updated 3rd party libs: CLapack 3.1.1.1 => 3.2.1, zlib 1.2.3 => 1.2.5, libpng 1.2.x => 1.4.3, libtiff 3.7.x => 3.9.4. fixed many 64-bit related VS2010 warnings
This commit is contained in:
@@ -333,7 +333,7 @@ mixChannels_( const void** _src, const int* sdelta0,
|
||||
typedef void (*MixChannelsFunc)( const void** src, const int* sdelta0,
|
||||
const int* sdelta1, void** dst, const int* ddelta0, const int* ddelta1, int n, Size size );
|
||||
|
||||
void mixChannels( const Mat* src, int nsrcs, Mat* dst, int ndsts, const int* fromTo, size_t npairs )
|
||||
void mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, const int* fromTo, size_t npairs )
|
||||
{
|
||||
size_t i;
|
||||
|
||||
|
@@ -3937,9 +3937,9 @@ void KDTree::findOrthoRange(const float* L, const float* R,
|
||||
|
||||
void KDTree::getPoints(const int* idx, size_t nidx, Mat& pts) const
|
||||
{
|
||||
int dims = points.cols;
|
||||
pts.create( nidx, dims, points.type());
|
||||
for( size_t i = 0; i < nidx; i++ )
|
||||
int dims = points.cols, n = (int)nidx;
|
||||
pts.create( n, dims, points.type());
|
||||
for( int i = 0; i < n; i++ )
|
||||
{
|
||||
int k = idx[i];
|
||||
CV_Assert( (unsigned)k < (unsigned)points.rows );
|
||||
@@ -3954,7 +3954,7 @@ void KDTree::getPoints(const Mat& idx, Mat& pts) const
|
||||
CV_Assert(idx.type() == CV_32S && idx.isContinuous() &&
|
||||
(idx.cols == 1 || idx.rows == 1));
|
||||
int dims = points.cols;
|
||||
size_t i, nidx = idx.cols + idx.rows - 1;
|
||||
int i, nidx = idx.cols + idx.rows - 1;
|
||||
pts.create( nidx, dims, points.type());
|
||||
const int* _idx = idx.ptr<int>();
|
||||
|
||||
|
@@ -121,9 +121,9 @@ Index::~Index()
|
||||
void Index::knnSearch(const vector<float>& query, vector<int>& indices, vector<float>& dists, int knn, const SearchParams& searchParams)
|
||||
{
|
||||
|
||||
::cvflann::Matrix<float> m_query(1, query.size(), (float*)&query[0]);
|
||||
::cvflann::Matrix<int> m_indices(1, indices.size(), &indices[0]);
|
||||
::cvflann::Matrix<float> m_dists(1, dists.size(), &dists[0]);
|
||||
::cvflann::Matrix<float> m_query(1, (int)query.size(), (float*)&query[0]);
|
||||
::cvflann::Matrix<int> m_indices(1, (int)indices.size(), &indices[0]);
|
||||
::cvflann::Matrix<float> m_dists(1, (int)dists.size(), &dists[0]);
|
||||
|
||||
nnIndex->knnSearch(m_query,m_indices,m_dists,knn,::cvflann::SearchParams(searchParams.checks));
|
||||
}
|
||||
@@ -149,9 +149,9 @@ void Index::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, con
|
||||
|
||||
int Index::radiusSearch(const vector<float>& query, vector<int>& indices, vector<float>& dists, float radius, const SearchParams& searchParams)
|
||||
{
|
||||
::cvflann::Matrix<float> m_query(1, query.size(), (float*)&query[0]);
|
||||
::cvflann::Matrix<int> m_indices(1, indices.size(), &indices[0]);
|
||||
::cvflann::Matrix<float> m_dists(1, dists.size(), &dists[0]);
|
||||
::cvflann::Matrix<float> m_query(1, (int)query.size(), (float*)&query[0]);
|
||||
::cvflann::Matrix<int> m_indices(1, (int)indices.size(), &indices[0]);
|
||||
::cvflann::Matrix<float> m_dists(1, (int)dists.size(), &dists[0]);
|
||||
|
||||
return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,::cvflann::SearchParams(searchParams.checks));
|
||||
}
|
||||
|
@@ -522,7 +522,7 @@ double invert( const Mat& src, Mat& dst, int method )
|
||||
else
|
||||
{
|
||||
src.copyTo(arr);
|
||||
lda = arr.step/sizeof(double);
|
||||
lda = (integer)(arr.step/sizeof(double));
|
||||
}
|
||||
|
||||
dgetrf_(&n, &n, (double*)arr.data, &lda, (integer*)buffer, &info);
|
||||
@@ -545,7 +545,7 @@ double invert( const Mat& src, Mat& dst, int method )
