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Fixing some static analysis issues

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This commit is contained in:
Maksim Shabunin 2017-06-26 14:09:21 +03:00
parent b991665b5d
commit 32d4af36e2
63 changed files with 238 additions and 94 deletions
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2
3rdparty/protobuf/src/google/protobuf/io/coded_stream.cc vendored
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@ -577,7 +577,7 @@ std::pair<uint64, bool> CodedInputStream::ReadVarint64Fallback() {
buffer_ = p.second;
return std::make_pair(temp, true);
} else {
uint64 temp;
uint64 temp = 0;
bool success = ReadVarint64Slow(&temp);
return std::make_pair(temp, success);
}

2
cmake/OpenCVCompilerOptions.cmake
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@ -127,6 +127,8 @@ if(CMAKE_COMPILER_IS_GNUCXX)
add_extra_compiler_option(-Wpointer-arith)
add_extra_compiler_option(-Wshadow)
add_extra_compiler_option(-Wsign-promo)
add_extra_compiler_option(-Wuninitialized)
add_extra_compiler_option(-Winit-self)
if(ENABLE_NOISY_WARNINGS)
add_extra_compiler_option(-Wcast-align)

12
modules/calib3d/src/calibration.cpp
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@ -252,7 +252,7 @@ CV_IMPL int cvRodrigues2( const CvMat* src, CvMat* dst, CvMat* jacobian )
{
int depth, elem_size;
int i, k;
double J[27];
double J[27] = {0};
CvMat matJ = cvMat( 3, 9, CV_64F, J );
if( !CV_IS_MAT(src) )
@ -1189,7 +1189,7 @@ CV_IMPL void cvInitIntrinsicParams2D( const CvMat* objectPoints,
int i, j, pos, nimages, ni = 0;
double a[9] = { 0, 0, 0, 0, 0, 0, 0, 0, 1 };
double H[9], f[2];
double H[9] = {0}, f[2] = {0};
CvMat _a = cvMat( 3, 3, CV_64F, a );
CvMat matH = cvMat( 3, 3, CV_64F, H );
CvMat _f = cvMat( 2, 1, CV_64F, f );
@ -1731,7 +1731,7 @@ void cvCalibrationMatrixValues( const CvMat *calibMatr, CvSize imgSize,
if(!CV_IS_MAT(calibMatr))
CV_Error(CV_StsUnsupportedFormat, "Input parameters must be a matrices!");
double dummy;
double dummy = .0;
Point2d pp;
cv::calibrationMatrixValues(cvarrToMat(calibMatr), imgSize, apertureWidth, apertureHeight,
fovx ? *fovx : dummy,
@ -2281,7 +2281,7 @@ void cvStereoRectify( const CvMat* _cameraMatrix1, const CvMat* _cameraMatrix2,
CvMat* matQ, int flags, double alpha, CvSize newImgSize,
CvRect* roi1, CvRect* roi2 )
{
double _om[3], _t[3], _uu[3]={0,0,0}, _r_r[3][3], _pp[3][4];
double _om[3], _t[3] = {0}, _uu[3]={0,0,0}, _r_r[3][3], _pp[3][4];
double _ww[3], _wr[3][3], _z[3] = {0,0,0}, _ri[3][3];
cv::Rect_<float> inner1, inner2, outer1, outer2;
@ -2574,7 +2574,7 @@ CV_IMPL int cvStereoRectifyUncalibrated(
int i, j, npoints;
double cx, cy;
double u[9], v[9], w[9], f[9], h1[9], h2[9], h0[9], e2[3];
double u[9], v[9], w[9], f[9], h1[9], h2[9], h0[9], e2[3] = {0};
CvMat E2 = cvMat( 3, 1, CV_64F, e2 );
CvMat U = cvMat( 3, 3, CV_64F, u );
CvMat V = cvMat( 3, 3, CV_64F, v );
@ -2722,7 +2722,7 @@ CV_IMPL int cvStereoRectifyUncalibrated(
cvPerspectiveTransform( _m1, _m1, &H0 );
cvPerspectiveTransform( _m2, _m2, &H2 );
CvMat A = cvMat( 1, npoints, CV_64FC3, lines1 ), BxBy, B;
double x[3];
double x[3] = {0};
CvMat X = cvMat( 3, 1, CV_64F, x );
cvConvertPointsHomogeneous( _m1, &A );
cvReshape( &A, &A, 1, npoints );

2
modules/calib3d/src/dls.h
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@ -720,7 +720,7 @@ private:
public:
EigenvalueDecomposition()
: n(0) { }
: n(0), cdivr(0), cdivi(0), d(0), e(0), ort(0), V(0), H(0) {}
// Initializes & computes the Eigenvalue Decomposition for a general matrix
// given in src. This function is a port of the EigenvalueSolver in JAMA,

