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fix build with GCC 3.3 on Ubuntu 8.04

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This commit is contained in:
Vadim Pisarevsky 2010-07-09 09:09:20 +00:00
parent 4187f11610
commit db82906067
5 changed files with 57 additions and 50 deletions
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9
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -1560,6 +1560,7 @@ struct DMatch
class CV_EXPORTS DescriptorMatcher
{
public:
virtual ~DescriptorMatcher() {}
/*
* Add descriptors to the training set
* descriptors Descriptors to add to the training set
@ -1806,7 +1807,7 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
for( int i = 0; i < descriptors_1.rows; i++ )
{
const ValueType* d1 = descriptors_1.ptr<ValueType>(i);
const ValueType* d1 = (const ValueType*)(descriptors_1.data + descriptors_1.step*i);
int matchIndex = -1;
DistanceType matchDistance = std::numeric_limits<DistanceType>::max();
@ -1814,7 +1815,7 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
{
if( possibleMatch(mask, i, j) )
{
const ValueType* d2 = descriptors_2.ptr<ValueType>(j);
const ValueType* d2 = (const ValueType*)(descriptors_2.data + descriptors_2.step*j);
DistanceType curDistance = distance(d1, d2, dimension);
if( curDistance < matchDistance )
{
@ -1854,13 +1855,13 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
for( int i = 0; i < descriptors_1.rows; i++ )
{
const ValueType* d1 = descriptors_1.ptr<ValueType>(i);
const ValueType* d1 = (const ValueType*)(descriptors_1.data + descriptors_1.step*i);
for( int j = 0; j < descriptors_2.rows; j++ )
{
if( possibleMatch(mask, i, j) )
{
const ValueType* d2 = descriptors_2.ptr<ValueType>(j);
const ValueType* d2 = (const ValueType*)(descriptors_2.data + descriptors_2.step*j);
DistanceType curDistance = distance(d1, d2, dimension);
if( curDistance < threshold )
{

2
modules/imgproc/src/moments.cpp
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@ -205,7 +205,7 @@ static void momentsInTile( const cv::Mat& img, double* moments )
for( y = 0; y < size.height; y++ )
{
const T* ptr = img.ptr<T>(y);
const T* ptr = (const T*)(img.data + y*img.step);
WT x0 = 0, x1 = 0, x2 = 0;
MT x3 = 0;

3
samples/cpp/calibration.cpp
View File

@ -171,7 +171,8 @@ void saveCameraParams( const string& filename,
for( size_t i = 0; i < imagePoints.size(); i++ )
{
Mat r = imagePtMat.row(i).reshape(2, imagePtMat.cols);
Mat(imagePoints[i]).copyTo(r);
Mat imgpti(imagePoints[i]);
imgpti.copyTo(r);
}
fs << "image_points" << imagePtMat;
}

2
tests/cv/src/areprojectImageTo3D.cpp
View File

@ -145,7 +145,7 @@ protected:
Mat_<double> res = Q * Mat_<double>(4, 1, from);
res /= res(3, 0);
out3d_t pixel_exp = *res.ptr<Vec3d>();
out3d_t pixel_exp = *(Vec3d*)res.data;
out3d_t pixel_out = _3dImg(y, x);
const int largeZValue = 10000; /* see documentation */

91
tests/cxcore/src/areduce.cpp
View File

@ -75,6 +75,11 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim
sum.setTo(Scalar(0));
max.setTo(Scalar(-DBL_MAX));
min.setTo(Scalar(DBL_MAX));
const Mat_<Type>& src_ = src;
Mat_<double>& sum_ = (Mat_<double>&)sum;
Mat_<double>& min_ = (Mat_<double>&)min;
Mat_<double>& max_ = (Mat_<double>&)max;
if( dim == 0 )
{
@ -82,9 +87,9 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim
{
for( int ci = 0; ci < src.cols; ci++ )
{
sum.at<double>(0, ci) += src.at<Type>(ri, ci);
max.at<double>(0, ci) = std::max( max.at<double>(0, ci), (double)src.at<Type>(ri, ci) );
min.at<double>(0, ci) = std::min( min.at<double>(0, ci), (double)src.at<Type>(ri, ci) );
sum_(0, ci) += src_(ri, ci);
max_(0, ci) = std::max( max_(0, ci), (double)src_(ri, ci) );
min_(0, ci) = std::min( min_(0, ci), (double)src_(ri, ci) );
}
}
}
@ -94,9 +99,9 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim
{
for( int ri = 0; ri < src.rows; ri++ )
{
sum.at<double>(ri, 0) += src.at<Type>(ri, ci);
max.at<double>(ri, 0) = std::max( max.at<double>(ri, 0), (double)src.at<Type>(ri, ci) );
min.at<double>(ri, 0) = std::min( min.at<double>(ri, 0), (double)src.at<Type>(ri, ci) );
sum_(ri, 0) += src_(ri, ci);
max_(ri, 0) = std::max( max_(ri, 0), (double)src_(ri, ci) );
min_(ri, 0) = std::min( min_(ri, 0), (double)src_(ri, ci) );
}
}
}
@ -119,37 +124,37 @@ int CV_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat&
{
int srcType = src.type();
bool support = false;
if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
{
if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16U && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16S && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_32F && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
else if( opType == CV_REDUCE_MAX )
{
if( srcType == CV_8U && dstType == CV_8U )
support = true;
if( srcType == CV_32F && dstType == CV_32F )
support = true;
if( srcType == CV_64F && dstType == CV_64F )
support = true;
}
else if( opType == CV_REDUCE_MIN )
{
if( srcType == CV_8U && dstType == CV_8U)
support = true;
if( srcType == CV_32F && dstType == CV_32F)
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
{
if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16U && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16S && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_32F && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
else if( opType == CV_REDUCE_MAX )
{
if( srcType == CV_8U && dstType == CV_8U )
support = true;
if( srcType == CV_32F && dstType == CV_32F )
support = true;
if( srcType == CV_64F && dstType == CV_64F )
support = true;
}
else if( opType == CV_REDUCE_MIN )
{
if( srcType == CV_8U && dstType == CV_8U)
support = true;
if( srcType == CV_32F && dstType == CV_32F)
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
if( !support )
return CvTS::OK;
@ -158,7 +163,7 @@ int CV_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat&
reduce( src, _dst, dim, opType, dstType );
_dst.convertTo( dst, CV_64FC1 );
if( norm( opRes, dst, NORM_INF ) > eps )
{
{
char msg[100];
const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" :
opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" :
@ -168,11 +173,11 @@ int CV_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat&
getMatTypeStr( src.type(), srcTypeStr );
getMatTypeStr( dstType, dstTypeStr );
const char* dimStr = dim == 0 ? "ROWS" : "COLS";
sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s",
srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr );
ts->printf( CvTS::LOG, msg );
return CvTS::FAIL_BAD_ACCURACY;
sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s",
srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr );
ts->printf( CvTS::LOG, msg );
return CvTS::FAIL_BAD_ACCURACY;
}
return CvTS::OK;
}