mirror of
https://github.com/opencv/opencv.git
synced 2025-01-18 22:44:02 +08:00
fixed compilation of some samples; fixed ANN_MLP::predict
This commit is contained in:
Vadim Pisarevsky
parent
e368f17caf
commit
223cdcd0ee
@ -262,9 +262,9 @@ public:
|
||||
int cols = layer_sizes[j];
|
||||
|
||||
layer_out = Mat(dn, cols, CV_64F, data);
|
||||
Mat w = weights[i].rowRange(0, layer_in.cols);
|
||||
Mat w = weights[j].rowRange(0, layer_in.cols);
|
||||
gemm(layer_in, w, 1, noArray(), 0, layer_out);
|
||||
calc_activ_func( layer_out, weights[i] );
|
||||
calc_activ_func( layer_out, weights[j] );
|
||||
|
||||
layer_in = layer_out;
|
||||
}
|
||||
@ -682,6 +682,8 @@ public:
|
||||
train_backprop( inputs, outputs, sw, termcrit ) :
|
||||
train_rprop( inputs, outputs, sw, termcrit );
|
||||
|
||||
trained = true;
|
||||
|
||||
return iter;
|
||||
}
|
||||
|
||||
|
||||
@ -23,6 +23,7 @@
|
||||
#define DEBUG_DESC_PROGRESS
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
using namespace std;
|
||||
|
||||
const string paramsFile = "params.xml";
|
||||
@ -677,7 +678,7 @@ void VocData::writeClassifierResultsFile( const string& out_dir, const string& o
|
||||
result_file.close();
|
||||
} else {
|
||||
string err_msg = "could not open classifier results file '" + output_file + "' for writing. Before running for the first time, a 'results' subdirectory should be created within the VOC dataset base directory. e.g. if the VOC data is stored in /VOC/VOC2010 then the path /VOC/results must be created.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -701,9 +702,9 @@ void VocData::writeClassifierResultsFile( const string& out_dir, const string& o
|
||||
string VocData::getResultsFilename(const string& obj_class, const VocTask task, const ObdDatasetType dataset, const int competition, const int number)
|
||||
{
|
||||
if ((competition < 1) && (competition != -1))
|
||||
CV_Error(CV_StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
|
||||
CV_Error(Error::StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
|
||||
if ((number < 1) && (number != -1))
|
||||
CV_Error(CV_StsBadArg,"number argument should be a positive non-zero number or -1 to accept the default");
|
||||
CV_Error(Error::StsBadArg,"number argument should be a positive non-zero number or -1 to accept the default");
|
||||
|
||||
string dset, task_type;
|
||||
|
||||
@ -815,7 +816,7 @@ void VocData::calcClassifierPrecRecall(const string& input_file, vector<float>&
|
||||
scoregt_file.close();
|
||||
} else {
|
||||
string err_msg = "could not open scoregt file '" + scoregt_file_str + "' for writing.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -974,7 +975,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
|
||||
if (target_idx_it == output_headers.end())
|
||||
{
|
||||
string err_msg = "could not find the target object class '" + obj_class + "' in list of valid classes.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
/* convert iterator to index */
|
||||
target_idx = (int)std::distance(output_headers.begin(),target_idx_it);
|
||||
@ -1037,7 +1038,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
|
||||
if (class_idx_it == output_headers.end())
|
||||
{
|
||||
string err_msg = "could not find object class '" + img_objects[obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
/* convert iterator to index */
|
||||
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
|
||||
@ -1189,7 +1190,7 @@ void VocData::calcDetectorConfMatRow(const string& obj_class, const ObdDatasetTy
|
||||
if (class_idx_it == output_headers.end())
|
||||
{
|
||||
string err_msg = "could not find object class '" + img_objects[max_gt_obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
/* convert iterator to index */
|
||||
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
|
||||
@ -1282,7 +1283,7 @@ void VocData::savePrecRecallToGnuplot(const string& output_file, const vector<fl
|
||||
plot_file.close();
|
||||
} else {
|
||||
string err_msg = "could not open plot file '" + output_file_std + "' for writing.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -1446,7 +1447,7 @@ void VocData::readClassifierGroundTruth(const string& filename, vector<string>&
