148 lines
6.0 KiB
Objective-C
148 lines
6.0 KiB
Objective-C
//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/face.hpp"
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#import "opencv2/face/facerec.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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#import "BasicFaceRecognizer.h"
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NS_ASSUME_NONNULL_BEGIN
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// C++: class FisherFaceRecognizer
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/**
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* The FisherFaceRecognizer module
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*
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* Member of `Face`
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*/
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CV_EXPORTS @interface FisherFaceRecognizer : BasicFaceRecognizer
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::face::FisherFaceRecognizer> nativePtrFisherFaceRecognizer;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::face::FisherFaceRecognizer>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::face::FisherFaceRecognizer>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// static Ptr_FisherFaceRecognizer cv::face::FisherFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
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//
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/**
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* @param num_components The number of components (read: Fisherfaces) kept for this Linear
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* Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
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* means the number of your classes c (read: subjects, persons you want to recognize). If you leave
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* this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
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* correct number (c-1) automatically.
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* @param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* - Training and prediction must be done on grayscale images, use cvtColor to convert between the
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* color spaces.
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* - **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
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* SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
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* input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
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* the images.
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* - This model does not support updating.
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*
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* ### Model internal data:
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*
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* - num_components see FisherFaceRecognizer::create.
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* - threshold see FisherFaceRecognizer::create.
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* - eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
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* - eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their
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* eigenvalue).
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* - mean The sample mean calculated from the training data.
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* - projections The projections of the training data.
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* - labels The labels corresponding to the projections.
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*/
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+ (FisherFaceRecognizer*)create:(int)num_components threshold:(double)threshold NS_SWIFT_NAME(create(num_components:threshold:));
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/**
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* @param num_components The number of components (read: Fisherfaces) kept for this Linear
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* Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
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* means the number of your classes c (read: subjects, persons you want to recognize). If you leave
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* this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
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* correct number (c-1) automatically.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* - Training and prediction must be done on grayscale images, use cvtColor to convert between the
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* color spaces.
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* - **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
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* SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
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* input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
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* the images.
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* - This model does not support updating.
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*
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* ### Model internal data:
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*
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* - num_components see FisherFaceRecognizer::create.
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* - threshold see FisherFaceRecognizer::create.
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* - eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
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* - eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their
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* eigenvalue).
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* - mean The sample mean calculated from the training data.
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* - projections The projections of the training data.
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* - labels The labels corresponding to the projections.
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*/
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+ (FisherFaceRecognizer*)create:(int)num_components NS_SWIFT_NAME(create(num_components:));
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/**
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* Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
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* means the number of your classes c (read: subjects, persons you want to recognize). If you leave
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* this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
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* correct number (c-1) automatically.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* - Training and prediction must be done on grayscale images, use cvtColor to convert between the
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* color spaces.
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* - **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
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* SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
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* input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
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* the images.
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* - This model does not support updating.
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*
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* ### Model internal data:
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*
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* - num_components see FisherFaceRecognizer::create.
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* - threshold see FisherFaceRecognizer::create.
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* - eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
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* - eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their
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* eigenvalue).
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* - mean The sample mean calculated from the training data.
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* - projections The projections of the training data.
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* - labels The labels corresponding to the projections.
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*/
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+ (FisherFaceRecognizer*)create NS_SWIFT_NAME(create());
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@end
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NS_ASSUME_NONNULL_END
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