@Namespace(value="cv::face") @Properties(inherit=opencv_face.class) public class EigenFaceRecognizer extends BasicFaceRecognizer
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter| Constructor and Description |
|---|
EigenFaceRecognizer(Pointer p)
Pointer cast constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static EigenFaceRecognizer |
create() |
static EigenFaceRecognizer |
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 EigenFaceRecognizer(Pointer p)
Pointer.Pointer(Pointer).@opencv_core.Ptr public static EigenFaceRecognizer create(int num_components, double threshold)
num_components - The number of components (read: Eigenfaces) kept for this Principal
Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
kept for good reconstruction capabilities. It is based on your input data, so experiment with the
number. Keeping 80 components should almost always be sufficient.threshold - The threshold applied in the prediction.
### Notes:
- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces. - **THE EIGENFACES 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 EigenFaceRecognizer::create. - threshold see EigenFaceRecognizer::create. - eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending). - eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue). - mean The sample mean calculated from the training data. - projections The projections of the training data. - labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
@opencv_core.Ptr public static EigenFaceRecognizer create()
Copyright © 2020. All rights reserved.