12
modules/calib3d/src/epnp.cpp
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@ -60,7 +60,7 @@ void epnp::choose_control_points(void)
// Take C1, C2, and C3 from PCA on the reference points:
CvMat * PW0 = cvCreateMat(number_of_correspondences, 3, CV_64F);
double pw0tpw0[3 * 3], dc[3], uct[3 * 3];
double pw0tpw0[3 * 3], dc[3] = {0}, uct[3 * 3] = {0};
CvMat PW0tPW0 = cvMat(3, 3, CV_64F, pw0tpw0);
CvMat DC = cvMat(3, 1, CV_64F, dc);
CvMat UCt = cvMat(3, 3, CV_64F, uct);
@ -240,7 +240,7 @@ void epnp::estimate_R_and_t(double R[3][3], double t[3])
pw0[j] /= number_of_correspondences;
}
double abt[3 * 3], abt_d[3], abt_u[3 * 3], abt_v[3 * 3];
double abt[3 * 3] = {0}, abt_d[3], abt_u[3 * 3], abt_v[3 * 3];
CvMat ABt = cvMat(3, 3, CV_64F, abt);
CvMat ABt_D = cvMat(3, 1, CV_64F, abt_d);
CvMat ABt_U = cvMat(3, 3, CV_64F, abt_u);
@ -332,7 +332,7 @@ double epnp::reprojection_error(const double R[3][3], const double t[3])
void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
{
double l_6x4[6 * 4], b4[4];
double l_6x4[6 * 4], b4[4] = {0};
CvMat L_6x4 = cvMat(6, 4, CV_64F, l_6x4);
CvMat B4 = cvMat(4, 1, CV_64F, b4);
@ -364,7 +364,7 @@ void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho,
void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
{
double l_6x3[6 * 3], b3[3];
double l_6x3[6 * 3], b3[3] = {0};
CvMat L_6x3 = cvMat(6, 3, CV_64F, l_6x3);
CvMat B3 = cvMat(3, 1, CV_64F, b3);
@ -396,7 +396,7 @@ void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho,
void epnp::find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho,
double * betas)
{
double l_6x5[6 * 5], b5[5];
double l_6x5[6 * 5], b5[5] = {0};
CvMat L_6x5 = cvMat(6, 5, CV_64F, l_6x5);
CvMat B5 = cvMat(5, 1, CV_64F, b5);
@ -506,7 +506,7 @@ void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, double betas[4]
{
const int iterations_number = 5;
double a[6*4], b[6], x[4];
double a[6*4], b[6], x[4] = {0};
CvMat A = cvMat(6, 4, CV_64F, a);
CvMat B = cvMat(6, 1, CV_64F, b);
CvMat X = cvMat(4, 1, CV_64F, x);

4
modules/calib3d/src/fundam.cpp
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@ -423,7 +423,7 @@ namespace cv
static int run7Point( const Mat& _m1, const Mat& _m2, Mat& _fmatrix )
{
double a[7*9], w[7], u[9*9], v[9*9], c[4], r[3];
double a[7*9], w[7], u[9*9], v[9*9], c[4], r[3] = {0};
double* f1, *f2;
double t0, t1, t2;
Mat A( 7, 9, CV_64F, a );
@ -766,7 +766,7 @@ void cv::computeCorrespondEpilines( InputArray _points, int whichImage,
{
CV_INSTRUMENT_REGION()
double f[9];
double f[9] = {0};
Mat tempF(3, 3, CV_64F, f);
Mat points = _points.getMat(), F = _Fmat.getMat();

48
modules/calib3d/src/rho.cpp
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@ -556,7 +556,55 @@ unsigned rhoHest(Ptr<RHO_HEST> p, /* Homography estimation context. */
*/
RHO_HEST_REFC::RHO_HEST_REFC() : initialized(0){
arg.src = 0;
arg.dst = 0;
arg.inl = 0;
arg.N = 0;
arg.maxD = 0;
arg.maxI = 0;
arg.rConvg = 0;
arg.cfd = 0;
arg.minInl = 0;
arg.beta = 0;
arg.flags = 0;
arg.guessH = 0;
arg.finalH = 0;
ctrl.i = 0;
ctrl.phNum = 0;
ctrl.phEndI = 0;
ctrl.phEndFpI = 0;
ctrl.phMax = 0;
ctrl.phNumInl = 0;
ctrl.numModels = 0;
ctrl.smpl = 0;
curr.pkdPts = 0;
curr.H = 0;
curr.inl = 0;
curr.numInl = 0;
best.H = 0;
best.inl = 0;
best.numInl = 0;
nr.size = 0;
nr.beta = 0;
eval.t_M = 0;
eval.m_S = 0;
eval.epsilon = 0;
eval.delta = 0;
eval.A = 0;
eval.Ntested = 0;
eval.Ntestedtotal = 0;
eval.good = 0;
eval.lambdaAccept = 0;
eval.lambdaReject = 0;
lm.JtJ = 0;
lm.tmp1 = 0;
lm.Jte = 0;
}
/**

4
modules/calib3d/src/upnp.cpp
View File

@ -175,7 +175,7 @@ void upnp::estimate_R_and_t(double R[3][3], double t[3])
pw0[j] /= number_of_correspondences;
}
double abt[3 * 3], abt_d[3], abt_u[3 * 3], abt_v[3 * 3];
double abt[3 * 3] = {0}, abt_d[3], abt_u[3 * 3], abt_v[3 * 3];
Mat ABt = Mat(3, 3, CV_64F, abt);
Mat ABt_D = Mat(3, 1, CV_64F, abt_d);
Mat ABt_U = Mat(3, 3, CV_64F, abt_u);
@ -575,7 +575,7 @@ void upnp::gauss_newton(const Mat * L_6x12, const Mat * Rho, double betas[4], do
{
const int iterations_number = 50;
double a[6*4], b[6], x[4];
double a[6*4], b[6], x[4] = {0};
Mat * A = new Mat(6, 4, CV_64F, a);
Mat * B = new Mat(6, 1, CV_64F, b);
Mat * X = new Mat(4, 1, CV_64F, x);