|
||||
if (!gtfile.is_open())
|
||||
{
|
||||
string err_msg = "could not open VOC ground truth textfile '" + filename + "'.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
|
||||
string line;
|
||||
@ -1462,7 +1463,7 @@ void VocData::readClassifierGroundTruth(const string& filename, vector<string>&
|
||||
image_codes.push_back(image);
|
||||
object_present.push_back(obj_present == 1);
|
||||
} else {
|
||||
if (!gtfile.eof()) CV_Error(CV_StsParseError,"error parsing VOC ground truth textfile.");
|
||||
if (!gtfile.eof()) CV_Error(Error::StsParseError,"error parsing VOC ground truth textfile.");
|
||||
}
|
||||
}
|
||||
gtfile.close();
|
||||
@ -1488,13 +1489,13 @@ void VocData::readClassifierResultsFile(const string& input_file, vector<string>
|
||||
image_codes.push_back(image);
|
||||
scores.push_back(score);
|
||||
} else {
|
||||
if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC classifier results file.");
|
||||
if(!result_file.eof()) CV_Error(Error::StsParseError,"error parsing VOC classifier results file.");
|
||||
}
|
||||
}
|
||||
result_file.close();
|
||||
} else {
|
||||
string err_msg = "could not open classifier results file '" + input_file + "' for reading.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -1545,13 +1546,13 @@ void VocData::readDetectorResultsFile(const string& input_file, vector<string>&
|
||||
bounding_boxes[image_idx].push_back(bounding_box);
|
||||
}
|
||||
} else {
|
||||
if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC detector results file.");
|
||||
if(!result_file.eof()) CV_Error(Error::StsParseError,"error parsing VOC detector results file.");
|
||||
}
|
||||
}
|
||||
result_file.close();
|
||||
} else {
|
||||
string err_msg = "could not open detector results file '" + input_file + "' for reading.";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -1595,23 +1596,23 @@ void VocData::extractVocObjects(const string filename, vector<ObdObject>& object
|
||||
|
||||
//object class -------------
|
||||
|
||||
if (extractXMLBlock(object_contents, "name", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <name> tag in object definition of '" + filename + "'");
|
||||
if (extractXMLBlock(object_contents, "name", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <name> tag in object definition of '" + filename + "'");
|
||||
object.object_class.swap(tag_contents);
|
||||
|
||||
//object bounding box -------------
|
||||
|
||||
int xmax, xmin, ymax, ymin;
|
||||
|
||||
if (extractXMLBlock(object_contents, "xmax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmax> tag in object definition of '" + filename + "'");
|
||||
if (extractXMLBlock(object_contents, "xmax", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <xmax> tag in object definition of '" + filename + "'");
|
||||
xmax = stringToInteger(tag_contents);
|
||||
|
||||
if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmin> tag in object definition of '" + filename + "'");
|
||||
if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <xmin> tag in object definition of '" + filename + "'");
|
||||
xmin = stringToInteger(tag_contents);
|
||||
|
||||
if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymax> tag in object definition of '" + filename + "'");
|
||||
if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <ymax> tag in object definition of '" + filename + "'");
|
||||
ymax = stringToInteger(tag_contents);
|
||||
|
||||
if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymin> tag in object definition of '" + filename + "'");
|
||||
if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <ymin> tag in object definition of '" + filename + "'");
|
||||
ymin = stringToInteger(tag_contents);
|
||||
|
||||
object.boundingBox.x = xmin-1; //convert to 0-based indexing
|
||||
@ -1714,11 +1715,11 @@ void VocData::extractDataFromResultsFilename(const string& input_file, string& c
|
||||
size_t fnameend = input_file_std.rfind(".txt");
|
||||
|
||||
if ((fnamestart == input_file_std.npos) || (fnameend == input_file_std.npos))
|
||||
CV_Error(CV_StsError,"Could not extract filename of results file.");
