using type hinting for backend functions

deepface/DeepFace.py

@@ -889,8 +889,12 @@ def extract_faces(
 
 @deprecated(version="0.0.78", reason="Use DeepFace.extract_faces instead of DeepFace.detectFace")
 def detectFace(
-    img_path, target_size=(224, 224), detector_backend="opencv", enforce_detection=True, align=True
+    img_path: Union[str, np.ndarray],
-):
+    target_size: tuple = (224, 224),
+    detector_backend: str = "opencv",
+    enforce_detection: bool = True,
+    align: bool = True,
+) -> np.ndarray:
     """
     Deprecated function. Use extract_faces for same functionality.
 
@@ -942,7 +946,7 @@ def detectFace(
 functions.initialize_folder()
 
 
-def cli():
+def cli() -> None:
     """
     command line interface function will be offered in this block
     """

deepface/basemodels/ArcFace.py

@@ -14,8 +14,8 @@ logger = Logger(module="basemodels.ArcFace")
 tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
 
 if tf_version == 1:
+    from keras.models import Model
     from keras.engine import training
-    import keras
     from keras.layers import (
         ZeroPadding2D,
         Input,
@@ -28,8 +28,8 @@ if tf_version == 1:
         Dense,
     )
 else:
+    from tensorflow.keras.models import Model
     from tensorflow.python.keras.engine import training
-    from tensorflow import keras
     from tensorflow.keras.layers import (
         ZeroPadding2D,
         Input,
@@ -41,15 +41,11 @@ else:
         Flatten,
         Dense,
     )
-# --------------------------------
-
-
-# url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
 
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5",
-):
+) -> Model:
     base_model = ResNet34()
     inputs = base_model.inputs[0]
     arcface_model = base_model.outputs[0]
@@ -62,7 +58,7 @@ def loadModel(
     embedding = BatchNormalization(momentum=0.9, epsilon=2e-5, name="embedding", scale=True)(
         arcface_model
     )
-    model = keras.models.Model(inputs, embedding, name=base_model.name)
+    model = Model(inputs, embedding, name=base_model.name)
 
     # ---------------------------------------
     # check the availability of pre-trained weights
@@ -84,7 +80,7 @@ def loadModel(
     return model
 
 
-def ResNet34():
+def ResNet34() -> Model:
 
     img_input = Input(shape=(112, 112, 3))
 

deepface/basemodels/DeepID.py

@@ -41,7 +41,7 @@ else:
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",
-):
+) -> Model:
 
     myInput = Input(shape=(55, 47, 3))
 

deepface/basemodels/DlibResNet.py

@@ -13,8 +13,14 @@ logger = Logger(module="basemodels.DlibResNet")
 class DlibResNet:
     def __init__(self):
 
-        # this is not a must dependency
+        ## this is not a must dependency. do not import it in the global level.
-        import dlib  # 19.20.0
+        try:
+            import dlib
+        except ModuleNotFoundError as e:
+            raise ImportError(
+                "Dlib is an optional dependency, ensure the library is installed."
+                "Please install using 'pip install dlib' "
+            ) from e
 
         self.layers = [DlibMetaData()]
 
@@ -49,7 +55,7 @@ class DlibResNet:
 
         # return None  # classes must return None
 
-    def predict(self, img_aligned):
+    def predict(self, img_aligned: np.ndarray) -> np.ndarray:
 
         # functions.detectFace returns 4 dimensional images
         if len(img_aligned.shape) == 4:

deepface/basemodels/DlibWrapper.py

@@ -1,5 +1,6 @@
+from typing import Any
 from deepface.basemodels.DlibResNet import DlibResNet
 
 
-def loadModel():
+def loadModel() -> Any:
     return DlibResNet()

deepface/basemodels/Facenet.py

@@ -47,7 +47,7 @@ def scaling(x, scale):
     return x * scale
 
 
-def InceptionResNetV2(dimension=128):
+def InceptionResNetV2(dimension=128) -> Model:
 
     inputs = Input(shape=(160, 160, 3))
     x = Conv2D(32, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_1a_3x3")(inputs)
@@ -1618,12 +1618,9 @@ def InceptionResNetV2(dimension=128):
     return model
 
