facial recognition, detector and demography models are now using interface

Author: serengil

deepface/DeepFace.py

@@ -8,7 +8,6 @@
 import numpy as np
 import pandas as pd
 import tensorflow as tf
-from deprecated import deprecated
 
 # package dependencies
 from deepface.commons import functions
@@ -23,8 +22,6 @@
     realtime,
 )
 
-# pylint: disable=no-else-raise, simplifiable-if-expression
-
 logger = Logger(module="DeepFace")
 
 # -----------------------------------
@@ -40,6 +37,8 @@
     from keras.models import Model
 # -----------------------------------
 
+functions.initialize_folder()
+
 
 def build_model(model_name: str) -> Union[Model, Any]:
     """
@@ -413,69 +412,6 @@ def extract_faces(
     )
 
 
-# ---------------------------
-# deprecated functions
-
-
-@deprecated(version="0.0.78", reason="Use DeepFace.extract_faces instead of DeepFace.detectFace")
-def detectFace(
-    img_path: Union[str, np.ndarray],
-    target_size: tuple = (224, 224),
-    detector_backend: str = "opencv",
-    enforce_detection: bool = True,
-    align: bool = True,
-) -> Union[np.ndarray, None]:
-    """
-    Deprecated function. Use extract_faces for same functionality.
-
-    This function applies pre-processing stages of a face recognition pipeline
-    including detection and alignment
-
-    Parameters:
-            img_path: exact image path, numpy array (BGR) or base64 encoded image.
-            Source image can have many face. Then, result will be the size of number
-            of faces appearing in that source image.
-
-            target_size (tuple): final shape of facial image. black pixels will be
-            added to resize the image.
-
-            detector_backend (string): face detection backends are retinaface, mtcnn,
-            opencv, ssd or dlib
-
-            enforce_detection (boolean): function throws exception if face cannot be
-            detected in the fed image. Set this to False if you do not want to get
-            an exception and run the function anyway.
-
-            align (boolean): alignment according to the eye positions.
-
-            grayscale (boolean): extracting faces in rgb or gray scale
-
-    Returns:
-            detected and aligned face as numpy array
-
-    """
-    logger.warn("Function detectFace is deprecated. Use extract_faces instead.")
-    face_objs = extract_faces(
-        img_path=img_path,
-        target_size=target_size,
-        detector_backend=detector_backend,
-        enforce_detection=enforce_detection,
-        align=align,
-        grayscale=False,
-    )
-
-    extracted_face = None
-    if len(face_objs) > 0:
-        extracted_face = face_objs[0]["face"]
-    return extracted_face
-
-
-# ---------------------------
-# main
-
-functions.initialize_folder()
-
-
 def cli() -> None:
     """
     command line interface function will be offered in this block

deepface/basemodels/ArcFace.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 from deepface.commons import functions
 from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="basemodels.ArcFace")
 
@@ -42,10 +43,25 @@
         Dense,
     )
 
+# pylint: disable=too-few-public-methods
+class ArcFace(FacialRecognition):
+    """
+    ArcFace model class
+    """
 
-def loadModel(
+    def __init__(self):
+        self.model = load_model()
+        self.model_name = "ArcFace"
+
+
+def load_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5",
 ) -> Model:
+    """
+    Construct ArcFace model, download its weights and load
+    Returns:
+        model (Model)
+    """
     base_model = ResNet34()
     inputs = base_model.inputs[0]
     arcface_model = base_model.outputs[0]
@@ -81,7 +97,11 @@ def loadModel(
 
 
 def ResNet34() -> Model:
-
+    """
+    ResNet34 model
+    Returns:
+        model (Model)
+    """
     img_input = Input(shape=(112, 112, 3))
 
     x = ZeroPadding2D(padding=1, name="conv1_pad")(img_input)

deepface/basemodels/DeepID.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 from deepface.commons import functions
 from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="basemodels.DeepID")
 
@@ -38,10 +39,23 @@
 
 # -------------------------------------
 
+# pylint: disable=too-few-public-methods
+class DeepId(FacialRecognition):
+    """
+    DeepId model class
+    """
 