22
modules/core/include/opencv2/core/hal/intrin_sse.hpp
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@ -63,7 +63,7 @@ struct v_uint8x16
typedef uchar lane_type;
enum { nlanes = 16 };
v_uint8x16() {}
v_uint8x16() : val(_mm_setzero_si128()) {}
explicit v_uint8x16(__m128i v) : val(v) {}
v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
@ -86,7 +86,7 @@ struct v_int8x16
typedef schar lane_type;
enum { nlanes = 16 };
v_int8x16() {}
v_int8x16() : val(_mm_setzero_si128()) {}
explicit v_int8x16(__m128i v) : val(v) {}
v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
@ -109,7 +109,7 @@ struct v_uint16x8
typedef ushort lane_type;
enum { nlanes = 8 };
v_uint16x8() {}
v_uint16x8() : val(_mm_setzero_si128()) {}
explicit v_uint16x8(__m128i v) : val(v) {}
v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
{
@ -129,7 +129,7 @@ struct v_int16x8
typedef short lane_type;
enum { nlanes = 8 };
v_int16x8() {}
v_int16x8() : val(_mm_setzero_si128()) {}
explicit v_int16x8(__m128i v) : val(v) {}
v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
{
@ -148,7 +148,7 @@ struct v_uint32x4
typedef unsigned lane_type;
enum { nlanes = 4 };
v_uint32x4() {}
v_uint32x4() : val(_mm_setzero_si128()) {}
explicit v_uint32x4(__m128i v) : val(v) {}
v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
{
@ -166,7 +166,7 @@ struct v_int32x4
typedef int lane_type;
enum { nlanes = 4 };
v_int32x4() {}
v_int32x4() : val(_mm_setzero_si128()) {}
explicit v_int32x4(__m128i v) : val(v) {}
v_int32x4(int v0, int v1, int v2, int v3)
{
@ -184,7 +184,7 @@ struct v_float32x4
typedef float lane_type;
enum { nlanes = 4 };
v_float32x4() {}
v_float32x4() : val(_mm_setzero_ps()) {}
explicit v_float32x4(__m128 v) : val(v) {}
v_float32x4(float v0, float v1, float v2, float v3)
{
@ -202,7 +202,7 @@ struct v_uint64x2
typedef uint64 lane_type;
enum { nlanes = 2 };
v_uint64x2() {}
v_uint64x2() : val(_mm_setzero_si128()) {}
explicit v_uint64x2(__m128i v) : val(v) {}
v_uint64x2(uint64 v0, uint64 v1)
{
@ -222,7 +222,7 @@ struct v_int64x2
typedef int64 lane_type;
enum { nlanes = 2 };
v_int64x2() {}
v_int64x2() : val(_mm_setzero_si128()) {}
explicit v_int64x2(__m128i v) : val(v) {}
v_int64x2(int64 v0, int64 v1)
{
@ -242,7 +242,7 @@ struct v_float64x2
typedef double lane_type;
enum { nlanes = 2 };
v_float64x2() {}
v_float64x2() : val(_mm_setzero_pd()) {}
explicit v_float64x2(__m128d v) : val(v) {}
v_float64x2(double v0, double v1)
{
@ -261,7 +261,7 @@ struct v_float16x4
typedef short lane_type;
enum { nlanes = 4 };
v_float16x4() {}
v_float16x4() : val(_mm_setzero_si128()) {}
explicit v_float16x4(__m128i v) : val(v) {}
v_float16x4(short v0, short v1, short v2, short v3)
{

32
modules/core/include/opencv2/core/mat.inl.hpp
View File

@ -397,13 +397,13 @@ inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem)
inline
Mat::Mat()
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{}
inline
Mat::Mat(int _rows, int _cols, int _type)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_rows, _cols, _type);
}
@ -411,7 +411,7 @@ Mat::Mat(int _rows, int _cols, int _type)
inline
Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_rows, _cols, _type);
*this = _s;
@ -420,7 +420,7 @@ Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s)
inline
Mat::Mat(Size _sz, int _type)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create( _sz.height, _sz.width, _type );
}
@ -428,7 +428,7 @@ Mat::Mat(Size _sz, int _type)
inline
Mat::Mat(Size _sz, int _type, const Scalar& _s)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_sz.height, _sz.width, _type);
*this = _s;
@ -437,7 +437,7 @@ Mat::Mat(Size _sz, int _type, const Scalar& _s)
inline
Mat::Mat(int _dims, const int* _sz, int _type)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_dims, _sz, _type);
}
@ -445,7 +445,7 @@ Mat::Mat(int _dims, const int* _sz, int _type)
inline
Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_dims, _sz, _type);
*this = _s;
@ -454,7 +454,7 @@ Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s)
inline
Mat::Mat(const std::vector<int>& _sz, int _type)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_sz, _type);
}
@ -462,7 +462,7 @@ Mat::Mat(const std::vector<int>& _sz, int _type)
inline
Mat::Mat(const std::vector<int>& _sz, int _type, const Scalar& _s)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
datalimit(0), allocator(0), u(0), size(&rows)
datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
create(_sz, _type);
*this = _s;
@ -472,7 +472,7 @@ inline
Mat::Mat(const Mat& m)
: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data),
datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator),
u(m.u), size(&rows)
u(m.u), size(&rows), step(0)
{
if( u )
CV_XADD(&u->refcount, 1);
@ -556,7 +556,7 @@ Mat::Mat(Size _sz, int _type, void* _data, size_t _step)
template<typename _Tp> inline
Mat::Mat(const std::vector<_Tp>& vec, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()),
cols(1), data(0), datastart(0), dataend(0), allocator(0), u(0), size(&rows)
cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if(vec.empty())
return;
@ -574,7 +574,7 @@ Mat::Mat(const std::vector<_Tp>& vec, bool copyData)
template<typename _Tp, std::size_t _Nm> inline
Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()),
cols(1), data(0), datastart(0), dataend(0), allocator(0), u(0), size(&rows)
cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if(arr.empty())
return;
@ -592,7 +592,7 @@ Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData)
template<typename _Tp, int n> inline
Mat::Mat(const Vec<_Tp, n>& vec, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0),
datastart(0), dataend(0), allocator(0), u(0), size(&rows)
datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if( !copyData )
{
@ -608,7 +608,7 @@ Mat::Mat(const Vec<_Tp, n>& vec, bool copyData)
template<typename _Tp, int m, int n> inline
Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0),
datastart(0), dataend(0), allocator(0), u(0), size(&rows)
datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if( !copyData )
{
@ -624,7 +624,7 @@ Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData)
template<typename _Tp> inline
Mat::Mat(const Point_<_Tp>& pt, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0),
datastart(0), dataend(0), allocator(0), u(0), size(&rows)
datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if( !copyData )
{
@ -643,7 +643,7 @@ Mat::Mat(const Point_<_Tp>& pt, bool copyData)
template<typename _Tp> inline
Mat::Mat(const Point3_<_Tp>& pt, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0),
datastart(0), dataend(0), allocator(0), u(0), size(&rows)
datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
{
if( !copyData )
{

2
modules/core/include/opencv2/core/softfloat.hpp
View File

@ -121,7 +121,7 @@ public:
struct CV_EXPORTS softdouble
{
public:
softdouble() { }
softdouble() : v(0) { }
softdouble( const softdouble& c) { v = c.v; }
softdouble& operator=( const softdouble& c )
{

2
modules/core/src/copy.cpp
View File

@ -284,7 +284,7 @@ void Mat::copyTo( OutputArray _dst ) const
_dst.create( dims, size.p, type() );
UMat dst = _dst.getUMat();
size_t i, sz[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
size_t i, sz[CV_MAX_DIM] = {0}, dstofs[CV_MAX_DIM], esz = elemSize();
for( i = 0; i < (size_t)dims; i++ )
sz[i] = size.p[i];
sz[dims-1] *= esz;

2
modules/core/src/lda.cpp
View File

@ -883,7 +883,7 @@ private:
public:
EigenvalueDecomposition()
: n(0) { }
: n(0), cdivr(0), cdivi(0), d(0), e(0), ort(0), V(0), H(0) {}
// Initializes & computes the Eigenvalue Decomposition for a general matrix
// given in src. This function is a port of the EigenvalueSolver in JAMA,

2
modules/core/src/parallel_pthreads.cpp
View File

@ -112,7 +112,7 @@ class ForThread
{
public:
ForThread(): m_task_start(false), m_parent(0), m_state(eFTNotStarted), m_id(0)
ForThread(): m_posix_thread(0), m_task_start(false), m_parent(0), m_state(eFTNotStarted), m_id(0)
{
}

2
modules/core/src/umatrix.cpp
View File

@ -824,7 +824,7 @@ void UMat::copyTo(OutputArray _dst) const
return;
}
size_t i, sz[CV_MAX_DIM], srcofs[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
size_t i, sz[CV_MAX_DIM] = {0}, srcofs[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
for( i = 0; i < (size_t)dims; i++ )
sz[i] = size.p[i];
sz[dims-1] *= esz;

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

@ -703,7 +703,6 @@ BRISK_Impl::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _ma
{
cv::KeyPoint& kp = keypoints[k];
const int& scale = kscales[k];
int* pvalues = _values;
const float& x = kp.pt.x;
const float& y = kp.pt.y;
@ -712,7 +711,7 @@ BRISK_Impl::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _ma
// get the gray values in the unrotated pattern
for (unsigned int i = 0; i < points_; i++)
{
*(pvalues++) = smoothedIntensity(image, _integral, x, y, scale, 0, i);
_values[i] = smoothedIntensity(image, _integral, x, y, scale, 0, i);
}
int direction0 = 0;
@ -765,11 +764,10 @@ BRISK_Impl::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _ma
int shifter = 0;
//unsigned int mean=0;
pvalues = _values;
// get the gray values in the rotated pattern
for (unsigned int i = 0; i < points_; i++)
{
*(pvalues++) = smoothedIntensity(image, _integral, x, y, scale, theta, i);
_values[i] = smoothedIntensity(image, _integral, x, y, scale, theta, i);
}
// now iterate through all the pairings

2
modules/features2d/src/evaluation.cpp
View File

@ -481,7 +481,7 @@ void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H
struct DMatchForEvaluation : public DMatch
{
uchar isCorrect;
DMatchForEvaluation( const DMatch &dm ) : DMatch( dm ) {}
DMatchForEvaluation( const DMatch &dm ) : DMatch( dm ), isCorrect(0) {}
};
static inline float recall( int correctMatchCount, int correspondenceCount )

2
modules/features2d/src/kaze/AKAZEFeatures.cpp
View File

@ -782,7 +782,7 @@ void AKAZEFeatures::Compute_Main_Orientation(KeyPoint& kpt, const std::vector<TE
int ix = 0, iy = 0, idx = 0, s = 0, level = 0;
float xf = 0.0, yf = 0.0, gweight = 0.0, ratio = 0.0;
const int ang_size = 109;
float resX[ang_size], resY[ang_size], Ang[ang_size];
float resX[ang_size] = {0}, resY[ang_size] = {0}, Ang[ang_size];
const int id[] = { 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, 6 };
// Variables for computing the dominant direction

1
modules/flann/include/opencv2/flann/allocator.h
View File

@ -97,6 +97,7 @@ public:
blocksize = blockSize;
remaining = 0;
base = NULL;
loc = NULL;
usedMemory = 0;
wastedMemory = 0;

1
modules/flann/include/opencv2/flann/autotuned_index.h
View File

@ -83,6 +83,7 @@ public:
memory_weight_ = get_param(params, "memory_weight", 0.0f);
sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
bestIndex_ = NULL;
speedup_ = 0;
}
AutotunedIndex(const AutotunedIndex&);

2
modules/flann/include/opencv2/flann/dynamic_bitset.h
View File

@ -59,7 +59,7 @@ class DynamicBitset
public:
/** default constructor
*/
DynamicBitset()
DynamicBitset() : size_(0)
{
}

1
modules/flann/include/opencv2/flann/kdtree_single_index.h
View File

@ -87,6 +87,7 @@ public:
{
size_ = dataset_.rows;
dim_ = dataset_.cols;
root_node_ = 0;
int dim_param = get_param(params,"dim",-1);
if (dim_param>0) dim_ = dim_param;
leaf_max_size_ = get_param(params,"leaf_max_size",10);

2
modules/flann/include/opencv2/flann/lsh_table.h
View File

@ -146,6 +146,8 @@ public:
*/
LshTable()
{
key_size_ = 0;
speed_level_ = kArray;
}
/** Default constructor

2
modules/flann/include/opencv2/flann/result_set.h
View File

@ -303,7 +303,7 @@ public:
/** Default cosntructor */
UniqueResultSet() :
worst_distance_(std::numeric_limits<DistanceType>::max())
is_full_(false), worst_distance_(std::numeric_limits<DistanceType>::max())
{
}

1
modules/imgcodecs/src/grfmt_base.cpp
View File

@ -96,6 +96,7 @@ ImageDecoder BaseImageDecoder::newDecoder() const
BaseImageEncoder::BaseImageEncoder()
{
m_buf = 0;
m_buf_supported = false;
}

5
modules/imgcodecs/src/grfmt_bmp.cpp
View File

@ -55,6 +55,9 @@ BmpDecoder::BmpDecoder()
m_signature = fmtSignBmp;
m_offset = -1;
m_buf_supported = true;
m_origin = 0;
m_bpp = 0;
m_rle_code = BMP_RGB;
}
@ -191,7 +194,7 @@ bool BmpDecoder::readData( Mat& img )
uchar* data = img.ptr();
int step = (int)img.step;
bool color = img.channels() > 1;
uchar gray_palette[256];
uchar gray_palette[256] = {0};
bool result = false;
int src_pitch = ((m_width*(m_bpp != 15 ? m_bpp : 16) + 7)/8 + 3) & -4;
int nch = color ? 3 : 1;

7
modules/imgcodecs/src/grfmt_exr.cpp
View File

@ -81,6 +81,13 @@ ExrDecoder::ExrDecoder()
m_signature = "\x76\x2f\x31\x01";
m_file = 0;
m_red = m_green = m_blue = 0;
m_type = ((Imf::PixelType)0);
m_iscolor = false;
m_bit_depth = 0;
m_isfloat = false;
m_ischroma = false;
m_native_depth = false;
}

2
modules/imgcodecs/src/grfmt_jpeg.cpp
View File

@ -341,7 +341,7 @@ int my_jpeg_load_dht (struct jpeg_decompress_struct *info, unsigned char *dht,
JHUFF_TBL **hufftbl;
unsigned char bits[17];
unsigned char huffval[256];
unsigned char huffval[256] = {0};
while (length > 16)
{

3
modules/imgcodecs/src/grfmt_pam.cpp
View File

@ -324,6 +324,9 @@ PAMDecoder::PAMDecoder()
m_buf_supported = true;
bit_mode = false;
selected_fmt = CV_IMWRITE_PAM_FORMAT_NULL;
m_maxval = 0;
m_channels = 0;
m_sampledepth = 0;
}

1
modules/imgcodecs/src/grfmt_png.cpp
View File

@ -91,6 +91,7 @@ PngDecoder::PngDecoder()
m_f = 0;
m_buf_supported = true;
m_buf_pos = 0;
m_bit_depth = 0;
}

3
modules/imgcodecs/src/grfmt_pxm.cpp
View File

@ -94,6 +94,9 @@ PxMDecoder::PxMDecoder()
{
m_offset = -1;
m_buf_supported = true;
m_bpp = 0;
m_binary = false;
m_maxval = 0;
}

6
modules/imgcodecs/src/grfmt_sunras.cpp
View File

@ -54,6 +54,10 @@ SunRasterDecoder::SunRasterDecoder()
{
m_offset = -1;
m_signature = fmtSignSunRas;
m_bpp = 0;
m_encoding = RAS_STANDARD;
m_maptype = RMT_NONE;
m_maplength = 0;
}
@ -157,7 +161,7 @@ bool SunRasterDecoder::readData( Mat& img )
int color = img.channels() > 1;
uchar* data = img.ptr();
int step = (int)img.step;
uchar gray_palette[256];
uchar gray_palette[256] = {0};
bool result = false;
int src_pitch = ((m_width*m_bpp + 7)/8 + 1) & -2;
int nch = color ? 3 : 1;

1
modules/imgcodecs/src/grfmt_webp.cpp
View File

@ -62,6 +62,7 @@ namespace cv
WebPDecoder::WebPDecoder()
{
m_buf_supported = true;
channels = 0;
}
WebPDecoder::~WebPDecoder() {}

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

@ -95,8 +95,8 @@ namespace cv{
std::vector<Point2ui64> integrals;
int _nextLoc;
CCStatsOp(){}
CCStatsOp(OutputArray _statsv, OutputArray _centroidsv) : _mstatsv(&_statsv), _mcentroidsv(&_centroidsv){}
CCStatsOp() : _mstatsv(0), _mcentroidsv(0), _nextLoc(0) {}
CCStatsOp(OutputArray _statsv, OutputArray _centroidsv) : _mstatsv(&_statsv), _mcentroidsv(&_centroidsv), _nextLoc(0){}
inline
void init(int nlabels){

3
modules/imgproc/src/drawing.cpp
View File

@ -178,6 +178,9 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2,
{
ptr = img.data;
err = plusDelta = minusDelta = plusStep = minusStep = count = 0;
ptr0 = 0;
step = 0;
elemSize = 0;
return;
}
}

