Cleaning up Extractors

bob/bio/face/extractor/GridGraph.py

@@ -7,12 +7,10 @@ import bob.io.base
 
 import numpy
 import math
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.utils import check_array
-from bob.pipelines.sample import SampleBatch
+from bob.bio.base.extractor import Extractor
 
 
-class GridGraph(TransformerMixin, BaseEstimator):
+class GridGraph(Extractor):
     """Extracts Gabor jets in a grid structure [GHW12]_ using functionalities from :ref:`bob.ip.gabor <bob.ip.gabor>`.
 
   The grid can be either aligned to the eye locations (in which case the grid might be rotated), or a fixed grid graph can be extracted.
@@ -73,6 +71,26 @@ class GridGraph(TransformerMixin, BaseEstimator):
         first_node=None,  # one or two integral values, or None -> automatically determined
     ):
 
+        # call base class constructor
+        Extractor.__init__(
+            self,
+            gabor_directions=gabor_directions,
+            gabor_scales=gabor_scales,
+            gabor_sigma=gabor_sigma,
+            gabor_maximum_frequency=gabor_maximum_frequency,
+            gabor_frequency_step=gabor_frequency_step,
+            gabor_power_of_k=gabor_power_of_k,
+            gabor_dc_free=gabor_dc_free,
+            normalize_gabor_jets=normalize_gabor_jets,
+            eyes=eyes,
+            nodes_between_eyes=nodes_between_eyes,
+            nodes_along_eyes=nodes_along_eyes,
+            nodes_above_eyes=nodes_above_eyes,
+            nodes_below_eyes=nodes_below_eyes,
+            node_distance=node_distance,
+            first_node=first_node,
+        )
+
         self.gabor_directions = gabor_directions
         self.gabor_scales = gabor_scales
         self.gabor_sigma = gabor_sigma
@@ -121,18 +139,11 @@ class GridGraph(TransformerMixin, BaseEstimator):
                 raise ValueError(
                     "Please specify either 'eyes' or the grid parameters 'node_distance' (and 'first_node')!"
                 )
-
-            if not hasattr(self, "_last_image_resolution"):
-                self._last_image_resolution = None
-
-            if not hasattr(self, "_aligned_graph"):
-                self._aligned_graph = None
-
+            self._aligned_graph = None
+            self._last_image_resolution = None
             if isinstance(self.node_distance, (int, float)):
                 self.node_distance = (int(self.node_distance), int(self.node_distance))
 
-        self._graph = None
-
     def _extractor(self, image):
         """Creates an extractor based on the given image.
     If an aligned graph was specified in the constructor, it is simply returned.
@@ -151,7 +162,7 @@ class GridGraph(TransformerMixin, BaseEstimator):
             return self._aligned_graph
 
         # check if a new extractor needs to be created
-        if self._graph is None or self._last_image_resolution != image.shape:
+        if self._last_image_resolution != image.shape:
             self._last_image_resolution = image.shape
             if self.first_node is None:
                 # automatically compute the first node
@@ -184,7 +195,7 @@ class GridGraph(TransformerMixin, BaseEstimator):
 
         return self._graph
 
-    def transform(self, X):
+    def __call__(self, image):
         """__call__(image) -> feature
 
     Returns a list of Gabor jets extracted from the given image.
@@ -201,32 +212,24 @@ class GridGraph(TransformerMixin, BaseEstimator):
       The 2D location of the jet's nodes is not returned.
     """
 
-        def _extract(image):
-            import ipdb; ipdb.set_trace()
-
-            assert image.ndim == 2
-            assert isinstance(image, numpy.ndarray)
-            image = image.astype(numpy.float64)
-            assert image.dtype == numpy.float64
+        assert image.ndim == 2
+        assert isinstance(image, numpy.ndarray)
+        image = image.astype(numpy.float64)
+        assert image.dtype == numpy.float64
 
-            extractor = self._extractor(image)
+        extractor = self._extractor(image)
 
-            # perform Gabor wavelet transform
-            self.gwt.transform(image, self.trafo_image)
-            # extract face graph
-            jets = extractor.extract(self.trafo_image)
+        # perform Gabor wavelet transform
+        self.gwt.transform(image, self.trafo_image)
+        # extract face graph
+        jets = extractor.extract(self.trafo_image)
 
-            # normalize the Gabor jets of the graph only
-            if self.normalize_jets:
-                [j.normalize() for j in jets]
+        # normalize the Gabor jets of the graph only
+        if self.normalize_jets:
+            [j.normalize() for j in jets]
 
-            # return the extracted face graph
-            return self.__class__.serialize_jets(jets)
-
-        if isinstance(X, SampleBatch):
-            return [_extract(x) for x in X]
-        else:
-            return _extract(X)
+        # return the extracted face graph
+        return self.__class__.serialize_jets(jets)
 
     def write_feature(self, feature, feature_file):
         """Writes the feature extracted by the `__call__` function to the given file.
@@ -265,6 +268,15 @@ class GridGraph(TransformerMixin, BaseEstimator):
             bob.ip.gabor.load_jets(bob.io.base.HDF5File(feature_file))
         )
 
+    # re-define the train function to get it non-documented
+    def train(*args, **kwargs):
+        raise NotImplementedError(
+            "This function is not implemented and should not be called."
+        )
+
+    def load(*args, **kwargs):
+        pass
+
     def __getstate__(self):
         d = dict(self.__dict__)
         d.pop("gwt")
@@ -285,9 +297,3 @@ class GridGraph(TransformerMixin, BaseEstimator):
             sj.jet = jet.jet
             serialize_jets.append(sj)
         return serialize_jets
-
-    def _more_tags(self):
-        return {"stateless": True, "requires_fit": False}
-
-    def fit(self, X, y=None):
-        return self

bob/bio/face/extractor/LGBPHS.py

@@ -278,7 +278,7 @@ class LGBPHS(TransformerMixin, BaseEstimator):
         if isinstance(X, SampleBatch):
             return [_extract(x) for x in X]
         else:
-            return _extract(X) 
+            return _extract(X)
 
     def __getstate__(self):
         d = dict(self.__dict__)

bob/bio/face/test/test_extractors.py

@@ -33,6 +33,14 @@ import pkg_resources
 
 regenerate_refs = False
 
+# Cropping
+CROPPED_IMAGE_HEIGHT = 80
+CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
+
+# eye positions for frontal images
+RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
+LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
+
 
 def _compare(
     data,
@@ -60,9 +68,10 @@ def _data():
 def test_dct_blocks():
     # read input
     data = _data()
-    dct = bob.bio.base.load_resource(
-        "dct-blocks", "extractor", preferred_package="bob.bio.face"
+    dct = bob.bio.face.extractor.DCTBlocks(
+        block_size=12, block_overlap=11, number_of_dct_coefficients=45
     )
+
     assert isinstance(dct, bob.bio.face.extractor.DCTBlocks)
 
     # generate smaller extractor, using mixed tuple and int input for the block size and overlap
@@ -81,8 +90,13 @@ def test_dct_blocks():
 
 def test_graphs():
     data = _data()
-    graph = bob.bio.base.load_resource(
-        "grid-graph", "extractor", preferred_package="bob.bio.face"
+    graph = bob.bio.face.extractor.GridGraph(
+        # Gabor parameters
+        gabor_sigma=math.sqrt(2.0) * math.pi,
+        # what kind of information to extract
+        normalize_gabor_jets=True,
+        # setup of the fixed grid
+        node_distance=(8, 8),
     )
     assert isinstance(graph, bob.bio.face.extractor.GridGraph)
 
@@ -90,7 +104,7 @@ def test_graphs():
     graph = bob.bio.face.extractor.GridGraph(node_distance=24)
 
     # extract features
-    feature = graph.transform(data)
+    feature = graph(data)
 
     reference = pkg_resources.resource_filename(
         "bob.bio.face.test", "data/graph_regular.hdf5"
@@ -106,8 +120,9 @@ def test_graphs():
     assert all(numpy.allclose(r.jet, f.jet) for r, f in zip(reference, feature))
 
     # get reference face graph extractor
-    cropper = bob.bio.base.load_resource(
-        "face-crop-eyes", "preprocessor", preferred_package="bob.bio.face"
+    cropper = bob.bio.face.preprocessor.FaceCrop(
+        cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
+        cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
     )
     eyes = cropper.cropped_positions
     # generate aligned graph extractor
@@ -131,10 +146,6 @@ def test_graphs():
 
 def test_lgbphs():
     data = _data()
-    lgbphs = bob.bio.base.load_resource(
-        "lgbphs", "extractor", preferred_package="bob.bio.face"
-    )
-    assert isinstance(lgbphs, bob.bio.face.extractor.LGBPHS)
 
     # in this test, we use a smaller setup of the LGBPHS features
     lgbphs = bob.bio.face.extractor.LGBPHS(
@@ -171,22 +182,3 @@ def test_lgbphs():
         "bob.bio.face.test", "data/lgbphs_with_phase.hdf5"
     )
     _compare(feature, reference)
-
-
-"""
-  def test05_sift_key_points(self):
-    # check if VLSIFT is available
-    import bob.ip.base
-    if not hasattr(bob.ip.base, "VLSIFT"):
-      raise SkipTest("VLSIFT is not part of bob.ip.base; maybe SIFT headers aren't installed in your system?")
-
-    # we need the preprocessor tool to actually read the data
-    preprocessor = facereclib.preprocessing.Keypoints()
-    data = preprocessor.read_data(self.input_dir('key_points.hdf5'))
-    # now, we extract features from it
-    extractor = self.config('sift')
-    feature = self.execute(extractor, data, 'sift.hdf5', epsilon=1e-4)
-    self.assertEqual(len(feature.shape), 1)
-
-
-"""