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
Download Patch File Download Diff File Expand all files Collapse all files

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

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