|
||||
else
|
||||
{
|
||||
src.copyTo(arr);
|
||||
lda = arr.step/sizeof(double);
|
||||
lda = (integer)(arr.step/sizeof(double));
|
||||
}
|
||||
|
||||
char L[] = {'L', '\0'};
|
||||
@@ -944,9 +944,9 @@ template<typename Real> bool jacobi(const Mat& _S0, Mat& _e, Mat& matE, bool com
|
||||
Real mv;
|
||||
Real* E = (Real*)matE.data;
|
||||
Real* e = (Real*)_e.data;
|
||||
int Sstep = matS.step/sizeof(Real);
|
||||
int estep = _e.rows == 1 ? 1 : _e.step/sizeof(Real);
|
||||
int Estep = matE.step/sizeof(Real);
|
||||
int Sstep = (int)(matS.step/sizeof(Real));
|
||||
int estep = _e.rows == 1 ? 1 : (int)(_e.step/sizeof(Real));
|
||||
int Estep = (int)(matE.step/sizeof(Real));
|
||||
|
||||
for( k = 0; k < n; k++ )
|
||||
{
|
||||
@@ -1144,7 +1144,7 @@ static bool eigen( const Mat& src, Mat& evals, Mat& evects, bool computeEvects,
|
||||
(liwork+2*n+1)*sizeof(integer));
|
||||
Mat a(n, n, type, (uchar*)buf);
|
||||
src.copyTo(a);
|
||||
lda = a.step1();
|
||||
lda = (integer)a.step1();
|
||||
work = a.data + n*n*elem_size;
|
||||
if( copy_evals )
|
||||
s = (float*)(work + lwork*elem_size);
|
||||
@@ -1174,7 +1174,7 @@ static bool eigen( const Mat& src, Mat& evals, Mat& evects, bool computeEvects,
|
||||
(liwork+2*n+1)*sizeof(integer));
|
||||
Mat a(n, n, type, (uchar*)buf);
|
||||
src.copyTo(a);
|
||||
lda = a.step1();
|
||||
lda = (integer)a.step1();
|
||||
work = a.data + n*n*elem_size;
|
||||
|
||||
if( copy_evals )
|
||||
@@ -1339,7 +1339,7 @@ SVD& SVD::operator ()(const Mat& a, int flags)
|
||||
bool temp_a = false;
|
||||
double u1=0, v1=0, work1=0;
|
||||
float uf1=0, vf1=0, workf1=0;
|
||||
integer lda, ldu, ldv, lwork=-1, iwork1=0, info=0, *iwork=0;
|
||||
integer lda, ldu, ldv, lwork=-1, iwork1=0, info=0;
|
||||
char mode[] = {u.data || vt.data ? 'S' : 'N', '\0'};
|
||||
|
||||
if( m != n && !(flags & NO_UV) && (flags & FULL_UV) )
|
||||
@@ -1378,7 +1378,7 @@ SVD& SVD::operator ()(const Mat& a, int flags)
|
||||
}
|
||||
work_ofs = buf_size;
|
||||
buf_size += lwork*elem_size;
|
||||
buf_size = cvAlign(buf_size, sizeof(iwork[0]));
|
||||
buf_size = cvAlign(buf_size, sizeof(integer));
|
||||
iwork_ofs = buf_size;
|
||||
buf_size += 8*nm*sizeof(integer);
|
||||
|
||||
|
@@ -832,7 +832,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
|
||||
{
|
||||
int i, j, k, dims = _data.cols, N = _data.rows;
|
||||
const float* data = _data.ptr<float>(0);
|
||||
int step = _data.step/sizeof(data[0]);
|
||||
int step = (int)(_data.step/sizeof(data[0]));
|
||||
vector<int> _centers(K);
|
||||
int* centers = &_centers[0];
|
||||
vector<float> _dist(N*3);
|
||||
@@ -2851,7 +2851,7 @@ void SparseMat::resizeHashTab(size_t newsize)
|
||||
{
|
||||
newsize = std::max(newsize, (size_t)8);
|
||||
if((newsize & (newsize-1)) != 0)
|
||||
newsize = 1 << cvCeil(std::log((double)newsize)/CV_LOG2);
|
||||
newsize = (size_t)1 << cvCeil(std::log((double)newsize)/CV_LOG2);
|
||||
|
||||
size_t i, hsize = hdr->hashtab.size();
|
||||
vector<size_t> _newh(newsize);
|
||||
@@ -2908,14 +2908,14 @@ uchar* SparseMat::newNode(const int* idx, size_t hashval)
|
||||
int i, d = hdr->dims;
|
||||
for( i = 0; i < d; i++ )
|
||||
elem->idx[i] = idx[i];
|
||||
d = elemSize();
|
||||
size_t esz = elemSize();
|
||||
uchar* p = &value<uchar>(elem);
|
||||
if( d == sizeof(float) )
|
||||
if( esz == sizeof(float) )
|
||||
*((float*)p) = 0.f;
|
||||
else if( d == sizeof(double) )
|
||||
else if( esz == sizeof(double) )
|
||||
*((double*)p) = 0.;
|
||||
else
|
||||
memset(p, 0, d);
|
||||
memset(p, 0, esz);
|
||||
|
||||
return p;
|
||||
}
|
||||
|
Reference in New Issue
Block a user