generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Warning fixes continued

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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
View File

@ -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})

21
modules/calib3d/src/calibinit.cpp
View File

@ -255,7 +255,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
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;
@ -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++)
@ -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 )
@ -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);
@ -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)
{
@ -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

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];

12
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;
}

68
modules/calib3d/src/epnp.cpp
View File

@ -500,8 +500,7 @@ void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rh
}
}
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;
@ -510,7 +509,8 @@ 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);
qr_solve(&A, &B, &X);
@ -524,50 +524,61 @@ 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;
}
double sigma = sqrt(sum);
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(sum2);
if (*ppAkk < 0)
sigma = -sigma;
*ppAkk += sigma;
A1[k] = sigma * *ppAkk;
A2[k] = -eta * sigma;
for(int j = k + 1; j < nc; j++) {
for(int j = k + 1; j < nc; j++)
{
double * ppAik = ppAkk, sum = 0;
for(int i = k; i < nr; i++) {
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++) {
for(int i = k; i < nr; i++)
{
ppAik[j - k] -= tau * *ppAik;
ppAik += nc;
}
@ -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++;
}

18
modules/calib3d/src/p3p.cpp
View File

@ -362,20 +362,20 @@ bool p3p::jacobi_4x4(double * A, double * D, double * U)
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;
double hh = D[j] - D[i], t;
if (fabs(hh) + eps_machine == fabs(hh))
t = Aij / hh;
else {
double theta = 0.5 * h / Aij;
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;
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);

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);

13
modules/calib3d/src/stereobm.cpp
View File

@ -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;
@ -463,7 +463,8 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right,
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
@ -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;
@ -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];
}

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

@ -773,11 +773,11 @@ 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 )
@ -798,11 +798,11 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
// 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 )

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

@ -529,14 +529,14 @@ void CV_CameraCalibrationTest::run( int start_from )
/* ---- Reproject points to the image ---- */
for( currImage = 0; currImage < numImages; currImage++ )
{
int numPoints = etalonSize.width * etalonSize.height;
project( numPoints,
objectPoints + currImage * numPoints,
int nPoints = etalonSize.width * etalonSize.height;
project( nPoints,
objectPoints + currImage * nPoints,
rotMatrs + currImage * 9,
transVects + currImage * 3,
cameraMatrix,
distortion,
reprojectPoints + currImage * numPoints);
reprojectPoints + currImage * nPoints);
}
/* ----- Compute reprojection error ----- */

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

@ -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;
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);
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]);
}

55
modules/calib3d/test/test_chesscorners.cpp
View File

@ -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;*/
@ -146,19 +145,19 @@ 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;
}
@ -176,9 +175,7 @@ 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
double max_rough_error = 0, max_precise_error = 0;
@ -187,13 +184,13 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
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;
}
@ -202,10 +199,10 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
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;
}
@ -216,7 +213,7 @@ 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];
@ -224,19 +221,19 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
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();
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();
@ -259,8 +256,8 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
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;
}
@ -291,19 +288,19 @@ 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
}
#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();
@ -312,7 +309,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
}
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)

21
modules/calib3d/test/test_compose_rt.cpp
View File

@ -139,8 +139,7 @@ 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);
Mat_<double> rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1);
@ -164,7 +163,7 @@ 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);
@ -179,8 +178,8 @@ protected:
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;
@ -188,8 +187,8 @@ protected:
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;
@ -197,8 +196,8 @@ protected:
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;
@ -206,8 +205,8 @@ protected:
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);
}
}
};

16
modules/calib3d/test/test_homography.cpp
View File

@ -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;

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)

4
modules/calib3d/test/test_stereomatching.cpp
View File

@ -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;
}

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

18
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;
}
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;
}
//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;

22
modules/contrib/src/facerec.cpp
View File

@ -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);
}

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;
}
}
}

68
modules/contrib/src/logpolar_bsm.cpp
View File

@ -62,7 +62,7 @@ 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);
@ -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);
@ -433,7 +433,7 @@ 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);

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;
}

20
modules/contrib/src/spinimages.cpp
View File

@ -336,18 +336,18 @@ void computeSpinImages( const Octree& Octree, const vector<Point3f>& points, con
__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
@ -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
{
@ -851,11 +851,11 @@ void cv::SpinImageModel::repackSpinImages(const vector<uchar>& mask, Mat& spinIm
for (; first != last; ++first)
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);
}
}

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

@ -204,10 +204,10 @@ void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
break;
}
float fi = Fi;
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 (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 A = static_cast<int>(pU[x]);
@ -219,8 +219,8 @@ void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
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) ;
+ _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];

6
modules/core/CMakeLists.txt
View File

@ -5,12 +5,10 @@ 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()

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

24
modules/core/include/opencv2/core/mat.hpp
View File

@ -336,12 +336,12 @@ inline Mat Mat::clone() const
return m;
}
inline void Mat::assignTo( Mat& m, int type ) const
inline void Mat::assignTo( Mat& m, int _type ) const
{
if( type < 0 )
if( _type < 0 )
m = *this;
else
convertTo(m, type);
convertTo(m, _type);
}
inline void Mat::create(int _rows, int _cols, int _type)
@ -370,9 +370,9 @@ inline void Mat::release()
refcount = 0;
}
inline Mat Mat::operator()( Range rowRange, Range colRange ) const
inline Mat Mat::operator()( Range _rowRange, Range _colRange ) const
{
return Mat(*this, rowRange, colRange);
return Mat(*this, _rowRange, _colRange);
}
inline Mat Mat::operator()( const Rect& roi ) const
@ -829,8 +829,8 @@ template<typename _Tp> inline Mat_<_Tp>::Mat_(const Mat_& m)
template<typename _Tp> inline Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps)
: Mat(_rows, _cols, DataType<_Tp>::type, _data, steps) {}
template<typename _Tp> inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& rowRange, const Range& colRange)
: Mat(m, rowRange, colRange) {}
template<typename _Tp> inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange)
: Mat(m, _rowRange, _colRange) {}
template<typename _Tp> inline Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi)
: Mat(m, roi) {}
@ -967,8 +967,8 @@ template<typename _Tp> inline size_t Mat_<_Tp>::step1(int i) const { return step
template<typename _Tp> inline Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright )
{ return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); }
template<typename _Tp> inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& rowRange, const Range& colRange ) const
{ return Mat_<_Tp>(*this, rowRange, colRange); }
template<typename _Tp> inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const
{ return Mat_<_Tp>(*this, _rowRange, _colRange); }
template<typename _Tp> inline Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const
{ return Mat_<_Tp>(*this, roi); }
@ -2123,12 +2123,12 @@ inline SparseMat SparseMat::clone() const
}
inline void SparseMat::assignTo( SparseMat& m, int type ) const
inline void SparseMat::assignTo( SparseMat& m, int _type ) const
{
if( type < 0 )
if( _type < 0 )
m = *this;
else
convertTo(m, type);
convertTo(m, _type);
}
inline void SparseMat::addref()

13
modules/core/include/opencv2/core/opengl_interop.hpp
View File

@ -70,9 +70,9 @@ namespace cv
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 create(Size size, int type, Usage usage);
void create(int rows, int cols, int type);
void create(Size size, int type);
void release();
@ -130,7 +130,7 @@ namespace cv
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 create(Size size, int type);
void release();
//! copy from host/device memory
@ -318,6 +318,11 @@ namespace cv
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

