@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, writegetLabelInfo, 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, writeclear, getDefaultName, position, save, save, write, writeaddress, 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, zeropublic 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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