|
||||
CV_Error(Error::StsError,"Could not extract filename of results file.");
|
||||
|
||||
++fnamestart;
|
||||
if (fnamestart >= fnameend)
|
||||
CV_Error(CV_StsError,"Could not extract filename of results file.");
|
||||
CV_Error(Error::StsError,"Could not extract filename of results file.");
|
||||
|
||||
//extract dataset and class names, triggering exception if the filename format is not correct
|
||||
string filename = input_file_std.substr(fnamestart, fnameend-fnamestart);
|
||||
@ -1729,11 +1730,11 @@ void VocData::extractDataFromResultsFilename(const string& input_file, string& c
|
||||
size_t classend = filename.find("_",classstart+1);
|
||||
if (classend == filename.npos) classend = filename.size();
|
||||
if ((datasetstart == filename.npos) || (classstart == filename.npos))
|
||||
CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
|
||||
CV_Error(Error::StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
|
||||
++datasetstart;
|
||||
++classstart;
|
||||
if (((datasetstart-classstart) < 1) || ((classend-datasetstart) < 1))
|
||||
CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
|
||||
CV_Error(Error::StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
|
||||
|
||||
dataset_name = filename.substr(datasetstart,classstart-datasetstart-1);
|
||||
class_name = filename.substr(classstart,classend-classstart);
|
||||
@ -1781,7 +1782,7 @@ bool VocData::getClassifierGroundTruthImage(const string& obj_class, const strin
|
||||
return m_classifier_gt_all_present[std::distance(m_classifier_gt_all_ids.begin(),it)] != 0;
|
||||
} else {
|
||||
string err_msg = "could not find classifier ground truth for image '" + id + "' and class '" + obj_class + "'";
|
||||
CV_Error(CV_StsError,err_msg.c_str());
|
||||
CV_Error(Error::StsError,err_msg.c_str());
|
||||
}
|
||||
|
||||
return true;
|
||||
@ -1814,7 +1815,7 @@ void VocData::getSortOrder(const vector<float>& values, vector<size_t>& order, b
|
||||
void VocData::readFileToString(const string filename, string& file_contents)
|
||||
{
|
||||
std::ifstream ifs(filename.c_str());
|
||||
if (!ifs.is_open()) CV_Error(CV_StsError,"could not open text file");
|
||||
if (!ifs.is_open()) CV_Error(Error::StsError,"could not open text file");
|
||||
|
||||
stringstream oss;
|
||||
oss << ifs.rdbuf();
|
||||
@ -1829,7 +1830,7 @@ int VocData::stringToInteger(const string input_str)
|
||||
stringstream ss(input_str);
|
||||
if ((ss >> result).fail())
|
||||
{
|
||||
CV_Error(CV_StsBadArg,"could not perform string to integer conversion");
|
||||
CV_Error(Error::StsBadArg,"could not perform string to integer conversion");
|
||||
}
|
||||
return result;
|
||||
}
|
||||
@ -1841,7 +1842,7 @@ string VocData::integerToString(const int input_int)
|
||||
stringstream ss;
|
||||
if ((ss << input_int).fail())
|
||||
{
|
||||
CV_Error(CV_StsBadArg,"could not perform integer to string conversion");
|
||||
CV_Error(Error::StsBadArg,"could not perform integer to string conversion");
|
||||
}
|
||||
result = ss.str();
|
||||
return result;
|
||||
@ -2325,7 +2326,7 @@ static void removeBowImageDescriptorsByCount( vector<ObdImage>& images, vector<M
|
||||
CV_Assert( bowImageDescriptors.size() == objectPresent.size() );
|
||||
}
|
||||
|
||||
static void setSVMParams( CvSVMParams& svmParams, CvMat& class_wts_cv, const Mat& responses, bool balanceClasses )
|
||||
static void setSVMParams( const SVM::Params& svmParams, Mat& class_wts_cv, const Mat& responses, bool balanceClasses )
|
||||
{
|
||||
int pos_ex = countNonZero(responses == 1);
|
||||
int neg_ex = countNonZero(responses == -1);
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
#include "opencv2/ml.hpp"
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
int main( int /*argc*/, char** /*argv*/ )
|
||||
{
|
||||
@ -34,8 +35,9 @@ int main( int /*argc*/, char** /*argv*/ )
|
||||
samples = samples.reshape(1, 0);
|
||||
|
||||
// cluster the data
|
||||
EM em_model(N, EM::COV_MAT_SPHERICAL, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1));
|
||||
em_model.train( samples, noArray(), labels, noArray() );
|
||||
