Diff: opencv_3_1/opencv2/face.hpp

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+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
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+(including, but not limited to, procurement of substitute goods or services;
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+and on any theory of liability, whether in contract, strict liability,
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+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_FACE_HPP__
+#define __OPENCV_FACE_HPP__
+
+/**
+@defgroup face Face Recognition
+
+- @ref face_changelog
+- @ref tutorial_face_main
+
+*/
+
+#include "opencv2/core.hpp"
+#include "face/predict_collector.hpp"
+#include <map>
+
+namespace cv { namespace face {
+
+//! @addtogroup face
+//! @{
+
+/** @brief Abstract base class for all face recognition models
+
+All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which
+provides a unified access to all face recongition algorithms in OpenCV.
+
+### Description
+
+I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful
+interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all
+model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept,
+which is available since the 2.4 release. I suggest you take a look at its description.
+
+Algorithm provides the following features for all derived classes:
+
+-   So called “virtual constructor”. That is, each Algorithm derivative is registered at program
+    start and you can get the list of registered algorithms and create instance of a particular
+    algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is
+    good practice to add a unique prefix to your algorithms to distinguish them from other
+    algorithms.
+-   Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from
+    OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty,
+ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar
+    method where instead of integer id's you specify the parameter names as text Strings. See
+    Algorithm::set and Algorithm::get for details.
+-   Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store
+    all its parameters and then read them back. There is no need to re-implement it each time.
+
+Moreover every FaceRecognizer supports the:
+
+-   **Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face
+    database!).
+-   **Prediction** of a given sample image, that means a face. The image is given as a Mat.
+-   **Loading/Saving** the model state from/to a given XML or YAML.
+-   **Setting/Getting labels info**, that is stored as a string. String labels info is useful for
+    keeping names of the recognized people.
+
+@note When using the FaceRecognizer interface in combination with Python, please stick to Python 2.
+Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the
+Thresholds +++++++++++++++++++++++
+
+Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common
+scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is
+unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the
+prediction, but rest assured: It's supported. It just means there's no generic way in an abstract
+class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer
+algorithm. The appropriate place to set the thresholds is in the constructor of the specific
+FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the
+thresholds at runtime!
+
+Here is an example of setting a threshold for the Eigenfaces method, when creating the model:
+
+@code
+// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0
+int num_components = 10;
+double threshold = 10.0;
+// Then if you want to have a cv::FaceRecognizer with a confidence threshold,
+// create the concrete implementation with the appropiate parameters:
+Ptr<FaceRecognizer> model = createEigenFaceRecognizer(num_components, threshold);
+@endcode
+
+Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to
+Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would
+set/get the prediction for the Eigenface model, we've created above:
+
+@code
+// The following line reads the threshold from the Eigenfaces model:
+double current_threshold = model->getDouble("threshold");
+// And this line sets the threshold to 0.0:
+model->set("threshold", 0.0);
+@endcode
+
+If you've set the threshold to 0.0 as we did above, then:
+
+@code
+//
+Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+// Get a prediction from the model. Note: We've set a threshold of 0.0 above,
+// since the distance is almost always larger than 0.0, you'll get -1 as
+// label, which indicates, this face is unknown
+int predicted_label = model->predict(img);
+// ...
+@endcode
+
+is going to yield -1 as predicted label, which states this face is unknown.
+
+### Getting the name of a FaceRecognizer
+
+Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a
+FaceRecognizer:
+
+@code
+// Create a FaceRecognizer:
+Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
+// And here's how to get its name:
+String name = model->name();
+@endcode
+
+ */
+class CV_EXPORTS_W FaceRecognizer : public Algorithm
+{
+public:
+    /** @brief Trains a FaceRecognizer with given data and associated labels.
+
+    @param src The training images, that means the faces you want to learn. The data has to be
+    given as a vector\<Mat\>.
+    @param labels The labels corresponding to the images have to be given either as a vector\<int\>
+    or a
+
+    The following source code snippet shows you how to learn a Fisherfaces model on a given set of
+    images. The images are read with imread and pushed into a std::vector\<Mat\>. The labels of each
+    image are stored within a std::vector\<int\> (you could also use a Mat of type CV_32SC1). Think of
+    the label as the subject (the person) this image belongs to, so same subjects (persons) should have
+    the same label. For the available FaceRecognizer you don't have to pay any attention to the order of
+    the labels, just make sure same persons have the same label:
+
+    @code
+    // holds images and labels
+    vector<Mat> images;
+    vector<int> labels;
+    // images for first person
+    images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
+    images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
+    images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
+    // images for second person
+    images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
+    images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
+    images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
+    @endcode
+
+    Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create
+    a Fisherfaces model and decide to keep all of the possible Fisherfaces:
+
+    @code
+    // Create a new Fisherfaces model and retain all available Fisherfaces,
+    // this is the most common usage of this specific FaceRecognizer:
+    //
+    Ptr<FaceRecognizer> model =  createFisherFaceRecognizer();
+    @endcode
+
+    And finally train it on the given dataset (the face images and labels):
+
+    @code
+    // This is the common interface to train all of the available cv::FaceRecognizer
+    // implementations:
+    //
+    model->train(images, labels);
+    @endcode
+     */
+    CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0;
+
+    /** @brief Updates a FaceRecognizer with given data and associated labels.
