, const cvtest::VideoFormat& fmt
if (!img)
break;
frames.push_back(Mat(img).clone());
if (writer == 0)
@ -393,7 +393,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor
{
string ext = fmt.ext;
int fourcc = fmt.fourcc;
string fourcc_str = cvtest::fourccToString(fourcc);
const string video_file = "video_" + fourcc_str + "." + ext;
@ -403,7 +403,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor
if (!writer.isOpened())
{
// call it repeatedly for easier debugging
VideoWriter writer(video_file, fourcc, 25, frame_size, true);
VideoWriter writer2(video_file, fourcc, 25, frame_size, true);
ts->printf(ts->LOG, "Creating a video in %s...\n", video_file.c_str());
ts->printf(ts->LOG, "Cannot create VideoWriter object with codec %s.\n", fourcc_str.c_str());
ts->set_failed_test_info(ts->FAIL_MISMATCH);
@ -412,7 +412,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor
const size_t IMAGE_COUNT = 30;
vector<Mat> images;
for( size_t i = 0; i < IMAGE_COUNT; ++i )
{
string file_path = format("%s../python/images/QCIF_%02d.bmp", dir.c_str(), i);
@ -432,7 +432,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor
if (img.at<Vec3b>(k, l) == Vec3b::all(0))
img.at<Vec3b>(k, l) = Vec3b(0, 255, 0);
else img.at<Vec3b>(k, l) = Vec3b(0, 0, 255);
resize(img, img, frame_size, 0.0, 0.0, INTER_CUBIC);
images.push_back(img);

958
modules/imgproc/src/color.cpp
View File

File diff suppressed because it is too large Load Diff

180
modules/imgproc/src/filter.cpp
View File

@ -48,11 +48,11 @@
/*
Various border types, image boundaries are denoted with '|'
* BORDER_REPLICATE: aaaaaa|abcdefgh|hhhhhhh
* BORDER_REFLECT: fedcba|abcdefgh|hgfedcb
* BORDER_REFLECT_101: gfedcb|abcdefgh|gfedcba
* BORDER_WRAP: cdefgh|abcdefgh|abcdefg
* BORDER_WRAP: cdefgh|abcdefgh|abcdefg
* BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii with some specified 'i'
*/
int cv::borderInterpolate( int p, int len, int borderType )
@ -113,7 +113,7 @@ FilterEngine::FilterEngine()
wholeSize = Size(-1,-1);
}
FilterEngine::FilterEngine( const Ptr<BaseFilter>& _filter2D,
const Ptr<BaseRowFilter>& _rowFilter,
@ -125,7 +125,7 @@ FilterEngine::FilterEngine( const Ptr<BaseFilter>& _filter2D,
init(_filter2D, _rowFilter, _columnFilter, _srcType, _dstType, _bufType,
_rowBorderType, _columnBorderType, _borderValue);
}
FilterEngine::~FilterEngine()
{
}
@ -141,24 +141,24 @@ void FilterEngine::init( const Ptr<BaseFilter>& _filter2D,
_srcType = CV_MAT_TYPE(_srcType);
_bufType = CV_MAT_TYPE(_bufType);
_dstType = CV_MAT_TYPE(_dstType);
srcType = _srcType;
int srcElemSize = (int)getElemSize(srcType);
dstType = _dstType;
bufType = _bufType;
filter2D = _filter2D;
rowFilter = _rowFilter;
columnFilter = _columnFilter;
if( _columnBorderType < 0 )
_columnBorderType = _rowBorderType;
rowBorderType = _rowBorderType;
columnBorderType = _columnBorderType;
CV_Assert( columnBorderType != BORDER_WRAP );
if( isSeparable() )
{
CV_Assert( !rowFilter.empty() && !columnFilter.empty() );
@ -175,7 +175,7 @@ void FilterEngine::init( const Ptr<BaseFilter>& _filter2D,
CV_Assert( 0 <= anchor.x && anchor.x < ksize.width &&
0 <= anchor.y && anchor.y < ksize.height );
borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1);
borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1);
int borderLength = std::max(ksize.width - 1, 1);
borderTab.resize(borderLength*borderElemSize);
@ -198,7 +198,7 @@ static const int VEC_ALIGN = CV_MALLOC_ALIGN;
int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows)
{
int i, j;
wholeSize = _wholeSize;
roi = _roi;
CV_Assert( roi.x >= 0 && roi.y >= 0 && roi.width >= 0 && roi.height >= 0 &&
@ -226,7 +226,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows)
int n = (int)constBorderValue.size(), N;
N = (maxWidth + ksize.width - 1)*esz;
tdst = isSeparable() ? &srcRow[0] : dst;
for( i = 0; i < N; i += n )
{
n = std::min( n, N - i );
@ -237,7 +237,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows)
if( isSeparable() )
(*rowFilter)(&srcRow[0], dst, maxWidth, cn);
}
int maxBufStep = bufElemSize*(int)alignSize(maxWidth +
(!isSeparable() ? ksize.width - 1 : 0),VEC_ALIGN);
ringBuf.resize(maxBufStep*rows.size()+VEC_ALIGN);
@ -265,10 +265,10 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows)
else
{
int xofs1 = std::min(roi.x, anchor.x) - roi.x;
int btab_esz = borderElemSize, wholeWidth = wholeSize.width;
int* btab = (int*)&borderTab[0];
for( i = 0; i < dx1; i++ )
{
int p0 = (borderInterpolate(i-dx1, wholeWidth, rowBorderType) + xofs1)*btab_esz;
@ -301,20 +301,20 @@ int FilterEngine::start(const Mat& src, const Rect& _srcRoi,
bool isolated, int maxBufRows)
{
Rect srcRoi = _srcRoi;
if( srcRoi == Rect(0,0,-1,-1) )
srcRoi = Rect(0,0,src.cols,src.rows);
CV_Assert( srcRoi.x >= 0 && srcRoi.y >= 0 &&
srcRoi.width >= 0 && srcRoi.height >= 0 &&
srcRoi.x + srcRoi.width <= src.cols &&
srcRoi.y + srcRoi.height <= src.rows );
Point ofs;
Size wholeSize(src.cols, src.rows);
Size wsz(src.cols, src.rows);
if( !isolated )
src.locateROI( wholeSize, ofs );
start( wholeSize, srcRoi + ofs, maxBufRows );
src.locateROI( wsz, ofs );
start( wsz, srcRoi + ofs, maxBufRows );
return startY - ofs.y;
}
@ -334,7 +334,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count,
uchar* dst, int dststep )
{
CV_Assert( wholeSize.width > 0 && wholeSize.height > 0 );
const int *btab = &borderTab[0];
int esz = (int)getElemSize(srcType), btab_esz = borderElemSize;
uchar** brows = &rows[0];
@ -365,7 +365,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count,
int bi = (startY - startY0 + rowCount) % bufRows;
uchar* brow = alignPtr(&ringBuf[0], VEC_ALIGN) + bi*bufStep;
uchar* row = isSep ? &srcRow[0] : brow;
if( ++rowCount > bufRows )
{
--rowCount;
@ -394,7 +394,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count,
row[i + (width1 - _dx2)*esz] = src[btab[i+_dx1*esz]];
}
}
if( isSep )
(*rowFilter)(row, brow, width, CV_MAT_CN(srcType));
}
@ -434,11 +434,11 @@ void FilterEngine::apply(const Mat& src, Mat& dst,
const Rect& _srcRoi, Point dstOfs, bool isolated)
{
CV_Assert( src.type() == srcType && dst.type() == dstType );
Rect srcRoi = _srcRoi;
if( srcRoi == Rect(0,0,-1,-1) )
srcRoi = Rect(0,0,src.cols,src.rows);
if( srcRoi.area() == 0 )
return;
@ -560,7 +560,7 @@ struct RowVec_8u32s
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
int i = 0, k, _ksize = kernel.rows + kernel.cols - 1;
int* dst = (int*)_dst;
const int* _kx = (const int*)kernel.data;
@ -593,7 +593,7 @@ struct RowVec_8u32s
s2 = _mm_add_epi32(s2, _mm_unpacklo_epi16(x2, x3));
s3 = _mm_add_epi32(s3, _mm_unpackhi_epi16(x2, x3));
}
_mm_store_si128((__m128i*)(dst + i), s0);
_mm_store_si128((__m128i*)(dst + i + 4), s1);
_mm_store_si128((__m128i*)(dst + i + 8), s2);
@ -652,7 +652,7 @@ struct SymmRowSmallVec_8u32s
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
int i = 0, j, k, _ksize = kernel.rows + kernel.cols - 1;
int* dst = (int*)_dst;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
@ -973,7 +973,7 @@ struct SymmColumnVec_32s8u
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
const float* ky = (const float*)kernel.data + ksize2;
int i = 0, k;
@ -1121,7 +1121,7 @@ struct SymmColumnSmallVec_32s16s
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
const float* ky = (const float*)kernel.data + ksize2;
int i = 0;
@ -1237,9 +1237,9 @@ struct SymmColumnSmallVec_32s16s
Mat kernel;
};
/////////////////////////////////////// 16s //////////////////////////////////
struct RowVec_16s32f
{
RowVec_16s32f() {}
@ -1248,17 +1248,17 @@ struct RowVec_16s32f
kernel = _kernel;
sse2_supported = checkHardwareSupport(CV_CPU_SSE2);
}
int operator()(const uchar* _src, uchar* _dst, int width, int cn) const
{
if( !sse2_supported )
return 0;
int i = 0, k, _ksize = kernel.rows + kernel.cols - 1;
float* dst = (float*)_dst;
const float* _kx = (const float*)kernel.data;
width *= cn;
for( ; i <= width - 8; i += 8 )
{
const short* src = (const short*)_src + i;
@ -1267,7 +1267,7 @@ struct RowVec_16s32f
{
f = _mm_load_ss(_kx+k);
f = _mm_shuffle_ps(f, f, 0);
__m128i x0i = _mm_loadu_si128((const __m128i*)src);
__m128i x1i = _mm_srai_epi32(_mm_unpackhi_epi16(x0i, x0i), 16);
x0i = _mm_srai_epi32(_mm_unpacklo_epi16(x0i, x0i), 16);
@ -1281,12 +1281,12 @@ struct RowVec_16s32f
}
return i;
}
Mat kernel;
bool sse2_supported;
};
struct SymmColumnVec_32f16s
{
SymmColumnVec_32f16s() { symmetryType=0; }
@ -1298,12 +1298,12 @@ struct SymmColumnVec_32f16s
CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 );
sse2_supported = checkHardwareSupport(CV_CPU_SSE2);
}
int operator()(const uchar** _src, uchar* _dst, int width) const
{
if( !sse2_supported )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
const float* ky = (const float*)kernel.data + ksize2;
int i = 0, k;
@ -1312,7 +1312,7 @@ struct SymmColumnVec_32f16s
const float *S, *S2;
short* dst = (short*)_dst;
__m128 d4 = _mm_set1_ps(delta);
if( symmetrical )
{
for( ; i <= width - 16; i += 16 )
@ -1330,7 +1330,7 @@ struct SymmColumnVec_32f16s
s3 = _mm_load_ps(S+12);
s2 = _mm_add_ps(_mm_mul_ps(s2, f), d4);
s3 = _mm_add_ps(_mm_mul_ps(s3, f), d4);
for( k = 1; k <= ksize2; k++ )
{
S = src[k] + i;
@ -1346,23 +1346,23 @@ struct SymmColumnVec_32f16s
s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f));
s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f));
}
__m128i s0i = _mm_cvtps_epi32(s0);
__m128i s1i = _mm_cvtps_epi32(s1);
__m128i s2i = _mm_cvtps_epi32(s2);
__m128i s3i = _mm_cvtps_epi32(s3);
_mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i));
_mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i));
}
for( ; i <= width - 4; i += 4 )
{
__m128 f = _mm_load_ss(ky);
f = _mm_shuffle_ps(f, f, 0);
__m128 x0, s0 = _mm_load_ps(src[0] + i);
s0 = _mm_add_ps(_mm_mul_ps(s0, f), d4);
for( k = 1; k <= ksize2; k++ )
{
f = _mm_load_ss(ky+k);
@ -1372,7 +1372,7 @@ struct SymmColumnVec_32f16s
x0 = _mm_add_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i));
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f));
}
__m128i s0i = _mm_cvtps_epi32(s0);
_mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i));
}
@ -1384,7 +1384,7 @@ struct SymmColumnVec_32f16s
__m128 f, s0 = d4, s1 = d4, s2 = d4, s3 = d4;
__m128 x0, x1;
S = src[0] + i;
for( k = 1; k <= ksize2; k++ )
{
S = src[k] + i;
@ -1400,20 +1400,20 @@ struct SymmColumnVec_32f16s
s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f));
s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f));
}
__m128i s0i = _mm_cvtps_epi32(s0);
__m128i s1i = _mm_cvtps_epi32(s1);
__m128i s2i = _mm_cvtps_epi32(s2);
__m128i s3i = _mm_cvtps_epi32(s3);
_mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i));
_mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i));
}
for( ; i <= width - 4; i += 4 )
{
__m128 f, x0, s0 = d4;
for( k = 1; k <= ksize2; k++ )
{
f = _mm_load_ss(ky+k);
@ -1421,21 +1421,21 @@ struct SymmColumnVec_32f16s
x0 = _mm_sub_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i));
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f));
}
__m128i s0i = _mm_cvtps_epi32(s0);
_mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i));
}
}
return i;
}
int symmetryType;
float delta;
Mat kernel;
bool sse2_supported;
};
};
/////////////////////////////////////// 32f //////////////////////////////////
@ -1451,7 +1451,7 @@ struct RowVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
int i = 0, k, _ksize = kernel.rows + kernel.cols - 1;
float* dst = (float*)_dst;
const float* _kx = (const float*)kernel.data;
@ -1494,7 +1494,7 @@ struct SymmRowSmallVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
int i = 0, _ksize = kernel.rows + kernel.cols - 1;
float* dst = (float*)_dst;
const float* src = (const float*)_src + (_ksize/2)*cn;
@ -1594,12 +1594,12 @@ struct SymmRowSmallVec_32f
y0 = _mm_mul_ps(_mm_add_ps(y0, y2), k1);
x0 = _mm_add_ps(x0, _mm_mul_ps(x1, k0));
y0 = _mm_add_ps(y0, _mm_mul_ps(y1, k0));
x2 = _mm_add_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2));
y2 = _mm_add_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4));
x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2));
y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2));
_mm_store_ps(dst + i, x0);
_mm_store_ps(dst + i + 4, y0);
}
@ -1654,12 +1654,12 @@ struct SymmRowSmallVec_32f
x0 = _mm_mul_ps(_mm_sub_ps(x0, x2), k1);
y0 = _mm_mul_ps(_mm_sub_ps(y0, y2), k1);
x2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2));
y2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4));
x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2));
y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2));
_mm_store_ps(dst + i, x0);
_mm_store_ps(dst + i + 4, y0);
}
@ -1689,7 +1689,7 @@ struct SymmColumnVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
const float* ky = (const float*)kernel.data + ksize2;
int i = 0, k;
@ -1829,7 +1829,7 @@ struct SymmColumnSmallVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
const float* ky = (const float*)kernel.data + ksize2;
int i = 0;
@ -1963,7 +1963,7 @@ struct FilterVec_8u
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const float* kf = (const float*)&coeffs[0];
int i = 0, k, nz = _nz;
__m128 d4 = _mm_set1_ps(delta);
@ -2046,7 +2046,7 @@ struct FilterVec_8u16s
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const float* kf = (const float*)&coeffs[0];
short* dst = (short*)_dst;
int i = 0, k, nz = _nz;
@ -2127,7 +2127,7 @@ struct FilterVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
const float* kf = (const float*)&coeffs[0];
const float** src = (const float**)_src;
float* dst = (float*)_dst;
@ -2217,7 +2217,7 @@ template<typename ST, typename DT, class VecOp> struct RowFilter : public BaseRo
(kernel.rows == 1 || kernel.cols == 1));
vecOp = _vecOp;
}
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
int _ksize = ksize;
@ -2242,7 +2242,7 @@ template<typename ST, typename DT, class VecOp> struct RowFilter : public BaseRo
s0 += f*S[0]; s1 += f*S[1];
s2 += f*S[2]; s3 += f*S[3];
}
D[i] = s0; D[i+1] = s1;
D[i+2] = s2; D[i+3] = s3;
}
@ -2275,7 +2275,7 @@ template<typename ST, typename DT, class VecOp> struct SymmRowSmallFilter :
symmetryType = _symmetryType;
CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 && this->ksize <= 5 );
}
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
int ksize2 = this->ksize/2, ksize2n = ksize2*cn;
@ -2397,7 +2397,7 @@ template<class CastOp, class VecOp> struct ColumnFilter : public BaseColumnFilte
{
typedef typename CastOp::type1 ST;
typedef typename CastOp::rtype DT;
ColumnFilter( const Mat& _kernel, int _anchor,
double _delta, const CastOp& _castOp=CastOp(),
const VecOp& _vecOp=VecOp() )
@ -2427,7 +2427,7 @@ template<class CastOp, class VecOp> struct ColumnFilter : public BaseColumnFilte
{
DT* D = (DT*)dst;
i = vecOp(src, dst, width);
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; i <= width - 4; i += 4 )
{
ST f = ky[0];
@ -2574,7 +2574,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
{
typedef typename CastOp::type1 ST;
typedef typename CastOp::rtype DT;
SymmColumnSmallFilter( const Mat& _kernel, int _anchor,
double _delta, int _symmetryType,
const CastOp& _castOp=CastOp(),
@ -2610,7 +2610,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
{
if( is_1_2_1 )
{
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; i <= width - 4; i += 4 )
{
ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta;
@ -2624,7 +2624,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
D[i+3] = castOp(s1);
}
#else
for( ; i < width; i ++ )
for( ; i < width; i ++ )
{
ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta;
D[i] = castOp(s0);
@ -2633,7 +2633,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
}
else if( is_1_m2_1 )
{
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; i <= width - 4; i += 4 )
{
ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta;
@ -2647,7 +2647,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
D[i+3] = castOp(s1);
}
#else
for( ; i < width; i ++ )
for( ; i < width; i ++ )
{
ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta;
D[i] = castOp(s0);
@ -2700,7 +2700,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter<CastOp, VecOp>
D[i+3] = castOp(s1);
}
#else
for( ; i < width; i ++ )
for( ; i < width; i ++ )
{
ST s0 = S2[i] - S0[i] + _delta;
D[i] = castOp(s0);
@ -2763,7 +2763,7 @@ template<typename ST, typename DT> struct FixedPtCastEx
};
}
cv::Ptr<cv::BaseRowFilter> cv::getLinearRowFilter( int srcType, int bufType,
InputArray _kernel, int anchor,
int symmetryType )
@ -2785,7 +2785,7 @@ cv::Ptr<cv::BaseRowFilter> cv::getLinearRowFilter( int srcType, int bufType,
return Ptr<BaseRowFilter>(new SymmRowSmallFilter<float, float, SymmRowSmallVec_32f>
(kernel, anchor, symmetryType, SymmRowSmallVec_32f(kernel, symmetryType)));
}
if( sdepth == CV_8U && ddepth == CV_32S )
return Ptr<BaseRowFilter>(new RowFilter<uchar, int, RowVec_8u32s>
(kernel, anchor, RowVec_8u32s(kernel)));
@ -2820,7 +2820,7 @@ cv::Ptr<cv::BaseRowFilter> cv::getLinearRowFilter( int srcType, int bufType,
cv::Ptr<cv::BaseColumnFilter> cv::getLinearColumnFilter( int bufType, int dstType,
InputArray _kernel, int anchor,
int symmetryType, double delta,
int symmetryType, double delta,
int bits )
{
Mat kernel = _kernel.getMat();
@ -3045,7 +3045,7 @@ template<typename ST, class CastOp, class VecOp> struct Filter2D : public BaseFi
{
typedef typename CastOp::type1 KT;
typedef typename CastOp::rtype DT;
Filter2D( const Mat& _kernel, Point _anchor,
double _delta, const CastOp& _castOp=CastOp(),
const VecOp& _vecOp=VecOp() )
@ -3143,7 +3143,7 @@ cv::Ptr<cv::BaseFilter> cv::getLinearFilter(int srcType, int dstType,
kernel = _kernel;
else
_kernel.convertTo(kernel, kdepth, _kernel.type() == CV_32S ? 1./(1 << bits) : 1.);
if( sdepth == CV_8U && ddepth == CV_8U )
return Ptr<BaseFilter>(new Filter2D<uchar, Cast<float, uchar>, FilterVec_8u>
(kernel, anchor, delta, Cast<float, uchar>(), FilterVec_8u(kernel, 0, delta)));
@ -3203,7 +3203,7 @@ cv::Ptr<cv::FilterEngine> cv::createLinearFilter( int _srcType, int _dstType,
{
Mat _kernel = filter_kernel.getMat();
_srcType = CV_MAT_TYPE(_srcType);
_dstType = CV_MAT_TYPE(_dstType);
_dstType = CV_MAT_TYPE(_dstType);
int cn = CV_MAT_CN(_srcType);
CV_Assert( cn == CV_MAT_CN(_dstType) );
@ -3211,14 +3211,14 @@ cv::Ptr<cv::FilterEngine> cv::createLinearFilter( int _srcType, int _dstType,
int bits = 0;
/*int sdepth = CV_MAT_DEPTH(_srcType), ddepth = CV_MAT_DEPTH(_dstType);
int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor);
int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor);
if( sdepth == CV_8U && (ddepth == CV_8U || ddepth == CV_16S) &&
_kernel.rows*_kernel.cols <= (1 << 10) )
{
bits = (ktype & KERNEL_INTEGER) ? 0 : 11;
_kernel.convertTo(kernel, CV_32S, 1 << bits);
}*/
Ptr<BaseFilter> _filter2D = getLinearFilter(_srcType, _dstType,
kernel, _anchor, _delta, bits);
@ -3233,7 +3233,7 @@ void cv::filter2D( InputArray _src, OutputArray _dst, int ddepth,
double delta, int borderType )
{
Mat src = _src.getMat(), kernel = _kernel.getMat();
if( ddepth < 0 )
ddepth = src.depth();
@ -3279,7 +3279,7 @@ void cv::sepFilter2D( InputArray _src, OutputArray _dst, int ddepth,
double delta, int borderType )
{
Mat src = _src.getMat(), kernelX = _kernelX.getMat(), kernelY = _kernelY.getMat();
if( ddepth < 0 )
ddepth = src.depth();

22
modules/imgproc/src/gcgraph.hpp
View File

@ -64,7 +64,7 @@ private:
int ts;
int dist;
TWeight weight;
uchar t;
uchar t;
};
class Edge
{
@ -174,7 +174,7 @@ TWeight GCGraph<TWeight>::maxFlow()
v->t = v->weight < 0;
}
else
v->parent = 0;
v->parent = 0;
}
first = first->next;
last->next = nilNode;
@ -290,14 +290,14 @@ TWeight GCGraph<TWeight>::maxFlow()
curr_ts++;
while( !orphans.empty() )
{
Vtx* v = orphans.back();
Vtx* v2 = orphans.back();
orphans.pop_back();
int d, minDist = INT_MAX;
e0 = 0;
vt = v->t;
vt = v2->t;
for( ei = v->first; ei != 0; ei = edgePtr[ei].next )
for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
{
if( edgePtr[ei^(vt^1)].weight == 0 )
continue;
@ -344,16 +344,16 @@ TWeight GCGraph<TWeight>::maxFlow()
}
}
if( (v->parent = e0) > 0 )
if( (v2->parent = e0) > 0 )
{
v->ts = curr_ts;
v->dist = minDist;
v2->ts = curr_ts;
v2->dist = minDist;
continue;
}
/* no parent is found */
v->ts = 0;
for( ei = v->first; ei != 0; ei = edgePtr[ei].next )
v2->ts = 0;
for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
{
u = vtxPtr+edgePtr[ei].dst;
ej = u->parent;
@ -364,7 +364,7 @@ TWeight GCGraph<TWeight>::maxFlow()
u->next = nilNode;
last = last->next = u;
}
if( ej > 0 && vtxPtr+edgePtr[ej].dst == v )
if( ej > 0 && vtxPtr+edgePtr[ej].dst == v2 )
{
orphans.push_back(u);
u->parent = ORPHAN;

408
modules/imgproc/src/histogram.cpp
View File

File diff suppressed because it is too large Load Diff

28
modules/imgproc/src/hough.cpp
View File

@ -92,8 +92,6 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta,
int step, width, height;
int numangle, numrho;
int total = 0;
float ang;
int r, n;
int i, j;
float irho = 1 / rho;
double scale;
@ -117,7 +115,8 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta,
memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) );
for( ang = 0, n = 0; n < numangle; ang += theta, n++ )
float ang = 0;
for(int n = 0; n < numangle; ang += theta, n++ )
{
tabSin[n] = (float)(sin(ang) * irho);
tabCos[n] = (float)(cos(ang) * irho);
@ -128,17 +127,17 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta,
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for( n = 0; n < numangle; n++ )
for(int n = 0; n < numangle; n++ )
{
r = cvRound( j * tabCos[n] + i * tabSin[n] );
int r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n+1) * (numrho+2) + r+1]++;
}
}
// stage 2. find local maximums
for( r = 0; r < numrho; r++ )
for( n = 0; n < numangle; n++ )
for(int r = 0; r < numrho; r++ )
for(int n = 0; n < numangle; n++ )
{
int base = (n+1) * (numrho+2) + r+1;
if( accum[base] > threshold &&
@ -529,7 +528,7 @@ icvHoughLinesProbabilistic( CvMat* image,
// choose random point out of the remaining ones
int idx = cvRandInt(&rng) % count;
int max_val = threshold-1, max_n = 0;
CvPoint* pt = (CvPoint*)cvGetSeqElem( seq, idx );
CvPoint* point = (CvPoint*)cvGetSeqElem( seq, idx );
CvPoint line_end[2] = {{0,0}, {0,0}};
float a, b;
int* adata = (int*)accum.data;
@ -537,11 +536,11 @@ icvHoughLinesProbabilistic( CvMat* image,
int good_line;
const int shift = 16;
i = pt->y;
j = pt->x;
i = point->y;
j = point->x;
// "remove" it by overriding it with the last element
*pt = *(CvPoint*)cvGetSeqElem( seq, count-1 );
*point = *(CvPoint*)cvGetSeqElem( seq, count-1 );
// check if it has been excluded already (i.e. belongs to some other line)
if( !mdata0[i*width + j] )
@ -852,7 +851,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
for( x = 0; x < cols; x++ )
{
float vx, vy;
int sx, sy, x0, y0, x1, y1, r, k;
int sx, sy, x0, y0, x1, y1, r;
CvPoint pt;
vx = dx_row[x];
@ -869,7 +868,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
x0 = cvRound((x*idp)*ONE);
y0 = cvRound((y*idp)*ONE);
// Step from min_radius to max_radius in both directions of the gradient
for( k = 0; k < 2; k++ )
for(int k1 = 0; k1 < 2; k1++ )
{
x1 = x0 + min_radius * sx;
y1 = y0 + min_radius * sy;
@ -934,7 +933,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
//Calculate circle's center in pixels
float cx = (float)((x + 0.5f)*dp), cy = (float)(( y + 0.5f )*dp);
float start_dist, dist_sum;
float r_best = 0, c[3];
float r_best = 0;
int max_count = 0;
// Check distance with previously detected circles
for( j = 0; j < circles->total; j++ )
@ -996,6 +995,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
// Check if the circle has enough support
if( max_count > acc_threshold )
{
float c[3];
c[0] = cx;
c[1] = cy;
c[2] = (float)r_best;

39
modules/imgproc/src/imgwarp.cpp
View File

@ -97,7 +97,6 @@ static inline void interpolateLanczos4( float x, float* coeffs )
static const double cs[][2]=
{{1, 0}, {-s45, -s45}, {0, 1}, {s45, -s45}, {-1, 0}, {s45, s45}, {0, -1}, {-s45, s45}};
int i;
if( x < FLT_EPSILON )
{
for( int i = 0; i < 8; i++ )
@ -108,7 +107,7 @@ static inline void interpolateLanczos4( float x, float* coeffs )
float sum = 0;
double y0=-(x+3)*CV_PI*0.25, s0 = sin(y0), c0=cos(y0);
for( i = 0; i < 8; i++ )
for(int i = 0; i < 8; i++ )
{
double y = -(x+3-i)*CV_PI*0.25;
coeffs[i] = (float)((cs[i][0]*s0 + cs[i][1]*c0)/(y*y));
@ -116,7 +115,7 @@ static inline void interpolateLanczos4( float x, float* coeffs )
}
sum = 1.f/sum;
for( i = 0; i < 8; i++ )
for(int i = 0; i < 8; i++ )
coeffs[i] *= sum;
}
@ -1091,14 +1090,14 @@ static void resizeGeneric_( const Mat& src, Mat& dst,
const T* srows[MAX_ESIZE]={0};
WT* rows[MAX_ESIZE]={0};
int prev_sy[MAX_ESIZE];
int k, dy;
int dy;
xmin *= cn;
xmax *= cn;
HResize hresize;
VResize vresize;
for( k = 0; k < ksize; k++ )
for(int k = 0; k < ksize; k++ )
{
prev_sy[k] = -1;
rows[k] = (WT*)_buffer + bufstep*k;
@ -1107,9 +1106,9 @@ static void resizeGeneric_( const Mat& src, Mat& dst,
// image resize is a separable operation. In case of not too strong
for( dy = 0; dy < dsize.height; dy++, beta += ksize )
{
int sy0 = yofs[dy], k, k0=ksize, k1=0, ksize2 = ksize/2;
int sy0 = yofs[dy], k0=ksize, k1=0, ksize2 = ksize/2;
for( k = 0; k < ksize; k++ )
for(int k = 0; k < ksize; k++ )
{
int sy = clip(sy0 - ksize2 + 1 + k, 0, ssize.height);
for( k1 = std::max(k1, k); k1 < ksize; k1++ )
@ -2374,25 +2373,25 @@ static void remapLanczos4( const Mat& _src, Mat& _dst, const Mat& _xy,
for( i = 0; i < 8; i++, w += 8 )
{
int yi = y[i];
const T* S = S0 + yi*sstep;
const T* S1 = S0 + yi*sstep;
if( yi < 0 )
continue;
if( x[0] >= 0 )
sum += (S[x[0]] - cv)*w[0];
sum += (S1[x[0]] - cv)*w[0];
if( x[1] >= 0 )
sum += (S[x[1]] - cv)*w[1];
sum += (S1[x[1]] - cv)*w[1];
if( x[2] >= 0 )
sum += (S[x[2]] - cv)*w[2];
sum += (S1[x[2]] - cv)*w[2];
if( x[3] >= 0 )
sum += (S[x[3]] - cv)*w[3];
sum += (S1[x[3]] - cv)*w[3];
if( x[4] >= 0 )
sum += (S[x[4]] - cv)*w[4];
sum += (S1[x[4]] - cv)*w[4];
if( x[5] >= 0 )
sum += (S[x[5]] - cv)*w[5];
sum += (S1[x[5]] - cv)*w[5];
if( x[6] >= 0 )
sum += (S[x[6]] - cv)*w[6];
sum += (S1[x[6]] - cv)*w[6];
if( x[7] >= 0 )
sum += (S[x[7]] - cv)*w[7];
sum += (S1[x[7]] - cv)*w[7];
}
D[k] = castOp(sum);
}
@ -2966,8 +2965,8 @@ void cv::warpAffine( InputArray _src, OutputArray _dst,
remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue );
else
{
Mat matA(bh, bw, CV_16U, A);
remap( src, dpart, _XY, matA, interpolation, borderType, borderValue );
Mat _matA(bh, bw, CV_16U, A);
remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue );
}
}
}
@ -3064,8 +3063,8 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0,
remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue );
else
{
Mat matA(bh, bw, CV_16U, A);
remap( src, dpart, _XY, matA, interpolation, borderType, borderValue );
Mat _matA(bh, bw, CV_16U, A);
remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue );
}
}
}

48
modules/imgproc/src/moments.cpp
View File

@ -106,7 +106,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* moments )
yi_1 = ((CvPoint2D32f*)(reader.ptr))->y;
}
CV_NEXT_SEQ_ELEM( contour->elem_size, reader );
xi_12 = xi_1 * xi_1;
yi_12 = yi_1 * yi_1;
@ -208,7 +208,7 @@ static void momentsInTile( const cv::Mat& img, double* moments )
const T* ptr = (const T*)(img.data + y*img.step);
WT x0 = 0, x1 = 0, x2 = 0;
MT x3 = 0;
for( x = 0; x < size.width; x++ )
{
WT p = ptr[x];
@ -248,21 +248,21 @@ template<> void momentsInTile<uchar, int, int>( const cv::Mat& img, double* mome
typedef int WT;
typedef int MT;
cv::Size size = img.size();
int x, y;
int y;
MT mom[10] = {0,0,0,0,0,0,0,0,0,0};
bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2);
for( y = 0; y < size.height; y++ )
{
const T* ptr = img.ptr<T>(y);
int x0 = 0, x1 = 0, x2 = 0, x3 = 0, x = 0;
if( useSIMD )
{
__m128i qx_init = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7);
__m128i dx = _mm_set1_epi16(8);
__m128i z = _mm_setzero_si128(), qx0 = z, qx1 = z, qx2 = z, qx3 = z, qx = qx_init;
for( ; x <= size.width - 8; x += 8 )
{
__m128i p = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(ptr + x)), z);
@ -272,34 +272,34 @@ template<> void momentsInTile<uchar, int, int>( const cv::Mat& img, double* mome
qx1 = _mm_add_epi32(qx1, _mm_madd_epi16(p, qx));
qx2 = _mm_add_epi32(qx2, _mm_madd_epi16(p, sx));
qx3 = _mm_add_epi32(qx3, _mm_madd_epi16(px, sx));
qx = _mm_add_epi16(qx, dx);
}
int CV_DECL_ALIGNED(16) buf[4];
_mm_store_si128((__m128i*)buf, qx0);
x0 = buf[0] + buf[1] + buf[2] + buf[3];
_mm_store_si128((__m128i*)buf, qx1);
x1 = buf[0] + buf[1] + buf[2] + buf[3];
x1 = buf[0] + buf[1] + buf[2] + buf[3];
_mm_store_si128((__m128i*)buf, qx2);
x2 = buf[0] + buf[1] + buf[2] + buf[3];
_mm_store_si128((__m128i*)buf, qx3);
x3 = buf[0] + buf[1] + buf[2] + buf[3];
}
for( ; x < size.width; x++ )
{
WT p = ptr[x];
WT xp = x * p, xxp;
x0 += p;
x1 += xp;
xxp = xp * x;
x2 += xxp;
x3 += xxp * x;
}
WT py = y * x0, sy = y*y;
mom[9] += ((MT)py) * sy; // m03
mom[8] += ((MT)x1) * sy; // m12
mom[7] += ((MT)x2) * y; // m21
@ -311,8 +311,8 @@ template<> void momentsInTile<uchar, int, int>( const cv::Mat& img, double* mome
mom[1] += x1; // m10
mom[0] += x0; // m00
}
for( x = 0; x < 10; x++ )
for(int x = 0; x < 10; x++ )
moments[x] = (double)mom[x];
}
@ -366,7 +366,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary )
type = CV_MAT_TYPE( mat->type );
depth = CV_MAT_DEPTH( type );
cn = CV_MAT_CN( type );
cv::Size size = cvGetMatSize( mat );
if( cn > 1 && coi == 0 )
@ -387,14 +387,14 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary )
func = momentsInTile<double, double, double>;
else
CV_Error( CV_StsUnsupportedFormat, "" );
cv::Mat src0(mat);
for( int y = 0; y < size.height; y += TILE_SIZE )
{
cv::Size tileSize;
tileSize.height = std::min(TILE_SIZE, size.height - y);
for( int x = 0; x < size.width; x += TILE_SIZE )
{
tileSize.width = std::min(TILE_SIZE, size.width - x);
@ -413,20 +413,20 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary )
cv::compare( src, 0, tmp, CV_CMP_NE );
src = tmp;
}
double mom[10];
func( src, mom );
if(binary)
{
double s = 1./255;
for( int k = 0; k < 10; k++ )
mom[k] *= s;
}
double xm = x * mom[0], ym = y * mom[0];
// accumulate moments computed in each tile
// accumulate moments computed in each tile
// + m00 ( = m00' )
moments->m00 += mom[0];
@ -451,7 +451,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary )
// + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20')
moments->m21 += mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3];
// + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02')
moments->m12 += mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5];
@ -601,9 +601,9 @@ Moments::operator CvMoments() const
return m;
}
}
cv::Moments cv::moments( InputArray _array, bool binaryImage )
{
CvMoments om;

82
modules/imgproc/src/shapedescr.cpp
View File

@ -49,7 +49,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed )
int i, j = 0, count;
const int N = 16;
float buf[N];
CvMat buffer = cvMat( 1, N, CV_32F, buf );
CvMat buffer = cvMat( 1, N, CV_32F, buf );
CvSeqReader reader;
CvContour contour_header;
CvSeq* contour = 0;
@ -74,7 +74,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed )
if( contour->total > 1 )
{
int is_float = CV_SEQ_ELTYPE( contour ) == CV_32FC2;
cvStartReadSeq( contour, &reader, 0 );
cvSetSeqReaderPos( &reader, slice.start_index );
count = cvSliceLength( slice, contour );
@ -110,7 +110,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed )
CV_NEXT_SEQ_ELEM( contour->elem_size, reader );
// Bugfix by Axel at rubico.com 2010-03-22, affects closed slices only
// wraparound not handled by CV_NEXT_SEQ_ELEM
if( is_closed && i == count - 2 )
if( is_closed && i == count - 2 )
cvSetSeqReaderPos( &reader, slice.start_index );
buffer.data.fl[j] = dx * dx + dy * dy;
@ -287,7 +287,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
*_radius = 0;
CvSeqReader reader;
int i, k, count;
int k, count;
CvPoint2D32f pts[8];
CvContour contour_header;
CvSeqBlock block;
@ -324,7 +324,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
pt_left = pt_right = pt_top = pt_bottom = (CvPoint *)(reader.ptr);
CV_READ_SEQ_ELEM( pt, reader );
for( i = 1; i < count; i++ )
for(int i = 1; i < count; i++ )
{
CvPoint* pt_ptr = (CvPoint*)reader.ptr;
CV_READ_SEQ_ELEM( pt, reader );
@ -351,7 +351,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
pt_left = pt_right = pt_top = pt_bottom = (CvPoint2D32f *) (reader.ptr);
CV_READ_SEQ_ELEM( pt, reader );
for( i = 1; i < count; i++ )
for(int i = 1; i < count; i++ )
{
CvPoint2D32f* pt_ptr = (CvPoint2D32f*)reader.ptr;
CV_READ_SEQ_ELEM( pt, reader );
@ -375,14 +375,14 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
for( k = 0; k < max_iters; k++ )
{
double min_delta = 0, delta;
CvPoint2D32f ptfl, farAway = { 0, 0};
/*only for first iteration because the alg is repared at the loop's foot*/
if(k==0)
icvFindEnslosingCicle4pts_32f( pts, &center, &radius );
CvPoint2D32f ptfl, farAway = { 0, 0};
/*only for first iteration because the alg is repared at the loop's foot*/
if(k==0)
icvFindEnslosingCicle4pts_32f( pts, &center, &radius );
cvStartReadSeq( sequence, &reader, 0 );
for( i = 0; i < count; i++ )
for(int i = 0; i < count; i++ )
{
if( !is_float )
{
@ -406,22 +406,22 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
if( result )
break;
CvPoint2D32f ptsCopy[4];
/* find good replacement partner for the point which is at most far away,
starting with the one that lays in the actual circle (i=3) */
for(int i = 3; i >=0; i-- )
{
for(int j = 0; j < 4; j++ )
{
ptsCopy[j]=(i != j)? pts[j]: farAway;
}
CvPoint2D32f ptsCopy[4];
/* find good replacement partner for the point which is at most far away,
starting with the one that lays in the actual circle (i=3) */
for(int i = 3; i >=0; i-- )
{
for(int j = 0; j < 4; j++ )
{
ptsCopy[j]=(i != j)? pts[j]: farAway;
}
icvFindEnslosingCicle4pts_32f(ptsCopy, &center, &radius );
if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle?
pts[i] = farAway;
break;
}
}
icvFindEnslosingCicle4pts_32f(ptsCopy, &center, &radius );
if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle?
pts[i] = farAway;
break;
}
}
}
if( !result )
@ -429,7 +429,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
cvStartReadSeq( sequence, &reader, 0 );
radius = 0.f;
for( i = 0; i < count; i++ )
for(int i = 0; i < count; i++ )
{
CvPoint2D32f ptfl;
float t, dx, dy;
@ -486,7 +486,7 @@ icvContourArea( const CvSeq* contour, double *area )
yi_1 = ((CvPoint2D32f*)(reader.ptr))->y;
}
CV_NEXT_SEQ_ELEM( contour->elem_size, reader );
while( lpt-- > 0 )
{
double dxy, xi, yi;
@ -520,7 +520,7 @@ icvContourArea( const CvSeq* contour, double *area )
/****************************************************************************************\
copy data from one buffer to other buffer
copy data from one buffer to other buffer
\****************************************************************************************/
@ -797,9 +797,9 @@ cvFitEllipse2( const CvArr* array )
n = ptseq->total;
if( n < 5 )
CV_Error( CV_StsBadSize, "Number of points should be >= 5" );
/*
* New fitellipse algorithm, contributed by Dr. Daniel Weiss
* New fitellipse algorithm, contributed by Dr. Daniel Weiss
*/
CvPoint2D32f c = {0,0};
double gfp[5], rp[5], t;
@ -818,7 +818,7 @@ cvFitEllipse2( const CvArr* array )
cvStartReadSeq( ptseq, &reader );
is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2;
for( i = 0; i < n; i++ )
{
CvPoint2D32f p;
@ -857,7 +857,7 @@ cvFitEllipse2( const CvArr* array )
Ad[i*5 + 3] = p.x;
Ad[i*5 + 4] = p.y;
}
cvSolve( &A, &b, &x, CV_SVD );
// now use general-form parameters A - E to find the ellipse center:
@ -1069,7 +1069,7 @@ cvBoundingRect( CvArr* array, int update )
xmin = ymin = 0;
}
else if( ptseq->total )
{
{
int is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2;
cvStartReadSeq( ptseq, &reader, 0 );
@ -1082,12 +1082,12 @@ cvBoundingRect( CvArr* array, int update )
ymin = ymax = pt.y;
for( i = 1; i < ptseq->total; i++ )
{
{
CV_READ_SEQ_ELEM( pt, reader );
if( xmin > pt.x )
xmin = pt.x;
if( xmax < pt.x )
xmax = pt.x;
@ -1108,14 +1108,14 @@ cvBoundingRect( CvArr* array, int update )
ymin = ymax = CV_TOGGLE_FLT(pt.y);
for( i = 1; i < ptseq->total; i++ )
{
{
CV_READ_SEQ_ELEM( pt, reader );
pt.x = CV_TOGGLE_FLT(pt.x);
pt.y = CV_TOGGLE_FLT(pt.y);
if( xmin > pt.x )
xmin = pt.x;
if( xmax < pt.x )
xmax = pt.x;
@ -1144,7 +1144,7 @@ cvBoundingRect( CvArr* array, int update )
if( update )
((CvContour*)ptseq)->rect = rect;
return rect;
}

70
modules/imgproc/src/undistort.cpp
View File

@ -48,7 +48,7 @@ cv::Mat cv::getDefaultNewCameraMatrix( InputArray _cameraMatrix, Size imgsize,
Mat cameraMatrix = _cameraMatrix.getMat();
if( !centerPrincipalPoint && cameraMatrix.type() == CV_64F )
return cameraMatrix;
Mat newCameraMatrix;
cameraMatrix.convertTo(newCameraMatrix, CV_64F);
if( centerPrincipalPoint )
@ -65,7 +65,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef
{
Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();
Mat matR = _matR.getMat(), newCameraMatrix = _newCameraMatrix.getMat();
if( m1type <= 0 )
m1type = CV_16SC2;
CV_Assert( m1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2 );
@ -106,7 +106,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef
double u0 = A(0, 2), v0 = A(1, 2);
double fx = A(0, 0), fy = A(1, 1);
CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) ||
CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) ||
distCoeffs.size() == Size(1, 5) || distCoeffs.size() == Size(5, 1) ||
distCoeffs.size() == Size(1, 8) || distCoeffs.size() == Size(8, 1));
@ -166,10 +166,10 @@ void cv::undistort( InputArray _src, OutputArray _dst, InputArray _cameraMatrix,
{
Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat();
Mat distCoeffs = _distCoeffs.getMat(), newCameraMatrix = _newCameraMatrix.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
CV_Assert( dst.data != src.data );
int stripe_size0 = std::min(std::max(1, (1 << 12) / std::max(src.cols, 1)), src.rows);
@ -289,11 +289,11 @@ void cvUndistortPoints( const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatr
(_distCoeffs->rows == 1 || _distCoeffs->cols == 1) &&
(_distCoeffs->rows*_distCoeffs->cols == 4 ||
_distCoeffs->rows*_distCoeffs->cols == 5 ||
_distCoeffs->rows*_distCoeffs->cols == 8));
_distCoeffs->rows*_distCoeffs->cols == 8));
_Dk = cvMat( _distCoeffs->rows, _distCoeffs->cols,
CV_MAKETYPE(CV_64F,CV_MAT_CN(_distCoeffs->type)), k);
cvConvert( _distCoeffs, &_Dk );
iters = 5;
}
@ -389,13 +389,13 @@ void cv::undistortPoints( InputArray _src, OutputArray _dst,
{
Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat();
Mat distCoeffs = _distCoeffs.getMat(), R = _Rmat.getMat(), P = _Pmat.getMat();
CV_Assert( src.isContinuous() && (src.depth() == CV_32F || src.depth() == CV_64F) &&
((src.rows == 1 && src.channels() == 2) || src.cols*src.channels() == 2));
_dst.create(src.size(), src.type(), -1, true);
Mat dst = _dst.getMat();
CvMat _csrc = src, _cdst = dst, _ccameraMatrix = cameraMatrix;
CvMat matR, matP, _cdistCoeffs, *pR=0, *pP=0, *pD=0;
if( R.data )
@ -416,11 +416,11 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr
double beta = 1 + 2*alpha;
double v = x*x + y*y + 1, iv = 1/v;
double u = sqrt(beta*v + alpha*alpha);
double k = (u - alpha)*iv;
double kv = (v*beta/u - (u - alpha)*2)*iv*iv;
double kx = kv*x, ky = kv*y;
if( projType == PROJ_SPHERICAL_ORTHO )
{
if(J)
@ -433,7 +433,7 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr
double iR = 1/(alpha + 1);
double x1 = std::max(std::min(x*k*iR, 1.), -1.);
double y1 = std::max(std::min(y*k*iR, 1.), -1.);
if(J)
{
double fx1 = iR/sqrt(1 - x1*x1);
@ -446,35 +446,35 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr
return Point2f();
}
static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType)
{
static int avgiter = 0, avgn = 0;
double eps = 1e-12;
Vec2d p(_p.x, _p.y), q(_p.x, _p.y), err;
Vec4d J;
int i, maxiter = 5;
for( i = 0; i < maxiter; i++ )
{
Point2f p1 = mapPointSpherical(Point2f((float)q[0], (float)q[1]), alpha, &J, projType);
err = Vec2d(p1.x, p1.y) - p;
if( err[0]*err[0] + err[1]*err[1] < eps )
break;
Vec4d JtJ(J[0]*J[0] + J[2]*J[2], J[0]*J[1] + J[2]*J[3],
J[0]*J[1] + J[2]*J[3], J[1]*J[1] + J[3]*J[3]);
double d = JtJ[0]*JtJ[3] - JtJ[1]*JtJ[2];
d = d ? 1./d : 0;
Vec4d iJtJ(JtJ[3]*d, -JtJ[1]*d, -JtJ[2]*d, JtJ[0]*d);
Vec2d JtErr(J[0]*err[0] + J[2]*err[1], J[1]*err[0] + J[3]*err[1]);
q -= Vec2d(iJtJ[0]*JtErr[0] + iJtJ[1]*JtErr[1], iJtJ[2]*JtErr[0] + iJtJ[3]*JtErr[1]);
//Matx22d J(kx*x + k, kx*y, ky*x, ky*y + k);
//q -= Vec2d((J.t()*J).inv()*(J.t()*err));
}
if( i < maxiter )
{
avgiter += i;
@ -482,12 +482,12 @@ static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType)
if( avgn == 1500 )
printf("avg iters = %g\n", (double)avgiter/avgn);
}
return i < maxiter ? Point2f((float)q[0], (float)q[1]) : Point2f(-FLT_MAX, -FLT_MAX);
}
}
float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeffs0,
Size imageSize, int destImageWidth, int m1type,
OutputArray _map1, OutputArray _map2, int projType, double _alpha )
@ -500,40 +500,40 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff
Point2f dcenter((destImageWidth-1)*0.5f, 0.f);
float xmin = FLT_MAX, xmax = -FLT_MAX, ymin = FLT_MAX, ymax = -FLT_MAX;
int N = 9;
std::vector<Point2f> u(1), v(1);
Mat _u(u), I = Mat::eye(3,3,CV_64F);
std::vector<Point2f> uvec(1), vvec(1);
Mat I = Mat::eye(3,3,CV_64F);
float alpha = (float)_alpha;
int ndcoeffs = distCoeffs0.cols*distCoeffs0.rows*distCoeffs0.channels();
CV_Assert((distCoeffs0.cols == 1 || distCoeffs0.rows == 1) &&
(ndcoeffs == 4 || ndcoeffs == 5 || ndcoeffs == 8));
CV_Assert(cameraMatrix0.size() == Size(3,3));
distCoeffs0.convertTo(distCoeffs,CV_64F);
cameraMatrix0.convertTo(cameraMatrix,CV_64F);
alpha = std::min(alpha, 0.999f);
for( int i = 0; i < N; i++ )
for( int j = 0; j < N; j++ )
{
Point2f p((float)j*imageSize.width/(N-1), (float)i*imageSize.height/(N-1));
u[0] = p;
undistortPoints(_u, v, cameraMatrix, distCoeffs, I, I);
Point2f q = mapPointSpherical(v[0], alpha, 0, projType);
uvec[0] = p;
undistortPoints(uvec, vvec, cameraMatrix, distCoeffs, I, I);
Point2f q = mapPointSpherical(vvec[0], alpha, 0, projType);
if( xmin > q.x ) xmin = q.x;
if( xmax < q.x ) xmax = q.x;
if( ymin > q.y ) ymin = q.y;
if( ymax < q.y ) ymax = q.y;
}
float scale = (float)std::min(dcenter.x/fabs(xmax), dcenter.x/fabs(xmin));
Size dsize(destImageWidth, cvCeil(std::max(scale*fabs(ymin)*2, scale*fabs(ymax)*2)));
dcenter.y = (dsize.height - 1)*0.5f;
Mat mapxy(dsize, CV_32FC2);
double k1 = k[0], k2 = k[1], k3 = k[2], p1 = k[3], p2 = k[4], k4 = k[5], k5 = k[6], k6 = k[7];
double fx = cameraMatrix.at<double>(0,0), fy = cameraMatrix.at<double>(1,1), cx = scenter.x, cy = scenter.y;
for( int y = 0; y < dsize.height; y++ )
{
Point2f* mxy = mapxy.ptr<Point2f>(y);
@ -551,11 +551,11 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff
double kr = 1 + ((k3*r2 + k2)*r2 + k1)*r2/(1 + ((k6*r2 + k5)*r2 + k4)*r2);
double u = fx*(q.x*kr + p1*_2xy + p2*(r2 + 2*x2)) + cx;
double v = fy*(q.y*kr + p1*(r2 + 2*y2) + p2*_2xy) + cy;
mxy[x] = Point2f((float)u, (float)v);
}
}
if(m1type == CV_32FC2)
{
_map1.create(mapxy.size(), mapxy.type());
@ -565,7 +565,7 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff
}
else
convertMaps(mapxy, Mat(), _map1, _map2, m1type, false);
return scale;
}

8
modules/imgproc/test/test_contours.cpp
View File

@ -266,7 +266,7 @@ void CV_FindContourTest::run_func()
// the whole testing is done here, run_func() is not utilized in this test
int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
{
int i, code = cvtest::TS::OK;
int code = cvtest::TS::OK;
cvCmpS( img[0], 0, img[0], CV_CMP_GT );
@ -284,7 +284,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
Mat _img[4];
for( int i = 0; i < 4; i++ )
_img[i] = cvarrToMat(img[i]);
code = cvtest::cmpEps2(ts, _img[0], _img[3], 0, true, "Comparing original image with the map of filled contours" );
if( code < 0 )
@ -303,7 +303,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
CvTreeNodeIterator iterator2;
int count3;
for( i = 0; i < 2; i++ )
for(int i = 0; i < 2; i++ )
{
CvTreeNodeIterator iterator;
cvInitTreeNodeIterator( &iterator, i == 0 ? contours : contours2, INT_MAX );
@ -353,7 +353,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
goto _exit_;
}
for( i = 0; i < seq1->total; i++ )
for(int i = 0; i < seq1->total; i++ )
{
CvPoint pt1;
CvPoint pt2;

66
modules/imgproc/test/test_convhull.cpp
View File

@ -193,7 +193,7 @@ protected:
void* result;
double low_high_range;
CvScalar low, high;
bool test_cpp;
};
@ -254,7 +254,7 @@ int CV_BaseShapeDescrTest::read_params( CvFileStorage* fs )
}
void CV_BaseShapeDescrTest::generate_point_set( void* points )
void CV_BaseShapeDescrTest::generate_point_set( void* pointsSet )
{
RNG& rng = ts->get_rng();
int i, k, n, total, point_type;
@ -269,16 +269,16 @@ void CV_BaseShapeDescrTest::generate_point_set( void* points )
}
memset( &reader, 0, sizeof(reader) );
if( CV_IS_SEQ(points) )
if( CV_IS_SEQ(pointsSet) )
{
CvSeq* ptseq = (CvSeq*)points;
CvSeq* ptseq = (CvSeq*)pointsSet;
total = ptseq->total;
point_type = CV_SEQ_ELTYPE(ptseq);
cvStartReadSeq( ptseq, &reader );
}
else
{
CvMat* ptm = (CvMat*)points;
CvMat* ptm = (CvMat*)pointsSet;
assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) );
total = ptm->rows + ptm->cols - 1;
point_type = CV_MAT_TYPE(ptm->type);
@ -362,7 +362,7 @@ int CV_BaseShapeDescrTest::prepare_test_case( int test_case_idx )
}
generate_point_set( points );
test_cpp = (cvtest::randInt(rng) & 16) == 0;
return 1;
}
@ -614,16 +614,16 @@ int CV_ConvHullTest::validate_test_results( int test_case_idx )
for( i = 0; i < point_count; i++ )
{
int idx = 0, on_edge = 0;
double result = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge );
double pptresult = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge );
if( result < 0 )
if( pptresult < 0 )
{
ts->printf( cvtest::TS::LOG, "The point #%d is outside of the convex hull\n", i );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( result < FLT_EPSILON && !on_edge )
if( pptresult < FLT_EPSILON && !on_edge )
mask->data.ptr[idx] = (uchar)1;
}
@ -735,15 +735,15 @@ int CV_MinAreaRectTest::validate_test_results( int test_case_idx )
for( i = 0; i < point_count; i++ )
{
int idx = 0, on_edge = 0;
double result = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge );
if( result < -eps )
double pptresult = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge );
if( pptresult < -eps )
{
ts->printf( cvtest::TS::LOG, "The point #%d is outside of the box\n", i );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( result < eps )
if( pptresult < eps )
{
for( j = 0; j < 4; j++ )
{
@ -997,7 +997,7 @@ CV_FitEllipseTest::CV_FitEllipseTest()
}
void CV_FitEllipseTest::generate_point_set( void* points )
void CV_FitEllipseTest::generate_point_set( void* pointsSet )
{
RNG& rng = ts->get_rng();
int i, total, point_type;
@ -1020,16 +1020,16 @@ void CV_FitEllipseTest::generate_point_set( void* points )
}
memset( &reader, 0, sizeof(reader) );
if( CV_IS_SEQ(points) )
if( CV_IS_SEQ(pointsSet) )
{
CvSeq* ptseq = (CvSeq*)points;
CvSeq* ptseq = (CvSeq*)pointsSet;
total = ptseq->total;
point_type = CV_SEQ_ELTYPE(ptseq);
cvStartReadSeq( ptseq, &reader );
}
else
{
CvMat* ptm = (CvMat*)points;
CvMat* ptm = (CvMat*)pointsSet;
assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) );
total = ptm->rows + ptm->cols - 1;
point_type = CV_MAT_TYPE(ptm->type);
@ -1171,7 +1171,7 @@ class CV_FitEllipseSmallTest : public cvtest::BaseTest
{
public:
CV_FitEllipseSmallTest() {}
~CV_FitEllipseSmallTest() {}
~CV_FitEllipseSmallTest() {}
protected:
void run(int)
{
@ -1188,7 +1188,7 @@ protected:
c[0].push_back(Point(8, 6)*scale+ofs);
c[0].push_back(Point(8, 2)*scale+ofs);
c[0].push_back(Point(6, 0)*scale+ofs);
RotatedRect e = fitEllipse(c[0]);
CV_Assert( fabs(e.center.x - 4) <= 1. &&
fabs(e.center.y - 4) <= 1. &&
@ -1226,7 +1226,7 @@ CV_FitLineTest::CV_FitLineTest()
}
void CV_FitLineTest::generate_point_set( void* points )
void CV_FitLineTest::generate_point_set( void* pointsSet )
{
RNG& rng = ts->get_rng();
int i, k, n, total, point_type;
@ -1250,16 +1250,16 @@ void CV_FitLineTest::generate_point_set( void* points )
memset( &reader, 0, sizeof(reader) );
if( CV_IS_SEQ(points) )
if( CV_IS_SEQ(pointsSet) )
{
CvSeq* ptseq = (CvSeq*)points;
CvSeq* ptseq = (CvSeq*)pointsSet;
total = ptseq->total;
point_type = CV_MAT_DEPTH(CV_SEQ_ELTYPE(ptseq));
cvStartReadSeq( ptseq, &reader );
}
else
{
CvMat* ptm = (CvMat*)points;
CvMat* ptm = (CvMat*)pointsSet;
assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) );
total = ptm->rows + ptm->cols - 1;
point_type = CV_MAT_DEPTH(CV_MAT_TYPE(ptm->type));
@ -1498,7 +1498,7 @@ CV_ContourMomentsTest::CV_ContourMomentsTest()
}
void CV_ContourMomentsTest::generate_point_set( void* points )
void CV_ContourMomentsTest::generate_point_set( void* pointsSet )
{
RNG& rng = ts->get_rng();
float max_sz;
@ -1518,7 +1518,7 @@ void CV_ContourMomentsTest::generate_point_set( void* points )
max_r_scale = cvtest::randReal(rng)*max_max_r_scale*0.01;
angle = cvtest::randReal(rng)*360;
cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, points, rng );
cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, pointsSet, rng );
if( points1 )
points1->flags = CV_SEQ_MAGIC_VAL + CV_SEQ_POLYGON;
@ -1614,8 +1614,8 @@ class CV_PerimeterAreaSliceTest : public cvtest::BaseTest
{
public:
CV_PerimeterAreaSliceTest();
~CV_PerimeterAreaSliceTest();
protected:
~CV_PerimeterAreaSliceTest();
protected:
void run(int);
};
@ -1629,7 +1629,7 @@ void CV_PerimeterAreaSliceTest::run( int )
Ptr<CvMemStorage> storage = cvCreateMemStorage();
RNG& rng = theRNG();
const double min_r = 90, max_r = 120;
for( int i = 0; i < 100; i++ )
{
ts->update_context( this, i, true );
@ -1640,7 +1640,7 @@ void CV_PerimeterAreaSliceTest::run( int )
CvPoint center;
center.x = rng.uniform(cvCeil(max_r), cvFloor(640-max_r));
center.y = rng.uniform(cvCeil(max_r), cvFloor(480-max_r));
for( int j = 0; j < n; j++ )
{
CvPoint pt;
@ -1650,7 +1650,7 @@ void CV_PerimeterAreaSliceTest::run( int )
pt.y = cvRound(center.y - r*sin(phi));
cvSeqPush(contour, &pt);
}
CvSlice slice;
for(;;)
{
@ -1664,14 +1664,14 @@ void CV_PerimeterAreaSliceTest::run( int )
/*printf( "%d. (%d, %d) of %d, length = %d, length1 = %d\n",
i, slice.start_index, slice.end_index,
contour->total, cvSliceLength(slice, contour), cslice->total );
double area0 = cvContourArea(cslice);
double area1 = cvContourArea(contour, slice);
double area1 = cvContourArea(contour, slice);
if( area0 != area1 )
{
ts->printf(cvtest::TS::LOG,
"The contour area slice is computed differently (%g vs %g)\n", area0, area1 );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}*/
@ -1681,7 +1681,7 @@ void CV_PerimeterAreaSliceTest::run( int )
{
ts->printf(cvtest::TS::LOG,
"The contour arc length is computed differently (%g vs %g)\n", len0, len1 );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}

