◆ __fromPtr__()
static new EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.__fromPtr__ 
( 
IntPtr 
addr  ) 


static 
◆ create() [1/3]
static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create 
( 
int 
num_components, 


double 
threshold 

) 
 

static 
 Parameters

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.** (capslock, 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. 
◆ create() [2/3]
static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create 
( 
int 
num_components  ) 


static 
 Parameters

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.** (capslock, 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. 
◆ create() [3/3]
 Parameters

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.** (capslock, 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. 
◆ Dispose()
override void OpenCVForUnity.FaceModule.EigenFaceRecognizer.Dispose 
( 
bool 
disposing  ) 


protectedvirtual 
The documentation for this class was generated from the following file: