Porting some baselines

bob/bio/face/config/baseline/gabor_graph.py

-from bob.bio.base.pipelines.vanilla_biometrics import Distance, VanillaBiometricsPipeline, BioAlgorithmLegacy
-from bob.bio.face.helpers import face_crop_solver
+from bob.bio.base.pipelines.vanilla_biometrics import (
+    Distance,
+    VanillaBiometricsPipeline,
+    BioAlgorithmLegacy,
+)
+from bob.bio.face.config.baseline.helpers import crop_80x64
 import math
 import numpy as np
 import bob.bio.face
@@ -19,76 +23,39 @@ else:
 ####### SOLVING THE FACE CROPPER TO BE USED ##########
 
 # 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)
-
-cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
-color_channel = "gray"
-
-
-if annotation_type == "bounding-box":
-    transform_extra_arguments = (("annotations", "annotations"),)
-    TOP_LEFT_POS = (0, 0)
-    BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
-
-    # Detects the face and crops it without eye detection
-    face_cropper = face_crop_solver(
-        cropped_image_size,
-        color_channel=color_channel,
-        cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS},
-        fixed_positions=fixed_positions,
-    )
-
-elif annotation_type == "eyes-center":
-    transform_extra_arguments = (("annotations", "annotations"),)
-    # eye positions for frontal images
-
-    # Detects the face and crops it without eye detection
-    face_cropper = face_crop_solver(
-        cropped_image_size,
-        color_channel=color_channel,
-        cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
-        fixed_positions=fixed_positions,
-    )
-
-else:
-    transform_extra_arguments = None
-    # DEFAULT TO FACE SIMPLE RESIZE
-    face_cropper = face_crop_solver(cropped_image_size)
+face_cropper, transform_extra_arguments = crop_80x64(
+    annotation_type, fixed_positions, color_channel="gray"
+)
 
 preprocessor = bob.bio.face.preprocessor.INormLBP(
-  face_cropper = face_cropper,
-  dtype = np.float64
+    face_cropper=face_cropper, dtype=np.float64
 )
 
 
-
 #### FEATURE EXTRACTOR ######
 
-gabor_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),
+# legacy objects needs to be wrapped with legacy transformers
+from bob.bio.base.transformers import ExtractorTransformer
+
+gabor_graph = ExtractorTransformer(
+    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),
+    )
 )
 
 transformer = make_pipeline(
     wrap(
-        ["sample"],
-        preprocessor,
-        transform_extra_arguments=transform_extra_arguments,
+        ["sample"], preprocessor, transform_extra_arguments=transform_extra_arguments,
     ),
     wrap(["sample"], gabor_graph),
 )
 
 
-
 gabor_jet = bob.bio.face.algorithm.GaborJet(
     gabor_jet_similarity_type="PhaseDiffPlusCanberra",
     multiple_feature_scoring="max_jet",
@@ -98,7 +65,4 @@ gabor_jet = bob.bio.face.algorithm.GaborJet(
 tempdir = tempfile.TemporaryDirectory()
 algorithm = BioAlgorithmLegacy(gabor_jet, base_dir=tempdir.name)
 
-pipeline = VanillaBiometricsPipeline(
-    transformer,
-    algorithm
-)
+pipeline = VanillaBiometricsPipeline(transformer, algorithm)

bob/bio/face/config/baseline/helpers.py

@@ -11,7 +11,7 @@ def embedding_transformer_160x160(embedding, annotation_type, fixed_positions):
     This transformer is suited for Facenet based architectures
     
     .. warning::
-       This will redirect images to :math:`160 \times 160`
+       This will resize images to :math:`160 \times 160`
     
     """
 
@@ -72,7 +72,7 @@ def embedding_transformer_112x112(embedding, annotation_type, fixed_positions):
     This transformer is suited for Facenet based architectures
     
     .. warning::
-       This will redirect images to :math:`160 \times 160`
+       This will resize images to :math:`112 \times 112`
     
     """
 
@@ -125,3 +125,78 @@ def embedding_transformer_112x112(embedding, annotation_type, fixed_positions):
     )
 
     return transformer
+
+
+def crop_80x64(annotation_type, fixed_positions=None, color_channel="gray"):
+    """
+    Crops a face to :math:`80 \times 64`
+
+
+    Parameters
+    ----------
+
+       annotation_type: str
+          Type of annotations. Possible values are: `bounding-box`, `eyes-center` and None
+
+       fixed_positions: tuple
+          A tuple containing the annotations. This is used in case your input is already registered
+          with fixed positions (eyes or bounding box)
+
+       color_channel: str
+
+
+    Returns
+    -------
+
+      face_cropper:
+         A face cropper to be used
+      
+      transform_extra_arguments:
+         The parameters to the transformer
+
+    """
+
+
+    # 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)
+
+    cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+    color_channel = color_channel
+
+
+    if annotation_type == "bounding-box":
+        transform_extra_arguments = (("annotations", "annotations"),)
+        TOP_LEFT_POS = (0, 0)
+        BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+
+        # Detects the face and crops it without eye detection
+        face_cropper = face_crop_solver(
+            cropped_image_size,
+            color_channel=color_channel,
+            cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS},
+            fixed_positions=fixed_positions,
+        )
+
+    elif annotation_type == "eyes-center":
+        transform_extra_arguments = (("annotations", "annotations"),)
+        # eye positions for frontal images
+
+        # Detects the face and crops it without eye detection
+        face_cropper = face_crop_solver(
+            cropped_image_size,
+            color_channel=color_channel,
+            cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
+            fixed_positions=fixed_positions,
+        )
+
+    else:
+        transform_extra_arguments = None
+        # DEFAULT TO FACE SIMPLE RESIZE
+        face_cropper = face_crop_solver(cropped_image_size)
+
+    return face_cropper, transform_extra_arguments

bob/bio/face/config/baseline/lda.py

+from bob.bio.base.pipelines.vanilla_biometrics import (
+    Distance,
+    VanillaBiometricsPipeline,
+    BioAlgorithmLegacy,
+)
+from bob.bio.face.config.baseline.helpers import crop_80x64
+import numpy as np
+import bob.bio.face
+from sklearn.pipeline import make_pipeline
+from bob.pipelines import wrap
+import tempfile
+from bob.bio.base.transformers import AlgorithmTransformer
+from bob.pipelines.transformers import SampleLinearize
+import os
+
+#### SOLVING IF THERE'S ANY DATABASE INFORMATION
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+####### SOLVING THE FACE CROPPER TO BE USED ##########
+
+# Cropping
+face_cropper, transform_extra_arguments = crop_80x64(
+    annotation_type, fixed_positions, color_channel="gray"
+)
+
+preprocessor = bob.bio.face.preprocessor.TanTriggs(
+    face_cropper=face_cropper, dtype=np.float64
+)
+
+
+#### FEATURE EXTRACTOR ######
+
+tempdir = tempfile.TemporaryDirectory()
+lda = bob.bio.base.algorithm.LDA(use_pinv=True, pca_subspace_dimension=0.90)
+
+lda_transformer = AlgorithmTransformer(
+    lda, projector_file=os.path.join(tempdir.name, "Projector.hdf5")
+)
+
+
+transformer = make_pipeline(
+    wrap(
+        ["sample"], preprocessor, transform_extra_arguments=transform_extra_arguments,
+    ),
+    SampleLinearize(),
+    wrap(["sample"], lda_transformer),
+)
+
+
+### BIOMETRIC ALGORITHM
+
+algorithm = BioAlgorithmLegacy(
+    lda,
+    base_dir=tempdir.name,
+    projector_file=os.path.join(tempdir.name, "Projector.hdf5"),
+)
+
+pipeline = VanillaBiometricsPipeline(transformer, algorithm)

bob/bio/face/config/baseline/lgbphs.py

+from bob.bio.base.pipelines.vanilla_biometrics import (
+    Distance,
+    VanillaBiometricsPipeline,
+    BioAlgorithmLegacy,
+)
+from bob.bio.face.config.baseline.helpers import crop_80x64
+import math
+import numpy as np
+import bob.bio.face
+from sklearn.pipeline import make_pipeline
+from bob.pipelines import wrap
+import tempfile
+import bob.math
+
+
+#### SOLVING IF THERE'S ANY DATABASE INFORMATION
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+####### SOLVING THE FACE CROPPER TO BE USED ##########
+
+# Cropping
+face_cropper, transform_extra_arguments = crop_80x64(
+    annotation_type, fixed_positions, color_channel="gray"
+)
+
+preprocessor = bob.bio.face.preprocessor.TanTriggs(
+    face_cropper=face_cropper, dtype=np.float64
+)
+
+
+#### FEATURE EXTRACTOR ######
+
+lgbphs = bob.bio.face.extractor.LGBPHS(
+    # block setup
+    block_size = 8,
+    block_overlap = 0,
+    # Gabor parameters
+    gabor_sigma = math.sqrt(2.) * math.pi,
+    # LBP setup (we use the defaults)
+
+    # histogram setup
+    sparse_histogram = True
+)
+
+transformer = make_pipeline(
+    wrap(
+        ["sample"], preprocessor, transform_extra_arguments=transform_extra_arguments,
+    ),
+    wrap(["sample"], lgbphs),
+)
+
+
+
+### BIOMETRIC ALGORITHM
+histogram = bob.bio.face.algorithm.Histogram(
+    distance_function = bob.math.histogram_intersection,
+    is_distance_function = False
+)
+
+
+tempdir = tempfile.TemporaryDirectory()
+algorithm = BioAlgorithmLegacy(histogram, base_dir=tempdir.name)
+
+pipeline = VanillaBiometricsPipeline(transformer, algorithm)

bob/bio/face/test/test_baselines.py

@@ -3,7 +3,10 @@ import pkg_resources
 import numpy as np
 from bob.pipelines import Sample, SampleSet, DelayedSample
 from bob.bio.base import load_resource
-from bob.bio.base.pipelines.vanilla_biometrics import checkpoint_vanilla_biometrics, dask_vanilla_biometrics
+from bob.bio.base.pipelines.vanilla_biometrics import (
+    checkpoint_vanilla_biometrics,
+    dask_vanilla_biometrics,
+)
 import tempfile
 import os
 import bob.io.base
@@ -44,23 +47,32 @@ def get_fake_sample_set(face_size=(160, 160), purpose="bioref"):
     ]
 
 
-def run_baseline(baseline):
+def get_fake_samples_for_training():
+
+    data = np.random.rand(10, 3, 400, 400)
+    annotations = {"reye": (131, 176), "leye": (222, 170)}
 
+    return [
+        Sample(x, key=str(i), subject=str(i), annotations=annotations) for i,x in enumerate(data)
+    ]
+
+
+def run_baseline(baseline, samples_for_training=[]):    
     biometric_references = get_fake_sample_set(purpose="bioref")
     probes = get_fake_sample_set(purpose="probe")
 
     # Regular pipeline
     pipeline = load_resource(baseline, "baseline")
-    scores = pipeline([], biometric_references, probes)
+    scores = pipeline(samples_for_training, biometric_references, probes)
     assert len(scores) == 1
     assert len(scores[0]) == 1
 
-
     # CHECKPOINTING
-    import ipdb; ipdb.set_trace()
     with tempfile.TemporaryDirectory() as d:
 
-        checkpoint_pipeline = checkpoint_vanilla_biometrics(copy.deepcopy(pipeline), base_dir=d)
+        checkpoint_pipeline = checkpoint_vanilla_biometrics(
+            copy.deepcopy(pipeline), base_dir=d
+        )
         checkpoint_scores = checkpoint_pipeline([], biometric_references, probes)
         assert len(checkpoint_scores) == 1
         assert len(checkpoint_scores[0]) == 1
@@ -72,13 +84,14 @@ def run_baseline(baseline):
         assert "samplewrapper-2" in dirs
         assert "scores" in dirs
 
-
     # DASK
     with tempfile.TemporaryDirectory() as d:
 
-        dask_pipeline = dask_vanilla_biometrics(checkpoint_vanilla_biometrics(copy.deepcopy(pipeline), base_dir=d))
+        dask_pipeline = dask_vanilla_biometrics(
+            checkpoint_vanilla_biometrics(copy.deepcopy(pipeline), base_dir=d)
+        )
         dask_scores = dask_pipeline([], biometric_references, probes)
-        dask_scores = dask_scores.compute(scheduler="single-threaded") 
+        dask_scores = dask_scores.compute(scheduler="single-threaded")
         assert len(dask_scores) == 1
         assert len(dask_scores[0]) == 1
         assert np.isclose(scores[0][0].data, dask_scores[0][0].data)
@@ -109,12 +122,18 @@ def test_inception_resnetv1_msceleb():
 def test_inception_resnetv1_casiawebface():
     run_baseline("inception_resnetv1_casiawebface")
 
+
 def test_arcface_insight_tf():
     import tensorflow as tf
+
     tf.compat.v1.reset_default_graph()
 
     run_baseline("arcface_insight_tf")
 
 
 def test_gabor_graph():
-    run_baseline("gabor_graph")    
\ No newline at end of file
+    run_baseline("gabor_graph")
+
+
+#def test_lda():
+#    run_baseline("lda", get_fake_samples_for_training())