28
modules/imgproc/test/test_filter.cpp
View File

@ -253,46 +253,46 @@ void CV_MorphologyBaseTest::prepare_to_validation( int /*test_case_idx*/ )
Mat _ielement(element->nRows, element->nCols, CV_32S, element->values);
Mat _element;
_ielement.convertTo(_element, CV_8U);
Point anchor(element->anchorX, element->anchorY);
int border = BORDER_REPLICATE;
Point _anchor(element->anchorX, element->anchorY);
int _border = BORDER_REPLICATE;
if( optype == CV_MOP_ERODE )
{
cvtest::erode( src, dst, _element, anchor, border );
cvtest::erode( src, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_DILATE )
{
cvtest::dilate( src, dst, _element, anchor, border );
cvtest::dilate( src, dst, _element, _anchor, _border );
}
else
{
Mat temp;
if( optype == CV_MOP_OPEN )
{
cvtest::erode( src, temp, _element, anchor, border );
cvtest::dilate( temp, dst, _element, anchor, border );
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( temp, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_CLOSE )
{
cvtest::dilate( src, temp, _element, anchor, border );
cvtest::erode( temp, dst, _element, anchor, border );
cvtest::dilate( src, temp, _element, _anchor, _border );
cvtest::erode( temp, dst, _element, _anchor, _border );
}
else if( optype == CV_MOP_GRADIENT )
{
cvtest::erode( src, temp, _element, anchor, border );
cvtest::dilate( src, dst, _element, anchor, border );
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( src, dst, _element, _anchor, _border );
cvtest::add( dst, 1, temp, -1, Scalar::all(0), dst, dst.type() );
}
else if( optype == CV_MOP_TOPHAT )
{
cvtest::erode( src, temp, _element, anchor, border );
cvtest::dilate( temp, dst, _element, anchor, border );
cvtest::erode( src, temp, _element, _anchor, _border );
cvtest::dilate( temp, dst, _element, _anchor, _border );
cvtest::add( src, 1, dst, -1, Scalar::all(0), dst, dst.type() );
}
else if( optype == CV_MOP_BLACKHAT )
{
cvtest::dilate( src, temp, _element, anchor, border );
cvtest::erode( temp, dst, _element, anchor, border );
cvtest::dilate( src, temp, _element, _anchor, _border );
cvtest::erode( temp, dst, _element, _anchor, _border );
cvtest::add( dst, 1, src, -1, Scalar::all(0), dst, dst.type() );
}
else

22
modules/imgproc/test/test_floodfill.cpp
View File

@ -56,7 +56,7 @@ protected:
void prepare_to_validation( int );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
/*int write_default_params(CvFileStorage* fs);
void get_timing_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types
CvSize** whole_sizes, bool *are_images );
@ -94,7 +94,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx,
RNG& rng = ts->get_rng();
int depth, cn;
int i;
double buf[8];
double buff[8];
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = cvtest::randInt(rng) % 3;
@ -111,7 +111,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx,
types[INPUT_OUTPUT][1] = types[REF_INPUT_OUTPUT][1] = CV_8UC1;
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(9,1);
if( !use_mask )
sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(0,0);
else
@ -119,7 +119,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx,
CvSize sz = sizes[INPUT_OUTPUT][0];
sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(sz.width+2,sz.height+2);
}
seed_pt.x = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].width;
seed_pt.y = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].height;
@ -127,7 +127,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx,
l_diff = u_diff = Scalar::all(0.);
else
{
Mat m( 1, 8, CV_16S, buf );
Mat m( 1, 8, CV_16S, buff );
rng.fill( m, RNG::NORMAL, Scalar::all(0), Scalar::all(32) );
for( i = 0; i < 4; i++ )
{
@ -139,7 +139,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx,
new_val = Scalar::all(0.);
for( i = 0; i < cn; i++ )
new_val.val[i] = cvtest::randReal(rng)*255;
test_cpp = (cvtest::randInt(rng) & 256) == 0;
}
@ -153,13 +153,13 @@ double CV_FloodFillTest::get_success_error_level( int /*test_case_idx*/, int i,
void CV_FloodFillTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
RNG& rng = ts->get_rng();
if( i != INPUT && i != INPUT_OUTPUT )
{
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
return;
}
if( j == 0 )
{
Mat tmp = arr;
@ -191,7 +191,7 @@ void CV_FloodFillTest::run_func()
int flags = connectivity + (mask_only ? CV_FLOODFILL_MASK_ONLY : 0) +
(range_type == 1 ? CV_FLOODFILL_FIXED_RANGE : 0) + (new_mask_val << 8);
double* odata = test_mat[OUTPUT][0].ptr<double>();
if(!test_cpp)
{
CvConnectedComp comp;
@ -255,7 +255,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val,
int cols = _img->cols, rows = _img->rows;
int u0 = 0, u1 = 0, u2 = 0;
double s0 = 0, s1 = 0, s2 = 0;
if( CV_MAT_DEPTH(_img->type) == CV_8U || CV_MAT_DEPTH(_img->type) == CV_32S )
{
tmp = cvCreateMat( rows, cols, CV_MAKETYPE(CV_32F,CV_MAT_CN(_img->type)) );
@ -395,7 +395,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val,
cvSeqPush( seq, &p );
}
}
}
}
}
r.x = r.width = seed_pt.x;

98
modules/imgproc/test/test_histograms.cpp
View File

@ -59,7 +59,7 @@ protected:
int prepare_test_case( int test_case_idx );
int validate_test_results( int test_case_idx );
virtual void init_hist( int test_case_idx, int i );
virtual void get_hist_params( int test_case_idx );
virtual float** get_hist_ranges( int test_case_idx );
@ -73,7 +73,7 @@ protected:
int uniform;
int gen_random_hist;
double gen_hist_max_val, gen_hist_sparse_nz_ratio;
int init_ranges;
int img_type;
int img_max_log_size;
@ -127,7 +127,7 @@ int CV_BaseHistTest::read_params( CvFileStorage* fs )
max_log_size = cvtest::clipInt( max_log_size, 1, 20 );
img_max_log_size = cvReadInt( find_param( fs, "max_log_array_size" ), img_max_log_size );
img_max_log_size = cvtest::clipInt( img_max_log_size, 1, 9 );
max_cdims = cvReadInt( find_param( fs, "max_cdims" ), max_cdims );
max_cdims = cvtest::clipInt( max_cdims, 1, 6 );
@ -146,13 +146,13 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ )
max_dim_size = cvRound(pow(hist_size,1./cdims));
total_size = 1;
uniform = cvtest::randInt(rng) % 2;
hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY;
hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY;
for( i = 0; i < cdims; i++ )
{
dims[i] = cvtest::randInt(rng) % (max_dim_size + 2) + 2;
if( !uniform )
dims[i] = MIN(dims[i], max_ni_dim_size);
dims[i] = MIN(dims[i], max_ni_dim_size);
total_size *= dims[i];
}
@ -178,12 +178,12 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ )
float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
{
double _low = low + range_delta, _high = high - range_delta;
if( !init_ranges )
return 0;
ranges.resize(cdims);
if( uniform )
{
_ranges.resize(cdims*2);
@ -200,7 +200,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
for( i = 0; i < cdims; i++ )
dims_sum += dims[i] + 1;
_ranges.resize(dims_sum);
for( i = 0; i < cdims; i++ )
{
int j, n = dims[i];
@ -212,7 +212,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
if( (pow(q,(double)n)-1)/(q-1.) >= _high-_low )
break;
}
if( j == 0 )
{
delta = (_high-_low)/n;
@ -223,9 +223,9 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
q = 1 + j*0.1;
delta = cvFloor((_high-_low)*(q-1)/(pow(q,(double)n) - 1));
delta = MAX(delta, 1.);
}
}
val = _low;
for( j = 0; j <= n; j++ )
{
_ranges[j+ofs] = (float)MIN(val,_high);
@ -236,7 +236,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
ofs += n + 1;
}
}
return &ranges[0];
}
@ -246,7 +246,7 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i )
if( gen_random_hist )
{
RNG& rng = ts->get_rng();
if( hist_type == CV_HIST_ARRAY )
{
Mat h = cvarrToMat(hist[hist_i]->bins);
@ -255,13 +255,13 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i )
else
{
CvArr* arr = hist[hist_i]->bins;
int i, j, total_size = 1, nz_count;
int i, j, totalSize = 1, nz_count;
int idx[CV_MAX_DIM];
for( i = 0; i < cdims; i++ )
total_size *= dims[i];
totalSize *= dims[i];
nz_count = cvtest::randInt(rng) % MAX( total_size/4, 100 );
nz_count = MIN( nz_count, total_size );
nz_count = cvtest::randInt(rng) % MAX( totalSize/4, 100 );
nz_count = MIN( nz_count, totalSize );
// a zero number of non-zero elements should be allowed
for( i = 0; i < nz_count; i++ )
@ -286,7 +286,7 @@ int CV_BaseHistTest::prepare_test_case( int test_case_idx )
get_hist_params( test_case_idx );
r = get_hist_ranges( test_case_idx );
hist.resize(hist_count);
for( i = 0; i < hist_count; i++ )
{
hist[i] = cvCreateHist( cdims, dims, hist_type, r, uniform );
@ -323,7 +323,7 @@ protected:
int prepare_test_case( int test_case_idx );
int validate_test_results( int test_case_idx );
void init_hist( int test_case_idx, int i );
CvMat* indices;
CvMat* values;
CvMat* values0;
@ -376,7 +376,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx )
iters = (cvtest::randInt(rng) % MAX(total_size/10,100)) + 1;
iters = MIN( iters, total_size*9/10 + 1 );
indices = cvCreateMat( 1, iters*cdims, CV_32S );
values = cvCreateMat( 1, iters, CV_32F );
values0 = cvCreateMat( 1, iters, CV_32F );
@ -422,7 +422,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx )
if( GET_BIT(lin_idx) )
values0->data.fl[i] = (float)(lin_idx+1);
}
cvReleaseMat( &bit_mask );
}
@ -539,7 +539,7 @@ int CV_QueryHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
int i, j, iters = values->cols;
for( i = 0; i < iters; i++ )
{
float v = values->data.fl[i], v0 = values0->data.fl[i];
@ -613,7 +613,7 @@ void CV_MinMaxHistTest::init_hist(int test_case_idx, int hist_i)
}
if( !eq || total_size == 1 )
break;
}
}
min_val0 = (float)(-cvtest::randReal(rng)*10 - FLT_EPSILON);
max_val0 = (float)(cvtest::randReal(rng)*10 + FLT_EPSILON + gen_hist_max_val);
@ -644,7 +644,7 @@ void CV_MinMaxHistTest::run_func(void)
int CV_MinMaxHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
if( cvIsNaN(min_val) || cvIsInf(min_val) ||
cvIsNaN(max_val) || cvIsInf(max_val) )
{
@ -728,7 +728,7 @@ void CV_NormHistTest::run_func(void)
if( hist_type != CV_HIST_ARRAY && test_cpp )
{
cv::SparseMat h((CvSparseMat*)hist[0]->bins);
cv::normalize(h, h, factor, CV_L1);
cv::normalize(h, h, factor, CV_L1);
cvReleaseSparseMat((CvSparseMat**)&hist[0]->bins);
hist[0]->bins = (CvSparseMat*)h;
}
@ -741,7 +741,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
double sum = 0;
if( hist_type == CV_HIST_ARRAY )
{
int i;
@ -755,7 +755,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ )
CvSparseMat* sparse = (CvSparseMat*)hist[0]->bins;
CvSparseMatIterator iterator;
CvSparseNode *node;
for( node = cvInitSparseMatIterator( sparse, &iterator );
node != 0; node = cvGetNextSparseNode( &iterator ))
{
@ -839,7 +839,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx )
if( hist_type == CV_HIST_ARRAY )
{
orig_nz_count = total_size;
values = cvCreateMat( 1, total_size, CV_32F );
memcpy( values->data.fl, cvPtr1D( hist[0]->bins, 0 ), total_size*sizeof(float) );
}
@ -859,7 +859,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx )
node != 0; node = cvGetNextSparseNode( &iterator ), i++ )
{
const int* idx = CV_NODE_IDX(sparse,node);
OPENCV_ASSERT( i < orig_nz_count, "CV_ThreshHistTest::prepare_test_case", "Buffer overflow" );
values->data.fl[i] = *(float*)CV_NODE_VAL(sparse,node);
@ -924,7 +924,7 @@ int CV_ThreshHistTest::validate_test_results( int /*test_case_idx*/ )
}
}
}
if( code > 0 && hist_type == CV_HIST_SPARSE )
{
if( sparse->heap->active_count > 0 )
@ -1003,7 +1003,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ )
{
float* ptr0 = (float*)cvPtr1D( hist[0]->bins, 0 );
float* ptr1 = (float*)cvPtr1D( hist[1]->bins, 0 );
for( i = 0; i < total_size; i++ )
{
double v0 = ptr0[i], v1 = ptr1[i];
@ -1031,7 +1031,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ )
const int* idx = CV_NODE_IDX(sparse0, node);
double v0 = *(float*)CV_NODE_VAL(sparse0, node);
double v1 = (float)cvGetRealND(sparse1, idx);
result0[CV_COMP_CORREL] += v0*v1;
result0[CV_COMP_INTERSECT] += MIN(v0,v1);
if( fabs(v0) > DBL_EPSILON )
@ -1134,7 +1134,7 @@ CV_CalcHistTest::~CV_CalcHistTest()
void CV_CalcHistTest::clear()
{
int i;
for( i = 0; i <= CV_MAX_DIM; i++ )
cvReleaseImage( &images[i] );
@ -1160,7 +1160,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx )
img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch );
channels[i] = cvtest::randInt(rng) % nch;
Mat images_i = cvarrToMat(images[i]);
cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) );
}
else if( i == CV_MAX_DIM && cvtest::randInt(rng) % 2 )
@ -1168,7 +1168,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx )
// create mask
images[i] = cvCreateImage( img_size, IPL_DEPTH_8U, 1 );
Mat images_i = cvarrToMat(images[i]);
// make ~25% pixels in the mask non-zero
cvtest::randUni( rng, images_i, Scalar::all(-2), Scalar::all(2) );
}
@ -1230,7 +1230,7 @@ cvTsCalcHist( IplImage** _images, CvHistogram* hist, IplImage* _mask, int* chann
{
float val[CV_MAX_DIM];
int idx[CV_MAX_DIM];
if( mptr && !mptr[x] )
continue;
if( img_depth == IPL_DEPTH_8U )
@ -1288,7 +1288,7 @@ int CV_CalcHistTest::validate_test_results( int /*test_case_idx*/ )
{
ts->printf( cvtest::TS::LOG, "The histogram does not match to the reference one\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
if( code < 0 )
@ -1345,7 +1345,7 @@ CV_CalcBackProjectTest::~CV_CalcBackProjectTest()
void CV_CalcBackProjectTest::clear()
{
int i;
for( i = 0; i < CV_MAX_DIM+3; i++ )
cvReleaseImage( &images[i] );
@ -1399,7 +1399,7 @@ int CV_CalcBackProjectTest::prepare_test_case( int test_case_idx )
{
int idx = cvtest::randInt(rng) % img_len;
double val = cvtest::randReal(rng)*(high - low) + low;
if( img_type == CV_8U )
((uchar*)data)[idx] = (uchar)cvRound(val);
else
@ -1453,7 +1453,7 @@ cvTsCalcBackProject( IplImage** images, IplImage* dst, CvHistogram* hist, int* c
float val[CV_MAX_DIM];
float bin_val = 0;
int idx[CV_MAX_DIM];
if( img_depth == IPL_DEPTH_8U )
for( k = 0; k < cdims; k++ )
val[k] = plane[k].ptr[x*nch[k]];
@ -1569,7 +1569,7 @@ CV_CalcBackProjectPatchTest::~CV_CalcBackProjectPatchTest()
void CV_CalcBackProjectPatchTest::clear()
{
int i;
for( i = 0; i < CV_MAX_DIM+2; i++ )
cvReleaseImage( &images[i] );
@ -1627,7 +1627,7 @@ int CV_CalcBackProjectPatchTest::prepare_test_case( int test_case_idx )
{
int idx = cvtest::randInt(rng) % img_len;
double val = cvtest::randReal(rng)*(high - low) + low;
if( img_type == CV_8U )
((uchar*)data)[idx] = (uchar)cvRound(val);
else
@ -1652,7 +1652,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size,
double factor, int* channels )
{
CvHistogram* model = 0;
IplImage imgstub[CV_MAX_DIM], *img[CV_MAX_DIM];
IplROI roi;
int i, dims;
@ -1679,7 +1679,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size,
for( x = 0; x < size.width; x++ )
{
double result;
roi.xOffset = x;
roi.yOffset = y;
roi.width = patch_size.width;
@ -1703,7 +1703,7 @@ int CV_CalcBackProjectPatchTest::validate_test_results( int /*test_case_idx*/ )
cvTsCalcBackProjectPatch( images, images[CV_MAX_DIM+1],
patch_size, hist[0], method, factor, channels );
Mat a = cvarrToMat(images[CV_MAX_DIM]), b = cvarrToMat(images[CV_MAX_DIM+1]);
code = cvtest::cmpEps2( ts, a, b, err_level, true, "BackProjectPatch result" );
@ -1756,7 +1756,7 @@ void CV_BayesianProbTest::init_hist( int test_case_idx, int hist_i )
int CV_BayesianProbTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
hist_count = (cvtest::randInt(rng) % (MAX_HIST/2-1) + 2)*2;
hist_count = MIN( hist_count, MAX_HIST );
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
@ -1833,5 +1833,5 @@ TEST(Imgproc_Hist_MinMaxVal, accuracy) { CV_MinMaxHistTest test; test.safe_run()
TEST(Imgproc_Hist_CalcBackProject, accuracy) { CV_CalcBackProjectTest test; test.safe_run(); }
TEST(Imgproc_Hist_CalcBackProjectPatch, accuracy) { CV_CalcBackProjectPatchTest test; test.safe_run(); }
TEST(Imgproc_Hist_BayesianProb, accuracy) { CV_BayesianProbTest test; test.safe_run(); }
/* End Of File */

166
modules/imgproc/test/test_imgwarp.cpp
View File

@ -135,7 +135,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
test_mat[INPUT_OUTPUT][0].rows >= img.rows )
space_scale = spatial_scale_zoom;
for( i = 0; i < img.rows; i++ )
{
uchar* ptr = img.ptr(i);
@ -192,7 +192,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
}*/
cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
cv::Mat dst(1, cols*cn, depth, ptr);
src.convertTo(dst, dst.type());
src.convertTo(dst, dst.type());
}
return code;
@ -279,7 +279,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ )
CvMat* x_idx = cvCreateMat( 1, dst->cols, CV_32SC1 );
CvMat* y_idx = cvCreateMat( 1, dst->rows, CV_32SC1 );
int* x_tab = x_idx->data.i;
int elem_size = CV_ELEM_SIZE(src->type);
int elem_size = CV_ELEM_SIZE(src->type);
int drows = dst->rows, dcols = dst->cols;
if( interpolation == CV_INTER_NN )
@ -302,7 +302,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ )
{
double scale_x = (double)src->cols/dcols;
double scale_y = (double)src->rows/drows;
for( j = 0; j < dcols; j++ )
{
double f = ((j+0.5)*scale_x - 0.5);
@ -322,7 +322,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ )
{
uchar* dptr = dst->data.ptr + dst->step*i;
const uchar* sptr0 = src->data.ptr + src->step*y_idx->data.i[i];
for( j = 0; j < dcols; j++, dptr += elem_size )
{
const uchar* sptr = sptr0 + x_tab[j];
@ -394,7 +394,7 @@ static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& ma
xs -= ixs;
ys -= iys;
switch( depth )
{
case CV_8U:
@ -508,7 +508,7 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx )
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
const Mat& dst = test_mat[INPUT_OUTPUT][0];
const Mat& dst = test_mat[INPUT_OUTPUT][0];
Mat& mat = test_mat[INPUT][1];
CvPoint2D32f center;
double scale, angle;
@ -516,8 +516,8 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx )
if( code <= 0 )
return code;
double buf[6];
Mat tmp( 2, 3, mat.type(), buf );
double buffer[6];
Mat tmp( 2, 3, mat.type(), buffer );
center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols);
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
@ -619,7 +619,7 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx )
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const CvMat& src = test_mat[INPUT][0];
const CvMat& dst = test_mat[INPUT_OUTPUT][0];
const CvMat& dst = test_mat[INPUT_OUTPUT][0];
Mat& mat = test_mat[INPUT][1];
Point2f s[4], d[4];
int i;
@ -636,17 +636,17 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx )
s[3] = Point2f(0,src.rows-1.f);
d[3] = Point2f(0,dst.rows-1.f);
float buf[16];
Mat tmp( 1, 16, CV_32FC1, buf );
float bufer[16];
Mat tmp( 1, 16, CV_32FC1, bufer );
rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(0.), Scalar::all(0.1) );
for( i = 0; i < 4; i++ )
{
s[i].x += buf[i*4]*src.cols/2;
s[i].y += buf[i*4+1]*src.rows/2;
d[i].x += buf[i*4+2]*dst.cols/2;
d[i].y += buf[i*4+3]*dst.rows/2;
s[i].x += bufer[i*4]*src.cols/2;
s[i].y += bufer[i*4+1]*src.rows/2;
d[i].x += bufer[i*4+2]*dst.cols/2;
d[i].y += bufer[i*4+3]*dst.rows/2;
}
cv::getPerspectiveTransform( s, d ).convertTo( mat, mat.depth() );
@ -675,11 +675,11 @@ void CV_WarpPerspectiveTest::prepare_to_validation( int /*test_case_idx*/ )
double xs = x*m[0] + y*m[1] + m[2];
double ys = x*m[3] + y*m[4] + m[5];
double ds = x*m[6] + y*m[7] + m[8];
ds = ds ? 1./ds : 0;
xs *= ds;
ys *= ds;
mapx.at<float>(y, x) = (float)xs;
mapy.at<float>(y, x) = (float)ys;
}
@ -806,15 +806,15 @@ protected:
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool useCPlus;
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool useCPlus;
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool zero_new_cam;
bool zero_distortion;
bool zero_new_cam;
bool zero_distortion;
};
@ -823,7 +823,7 @@ CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false )
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
@ -834,14 +834,14 @@ void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int type = types[INPUT][0];
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][1] = cvSize(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
interpolation = CV_INTER_LINEAR;
}
@ -855,22 +855,22 @@ void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
void CV_UndistortTest::run_func()
{
if (!useCPlus)
{
if (!useCPlus)
{
CvMat a = test_mat[INPUT][1], k = test_mat[INPUT][2];
cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k);
}
else
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k);
}
else
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
}
@ -888,10 +888,10 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx )
const Mat& src = test_mat[INPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(src.rows, src.cols);
Mat& _new_cam0 = test_mat[INPUT][3];
Mat& _new_cam0 = test_mat[INPUT][3];
Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam);
Mat& _a0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
@ -925,21 +925,21 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx )
_a.convertTo(_a0, _a0.depth());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
_k.convertTo(_k0, _k0.depth());
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
_new_cam.convertTo(_new_cam0, _new_cam0.depth());
//Testing C++ code
useCPlus = ((cvtest::randInt(rng) % 2)!=0);
if (useCPlus)
{
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
}
//Testing C++ code
useCPlus = ((cvtest::randInt(rng) % 2)!=0);
if (useCPlus)
{
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
}
return code;
}
@ -947,11 +947,11 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx )
void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ )
{
if (useCPlus)
{
if (useCPlus)
{
Mat& output = test_mat[INPUT_OUTPUT][0];
input_output.convertTo(output, output.type());
}
}
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
@ -978,7 +978,7 @@ protected:
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool dualChannel;
bool dualChannel;
};
@ -1003,8 +1003,8 @@ void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vec
CvSize sz = sizes[OUTPUT][0];
types[INPUT][0] = types[INPUT][1] = depth;
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F;
sizes[INPUT][0] = cvSize(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
@ -1026,11 +1026,11 @@ void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr
void CV_UndistortMapTest::run_func()
{
CvMat a = test_mat[INPUT][0], k = test_mat[INPUT][1];
if (!dualChannel )
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] );
else
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 );
if (!dualChannel )
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] );
else
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 );
}
@ -1069,11 +1069,11 @@ int CV_UndistortMapTest::prepare_test_case( int test_case_idx )
_a.convertTo(_a0, _a0.depth());
_k.convertTo(_k0, _k0.depth());
if (dualChannel)
{
if (dualChannel)
{
test_mat[REF_OUTPUT][1] = Scalar::all(0);
test_mat[OUTPUT][1] = Scalar::all(0);
}
test_mat[OUTPUT][1] = Scalar::all(0);
}
return code;
}
@ -1102,7 +1102,7 @@ test_getQuadrangeSubPix( const Mat& src, Mat& dst, double* a )
{
int sstep = (int)(src.step / sizeof(float));
int scols = src.cols, srows = src.rows;
CV_Assert( src.depth() == CV_32F && src.type() == dst.type() );
int cn = dst.channels();
@ -1167,11 +1167,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve
int src_depth = cvtest::randInt(rng) % 2, dst_depth;
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
CvSize src_size, dst_size;
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
dst_depth = CV_32F;
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
@ -1181,11 +1181,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve
dst_size.width = MIN(dst_size.width,src_size.width);
dst_size.height = MIN(dst_size.width,src_size.height);
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dst_size;
center.x = (float)(cvtest::randReal(rng)*src_size.width);
center.y = (float)(cvtest::randReal(rng)*src_size.height);
interpolation = CV_INTER_LINEAR;
test_cpp = (cvtest::randInt(rng) & 256) == 0;
}
@ -1274,11 +1274,11 @@ void CV_GetQuadSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve
RNG& rng = ts->get_rng();
int msz, src_depth = cvtest::randInt(rng) % 2, dst_depth;
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
dst_depth = CV_32F;
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
@ -1333,7 +1333,7 @@ int CV_GetQuadSubPixTest::prepare_test_case( int test_case_idx )
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
angle = cvtest::randReal(rng)*360;
scale = cvtest::randReal(rng)*0.2 + 0.9;
// y = Ax + b -> x = A^-1(y - b) = A^-1*y - A^-1*b
scale = 1./scale;
angle = angle*(CV_PI/180.);
@ -1413,7 +1413,7 @@ TEST(Imgproc_fitLine_vector_2d, regression)
points_vector.push_back(p21);
points_vector.push_back(p22);
points_vector.push_back(p23);
points_vector.push_back(p23);
std::vector<float> line;

3
modules/imgproc/test/test_thresh.cpp
View File

@ -91,7 +91,8 @@ void CV_ThreshTest::get_test_array_types_and_sizes( int test_case_idx,
}
else if( depth == CV_16S )
{
float min_val = SHRT_MIN-100.f, max_val = SHRT_MAX+100.f;
float min_val = SHRT_MIN-100.f;
max_val = SHRT_MAX+100.f;
thresh_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val);
max_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val);
if( cvtest::randInt(rng)%4 == 0 )

48
modules/legacy/include/opencv2/legacy/legacy.hpp
View File

@ -1347,9 +1347,9 @@ class CV_EXPORTS CvImage
{
public:
CvImage() : image(0), refcount(0) {}
CvImage( CvSize size, int depth, int channels )
CvImage( CvSize _size, int _depth, int _channels )
{
image = cvCreateImage( size, depth, channels );
image = cvCreateImage( _size, _depth, _channels );
refcount = image ? new int(1) : 0;
}
@ -1383,12 +1383,12 @@ public:
CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); }
void create( CvSize size, int depth, int channels )
void create( CvSize _size, int _depth, int _channels )
{
if( !image || !refcount ||
image->width != size.width || image->height != size.height ||
image->depth != depth || image->nChannels != channels )
attach( cvCreateImage( size, depth, channels ));
image->width != _size.width || image->height != _size.height ||
image->depth != _depth || image->nChannels != _channels )
attach( cvCreateImage( _size, _depth, _channels ));
}
void release() { detach(); }
@ -1447,9 +1447,9 @@ public:
int coi() const { return !image || !image->roi ? 0 : image->roi->coi; }
void set_roi(CvRect roi) { cvSetImageROI(image,roi); }
void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); }
void reset_roi() { cvResetImageROI(image); }
void set_coi(int coi) { cvSetImageCOI(image,coi); }
void set_coi(int _coi) { cvSetImageCOI(image,_coi); }
int depth() const { return image ? image->depth : 0; }
int channels() const { return image ? image->nChannels : 0; }
int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; }
@ -1511,18 +1511,18 @@ class CV_EXPORTS CvMatrix
{
public:
CvMatrix() : matrix(0) {}
CvMatrix( int rows, int cols, int type )
{ matrix = cvCreateMat( rows, cols, type ); }
CvMatrix( int _rows, int _cols, int _type )
{ matrix = cvCreateMat( _rows, _cols, _type ); }
CvMatrix( int rows, int cols, int type, CvMat* hdr,
void* data=0, int step=CV_AUTOSTEP )
{ matrix = cvInitMatHeader( hdr, rows, cols, type, data, step ); }
CvMatrix( int _rows, int _cols, int _type, CvMat* hdr,
void* _data=0, int _step=CV_AUTOSTEP )
{ matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); }
CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true );
CvMatrix( int rows, int cols, int type, void* data, int step=CV_AUTOSTEP )
{ matrix = cvCreateMatHeader( rows, cols, type );
cvSetData( matrix, data, step ); }
CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP )
{ matrix = cvCreateMatHeader( _rows, _cols, _type );
cvSetData( matrix, _data, _step ); }
CvMatrix( CvMat* m )
{ matrix = m; }
@ -1557,12 +1557,12 @@ public:
addref();
}
void create( int rows, int cols, int type )
void create( int _rows, int _cols, int _type )
{
if( !matrix || !matrix->refcount ||
matrix->rows != rows || matrix->cols != cols ||
CV_MAT_TYPE(matrix->type) != type )
set( cvCreateMat( rows, cols, type ), false );
matrix->rows != _rows || matrix->cols != _cols ||
CV_MAT_TYPE(matrix->type) != _type )
set( cvCreateMat( _rows, _cols, _type ), false );
}
void addref() const
@ -1626,8 +1626,8 @@ public:
const uchar* data() const { return matrix ? matrix->data.ptr : 0; }
int step() const { return matrix ? matrix->step : 0; }
void set_data( void* data, int step=CV_AUTOSTEP )
{ cvSetData( matrix, data, step ); }
void set_data( void* _data, int _step=CV_AUTOSTEP )
{ cvSetData( matrix, _data, _step ); }
uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
const uchar* row(int i) const
@ -2014,8 +2014,8 @@ struct CV_EXPORTS BaseKeypoint
: x(0), y(0), image(NULL)
{}
BaseKeypoint(int x, int y, IplImage* image)
: x(x), y(y), image(image)
BaseKeypoint(int _x, int _y, IplImage* _image)
: x(_x), y(_y), image(_image)
{}
};

8
modules/legacy/src/blobtrackanalysistrackdist.cpp
View File

@ -350,11 +350,10 @@ public:
virtual void Process(IplImage* pImg, IplImage* /*pFG*/)
{
int i;
double MinTv = pImg->width/1440.0; /* minimal threshold for speed difference */
double MinTv2 = MinTv*MinTv;
for(i=m_Tracks.GetBlobNum(); i>0; --i)
for(int i=m_Tracks.GetBlobNum(); i>0; --i)
{
DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1);
pF->state = 0;
@ -466,14 +465,13 @@ public:
if(m_Wnd)
{ /* Debug output: */
int i;
if(m_pDebugImg==NULL)
m_pDebugImg = cvCloneImage(pImg);
else
cvCopy(pImg, m_pDebugImg);
for(i=m_TrackDataBase.GetBlobNum(); i>0; --i)
for(int i=m_TrackDataBase.GetBlobNum(); i>0; --i)
{ /* Draw all elements in track data base: */
int j;
DefTrackForDist* pF = (DefTrackForDist*)m_TrackDataBase.GetBlob(i-1);
@ -497,7 +495,7 @@ public:
pF->close = 0;
} /* Draw all elements in track data base. */
for(i=m_Tracks.GetBlobNum(); i>0; --i)
for(int i=m_Tracks.GetBlobNum(); i>0; --i)
{ /* Draw all elements for all trajectories: */
DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1);
int j;

4
modules/legacy/src/blobtrackingcc.cpp
View File

@ -301,8 +301,8 @@ public:
{ /* Find a neighbour on current frame
* for each blob from previous frame:
*/
CvBlob* pB = m_BlobList.GetBlob(i-1);
DefBlobTracker* pBT = (DefBlobTracker*)pB;
CvBlob* pBl = m_BlobList.GetBlob(i-1);
DefBlobTracker* pBT = (DefBlobTracker*)pBl;
//int BlobID = CV_BLOB_ID(pB);
//CvBlob* pBBest = NULL;
//double DistBest = -1;

13
modules/legacy/src/kdtree.cpp
View File

@ -93,18 +93,19 @@ class CvKDTreeWrap : public CvFeatureTree {
assert(results->cols == k);
assert(dist->cols == k);
for (int j = 0; j < d->rows; ++j) {
const typename __treetype::scalar_type* dj =
(const typename __treetype::scalar_type*) dptr;
for (int j = 0; j < d->rows; ++j)
{
const typename __treetype::scalar_type* dj = (const typename __treetype::scalar_type*) dptr;
int* resultsj = (int*) resultsptr;
double* distj = (double*) distptr;
tr->find_nn_bbf(dj, k, emax, nn);
assert((int)nn.size() <= k);
for (unsigned int j = 0; j < nn.size(); ++j) {
*resultsj++ = *nn[j].p;
*distj++ = nn[j].dist;
for (unsigned int i = 0; i < nn.size(); ++i)
{
*resultsj++ = *nn[i].p;
*distj++ = nn[i].dist;
}
std::fill(resultsj, resultsj + k - nn.size(), -1);
std::fill(distj, distj + k - nn.size(), 0);

