added reviewed changes

deepface/DeepFace.py

@@ -54,7 +54,7 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
     Args:
         model_name (str): model identifier
             - VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
-                ArcFace, SFace GhostFaceNet and buffalo_l for face recognition
+                ArcFace, SFace GhostFaceNet and Buffalo_L for face recognition
             - Age, Gender, Emotion, Race for facial attributes
             - opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yolov11n,
               yolov11s, yolov11m, yunet, fastmtcnn or centerface for face detectors

deepface/models/facial_recognition/Buffalo_L.py

@@ -1,73 +1,73 @@
-import cv2
-import numpy as np
-from deepface.models.FacialRecognition import FacialRecognition
-from deepface.commons.logger import Logger
-from deepface.basemodel import get_weights_path
-from deepface.common import weight_utils
 import os
+import numpy as np
+from deepface.commons import weight_utils
+from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger()
 
-# Check for insightface dependency
 try:
     from insightface.model_zoo import get_model
-except ModuleNotFoundError:
+except ModuleNotFoundError as err:
     raise ModuleNotFoundError(
         "InsightFace is an optional dependency for the Buffalo_L model."
         "You can install it with: pip install insightface>=0.7.3"
-    )
+    ) from err
 
 class Buffalo_L(FacialRecognition):
     def __init__(self):
         self.model = None
-        self.input_shape = (112, 112)  # Buffalo_L recognition model expects 112x112
-        self.output_shape = 512  # Embedding size for Buffalo_L
+        self.input_shape = (112, 112)  # Buffalo_L expects 112x112
+        self.output_shape = 512  # Embedding size
         self.load_model()
 
     def load_model(self):
-        root = os.path.join(get_weights_path(), 'insightface')
-        model_name = 'buffalo_l/w600k_r50.onnx'
-        model_path = os.path.join(root, model_name)
-        
-        if not os.path.exists(model_path):
-            url = 'https://drive.google.com/file/d/1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg/view?usp=sharing'
-            weight_utils.download_file(url, model_path)
-        
-        self.model = get_model(model_name, root=root)
+        """
+        Load the InsightFace Buffalo_L recognition model.
+        """
+        # Use DeepFace's utility to download weights if necessary
+        model_rel_path = os.path.join("insightface", "buffalo_l", "w600k_r50.onnx")
+        weights_path = weight_utils.download_weights_if_necessary(
+            file_name="webface_r50.onnx",
+            source_url="https://drive.google.com/uc?export=download&confirm=pbef&id=1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg"
+        )
+        # Load model from weights folder
+        self.model = get_model("buffalo_l/w600k_r50.onnx", root=os.path.dirname(weights_path))
         self.model.prepare(ctx_id=-1, input_size=self.input_shape)
 
-    def preprocess(self, img):
+    def preprocess(self, img: np.ndarray) -> np.ndarray:
         """
         Preprocess the image to match InsightFace recognition model expectations.
         Args:
-            img (numpy array): Image in shape (1, 112, 112, 3) or (112, 112, 3)
+            img: Image in shape (1, 112, 112, 3) or (112, 112, 3)
         Returns:
-            numpy array: Preprocessed image
+            Preprocessed image as numpy array
         """
         if len(img.shape) == 4:  # (1, 112, 112, 3)
             img = img[0]  # Remove batch dimension
         if img.max() <= 1.0:  # If normalized to [0, 1]
             img = (img * 255).astype(np.uint8)
-        # Always convert RGB to BGR (InsightFace expects BGR, DeepFace provides RGB)
+        # Always convert RGB to BGR (DeepFace outputs RGB, InsightFace expects BGR)
         img = img[:, :, ::-1]
         return img
 
-    def forward(self, img):
+    def forward(self, img: np.ndarray) -> list[float]:
         """
         Extract face embedding from a pre-cropped face image.
         Args:
-            img (numpy array): Preprocessed face image with shape (1, 112, 112, 3)
+            img: Preprocessed face image with shape (1, 112, 112, 3)
         Returns:
-            numpy array: Face embedding
+            Face embedding as a list of floats
         """
         img = self.preprocess(img)
         embedding = self.model.get_feat(img)
         
         # Handle different embedding formats
-        if isinstance(embedding, np.ndarray):
-            if len(embedding.shape) > 1:
-                embedding = embedding.flatten()
+        if isinstance(embedding, np.ndarray) and len(embedding.shape) > 1:
+            embedding = embedding.flatten()
         elif isinstance(embedding, list):
             embedding = np.array(embedding).flatten()
-            
-        return embedding
+        else:
+            raise ValueError(f"Unexpected embedding type: {type(embedding)}")
+        
+        return embedding.tolist()  # Convert to list per FacialRecognition spec