Removed EigenFace Baseline. This is covered in other base packages

bob/bio/face/baseline/baseline.py

@@ -8,11 +8,6 @@ Defining some face recognition baselines
 
 from bob.bio.base.baseline import Baseline
 
-eigenface = Baseline(name="eigenface",
-                     preprocessors={'default': 'face-crop-eyes', 'atnt': 'base'},
-                     extractor='linearize',
-                     algorithm='pca')
-
 lda = Baseline(name="lda",
                preprocessors={'default': 'face-crop-eyes', 'atnt': 'base'},
                extractor='eigenface',

bob/bio/face/config/extractor/eigenface.py

-#!/usr/bin/env python
-
-import bob.bio.face
-
-# compute eigenfaces using the training database
-extractor = bob.bio.face.extractor.Eigenface(
-    subspace_dimension = .95
-)

bob/bio/face/extractor/Eigenface.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Manuel Guenther <Manuel.Guenther@idiap.ch>
-
-import numpy
-
-import bob.learn.linear
-import bob.io.base
-
-from bob.bio.base.extractor import Extractor
-
-import logging
-logger = logging.getLogger("bob.bio.face")
-
-class Eigenface (Extractor):
-  """Performs a principal component analysis (PCA) on the given data.
-
-  This algorithm computes a PCA projection (:py:class:`bob.learn.linear.PCATrainer`) on the given training images, and projects the images into face space.
-  In opposition to :py:class:`bob.bio.base.algorithm.PCA`, here the eigenfces are used as features, i.e., to apply advanced face recognition algorithms on top of them.
-
-  **Parameters:**
-
-  subspace_dimension : int or float
-    If specified as ``int``, defines the number of eigenvectors used in the PCA projection matrix.
-    If specified as ``float`` (between 0 and 1), the number of eigenvectors is calculated such that the given percentage of variance is kept.
-
-  kwargs : ``key=value`` pairs
-    A list of keyword arguments directly passed to the :py:class:`bob.bio.base.extractor.Extractor` base class constructor.
-  """
-
-  def __init__(self, subspace_dimension):
-    # We have to register that this function will need a training step
-    Extractor.__init__(self, requires_training = True, subspace_dimension = subspace_dimension)
-    self.subspace_dimension = subspace_dimension
-
-
-  def _check_data(self, data):
-    """Checks that the given data are appropriate."""
-    assert isinstance(data, numpy.ndarray)
-    assert data.ndim == 2
-    assert data.dtype == numpy.float64
-
-
-  def train(self, training_images, extractor_file):
-    """Generates the PCA covariance matrix and writes it into the given extractor_file.
-
-    Beforehand, all images are turned into a 1D pixel vector.
-
-    **Parameters:**
-
-    training_images : [2D :py:class:`numpy.ndarray`]
-      A list of 2D training images to train the PCA projection matrix with.
-
-    extractor_file : str
-      A writable file, into which the PCA projection matrix (as a :py:class:`bob.learn.linear.Machine`) will be written.
-    """
-    [self._check_data(image) for image in training_images]
-
-    # Initializes an array for the data
-    data = numpy.vstack([image.flatten() for image in training_images])
-
-    logger.info("  -> Training LinearMachine using PCA (SVD)")
-    t = bob.learn.linear.PCATrainer()
-    self.machine, variances = t.train(data)
-
-    # compute variance percentage, if desired
-    if isinstance(self.subspace_dimension, float):
-      cummulated = numpy.cumsum(variances) / numpy.sum(variances)
-      for index in range(len(cummulated)):
-        if cummulated[index] > self.subspace_dimension:
-          self.subspace_dimension = index
-          break
-      self.subspace_dimension = index
-      logger.info("  -> Keeping %d eigenvectors" % self.subspace_dimension)
-
-    # Machine: get shape, then resize
-    self.machine.resize(self.machine.shape[0], self.subspace_dimension)
-    self.machine.save(bob.io.base.HDF5File(extractor_file, "w"))
-
-
-  def load(self, extractor_file):
-    """Reads the PCA projection matrix from file.
-
-    **Parameters:**
-
-    extractor_file : str
-      An existing file, from which the PCA projection matrix are read.
-    """
-    # read PCA projector
-    self.machine = bob.learn.linear.Machine(bob.io.base.HDF5File(extractor_file))
-
-
-  def __call__(self, image):
-    """__call__(image) -> feature
-
-    Projects the given image using the stored covariance matrix.
-
-    Beforehand, the image is turned into a 1D pixel vector.
-
-    **Parameters:**
-
-    image : 2D :py:class:`numpy.ndarray` (floats)
-      The image to extract the eigenface feature from.
-
-    **Returns:**
-
-    feature : 1D :py:class:`numpy.ndarray` (floats)
-      The extracted eigenface feature.
-    """
-    self._check_data(image)
-    # Projects the data
-    return self.machine(image.flatten())

bob/bio/face/test/test_extractors.py

@@ -153,44 +153,6 @@ def test_lgbphs():
   _compare(feature, reference, lgbphs.write_feature, lgbphs.read_feature)
 
 
-def test_eigenface():
-  temp_file = bob.io.base.test_utils.temporary_filename()
-  data = _data()
-  eigen1 = bob.bio.base.load_resource('eigenface', 'extractor', preferred_package='bob.bio.face')
-  assert isinstance(eigen1, bob.bio.face.extractor.Eigenface)
-  assert isinstance(eigen1, bob.bio.base.extractor.Extractor)
-  assert eigen1.requires_training
-
-  # create extractor with a smaller number of kept eigenfaces
-  train_data = utils.random_training_set(data.shape, 400, 0., 255.)
-  eigen2 = bob.bio.face.extractor.Eigenface(subspace_dimension = 5)
-  reference = pkg_resources.resource_filename('bob.bio.face.test', 'data/eigenface_extractor.hdf5')
-  try:
-    # train the projector
-    eigen2.train(train_data, temp_file)
-
-    assert os.path.exists(temp_file)
-
-    if regenerate_refs: shutil.copy(temp_file, reference_file)
-
-    # check projection matrix
-    eigen1.load(reference)
-    eigen2.load(temp_file)
-
-    assert eigen1.machine.shape == eigen2.machine.shape
-    for i in range(5):
-      assert numpy.abs(eigen1.machine.weights[:,i] - eigen2.machine.weights[:,i] < 1e-5).all() or numpy.abs(eigen1.machine.weights[:,i] + eigen2.machine.weights[:,i] < 1e-5).all()
-
-  finally:
-    if os.path.exists(temp_file): os.remove(temp_file)
-
-  # now, we can execute the extractor and check that the feature is still identical
-  feature = eigen1(data)
-  assert feature.ndim == 1
-  reference = pkg_resources.resource_filename('bob.bio.face.test', 'data/eigenface_feature.hdf5')
-  _compare(feature, reference, eigen1.write_feature, eigen1.read_feature)
-
-
 """
   def test05_sift_key_points(self):
     # check if VLSIFT is available

setup.py

@@ -185,7 +185,6 @@ setup(
             'dct-blocks        = bob.bio.face.config.extractor.dct_blocks:extractor',  # DCT blocks
             'grid-graph        = bob.bio.face.config.extractor.grid_graph:extractor',  # Grid graph
             'lgbphs            = bob.bio.face.config.extractor.lgbphs:extractor',  # LGBPHS
-            'eigenface         = bob.bio.face.config.extractor.eigenface:extractor',  # Eigenface
         ],
 
         'bob.bio.algorithm': [
@@ -196,7 +195,6 @@ setup(
 
         #baselines
         'bob.bio.baseline':[
-          'eigenface = bob.bio.face.baseline.baseline:eigenface',
           'lda = bob.bio.face.baseline.baseline:lda',
           'plda = bob.bio.face.baseline.baseline:plda',
           'gabor_graph = bob.bio.face.baseline.baseline:gabor_graph',