232
modules/ml/include/opencv2/ml/ml.hpp
View File

@ -170,12 +170,7 @@ struct CV_EXPORTS_W_MAP CvParamGrid
min_val = max_val = step = 0;
}
CvParamGrid( double min_val, double max_val, double log_step )
{
this->min_val = min_val;
this->max_val = max_val;
step = log_step;
}
CvParamGrid( double min_val, double max_val, double log_step );
//CvParamGrid( int param_id );
bool check() const;
@ -184,6 +179,13 @@ struct CV_EXPORTS_W_MAP CvParamGrid
CV_PROP_RW double step;
};
inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step )
{
min_val = _min_val;
max_val = _max_val;
step = _log_step;
}
class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
{
public:
@ -192,10 +194,10 @@ public:
CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
@ -207,7 +209,7 @@ public:
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
@ -243,31 +245,31 @@ public:
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* sampleIdx=0, bool is_regression=false,
int maxK=32, bool updateBase=false );
virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
#ifndef SWIG
CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false,
int maxK=32, bool updateBase=false );
int maxK=32, bool updateBase=false );
virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0,
const float** neighbors=0, cv::Mat* neighborResponses=0,
cv::Mat* dist=0 ) const;
CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
#endif
virtual void clear();
int get_max_k() const;
int get_var_count() const;
int get_sample_count() const;
bool is_regression() const;
virtual float write_results( int k, int k1, int start, int end,
const float* neighbor_responses, const float* dist, CvMat* _results,
CvMat* _neighbor_responses, CvMat* _dist, Cv32suf* sort_buf ) const;
@ -473,7 +475,7 @@ public:
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0,
CvSVMParams params=CvSVMParams() );
virtual bool train_auto( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params,
int kfold = 10,
@ -487,16 +489,16 @@ public:
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* samples, CvMat* results ) const;
#ifndef SWIG
CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params,
int k_fold = 10,
@ -509,7 +511,7 @@ public:
bool balanced=false);
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
#endif
CV_WRAP virtual int get_support_vector_count() const;
virtual const float* get_support_vector(int i) const;
virtual CvSVMParams get_params() const { return params; };
@ -564,14 +566,14 @@ public:
// Default parameters
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
// The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
virtual ~EM();
CV_WRAP virtual void clear();
@ -579,7 +581,7 @@ public:
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
@ -587,13 +589,13 @@ public:
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP Vec2d predict(InputArray sample,
OutputArray probs=noArray()) const;
@ -603,7 +605,7 @@ public:
virtual void read(const FileNode& fn);
protected:
virtual void setTrainData(int startStep, const Mat& samples,
const Mat* probs0,
const Mat* means0,
@ -802,7 +804,7 @@ struct CV_EXPORTS CvDTreeTrainData
int buf_count, buf_size;
bool shared;
int is_buf_16u;
CvMat* cat_count;
CvMat* cat_ofs;
CvMat* cat_map;
@ -871,12 +873,12 @@ public:
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvDTreeParams params=CvDTreeParams() );
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
bool preprocessedInput=false ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
virtual const CvMat* get_var_importance();
CV_WRAP virtual void clear();
@ -900,13 +902,13 @@ protected:
virtual void try_split_node( CvDTreeNode* n );
virtual void split_node_data( CvDTreeNode* n );
virtual CvDTreeSplit* find_best_split( CvDTreeNode* n );
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
@ -1003,7 +1005,7 @@ public:
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvRTParams params=CvRTParams() );
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
@ -1018,16 +1020,16 @@ public:
CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
CV_WRAP virtual void clear();
virtual const CvMat* get_var_importance();
virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,
const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;
virtual float calc_error( CvMLData* data, int type , std::vector<float>* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
virtual float get_train_error();
virtual float get_train_error();
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
@ -1083,13 +1085,13 @@ class CV_EXPORTS CvForestERTree : public CvForestTree
{
protected:
virtual double calc_node_dir( CvDTreeNode* node );
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual void split_node_data( CvDTreeNode* n );
};
@ -1169,13 +1171,13 @@ protected:
virtual void try_split_node( CvDTreeNode* n );
virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
virtual void calc_node_value( CvDTreeNode* n );
virtual double calc_node_dir( CvDTreeNode* n );
@ -1201,14 +1203,14 @@ public:
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvBoostParams params=CvBoostParams() );
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvBoostParams params=CvBoostParams(),
bool update=false );
virtual bool train( CvMLData* data,
CvBoostParams params=CvBoostParams(),
bool update=false );
@ -1223,19 +1225,19 @@ public:
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvBoostParams params=CvBoostParams() );
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvBoostParams params=CvBoostParams(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
const cv::Range& slice=cv::Range::all(), bool rawMode=false,
bool returnSum=false ) const;
#endif
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
CV_WRAP virtual void prune( CvSlice slice );
@ -1315,7 +1317,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
// DataType: CLASS CvGBTrees
// Gradient Boosting Trees (GBT) algorithm implementation.
//
//
// data - training dataset
// params - parameters of the CvGBTrees
// weak - array[0..(class_count-1)] of CvSeq
@ -1347,7 +1349,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
// missing - mask of the missing values in the training set. This
// matrix has the same size as train_data. 1 - missing
// value, 0 - not a missing value.
// class_labels - output class labels map.
// class_labels - output class labels map.
// rng - random number generator. Used for spliting the
// training set.
// class_count - count of output classes.
@ -1368,15 +1370,15 @@ public:
/*
// DataType: ENUM
// Loss functions implemented in CvGBTrees.
//
//
// SQUARED_LOSS
// problem: regression
// loss = (x - x')^2
//
//
// ABSOLUTE_LOSS
// problem: regression
// loss = abs(x - x')
//
//
// HUBER_LOSS
// problem: regression
// loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta
@ -1386,18 +1388,18 @@ public:
//
// DEVIANCE_LOSS
// problem: classification
//
*/
//
*/
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
/*
// Default constructor. Creates a model only (without training).
// Should be followed by one form of the train(...) function.
//
// API
// CvGBTrees();
// INPUT
// OUTPUT
// RESULT
@ -1415,7 +1417,7 @@ public:
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams() );
// INPUT
// trainData - a set of input feature vectors.
// size of matrix is
@ -1448,13 +1450,13 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams() );
/*
// Destructor.
*/
virtual ~CvGBTrees();
/*
// Gradient tree boosting model training
//
@ -1465,7 +1467,7 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
// INPUT
// trainData - a set of input feature vectors.
// size of matrix is
@ -1500,8 +1502,8 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
/*
// Gradient tree boosting model training
//
@ -1509,7 +1511,7 @@ public:
// virtual bool train( CvMLData* data,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false ) {return false;};
// INPUT
// data - training set.
// params - parameters of GTB algorithm.
@ -1522,7 +1524,7 @@ public:
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
/*
// Response value prediction
//
@ -1530,7 +1532,7 @@ public:
// virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
// INPUT
// sample - input sample of the same type as in the training set.
// missing - missing values mask. missing=0 if there are no
@ -1541,7 +1543,7 @@ public:
// slice = CV_WHOLE_SEQ when all trees are used.
// k - number of ensemble used.
// k is in {-1,0,1,..,<count of output classes-1>}.
// in the case of classification problem
// in the case of classification problem
// <count of output classes-1> ensembles are built.
// If k = -1 ordinary prediction is the result,
// otherwise function gives the prediction of the
@ -1553,7 +1555,7 @@ public:
virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
/*
// Response value prediction.
// Parallel version (in the case of TBB existence)
@ -1562,7 +1564,7 @@ public:
// virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
// INPUT
// sample - input sample of the same type as in the training set.
// missing - missing values mask. missing=0 if there are no
@ -1573,7 +1575,7 @@ public:
// slice = CV_WHOLE_SEQ when all trees are used.
// k - number of ensemble used.
// k is in {-1,0,1,..,<count of output classes-1>}.
// in the case of classification problem
// in the case of classification problem
// <count of output classes-1> ensembles are built.
// If k = -1 ordinary prediction is the result,
// otherwise function gives the prediction of the
@ -1581,7 +1583,7 @@ public:
// OUTPUT
// RESULT
// Predicted value.
*/
*/
virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
@ -1591,7 +1593,7 @@ public:
//
// API
// virtual void clear();
// INPUT
// OUTPUT
// delete data, weak, orig_response, sum_response,
@ -1622,7 +1624,7 @@ public:
std::vector<float> *resp = 0 );
/*
//
//
// Write parameters of the gtb model and data. Write learned model.
//
// API
@ -1638,7 +1640,7 @@ public:
/*
//
//
// Read parameters of the gtb model and data. Read learned model.
//
// API
@ -1652,14 +1654,14 @@ public:
*/
virtual void read( CvFileStorage* fs, CvFileNode* node );
// new-style C++ interface
CV_WRAP CvGBTrees( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvGBTreesParams params=CvGBTreesParams() );
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@ -1670,7 +1672,7 @@ public:
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
const cv::Range& slice = cv::Range::all(),
int k=-1 ) const;
protected:
/*
@ -1678,7 +1680,7 @@ protected:
//
// API
// virtual void find_gradient( const int k = 0);
// INPUT
// k - used for classification problem, determining current
// tree ensemble.
@ -1690,9 +1692,9 @@ protected:
*/
virtual void find_gradient( const int k = 0);
/*
//
//
// Change values in tree leaves according to the used loss function.
//
// API
@ -1711,7 +1713,7 @@ protected:
/*
//
//
// Find optimal constant prediction value according to the used loss
// function.
// The goal is to find a constant which gives the minimal summary loss
@ -1728,9 +1730,9 @@ protected:
*/
virtual float find_optimal_value( const CvMat* _Idx );
/*
//
//
// Randomly split the whole training set in two parts according
// to params.portion.
//
@ -1747,7 +1749,7 @@ protected:
/*
//
//
// Internal recursive function giving an array of subtree tree leaves.
//
// API
@ -1761,10 +1763,10 @@ protected:
// RESULT
*/
void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
/*
//
//
// Get leaves of the tree.
//
// API
@ -1779,9 +1781,9 @@ protected:
*/
CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
/*
//
//
// Is it a regression or a classification.
//
// API
@ -1797,7 +1799,7 @@ protected:
/*
//
//
// Write parameters of the gtb model.
//
// API
@ -1812,7 +1814,7 @@ protected:
/*
//
//
// Read parameters of the gtb model and data.
//
// API
@ -1829,9 +1831,9 @@ protected:
// RESULT
*/
virtual void read_params( CvFileStorage* fs, CvFileNode* fnode );
int get_len(const CvMat* mat) const;
int get_len(const CvMat* mat) const;
CvDTreeTrainData* data;
CvGBTreesParams params;
@ -1894,30 +1896,30 @@ public:
virtual void create( const CvMat* layerSizes,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
virtual int train( const CvMat* inputs, const CvMat* outputs,
const CvMat* sampleWeights, const CvMat* sampleIdx=0,
CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
int flags=0 );
virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
#ifndef SWIG
CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
CV_WRAP virtual void create( const cv::Mat& layerSizes,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
double fparam1=0, double fparam2=0 );
CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs,
const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(),
CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
int flags=0 );
int flags=0 );
CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
#endif
CV_WRAP virtual void clear();
// possible activation functions
@ -2031,7 +2033,7 @@ public:
virtual ~CvMLData();
// returns:
// 0 - OK
// 0 - OK
// -1 - file can not be opened or is not correct
int read_csv( const char* filename );
@ -2039,8 +2041,8 @@ public:
const CvMat* get_responses();
const CvMat* get_missing() const;
void set_header_lines_number( int n );
int get_header_lines_number() const;
void set_header_lines_number( int n );
int get_header_lines_number() const;
void set_response_idx( int idx ); // old response become predictors, new response_idx = idx
// if idx < 0 there will be no response
@ -2050,7 +2052,7 @@ public:
const CvMat* get_train_sample_idx() const;
const CvMat* get_test_sample_idx() const;
void mix_train_and_test_idx();
const CvMat* get_var_idx();
void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability),
// use change_var_idx
@ -2064,14 +2066,14 @@ public:
void set_var_types( const char* str ); // str examples:
// "ord[0-17],cat[18]", "ord[0,2,4,10-12], cat[1,3,5-9,13,14]",
// "cat", "ord" (all vars are categorical/ordered)
void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL }
void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL }
void set_delimiter( char ch );
char get_delimiter() const;
void set_miss_ch( char ch );
char get_miss_ch() const;
const std::map<std::string, int>& get_class_labels_map() const;
protected:
@ -2079,7 +2081,7 @@ protected:
void str_to_flt_elem( const char* token, float& flt_elem, int& type);
void free_train_test_idx();
char delimiter;
char miss_ch;
//char flt_separator;
@ -2093,13 +2095,13 @@ protected:
CvMat* var_idx_out; // mat
CvMat* var_types_out; // mat
int header_lines_number;
int header_lines_number;
int response_idx;
int train_sample_count;
bool mix;
int total_class_count;
std::map<std::string, int> class_map;
@ -2113,7 +2115,7 @@ protected:
namespace cv
{
typedef CvStatModel StatModel;
typedef CvParamGrid ParamGrid;
typedef CvNormalBayesClassifier NormalBayesClassifier;
@ -2142,7 +2144,7 @@ typedef CvGBTrees GradientBoostingTrees;
template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
CV_EXPORTS bool initModule_ml(void);
}
#endif // __cplusplus

54
modules/ml/src/ann_mlp.cpp
View File

@ -504,7 +504,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df,
n *= cols;
xf -= n; df -= n;
for( i = 0; i < n; i++ )
df[i] *= xf[i];
}
@ -517,7 +517,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df,
xf[j] = (xf[j] + bias[j])*scale;
df[j] = -fabs(xf[j]);
}
cvExp( _df, _df );
n *= cols;
@ -1023,9 +1023,9 @@ int CvANN_MLP::train_backprop( CvVectors x0, CvVectors u, const double* sw )
}
struct rprop_loop {
rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0,
rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0,
int& _l_count, CvMat*& _layer_sizes, int& _ovcount, int& _max_count,
CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz)
CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz)
{
point = _point;
weights = _weights;
@ -1044,7 +1044,7 @@ struct rprop_loop {
E = _E;
buf_sz = _buf_sz;
}
const CvANN_MLP* point;
double** weights;
int count;
@ -1062,14 +1062,14 @@ struct rprop_loop {
double* E;
int buf_sz;
void operator()( const cv::BlockedRange& range ) const
{
double* buf_ptr;
double** x = 0;
double **df = 0;
double **df = 0;
int total = 0;
for(int i = 0; i < l_count; i++ )
total += layer_sizes->data.i[i];
CvMat* buf;
@ -1087,7 +1087,7 @@ struct rprop_loop {
for(int si = range.begin(); si < range.end(); si++ )
{
if (si % dcount0 != 0) continue;
int n1, n2, j, k;
int n1, n2, k;
double* w;
CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
CvMat *x1, *x2, *grad1, *grad2, *temp;
@ -1100,23 +1100,23 @@ struct rprop_loop {
// grab and preprocess input data
if( x0->type == CV_32F )
{
{
for(int i = 0; i < dcount; i++ )
{
const float* x0data = x0->data.fl[si+i];
double* xdata = x[0]+i*ivcount;
for( j = 0; j < ivcount; j++ )
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
}
}
else
for(int i = 0; i < dcount; i++ )
{
const double* x0data = x0->data.db[si+i];
double* xdata = x[0]+i*ivcount;
for( j = 0; j < ivcount; j++ )
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
}
cvInitMatHeader( x1, dcount, ivcount, CV_64F, x[0] );
// forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i])
@ -1144,7 +1144,7 @@ struct rprop_loop {
double* gdata = grad1->data.db + i*ovcount;
double sweight = sw ? sw[si+i] : inv_count, E1 = 0;
for( j = 0; j < ovcount; j++ )
for(int j = 0; j < ovcount; j++ )
{
double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j];
gdata[j] = t*sweight;
@ -1168,7 +1168,7 @@ struct rprop_loop {
}
*E += sweight*E1;
}
// backward pass, update dEdw
#ifdef HAVE_TBB
static tbb::spin_mutex mutex;
@ -1191,10 +1191,10 @@ struct rprop_loop {
{
double* dst = _dEdw.data.db + n1*n2;
const double* src = grad1->data.db + k*n2;
for( j = 0; j < n2; j++ )
for(int j = 0; j < n2; j++ )
dst[j] += src[j];
}
if (i > 1)
cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] );
#ifdef HAVE_TBB
@ -1215,7 +1215,7 @@ struct rprop_loop {
int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
{
const int max_buf_sz = 1 << 16;
const int max_buf_size = 1 << 16;
CvMat* dw = 0;
CvMat* dEdw = 0;
CvMat* prev_dEdw_sign = 0;
@ -1256,7 +1256,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
cvZero( prev_dEdw_sign );
inv_count = 1./count;
dcount0 = max_buf_sz/(2*total);
dcount0 = max_buf_size/(2*total);
dcount0 = MAX( dcount0, 1 );
dcount0 = MIN( dcount0, count );
buf_sz = dcount0*(total + max_count)*2;
@ -1297,8 +1297,8 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
double E = 0;
// first, iterate through all the samples and compute dEdw
cv::parallel_for(cv::BlockedRange(0, count),
rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes,
cv::parallel_for(cv::BlockedRange(0, count),
rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes,
ovcount, max_count, &u, sw, inv_count, dEdw, dcount0, &E, buf_sz)
);
@ -1600,8 +1600,8 @@ CvANN_MLP::CvANN_MLP( const Mat& _layer_sizes, int _activ_func,
void CvANN_MLP::create( const Mat& _layer_sizes, int _activ_func,
double _f_param1, double _f_param2 )
{
CvMat layer_sizes = _layer_sizes;
create( &layer_sizes, _activ_func, _f_param1, _f_param2 );
CvMat cvlayer_sizes = _layer_sizes;
create( &cvlayer_sizes, _activ_func, _f_param1, _f_param2 );
}
int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs,
@ -1610,7 +1610,7 @@ int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs,
{
CvMat inputs = _inputs, outputs = _outputs, sweights = _sample_weights, sidx = _sample_idx;
return train(&inputs, &outputs, sweights.data.ptr ? &sweights : 0,
sidx.data.ptr ? &sidx : 0, _params, flags);
sidx.data.ptr ? &sidx : 0, _params, flags);
}
float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const
@ -1618,8 +1618,8 @@ float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const
CV_Assert(layer_sizes != 0);
_outputs.create(_inputs.rows, layer_sizes->data.i[layer_sizes->cols-1], _inputs.type());
CvMat inputs = _inputs, outputs = _outputs;
return predict(&inputs, &outputs);
return predict(&inputs, &outputs);
}
/* End of file. */

56
modules/ml/src/boost.cpp
View File

@ -129,7 +129,7 @@ CvBoostTree::train( CvDTreeTrainData*, const CvMat* )
void
CvBoostTree::scale( double scale )
CvBoostTree::scale( double _scale )
{
CvDTreeNode* node = root;
@ -139,7 +139,7 @@ CvBoostTree::scale( double scale )
CvDTreeNode* parent;
for(;;)
{
node->value *= scale;
node->value *= _scale;
if( !node->left )
break;
node = node->left;
@ -501,7 +501,7 @@ CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality,
int i, best_i = -1;
double L = 0, R = weights[n];
double best_val = init_quality, lsum = 0, rsum = node->value*R;
// compensate for missing values
for( i = n1; i < n; i++ )
{
@ -590,7 +590,7 @@ CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality,
{
R += counts[i];
rsum += sum[i];
sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0;
sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0;
sum_ptr[i] = sum + i;
}
@ -1030,7 +1030,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag,
__BEGIN__;
int i;
set_params( _params );
cvReleaseMat( &active_vars );
@ -1057,7 +1057,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag,
if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) )
data->do_responses_copy();
update_weights( 0 );
for( i = 0; i < params.weak_count; i++ )
@ -1088,7 +1088,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag,
}
bool CvBoost::train( CvMLData* _data,
CvBoostParams params,
CvBoostParams _params,
bool update )
{
bool result = false;
@ -1105,7 +1105,7 @@ bool CvBoost::train( CvMLData* _data,
const CvMat* var_idx = _data->get_var_idx();
CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx,
train_sidx, var_types, missing, params, update ) );
train_sidx, var_types, missing, _params, update ) );
__END__;
@ -1258,7 +1258,7 @@ CvBoost::update_weights( CvBoostTree* tree )
// invert the subsample mask
cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
data->get_vectors( subsample_mask, values, missing, 0 );
_sample = cvMat( 1, data->var_count, CV_32F );
_mask = cvMat( 1, data->var_count, CV_8U );
@ -1458,17 +1458,17 @@ CvBoost::trim_weights()
}
const CvMat*
const CvMat*
CvBoost::get_active_vars( bool absolute_idx )
{
CvMat* mask = 0;
CvMat* inv_map = 0;
CvMat* result = 0;
CV_FUNCNAME( "CvBoost::get_active_vars" );
__BEGIN__;
if( !weak )
CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" );
@ -1478,7 +1478,7 @@ CvBoost::get_active_vars( bool absolute_idx )
int i, j, nactive_vars;
CvBoostTree* wtree;
const CvDTreeNode* node;
assert(!active_vars && !active_vars_abs);
mask = cvCreateMat( 1, data->var_count, CV_8U );
inv_map = cvCreateMat( 1, data->var_count, CV_32S );
@ -1518,7 +1518,7 @@ CvBoost::get_active_vars( bool absolute_idx )
}
nactive_vars = cvCountNonZero(mask);
//if ( nactive_vars > 0 )
{
active_vars = cvCreateMat( 1, nactive_vars, CV_32S );
@ -1538,7 +1538,7 @@ CvBoost::get_active_vars( bool absolute_idx )
j++;
}
}
// second pass: now compute the condensed indices
cvStartReadSeq( weak, &reader );
@ -1638,7 +1638,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
"floating-point vector of the same number of components as the length of input slice" );
wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float);
}
int var_count = active_vars->cols;
const int* vtype = data->var_type->data.i;
const int* cmap = data->cat_map->data.i;
@ -1738,7 +1738,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
CvBoostTree* wtree;
const CvDTreeNode* node;
CV_READ_SEQ_ELEM( wtree, reader );
node = wtree->get_root();
while( node->left )
{
@ -1757,14 +1757,14 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
{
const int* avars = active_vars->data.i;
const uchar* m = _missing ? _missing->data.ptr : 0;
// full-featured version
for( i = 0; i < weak_count; i++ )
{
CvBoostTree* wtree;
const CvDTreeNode* node;
CV_READ_SEQ_ELEM( wtree, reader );
node = wtree->get_root();
while( node->left )
{
@ -1841,9 +1841,9 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
@ -1859,15 +1859,15 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@ -2097,10 +2097,10 @@ CvBoost::CvBoost( const Mat& _train_data, int _tflag,
default_model_name = "my_boost_tree";
active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
subsample_mask = weights = subtree_weights = 0;
train( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params );
}
}
bool
@ -2130,7 +2130,7 @@ CvBoost::predict( const Mat& _sample, const Mat& _missing,
weak_count = weak->total;
slice.start_index = 0;
}
if( !(weak_responses->data && weak_responses->type() == CV_32FC1 &&
(weak_responses->cols == 1 || weak_responses->rows == 1) &&
weak_responses->cols + weak_responses->rows - 1 == weak_count) )

