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integrated parallel SVM prediction; fixed warnings after meanshift integration

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This commit is contained in:
Vadim Pisarevsky
2011-04-19 16:20:44 +00:00
parent 537a36115f
commit 17a2480a21
5 changed files with 133 additions and 10 deletions
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5
modules/ml/include/opencv2/ml/ml.hpp
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@@ -543,7 +543,8 @@ public:
bool balanced=false );
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* samples, CvMat* results ) const;
#ifndef SWIG
CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
@@ -563,7 +564,7 @@ public:
CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF),
CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE),
bool balanced=false);
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
#endif
CV_WRAP virtual int get_support_vector_count() const;

40
modules/ml/src/svm.cpp
View File

@@ -2081,7 +2081,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
CV_CALL( cvPreparePredictData( sample, var_all, var_idx,
class_count, 0, &row_sample ));
result = predict( row_sample, get_var_count(), returnDFVal );
__END__;
if( sample && (!CV_IS_MAT(sample) || sample->data.fl != row_sample) )
@@ -2090,6 +2090,44 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
return result;
}
struct predict_body {
predict_body(const CvSVM* _pointer, float* _result, const CvMat* _samples, CvMat* _results)
{
pointer = _pointer;
result = _result;
samples = _samples;
results = _results;
}
const CvSVM* pointer;
float* result;
const CvMat* samples;
CvMat* results;
void operator()( const cv::BlockedRange& range ) const
{
for(int i = range.begin(); i < range.end(); i++ )
{
CvMat sample;
cvGetRow( samples, &sample, i );
int r = (int)pointer->predict(&sample);
if (results)
results->data.fl[i] = r;
if (i == 0)
*result = r;
}
}
};
float CvSVM::predict(const CvMat* samples, CV_OUT CvMat* results) const
{
float result = 0;
cv::parallel_for(cv::BlockedRange(0, samples->rows),
predict_body(this, &result, samples, results)
);
return result;
}
CvSVM::CvSVM( const Mat& _train_data, const Mat& _responses,
const Mat& _var_idx, const Mat& _sample_idx, CvSVMParams _params )