32
modules/imgproc/src/filter.cpp
View File

@ -77,6 +77,9 @@ FilterEngine::FilterEngine()
maxWidth = 0;
wholeSize = Size(-1,-1);
dx1 = 0;
borderElemSize = 0;
dx2 = 0;
}
@ -87,6 +90,11 @@ FilterEngine::FilterEngine( const Ptr<BaseFilter>& _filter2D,
int _rowBorderType, int _columnBorderType,
const Scalar& _borderValue )
{
startY0 = 0;
endY = 0;
dstY = 0;
dx2 = 0;
rowCount = 0;
init(_filter2D, _rowFilter, _columnFilter, _srcType, _dstType, _bufType,
_rowBorderType, _columnBorderType, _borderValue);
}
@ -566,7 +574,7 @@ struct RowVec_8u32s
struct SymmRowSmallVec_8u32s
{
SymmRowSmallVec_8u32s() { smallValues = false; }
SymmRowSmallVec_8u32s() { smallValues = false; symmetryType = 0; }
SymmRowSmallVec_8u32s( const Mat& _kernel, int _symmetryType )
{
kernel = _kernel;
@ -870,7 +878,7 @@ struct SymmRowSmallVec_8u32s
struct SymmColumnVec_32s8u
{
SymmColumnVec_32s8u() { symmetryType=0; }
SymmColumnVec_32s8u() { symmetryType=0; delta = 0; }
SymmColumnVec_32s8u(const Mat& _kernel, int _symmetryType, int _bits, double _delta)
{
symmetryType = _symmetryType;
@ -1018,7 +1026,7 @@ struct SymmColumnVec_32s8u
struct SymmColumnSmallVec_32s16s
{
SymmColumnSmallVec_32s16s() { symmetryType=0; }
SymmColumnSmallVec_32s16s() { symmetryType=0; delta = 0; }
SymmColumnSmallVec_32s16s(const Mat& _kernel, int _symmetryType, int _bits, double _delta)
{
symmetryType = _symmetryType;
@ -1152,7 +1160,7 @@ struct SymmColumnSmallVec_32s16s
struct RowVec_16s32f
{
RowVec_16s32f() {}
RowVec_16s32f() { sse2_supported = false; }
RowVec_16s32f( const Mat& _kernel )
{
kernel = _kernel;
@ -1199,7 +1207,7 @@ struct RowVec_16s32f
struct SymmColumnVec_32f16s
{
SymmColumnVec_32f16s() { symmetryType=0; }
SymmColumnVec_32f16s() { symmetryType=0; delta = 0; sse2_supported = false; }
SymmColumnVec_32f16s(const Mat& _kernel, int _symmetryType, int, double _delta)
{
symmetryType = _symmetryType;
@ -1355,6 +1363,9 @@ struct RowVec_32f
{
haveSSE = checkHardwareSupport(CV_CPU_SSE);
haveAVX2 = checkHardwareSupport(CV_CPU_AVX2);
#if defined USE_IPP_SEP_FILTERS
bufsz = -1;
#endif
}
RowVec_32f( const Mat& _kernel )
@ -1478,7 +1489,7 @@ private:
struct SymmRowSmallVec_32f
{
SymmRowSmallVec_32f() {}
SymmRowSmallVec_32f() { symmetryType = 0; }
SymmRowSmallVec_32f( const Mat& _kernel, int _symmetryType )
{
kernel = _kernel;
@ -1675,6 +1686,7 @@ struct SymmColumnVec_32f
symmetryType=0;
haveSSE = checkHardwareSupport(CV_CPU_SSE);
haveAVX2 = checkHardwareSupport(CV_CPU_AVX2);
delta = 0;
}
SymmColumnVec_32f(const Mat& _kernel, int _symmetryType, int, double _delta)
{
@ -1898,7 +1910,7 @@ if ( haveAVX2 )
struct SymmColumnSmallVec_32f
{
SymmColumnSmallVec_32f() { symmetryType=0; }
SymmColumnSmallVec_32f() { symmetryType=0; delta = 0; }
SymmColumnSmallVec_32f(const Mat& _kernel, int _symmetryType, int, double _delta)
{
symmetryType = _symmetryType;
@ -2030,7 +2042,7 @@ struct SymmColumnSmallVec_32f
struct FilterVec_8u
{
FilterVec_8u() {}
FilterVec_8u() { delta = 0; _nz = 0; }
FilterVec_8u(const Mat& _kernel, int _bits, double _delta)
{
Mat kernel;
@ -2113,7 +2125,7 @@ struct FilterVec_8u
struct FilterVec_8u16s
{
FilterVec_8u16s() {}
FilterVec_8u16s() { delta = 0; _nz = 0; }
FilterVec_8u16s(const Mat& _kernel, int _bits, double _delta)
{
Mat kernel;
@ -2196,7 +2208,7 @@ struct FilterVec_8u16s
struct FilterVec_32f
{
FilterVec_32f() {}
FilterVec_32f() { delta = 0; _nz = 0; }
FilterVec_32f(const Mat& _kernel, int, double _delta)
{
delta = (float)_delta;

1
modules/imgproc/src/grabcut.cpp
View File

@ -104,6 +104,7 @@ GMM::GMM( Mat& _model )
for( int ci = 0; ci < componentsCount; ci++ )
if( coefs[ci] > 0 )
calcInverseCovAndDeterm( ci );
totalSampleCount = 0;
}
double GMM::operator()( const Vec3d color ) const

9
modules/imgproc/src/histogram.cpp
View File

@ -1219,16 +1219,15 @@ public:
m_type = ippiGetDataType(src.type());
m_levelsNum = histSize+1;
ippiHistogram_C1 = getIppiHistogramFunction_C1(src.type());
m_fullRoi = ippiSize(src.size());
m_bufferSize = 0;
m_specSize = 0;
if(!ippiHistogram_C1)
{
ok = false;
return;
}
m_fullRoi = ippiSize(src.size());
m_bufferSize = 0;
m_specSize = 0;
if(ippiHistogramGetBufferSize(m_type, m_fullRoi, &m_levelsNum, 1, 1, &m_specSize, &m_bufferSize) < 0)
{
ok = false;
@ -3530,6 +3529,8 @@ cvCalcArrBackProjectPatch( CvArr** arr, CvArr* dst, CvSize patch_size, CvHistogr
CV_Error( CV_StsBadSize, "The patch width and height must be positive" );
dims = cvGetDims( hist->bins );
if (dims < 1)
CV_Error( CV_StsOutOfRange, "Invalid number of dimensions");
cvNormalizeHist( hist, norm_factor );
for( i = 0; i < dims; i++ )

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

@ -6171,7 +6171,7 @@ static bool ocl_warpTransform_cols4(InputArray _src, OutputArray _dst, InputArra
_dst.create( dsize.area() == 0 ? src.size() : dsize, src.type() );
UMat dst = _dst.getUMat();
float M[9];
float M[9] = {0};
int matRows = (op_type == OCL_OP_AFFINE ? 2 : 3);
Mat matM(matRows, 3, CV_32F, M), M1 = _M0.getMat();
CV_Assert( (M1.type() == CV_32F || M1.type() == CV_64F) && M1.rows == matRows && M1.cols == 3 );
@ -6269,7 +6269,7 @@ static bool ocl_warpTransform(InputArray _src, OutputArray _dst, InputArray _M0,
_dst.create( dsize.area() == 0 ? src.size() : dsize, src.type() );
UMat dst = _dst.getUMat();
double M[9];
double M[9] = {0};
int matRows = (op_type == OCL_OP_AFFINE ? 2 : 3);
Mat matM(matRows, 3, CV_64F, M), M1 = _M0.getMat();
CV_Assert( (M1.type() == CV_32F || M1.type() == CV_64F) &&
@ -6364,7 +6364,7 @@ void cv::warpAffine( InputArray _src, OutputArray _dst,
if( dst.data == src.data )
src = src.clone();
double M[6];
double M[6] = {0};
Mat matM(2, 3, CV_64F, M);
if( interpolation == INTER_AREA )
interpolation = INTER_LINEAR;

