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formatted RSKNN and RSClassifier, added multithreading to KNNAnnotator and RFAnnotator #13

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formatted RSKNN and RSClassifier, added multithreading to KNNAnnotator and RFAnnotator #13

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99c958c
formatted RSKNN and RSClassifier, added multithreading to KNNAnnotato…
Jun 3, 2022
1b41b90
added missing curly brace
Jun 3, 2022
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138 changes: 70 additions & 68 deletions src/KnnAnnotator.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -26,100 +26,102 @@ class KnnAnnotator : public DrawingAnnotator
{
private:

cv::Mat color;
cv::Mat color;

//set_mode should be GT(groundTruth) or CL (classify)....
std::string mode;
//set_mode should be GT(groundTruth) or CL (classify)....
std::string mode;

//the value of k-neighbors in the knn-classifier
int default_k;
//the value of k-neighbors in the knn-classifier
int default_k;

//feature_use should be VFH, CVFH, CNN, VGG16 .....
std::string feature_use;
//feature_use should be VFH, CVFH, CNN, VGG16 .....
std::string feature_use;

//the name of train matrix and its labels in path rs_resources/objects_dataset/extractedFeat/
std::string trainKNN_matrix;
std::string trainKNNLabel_matrix;
//the name of train matrix and its labels in path rs_resources/objects_dataset/extractedFeat/
std::string trainKNN_matrix;
std::string trainKNNLabel_matrix;

//vector to hold split trained_model_name
std::vector<std::string> split_model;
//vector to hold split trained_model_name
std::vector<std::string> split_model;

//the name of actual_class_label map file in path rs_resources/objects_dataset/extractedFeat/
std::string classNrMapping;
//the name of actual_class_label map file in path rs_resources/objects_dataset/extractedFeat/
std::string classNrMapping;

//vector to hold classes name
std::vector<std::string> model_labels;
//vector to hold classes name
std::vector<std::string> model_labels;

RSKNN *knnObject;
RSKNN *knnObject;

public:

KnnAnnotator(): DrawingAnnotator(__func__)
{}
KnnAnnotator(): DrawingAnnotator(__func__)
{}



TyErrorId initialize(AnnotatorContext &ctx)
{
ctx.extractValue("set_mode", mode);
outInfo("set_mode:" << mode << std::endl);
TyErrorId initialize(AnnotatorContext &ctx)
{
ctx.extractValue("set_mode", mode);
outInfo("set_mode:" << mode << std::endl);

ctx.extractValue("default_k", default_k);
outInfo("Value of k-neighbors: " << default_k);
ctx.extractValue("default_k", default_k);
outInfo("Value of k-neighbors: " << default_k);

ctx.extractValue("training_data", trainKNN_matrix);
outInfo("training_data:" << trainKNN_matrix);
ctx.extractValue("training_data", trainKNN_matrix);
outInfo("training_data:" << trainKNN_matrix);

ctx.extractValue("class_label_mapping", classNrMapping);
outInfo("class_label_mapping:" << classNrMapping );
ctx.extractValue("class_label_mapping", classNrMapping);
outInfo("class_label_mapping:" << classNrMapping );



ctx.extractValue("feature_descriptor_type", feature_use);
outInfo("feature descriptor set: "<<feature_use);
ctx.extractValue("feature_descriptor_type", feature_use);
outInfo("feature descriptor set: "<<feature_use);

knnObject = new RSKNN(default_k);
knnObject->loadModelFile(trainKNN_matrix);
knnObject->setLabels(classNrMapping, model_labels);
knnObject = new RSKNN(default_k);
knnObject->loadModelFile(trainKNN_matrix);
knnObject->setLabels(classNrMapping, model_labels);

return UIMA_ERR_NONE;
}

TyErrorId destroy()
{
outInfo("destroy");
return UIMA_ERR_NONE;
}

TyErrorId processWithLock(CAS &tcas, ResultSpecification const &res_spec)
{
outInfo("RSKNNAnnotator is running:");
rs::SceneCas cas(tcas);
rs::Scene scene = cas.getScene();
cas.get(VIEW_COLOR_IMAGE_HD, color);
std::vector<rs::ObjectHypothesis> clusters;
scene.identifiables.filter(clusters);
outInfo("Feature to use: "<<feature_use);
if(feature_use == "VFH" || feature_use == "CVFH") {
outInfo("Calculation starts with : " << mode << "::" << feature_use);
knnObject->processPCLFeatureKNN(mode, feature_use, clusters, color, model_labels, tcas);
}
else if(feature_use == "BVLC_REF" || feature_use == "VGG16") {
outInfo("Calculation starts with : " << mode << "::" << feature_use);
knnObject->processCaffeFeatureKNN(mode, feature_use, clusters, color, model_labels, tcas);
return UIMA_ERR_NONE;
}
else {
outError("Please sellect the correct value of parameter(feature_use): VFH, CVFH, BVLC_REF, VGG16");

TyErrorId destroy()
{
outInfo("destroy");
return UIMA_ERR_NONE;
}

outInfo("calculation is done with RSKNN" << std::endl);
return UIMA_ERR_NONE;
}
TyErrorId processWithLock(CAS &tcas, ResultSpecification const &res_spec)
{
rs::StopWatch clock;
outInfo("RSKNNAnnotator is running:");
rs::SceneCas cas(tcas);
rs::Scene scene = cas.getScene();
cas.get(VIEW_COLOR_IMAGE_HD, color);
std::vector<rs::ObjectHypothesis> clusters;
scene.identifiables.filter(clusters);
outInfo("Feature to use: "<<feature_use);
if(feature_use == "VFH" || feature_use == "CVFH") {
outInfo("Calculation starts with : " << mode << "::" << feature_use);
knnObject->processPCLFeatureKNN(mode, feature_use, clusters, color, model_labels, tcas);
}
else if(feature_use == "BVLC_REF" || feature_use == "VGG16") {
outInfo("Calculation starts with : " << mode << "::" << feature_use);
knnObject->processCaffeFeatureKNN(mode, feature_use, clusters, color, model_labels, tcas);
}
else {
outError("Please sellect the correct value of parameter(feature_use): VFH, CVFH, BVLC_REF, VGG16");
}

outInfo("calculation is done with RSKNN" << std::endl);
outInfo("took: " << clock.getTime() << " ms. with the Cluster size of : " << clusters.size());
return UIMA_ERR_NONE;
}

void drawImageWithLock(cv::Mat &disp)
{
disp = color.clone();
}
void drawImageWithLock(cv::Mat &disp)
{
disp = color.clone();
}

};

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139 changes: 70 additions & 69 deletions src/RfAnnotator.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -25,102 +25,103 @@ class RfAnnotator : public DrawingAnnotator
{
private:

cv::Mat color;
cv::Mat color;

//set_mode should be GT(groundTruth) or CF (classify) ...........
std::string set_mode;
//set_mode should be GT(groundTruth) or CF (classify) ...........
std::string set_mode;

//dataset_use should be IAI (kitchen data from IAI) or WU (data from Washington University) or both...
std::string dataset_use;
//dataset_use should be IAI (kitchen data from IAI) or WU (data from Washington University) or both...
std::string dataset_use;

//feature_use should be VFH, CVFH, CNN, VGG16 .....
std::string feature_use;
//feature_use should be VFH, CVFH, CNN, VGG16 .....
std::string feature_use;

//the name of trained model ifle in folder rs_addons/trainedData/
std::string trained_model_name;
//the name of trained model ifle in folder rs_addons/trainedData/
std::string trained_model_name;

//vector to hold split trained_model_name
std::vector<std::string> split_model;
//vector to hold split trained_model_name
std::vector<std::string> split_model;

//the name of actual_class_label map file in path rs_resources/objects_dataset/extractedFeat/
std::string actual_class_label;
//the name of actual_class_label map file in path rs_resources/objects_dataset/extractedFeat/
std::string actual_class_label;

//vector to hold classes name
std::vector<std::string> model_labels;
//vector to hold classes name
std::vector<std::string> model_labels;

public:

RfAnnotator(): DrawingAnnotator(__func__)
{}
RfAnnotator(): DrawingAnnotator(__func__)
{}

RSClassifier* rfObject= new RSRF;
RSClassifier* rfObject= new RSRF;

TyErrorId initialize(AnnotatorContext &ctx)
{
outInfo("initialize");
ctx.extractValue("set_mode", set_mode);
ctx.extractValue("trained_model_name", trained_model_name);
ctx.extractValue("actual_class_label", actual_class_label);

outInfo("Name of the loaded files for RF are:"<<std::endl);

outInfo("set_mode:"<<set_mode<<std::endl);
outInfo("trained_model_name:"<<trained_model_name<<std::endl);
outInfo("actual_class_label:"<<actual_class_label<<std::endl);

rfObject->setLabels(actual_class_label, model_labels);
TyErrorId initialize(AnnotatorContext &ctx)
{
outInfo("initialize");
ctx.extractValue("set_mode", set_mode);
ctx.extractValue("trained_model_name", trained_model_name);
ctx.extractValue("actual_class_label", actual_class_label);

boost::split(split_model, trained_model_name, boost::is_any_of("_"));
outInfo("Name of the loaded files for RF are:"<<std::endl);

dataset_use= split_model[0];
outInfo("dataset_use:"<<dataset_use<<std::endl);
outInfo("set_mode:"<<set_mode<<std::endl);
outInfo("trained_model_name:"<<trained_model_name<<std::endl);
outInfo("actual_class_label:"<<actual_class_label<<std::endl);

feature_use= split_model[1];
outInfo("feature_use:"<<feature_use<<std::endl);
rfObject->setLabels(actual_class_label, model_labels);

return UIMA_ERR_NONE;
}
boost::split(split_model, trained_model_name, boost::is_any_of("_"));

TyErrorId destroy()
{
outInfo("destroy");
return UIMA_ERR_NONE;
}
dataset_use= split_model[0];
outInfo("dataset_use:"<<dataset_use<<std::endl);

TyErrorId processWithLock(CAS &tcas, ResultSpecification const &res_spec)
{
outInfo("RSRFAnnotator is running:");
rs::SceneCas cas(tcas);
rs::Scene scene = cas.getScene();
cas.get(VIEW_COLOR_IMAGE_HD, color);
std::vector<rs::ObjectHypothesis> clusters;
scene.identifiables.filter(clusters);
feature_use= split_model[1];
outInfo("feature_use:"<<feature_use<<std::endl);

return UIMA_ERR_NONE;
}

if(feature_use == "VFH" || feature_use == "CVFH")
TyErrorId destroy()
{
outInfo("Calculation starts with : " << set_mode << "::" << dataset_use << "::" << feature_use);
rfObject->processPCLFeature(trained_model_name,set_mode,feature_use,clusters, rfObject, color,model_labels, tcas);
outInfo("destroy");
return UIMA_ERR_NONE;
}
else if(feature_use == "CNN" || feature_use == "VGG16")

TyErrorId processWithLock(CAS &tcas, ResultSpecification const &res_spec)
{
outInfo("Calculation starts with : " << set_mode << "::" << dataset_use << "::" << feature_use);
rfObject->processCaffeFeature(trained_model_name,set_mode,feature_use,clusters, rfObject, color, model_labels, tcas);
rs::StopWatch clock;
outInfo("RSRFAnnotator is running:");
rs::SceneCas cas(tcas);
rs::Scene scene = cas.getScene();
cas.get(VIEW_COLOR_IMAGE_HD, color);
std::vector<rs::ObjectHypothesis> clusters;
scene.identifiables.filter(clusters);

if(feature_use == "VFH" || feature_use == "CVFH")
{
outInfo("Calculation starts with : " << set_mode << "::" << dataset_use << "::" << feature_use);
rfObject->processPCLFeature(trained_model_name,set_mode,feature_use,clusters, rfObject, color,model_labels, tcas);
}
else if(feature_use == "CNN" || feature_use == "VGG16")
{
outInfo("Calculation starts with : " << set_mode << "::" << dataset_use << "::" << feature_use);
rfObject->processCaffeFeature(trained_model_name,set_mode,feature_use,clusters, rfObject, color, model_labels, tcas);
}
else
{
outError("Please sellect the correct value of parameter(feature_use): VFH, CVFH, CNN, VGG16");
}

outInfo("calculation is done with RSRF"<<std::endl);
outInfo("took: " << clock.getTime() << " ms. with the Cluster size of : " << clusters.size());
return UIMA_ERR_NONE;
}
else

void drawImageWithLock(cv::Mat &disp)
{
outError("Please sellect the correct value of parameter(feature_use): VFH, CVFH, CNN, VGG16");
disp = color.clone();
}

outInfo("calculation is done with RSRF"<<std::endl);
return UIMA_ERR_NONE;
}

void drawImageWithLock(cv::Mat &disp)
{
disp = color.clone();
}

};

// This macro exports an entry point that is used to create the annotator.
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