◆ __fromPtr__()
| static new EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.__fromPtr__ |
( |
IntPtr |
addr | ) |
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static |
◆ create() [1/3]
| static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create |
( |
int |
num_components, |
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double |
threshold |
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) |
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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.** (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. |
◆ 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.** (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. |
◆ 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.** (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. |
◆ Dispose()
| override void OpenCVForUnity.FaceModule.EigenFaceRecognizer.Dispose |
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bool |
disposing | ) |
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protectedvirtual |
The documentation for this class was generated from the following file:
Static Public Member Functions inherited from OpenCVForUnity.FaceModule.BasicFaceRecognizer