@Namespace(value="cv::face") @Properties(inherit=opencv_face.class) public class FisherFaceRecognizer extends BasicFaceRecognizer
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter
Constructor and Description |
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FisherFaceRecognizer(Pointer p)
Pointer cast constructor.
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Modifier and Type | Method and Description |
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static FisherFaceRecognizer |
create() |
static FisherFaceRecognizer |
create(int num_components,
double threshold) |
empty, getEigenValues, getEigenVectors, getLabels, getMean, getNumComponents, getProjections, getThreshold, read, setNumComponents, setThreshold, write
getLabelInfo, getLabelsByString, getLabelsByString, predict_collect, predict_collect, predict_collect, predict_label, predict_label, predict_label, predict, predict, predict, predict, predict, predict, predict, predict, predict, read, read, setLabelInfo, setLabelInfo, train, train, train, train, train, train, train, train, train, update, update, update, update, update, update, update, update, update, write, write
clear, getDefaultName, position, save, save, write, write
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero
public FisherFaceRecognizer(Pointer p)
Pointer.Pointer(Pointer)
.@opencv_core.Ptr public static FisherFaceRecognizer create(int num_components, double threshold)
num_components
- The number of components (read: Fisherfaces) kept for this Linear
Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
means the number of your classes c (read: subjects, persons you want to recognize). If you leave
this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
correct number (c-1) automatically.threshold
- The threshold applied in the prediction. If the distance to the nearest neighbor
is larger than the threshold, this method returns -1.
### Notes:
- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces. - **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images. - This model does not support updating.
### Model internal data:
- num_components see FisherFaceRecognizer::create. - threshold see FisherFaceRecognizer::create. - eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending). - eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue). - mean The sample mean calculated from the training data. - projections The projections of the training data. - labels The labels corresponding to the projections.
@opencv_core.Ptr public static FisherFaceRecognizer create()
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