Class cv::face::FaceRecognizer#
Abstract base class for all face recognition models. View details
#include <opencv2/face.hpp>Collaboration diagram for cv::face::FaceRecognizer:
Public Member Functions#
Public Member Functions inherited from cv::Algorithm
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Clears the algorithm state. |
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Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. |
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Reads algorithm parameters from a file storage. |
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Stores algorithm parameters in a file storage. |
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Static Public Member Functions#
Static Public Member Functions inherited from cv::Algorithm
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Loads algorithm from the file. |
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Loads algorithm from a String. |
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Reads algorithm from the file node. |
Additional Inherited Members#
Protected Member Functions inherited from cv::Algorithm
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Detailed Description#
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class FaceRecognizer : public cv::Algorithm#
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.
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:
// 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 appropriate parameters: Ptr<FaceRecognizer> model = EigenFaceRecognizer::create(num_components, threshold);
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:
// 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);
If you’ve set the threshold to 0.0 as we did above, then:
// Mat img = imread("person1/3.jpg", IMREAD_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); // ...
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:
// Create a FaceRecognizer: Ptr<FaceRecognizer> model = EigenFaceRecognizer::create(); // And here's how to get its name: String name = model->name();
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 +++++++++++++++++++++++
Subclassed by cv::face::BasicFaceRecognizer, cv::face::LBPHFaceRecognizer
Member Function Documentation#
empty()#
bool cv::face::FaceRecognizer::empty()
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
getLabelInfo()#
String cv::face::FaceRecognizer::getLabelInfo(int label)
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.
getLabelsByString()#
std::vector< int > cv::face::FaceRecognizer::getLabelsByString(const String & str)
Gets vector of labels by string.
The function searches for the labels containing the specified sub-string in the associated string info.
getThreshold()#
double cv::face::FaceRecognizer::getThreshold()
threshold parameter accessor - required for default BestMinDist collector
predict()#
int cv::face::FaceRecognizer::predict(InputArray src)
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
predict()#
void cv::face::FaceRecognizer::predict(
InputArray src,
int & label,
double & confidence )
Predicts a label and associated confidence (e.g. distance) for a given input image.
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:
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
// Read in a sample image:
Mat img = imread("person1/3.jpg", IMREAD_GRAYSCALE);
// And get a prediction from the cv::FaceRecognizer:
int predicted = model->predict(img);
Or to get a prediction and the associated confidence (e.g. distance):
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
Mat img = imread("person1/3.jpg", IMREAD_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);
Parameters
src— Sample image to get a prediction from.label— The predicted label for the given image.confidence— Associated confidence (e.g. distance) for the predicted label.
predict()#
void cv::face::FaceRecognizer::predict(
InputArray src,
Ptr< PredictCollector > collector )
if implemented - send all result of prediction to collector that can be used for somehow custom result handling
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
Parameters
src— Sample image to get a prediction from.collector— User-defined collector object that accepts all results
read()#
void cv::face::FaceRecognizer::read(const FileNode & fn)
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
read()#
void cv::face::FaceRecognizer::read(const String & filename)
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.
setLabelInfo()#
void cv::face::FaceRecognizer::setLabelInfo(
int label,
const String & strInfo )
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.
setThreshold()#
void cv::face::FaceRecognizer::setThreshold(double val)
Sets threshold of model.
train()#
void cv::face::FaceRecognizer::train(
InputArrayOfArrays src,
InputArray labels )
Trains a FaceRecognizer with given data and associated labels.
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
// holds images and labels
vector<Mat> images;
vector<int> labels;
// using Mat of type CV_32SC1
// Mat labels(number_of_samples, 1, CV_32SC1);
// images for first person
images.push_back(imread("person0/0.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/1.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/2.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
// images for second person
images.push_back(imread("person1/0.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/1.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/2.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
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:
// Create a new Fisherfaces model and retain all available Fisherfaces,
// this is the most common usage of this specific FaceRecognizer:
//
Ptr<FaceRecognizer> model = FisherFaceRecognizer::create();
And finally train it on the given dataset (the face images and labels):
// This is the common interface to train all of the available cv::FaceRecognizer
// implementations:
//
model->train(images, labels);
Parameters
update()#
void cv::face::FaceRecognizer::update(
InputArrayOfArrays src,
InputArray labels )
Updates a FaceRecognizer with given data and associated labels.
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.
// 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 = LBPHFaceRecognizer::create();
// 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!
Calling update on an Eigenfaces model (see EigenFaceRecognizer::create), which doesn’t support updating, will throw an error similar to:
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'
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.
Parameters
write()#
void cv::face::FaceRecognizer::write(const String & filename)
Saves a FaceRecognizer and its model state.
Saves this model to a given filename, either as XML or 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.
Parameters
filename— The filename to store this FaceRecognizer to (either XML/YAML).
write()#
void cv::face::FaceRecognizer::write(FileStorage & fs)
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. Saves this model to a given FileStorage.
Parameters
fs— The FileStorage to store this FaceRecognizer to.
Member Data Documentation#
_labelsInfo#
std::map< int, String > cv::face::FaceRecognizer::_labelsInfo
Source file#
The documentation for this class was generated from the following file:
opencv2/face.hpp