◆ __fromPtr__()
static new FisherFaceRecognizer OpenCVForUnity.FaceModule.FisherFaceRecognizer.__fromPtr__ |
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IntPtr |
addr | ) |
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static |
◆ create() [1/3]
static FisherFaceRecognizer OpenCVForUnity.FaceModule.FisherFaceRecognizer.create |
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int |
num_components, |
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double |
threshold |
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static |
- Parameters
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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.
◆ create() [2/3]
static FisherFaceRecognizer OpenCVForUnity.FaceModule.FisherFaceRecognizer.create |
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int |
num_components | ) |
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static |
- Parameters
-
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.
◆ create() [3/3]
- Parameters
-
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.
◆ Dispose()
override void OpenCVForUnity.FaceModule.FisherFaceRecognizer.Dispose |
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bool |
disposing | ) |
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protectedvirtual |
The documentation for this class was generated from the following file: