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OpenCV 2.4.3.1 | |||||||
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java.lang.Object org.opencv.core.Algorithm org.opencv.contrib.FaceRecognizer
public class FaceRecognizer
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.
class FaceRecognizer : public Algorithm
// C++ code:
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
virtual void train(InputArray src, InputArray labels) = 0;
// Updates a FaceRecognizer.
virtual void update(InputArrayOfArrays src, InputArray labels);
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
virtual void predict(InputArray src, int &label, double &confidence) const = 0;
// Serializes this object to a given filename.
virtual void save(const string& filename) const;
// Deserializes this object from a given filename.
virtual void load(const string& filename);
// Serializes this object to a given cv.FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv.FileStorage.
virtual void load(const FileStorage& fs) = 0;
};
Field Summary |
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Fields inherited from class org.opencv.core.Algorithm |
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nativeObj |
Constructor Summary | |
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protected |
FaceRecognizer(long addr)
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Method Summary | |
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protected void |
finalize()
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void |
load(java.lang.String filename)
Loads a "FaceRecognizer" and its model state. |
void |
predict(Mat src,
int[] label,
double[] confidence)
Predicts a label and associated confidence (e.g. distance) for a given input image. |
void |
save(java.lang.String filename)
Saves a "FaceRecognizer" and its model state. |
void |
train(java.util.List<Mat> src,
Mat labels)
Trains a FaceRecognizer with given data and associated labels. |
void |
update(java.util.List<Mat> src,
Mat labels)
Updates a FaceRecognizer with given data and associated labels. |
Methods inherited from class org.opencv.core.Algorithm |
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getBool, getDouble, getInt, getMat, getMatVector, getString, paramHelp, paramType, setBool, setDouble, setInt, setMat, setMatVector, setString |
Methods inherited from class java.lang.Object |
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clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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protected FaceRecognizer(long addr)
Method Detail |
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protected void finalize() throws java.lang.Throwable
finalize
in class Algorithm
java.lang.Throwable
public void load(java.lang.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.
filename
- a filenamepublic void predict(Mat 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;
// C++ code:
// 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);
Or to get a prediction and the associated confidence (e.g. distance):
using namespace cv;
// C++ code:
// 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);
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.public void save(java.lang.String filename)
Saves a "FaceRecognizer" and its model state.
Saves this model to a given filename, either as XML or YAML.
Saves this model to a given "FileStorage".
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.
filename
- The filename to store this "FaceRecognizer" to (either
XML/YAML).public void train(java.util.List<Mat> src, Mat 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
. The labels of each image are stored within a
std.vector
(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: // holds
images and labels
// C++ code:
vector
vector
// 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);
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,
// C++ code:
// this is the most common usage of this specific FaceRecognizer:
//
Ptr
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
// C++ code:
// implementations:
//
model->train(images, labels);
src
- The training images, that means the faces you want to learn. The
data has to be given as a vector
.labels
- The labels corresponding to the images have to be given either
as a vector
or apublic void update(java.util.List<Mat> src, Mat 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,
// C++ code:
// this is the most common usage of this specific FaceRecognizer:
//
Ptr
// 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
vector
// 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 "createEigenFaceRecognizer"), 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
// C++ code:
terminate called after throwing an instance of 'cv.Exception'
Please 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.
src
- The training images, that means the faces you want to learn. The
data has to be given as a vector
.labels
- The labels corresponding to the images have to be given either
as a vector
or a
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Official OpenCV 2.4 Documentation | |||||||
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