176
modules/ml/src/ertrees.cpp
View File

@ -71,7 +71,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
CV_FUNCNAME( "CvERTreeTrainData::set_data" );
__BEGIN__;
int sample_all = 0, r_type, cv_n;
int total_c_count = 0;
int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0;
@ -79,10 +79,10 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int vi, i, size;
char err[100];
const int *sidx = 0, *vidx = 0;
if ( _params.use_surrogates )
CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits");
if( _update_data && data_root )
{
CV_ERROR(CV_StsBadArg, "CvERTrees do not support data update");
@ -143,17 +143,17 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
"floating-point vector containing as many elements as "
"the total number of samples in the training data matrix" );
is_buf_16u = false;
if ( sample_count < 65536 )
is_buf_16u = true;
if ( sample_count < 65536 )
is_buf_16u = true;
r_type = CV_VAR_CATEGORICAL;
if( _var_type )
CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type ));
CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 ));
cat_var_count = 0;
ord_var_count = -1;
@ -182,7 +182,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
buf_size = (work_var_count + 1)*sample_count;
shared = _shared;
buf_count = shared ? 2 : 1;
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
@ -192,13 +192,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
}
}
size = is_classifier ? cat_var_count+1 : cat_var_count;
size = !size ? 1 : size;
CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 ));
CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 ));
size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories;
size = !size ? 1 : size;
CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 ));
@ -283,12 +283,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int c_count, prev_label;
int* c_map;
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + ci*sample_count);
else
idst = buf->data.i + ci*sample_count;
// copy data
for( i = 0; i < sample_count; i++ )
{
@ -322,7 +322,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
_idst[i] = val;
pair16u32s_ptr[i].u = udst + i;
pair16u32s_ptr[i].i = _idst + i;
}
}
else
{
idst[i] = val;
@ -397,7 +397,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
// replace labels for missing values with -1
for( ; i < sample_count; i++ )
*int_ptr[i] = -1;
}
}
}
else if( ci < 0 ) // process ordered variable
{
@ -442,15 +442,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( cv_n )
{
unsigned short* udst = 0;
int* idst = 0;
unsigned short* usdst = 0;
int* idst2 = 0;
if (is_buf_16u)
{
udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
for( i = vi = 0; i < sample_count; i++ )
{
udst[i] = (unsigned short)vi++;
usdst[i] = (unsigned short)vi++;
vi &= vi < cv_n ? -1 : 0;
}
@ -459,15 +459,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
unsigned short unsh = (unsigned short)vi;
CV_SWAP( udst[a], udst[b], unsh );
CV_SWAP( usdst[a], usdst[b], unsh );
}
}
else
{
idst = buf->data.i + (get_work_var_count()-1)*sample_count;
idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
for( i = vi = 0; i < sample_count; i++ )
{
idst[i] = vi++;
idst2[i] = vi++;
vi &= vi < cv_n ? -1 : 0;
}
@ -475,12 +475,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
CV_SWAP( idst[a], idst[b], vi );
CV_SWAP( idst2[a], idst2[b], vi );
}
}
}
if ( cat_map )
if ( cat_map )
cat_map->cols = MAX( total_c_count, 1 );
max_split_size = cvAlign(sizeof(CvDTreeSplit) +
@ -532,7 +532,7 @@ void CvERTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_val
const float** ord_values, const int** missing, int* sample_indices_buf )
{
int vidx = var_idx ? var_idx->data.i[vi] : vi;
int node_sample_count = n->sample_count;
int node_sample_count = n->sample_count;
// may use missing_buf as buffer for sample indices!
const int* sample_indices = get_sample_indices(n, sample_indices_buf ? sample_indices_buf : missing_buf);
@ -580,7 +580,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat
if( !is_buf_16u )
cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset;
else {
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
ci*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
@ -591,7 +591,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat
void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
float* values, uchar* missing,
float* responses, bool get_class_idx )
float* _responses, bool get_class_idx )
{
CvMat* subsample_idx = 0;
CvMat* subsample_co = 0;
@ -664,7 +664,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
}
// copy responses
if( responses )
if( _responses )
{
if( is_classifier )
{
@ -675,10 +675,10 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
int idx = sidx ? sidx[i] : i;
int val = get_class_idx ? src[idx] :
cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]];
responses[i] = (float)val;
_responses[i] = (float)val;
}
}
else
else
{
float* _values_buf = (float*)(uchar*)inn_buf;
int* sample_idx_buf = (int*)(_values_buf + sample_count);
@ -686,7 +686,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
responses[i] = _values[idx];
_responses[i] = _values[idx];
}
}
}
@ -700,7 +700,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
CvDTreeNode* CvERTreeTrainData::subsample_data( const CvMat* _subsample_idx )
{
CvDTreeNode* root = 0;
CV_FUNCNAME( "CvERTreeTrainData::subsample_data" );
__BEGIN__;
@ -853,7 +853,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
const float epsilon = FLT_EPSILON*2;
const float split_delta = (1 + FLT_EPSILON) * FLT_EPSILON;
int n = node->sample_count, i;
int n = node->sample_count;
int m = data->get_num_classes();
cv::AutoBuffer<uchar> inn_buf;
@ -882,8 +882,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
for (; smpi < n; smpi++)
{
float ptemp = values[smpi];
int m = missing[smpi];
if (m) continue;
int ms = missing[smpi];
if (ms) continue;
if ( ptemp < pmin)
pmin = ptemp;
if ( ptemp > pmax)
@ -898,7 +898,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
if (split_val - pmin <= FLT_EPSILON)
split_val = pmin + split_delta;
if (pmax - split_val <= FLT_EPSILON)
split_val = pmax - split_delta;
split_val = pmax - split_delta;
// calculate Gini index
if ( !priors )
@ -906,9 +906,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
cv::AutoBuffer<int> lrc(m*2);
int *lc = lrc, *rc = lc + m;
int L = 0, R = 0;
// init arrays of class instance counters on both sides of the split
for( i = 0; i < m; i++ )
for(int i = 0; i < m; i++ )
{
lc[i] = 0;
rc[i] = 0;
@ -917,8 +917,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
{
int r = responses[si];
float val = values[si];
int m = missing[si];
if (m) continue;
int ms = missing[si];
if (ms) continue;
if ( val < split_val )
{
lc[r]++;
@ -942,9 +942,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
cv::AutoBuffer<double> lrc(m*2);
double *lc = lrc, *rc = lc + m;
double L = 0, R = 0;
// init arrays of class instance counters on both sides of the split
for( i = 0; i < m; i++ )
for(int i = 0; i < m; i++ )
{
lc[i] = 0;
rc[i] = 0;
@ -953,9 +953,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
{
int r = responses[si];
float val = values[si];
int m = missing[si];
int ms = missing[si];
double p = priors[r];
if (m) continue;
if (ms) continue;
if ( val < split_val )
{
lc[r] += p;
@ -974,7 +974,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
}
best_val = (lbest_val*R + rbest_val*L) / (L*R);
}
}
CvDTreeSplit* split = 0;
@ -995,7 +995,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
{
int ci = data->get_var_type(vi);
int n = node->sample_count;
int cm = data->get_num_classes();
int cm = data->get_num_classes();
int vm = data->cat_count->data.i[ci];
double best_val = init_quality;
CvDTreeSplit *split = 0;
@ -1009,8 +1009,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
const int* labels = data->get_cat_var_data( node, vi, ext_buf );
const int* responses = data->get_class_labels( node, ext_buf + n );
const double* priors = data->have_priors ? data->priors_mult->data.db : 0;
const double* priors = data->have_priors ? data->priors_mult->data.db : 0;
// create random class mask
cv::AutoBuffer<int> valid_cidx(vm);
@ -1078,7 +1078,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
if (var_class_mask->data.ptr[mask_class_idx])
{
lc[r]++;
L++;
L++;
split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
}
else
@ -1091,7 +1091,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
{
lbest_val += lc[i]*lc[i];
rbest_val += rc[i]*rc[i];
}
}
best_val = (lbest_val*R + rbest_val*L) / ((double)(L*R));
}
else
@ -1113,11 +1113,11 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
continue;
double p = priors[si];
int mask_class_idx = valid_cidx[var_class_idx];
if (var_class_mask->data.ptr[mask_class_idx])
{
lc[r]+=(int)p;
L+=p;
L+=p;
split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
}
else
@ -1136,8 +1136,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f
split->quality = (float)best_val;
cvReleaseMat(&var_class_mask);
}
}
}
}
return split;
}
@ -1193,7 +1193,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo
if (split_val - pmin <= FLT_EPSILON)
split_val = pmin + split_delta;
if (pmax - split_val <= FLT_EPSILON)
split_val = pmax - split_delta;
split_val = pmax - split_delta;
for (int si = 0; si < n; si++)
{
@ -1209,7 +1209,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo
else
{
rsum += r;
R++;
R++;
}
}
best_val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R);
@ -1306,7 +1306,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo
if (var_class_mask->data.ptr[mask_class_idx])
{
lsum += r;
L++;
L++;
split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
}
else
@ -1320,8 +1320,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo
split->quality = (float)best_val;
cvReleaseMat(&var_class_mask);
}
}
}
}
return split;
}
@ -1358,7 +1358,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
{
int ci = data->get_var_type(vi);
if (ci >= 0) continue;
int n1 = node->get_num_valid(vi), nr1 = 0;
float* values_buf = (float*)(uchar*)inn_buf;
int* missing_buf = (int*)(values_buf + n);
@ -1369,7 +1369,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
for( i = 0; i < n; i++ )
nr1 += ((!missing[i]) & dir[i]);
left->set_num_valid(vi, n1 - nr1);
right->set_num_valid(vi, nr1);
right->set_num_valid(vi, nr1);
}
// split categorical vars, responses and cv_labels using new_idx relocation table
for( vi = 0; vi < data->get_work_var_count() + data->ord_var_count; vi++ )
@ -1385,11 +1385,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
ci*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
ci*scount + right->offset);
for( i = 0; i < n; i++ )
{
int d = dir[i];
@ -1415,11 +1415,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*buf->cols +
ci*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*buf->cols +
ci*scount + right->offset;
for( i = 0; i < n; i++ )
{
int d = dir[i];
@ -1435,7 +1435,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
*ldst = idx;
ldst++;
}
}
if( vi < data->var_count )
@ -1443,7 +1443,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
left->set_num_valid(vi, n1 - nr1);
right->set_num_valid(vi, nr1);
}
}
}
}
// split sample indices
@ -1457,14 +1457,14 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
temp_buf[i] = sample_idx_src[i];
int pos = data->get_work_var_count();
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
pos*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
pos*scount + right->offset);
for (i = 0; i < n; i++)
{
int d = dir[i];
@ -1483,9 +1483,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*buf->cols +
pos*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*buf->cols +
pos*scount + right->offset;
for (i = 0; i < n; i++)
{
@ -1504,9 +1504,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
}
}
}
// deallocate the parent node data that is not needed anymore
data->free_node_data(node);
data->free_node_data(node);
}
CvERTrees::CvERTrees()
@ -1576,10 +1576,10 @@ bool CvERTrees::train( const CvMat* _train_data, int _tflag,
__END__
return result;
}
bool CvERTrees::train( CvMLData* data, CvRTParams params)
bool CvERTrees::train( CvMLData* _data, CvRTParams params)
{
bool result = false;
@ -1587,7 +1587,7 @@ bool CvERTrees::train( CvMLData* data, CvRTParams params)
__BEGIN__;
CV_CALL( result = CvRTrees::train( data, params) );
CV_CALL( result = CvRTrees::train( _data, params) );
__END__;
@ -1609,7 +1609,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
const int dims = data->var_count;
float maximal_response = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_responses = 0;
float* oob_samples_perm_ptr= 0;
@ -1625,7 +1625,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
// initialize these variable to avoid warning C4701
CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );
nsamples = data->sample_count;
nclasses = data->get_num_classes();
@ -1647,11 +1647,11 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
cvGetRow( oob_responses, &oob_predictions_sum, 0 );
cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
}
CV_CALL(oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
CV_CALL(samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
CV_CALL(missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ));
CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ));
CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ));
CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ));
{
@ -1661,7 +1661,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 );
}
}
trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees );
memset( trees, 0, sizeof(trees[0])*max_ntrees );
@ -1692,7 +1692,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
sample.data.fl += dims, missing.data.ptr += dims )
{
CvDTreeNode* predicted_node = 0;
// predict oob samples
if( !predicted_node )
CV_CALL(predicted_node = tree->predict(&sample, &missing, true));
@ -1796,12 +1796,12 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
}
result = true;
cvFree( &oob_samples_perm_ptr );
cvFree( &samples_ptr );
cvFree( &missing_ptr );
cvFree( &true_resp_ptr );
cvReleaseMat( &sample_idx_for_tree );
cvReleaseMat( &oob_sample_votes );

32
modules/ml/src/gbt.cpp
View File

@ -166,13 +166,13 @@ bool CvGBTrees::problem_type() const
//===========================================================================
bool
CvGBTrees::train( CvMLData* data, CvGBTreesParams params, bool update )
CvGBTrees::train( CvMLData* _data, CvGBTreesParams _params, bool update )
{
bool result;
result = train ( data->get_values(), CV_ROW_SAMPLE,
data->get_responses(), data->get_var_idx(),
data->get_train_sample_idx(), data->get_var_types(),
data->get_missing(), params, update);
result = train ( _data->get_values(), CV_ROW_SAMPLE,
_data->get_responses(), _data->get_var_idx(),
_data->get_train_sample_idx(), _data->get_var_types(),
_data->get_missing(), _params, update);
//update is not supported
return result;
}
@ -1294,12 +1294,12 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
{
float err = 0.0f;
const CvMat* sample_idx = (type == CV_TRAIN_ERROR) ?
const CvMat* _sample_idx = (type == CV_TRAIN_ERROR) ?
_data->get_train_sample_idx() :
_data->get_test_sample_idx();
const CvMat* response = _data->get_responses();
int n = sample_idx ? get_len(sample_idx) : 0;
int n = _sample_idx ? get_len(_sample_idx) : 0;
n = (type == CV_TRAIN_ERROR && n == 0) ? _data->get_values()->rows : n;
if (!n)
@ -1315,7 +1315,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
pred_resp = new float[n];
Sample_predictor predictor = Sample_predictor(this, pred_resp, _data->get_values(),
_data->get_missing(), sample_idx);
_data->get_missing(), _sample_idx);
//#ifdef HAVE_TBB
// tbb::parallel_for(cv::BlockedRange(0,n), predictor, tbb::auto_partitioner());
@ -1323,7 +1323,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
cv::parallel_for(cv::BlockedRange(0,n), predictor);
//#endif
int* sidx = sample_idx ? sample_idx->data.i : 0;
int* sidx = _sample_idx ? _sample_idx->data.i : 0;
int r_step = CV_IS_MAT_CONT(response->type) ?
1 : response->step / CV_ELEM_SIZE(response->type);
@ -1357,7 +1357,7 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType,
const cv::Mat& missingDataMask,
CvGBTreesParams params )
CvGBTreesParams _params )
{
data = 0;
weak = 0;
@ -1371,14 +1371,14 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag,
clear();
train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, params, false);
train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, _params, false);
}
bool CvGBTrees::train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType,
const cv::Mat& missingDataMask,
CvGBTreesParams params,
CvGBTreesParams _params,
bool update )
{
CvMat _trainData = trainData, _responses = responses;
@ -1387,13 +1387,13 @@ bool CvGBTrees::train( const cv::Mat& trainData, int tflag,
return train( &_trainData, tflag, &_responses, varIdx.empty() ? 0 : &_varIdx,
sampleIdx.empty() ? 0 : &_sampleIdx, varType.empty() ? 0 : &_varType,
missingDataMask.empty() ? 0 : &_missingDataMask, params, update);
missingDataMask.empty() ? 0 : &_missingDataMask, _params, update);
}
float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& missing,
float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& _missing,
const cv::Range& slice, int k ) const
{
CvMat _sample = sample, _missing = missing;
return predict(&_sample, missing.empty() ? 0 : &_missing, 0,
CvMat _sample = sample, miss = _missing;
return predict(&_sample, _missing.empty() ? 0 : &miss, 0,
slice==cv::Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end), k);
}

20
modules/ml/src/knearest.cpp
View File

@ -141,7 +141,7 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses,
ok = true;
__END__;
if( responses && responses->data.ptr != _responses->data.ptr )
cvReleaseMat(&responses);
@ -318,7 +318,7 @@ struct P1 {
result = _result;
buf_sz = _buf_sz;
}
const CvKNearest* pointer;
int k;
const CvMat* _samples;
@ -329,7 +329,7 @@ struct P1 {
CvMat* _dist;
float* result;
int buf_sz;
void operator()( const cv::BlockedRange& range ) const
{
cv::AutoBuffer<float> buf(buf_sz);
@ -429,7 +429,7 @@ bool CvKNearest::train( const Mat& _train_data, const Mat& _responses,
int _max_k, bool _update_base )
{
CvMat tdata = _train_data, responses = _responses, sidx = _sample_idx;
return train(&tdata, &responses, sidx.data.ptr ? &sidx : 0, _is_regression, _max_k, _update_base );
}
@ -439,7 +439,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
Mat* _dist ) const
{
CvMat s = _samples, results, *presults = 0, nresponses, *pnresponses = 0, dist, *pdist = 0;
if( _results )
{
if(!(_results->data && (_results->type() == CV_32F ||
@ -449,7 +449,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
_results->create(_samples.rows, 1, CV_32F);
presults = &(results = *_results);
}
if( _neighbor_responses )
{
if(!(_neighbor_responses->data && _neighbor_responses->type() == CV_32F &&
@ -457,7 +457,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
_neighbor_responses->create(_samples.rows, k, CV_32F);
pnresponses = &(nresponses = *_neighbor_responses);
}
if( _dist )
{
if(!(_dist->data && _dist->type() == CV_32F &&
@ -465,15 +465,15 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
_dist->create(_samples.rows, k, CV_32F);
pdist = &(dist = *_dist);
}
return find_nearest(&s, k, presults, _neighbors, pnresponses, pdist );
}
float CvKNearest::find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat& results,
CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const
{
return find_nearest(samples, k, &results, 0, &neighborResponses, &dists);
return find_nearest(_samples, k, &results, 0, &neighborResponses, &dists);
}
/* End of file */

22
modules/ml/src/nbayes.cpp
View File

@ -241,13 +241,13 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res
double* cov_data = cov->data.db + i*_var_count;
double s1val = sum1[i];
double avg1 = avg_data[i];
int count = count_data[i];
int _count = count_data[i];
for( j = 0; j <= i; j++ )
{
double avg2 = avg2_data[j];
double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * count;
cov_val = (count > 1) ? cov_val / (count - 1) : cov_val;
double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * _count;
cov_val = (_count > 1) ? cov_val / (_count - 1) : cov_val;
cov_data[j] = cov_val;
}
}
@ -294,7 +294,7 @@ struct predict_body {
value = _value;
var_count1 = _var_count1;
}
CvMat* c;
CvMat** cov_rotate_mats;
CvMat** inv_eigen_values;
@ -306,15 +306,15 @@ struct predict_body {
CvMat* results;
float* value;
int var_count1;
void operator()( const cv::BlockedRange& range ) const
{
int cls = -1;
int rtype = 0, rstep = 0;
int rtype = 0, rstep = 0;
int nclasses = cls_labels->cols;
int _var_count = avg[0]->cols;
if (results)
{
rtype = CV_MAT_TYPE(results->type);
@ -323,7 +323,7 @@ struct predict_body {
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclasses + var_count1);
CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] );
for(int k = range.begin(); k < range.end(); k += 1 )
{
int ival;
@ -592,7 +592,7 @@ CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& _train_data, const
cov_rotate_mats = 0;
c = 0;
default_model_name = "my_nb";
CvMat tdata = _train_data, responses = _responses, vidx = _var_idx, sidx = _sample_idx;
train(&tdata, &responses, vidx.data.ptr ? &vidx : 0,
sidx.data.ptr ? &sidx : 0);
@ -609,7 +609,7 @@ bool CvNormalBayesClassifier::train( const Mat& _train_data, const Mat& _respons
float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) const
{
CvMat samples = _samples, results, *presults = 0;
if( _results )
{
if( !(_results->data && _results->type() == CV_32F &&
@ -618,7 +618,7 @@ float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) con
_results->create(_samples.rows, 1, CV_32F);
presults = &(results = *_results);
}
return predict(&samples, presults);
}

