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Commit f58d4027 authored by lluis's avatar lluis
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update to new OpenCV 3.0 ml API

parent 1cbeb8ce
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Showing with 45 additions and 28 deletions
modules/text/samples/trained_classifierNM1.xml
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modules/text/samples/trained_classifierNM2.xml
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modules/text/samples/trained_classifier_erGrouping.xml
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modules/text/src/erfilter.cpp
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......@@ -61,6 +61,7 @@ namespace text
{
using namespace std;
using namespace cv::ml;
// Deletes a tree of ERStat regions starting at root. Used only
// internally to this implementation.
......@@ -178,7 +179,7 @@ public:
double eval(const ERStat& stat);
private:
CvBoost boost;
Ptr<Boost> boost;
};
// default 2nd stage classifier
......@@ -194,7 +195,7 @@ public:
double eval(const ERStat& stat);
private:
CvBoost boost;
Ptr<Boost> boost;
};
......@@ -1016,22 +1017,27 @@ ERClassifierNM1::ERClassifierNM1(const string& filename)
{
if (ifstream(filename.c_str()))
boost.load( filename.c_str(), "boost" );
{
boost = StatModel::load<Boost>( filename.c_str() );
if( boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(CV_StsBadArg, "Default classifier file not found!");
CV_Error(Error::StsBadArg, "Default classifier file not found!");
}
double ERClassifierNM1::eval(const ERStat& stat)
{
//Classify
float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
Mat sample = (Mat_<float>(1,4) << (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
sqrt((float)(stat.area))/stat.perimeter, // compactness
(float)(1-stat.euler), //number of holes
stat.med_crossings};
stat.med_crossings);
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
// Logistic Correction returns a probability value (in the range(0,1))
return (double)1-(double)1/(1+exp(-2*votes));
......@@ -1042,23 +1048,28 @@ double ERClassifierNM1::eval(const ERStat& stat)
ERClassifierNM2::ERClassifierNM2(const string& filename)
{
if (ifstream(filename.c_str()))
boost.load( filename.c_str(), "boost" );
{
boost = StatModel::load<Boost>( filename.c_str() );
if( boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(CV_StsBadArg, "Default classifier file not found!");
CV_Error(Error::StsBadArg, "Default classifier file not found!");
}
double ERClassifierNM2::eval(const ERStat& stat)
{
//Classify
float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
Mat sample = (Mat_<float>(1,7) << (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
sqrt((float)(stat.area))/stat.perimeter, // compactness
(float)(1-stat.euler), //number of holes
stat.med_crossings, stat.hole_area_ratio,
stat.convex_hull_ratio, stat.num_inflexion_points};
stat.convex_hull_ratio, stat.num_inflexion_points);
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
// Logistic Correction returns a probability value (in the range(0,1))
return (double)1-(double)1/(1+exp(-2*votes));
......@@ -1397,7 +1408,7 @@ static double NFA(int n, int k, double p, double logNT)
/* check parameters */
if( n<0 || k<0 || k>n || p<=0.0 || p>=1.0 )
{
CV_Error(CV_StsBadArg, "erGrouping wrong n, k or p values in NFA call!");
CV_Error(Error::StsBadArg, "erGrouping wrong n, k or p values in NFA call!");
}
/* trivial cases */
......@@ -2137,15 +2148,15 @@ static int linkage_vector(double *X, int N, int dim, double * Z, unsigned char m
} // try
catch (const bad_alloc&)
{
CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
}
catch(const exception&)
{
CV_Error(CV_StsError, "Uncaught exception in erGrouping!");
CV_Error(Error::StsError, "Uncaught exception in erGrouping!");
}
catch(...)
{
CV_Error(CV_StsError, "C++ exception (unknown reason) in erGrouping!");
CV_Error(Error::StsError, "C++ exception (unknown reason) in erGrouping!");
}
return 0;
}
......@@ -2206,7 +2217,7 @@ public:
private:
double minProbability;
CvBoost group_boost;
Ptr<Boost> group_boost;
vector<ERFeatures> &regions;
Size imsize;
......@@ -2230,9 +2241,16 @@ MaxMeaningfulClustering::MaxMeaningfulClustering(unsigned char _method, unsigned
minProbability = _minProbability;
if (ifstream(filename.c_str()))
group_boost.load( filename.c_str(), "boost" );
{
group_boost = StatModel::load<Boost>( filename.c_str() );
if( group_boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(CV_StsBadArg, "erGrouping: Default classifier file not found!");
CV_Error(Error::StsBadArg, "erGrouping: Default classifier file not found!");
}
......@@ -2242,7 +2260,7 @@ void MaxMeaningfulClustering::operator()(double *data, unsigned int num, int dim
double *Z = (double*)malloc(((num-1)*4) * sizeof(double)); // we need 4 floats foreach sample merge.
if (Z == NULL)
CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
linkage_vector(data, (int)num, dim, Z, method, metric);
......@@ -2541,7 +2559,6 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
return 0.;
vector<float> sample;
sample.push_back(0);
sample.push_back((float)cluster.size());
Mat diameters ( (int)cluster.size(), 1, CV_32F, 1 );
......@@ -2723,7 +2740,7 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
sample.push_back((float)mean[0]);
sample.push_back((float)std[0]);
float votes_group = group_boost.predict( Mat(sample), Mat(), Range::all(), false, true );
float votes_group = group_boost->predict( Mat(sample), noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
return (double)1-(double)1/(1+exp(-2*votes_group));
}
......@@ -3021,7 +3038,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
// assert correct image type
CV_Assert( channel.type() == CV_8UC1 );
CV_Assert( !regions.at(c).empty() );
//CV_Assert( !regions.at(c).empty() );
if ( regions.at(c).size() < 3 )
continue;
......@@ -3039,7 +3056,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
int dim = 7; //dimensionality of feature space
double *data = (double*)malloc(dim*N * sizeof(double));
if (data == NULL)
CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
//Learned weights
float weight_param1 = 1.00f;
......
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