-def loadModel(
+    def __init__(self):
+        self.model = load_model()
+        self.model_name = "DeepId"
+
+
+def load_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",
 ) -> Model:
+    """
+    Construct DeepId model, download its weights and load
+    """
 
     myInput = Input(shape=(55, 47, 3))
 

deepface/basemodels/DlibResNet.py

@@ -4,12 +4,33 @@
 import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="basemodels.DlibResNet")
 
 # pylint: disable=too-few-public-methods
 
 
+class Dlib(FacialRecognition):
+    """
+    Dlib model class
+    """
+
+    def __init__(self):
+        self.model = DlibResNet()
+        self.model_name = "Dlib"
+
+    def find_embeddings(self, img: np.ndarray) -> list:
+        """
+        Custom find embeddings function of Dlib different than FacialRecognition's one
+        Args:
+            img (np.ndarray)
+        Retunrs:
+            embeddings (list)
+        """
+        return self.model.predict(img)[0].tolist()
+
+
 class DlibResNet:
     def __init__(self):
 

deepface/basemodels/DlibWrapper.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 from deepface.commons import functions
 from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="basemodels.Facenet")
 
@@ -42,12 +43,39 @@
 
 # --------------------------------
 
+# pylint: disable=too-few-public-methods
+class FaceNet128d(FacialRecognition):
+    """
+    FaceNet-128d model class
+    """
+
+    def __init__(self):
+        self.model = load_facenet128d_model()
+        self.model_name = "FaceNet-128d"
+
+
+class FaceNet512d(FacialRecognition):
+    """
+    FaceNet-1512d model class
+    """
+
+    def __init__(self):
+        self.model = load_facenet512d_model()
+        self.model_name = "FaceNet-512d"
+
 
 def scaling(x, scale):
     return x * scale
 
 
-def InceptionResNetV2(dimension=128) -> Model:
+def InceptionResNetV2(dimension: int = 128) -> Model:
+    """
+    InceptionResNetV2 model
+    Args:
+        dimension (int): number of dimensions in the embedding layer
+    Returns:
+        model (Model)
+    """
 
     inputs = Input(shape=(160, 160, 3))
     x = Conv2D(32, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_1a_3x3")(inputs)
@@ -1618,9 +1646,16 @@ def InceptionResNetV2(dimension=128) -> Model:
     return model
 
 
-def loadModel(
+def load_facenet128d_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5",
 ) -> Model:
+    """
+    Construct FaceNet-128d model, download weights and then load weights
+    Args:
+        dimension (int): construct FaceNet-128d or FaceNet-512d models
+    Returns:
+        model (Model)
+    """
     model = InceptionResNetV2()
 
     # -----------------------------------
@@ -1640,3 +1675,33 @@ def loadModel(
     # -----------------------------------
 
     return model
+
+
+def load_facenet512d_model(
+    url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5",
+) -> Model:
+    """
+    Construct FaceNet-512d model, download its weights and load
+    Returns:
+        model (Model)
+    """
+
+    model = InceptionResNetV2(dimension=512)
+
+    # -------------------------
+
+    home = functions.get_deepface_home()
+
+    if os.path.isfile(home + "/.deepface/weights/facenet512_weights.h5") != True:
+        logger.info("facenet512_weights.h5 will be downloaded...")
+
+        output = home + "/.deepface/weights/facenet512_weights.h5"
+        gdown.download(url, output, quiet=False)
+
+    # -------------------------
+
+    model.load_weights(home + "/.deepface/weights/facenet512_weights.h5")
+
+    # -------------------------
+
+    return model

deepface/basemodels/Facenet.py

@@ -4,6 +4,7 @@
 import tensorflow as tf
 from deepface.commons import functions
 from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="basemodels.FbDeepFace")
 
@@ -35,12 +36,23 @@
 
 
 # -------------------------------------
-# pylint: disable=line-too-long
+# pylint: disable=line-too-long, too-few-public-methods
+class DeepFace(FacialRecognition):
+    """
+    Fb's DeepFace model class
+    """
 
+    def __init__(self):
+        self.model = load_model()
+        self.model_name = "DeepFace"
 
-def loadModel(
+
+def load_model(
     url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
 ) -> Model:
+    """
+    Construct DeepFace model, download its weights and load
+    """
     base_model = Sequential()
     base_model.add(
         Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))