5
modules/imgproc/src/lsd.cpp
View File

@ -398,8 +398,9 @@ CV_EXPORTS Ptr<LineSegmentDetector> createLineSegmentDetector(
LineSegmentDetectorImpl::LineSegmentDetectorImpl(int _refine, double _scale, double _sigma_scale, double _quant,
double _ang_th, double _log_eps, double _density_th, int _n_bins)
:SCALE(_scale), doRefine(_refine), SIGMA_SCALE(_sigma_scale), QUANT(_quant),
ANG_TH(_ang_th), LOG_EPS(_log_eps), DENSITY_TH(_density_th), N_BINS(_n_bins)
: img_width(0), img_height(0), LOG_NT(0), w_needed(false), p_needed(false), n_needed(false),
SCALE(_scale), doRefine(_refine), SIGMA_SCALE(_sigma_scale), QUANT(_quant),
ANG_TH(_ang_th), LOG_EPS(_log_eps), DENSITY_TH(_density_th), N_BINS(_n_bins)
{
CV_Assert(_scale > 0 && _sigma_scale > 0 && _quant >= 0 &&
_ang_th > 0 && _ang_th < 180 && _density_th >= 0 && _density_th < 1 &&

2
modules/imgproc/src/samplers.cpp
View File

@ -441,7 +441,7 @@ cvGetQuadrangleSubPix( const void* srcarr, void* dstarr, const CvMat* mat )
CV_Assert( src.channels() == dst.channels() );
cv::Size win_size = dst.size();
double matrix[6];
double matrix[6] = {0};
cv::Mat M(2, 3, CV_64F, matrix);
m.convertTo(M, CV_64F);
double dx = (win_size.width - 1)*0.5;

2
modules/imgproc/src/shapedescr.cpp
View File

@ -359,7 +359,7 @@ cv::RotatedRect cv::fitEllipse( InputArray _points )
// New fitellipse algorithm, contributed by Dr. Daniel Weiss
Point2f c(0,0);
double gfp[5], rp[5], t;
double gfp[5] = {0}, rp[5] = {0}, t;
const double min_eps = 1e-8;
bool is_float = depth == CV_32F;
const Point* ptsi = points.ptr<Point>();

1
modules/ml/src/nbayes.cpp
View File

@ -203,6 +203,7 @@ public:
results = &_results;
results_prob = !_results_prob.empty() ? &_results_prob : 0;
rawOutput = _rawOutput;
value = 0;
}
const Mat* c;

1
modules/ml/src/rtrees.cpp
View File

@ -81,6 +81,7 @@ public:
params.use1SERule = false;
params.truncatePrunedTree = false;
params.priors = Mat();
oobError = 0;
}
virtual ~DTreesImplForRTrees() {}

2
modules/ml/src/tree.cpp
View File

@ -115,7 +115,7 @@ DTreesImpl::WorkData::WorkData(const Ptr<TrainData>& _data)
maxSubsetSize = 0;
}
DTreesImpl::DTreesImpl() {}
DTreesImpl::DTreesImpl() : _isClassifier(false) {}
DTreesImpl::~DTreesImpl() {}
void DTreesImpl::clear()
{

10
modules/objdetect/src/cascadedetect.cpp
View File

@ -569,6 +569,9 @@ HaarEvaluator::HaarEvaluator()
lbufSize = Size(0, 0);
nchannels = 0;
tofs = 0;
sqofs = 0;
varianceNormFactor = 0;
hasTiltedFeatures = false;
}
HaarEvaluator::~HaarEvaluator()
@ -770,6 +773,8 @@ LBPEvaluator::LBPEvaluator()
features = makePtr<std::vector<Feature> >();
optfeatures = makePtr<std::vector<OptFeature> >();
scaleData = makePtr<std::vector<ScaleData> >();
optfeaturesPtr = 0;
pwin = 0;
}
LBPEvaluator::~LBPEvaluator()
@ -885,6 +890,9 @@ Ptr<FeatureEvaluator> FeatureEvaluator::create( int featureType )
CascadeClassifierImpl::CascadeClassifierImpl()
{
#ifdef HAVE_OPENCL
tryOpenCL = false;
#endif
}
CascadeClassifierImpl::~CascadeClassifierImpl()
@ -1440,7 +1448,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
CascadeClassifierImpl::Data::Data()
{
stageType = featureType = ncategories = maxNodesPerTree = 0;
stageType = featureType = ncategories = maxNodesPerTree = minNodesPerTree = 0;
}
bool CascadeClassifierImpl::Data::read(const FileNode &root)

2
modules/objdetect/src/cascadedetect.hpp
View File

@ -184,7 +184,7 @@ protected:
struct Stump
{
Stump() { }
Stump() : featureIdx(0), threshold(0), left(0), right(0) { }
Stump(int _featureIdx, float _threshold, float _left, float _right)
: featureIdx(_featureIdx), threshold(_threshold), left(_left), right(_right) {}

1
modules/objdetect/src/detection_based_tracker.cpp
View File

@ -218,6 +218,7 @@ cv::DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork(Detectio
cascadeInThread = _detector;
#ifndef USE_STD_THREADS
second_workthread = 0;
int res=0;
res=pthread_mutex_init(&mutex, NULL);//TODO: should be attributes?
if (res) {

2
modules/shape/src/aff_trans.cpp
View File

@ -53,12 +53,14 @@ public:
{
fullAffine = true;
name_ = "ShapeTransformer.AFF";
transformCost = 0;
}
AffineTransformerImpl(bool _fullAffine)
{
fullAffine = _fullAffine;
name_ = "ShapeTransformer.AFF";
transformCost = 0;
}
/* Destructor */

8
modules/shape/src/emdL1_def.hpp
View File

@ -90,6 +90,14 @@ public:
binsDim3 = 0;
dimension = 0;
nMaxIt = 500;
m_pLeave = 0;
m_iEnter = 0;
nNBV = 0;
m_nItr = 0;
m_iTo = 0;
m_iFrom = 0;
m_pEnter = 0;
}
~EmdL1()