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;
}

154
modules/core/src/algorithm.cpp
View File

@ -181,124 +181,124 @@ 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);
info()->set(this, parameter.c_str(), ParamType<vector<Mat> >::type, &value);
}
void Algorithm::set(const string& name, const Ptr<Algorithm>& value)
void Algorithm::set(const string& parameter, const Ptr<Algorithm>& value)
{
info()->set(this, name.c_str(), ParamType<Algorithm>::type, &value);
info()->set(this, parameter.c_str(), ParamType<Algorithm>::type, &value);
}
void Algorithm::set(const char* name, int value)
void Algorithm::set(const char* parameter, int value)
{
info()->set(this, name, ParamType<int>::type, &value);
info()->set(this, parameter, ParamType<int>::type, &value);
}
void Algorithm::set(const char* name, double value)
void Algorithm::set(const char* parameter, double value)
{
info()->set(this, name, ParamType<double>::type, &value);
info()->set(this, parameter, ParamType<double>::type, &value);
}
void Algorithm::set(const char* name, bool value)
void Algorithm::set(const char* parameter, bool value)
{
info()->set(this, name, ParamType<bool>::type, &value);
info()->set(this, parameter, ParamType<bool>::type, &value);
}
void Algorithm::set(const char* name, const string& value)
void Algorithm::set(const char* parameter, const string& value)
{
info()->set(this, name, ParamType<string>::type, &value);
info()->set(this, parameter, ParamType<string>::type, &value);
}
void Algorithm::set(const char* name, const Mat& value)
void Algorithm::set(const char* parameter, const Mat& value)
{
info()->set(this, name, ParamType<Mat>::type, &value);
info()->set(this, parameter, ParamType<Mat>::type, &value);
}
void Algorithm::set(const char* name, const vector<Mat>& value)
void Algorithm::set(const char* parameter, const vector<Mat>& value)
{
info()->set(this, name, ParamType<vector<Mat> >::type, &value);
info()->set(this, parameter, ParamType<vector<Mat> >::type, &value);
}
void Algorithm::set(const char* name, const Ptr<Algorithm>& value)
void Algorithm::set(const char* parameter, const Ptr<Algorithm>& value)
{
info()->set(this, name, ParamType<Algorithm>::type, &value);
info()->set(this, parameter, ParamType<Algorithm>::type, &value);
}
int Algorithm::getInt(const string& name) const
int Algorithm::getInt(const string& parameter) const
{
return get<int>(name);
return get<int>(parameter);
}
double Algorithm::getDouble(const string& name) const
double Algorithm::getDouble(const string& parameter) const
{
return get<double>(name);
return get<double>(parameter);
}
bool Algorithm::getBool(const string& name) const
bool Algorithm::getBool(const string& parameter) const
{
return get<bool>(name);
return get<bool>(parameter);
}
string Algorithm::getString(const string& name) const
string Algorithm::getString(const string& parameter) const
{
return get<string>(name);
return get<string>(parameter);
}
Mat Algorithm::getMat(const string& name) const
Mat Algorithm::getMat(const string& parameter) const
{
return get<Mat>(name);
return get<Mat>(parameter);
}
vector<Mat> Algorithm::getMatVector(const string& name) const
vector<Mat> Algorithm::getMatVector(const string& parameter) const
{
return get<vector<Mat> >(name);
return get<vector<Mat> >(parameter);
}
Ptr<Algorithm> Algorithm::getAlgorithm(const string& name) const
Ptr<Algorithm> Algorithm::getAlgorithm(const string& parameter) const
{
return get<Algorithm>(name);
return get<Algorithm>(parameter);
}
string Algorithm::paramHelp(const string& name) const
string Algorithm::paramHelp(const string& parameter) const
{
return info()->paramHelp(name.c_str());
return info()->paramHelp(parameter.c_str());
}
int Algorithm::paramType(const string& name) const
int Algorithm::paramType(const string& parameter) const
{
return info()->paramType(name.c_str());
return info()->paramType(parameter.c_str());
}
int Algorithm::paramType(const char* name) const
int Algorithm::paramType(const char* parameter) const
{
return info()->paramType(name);
return info()->paramType(parameter);
}
void Algorithm::getParams(vector<string>& names) const
@ -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;
@ -532,11 +532,11 @@ void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const vo
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;
@ -606,20 +606,20 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo
}
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;
}
@ -630,7 +630,7 @@ void AlgorithmInfo::getParams(vector<string>& names) const
}
void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType,
void AlgorithmInfo::addParam_(Algorithm& algo, const char* parameter, int argType,
void* value, bool readOnly,
Algorithm::Getter getter, Algorithm::Setter setter,
const string& help)
@ -639,79 +639,79 @@ 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,
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,
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,
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,
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,
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,
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,
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);
}

10
modules/core/src/convert.cpp
View File

@ -335,8 +335,8 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst)
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;
}
}
}
@ -489,12 +489,12 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
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;

26
modules/core/src/copy.cpp
View File

@ -193,10 +193,10 @@ void Mat::copyTo( OutputArray _dst ) const
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);
}
}
@ -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
{
@ -259,17 +259,17 @@ Mat& Mat::operator = (const Scalar& s)
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;
@ -292,16 +292,16 @@ Mat& Mat::setTo(InputArray _value, InputArray _mask)
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] )
{

32
modules/core/src/datastructs.cpp
View File

@ -3653,7 +3653,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
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();
@ -3669,7 +3669,7 @@ 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);
@ -3700,7 +3700,7 @@ 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;
@ -3709,7 +3709,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
}
// 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);
@ -3758,7 +3758,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
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));
@ -3819,13 +3819,13 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
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;
@ -3898,14 +3898,14 @@ 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>();
@ -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;
}
@ -3957,7 +3957,7 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
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 )
{
@ -3968,7 +3968,7 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
if( _pts.needed() )
{
_pts.create( nidx, dims, points.type());
_pts.create( nidx, ptdims, points.type());
pts = _pts.getMat();
}
@ -3987,7 +3987,7 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c
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;
}

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;
}

20
modules/core/src/matmul.cpp
View File

@ -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 );
@ -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();
}
@ -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)

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

@ -1181,14 +1181,14 @@ int MatExpr::type() const
/////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Identity::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Identity::assign(const MatExpr& e, Mat& m, int _type) const
{
if( type == -1 || type == e.a.type() )
if( _type == -1 || _type == e.a.type() )
m = e.a;
else
{
CV_Assert( CV_MAT_CN(type) == e.a.channels() );
e.a.convertTo(m, type);
CV_Assert( CV_MAT_CN(_type) == e.a.channels() );
e.a.convertTo(m, _type);
}
}
@ -1199,9 +1199,9 @@ inline void MatOp_Identity::makeExpr(MatExpr& res, const Mat& m)
/////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp;
Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp;
if( e.b.data )
{
if( e.s == Scalar() || !e.s.isReal() )
@ -1233,7 +1233,7 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const
}
else if( e.s.isReal() && (dst.data != m.data || fabs(e.alpha) != 1))
{
e.a.convertTo(m, type, e.alpha, e.s[0]);
e.a.convertTo(m, _type, e.alpha, e.s[0]);
return;
}
else if( e.alpha == 1 )
@ -1308,9 +1308,9 @@ inline void MatOp_AddEx::makeExpr(MatExpr& res, const Mat& a, const Mat& b, doub
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Bin::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp;
Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp;
if( e.flags == '*' )
cv::multiply(e.a, e.b, dst, e.alpha);
@ -1348,7 +1348,7 @@ void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const
CV_Error(CV_StsError, "Unknown operation");
if( dst.data != m.data )
dst.convertTo(m, type);
dst.convertTo(m, _type);
}
void MatOp_Bin::multiply(const MatExpr& e, double s, MatExpr& res) const
@ -1382,9 +1382,9 @@ inline void MatOp_Bin::makeExpr(MatExpr& res, char op, const Mat& a, const Scala
///////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || type == CV_8U ? m : temp;
Mat temp, &dst = _type == -1 || _type == CV_8U ? m : temp;
if( e.b.data )
cv::compare(e.a, e.b, dst, e.flags);
@ -1392,7 +1392,7 @@ void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int type) const
cv::compare(e.a, e.alpha, dst, e.flags);
if( dst.data != m.data )
dst.convertTo(m, type);
dst.convertTo(m, _type);
}
inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, const Mat& b)
@ -1407,14 +1407,14 @@ inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, double al
/////////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_T::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_T::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp;
Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp;
cv::transpose(e.a, dst);
if( dst.data != m.data || e.alpha != 1 )
dst.convertTo(m, type, e.alpha);
dst.convertTo(m, _type, e.alpha);
}
void MatOp_T::multiply(const MatExpr& e, double s, MatExpr& res) const
@ -1438,13 +1438,13 @@ inline void MatOp_T::makeExpr(MatExpr& res, const Mat& a, double alpha)
/////////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp;
Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp;
cv::gemm(e.a, e.b, e.alpha, e.c, e.beta, dst, e.flags);
if( dst.data != m.data )
dst.convertTo(m, type);
dst.convertTo(m, _type);
}
void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
@ -1503,13 +1503,13 @@ inline void MatOp_GEMM::makeExpr(MatExpr& res, int flags, const Mat& a, const Ma
///////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Invert::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Invert::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp;
Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp;
cv::invert(e.a, dst, e.flags);
if( dst.data != m.data )
dst.convertTo(m, type);
dst.convertTo(m, _type);
}
void MatOp_Invert::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
@ -1529,13 +1529,13 @@ inline void MatOp_Invert::makeExpr(MatExpr& res, int method, const Mat& m)
/////////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Solve::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Solve::assign(const MatExpr& e, Mat& m, int _type) const
{
Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp;
Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp;
cv::solve(e.a, e.b, dst, e.flags);
if( dst.data != m.data )
dst.convertTo(m, type);
dst.convertTo(m, _type);
}
inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const Mat& b)
@ -1545,11 +1545,11 @@ inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const
//////////////////////////////////////////////////////////////////////////////////////////////////////////
void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int type) const
void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int _type) const
{
if( type == -1 )
type = e.a.type();
m.create(e.a.size(), type);
if( _type == -1 )
_type = e.a.type();
m.create(e.a.size(), _type);
if( e.flags == 'I' )
setIdentity(m, Scalar(e.alpha));
else if( e.flags == '0' )

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;

154
modules/core/src/opengl_interop.cpp
View File

@ -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)_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
}
@ -594,10 +594,10 @@ void cv::GlBuffer::copyFrom(InputArray 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)
{
@ -926,32 +926,32 @@ 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
}
@ -963,8 +963,8 @@ cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_
throw_nogl;
#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
}
@ -1035,10 +1035,10 @@ void cv::GlTexture::copyFrom(InputArray mat_, bool 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
}

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

4
modules/core/test/test_mat.cpp
View File

@ -284,8 +284,6 @@ void Core_ReduceTest::run( int )
#define CHECK_C
Size sz(200, 500);
class Core_PCATest : public cvtest::BaseTest
{
public:
@ -293,6 +291,8 @@ public:
protected:
void run(int)
{
const Size sz(200, 500);
double diffPrjEps, diffBackPrjEps,
prjEps, backPrjEps,
evalEps, evecEps;

14
modules/core/test/test_rand.cpp
View File

@ -54,17 +54,17 @@ 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;
@ -253,7 +253,7 @@ void Core_RandTest::run( int )
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];
@ -276,13 +276,13 @@ 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;

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]++;
}

8
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 );
}
}

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];

29
modules/features2d/src/stardetector.cpp
View File

@ -121,7 +121,7 @@ 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 );
@ -129,21 +129,20 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
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);
@ -169,7 +168,7 @@ 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;
@ -180,13 +179,13 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma
#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
@ -206,7 +205,7 @@ 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);
@ -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);
@ -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;

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)

8
modules/highgui/src/cap_gstreamer.cpp
View File

@ -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",
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_app_sink_set_caps(GST_APP_SINK(sink), caps);
NULL));
gst_caps_unref(caps);
}
if(gst_element_set_state(GST_ELEMENT(pipeline), GST_STATE_READY) ==
GST_STATE_CHANGE_FAILURE) {

4
modules/highgui/src/cap_libv4l.cpp
View File

@ -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");
}
}

8
modules/highgui/src/grfmt_png.cpp
View File

@ -157,16 +157,16 @@ 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;

10
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)))

4
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 );

2
modules/highgui/test/test_video_io.cpp
View File

@ -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);

