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Fix x64 build warnings

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This commit is contained in:
Andrey Kamaev 2013-02-25 11:08:27 +04:00
parent 2a6fb2867e
commit d586f4a103
10 changed files with 35 additions and 29 deletions
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4
modules/core/test/test_mat.cpp
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@ -869,7 +869,7 @@ int calcDiffElemCountImpl(const vector<Mat>& mv, const Mat& m)
for(int x = 0; x < m.cols; x++)
{
const ElemType* mElem = &m.at<ElemType>(y,x*mChannels);
int loc = 0;
size_t loc = 0;
for(size_t i = 0; i < mv.size(); i++)
{
const size_t mvChannel = mv[i].channels();
@ -879,7 +879,7 @@ int calcDiffElemCountImpl(const vector<Mat>& mv, const Mat& m)
diffElemCount++;
loc += mvChannel;
}
CV_Assert(loc == mChannels);
CV_Assert(loc == (size_t)mChannels);
}
}
return diffElemCount;

8
modules/imgproc/src/demosaicing.cpp
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@ -1306,7 +1306,7 @@ public:
int dcn = dst.channels();
int dcn2 = dcn<<1;
int start_with_green = Start_with_green, blue = Blue;
int sstep = src.step / src.elemSize1(), dstep = dst.step / dst.elemSize1();
int sstep = int(src.step / src.elemSize1()), dstep = int(dst.step / dst.elemSize1());
SIMDInterpolator vecOp;
const T* S = reinterpret_cast<const T*>(src.data + (range.start + 1) * src.step) + 1;
@ -1415,7 +1415,7 @@ static void Bayer2RGB_EdgeAware_T(const Mat& src, Mat& dst, int code)
}
size = dst.size();
size.width *= dst.channels();
int dstep = dst.step / dst.elemSize1();
size_t dstep = dst.step / dst.elemSize1();
T* firstRow = reinterpret_cast<T*>(dst.data);
T* lastRow = reinterpret_cast<T*>(dst.data) + (size.height-1) * dstep;
@ -1423,8 +1423,8 @@ static void Bayer2RGB_EdgeAware_T(const Mat& src, Mat& dst, int code)
{
for (int x = 0; x < size.width; ++x)
{
firstRow[x] = firstRow[dstep+x];
lastRow[x] = lastRow[-dstep+x];
firstRow[x] = (firstRow+dstep)[x];
lastRow[x] = (lastRow-dstep)[x];
}
}
else

9
modules/imgproc/src/floodfill.cpp
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@ -505,14 +505,15 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
if( is_simple )
{
int elem_size = img.elemSize();
size_t elem_size = img.elemSize();
const uchar* seed_ptr = img.data + img.step*seedPoint.y + elem_size*seedPoint.x;
for(i = 0; i < elem_size; i++)
if (seed_ptr[i] != nv_buf.b[i])
size_t k = 0;
for(; k < elem_size; k++)
if (seed_ptr[k] != nv_buf.b[k])
break;
if( i != elem_size )
if( k != elem_size )
{
if( type == CV_8UC1 )
floodFill_CnIR(img, seedPoint, nv_buf.b[0], &comp, flags, &buffer);

14
modules/imgproc/src/samplers.cpp
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@ -46,7 +46,7 @@ namespace cv
{
static const uchar*
adjustRect( const uchar* src, int src_step, int pix_size,
adjustRect( const uchar* src, size_t src_step, int pix_size,
Size src_size, Size win_size,
Point ip, Rect* pRect )
{
@ -127,8 +127,8 @@ struct nop
template<typename _Tp, typename _DTp, typename _WTp, class ScaleOp, class CastOp>
void getRectSubPix_Cn_(const _Tp* src, int src_step, Size src_size,
_DTp* dst, int dst_step, Size win_size, Point2f center, int cn )
void getRectSubPix_Cn_(const _Tp* src, size_t src_step, Size src_size,
_DTp* dst, size_t dst_step, Size win_size, Point2f center, int cn )
{
ScaleOp scale_op;
CastOp cast_op;
@ -217,8 +217,8 @@ void getRectSubPix_Cn_(const _Tp* src, int src_step, Size src_size,
static void getRectSubPix_8u32f
( const uchar* src, int src_step, Size src_size,
float* dst, int dst_step, Size win_size, Point2f center0, int cn )
( const uchar* src, size_t src_step, Size src_size,
float* dst, size_t dst_step, Size win_size, Point2f center0, int cn )
{
Point2f center = center0;
Point ip;
@ -269,8 +269,8 @@ static void getRectSubPix_8u32f
static void
getQuadrangleSubPix_8u32f_CnR( const uchar* src, int src_step, Size src_size,
float* dst, int dst_step, Size win_size,
getQuadrangleSubPix_8u32f_CnR( const uchar* src, size_t src_step, Size src_size,
float* dst, size_t dst_step, Size win_size,
const double *matrix, int cn )
{
int x, y, k;

