Close #532, add docstring and some refactoring.

deepface/commons/functions.py

@@ -29,7 +29,12 @@ elif tf_major_version == 2:
 # --------------------------------------------------
 
 
-def initialize_folder():
+def initialize_folder() -> None:
+    """Initialize the folder for storing weights and models.
+
+    Raises:
+        OSError: if the folder cannot be created.
+    """
     home = get_deepface_home()
 
     if not os.path.exists(home + "/.deepface"):
@@ -41,7 +46,12 @@ def initialize_folder():
         print("Directory ", home, "/.deepface/weights created")
 
 
-def get_deepface_home():
+def get_deepface_home() -> str:
+    """Get the home directory for storing weights and models.
+
+    Returns:
+        str: the home directory.
+    """
     return str(os.getenv("DEEPFACE_HOME", default=str(Path.home())))
 
 
@@ -49,6 +59,14 @@ def get_deepface_home():
 
 
 def loadBase64Img(uri):
+    """Load image from base64 string.
+
+    Args:
+        uri: a base64 string.
+
+    Returns:
+        numpy array: the loaded image.
+    """
     encoded_data = uri.split(",")[1]
     nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
@@ -56,32 +74,38 @@ def loadBase64Img(uri):
 
 
 def load_image(img):
+    """Load image from path, url, base64 or numpy array.
+
+    Args:
+        img: a path, url, base64 or numpy array.
+
+    Raises:
+        ValueError: if the image path does not exist.
+
+    Returns:
+        numpy array: the loaded image.
+    """
     exact_image = False
-    base64_img = False
-    url_img = False
 
+    # The image is already a numpy array
     if type(img).__module__ == np.__name__:
-        exact_image = True
+        # exact_image = True
+        return img
 
+    # The image is a base64 string
     elif img.startswith("data:image/"):
-        base64_img = True
+        return loadBase64Img(img)
 
+    # The image is a url
     elif img.startswith("http"):
-        url_img = True
+        return np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))[:, :, ::-1]
 
-    # ---------------------------
+    # The image is a path
-
+    if exact_image is not True:  # image path passed as input
-    if base64_img is True:
-        img = loadBase64Img(img)
-
-    elif url_img is True:
-        img = np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("RGB"))
-
-    elif exact_image is not True:  # image path passed as input
         if os.path.isfile(img) is not True:
             raise ValueError(f"Confirm that {img} exists")
 
-        img = cv2.imread(img)
+        return cv2.imread(img)
 
     return img
 
@@ -96,7 +120,23 @@ def extract_faces(
     grayscale=False,
     enforce_detection=True,
     align=True,
-):
+) -> list:
+    """Extract faces from an image.
+
+    Args:
+        img: a path, url, base64 or numpy array.
+        target_size (tuple, optional): the target size of the extracted faces. Defaults to (224, 224).
+        detector_backend (str, optional): the face detector backend. Defaults to "opencv".
+        grayscale (bool, optional): whether to convert the extracted faces to grayscale. Defaults to False.
+        enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
+        align (bool, optional): whether to align the extracted faces. Defaults to True.
+
+    Raises:
+        ValueError: if face could not be detected and enforce_detection is True.
+
+    Returns:
+        list: a list of extracted faces.
+    """
 
     # this is going to store a list of img itself (numpy), it region and confidence
     extracted_faces = []
@@ -109,7 +149,8 @@ def extract_faces(
         face_objs = [(img, img_region, 0)]
     else:
         face_detector = FaceDetector.build_model(detector_backend)
-        face_objs = FaceDetector.detect_faces(face_detector, detector_backend, img, align)
+        face_objs = FaceDetector.detect_faces(
+            face_detector, detector_backend, img, align)
 
     # in case of no face found
     if len(face_objs) == 0 and enforce_detection is True:
@@ -133,7 +174,8 @@ def extract_faces(
                 factor_1 = target_size[1] / current_img.shape[1]
                 factor = min(factor_0, factor_1)
 
-                dsize = (int(current_img.shape[1] * factor), int(current_img.shape[0] * factor))
+                dsize = (
+                    int(current_img.shape[1] * factor), int(current_img.shape[0] * factor))
                 current_img = cv2.resize(current_img, dsize)
 
                 diff_0 = target_size[0] - current_img.shape[0]
@@ -152,7 +194,8 @@ def extract_faces(
                 else:
                     current_img = np.pad(
                         current_img,
-                        ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)),
+                        ((diff_0 // 2, diff_0 - diff_0 // 2),
+                         (diff_1 // 2, diff_1 - diff_1 // 2)),
                         "constant",
                     )
 
@@ -161,7 +204,8 @@ def extract_faces(
                 current_img = cv2.resize(current_img, target_size)
 
             # normalizing the image pixels
-            img_pixels = image.img_to_array(current_img)  # what this line doing? must?
+            # what this line doing? must?
+            img_pixels = image.img_to_array(current_img)
             img_pixels = np.expand_dims(img_pixels, axis=0)
             img_pixels /= 255  # normalize input in [0, 1]
 
@@ -185,6 +229,15 @@ def extract_faces(
 
 
 def normalize_input(img, normalization="base"):
+    """Normalize input image.
+
+    Args:
+        img (numpy array): the input image.
+        normalization (str, optional): the normalization technique. Defaults to "base", for no normalization.
+
+    Returns:
+        numpy array: the normalized image.
+    """
 
     # issue 131 declares that some normalization techniques improves the accuracy
 
@@ -232,7 +285,15 @@ 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:
+        model_name (str): the model name.
+
+    Returns:
+        tuple: the target size.
+    """
 
     target_sizes = {
         "VGG-Face": (224, 224),
@@ -267,6 +328,25 @@ def preprocess_face(
     enforce_detection=True,
     align=True,
 ):
+    """Preprocess face.
+
+    Args:
+        img (numpy array): 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.
+        enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
+        align (bool, optional): whether to align the face. Defaults to True.
+
+    Returns:
+        numpy array: the preprocessed face.
+
+    Raises:
+        ValueError: if face is not detected and enforce_detection is True.
+
+    Deprecated:
+        0.0.78: Use extract_faces instead of preprocess_face.
+    """
     print("⚠️ Function preprocess_face is deprecated. Use extract_faces instead.")
     result = None
     img_objs = extract_faces(