generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

a part of PR269 (parallelization of several functions) by Alexander Mordvintsev

Browse Source
This commit is contained in:
Vadim Pisarevsky
2013-02-01 14:01:44 +04:00
parent b179e2dd2d
commit 51eba617a8
2 changed files with 15 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
modules/core/src/matrix.cpp
View File

@@ -2459,7 +2459,7 @@ static void generateRandomCenter(const vector<Vec2f>& box, float* center, RNG& r
center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
}
class KMeansPPDistanceComputer
class KMeansPPDistanceComputer : public ParallelLoopBody
{
public:
KMeansPPDistanceComputer( float *_tdist2,
@@ -2475,10 +2475,10 @@ public:
step(_step),
stepci(_stepci) { }
void operator()( const cv::BlockedRange& range ) const
void operator()( const cv::Range& range ) const
{
const int begin = range.begin();
const int end = range.end();
const int begin = range.start;
const int end = range.end;
for ( int i = begin; i<end; i++ )
{
@@ -2534,7 +2534,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
break;
int ci = i;
parallel_for(BlockedRange(0, N),
parallel_for_(Range(0, N),
KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
for( i = 0; i < N; i++ )
{
@@ -2562,7 +2562,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
}
}
class KMeansDistanceComputer
class KMeansDistanceComputer : public ParallelLoopBody
{
public:
KMeansDistanceComputer( double *_distances,
@@ -2576,10 +2576,10 @@ public:
{
}
void operator()( const BlockedRange& range ) const
void operator()( const Range& range ) const
{
const int begin = range.begin();
const int end = range.end();
const int begin = range.start;
const int end = range.end;
const int K = centers.rows;
const int dims = centers.cols;
@@ -2836,7 +2836,7 @@ double cv::kmeans( InputArray _data, int K,
// assign labels
Mat dists(1, N, CV_64F);
double* dist = dists.ptr<double>(0);
parallel_for(BlockedRange(0, N),
parallel_for_(Range(0, N),
KMeansDistanceComputer(dist, labels, data, centers));
compactness = 0;
for( i = 0; i < N; i++ )