 
-# url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'
-
-
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5",
-):
+) -> Model:
     model = InceptionResNetV2()
 
     # -----------------------------------

deepface/basemodels/Facenet512.py

@@ -1,14 +1,23 @@
 import os
 import gdown
+import tensorflow as tf
 from deepface.basemodels import Facenet
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 
 logger = Logger(module="basemodels.Facenet512")
 
+tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
+
+if tf_version == 1:
+    from keras.models import Model
+else:
+    from tensorflow.keras.models import Model
+
+
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5",
-):
+) -> Model:
 
     model = Facenet.InceptionResNetV2(dimension=512)
 

deepface/basemodels/FbDeepFace.py

@@ -40,7 +40,7 @@ else:
 
 def loadModel(
     url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
-):
+) -> Model:
     base_model = Sequential()
     base_model.add(
         Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))

deepface/basemodels/OpenFace.py

@@ -27,7 +27,7 @@ else:
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",
-):
+) -> Model:
     myInput = Input(shape=(96, 96, 3))
 
     x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)

deepface/basemodels/SFace.py

@@ -1,4 +1,6 @@
 import os
+from typing import Any
+
 import numpy as np
 import cv2 as cv
 import gdown
@@ -25,7 +27,7 @@ class SFaceModel:
 
         self.layers = [_Layer()]
 
-    def predict(self, image):
+    def predict(self, image: np.ndarray) -> np.ndarray:
         # Preprocess
         input_blob = (image[0] * 255).astype(
             np.uint8
@@ -39,7 +41,7 @@ class SFaceModel:
 
 def load_model(
     url="https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx",
-):
+) -> Any:
 
     home = functions.get_deepface_home()
 

deepface/basemodels/VGGFace.py

@@ -34,7 +34,7 @@ else:
 # ---------------------------------------
 
 
-def baseModel():
+def baseModel() -> Sequential:
     model = Sequential()
     model.add(ZeroPadding2D((1, 1), input_shape=(224, 224, 3)))
     model.add(Convolution2D(64, (3, 3), activation="relu"))
@@ -83,17 +83,12 @@ def baseModel():
     return model
 
 
-# url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'
-
-
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5",
-):
+) -> Model:
 
     model = baseModel()
 
-    # -----------------------------------
-
     home = functions.get_deepface_home()
     output = home + "/.deepface/weights/vgg_face_weights.h5"
 
@@ -101,13 +96,8 @@ def loadModel(
         logger.info("vgg_face_weights.h5 will be downloaded...")
         gdown.download(url, output, quiet=False)
 
-    # -----------------------------------
-
     model.load_weights(output)
 
-    # -----------------------------------
-
-    # TO-DO: why?
     vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
 
     return vgg_face_descriptor

deepface/commons/distance.py

@@ -1,14 +1,25 @@
+from typing import Union
 import numpy as np
 
 
-def findCosineDistance(source_representation, test_representation):
+def findCosineDistance(
+    source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
+) -> np.float64:
+    if isinstance(source_representation, list):
+        source_representation = np.array(source_representation)
+
+    if isinstance(test_representation, list):
+        test_representation = np.array(test_representation)
+
     a = np.matmul(np.transpose(source_representation), test_representation)
     b = np.sum(np.multiply(source_representation, source_representation))
     c = np.sum(np.multiply(test_representation, test_representation))
     return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
 
 
-def findEuclideanDistance(source_representation, test_representation):
+def findEuclideanDistance(
+    source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
+) -> np.float64:
     if isinstance(source_representation, list):
         source_representation = np.array(source_representation)
 
@@ -21,11 +32,11 @@ def findEuclideanDistance(source_representation, test_representation):
     return euclidean_distance
 
 
-def l2_normalize(x):
+def l2_normalize(x: np.ndarray) -> np.ndarray:
     return x / np.sqrt(np.sum(np.multiply(x, x)))
 
 
-def findThreshold(model_name, distance_metric):
+def findThreshold(model_name: str, distance_metric: str) -> float:
 
     base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}
 

deepface/commons/functions.py

@@ -1,10 +1,11 @@
 import os
+from typing import Union, Tuple
 import base64
 from pathlib import Path
-from PIL import Image
-import requests
 
 # 3rd party dependencies
+from PIL import Image
+import requests
 import numpy as np
 import cv2
 import tensorflow as tf
@@ -33,7 +34,7 @@ elif tf_major_version == 2:
 # --------------------------------------------------
 
 
-def initialize_folder():
+def initialize_folder() -> None:
     """Initialize the folder for storing weights and models.
 
     Raises:
@@ -52,7 +53,7 @@ def initialize_folder():
         logger.info(f"Directory {home}/.deepface/weights created")
 
 
-def get_deepface_home():
+def get_deepface_home() -> str:
     """Get the home directory for storing weights and models.
 
     Returns:
@@ -64,7 +65,7 @@ def get_deepface_home():
 # --------------------------------------------------
 
 
-def loadBase64Img(uri):
+def loadBase64Img(uri: str) -> np.ndarray:
     """Load image from base64 string.
 
     Args:
@@ -80,7 +81,7 @@ def loadBase64Img(uri):
     return img_bgr
 
 
-def load_image(img):
+def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
     """
     Load image from path, url, base64 or numpy array.
     Args:
@@ -91,15 +92,18 @@ def load_image(img):
     """
 
     # The image is already a numpy array
-    if type(img).__module__ == np.__name__:
+    if isinstance(img, np.ndarray):
-        return img, None
+        return img, "numpy array"
 
     if isinstance(img, Path):
         img = str(img)
 
+    if not isinstance(img, str):
+        raise ValueError(f"img must be numpy array or str but it is {type(img)}")
+
     # The image is a base64 string
     if img.startswith("data:image/"):
-        return loadBase64Img(img), None
+        return loadBase64Img(img), "base64 encoded string"
 
     # The image is a url
     if img.startswith("http"):
@@ -128,13 +132,13 @@ def load_image(img):
 
 
 def extract_faces(
-    img,
+    img: Union[str, np.ndarray],
-    target_size=(224, 224),
+    target_size: tuple = (224, 224),
-    detector_backend="opencv",
+    detector_backend: str = "opencv",
-    grayscale=False,
+    grayscale: bool = False,
-    enforce_detection=True,
+    enforce_detection: bool = True,
-    align=True,
+    align: bool = True,
-):
+) -> list:
     """Extract faces from an image.
 
     Args:
@@ -252,7 +256,7 @@ def extract_faces(
     return extracted_faces
 
 
-def normalize_input(img, normalization="base"):
+def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
     """Normalize input image.
 
     Args:
@@ -310,7 +314,7 @@ def normalize_input(img, normalization="base"):
     return img
 
 
-def find_target_size(model_name):
+def find_target_size(model_name: str) -> tuple:
     """Find the target size of the model.
 
     Args:
@@ -346,17 +350,18 @@ def find_target_size(model_name):
 
 @deprecated(version="0.0.78", reason="Use extract_faces instead of preprocess_face")
 def preprocess_face(
-    img,
+    img: Union[str, np.ndarray],
     target_size=(224, 224),
     detector_backend="opencv",
     grayscale=False,
     enforce_detection=True,
     align=True,
-):
+) -> Union[np.ndarray, None]:
-    """Preprocess face.
+    """
+    Preprocess only one face
 
     Args:
-        img (numpy array): the input image.
+        img (str or numpy): the input image.
         target_size (tuple, optional): the target size. Defaults to (224, 224).
         detector_backend (str, optional): the detector backend. Defaults to "opencv".
         grayscale (bool, optional): whether to convert to grayscale. Defaults to False.
@@ -364,7 +369,7 @@ def preprocess_face(
         align (bool, optional): whether to align the face. Defaults to True.
 
     Returns:
-        numpy array: the preprocessed face.
+        loaded image (numpt): the preprocessed face.
 