72
modules/ml/src/rtrees.cpp
View File

@ -307,14 +307,14 @@ bool CvRTrees::train( const CvMat* _train_data, int _tflag,
return grow_forest( params.term_crit );
}
bool CvRTrees::train( CvMLData* data, CvRTParams params )
bool CvRTrees::train( CvMLData* _data, CvRTParams params )
{
const CvMat* values = data->get_values();
const CvMat* response = data->get_responses();
const CvMat* missing = data->get_missing();
const CvMat* var_types = data->get_var_types();
const CvMat* train_sidx = data->get_train_sample_idx();
const CvMat* var_idx = data->get_var_idx();
const CvMat* values = _data->get_values();
const CvMat* response = _data->get_responses();
const CvMat* missing = _data->get_missing();
const CvMat* var_types = _data->get_var_types();
const CvMat* train_sidx = _data->get_train_sample_idx();
const CvMat* var_idx = _data->get_var_idx();
return train( values, CV_ROW_SAMPLE, response, var_idx,
train_sidx, var_types, missing, params );
@ -331,7 +331,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
const int dims = data->var_count;
float maximal_response = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_responses = 0;
float* oob_samples_perm_ptr= 0;
@ -347,7 +347,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
// initialize these variable to avoid warning C4701
CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );
nsamples = data->sample_count;
nclasses = data->get_num_classes();
@ -369,14 +369,14 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
cvGetRow( oob_responses, &oob_predictions_sum, 0 );
cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
}
oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims );
samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims );
missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims );
true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples );
true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples );
data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr );
double minval, maxval;
CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr);
cvMinMaxLoc( &responses, &minval, &maxval );
@ -536,7 +536,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
cvFree( &samples_ptr );
cvFree( &missing_ptr );
cvFree( &true_resp_ptr );
cvReleaseMat( &sample_idx_mask_for_tree );
cvReleaseMat( &sample_idx_for_tree );
@ -592,9 +592,9 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector<float> *resp
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
@ -610,15 +610,15 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector<float> *resp
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@ -635,12 +635,12 @@ float CvRTrees::get_train_error()
float *responses_ptr = (float*)cvAlloc( sizeof(float)*sample_count );
data->get_vectors( 0, values_ptr, missing_ptr, responses_ptr);
if (data->is_classifier)
{
int err_count = 0;
float *vp = values_ptr;
uchar *mp = missing_ptr;
uchar *mp = missing_ptr;
for (int si = 0; si < sample_count; si++, vp += var_count, mp += var_count)
{
CvMat sample = cvMat( 1, var_count, CV_32FC1, vp );
@ -653,10 +653,10 @@ float CvRTrees::get_train_error()
}
else
CV_Error( CV_StsBadArg, "This method is not supported for regression problems" );
cvFree( &values_ptr );
cvFree( &missing_ptr );
cvFree( &responses_ptr );
cvFree( &responses_ptr );
return err;
}
@ -701,7 +701,7 @@ float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const
float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const
{
if( nclasses == 2 ) //classification
if( nclasses == 2 ) //classification
{
cv::AutoBuffer<int> _votes(nclasses);
int* votes = _votes;
@ -711,15 +711,15 @@ float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const
CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
int class_idx = predicted_node->class_idx;
CV_Assert( 0 <= class_idx && class_idx < nclasses );
++votes[class_idx];
}
return float(votes[1])/ntrees;
return float(votes[1])/ntrees;
}
else // regression
CV_Error(CV_StsBadArg, "This function works for binary classification problems only...");
CV_Error(CV_StsBadArg, "This function works for binary classification problems only...");
return -1;
}
@ -809,15 +809,15 @@ void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode )
{
// initialize active variables mask
CvMat submask1;
cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
cvSet( &submask1, cvScalar(1) );
if( nactive_vars < var_count )
{
CvMat submask2;
cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
cvZero( &submask2 );
}
if( nactive_vars < var_count )
{
CvMat submask2;
cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
cvZero( &submask2 );
}
}
}

31
modules/ml/src/svm.cpp
View File

@ -1065,10 +1065,10 @@ bool CvSVMSolver::solve_eps_svr( int _sample_count, int _var_count, const float*
CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
double p = _kernel->params->p, C = _kernel->params->C;
double p = _kernel->params->p, _C = _kernel->params->C;
if( !create( _sample_count, _var_count, _samples, 0,
_sample_count*2, 0, C, C, _storage, _kernel, &CvSVMSolver::get_row_svr,
_sample_count*2, 0, _C, _C, _storage, _kernel, &CvSVMSolver::get_row_svr,
&CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho ))
return false;
@ -1101,7 +1101,7 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float**
CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
double C = _kernel->params->C, sum;
double _C = _kernel->params->C, sum;
if( !create( _sample_count, _var_count, _samples, 0,
_sample_count*2, 0, 1., 1., _storage, _kernel, &CvSVMSolver::get_row_svr,
@ -1110,11 +1110,11 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float**
y = (schar*)cvMemStorageAlloc( storage, sample_count*2*sizeof(y[0]) );
alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) );
sum = C * _kernel->params->nu * sample_count * 0.5;
sum = _C * _kernel->params->nu * sample_count * 0.5;
for( i = 0; i < sample_count; i++ )
{
alpha[i] = alpha[i + sample_count] = MIN(sum, C);
alpha[i] = alpha[i + sample_count] = MIN(sum, _C);
sum -= alpha[i];
b[i] = -_y[i];
@ -1628,12 +1628,11 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
int svm_type, sample_count, var_count, sample_size;
int block_size = 1 << 16;
double* alpha;
int i, k;
RNG* rng = &theRNG();
// all steps are logarithmic and must be > 1
double degree_step = 10, g_step = 10, coef_step = 10, C_step = 10, nu_step = 10, p_step = 10;
double gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0;
double gamma = 0, _C = 0, degree = 0, coef = 0, p = 0, nu = 0;
double best_degree = 0, best_gamma = 0, best_coef = 0, best_C = 0, best_nu = 0, best_p = 0;
float min_error = FLT_MAX, error;
@ -1760,7 +1759,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
cvZero( responses_local );
// randomly permute samples and responses
for( i = 0; i < sample_count; i++ )
for(int i = 0; i < sample_count; i++ )
{
int i1 = (*rng)(sample_count);
int i2 = (*rng)(sample_count);
@ -1779,7 +1778,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
{
// count class samples
int num_0=0,num_1=0;
for (i=0; i<sample_count; ++i)
for (int i=0; i<sample_count; ++i)
{
if (responses->data.i[i]==class_labels->data.i[0])
++num_0;
@ -1875,10 +1874,10 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
}
int* cls_lbls = class_labels ? class_labels->data.i : 0;
C = C_grid.min_val;
_C = C_grid.min_val;
do
{
params.C = C;
params.C = _C;
gamma = gamma_grid.min_val;
do
{
@ -1906,7 +1905,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
int train_size = trainset_size;
error = 0;
for( k = 0; k < k_fold; k++ )
for(int k = 0; k < k_fold; k++ )
{
memcpy( samples_local, samples, sizeof(samples[0])*test_size*k );
memcpy( samples_local + test_size*k, test_samples_ptr + test_size,
@ -1930,7 +1929,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
EXIT;
// Compute test set error on <test_size> samples
for( i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ )
for(int i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ )
{
float resp = predict( *test_samples_ptr, var_count );
error += is_regression ? powf( resp - *(float*)true_resp, 2 )
@ -1943,7 +1942,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
best_degree = degree;
best_gamma = gamma;
best_coef = coef;
best_C = C;
best_C = _C;
best_nu = nu;
best_p = p;
}
@ -1962,9 +1961,9 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses,
gamma *= gamma_grid.step;
}
while( gamma < gamma_grid.max_val );
C *= C_grid.step;
_C *= C_grid.step;
}
while( C < C_grid.max_val );
while( _C < C_grid.max_val );
}
min_error /= (float) sample_count;

166
modules/ml/src/tree.cpp
View File

@ -156,7 +156,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int vi, i, size;
char err[100];
const int *sidx = 0, *vidx = 0;
if( _update_data && data_root )
{
data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
@ -224,7 +224,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
sample_count = sample_all;
var_count = var_all;
if( _sample_idx )
{
CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all ));
@ -239,10 +239,10 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
var_count = var_idx->rows + var_idx->cols - 1;
}
is_buf_16u = false;
if ( sample_count < 65536 )
is_buf_16u = true;
is_buf_16u = false;
if ( sample_count < 65536 )
is_buf_16u = true;
if( !CV_IS_MAT(_responses) ||
(CV_MAT_TYPE(_responses->type) != CV_32SC1 &&
CV_MAT_TYPE(_responses->type) != CV_32FC1) ||
@ -251,13 +251,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
"floating-point vector containing as many elements as "
"the total number of samples in the training data matrix" );
r_type = CV_VAR_CATEGORICAL;
if( _var_type )
CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type ));
CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 ));
cat_var_count = 0;
ord_var_count = -1;
@ -284,11 +284,11 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
+ (have_labels ? 1 : 0); // for cv_labels
buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count;
shared = _shared;
buf_count = shared ? 2 : 1;
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
@ -298,13 +298,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
}
}
size = is_classifier ? (cat_var_count+1) : cat_var_count;
size = !size ? 1 : size;
CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 ));
CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 ));
size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories;
size = !size ? 1 : size;
CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 ));
@ -389,12 +389,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int c_count, prev_label;
int* c_map;
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + vi*sample_count);
else
idst = buf->data.i + vi*sample_count;
// copy data
for( i = 0; i < sample_count; i++ )
{
@ -428,7 +428,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
_idst[i] = val;
pair16u32s_ptr[i].u = udst + i;
pair16u32s_ptr[i].i = _idst + i;
}
}
else
{
idst[i] = val;
@ -502,7 +502,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
// replace labels for missing values with -1
for( ; i < sample_count; i++ )
*int_ptr[i] = -1;
}
}
}
else if( ci < 0 ) // process ordered variable
{
@ -536,14 +536,14 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
else
idst[i] = i;
_fdst[i] = val;
}
if (is_buf_16u)
icvSortUShAux( udst, sample_count, _fdst);
else
icvSortIntAux( idst, sample_count, _fdst );
}
if( vi < var_count )
data_root->set_num_valid(vi, num_valid);
}
@ -564,15 +564,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( cv_n )
{
unsigned short* udst = 0;
int* idst = 0;
unsigned short* usdst = 0;
int* idst2 = 0;
if (is_buf_16u)
{
udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
for( i = vi = 0; i < sample_count; i++ )
{
udst[i] = (unsigned short)vi++;
usdst[i] = (unsigned short)vi++;
vi &= vi < cv_n ? -1 : 0;
}
@ -581,15 +581,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
unsigned short unsh = (unsigned short)vi;
CV_SWAP( udst[a], udst[b], unsh );
CV_SWAP( usdst[a], usdst[b], unsh );
}
}
else
{
idst = buf->data.i + (get_work_var_count()-1)*sample_count;
idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
for( i = vi = 0; i < sample_count; i++ )
{
idst[i] = vi++;
idst2[i] = vi++;
vi &= vi < cv_n ? -1 : 0;
}
@ -597,12 +597,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
CV_SWAP( idst[a], idst[b], vi );
CV_SWAP( idst2[a], idst2[b], vi );
}
}
}
if ( cat_map )
if ( cat_map )
cat_map->cols = MAX( total_c_count, 1 );
max_split_size = cvAlign(sizeof(CvDTreeSplit) +
@ -751,7 +751,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
vi*sample_count + root->offset);
for( i = 0; i < count; i++ )
{
@ -762,7 +762,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst = buf->data.i + root->buf_idx*buf->cols +
int* idst = buf->data.i + root->buf_idx*buf->cols +
vi*sample_count + root->offset;
for( i = 0; i < count; i++ )
{
@ -788,7 +788,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
vi*sample_count + data_root->offset);
for( i = 0; i < num_valid; i++ )
{
@ -812,7 +812,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
vi*sample_count + root->offset;
for( i = 0; i < num_valid; i++ )
{
@ -840,14 +840,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
workVarCount*sample_count + root->offset);
for (i = 0; i < count; i++)
sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
}
else
{
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
@ -865,7 +865,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
float* values, uchar* missing,
float* responses, bool get_class_idx )
float* _responses, bool get_class_idx )
{
CvMat* subsample_idx = 0;
CvMat* subsample_co = 0;
@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
}
// copy responses
if( responses )
if( _responses )
{
if( is_classifier )
{
@ -972,7 +972,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
int idx = sidx ? sidx[i] : i;
int val = get_class_idx ? src[idx] :
cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]];
responses[i] = (float)val;
_responses[i] = (float)val;
}
}
else
@ -983,7 +983,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
responses[i] = _values[idx];
_responses[i] = _values[idx];
}
}
}
@ -1122,7 +1122,7 @@ void CvDTreeTrainData::clear()
cvReleaseMat( &cat_map );
cvReleaseMat( &priors );
cvReleaseMat( &priors_mult );
node_heap = split_heap = 0;
sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0;
@ -1130,7 +1130,7 @@ void CvDTreeTrainData::clear()
buf_count = buf_size = 0;
shared = false;
data_root = 0;
rng = &cv::theRNG();
@ -1152,7 +1152,7 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
const float** ord_values, const int** sorted_indices, int* sample_indices_buf )
{
int vidx = var_idx ? var_idx->data.i[vi] : vi;
int node_sample_count = n->sample_count;
int node_sample_count = n->sample_count;
int td_step = train_data->step/CV_ELEM_SIZE(train_data->type);
const int* sample_indices = get_sample_indices(n, sample_indices_buf);
@ -1161,16 +1161,16 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
*sorted_indices = buf->data.i + n->buf_idx*buf->cols +
vi*sample_count + n->offset;
else {
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
vi*sample_count + n->offset );
for( int i = 0; i < node_sample_count; i++ )
sorted_indices_buf[i] = short_indices[i];
*sorted_indices = sorted_indices_buf;
}
if( tflag == CV_ROW_SAMPLE )
{
for( int i = 0; i < node_sample_count &&
for( int i = 0; i < node_sample_count &&
((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
{
int idx = (*sorted_indices)[i];
@ -1179,14 +1179,14 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
}
}
else
for( int i = 0; i < node_sample_count &&
for( int i = 0; i < node_sample_count &&
((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
{
int idx = (*sorted_indices)[i];
idx = sample_indices[idx];
ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx);
}
*ord_values = ord_values_buf;
}
@ -1205,17 +1205,17 @@ const int* CvDTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_bu
const float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n, float* values_buf, int*sample_indices_buf )
{
int sample_count = n->sample_count;
int _sample_count = n->sample_count;
int r_step = CV_IS_MAT_CONT(responses->type) ? 1 : responses->step/CV_ELEM_SIZE(responses->type);
const int* indices = get_sample_indices(n, sample_indices_buf);
for( int i = 0; i < sample_count &&
for( int i = 0; i < _sample_count &&
(((indices[i] >= 0) && !is_buf_16u) || ((indices[i] != 65535) && is_buf_16u)); i++ )
{
int idx = indices[i];
values_buf[i] = *(responses->data.fl + idx * r_step);
}
return values_buf;
}
@ -1235,7 +1235,7 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_
cat_values = buf->data.i + n->buf_idx*buf->cols +
vi*sample_count + n->offset;
else {
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
vi*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
@ -1562,7 +1562,7 @@ bool CvDTree::train( const Mat& _train_data, int _tflag,
const Mat& _missing_mask, CvDTreeParams _params )
{
CvMat tdata = _train_data, responses = _responses, vidx=_var_idx,
sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0,
vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params);
}
@ -1794,7 +1794,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
const float* val = 0;
const int* sorted = 0;
data->get_ord_var_data( node, vi, val_buf, sorted_buf, &val, &sorted, sample_idx_buf);
assert( 0 <= split_point && split_point < n1-1 );
if( !data->have_priors )
@ -1848,7 +1848,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj()
{
fastFree(obj);
}
DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node)
{
tree = _tree;
@ -2310,7 +2310,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in
}
CvDTreeSplit* split = 0;
if( best_subset >= 0 )
if( best_subset >= 0 )
{
split = _split ? _split : data->new_split_cat( 0, -1.0f );
split->var_idx = vi;
@ -2933,7 +2933,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
{
int idx = labels[i];
if( !dir[i] && ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) ))
{
int d = CV_DTREE_CAT_DIR(idx,subset);
dir[i] = (char)((d ^ inversed_mask) - inversed_mask);
@ -3049,7 +3049,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
{
unsigned short *ldst, *rdst, *ldst0, *rdst0;
//unsigned short tl, tr;
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
vi*scount + left->offset);
rdst0 = rdst = (unsigned short*)(ldst + nl);
@ -3095,9 +3095,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
else
{
int *ldst0, *ldst, *rdst0, *rdst;
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
vi*scount + left->offset;
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
vi*scount + right->offset;
// split sorted
@ -3146,7 +3146,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
{
int ci = data->get_var_type(vi);
int n1 = node->get_num_valid(vi), nr1 = 0;
if( ci < 0 || (vi < data->var_count && !split_input_data) )
continue;
@ -3158,11 +3158,11 @@ void CvDTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
vi*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
vi*scount + right->offset);
for( i = 0; i < n; i++ )
{
int d = dir[i];
@ -3188,11 +3188,11 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*buf->cols +
vi*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*buf->cols +
vi*scount + right->offset;
for( i = 0; i < n; i++ )
{
int d = dir[i];
@ -3208,7 +3208,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
*ldst = idx;
ldst++;
}
}
if( vi < data->var_count )
@ -3216,7 +3216,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
left->set_num_valid(vi, n1 - nr1);
right->set_num_valid(vi, nr1);
}
}
}
}
@ -3230,9 +3230,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
int pos = data->get_work_var_count();
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
pos*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
pos*scount + right->offset);
for (i = 0; i < n; i++)
{
@ -3252,9 +3252,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*buf->cols +
pos*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*buf->cols +
pos*scount + right->offset;
for (i = 0; i < n; i++)
{
@ -3272,9 +3272,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
}
}
// deallocate the parent node data that is not needed anymore
data->free_node_data(node);
data->free_node_data(node);
}
float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
@ -3304,9 +3304,9 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
@ -3321,16 +3321,16 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@ -3527,7 +3527,7 @@ int CvDTree::cut_tree( int T, int fold, double min_alpha )
}
void CvDTree::free_prune_data(bool cut_tree)
void CvDTree::free_prune_data(bool _cut_tree)
{
CvDTreeNode* node = root;
@ -3548,7 +3548,7 @@ void CvDTree::free_prune_data(bool cut_tree)
for( parent = node->parent; parent && parent->right == node;
node = parent, parent = parent->parent )
{
if( cut_tree && parent->Tn <= pruned_tree_idx )
if( _cut_tree && parent->Tn <= pruned_tree_idx )
{
data->free_node( parent->left );
data->free_node( parent->right );
@ -3650,12 +3650,12 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
{
int a = c = cofs[ci];
int b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1];
int ival = cvRound(val);
if( ival != val )
CV_Error( CV_StsBadArg,
"one of input categorical variable is not an integer" );
int sh = 0;
while( a < b )
{

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