Ptr<EM> em_model = EM::train( samples, noArray(), labels, noArray(),
|
||||
EM::Params(N, EM::COV_MAT_SPHERICAL,
|
||||
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1)));
|
||||
|
||||
// classify every image pixel
|
||||
for( i = 0; i < img.rows; i++ )
|
||||
@ -44,7 +46,7 @@ int main( int /*argc*/, char** /*argv*/ )
|
||||
{
|
||||
sample.at<float>(0) = (float)j;
|
||||
sample.at<float>(1) = (float)i;
|
||||
int response = cvRound(em_model.predict( sample )[1]);
|
||||
int response = cvRound(em_model->predict2( sample, noArray() )[1]);
|
||||
Scalar c = colors[response];
|
||||
|
||||
circle( img, Point(j, i), 1, c*0.75, FILLED );
|
||||
|
||||
@ -12,6 +12,7 @@
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
const Scalar WHITE_COLOR = Scalar(255,255,255);
|
||||
const string winName = "points";
|
||||
@ -22,18 +23,20 @@ RNG rng;
|
||||
|
||||
vector<Point> trainedPoints;
|
||||
vector<int> trainedPointsMarkers;
|
||||
vector<Scalar> classColors;
|
||||
const int MAX_CLASSES = 2;
|
||||
vector<Vec3b> classColors(MAX_CLASSES);
|
||||
int currentClass = 0;
|
||||
vector<int> classCounters(MAX_CLASSES);
|
||||
|
||||
#define _NBC_ 0 // normal Bayessian classifier
|
||||
#define _KNN_ 0 // k nearest neighbors classifier
|
||||
#define _SVM_ 0 // support vectors machine
|
||||
#define _NBC_ 1 // normal Bayessian classifier
|
||||
#define _KNN_ 1 // k nearest neighbors classifier
|
||||
#define _SVM_ 1 // support vectors machine
|
||||
#define _DT_ 1 // decision tree
|
||||
#define _BT_ 0 // ADA Boost
|
||||
#define _BT_ 1 // ADA Boost
|
||||
#define _GBT_ 0 // gradient boosted trees
|
||||
#define _RF_ 0 // random forest
|
||||
#define _ERT_ 0 // extremely randomized trees
|
||||
#define _ANN_ 0 // artificial neural networks
|
||||
#define _EM_ 0 // expectation-maximization
|
||||
#define _RF_ 1 // random forest
|
||||
#define _ANN_ 1 // artificial neural networks
|
||||
#define _EM_ 1 // expectation-maximization
|
||||
|
||||
static void on_mouse( int event, int x, int y, int /*flags*/, void* )
|
||||
{
|
||||
@ -44,76 +47,43 @@ static void on_mouse( int event, int x, int y, int /*flags*/, void* )
|
||||
|
||||
if( event == EVENT_LBUTTONUP )
|
||||
{
|
||||
if( classColors.empty() )
|
||||
return;
|
||||
|
||||
trainedPoints.push_back( Point(x,y) );
|
||||
trainedPointsMarkers.push_back( (int)(classColors.size()-1) );
|
||||
trainedPointsMarkers.push_back( currentClass );
|
||||
classCounters[currentClass]++;
|
||||
updateFlag = true;
|
||||
}
|
||||
else if( event == EVENT_RBUTTONUP )
|
||||
{
|
||||
#if _BT_
|
||||
if( classColors.size() < 2 )
|
||||
{
|
||||
#endif
|
||||
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
|
||||
updateFlag = true;
|
||||
#if _BT_
|
||||
}
|
||||
else
|
||||
cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
//draw
|
||||
if( updateFlag )
|
||||
{
|
||||
img = Scalar::all(0);
|
||||
|
||||
// put the text
|
||||
stringstream text;
|
||||
text << "current class " << classColors.size()-1;
|
||||
putText( img, text.str(), Point(10,25), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
text.str("");
|
||||
text << "total classes " << classColors.size();
|
||||
putText( img, text.str(), Point(10,50), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
text.str("");
|
||||
text << "total points " << trainedPoints.size();
|
||||
putText(img, text.str(), Point(10,75), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
// draw points
|
||||
for( size_t i = 0; i < trainedPoints.size(); i++ )
|
||||
circle( img, trainedPoints[i], 5, classColors[trainedPointsMarkers[i]], -1 );
|
||||
{
|
||||
Vec3b c = classColors[trainedPointsMarkers[i]];
|
||||
circle( img, trainedPoints[i], 5, Scalar(c), -1 );
|
||||
}
|
||||
|
||||
imshow( winName, img );
|
||||
}
|
||||
}
|
||||
|
||||
static void prepare_train_data( Mat& samples, Mat& classes )
|
||||
static Mat prepare_train_samples(const vector<Point>& pts)
|
||||
{
|
||||
Mat( trainedPoints ).copyTo( samples );
|
||||
Mat( trainedPointsMarkers ).copyTo( classes );
|
||||
|
||||
// reshape trainData and change its type
|
||||
samples = samples.reshape( 1, samples.rows );
|
||||