     Raises:
         ValueError: if face is not detected and enforce_detection is True.

deepface/commons/logger.py

@@ -12,7 +12,8 @@ class Logger:
         except Exception as err:
             self.dump_log(
                 f"Exception while parsing $DEEPFACE_LOG_LEVEL."
-                f"Expected int but it is {log_level} ({str(err)})"
+                f"Expected int but it is {log_level} ({str(err)})."
+                "Setting app log level to info."
             )
             self.log_level = logging.INFO
 

deepface/detectors/DlibWrapper.py

@@ -1,14 +1,19 @@
 import os
 import bz2
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 
 logger = Logger(module="detectors.DlibWrapper")
 
 
-def build_model():
+def build_model() -> dict:
-
+    """
+    Build a dlib hog face detector model
+    Returns:
+        model (Any)
+    """
     home = functions.get_deepface_home()
 
     # this is not a must dependency. do not import it in the global level.
@@ -46,8 +51,16 @@ def build_model():
     return detector
 
 
-def detect_face(detector, img, align=True):
+def detect_face(detector: dict, img: np.ndarray, align: bool = True) -> list:
-
+    """
+    Detect and align face with dlib
+    Args:
+        face_detector (Any): dlib face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     # this is not a must dependency. do not import it in the global level.
     try:
         import dlib

deepface/detectors/FaceDetector.py

@@ -1,3 +1,4 @@
+from typing import Any, Union
 from PIL import Image
 import numpy as np
 from deepface.detectors import (
@@ -13,7 +14,14 @@ from deepface.detectors import (
 )
 
 
-def build_model(detector_backend):
+def build_model(detector_backend: str) -> Any:
+    """
+    Build a face detector model
+    Args:
+        detector_backend (str): backend detector name
+    Returns:
+        built detector (Any)
+    """
     global face_detector_obj  # singleton design pattern
 
     backends = {
@@ -44,7 +52,20 @@ def build_model(detector_backend):
     return face_detector_obj[detector_backend]
 
 
-def detect_face(face_detector, detector_backend, img, align=True):
+def detect_face(
+    face_detector: Any, detector_backend: str, img: np.ndarray, align: bool = True
+) -> tuple:
+    """
+    Detect a single face from a given image
+    Args:
+        face_detector (Any): pre-built face detector object
+        detector_backend (str): detector name
+        img (np.ndarray): pre-loaded image
+        alig (bool): enable or disable alignment after detection
+    Returns
+        result (tuple): tuple of face (np.ndarray), face region (list)
+            , confidence score (float)
+    """
     obj = detect_faces(face_detector, detector_backend, img, align)
 
     if len(obj) > 0:
@@ -60,7 +81,20 @@ def detect_face(face_detector, detector_backend, img, align=True):
     return face, region, confidence
 
 
-def detect_faces(face_detector, detector_backend, img, align=True):
+def detect_faces(
+    face_detector: Any, detector_backend: str, img: np.ndarray, align: bool = True
+) -> list:
+    """
+    Detect face(s) from a given image
+    Args:
+        face_detector (Any): pre-built face detector object
+        detector_backend (str): detector name
+        img (np.ndarray): pre-loaded image
+        alig (bool): enable or disable alignment after detection
+    Returns
+        result (list): tuple of face (np.ndarray), face region (list)
+            , confidence score (float)
+    """
     backends = {
         "opencv": OpenCvWrapper.detect_face,
         "ssd": SsdWrapper.detect_face,
@@ -83,18 +117,32 @@ def detect_faces(face_detector, detector_backend, img, align=True):
         raise ValueError("invalid detector_backend passed - " + detector_backend)
 
 
-def get_alignment_angle_arctan2(left_eye, right_eye):
+def get_alignment_angle_arctan2(
+    left_eye: Union[list, tuple], right_eye: Union[list, tuple]
+) -> float:
     """
-    The left_eye is the eye to the left of the viewer,
+    Find the angle between eyes
-    i.e., right eye of the person in the image.
+    Args:
-    The top-left point of the frame is (0, 0).
+        left_eye: coordinates of left eye with respect to the you
+        right_eye: coordinates of right eye with respect to the you
+    Returns:
+        angle (float)
     """
-    return float(np.degrees(
+    return float(np.degrees(np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0])))
-        np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0])
-    ))
 
