import scipy.spatial.distance
from sklearn.utils.validation import check_array
import numpy as np
from .abstract_classes import BioAlgorithm
from scipy.spatial.distance import cdist
import os
from bob.pipelines import DelayedSample, Sample, SampleSet
import functools
class Distance(BioAlgorithm):
def __init__(
self, distance_function=scipy.spatial.distance.cosine, factor=-1, **kwargs
):
super().__init__(**kwargs)
self.distance_function = distance_function
self.factor = factor
def _make_2d(self, X):
"""
This function will make sure that the inputs are ndim=2 before enrollment and scoring.
For instance, when the source is `VideoLikeContainer` the input of `enroll:enroll_features` and `score:probes` are
[`VideoLikeContainer`, ....].
The concatenation of them makes and array of `ZxNxD`. Hence we need to stack them in `Z`.
"""
if X.ndim == 3:
return np.vstack(X)
elif X.ndim == 1:
return np.expand_dims(X, axis=0)
else:
return X
[docs] def enroll(self, enroll_features):
"""enroll(enroll_features) -> model
Enrolls the model by storing all given input vectors.
Parameters
----------
``enroll_features`` : [:py:class:`numpy.ndarray`]
The list of projected features to enroll the model from.
Returns
-------
``model`` : 2D :py:class:`numpy.ndarray`
The enrolled model.
"""
enroll_features = check_array(enroll_features, allow_nd=True, ensure_2d=True)
enroll_features = self._make_2d(enroll_features)
# This avoids some possible mistakes in the feature extraction
# That dumps vectors in the format `Nx1xd`
assert enroll_features.ndim == 2
return np.mean(enroll_features, axis=0)
[docs] def score(self, biometric_reference, data):
"""score(model, probe) -> float
Computes the distance of the model to the probe using the distance function specified in the constructor.
Parameters
----------
``model`` : 2D :py:class:`numpy.ndarray`
The model storing all enrollment features
``probe`` : :py:class:`numpy.ndarray`
The probe feature vector
Returns
-------
``score`` : float
A similarity value between ``model`` and ``probe``
"""
# We have to do this `check_array` because we can
# have other array formats that are not necessarily numpy arrays but extensions of it
data = check_array(data, allow_nd=True, ensure_2d=False)
data = self._make_2d(data)
assert data.ndim == 2
# return the negative distance (as a similarity measure)
return self.factor * self.distance_function(biometric_reference, data)
[docs] def score_multiple_biometric_references(self, biometric_references, data):
# We have to do this `check_array` because we can
# have other array formats that are not necessarily numpy arrays but extensions of it
data = check_array(data, allow_nd=True, ensure_2d=False)
data = self._make_2d(data)
assert data.ndim == 2
references_stacked = np.vstack(biometric_references)
scores = self.factor * cdist(references_stacked, data, self.distance_function)
return scores