4
modules/imgproc/src/segmentation.cpp
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@ -148,9 +148,9 @@ void cv::watershed( InputArray _src, InputOutputArray _markers )
CV_Assert( src.size() == dst.size() );
const uchar* img = src.data;
int istep = src.step/sizeof(img[0]);
int istep = int(src.step/sizeof(img[0]));
int* mask = dst.ptr<int>();
int mstep = dst.step / sizeof(mask[0]);
int mstep = int(dst.step / sizeof(mask[0]));
for( i = 0; i < 256; i++ )
subs_tab[i] = 0;

3
modules/softcascade/CMakeLists.txt
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@ -1,2 +1,3 @@
set(the_description "Soft Cascade detection and training")
ocv_define_module(softcascade opencv_core opencv_imgproc opencv_ml)
ocv_define_module(softcascade opencv_core opencv_imgproc opencv_ml)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4310)

4
modules/softcascade/src/_random.hpp
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@ -52,6 +52,7 @@ namespace cv { namespace softcascade { namespace internal
struct Random
{
typedef std::mt19937 engine;
typedef engine::result_type seed_type;
typedef std::uniform_int<int> uniform;
};
@ -67,6 +68,7 @@ namespace cv { namespace softcascade { namespace internal
struct Random
{
typedef std::mt19937 engine;
typedef engine::result_type seed_type;
// True if we're using C++11.
#if __cplusplus >= 201103L
// C++11 removes uniform_int.
@ -86,6 +88,7 @@ namespace cv { namespace softcascade { namespace internal
struct Random
{
typedef std::tr1::mt19937 engine;
typedef engine::result_type seed_type;
typedef std::tr1::uniform_int<int> uniform;
};
@ -125,6 +128,7 @@ private:
struct Random
{
typedef rnd::engine engine;
typedef uint64 seed_type;
typedef rnd::uniform_int<int> uniform;
};

2
modules/softcascade/src/integral_channel_builder.cpp
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@ -217,7 +217,7 @@ void ChannelFeaturePool::fill(int desired)
int nfeatures = std::min(desired, maxPoolSize);
pool.reserve(nfeatures);
Random::engine eng(FEATURE_RECT_SEED);
Random::engine eng((Random::seed_type)FEATURE_RECT_SEED);
Random::engine eng_ch(DCHANNELS_SEED);
Random::uniform chRand(0, N_CHANNELS - 1);

4
modules/softcascade/src/soft_cascade_octave.cpp
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@ -116,7 +116,7 @@ BoostedSoftCascadeOctave::BoostedSoftCascadeOctave(cv::Rect bb, int np, int nn,
_params.truncate_pruned_tree = false;
_params.use_surrogates = false;
_params.use_1se_rule = false;
_params.regression_accuracy = 1.0e-6;
_params.regression_accuracy = 1.0e-6f;
// boost params
_params.boost_type = CvBoost::GENTLE;
@ -221,7 +221,7 @@ void BoostedSoftCascadeOctave::generateNegatives(const Dataset* dataset)
using namespace cv::softcascade::internal;
// ToDo: set seed, use offsets
Random::engine eng(DX_DY_SEED);
Random::engine idxEng(INDEX_ENGINE_SEED);
Random::engine idxEng((Random::seed_type)INDEX_ENGINE_SEED);
int h = boundingBox.height;

12
modules/softcascade/src/softcascade.cpp
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@ -180,7 +180,7 @@ struct ChannelStorage
cv::Mat hog;
int shrinkage;
int offset;
int step;
size_t step;
int model_height;
cv::Ptr<ChannelFeatureBuilder> builder;
@ -398,7 +398,7 @@ struct Detector::Fields
fns = (*st)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node(features.size(), inIt));
nodes.push_back(Node((int)features.size(), inIt));
fns = (*st)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
@ -504,7 +504,7 @@ void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects
{
for (int dx = 0; dx < level.workRect.width; ++dx)
{
storage.offset = dy * storage.step + dx;
storage.offset = (int)(dy * storage.step + dx);
fld.detectAt(dx, dy, level, storage, objects);
}
}
@ -555,7 +555,7 @@ void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<D
{
if (m[dx])
{
storage.offset = dy * storage.step + dx;
storage.offset = (int)(dy * storage.step + dx);
fld.detectAt(dx, dy, level, storage, objects);
}
}
@ -570,11 +570,11 @@ void Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects,
std::vector<Detection> objects;
detect( _image, _rois, objects);
_rects.create(1, objects.size(), CV_32SC4);
_rects.create(1, (int)objects.size(), CV_32SC4);
cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat();
cv::Rect* rectPtr = rects.ptr<cv::Rect>(0);
_confs.create(1, objects.size(), CV_32F);
_confs.create(1, (int)objects.size(), CV_32F);
cv::Mat confs = _confs.getMat();
float* confPtr = confs.ptr<float>(0);