samples.convertTo( samples, CV_32FC1 );
|
||||
Mat samples;
|
||||
Mat(pts).reshape(1, (int)pts.size()).convertTo(samples, CV_32F);
|
||||
return samples;
|
||||
}
|
||||
|
||||
#if _NBC_
|
||||
static void find_decision_boundary_NBC()
|
||||
static Ptr<TrainData> prepare_train_data()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvNormalBayesClassifier normalBayesClassifier( trainSamples, trainClasses );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
return TrainData::create(samples, ROW_SAMPLE, Mat(trainedPointsMarkers));
|
||||
}
|
||||
|
||||
static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst)
|
||||
{
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
@ -122,328 +92,146 @@ static void find_decision_boundary_NBC()
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)normalBayesClassifier.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 1, classColors[response] );
|
||||
int response = (int)model->predict( testSample );
|
||||
dst.at<Vec3b>(y, x) = classColors[response];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#if _NBC_
|
||||
static void find_decision_boundary_NBC()
|
||||
{
|
||||
// learn classifier
|
||||
Ptr<NormalBayesClassifier> normalBayesClassifier = NormalBayesClassifier::create();
|
||||
normalBayesClassifier->train(prepare_train_data());
|
||||
|
||||
predict_and_paint(normalBayesClassifier, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
#if _KNN_
|
||||
static void find_decision_boundary_KNN( int K )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
Ptr<KNearest> knn = KNearest::create(true);
|
||||
knn->setDefaultK(K);
|
||||
knn->train(prepare_train_data());
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_KNN_
|
||||
cv::ocl::KNearestNeighbour knnClassifier;
|
||||
Mat temp, result;
|
||||
knnClassifier.train(trainSamples, trainClasses, temp, false, K);
|
||||
cv::ocl::oclMat testSample_ocl, reslut_ocl;
|
||||
#else
|
||||
CvKNearest knnClassifier( trainSamples, trainClasses, Mat(), false, K );
|
||||
#endif
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_KNN_
|
||||
testSample_ocl.upload(testSample);
|
||||
|
||||
knnClassifier.find_nearest(testSample_ocl, K, reslut_ocl);
|
||||
|
||||
reslut_ocl.download(result);
|
||||
int response = saturate_cast<int>(result.at<float>(0));
|
||||
circle(imgDst, Point(x, y), 1, classColors[response]);
|
||||
#else
|
||||
|
||||
int response = (int)knnClassifier.find_nearest( testSample, K );
|
||||
circle( imgDst, Point(x,y), 1, classColors[response] );
|
||||
#endif
|
||||
}
|
||||
}
|
||||
predict_and_paint(knn, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _SVM_
|
||||
static void find_decision_boundary_SVM( CvSVMParams params )
|
||||
static void find_decision_boundary_SVM( SVM::Params params )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
Ptr<SVM> svm = SVM::create(params);
|
||||
svm->train(prepare_train_data());
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
predict_and_paint(svm, imgDst);
|
||||
|
||||
// learn classifier
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_SVM_
|
||||
cv::ocl::CvSVM_OCL svmClassifier(trainSamples, trainClasses, Mat(), Mat(), params);
|
||||
#else
|
||||
CvSVM svmClassifier( trainSamples, trainClasses, Mat(), Mat(), params );
|
||||
#endif
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
Mat sv = svm->getSupportVectors();
|
||||
for( int i = 0; i < sv.rows; i++ )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)svmClassifier.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
const float* supportVector = sv.ptr<float>(i);
|
||||
circle( imgDst, Point(saturate_cast<int>(supportVector[0]),saturate_cast<int>(supportVector[1])), 5, Scalar(255,255,255), -1 );
|
||||
}
|
||||
|
||||
|
||||
for( int i = 0; i < svmClassifier.get_support_vector_count(); i++ )
|
||||
{
|
||||
const float* supportVector = svmClassifier.get_support_vector(i);
|
||||
circle( imgDst, Point(saturate_cast<int>(supportVector[0]),saturate_cast<int>(supportVector[1])), 5, CV_RGB(255,255,255), -1 );
|
||||
}
|
||||
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _DT_
|
||||