22
modules/imgproc/src/color.cpp
View File

@ -434,7 +434,7 @@ template<> struct RGB2Gray<uchar>
for(int i = 0; i < n; i++, src += scn)
dst[i] = (uchar)((_tab[src[0]] + _tab[src[1]+256] + _tab[src[2]+512]) >> yuv_shift);
}
int srccn, blueIdx;
int srccn;
int tab[256*3];
};
@ -3510,8 +3510,8 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
// http://www.fourcc.org/yuv.php#NV12 -> a plane of 8 bit Y samples followed by an interleaved U/V plane containing 8 bit 2x2 subsampled colour difference samples
if (dcn <= 0) dcn = (code==CV_YUV420sp2BGRA || code==CV_YUV420sp2RGBA || code==CV_YUV2BGRA_NV12 || code==CV_YUV2RGBA_NV12) ? 4 : 3;
const int bidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2;
const int uidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0;
const int bIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2;
const int uIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0;
CV_Assert( dcn == 3 || dcn == 4 );
CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U );
@ -3524,7 +3524,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
const uchar* y = src.ptr();
const uchar* uv = y + srcstep * dstSz.height;
switch(dcn*100 + bidx * 10 + uidx)
switch(dcn*100 + bIdx * 10 + uIdx)
{
case 300: cvtYUV420sp2RGB<0, 0> (dst, srcstep, y, uv); break;
case 301: cvtYUV420sp2RGB<0, 1> (dst, srcstep, y, uv); break;
@ -3545,8 +3545,8 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
//http://www.fourcc.org/yuv.php#IYUV == I420 -> It comprises an NxN Y plane followed by (N/2)x(N/2) U and V planes
if (dcn <= 0) dcn = (code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12 || code==CV_YUV2RGBA_IYUV || code==CV_YUV2BGRA_IYUV) ? 4 : 3;
const int bidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2;
const int uidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0;
const int bIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2;
const int uIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0;
CV_Assert( dcn == 3 || dcn == 4 );
CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U );
@ -3563,9 +3563,9 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
int ustepIdx = 0;
int vstepIdx = dstSz.height % 4 == 2 ? 1 : 0;
if(uidx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); };
if(uIdx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); };
switch(dcn*10 + bidx)
switch(dcn*10 + bIdx)
{
case 30: cvtYUV420p2RGB<0>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break;
case 32: cvtYUV420p2RGB<2>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break;
@ -3598,9 +3598,9 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
//http://www.fourcc.org/yuv.php#YVYU
if (dcn <= 0) dcn = (code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2RGBA_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 4 : 3;
const int bidx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2;
const int bIdx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2;
const int ycn = (code==CV_YUV2RGB_UYVY || code==CV_YUV2BGR_UYVY || code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY) ? 1 : 0;
const int uidx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0;
const int uIdx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0;
CV_Assert( dcn == 3 || dcn == 4 );
CV_Assert( scn == 2 && depth == CV_8U );
@ -3608,7 +3608,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
_dst.create(sz, CV_8UC(dcn));
dst = _dst.getMat();
switch(dcn*1000 + bidx*100 + uidx*10 + ycn)
switch(dcn*1000 + bIdx*100 + uIdx*10 + ycn)
{
case 3000: cvtYUV422toRGB<0,0,0>(dst, (int)src.step, src.ptr<uchar>()); break;
case 3001: cvtYUV422toRGB<0,0,1>(dst, (int)src.step, src.ptr<uchar>()); break;

6
modules/imgproc/src/filter.cpp
View File

@ -311,10 +311,10 @@ int FilterEngine::start(const Mat& src, const Rect& _srcRoi,
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;
}

18
modules/imgproc/src/gcgraph.hpp
View File

@ -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;

26
modules/imgproc/src/histogram.cpp
View File

@ -198,9 +198,9 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe
{
for( i = 0; i < dims; i++ )
{
size_t j, n = histSize[i];
for( j = 0; j < n; j++ )
CV_Assert( ranges[i][j] < ranges[i][j+1] );
size_t n = histSize[i];
for(size_t k = 0; k < n; k++ )
CV_Assert( ranges[i][k] < ranges[i][k+1] );
}
}
}
@ -431,7 +431,7 @@ calcHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
uchar* H = hist.data;
int i, x;
int x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
vector<size_t> _tab;
@ -480,7 +480,7 @@ calcHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
matH[*p0]++;
}
for( i = 0; i < 256; i++ )
for(int i = 0; i < 256; i++ )
{
size_t hidx = tab[i];
if( hidx < OUT_OF_RANGE )
@ -548,7 +548,8 @@ calcHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
for( x = 0; x < imsize.width; x++ )
{
uchar* Hptr = H;
for( i = 0; i < dims; i++ )
int i = 0;
for( ; i < dims; i++ )
{
size_t idx = tab[*ptrs[i] + i*256];
if( idx >= OUT_OF_RANGE )
@ -584,7 +585,7 @@ calcHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
for( ; i < dims; i++ )
ptrs[i] += deltas[i*2];
}
for( i = 0; i < dims; i++ )
for(int i = 0; i < dims; i++ )
ptrs[i] += deltas[i*2 + 1];
}
}
@ -729,7 +730,7 @@ calcSparseHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
{
uchar** ptrs = (uchar**)&_ptrs[0];
const int* deltas = &_deltas[0];
int i, x;
int x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
int idx[CV_MAX_DIM];
@ -759,7 +760,7 @@ calcSparseHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas,
for( ; i < dims; i++ )
ptrs[i] += deltas[i*2];
}
for( i = 0; i < dims; i++ )
for(int i = 0; i < dims; i++ )
ptrs[i] += deltas[i*2 + 1];
}
}
@ -1749,7 +1750,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist,
int* idx_min, int* idx_max )
{
double minVal, maxVal;
int i, dims, size[CV_MAX_DIM];
int dims, size[CV_MAX_DIM];
if( !CV_IS_HIST(hist) )
CV_Error( CV_StsBadArg, "Invalid histogram header" );
@ -1782,9 +1783,8 @@ cvGetMinMaxHistValue( const CvHistogram* hist,
{
int imin = minPt.y*mat.cols + minPt.x;
int imax = maxPt.y*mat.cols + maxPt.x;
int i;
for( i = dims - 1; i >= 0; i-- )
for(int i = dims - 1; i >= 0; i-- )
{
if( idx_min )
{
@ -1844,7 +1844,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist,
minVal = maxVal = 0;
}
for( i = 0; i < dims; i++ )
for(int i = 0; i < dims; i++ )
{
if( idx_min )
idx_min[i] = _idx_min ? _idx_min[i] : -1;

