generic-library/opencv
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Several type of formal refactoring:

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1. someMatrix.data -> someMatrix.prt()
2. someMatrix.data + someMatrix.step * lineIndex -> someMatrix.ptr( lineIndex )
3. (SomeType*) someMatrix.data -> someMatrix.ptr<SomeType>()
4. someMatrix.data -> !someMatrix.empty() ( or !someMatrix.data -> someMatrix.empty() ) in logical expressions
This commit is contained in:
Adil Ibragimov
2014-08-13 15:08:27 +04:00
parent 30111a786a
commit 8a4a1bb018
134 changed files with 988 additions and 986 deletions
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16
apps/traincascade/HOGfeatures.cpp
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@@ -187,11 +187,11 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
for( y = 0; y < gradSize.height; y++ )
{
const uchar* currPtr = img.data + img.step*ymap[y];
const uchar* prevPtr = img.data + img.step*ymap[y-1];
const uchar* nextPtr = img.data + img.step*ymap[y+1];
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
const uchar* currPtr = img.ptr(ymap[y]);
const uchar* prevPtr = img.ptr(ymap[y-1]);
const uchar* nextPtr = img.ptr(ymap[y+1]);
float* gradPtr = grad.ptr<float>(y);
uchar* qanglePtr = qangle.ptr(y);
for( x = 0; x < width; x++ )
{
@@ -226,9 +226,9 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
int magStep = (int)( grad.step / sizeof(float) );
for( binIdx = 0; binIdx < nbins; binIdx++ )
{
histBuf = (float*)histogram[binIdx].data;
magBuf = (const float*)grad.data;
binsBuf = (const uchar*)qangle.data;
histBuf = histogram[binIdx].ptr<float>();
magBuf = grad.ptr<float>();
binsBuf = qangle.ptr();
memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
histBuf += histStep + 1;

4
apps/traincascade/features.cpp
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@@ -13,9 +13,9 @@ float calcNormFactor( const Mat& sum, const Mat& sqSum )
size_t p0, p1, p2, p3;
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
double area = normrect.width * normrect.height;
const int *sp = (const int*)sum.data;
const int *sp = sum.ptr<int>();
int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
const double *sqp = (const double *)sqSum.data;
const double *sqp = sqSum.ptr<double>();
double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
}

2
apps/traincascade/imagestorage.cpp
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@@ -98,7 +98,7 @@ bool CvCascadeImageReader::NegReader::get( Mat& _img )
return false;
Mat mat( winSize.height, winSize.width, CV_8UC1,
(void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
(void*)(img.ptr(point.y) + point.x * img.elemSize()), img.step );
mat.copyTo(_img);
if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )