int trainASM(path inputDir, path outputDir, path modelDir, path partsPath, path detectorPath){
const Size dsize = Constants::instance()->getSize(); // User-defined image scale size
Model model(Constants::instance()->getNum1DLevels(), Constants::instance()->getNum2DLevels(), Constants::instance()->getProfLength1d(), Constants::instance()->getProfLength2d()); // Implicitly calls the default constructor
vector<Parts> allParts; // Vector to hold the Parts information
if(Constants::instance()->isVerbose())
cout << "Loading parts file... ";
if(Parts::loadPartsFile(allParts, partsPath) == 0){ // Load the parts file
cerr << "Load Parts File Error...";
return FAILURE;
}
model.setModelParts(allParts);
if(Constants::instance()->isVerbose())
cout << "done\n";
vector<Shape> allShapes;
if(Constants::instance()->isVerbose())
cout << "Loading directory of images... ";
if(Shape::loadDirectory(allShapes, inputDir) == 0){ // Load all points into allShapes vector, also computes the profiles for each image
cerr << "Load Image/Pts Directory Error...";
return FAILURE;
}
int nSamples = allShapes.size();
if(Constants::instance()->isVerbose())
cout << "done, Loaded " << nSamples << " images\n";
model.setNPoints(allShapes[0].getNPoints());
Shape::preScalePtsAndMat(allShapes, dsize, nSamples); // scale all images and points to preset size
if(Constants::instance()->isVerbose()){
cout << "All images and points pre-scaled or padded to " << dsize.width << "x" << dsize.height << endl;
cout << "Training all profiles... ";
}
Shape::trainAllProfiles(allShapes, model.getModelParts(), dsize); // Train all of the profiles for each image
if(Constants::instance()->isVerbose())
cout << "done\n";
vector< vector <Profile1D> > allMean1DProfiles; // Profile and Covariance data structures that will get stored to the Model
vector< vector <Profile2D> > allMean2DProfiles; //
vector< vector <Mat> > all1DCovarMatrices; //
vector< vector <Mat> > all2DCovarMatrices; //
if(Constants::instance()->isVerbose())
cout << "Training all Covariance Matrices and Mean Profiles... ";
Shape::trainAll1DCovars(allShapes, allMean1DProfiles, all1DCovarMatrices, nSamples); // Train all of the covariance matrices for each level of resolution
Shape::trainAll2DCovars(allShapes, allMean2DProfiles, all2DCovarMatrices, nSamples); // Also finds the mean profiles for each level
if(Constants::instance()->isVerbose())
cout << "done\n";
// Set the model objects
model.setMean1DProfs(allMean1DProfiles);
model.setMean2DProfs(allMean2DProfiles);
model.set1DCovars(all1DCovarMatrices);
model.set2DCovars(all2DCovarMatrices);
Shape avgShape;
if(Constants::instance()->isVerbose())
cout << "Aligning all Shapes... \n";
Shape::alignShapes(allShapes,avgShape); // Align all shapes
if(Constants::instance()->isVerbose())
cout << "done\n";
model.setAvgShape(avgShape);
Mat eigenValues;
Mat eigenVectors;
if(Constants::instance()->isVerbose())
cout << "Computing PCA... \n";
Shape::computePCA(allShapes, eigenValues, eigenVectors); // Compute the PCA
if(Constants::instance()->isVerbose())
cout << "done\n";
model.setEigenValues(eigenValues);
model.setEigenVectors(eigenVectors);
Detector *det;
if(Constants::instance()->getDetector_t() == Constants::VJ){
VJ_Detector VJDet;
VJDet.face_cascade = new CascadeClassifier();
det = &VJDet;
det->init(detectorPath);
}
#ifdef WITH_PITTPATT
else if(Constants::instance()->getDetector_t() == Constants::PP){
PP_Detector PPDet;
det = &PPDet;
det->init(detectorPath);
}
#endif
if(Constants::instance()->isVerbose())
cout << "Computing Face Detection... \n";
if(det->detectAllImages(allShapes) == 0){
cerr << "Face Detection Function Error... Program Exiting";
return FAILURE;
}
if(Constants::instance()->isVerbose())
cout << "done\n";
Shape avgDetectorShape;
float transX, transY;
int ref_width = Shape::alignShapesFromDetector(allShapes,avgDetectorShape, transX, transY);
// Assign the detector average shape to the model
model.setAvgDetectorShape(avgDetectorShape);
model.setRefWidth(ref_width);
model.setDetTranslate(transX, transY);
// Training Complete
// Write ASM to binary/text file
if(Constants::instance()->isVerbose())
cout << "Writing Model to file... \n\n";
#ifdef _MSC_VER
if(model.writeASMtoTxtFile(modelDir) == 0){
cerr << "Failed to write ASM to specified .txt file!!!" << endl;
}
#endif
if(model.writeASMtoBinFile(modelDir) == 0){
cerr << "Failed to write ASM to specified file!!!" << endl;
return FAILURE;
}
eigenValues.release();
eigenVectors.release();
if(Constants::instance()->isVerbose())
cout << "Finished!" << endl;
return SUCCESS;
}
