generic-library/opencv
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Replaced most of the usages of parallel_for with that of parallel_for_.

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This should allow many algorithms to take advantage of more parallelization
technologies.
This commit is contained in:
Roman Donchenko
2013-05-30 18:44:33 +04:00
parent 37091b086c
commit 29b13ec1de
24 changed files with 232 additions and 352 deletions
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12
modules/ml/src/nbayes.cpp
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@@ -277,7 +277,7 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res
return result;
}
struct predict_body {
struct predict_body : cv::ParallelLoopBody {
predict_body(CvMat* _c, CvMat** _cov_rotate_mats, CvMat** _inv_eigen_values, CvMat** _avg,
const CvMat* _samples, const int* _vidx, CvMat* _cls_labels,
CvMat* _results, float* _value, int _var_count1
@@ -307,7 +307,7 @@ struct predict_body {
float* value;
int var_count1;
void operator()( const cv::BlockedRange& range ) const
void operator()( const cv::Range& range ) const
{
int cls = -1;
@@ -324,7 +324,7 @@ struct predict_body {
cv::AutoBuffer<double> buffer(nclasses + var_count1);
CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] );
for(int k = range.begin(); k < range.end(); k += 1 )
for(int k = range.start; k < range.end; k += 1 )
{
int ival;
double opt = FLT_MAX;
@@ -397,9 +397,9 @@ float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) c
const int* vidx = var_idx ? var_idx->data.i : 0;
cv::parallel_for(cv::BlockedRange(0, samples->rows), predict_body(c, cov_rotate_mats, inv_eigen_values, avg, samples,
vidx, cls_labels, results, &value, var_count
));
cv::parallel_for_(cv::Range(0, samples->rows),
predict_body(c, cov_rotate_mats, inv_eigen_values, avg, samples,
vidx, cls_labels, results, &value, var_count));
return value;
}