static void find_decision_boundary_DT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
DTrees::Params params;
|
||||
params.maxDepth = 8;
|
||||
params.minSampleCount = 2;
|
||||
params.useSurrogates = false;
|
||||
params.CVFolds = 0; // the number of cross-validation folds
|
||||
params.use1SERule = false;
|
||||
params.truncatePrunedTree = false;
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
Ptr<DTrees> dtree = DTrees::create(params);
|
||||
dtree->train(prepare_train_data());
|
||||
|
||||
// learn classifier
|
||||
CvDTree dtree;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvDTreeParams params;
|
||||
params.max_depth = 8;
|
||||
params.min_sample_count = 2;
|
||||
params.use_surrogates = false;
|
||||
params.cv_folds = 0; // the number of cross-validation folds
|
||||
params.use_1se_rule = false;
|
||||
params.truncate_pruned_tree = false;
|
||||
|
||||
dtree.train( trainSamples, CV_ROW_SAMPLE, trainClasses,
|
||||
Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)dtree.predict( testSample )->value;
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
predict_and_paint(dtree, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _BT_
|
||||
void find_decision_boundary_BT()
|
||||
static void find_decision_boundary_BT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
Boost::Params params( Boost::DISCRETE, // boost_type
|
||||
100, // weak_count
|
||||
0.95, // weight_trim_rate
|
||||
2, // max_depth
|
||||
false, //use_surrogates
|
||||
Mat() // priors
|
||||
);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvBoost boost;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvBoostParams params( CvBoost::DISCRETE, // boost_type
|
||||
100, // weak_count
|
||||
0.95, // weight_trim_rate
|
||||
2, // max_depth
|
||||
false, //use_surrogates
|
||||
0 // priors
|
||||
);
|
||||
|
||||
boost.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)boost.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<Boost> boost = Boost::create(params);
|
||||
boost->train(prepare_train_data());
|
||||
predict_and_paint(boost, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _GBT_
|
||||
void find_decision_boundary_GBT()
|
||||
static void find_decision_boundary_GBT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
GBTrees::Params params( GBTrees::DEVIANCE_LOSS, // loss_function_type
|
||||
100, // weak_count
|
||||
0.1f, // shrinkage
|
||||
1.0f, // subsample_portion
|
||||
2, // max_depth
|
||||
false // use_surrogates )
|
||||
);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvGBTrees gbtrees;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvGBTreesParams params( CvGBTrees::DEVIANCE_LOSS, // loss_function_type
|
||||
100, // weak_count
|
||||
0.1f, // shrinkage
|
||||
1.0f, // subsample_portion
|
||||
2, // max_depth
|
||||
false // use_surrogates )
|
||||
);
|
||||
|
||||
gbtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)gbtrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<GBTrees> gbtrees = GBTrees::create(params);
|
||||
gbtrees->train(prepare_train_data());
|
||||
predict_and_paint(gbtrees, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _RF_
|
||||
void find_decision_boundary_RF()
|
||||
static void find_decision_boundary_RF()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvRTrees rtrees;
|
||||
CvRTParams params( 4, // max_depth,
|
||||
RTrees::Params params( 4, // max_depth,
|
||||
2, // min_sample_count,
|
||||
0.f, // regression_accuracy,
|
||||
false, // use_surrogates,
|
||||
16, // max_categories,
|
||||
0, // priors,
|
||||
Mat(), // priors,
|
||||
false, // calc_var_importance,
|
||||
1, // nactive_vars,
|
||||
5, // max_num_of_trees_in_the_forest,
|
||||
0, // forest_accuracy,
|
||||
CV_TERMCRIT_ITER // termcrit_type
|
||||
TermCriteria(TermCriteria::MAX_ITER, 5, 0) // max_num_of_trees_in_the_forest,
|
||||
);
|
||||
|
||||
rtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), Mat(), Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)rtrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<RTrees> rtrees = RTrees::create(params);
|
||||
rtrees->train(prepare_train_data());
|
||||