 
-def alignment_procedure(img, left_eye, right_eye):
+def alignment_procedure(
+    img: np.ndarray, left_eye: Union[list, tuple], right_eye: Union[list, tuple]
+) -> np.ndarray:
+    """
+    Rotate given image until eyes are on a horizontal line
+    Args:
+        img (np.ndarray): pre-loaded image
+        left_eye: coordinates of left eye with respect to the you
+        right_eye: coordinates of right eye with respect to the you
+    Returns:
+        result (np.ndarray): aligned face
+    """
     angle = get_alignment_angle_arctan2(left_eye, right_eye)
     img = Image.fromarray(img)
     img = np.array(img.rotate(angle))

deepface/detectors/FastMtcnnWrapper.py

@@ -1,11 +1,18 @@
+from typing import Any, Union
 import cv2
+import numpy as np
 from deepface.detectors import FaceDetector
 
 # Link -> https://github.com/timesler/facenet-pytorch
 # Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
 
 
-def build_model():
+def build_model() -> Any:
+    """
+    Build a fast mtcnn face detector model
+    Returns:
+        model (Any)
+    """
     # this is not a must dependency. do not import it in the global level.
     try:
         from facenet_pytorch import MTCNN as fast_mtcnn
@@ -25,7 +32,7 @@ def build_model():
     return face_detector
 
 
-def xyxy_to_xywh(xyxy):
+def xyxy_to_xywh(xyxy: Union[list, tuple]) -> list:
     """
     Convert xyxy format to xywh format.
     """
@@ -35,8 +42,16 @@ def xyxy_to_xywh(xyxy):
     return [x, y, w, h]
 
 
-def detect_face(face_detector, img, align=True):
+def detect_face(face_detector: Any, img: np.ndarray, align: bool = True) -> list:
-
+    """
+    Detect and align face with mtcnn
+    Args:
+        face_detector (Any): mtcnn face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
     detected_face = None

deepface/detectors/MediapipeWrapper.py

@@ -1,9 +1,16 @@
+from typing import Any
+import numpy as np
 from deepface.detectors import FaceDetector
 
 # Link - https://google.github.io/mediapipe/solutions/face_detection
 
 
-def build_model():
+def build_model() -> Any:
+    """
+    Build a mediapipe face detector model
+    Returns:
+        model (Any)
+    """
     # this is not a must dependency. do not import it in the global level.
     try:
         import mediapipe as mp
@@ -18,7 +25,16 @@ def build_model():
     return face_detection
 
 
-def detect_face(face_detector, img, align=True):
+def detect_face(face_detector: Any, img: np.ndarray, align: bool = True) -> list:
+    """
+    Detect and align face with mediapipe
+    Args:
+        face_detector (Any): mediapipe face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
     img_width = img.shape[1]

deepface/detectors/MtcnnWrapper.py

@@ -1,15 +1,31 @@
+from typing import Any
 import cv2
+import numpy as np
 from deepface.detectors import FaceDetector
 
 
-def build_model():
+def build_model() -> Any:
+    """
+    Build a mtcnn face detector model
+    Returns:
+        model (Any)
+    """
     from mtcnn import MTCNN
 
     face_detector = MTCNN()
     return face_detector
 
 
-def detect_face(face_detector, img, align=True):
+def detect_face(face_detector: Any, img: np.ndarray, align: bool = True) -> list:
+    """
+    Detect and align face with mtcnn
+    Args:
+        face_detector (mtcnn.MTCNN): mtcnn face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
 
     resp = []
 

deepface/detectors/OpenCvWrapper.py

@@ -1,16 +1,28 @@
 import os
+from typing import Any
 import cv2
+import numpy as np
 from deepface.detectors import FaceDetector
 
 
-def build_model():
+def build_model() -> dict:
+    """
+    Build a opencv face&eye detector models
+    Returns:
+        model (Any)
+    """
     detector = {}
     detector["face_detector"] = build_cascade("haarcascade")
     detector["eye_detector"] = build_cascade("haarcascade_eye")
     return detector
 
