// Generate an approximately reconstructed face by back-projecting the eigenvectors & eigenvalues of the given (preprocessed) face.
public static Mat ReconstructFace(BasicFaceRecognizer model, Mat preprocessedFace)
{
// Since we can only reconstruct the face for some types of FaceRecognizer models (ie: Eigenfaces or Fisherfaces),
// we should surround the OpenCV calls by a try/catch block so we don't crash for other models.
try
{
// Get some required data from the FaceRecognizer model.
Mat eigenvectors = model.getEigenVectors();
Mat averageFaceRow = model.getMean();
int faceHeight = preprocessedFace.rows();
// Project the input image onto the PCA subspace.
Mat projection = subspaceProject(eigenvectors, averageFaceRow, preprocessedFace.reshape(1, 1));
//printMatInfo(projection, "projection");
// Generate the reconstructed face back from the PCA subspace.
Mat reconstructionRow = subspaceReconstruct(eigenvectors, averageFaceRow, projection);
//printMatInfo(reconstructionRow, "reconstructionRow");
// Convert the float row matrix to a regular 8-bit image. Note that we
// shouldn't use "getImageFrom1DFloatMat()" because we don't want to normalize
// the data since it is already at the perfect scale.
// Make it a rectangular shaped image instead of a single row.
Mat reconstructionMat = reconstructionRow.reshape(1, faceHeight);
// Convert the floating-point pixels to regular 8-bit uchar pixels.
Mat reconstructedFace = new Mat(reconstructionMat.size(), CvType.CV_8UC1);
reconstructionMat.convertTo(reconstructedFace, CvType.CV_8UC1, 1, 0);
//printMatInfo(reconstructedFace, "reconstructedFace");
return(reconstructedFace);
}
catch (CvException e)
{
Debug.Log("WARNING: Missing FaceRecognizer properties." + e);
return(new Mat());
}
}
