Commit f99174fd authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
Browse files

[py] Removed BIC

parent 4930dda2
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Manuel Guenther <Manuel.Guenther@idiap.ch>
import bob.io.base
import bob.learn.linear
import numpy
import math
from bob.bio.base.algorithm import Algorithm
from .. import utils
import logging
logger = logging.getLogger("bob.bio.base")
class BIC(Algorithm):
"""Computes the Intrapersonal/Extrapersonal classifier using a generic feature type and feature comparison function.
In this generic implementation, any distance or similarity vector that results as a comparison of two feature vectors can be used.
Currently two different versions are implemented: One with [MWP98]_ and one without (a generalization of [GW09]_) subspace projection of the features.
The implementation of the BIC training is taken from :ref:`bob.learn.linear <bob.learn.linear>`.
**Parameters:**
comparison_function : function
The function to compare the features in the original feature space.
For a given pair of features, this function is supposed to compute a vector of similarity (or distance) values.
In the easiest case, it just computes the element-wise difference of the feature vectors, but more difficult functions can be applied, and the function might be specialized for the features you put in.
maximum_training_pair_count : int or None
Limit the number of training image pairs to the given value, i.e., to avoid memory issues.
subspace_dimensions : (int, int) or None
A tuple of sizes of the intrapersonal and extrapersonal subspaces.
If given, subspace projection is performed (cf. [MWP98]_) and the subspace projection matrices are truncated to the given sizes.
If omitted, no subspace projection is performed (cf. [GW09]_).
uses_dffs : bool
Only valid, when ``subspace_dimensions`` are specified.
Use the *Distance From Feature Space* (DFFS) (cf. [MWP98]_) during scoring.
Use this flag with care!
read_function : function
A function to read a feature from :py:class:`bob.io.base.HDF5File`.
This function need to be appropriate to read the type of features that you are using.
By default, :py:func:`bob.bio.base.load` is used.
write_function : function
A function to write a feature to :py:class:`bob.io.base.HDF5File`.
This function is used to write the model and need to be appropriate to write the type of features that you are using.
By default, :py:func:`bob.bio.base.save` is used.
kwargs : ``key=value`` pairs
A list of keyword arguments directly passed to the :py:class:`Algorithm` base class constructor.
"""
def __init__(
self,
comparison_function,
# the function to be used to compare two features; this highly depends on the type of features that are used
maximum_training_pair_count=None,
# if set, limit the number of training pairs to the given number in a non-random manner
subspace_dimensions=None,
# if set as a pair (intra_dim, extra_dim), PCA subspace truncation for the two classes is performed
uses_dffs=False,
# use the distance from feature space; only valid when PCA truncation is enabled; WARNING: uses this flag with care
read_function=utils.load,
write_function=utils.save,
**kwargs # parameters directly sent to the base class
):
# call base class function and register that this tool requires training for the enrollment
super(BIC, self).__init__(
requires_enroller_training=True,
comparison_function=str(comparison_function),
maximum_training_pair_count=maximum_training_pair_count,
subspace_dimensions=subspace_dimensions,
uses_dffs=uses_dffs,
read_function=str(read_function),
write_function=str(write_function),
**kwargs
)
# set up the BIC tool
self.comparison_function = comparison_function
self.read_function = read_function
self.write_function = write_function
self.maximum_pair_count = maximum_training_pair_count
self.use_dffs = uses_dffs
if subspace_dimensions is not None:
self.M_I = subspace_dimensions[0]
self.M_E = subspace_dimensions[1]
self.bic_machine = bob.learn.linear.BICMachine(self.use_dffs)
else:
self.bic_machine = bob.learn.linear.BICMachine(False)
self.M_I = None
self.M_E = None
def _trainset_for(self, pairs):
"""Computes the array containing the comparison results for the given set of image pairs."""
return numpy.vstack(self.comparison_function(f1, f2) for (f1, f2) in pairs)
def train_enroller(self, train_features, enroller_file):
"""Trains the BIC by computing intra-personal and extra-personal subspaces.
First, two lists of pairs are computed, which contain intra-personal and extra-personal feature pairs, respectively.
Afterward, the comparison vectors are computed using the ``comparison_function`` specified in the constructor.
Finally, the :py:class:`bob.learn.linear.BICTrainer` is used to train a :py:class:`bob.learn.linear.BICMachine`.
**Parameters:**
train_features : [[object]]
A list of lists of feature vectors, which are used to train the BIC.
Each sub-list contains the features of one client.
enroller_file : str
A writable file, into which the resulting :py:class:`bob.learn.linear.BICMachine` will be written.
