Reorganized baselines

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

+import bob.bio.face
+
+from sklearn.pipeline import make_pipeline
+from bob.bio.base.wrappers import wrap_sample_preprocessor
+from bob.pipelines import wrap
+from bob.bio.face.embeddings import FaceNetSanderberg
+from bob.bio.face.helpers import face_crop_solver
+
+# This is the size of the image that this model expects
+CROPPED_IMAGE_HEIGHT = 160
+CROPPED_IMAGE_WIDTH = 160
+cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+color_channel = "rgb"
+
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = "eyes-center"
+    fixed_positions = None
+
+#### SOLVING THE FACE CROPPER TO BE USED
+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
+    RIGHT_EYE_POS = (46, 53)
+    LEFT_EYE_POS = (46, 107)
+
+    # 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)
+
+
+embedding = FaceNetSanderberg()
+
+transformer = make_pipeline(
+    wrap(
+        ["sample"],
+        face_cropper,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
+    wrap(["sample"], embedding),
+)

bob/bio/face/config/transformers/eyes_crop/facenet.py

-import bob.bio.face
-
-from sklearn.pipeline import make_pipeline
-from bob.bio.base.wrappers import wrap_sample_preprocessor
-from bob.pipelines import wrap
-from bob.bio.face.transformers import FaceNetSanderberg
-
-
-# This is the size of the image that this model expects
-CROPPED_IMAGE_HEIGHT = 160
-CROPPED_IMAGE_WIDTH = 160
-
-
-# eye positions for frontal images
-RIGHT_EYE_POS = (46, 53)
-LEFT_EYE_POS = (46, 107)
-
-
-legacy_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},
-    color_channel="rgb",
-)
-
-embedding = FaceNetSanderberg()
-
-transformer = make_pipeline(
-    wrap_sample_preprocessor(
-        legacy_face_cropper,
-        transform_extra_arguments=(("annotations", "annotations"),),
-    ),
-    wrap(["sample"], embedding),
-)

bob/bio/face/test/test_pipelines.py → bob/bio/face/test/test_baseline.py

@@ -6,24 +6,23 @@ from bob.pipelines import Sample, SampleSet
 
 def get_fake_sample(face_size=(160, 160), eyes={"leye": (46, 107), "reye": (46, 53)}):
 
-    data = np.random.rand(3,400,400)
-    annotations={"leye": (115, 267), "reye": (115, 132)}
+    data = np.random.rand(3, 400, 400)
+    annotations = {"leye": (115, 267), "reye": (115, 132)}
     return Sample(data, key="1", annotations=annotations)
 
 
-
-
-def test_facenet_pipeline():
-    config_name = pkg_resources.resource_filename('bob.bio.face', 'config/transformers/eyes_crop/facenet.py')
+def test_facenet_baseline():
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/facenet.py"
+    )
     transformer = load([config_name]).transformer
-    
-    #import ipdb; ipdb.set_trace()
+
     fake_sample = get_fake_sample()
 
-    #transformed_sample = transformer.transform([fake_sample])[0].data
+    # transformed_sample = transformer.transform([fake_sample])[0].data
 
-    #import ipdb; ipdb.set_trace()
     transformed_sample = transformer.transform([fake_sample])[0]
-    assert transformed_sample.data.size == 160
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 128
 
-    pass
\ No newline at end of file
+    pass