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 );
}
}
}

4
modules/imgproc/src/moments.cpp
View File

@ -248,7 +248,7 @@ 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);
@ -312,7 +312,7 @@ template<> void momentsInTile<uchar, int, int>( const cv::Mat& img, double* mome
mom[0] += x0; // m00
}
for( x = 0; x < 10; x++ )
for(int x = 0; x < 10; x++ )
moments[x] = (double)mom[x];
}

10
modules/imgproc/src/shapedescr.cpp
View File

@ -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 );
@ -382,7 +382,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius
cvStartReadSeq( sequence, &reader, 0 );
for( i = 0; i < count; i++ )
for(int i = 0; i < count; i++ )
{
if( !is_float )
{
@ -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;

10
modules/imgproc/src/undistort.cpp
View File

@ -500,8 +500,8 @@ 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();
@ -517,9 +517,9 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff
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;

6
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 );
@ -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;

40
modules/imgproc/test/test_convhull.cpp
View File

@ -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);
@ -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);
@ -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;

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

4
modules/imgproc/test/test_floodfill.cpp
View File

@ -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;
@ -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++ )
{

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

@ -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++ )

16
modules/imgproc/test/test_imgwarp.cpp
View File

@ -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);
@ -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() );

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);

14
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:

18
modules/ml/src/ann_mlp.cpp
View File

@ -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;
@ -1105,7 +1105,7 @@ struct rprop_loop {
{
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];
}
}
@ -1114,7 +1114,7 @@ struct rprop_loop {
{
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] );
@ -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;
@ -1191,7 +1191,7 @@ 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];
}
@ -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;
@ -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,

8
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;
@ -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__;

46
modules/ml/src/ertrees.cpp
View File

@ -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;
}
@ -475,7 +475,7 @@ 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 );
}
}
}
@ -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 )
{
@ -675,7 +675,7 @@ 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
@ -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];
}
}
}
@ -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)
@ -908,7 +908,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
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]++;
@ -944,7 +944,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f
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;
@ -1579,7 +1579,7 @@ bool CvERTrees::train( const CvMat* _train_data, int _tflag,
}
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__;

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);
}

4
modules/ml/src/knearest.cpp
View File

@ -470,10 +470,10 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
}
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 */

6
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;
}
}

14
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 );

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;

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

@ -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;
}
@ -597,7 +597,7 @@ 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 );
}
}
}
@ -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];
}
}
}
@ -1205,11 +1205,11 @@ 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];
@ -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 );

12
modules/ml/test/test_emknearestkmeans.cpp
View File

@ -327,12 +327,12 @@ void CV_KNearestTest::run( int /*start_from*/ )
class EM_Params
{
public:
EM_Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* probs=0, const cv::Mat* weights=0,
const cv::Mat* means=0, const std::vector<cv::Mat>* covs=0)
: nclusters(nclusters), covMatType(covMatType), startStep(startStep),
probs(probs), weights(weights), means(means), covs(covs), termCrit(termCrit)
EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* _probs=0, const cv::Mat* _weights=0,
const cv::Mat* _means=0, const std::vector<cv::Mat>* _covs=0)
: nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep),
probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit)
{}
int nclusters;

Some files were not shown because too many files have changed in this diff Show More