1
modules/shape/src/sc_dis.cpp
View File

@ -72,6 +72,7 @@ public:
shapeContextWeight=1.0f;
sigma=10.0f;
name_ = "ShapeDistanceExtractor.SCD";
costFlag = 0;
}
/* Destructor */

3
modules/shape/src/scd_def.hpp
View File

@ -61,6 +61,7 @@ public:
setInnerRadius(_innerRadius);
setOuterRadius(_outerRadius);
setRotationInvariant(_rotationInvariant);
meanDistance = 0;
}
void extractSCD(cv::Mat& contour, cv::Mat& descriptors,
@ -107,7 +108,7 @@ class SCDMatcher
{
public:
// the full constructor
SCDMatcher()
SCDMatcher() : minMatchCost(0)
{
}

2
modules/shape/src/tps_trans.cpp
View File

@ -54,6 +54,7 @@ public:
regularizationParameter=0;
name_ = "ShapeTransformer.TPS";
tpsComputed=false;
transformCost = 0;
}
ThinPlateSplineShapeTransformerImpl(double _regularizationParameter)
@ -61,6 +62,7 @@ public:
regularizationParameter=_regularizationParameter;
name_ = "ShapeTransformer.TPS";
tpsComputed=false;
transformCost = 0;
}
/* Destructor */

6
modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp
View File

@ -149,8 +149,10 @@ protected:
@param num_errs_per_measurement Number of error terms (components) per match
*/
BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement)
: num_params_per_cam_(num_params_per_cam),
num_errs_per_measurement_(num_errs_per_measurement)
: num_images_(0), total_num_matches_(0),
num_params_per_cam_(num_params_per_cam),
num_errs_per_measurement_(num_errs_per_measurement),
features_(0), pairwise_matches_(0), conf_thresh_(0)
{
setRefinementMask(Mat::ones(3, 3, CV_8U));
setConfThresh(1.);

1
modules/stitching/src/blenders.cpp
View File

@ -217,6 +217,7 @@ Rect FeatherBlender::createWeightMaps(const std::vector<UMat> &masks, const std:
MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands, int weight_type)
{
num_bands_ = 0;
setNumBands(num_bands);
#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)

2
modules/stitching/src/seam_finders.cpp
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@ -162,7 +162,7 @@ void VoronoiSeamFinder::findInPair(size_t first, size_t second, Rect roi)
}
DpSeamFinder::DpSeamFinder(CostFunction costFunc) : costFunc_(costFunc) {}
DpSeamFinder::DpSeamFinder(CostFunction costFunc) : costFunc_(costFunc), ncomps_(0) {}
void DpSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners, std::vector<UMat> &masks)

3
modules/superres/src/btv_l1.cpp
View File

@ -854,6 +854,9 @@ namespace
BTVL1::BTVL1()
{
temporalAreaRadius_ = 4;
procPos_ = 0;
outPos_ = 0;
storePos_ = 0;
}
void BTVL1::collectGarbage()

6
modules/video/src/bgfg_KNN.cpp
View File

@ -89,6 +89,9 @@ public:
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;// Tau - shadow threshold
name_ = "BackgroundSubtractor.KNN";
nLongCounter = 0;
nMidCounter = 0;
nShortCounter = 0;
}
//! the full constructor that takes the length of the history,
// the number of gaussian mixtures, the background ratio parameter and the noise strength
@ -113,6 +116,9 @@ public:
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;
name_ = "BackgroundSubtractor.KNN";
nLongCounter = 0;
nMidCounter = 0;
nShortCounter = 0;
}
//! the destructor
~BackgroundSubtractorKNNImpl() {}

2
modules/video/src/lkpyramid.cpp
View File

@ -1451,7 +1451,7 @@ getRTMatrix( const Point2f* a, const Point2f* b,
}
else
{
double sa[4][4]={{0.}}, sb[4]={0.}, m[4];
double sa[4][4]={{0.}}, sb[4]={0.}, m[4] = {0};
Mat A( 4, 4, CV_64F, sa ), B( 4, 1, CV_64F, sb );
Mat MM( 4, 1, CV_64F, m );

8
modules/videoio/src/cap_mjpeg_encoder.cpp
View File

@ -96,6 +96,8 @@ public:
m_end = m_start + DEFAULT_BLOCK_SIZE;
m_is_opened = false;
m_f = 0;
m_current = 0;
m_pos = 0;
}
~BitStream()
@ -591,6 +593,12 @@ public:
{
rawstream = false;
nstripes = -1;
height = 0;
width = 0;
moviPointer = 0;
channels = 0;
outfps = 0;
quality = 0;
}
MotionJpegWriter(const String& filename, double fps, Size size, bool iscolor)

2
modules/videostab/include/opencv2/videostab/fast_marching.hpp
View File

@ -63,7 +63,7 @@ namespace videostab
class CV_EXPORTS FastMarchingMethod
{
public:
FastMarchingMethod() : inf_(1e6f) {}
FastMarchingMethod() : inf_(1e6f), size_(0) {}
/** @brief Template method that runs the Fast Marching Method.

5
modules/videostab/src/stabilizer.cpp
View File

@ -63,6 +63,11 @@ StabilizerBase::StabilizerBase()
setTrimRatio(0);
setCorrectionForInclusion(false);
setBorderMode(BORDER_REPLICATE);
curPos_ = 0;
doDeblurring_ = false;
doInpainting_ = false;
processingStartTime_ = 0;
curStabilizedPos_ = 0;
}

2
modules/videostab/src/wobble_suppression.cpp
View File

@ -85,7 +85,7 @@ namespace cv
namespace videostab
{
WobbleSuppressorBase::WobbleSuppressorBase() : motions_(0), stabilizationMotions_(0)
WobbleSuppressorBase::WobbleSuppressorBase() : frameCount_(0), motions_(0), motions2_(0), stabilizationMotions_(0)
{
setMotionEstimator(makePtr<KeypointBasedMotionEstimator>(makePtr<MotionEstimatorRansacL2>(MM_HOMOGRAPHY)));
}