+
+    @param src The training images, that means the faces you want to learn. The data has to be given
+    as a vector\<Mat\>.
+    @param labels The labels corresponding to the images have to be given either as a vector\<int\> or
+    a
+
+    This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The
+    Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated.
+    For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to
+    re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing
+    model and learns a new model, while update does not delete any model data.
+
+    @code
+    // Create a new LBPH model (it can be updated) and use the default parameters,
+    // this is the most common usage of this specific FaceRecognizer:
+    //
+    Ptr<FaceRecognizer> model =  createLBPHFaceRecognizer();
+    // This is the common interface to train all of the available cv::FaceRecognizer
+    // implementations:
+    //
+    model->train(images, labels);
+    // Some containers to hold new image:
+    vector<Mat> newImages;
+    vector<int> newLabels;
+    // You should add some images to the containers:
+    //
+    // ...
+    //
+    // Now updating the model is as easy as calling:
+    model->update(newImages,newLabels);
+    // This will preserve the old model data and extend the existing model
+    // with the new features extracted from newImages!
+    @endcode
+
+    Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support
+    updating, will throw an error similar to:
+
+    @code
+    OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305
+    terminate called after throwing an instance of 'cv::Exception'
+    @endcode
+
+    @note The FaceRecognizer does not store your training images, because this would be very
+    memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is
+    responsible for maintaining the dataset, he want to work with.
+     */
+    CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels);
+
+    /** @overload */
+    CV_WRAP_AS(predict_label) int predict(InputArray src) const;
+
+
+    /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image.
+
+    @param src Sample image to get a prediction from.
+    @param label The predicted label for the given image.
+    @param confidence Associated confidence (e.g. distance) for the predicted label.
+
+    The suffix const means that prediction does not affect the internal model state, so the method can
+    be safely called from within different threads.
+
+    The following example shows how to get a prediction from a trained model:
+
+    @code
+    using namespace cv;
+    // Do your initialization here (create the cv::FaceRecognizer model) ...
+    // ...
+    // Read in a sample image:
+    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+    // And get a prediction from the cv::FaceRecognizer:
+    int predicted = model->predict(img);
+    @endcode
+
+    Or to get a prediction and the associated confidence (e.g. distance):
+
+    @code
+    using namespace cv;
+    // Do your initialization here (create the cv::FaceRecognizer model) ...
+    // ...
+    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+    // Some variables for the predicted label and associated confidence (e.g. distance):
+    int predicted_label = -1;
+    double predicted_confidence = 0.0;
+    // Get the prediction and associated confidence from the model
+    model->predict(img, predicted_label, predicted_confidence);
+    @endcode
+     */
+    CV_WRAP void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const;
+
+
+    /** @brief - if implemented - send all result of prediction to collector that can be used for somehow custom result handling
+    @param src Sample image to get a prediction from.
+    @param collector User-defined collector object that accepts all results
+
+    To implement this method u just have to do same internal cycle as in predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) but
+    not try to get "best@ result, just resend it to caller side with given collector
+    */
+    CV_WRAP_AS(predict_collect) virtual void predict(InputArray src, Ptr<PredictCollector> collector) const = 0;
+
+    /** @brief Saves a FaceRecognizer and its model state.
+
+    Saves this model to a given filename, either as XML or YAML.
+    @param filename The filename to store this FaceRecognizer to (either XML/YAML).
+
+    Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model
+    state. FaceRecognizer::save(const String& filename) saves the state of a model to the given
+    filename.
+
+    The suffix const means that prediction does not affect the internal model state, so the method can
+    be safely called from within different threads.
+     */
+    CV_WRAP virtual void save(const String& filename) const;
+
+    /** @brief Loads a FaceRecognizer and its model state.
+
+    Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to
+    overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state.
+    FaceRecognizer::load(FileStorage& fs) in turn gets called by
+    FaceRecognizer::load(const String& filename), to ease saving a model.
+     */
+    CV_WRAP virtual void load(const String& filename);
+
+    /** @overload
+    Saves this model to a given FileStorage.
+    @param fs The FileStorage to store this FaceRecognizer to.
+    */
+    virtual void save(FileStorage& fs) const = 0;
+
+    /** @overload */
+    virtual void load(const FileStorage& fs) = 0;
+
+    /** @brief Sets string info for the specified model's label.
+
+    The string info is replaced by the provided value if it was set before for the specified label.
+     */
+    CV_WRAP virtual void setLabelInfo(int label, const String& strInfo);
+
+    /** @brief Gets string information by label.
+
+    If an unknown label id is provided or there is no label information associated with the specified
+    label id the method returns an empty string.
+     */
+    CV_WRAP virtual String getLabelInfo(int label) const;
+
+    /** @brief Gets vector of labels by string.
+
+    The function searches for the labels containing the specified sub-string in the associated string
+    info.
+     */
+    CV_WRAP virtual std::vector<int> getLabelsByString(const String& str) const;
+    /** @brief threshold parameter accessor - required for default BestMinDist collector */
+    virtual double getThreshold() const = 0;
+    /** @brief Sets threshold of model */
+    virtual void setThreshold(double val) = 0;
+protected:
+    // Stored pairs "label id - string info"
+    std::map<int, String> _labelsInfo;
+};
+
+//! @}
+
+}}
+
+#include "opencv2/face/facerec.hpp"
+
+#endif
+