192 lines
8.1 KiB
C++
192 lines
8.1 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (c) 2011,2012. Philipp Wagner <bytefish[at]gmx[dot]de>.
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// Third party copyrights are property of their respective owners.
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#ifndef __OPENCV_FACEREC_HPP__
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#define __OPENCV_FACEREC_HPP__
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#include "opencv2/face.hpp"
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#include "opencv2/core.hpp"
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namespace cv { namespace face {
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//! @addtogroup face
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//! @{
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// base for two classes
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class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer
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{
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public:
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/** @see setNumComponents */
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CV_WRAP int getNumComponents() const;
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/** @copybrief getNumComponents @see getNumComponents */
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CV_WRAP void setNumComponents(int val);
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/** @see setThreshold */
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CV_WRAP double getThreshold() const CV_OVERRIDE;
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/** @copybrief getThreshold @see getThreshold */
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CV_WRAP void setThreshold(double val) CV_OVERRIDE;
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CV_WRAP std::vector<cv::Mat> getProjections() const;
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CV_WRAP cv::Mat getLabels() const;
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CV_WRAP cv::Mat getEigenValues() const;
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CV_WRAP cv::Mat getEigenVectors() const;
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CV_WRAP cv::Mat getMean() const;
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virtual void read(const FileNode& fn) CV_OVERRIDE;
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virtual void write(FileStorage& fs) const CV_OVERRIDE;
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virtual bool empty() const CV_OVERRIDE;
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using FaceRecognizer::read;
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using FaceRecognizer::write;
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protected:
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int _num_components;
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double _threshold;
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std::vector<Mat> _projections;
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Mat _labels;
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Mat _eigenvectors;
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Mat _eigenvalues;
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Mat _mean;
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};
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class CV_EXPORTS_W EigenFaceRecognizer : public BasicFaceRecognizer
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{
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public:
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/**
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@param num_components The number of components (read: Eigenfaces) kept for this Principal
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Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
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kept for good reconstruction capabilities. It is based on your input data, so experiment with the
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number. Keeping 80 components should almost always be sufficient.
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@param threshold The threshold applied in the prediction.
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### Notes:
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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 EIGENFACES 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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### Model internal data:
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- num_components see EigenFaceRecognizer::create.
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- threshold see EigenFaceRecognizer::create.
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- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
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- eigenvectors The eigenvectors for this Principal Component 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 threshold applied in the prediction. If the distance to the nearest neighbor is
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larger than the threshold, this method returns -1.
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*/
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CV_WRAP static Ptr<EigenFaceRecognizer> create(int num_components = 0, double threshold = DBL_MAX);
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};
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class CV_EXPORTS_W FisherFaceRecognizer : public BasicFaceRecognizer
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{
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public:
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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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### Notes:
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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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### Model internal data:
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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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CV_WRAP static Ptr<FisherFaceRecognizer> create(int num_components = 0, double threshold = DBL_MAX);
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};
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class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer
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{
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public:
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/** @see setGridX */
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CV_WRAP virtual int getGridX() const = 0;
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/** @copybrief getGridX @see getGridX */
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CV_WRAP virtual void setGridX(int val) = 0;
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/** @see setGridY */
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CV_WRAP virtual int getGridY() const = 0;
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/** @copybrief getGridY @see getGridY */
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CV_WRAP virtual void setGridY(int val) = 0;
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/** @see setRadius */
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CV_WRAP virtual int getRadius() const = 0;
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/** @copybrief getRadius @see getRadius */
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CV_WRAP virtual void setRadius(int val) = 0;
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/** @see setNeighbors */
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CV_WRAP virtual int getNeighbors() const = 0;
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/** @copybrief getNeighbors @see getNeighbors */
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CV_WRAP virtual void setNeighbors(int val) = 0;
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/** @see setThreshold */
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CV_WRAP virtual double getThreshold() const CV_OVERRIDE = 0;
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/** @copybrief getThreshold @see getThreshold */
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CV_WRAP virtual void setThreshold(double val) CV_OVERRIDE = 0;
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CV_WRAP virtual std::vector<cv::Mat> getHistograms() const = 0;
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CV_WRAP virtual cv::Mat getLabels() const = 0;
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/**
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@param radius The radius used for building the Circular Local Binary Pattern. The greater the
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radius, the smoother the image but more spatial information you can get.
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@param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
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appropriate value is to use `8` sample points. Keep in mind: the more sample points you include,
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the higher the computational cost.
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@param grid_x The number of cells in the horizontal direction, 8 is a common value used in
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publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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feature vector.
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@param grid_y The number of cells in the vertical direction, 8 is a common value used in
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publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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feature vector.
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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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### Notes:
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- The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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grayscale images, use cvtColor to convert between the color spaces.
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- This model supports updating.
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### Model internal data:
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- radius see LBPHFaceRecognizer::create.
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- neighbors see LBPHFaceRecognizer::create.
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- grid_x see LLBPHFaceRecognizer::create.
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- grid_y see LBPHFaceRecognizer::create.
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- threshold see LBPHFaceRecognizer::create.
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- histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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none was given).
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- labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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*/
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CV_WRAP static Ptr<LBPHFaceRecognizer> create(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
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};
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//! @}
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}} //namespace cv::face
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#endif //__OPENCV_FACEREC_HPP__
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