Added arcface

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

@@ -64,3 +64,64 @@ def embedding_transformer_160x160(embedding, annotation_type, fixed_positions):
     )
 
     return transformer
+
+
+def embedding_transformer_112x112(embedding, annotation_type, fixed_positions):
+    """
+    Creates a pipeline composed by and FaceCropper and an Embedding extractor.
+    This transformer is suited for Facenet based architectures
+    
+    .. warning::
+       This will redirect images to :math:`160 \times 160`
+    
+    """
+
+    # This is the size of the image that this model expects
+    CROPPED_IMAGE_HEIGHT = 112
+    CROPPED_IMAGE_WIDTH = 112
+    cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+    color_channel = "rgb"
+
+    #### 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 = (32, 34)
+        LEFT_EYE_POS = (32, 77)
+
+        # 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)
+
+    transformer = make_pipeline(
+        wrap(
+            ["sample"],
+            face_cropper,
+            transform_extra_arguments=transform_extra_arguments,
+        ),
+        wrap(["sample"], embedding),
+    )
+
+    return transformer

bob/bio/face/embeddings/arface.py

@@ -40,7 +40,6 @@ class ArcFace_InsightFaceTF(TensorflowCompatV1):
         # https://github.com/luckycallor/InsightFace-tensorflow/blob/master/evaluate.py#L42
         data = check_array(data, allow_nd=True)
         data = data / 127.5 - 1.0
-
         return super().transform(data)
 
     def load_model(self):

bob/bio/face/test/test_baseline.py

@@ -74,3 +74,19 @@ def test_inception_resnetv1_casiawebface():
     transformed_sample = transformer.transform([fake_sample])[0]
     transformed_data = transformed_sample.data
     assert transformed_sample.data.size == 128
+
+
+def test_arcface_insight_tf():
+    import tensorflow as tf
+    tf.compat.v1.reset_default_graph()
+    
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/arcface_insight_tf.py"
+    )
+    transformer = load([config_name]).transformer
+
+    fake_sample = get_fake_sample()
+
+    transformed_sample = transformer.transform([fake_sample])[0]
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 512