"""
# compute intrapersonal and extrapersonal pairs
logger.info(" -> Computing pairs")
intra_pairs, extra_pairs = bob.learn.linear.bic_intra_extra_pairs(train_features)
# limit pairs, if desired
if self.maximum_pair_count is not None:
if len(intra_pairs) > self.maximum_pair_count:
logger.info(
" -> Limiting intrapersonal pairs from %d to %d" % (len(intra_pairs), self.maximum_pair_count))
intra_pairs = utils.selected_elements(intra_pairs, self.maximum_pair_count)
if len(extra_pairs) > self.maximum_pair_count:
logger.info(
" -> Limiting extrapersonal pairs from %d to %d" % (len(extra_pairs), self.maximum_pair_count))
extra_pairs = utils.selected_elements(extra_pairs, self.maximum_pair_count)
# train the BIC Machine with these pairs
logger.info(" -> Computing %d intrapersonal results", len(intra_pairs))
intra_vectors = self._trainset_for(intra_pairs)
logger.info(" -> Computing %d extrapersonal results", len(extra_pairs))
extra_vectors = self._trainset_for(extra_pairs)
logger.info(" -> Training BIC machine")
trainer = bob.learn.linear.BICTrainer(self.M_I,
self.M_E) if self.M_I is not None else bob.learn.linear.BICTrainer()
trainer.train(intra_vectors, extra_vectors, self.bic_machine)
# save the machine to file
self.bic_machine.save(bob.io.base.HDF5File(enroller_file, 'w'))
def load_enroller(self, enroller_file):
"""Reads the :py:class:`bob.learn.linear.BICMachine` from file.
The :py:attr:`bob.learn.linear.BICMachine.use_DFFS` will be overwritten by the ``use_dffs`` value specified in this class' constructor.
**Parameters:**
enroller_file : str
An existing file, from which the :py:class:`bob.learn.linear.BICMachine` will be read.
"""
self.bic_machine.load(bob.io.base.HDF5File(enroller_file, 'r'))
# to set this should not be required, but just in case
# you re-use a trained enroller file that hat different setup of use_DFFS
self.bic_machine.use_DFFS = self.use_dffs
def enroll(self, enroll_features):
"""enroll(enroll_features) -> model
Enrolls the model by storing all given input features.
The features must be writable with the ``write_function`` defined in the constructor.
**Parameters:**
enroll_features : [object]
The list of projected features to enroll the model from.
**Returns:**
model : [object]
The enrolled model (which is identical to the input features).
"""
return enroll_features
def write_model(self, model, model_file):
"""Writes all features of the model into one HDF5 file.
To write the features, the ``write_function`` specified in the constructor is employed.
**Parameters:**
model : [object]
The model to write, which is a list of features.
model_file : str or :py:class:`bob.io.base.HDF5File`
The file (open for writing) or a file name to write into.
"""
hdf5 = model_file if isinstance(model_file, bob.io.base.HDF5File) else bob.io.base.HDF5File(model_file, 'w')
for i, f in enumerate(model):
hdf5.create_group("Feature%d" % i)
hdf5.cd("Feature%d" % i)
self.write_function(f, hdf5)
hdf5.cd("..")
def read_model(self, model_file):
"""read_model(model_file) -> model
Reads all features of the model from the given HDF5 file.
To read the features, the ``read_function`` specified in the constructor is employed.
**Parameters:**
model_file : str or :py:class:`bob.io.base.HDF5File`
The file (open for reading) or the name of an existing file to read from.
**Returns:**
model : [object]
The read model, which is a list of features.
"""
hdf5 = bob.io.base.HDF5File(model_file)
i = 0
model = []
while hdf5.has_group("Feature%d" % i):
hdf5.cd("Feature%d" % i)
model.append(self.read_function(hdf5))
hdf5.cd("..")
i += 1
return model
def score(self, model, probe):
"""score(model, probe) -> float
Computes the BIC score between the model and the probe.
First, the ``comparison_function`` is used to create the comparison vectors between all model features and the probe feature.
Then, a BIC score is computed for each comparison vector, and the BIC scores are fused using
the `model_fusion_function` defined in the :py:class:`bob.bio.base.algorithm.Algorithm` base class.
**Parameters:**
model : [object]
The model storing all model features.
probe : object
The probe feature.
**Returns:**
score : float
A fused BIC similarity value between ``model`` and ``probe``.
"""
# compute average score for the models
scores = []
for i in range(len(model)):
diff = self.comparison_function(model[i], probe)
assert len(diff) == self.bic_machine.input_size
scores.append(self.bic_machine(diff))
return self.model_fusion_function(scores)
# re-define unused functions, just so that they do not get documented
def train_projector(*args, **kwargs):
raise NotImplementedError()
def load_projector(*args, **kwargs):
pass
def project(*args, **kwargs):
raise NotImplementedError()
def write_feature(*args, **kwargs):
raise NotImplementedError()
def read_feature(*args, **kwargs):
raise NotImplementedError()
......@@ -103,9 +103,6 @@ References
.. [TP91] *M. Turk and A. Pentland*. **Eigenfaces for recognition**. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.
.. [ZKC+98] *W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets and J. Weng*. **Discriminant analysis of principal components for face recognition**, pages 73-85. Springer Verlag Berlin, 1998.
.. [MWP98] *B. Moghaddam, W. Wahid and A. Pentland*. **Beyond eigenfaces: probabilistic matching for face recognition**. IEEE International Conference on Automatic Face and Gesture Recognition, pages 30-35. 1998.
.. [GW09] *M. Günther and R.P. Würtz*. **Face detection and recognition using maximum likelihood classifiers on Gabor graphs**. International Journal of Pattern Recognition and Artificial Intelligence, 23(3):433-461, 2009.
......
......@@ -102,7 +102,6 @@ Implementations
bob.bio.base.algorithm.PCA
bob.bio.base.algorithm.LDA
bob.bio.base.algorithm.PLDA
bob.bio.base.algorithm.BIC
......
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