predict_and_paint(rtrees, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _ERT_
|
||||
void find_decision_boundary_ERT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvERTrees ertrees;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvRTParams params( 4, // max_depth,
|
||||
2, // min_sample_count,
|
||||
0.f, // regression_accuracy,
|
||||
false, // use_surrogates,
|
||||
16, // max_categories,
|
||||
0, // priors,
|
||||
false, // calc_var_importance,
|
||||
1, // nactive_vars,
|
||||
5, // max_num_of_trees_in_the_forest,
|
||||
0, // forest_accuracy,
|
||||
CV_TERMCRIT_ITER // termcrit_type
|
||||
);
|
||||
|
||||
ertrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)ertrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _ANN_
|
||||
void find_decision_boundary_ANN( const Mat& layer_sizes )
|
||||
static void find_decision_boundary_ANN( const Mat& layer_sizes )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
ANN_MLP::Params params(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON),
|
||||
ANN_MLP::Params::BACKPROP, 0.001);
|
||||
Ptr<ANN_MLP> ann = ANN_MLP::create(layer_sizes, params, ANN_MLP::SIGMOID_SYM, 1, 1 );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// prerare trainClasses
|
||||
trainClasses.create( trainedPoints.size(), classColors.size(), CV_32FC1 );
|
||||
for( int i = 0; i < trainClasses.rows; i++ )
|
||||
Mat trainClasses = Mat::zeros( trainedPoints.size(), classColors.size(), CV_32FC1 );
|
||||
for( int i = 0; i < trainClasses.rows; i++ )
|
||||
{
|
||||
for( int k = 0; k < trainClasses.cols; k++ )
|
||||
{
|
||||
if( k == trainedPointsMarkers[i] )
|
||||
trainClasses.at<float>(i,k) = 1;
|
||||
else
|
||||
trainClasses.at<float>(i,k) = 0;
|
||||
}
|
||||
trainClasses.at<float>(i, trainedPointsMarkers[i]) = 1.f;
|
||||
}
|
||||
|
||||
Mat weights( 1, trainedPoints.size(), CV_32FC1, Scalar::all(1) );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
|
||||
|
||||
// learn classifier
|
||||
CvANN_MLP ann( layer_sizes, CvANN_MLP::SIGMOID_SYM, 1, 1 );
|
||||
ann.train( trainSamples, trainClasses, weights );
|
||||
ann->train(tdata);
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
Mat outputs;
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
@ -451,49 +239,50 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
Mat outputs( 1, classColors.size(), CV_32FC1, testSample.data );
|
||||
ann.predict( testSample, outputs );
|
||||
ann->predict( testSample, outputs );
|
||||
Point maxLoc;
|
||||
minMaxLoc( outputs, 0, 0, 0, &maxLoc );
|
||||
circle( imgDst, Point(x,y), 2, classColors[maxLoc.x], 1 );
|
||||
imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _EM_
|
||||
void find_decision_boundary_EM()
|
||||
static void find_decision_boundary_EM()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
|
||||
vector<cv::EM> em_models(classColors.size());
|
||||
int i, j, nmodels = (int)classColors.size();
|
||||
vector<Ptr<EM> > em_models(nmodels);
|
||||
Mat modelSamples;
|
||||
|
||||
CV_Assert((int)trainClasses.total() == trainSamples.rows);
|
||||
CV_Assert((int)trainClasses.type() == CV_32SC1);
|
||||
|
||||
for(size_t modelIndex = 0; modelIndex < em_models.size(); modelIndex++)
|
||||
for( i = 0; i < nmodels; i++ )
|
||||
{
|
||||
const int componentCount = 3;
|
||||
em_models[modelIndex] = EM(componentCount, cv::EM::COV_MAT_DIAGONAL);
|
||||
|
||||
Mat modelSamples;
|
||||
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
|
||||
modelSamples.release();
|
||||
for( j = 0; j < samples.rows; j++ )
|
||||
{
|
||||
if(trainClasses.at<int>(sampleIndex) == (int)modelIndex)
|
||||
modelSamples.push_back(trainSamples.row(sampleIndex));
|
||||
if( trainedPointsMarkers[j] == i )
|
||||
modelSamples.push_back(samples.row(j));
|
||||
}
|
||||
|
||||
// learn models
|
||||
if(!modelSamples.empty())
|
||||
em_models[modelIndex].train(modelSamples);
|
||||
if( !modelSamples.empty() )
|
||||
{
|
||||
em_models[i] = EM::train(modelSamples, noArray(), noArray(), noArray(),
|
||||
EM::Params(componentCount, EM::COV_MAT_DIAGONAL));
|
||||
}
|
||||
}
|
||||
|
||||
// classify coordinate plane points using the bayes classifier, i.e.