 
-def build_cascade(model_name="haarcascade"):
+def build_cascade(model_name="haarcascade") -> Any:
+    """
+    Build a opencv face&eye detector models
+    Returns:
+        model (Any)
+    """
     opencv_path = get_opencv_path()
     if model_name == "haarcascade":
         face_detector_path = opencv_path + "haarcascade_frontalface_default.xml"
@@ -38,7 +50,16 @@ def build_cascade(model_name="haarcascade"):
     return detector
 
 
-def detect_face(detector, img, align=True):
+def detect_face(detector: dict, img: np.ndarray, align: bool = True) -> list:
+    """
+    Detect and align face with opencv
+    Args:
+        face_detector (Any): opencv face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
     detected_face = None

deepface/detectors/RetinaFaceWrapper.py

@@ -1,21 +1,31 @@
-def build_model():
+from typing import Any
-    from retinaface import RetinaFace  # this is not a must dependency
+import numpy as np
+from retinaface import RetinaFace
+from retinaface.commons import postprocess
 
+
+def build_model() -> Any:
+    """
+    Build a retinaface detector model
+    Returns:
+        model (Any)
+    """
     face_detector = RetinaFace.build_model()
     return face_detector
 
 
-def detect_face(face_detector, img, align=True):
+def detect_face(face_detector: Any, img: np.ndarray, align: bool = True) -> list:
-
+    """
-    from retinaface import RetinaFace  # this is not a must dependency
+    Detect and align face with retinaface
-    from retinaface.commons import postprocess
+    Args:
-
+        face_detector (Any): retinaface face detector object
-    # ---------------------------------
+        img (np.ndarray): pre-loaded image
-
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
-    # --------------------------
-
     obj = RetinaFace.detect_faces(img, model=face_detector, threshold=0.9)
 
     if isinstance(obj, dict):

deepface/detectors/SsdWrapper.py

@@ -2,6 +2,7 @@ import os
 import gdown
 import cv2
 import pandas as pd
+import numpy as np
 from deepface.detectors import OpenCvWrapper
 from deepface.commons import functions
 from deepface.commons.logger import Logger
@@ -11,7 +12,12 @@ logger = Logger(module="detectors.SsdWrapper")
 # pylint: disable=line-too-long
 
 
-def build_model():
+def build_model() -> dict:
+    """
+    Build a ssd detector model
+    Returns:
+        model (Any)
+    """
 
     home = functions.get_deepface_home()
 
@@ -51,8 +57,16 @@ def build_model():
     return detector
 
 
-def detect_face(detector, img, align=True):
+def detect_face(detector: dict, img: np.ndarray, align: bool = True) -> list:
-
+    """
+    Detect and align face with ssd
+    Args:
+        face_detector (Any): ssd face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
     detected_face = None

deepface/detectors/YoloWrapper.py

@@ -1,3 +1,5 @@
+from typing import Any
+import numpy as np
 from deepface.detectors import FaceDetector
 from deepface.commons.logger import Logger
 
@@ -14,8 +16,12 @@ WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
 LANDMARKS_CONFIDENCE_THRESHOLD = 0.5
 
 
-def build_model():
+def build_model() -> Any:
-    """Build YOLO (yolov8n-face) model"""
+    """
+    Build a yolo detector model
+    Returns:
+        model (Any)
+    """
     import gdown
     import os
 
@@ -41,7 +47,16 @@ def build_model():
     return YOLO(weight_path)
 
 
-def detect_face(face_detector, img, align=False):
+def detect_face(face_detector: Any, img: np.ndarray, align: bool = False) -> list:
+    """
+    Detect and align face with yolo
+    Args:
+        face_detector (Any): yolo face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     resp = []
 
     # Detect faces

deepface/detectors/YunetWrapper.py

@@ -1,5 +1,7 @@
 import os
+from typing import Any
 import cv2
+import numpy as np
 import gdown
 from deepface.detectors import FaceDetector
 from deepface.commons import functions
@@ -7,7 +9,13 @@ from deepface.commons.logger import Logger
 
 logger = Logger(module="detectors.YunetWrapper")
 
-def build_model():
+
+def build_model() -> Any:
+    """
+    Build a yunet detector model
+    Returns:
+        model (Any)
+    """
     # pylint: disable=C0301
     url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
     file_name = "face_detection_yunet_2023mar.onnx"
@@ -20,7 +28,18 @@ def build_model():
     return face_detector
 
