Models API¶
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class
face_engine.models.
Model
¶ FaceEngine model base class. Used to register all inheriting and imported subclasses (subclass registration PEP 487).
Note
- implementing model classes must have
name
class descriptor
- implementing model classes must have
Detector¶
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class
face_engine.models.
Detector
¶ Human face detector model base class.
Note
- bounding box format is (left, upper, right, lower)
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detect
(image)¶ Detect all faces in the image.
Parameters: image (numpy.ndarray) – RGB Image with shape (rows, cols, 3) Returns: bounding boxes with shape (n_faces, 4), detector model dependent extra information. Return type: (numpy.ndarray, dict) Raises: FaceNotFoundError
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name
= 'abstract_detector'¶
Embedder¶
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class
face_engine.models.
Embedder
¶ This object calculates embedding vectors from the face containing image.
Note
- implementing model classes should have
dim
class descriptor
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compute_embeddings
(image, bounding_boxes, **kwargs)¶ Compute image embeddings for given bounding boxes
Parameters: - image (numpy.ndarray) – RGB image with shape (rows, cols, 3)
- bounding_boxes (numpy.ndarray) – bounding boxes with shape (n_faces, 4)
- kwargs – model dependent
Returns: array of embedding vectors with shape (n_faces, embedding_dim)
Return type: numpy.ndarray
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name
= 'abstract_embedder'¶
- implementing model classes should have
Estimator¶
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class
face_engine.models.
Estimator
¶ Estimator model base class. Used to make predictions for face embedding vectors.
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fit
(embeddings, class_names, **kwargs)¶ Fit (train) estimator model with given embeddings for given class names.
Note that the passed number of samples for
embbedings
andclass_names
has to be equal.Parameters: - embeddings (numpy.ndarray) – face embedding vectors with shape (n_samples, embedding_dim)
- class_names (list) – sequence of class names
- kwargs – model and data dependent
Returns: self
Raises: TrainError
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predict
(embeddings)¶ Make predictions for given embeddings.
Note
Model previously has to be fitted.
Parameters: embeddings (numpy.array) – array of embedding vectors with shape (n_faces, embedding_dim) Returns: prediction scores and class names Return type: (list, list) Raises: TrainError
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name
= 'abstract_estimator'¶
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save
(dirname)¶ Persist estimators’s model state to given directory.
- File naming format convention:
name = '%s.estimator.%s' % (self.name, ext)
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load
(dirname)¶ Restore estimator’s model state from given directory.
- File naming format convention:
name = '%s.estimator.%s' % (self.name, ext)
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