|
||||
// y(x) = arg max_i=1_modelsCount likelihoods_i(x)
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
Mat logLikelihoods(1, nmodels, CV_64FC1, Scalar(-DBL_MAX));
|
||||
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
@ -501,17 +290,14 @@ void find_decision_boundary_EM()
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
Mat logLikelihoods(1, em_models.size(), CV_64FC1, Scalar(-DBL_MAX));
|
||||
for(size_t modelIndex = 0; modelIndex < em_models.size(); modelIndex++)
|
||||
for( i = 0; i < nmodels; i++ )
|
||||
{
|
||||
if(em_models[modelIndex].isTrained())
|
||||
logLikelihoods.at<double>(modelIndex) = em_models[modelIndex].predict(testSample)[0];
|
||||
if( !em_models[i].empty() )
|
||||
logLikelihoods.at<double>(i) = em_models[i]->predict2(testSample, noArray())[0];
|
||||
}
|
||||
Point maxLoc;
|
||||
minMaxLoc(logLikelihoods, 0, 0, 0, &maxLoc);
|
||||
|
||||
int response = maxLoc.x;
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -520,7 +306,7 @@ void find_decision_boundary_EM()
|
||||
int main()
|
||||
{
|
||||
cout << "Use:" << endl
|
||||
<< " right mouse button - to add new class;" << endl
|
||||
<< " key '0' .. '1' - switch to class #n" << endl
|
||||
<< " left mouse button - to add new point;" << endl
|
||||
<< " key 'r' - to run the ML model;" << endl
|
||||
<< " key 'i' - to init (clear) the data." << endl << endl;
|
||||
@ -532,6 +318,9 @@ int main()
|
||||
imshow( "points", img );
|
||||
setMouseCallback( "points", on_mouse );
|
||||
|
||||
classColors[0] = Vec3b(0, 255, 0);
|
||||
classColors[1] = Vec3b(0, 0, 255);
|
||||
|
||||
for(;;)
|
||||
{
|
||||
uchar key = (uchar)waitKey();
|
||||
@ -542,15 +331,28 @@ int main()
|
||||
{
|
||||
img = Scalar::all(0);
|
||||
|
||||
classColors.clear();
|
||||
trainedPoints.clear();
|
||||
trainedPointsMarkers.clear();
|
||||
classCounters.assign(MAX_CLASSES, 0);
|
||||
|
||||
imshow( winName, img );
|
||||
}
|
||||
|
||||
if( key == '0' || key == '1' )
|
||||
{
|
||||
currentClass = key - '0';
|
||||
}
|
||||
|
||||
if( key == 'r' ) // run
|
||||
{
|
||||
double minVal = 0;
|
||||
minMaxLoc(classCounters, &minVal, 0, 0, 0);
|
||||
if( minVal == 0 )
|
||||
{
|
||||
printf("each class should have at least 1 point\n");
|
||||
continue;
|
||||
}
|
||||
img.copyTo( imgDst );
|
||||
#if _NBC_
|
||||
find_decision_boundary_NBC();
|
||||
namedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
|
||||
@ -570,16 +372,16 @@ int main()
|
||||
|
||||
#if _SVM_
|
||||
//(1)-(2)separable and not sets
|
||||
CvSVMParams params;
|
||||
params.svm_type = CvSVM::C_SVC;
|
||||
params.kernel_type = CvSVM::POLY; //CvSVM::LINEAR;
|
||||
SVM::Params params;
|
||||
params.svmType = SVM::C_SVC;
|
||||
params.kernelType = SVM::POLY; //CvSVM::LINEAR;
|
||||
params.degree = 0.5;
|
||||
params.gamma = 1;
|
||||
params.coef0 = 1;
|
||||
params.C = 1;
|
||||
params.nu = 0.5;
|
||||
params.p = 0;
|
||||
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.01);
|
||||
params.termCrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01);
|
||||
|
||||
find_decision_boundary_SVM( params );
|
||||
namedWindow( "classificationSVM1", WINDOW_AUTOSIZE );
|
||||
@ -615,12 +417,6 @@ int main()
|
||||
imshow( "RF", imgDst);
|
||||
#endif
|
||||
|
||||
#if _ERT_
|
||||
find_decision_boundary_ERT();
|
||||
namedWindow( "ERT", WINDOW_AUTOSIZE );
|
||||
imshow( "ERT", imgDst);
|
||||
#endif
|
||||
|
||||
#if _ANN_
|
||||
Mat layer_sizes1( 1, 3, CV_32SC1 );
|
||||
layer_sizes1.at<int>(0) = 2;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user