 
-def detect_face(detector, image, align=True, score_threshold=0.9):
+def detect_face(
+    detector: Any, image: np.ndarray, align: bool = True, score_threshold: float = 0.9
+) -> list:
+    """
+    Detect and align face with yunet
+    Args:
+        face_detector (Any): yunet face detector object
+        img (np.ndarray): pre-loaded image
+        align (bool): default is true
+    Returns:
+        list of detected and aligned faces
+    """
     # FaceDetector.detect_faces does not support score_threshold parameter.
     # We can set it via environment variable.
     score_threshold = os.environ.get("yunet_score_threshold", score_threshold)
@@ -78,12 +97,8 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
         detected_face = image[int(y) : int(y + h), int(x) : int(x + w)]
         img_region = [x, y, w, h]
         if align:
-            detected_face = yunet_align_face(detected_face, x_re, y_re, x_le, y_le)
+            detected_face = FaceDetector.alignment_procedure(
+                detected_face, (x_re, y_re), (x_le, y_le)
+            )
         resp.append((detected_face, img_region, confidence))
     return resp
-
-
-# x_re, y_re, x_le, y_le stands for the coordinates of right eye, left eye
-def yunet_align_face(img, x_re, y_re, x_le, y_le):
-    img = FaceDetector.alignment_procedure(img, (x_re, y_re), (x_le, y_le))
-    return img

deepface/extendedmodels/Age.py

@@ -16,7 +16,7 @@ tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
 if tf_version == 1:
     from keras.models import Model, Sequential
     from keras.layers import Convolution2D, Flatten, Activation
-elif tf_version == 2:
+else:
     from tensorflow.keras.models import Model, Sequential
     from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
@@ -25,7 +25,7 @@ elif tf_version == 2:
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5",
-):
+) -> Model:
 
     model = VGGFace.baseModel()
 
@@ -60,7 +60,7 @@ def loadModel(
     # --------------------------
 
 
-def findApparentAge(age_predictions):
+def findApparentAge(age_predictions) -> np.float64:
     output_indexes = np.array(list(range(0, 101)))
     apparent_age = np.sum(age_predictions * output_indexes)
     return apparent_age

deepface/extendedmodels/Emotion.py

@@ -15,7 +15,7 @@ tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
 if tf_version == 1:
     from keras.models import Sequential
     from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
-elif tf_version == 2:
+else:
     from tensorflow.keras.models import Sequential
     from tensorflow.keras.layers import (
         Conv2D,
@@ -33,7 +33,7 @@ labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
-):
+) -> Sequential:
 
     num_classes = 7
 

deepface/extendedmodels/Gender.py

@@ -17,7 +17,7 @@ tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
 if tf_version == 1:
     from keras.models import Model, Sequential
     from keras.layers import Convolution2D, Flatten, Activation
-elif tf_version == 2:
+else:
     from tensorflow.keras.models import Model, Sequential
     from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 # -------------------------------------
@@ -28,7 +28,7 @@ labels = ["Woman", "Man"]
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5",
-):
+) -> Model:
 
     model = VGGFace.baseModel()
 

deepface/extendedmodels/Race.py

@@ -16,7 +16,7 @@ tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
 if tf_version == 1:
     from keras.models import Model, Sequential
     from keras.layers import Convolution2D, Flatten, Activation
-elif tf_version == 2:
+else:
     from tensorflow.keras.models import Model, Sequential
     from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 # --------------------------
@@ -26,7 +26,7 @@ labels = ["asian", "indian", "black", "white", "middle eastern", "latino hispani
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
-):
+) -> Model:
 
     model = VGGFace.baseModel()
 

tests/test_represent.py

@@ -9,7 +9,7 @@ def test_standard_represent():
     embedding_objs = DeepFace.represent(img_path)
     for embedding_obj in embedding_objs:
         embedding = embedding_obj["embedding"]
-        logger.info(f"Function returned {len(embedding)} dimensional vector")
+        logger.debug(f"Function returned {len(embedding)} dimensional vector")
         assert len(embedding) == 2622
     logger.info("✅ test standard represent function done")