code review changes

deepface/models/facial_recognition/Buffalo_L.py

@@ -1,11 +1,19 @@
 import cv2
 import numpy as np
-from insightface.model_zoo import get_model
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
 logger = Logger()
 
+# Check for insightface dependency
+try:
+    from insightface.model_zoo import get_model
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        "InsightFace is an optional dependency for the Buffalo_L model."
+        "You can install it with: pip install insightface>=0.7.3"
+    )
+
 class Buffalo_L(FacialRecognition):
     def __init__(self):
         self.model = None
@@ -17,8 +25,6 @@ class Buffalo_L(FacialRecognition):
         """
         Load the InsightFace Buffalo_L recognition model.
         """
-        # Load the recognition model directly (e.g., w600k_r50 from buffalo_l)
-        # The buffalo_l package includes recognition model weights
         self.model = get_model('buffalo_l/w600k_r50.onnx', download=True)
         self.model.prepare(ctx_id=-1, input_size=self.input_shape)  # ctx_id=-1 for CPU
 
@@ -32,12 +38,10 @@ class Buffalo_L(FacialRecognition):
         """
         if len(img.shape) == 4:  # (1, 112, 112, 3)
             img = img[0]  # Remove batch dimension
-        # Ensure image is in uint8 format (0-255 range)
         if img.max() <= 1.0:  # If normalized to [0, 1]
             img = (img * 255).astype(np.uint8)
-        # Convert to BGR if in RGB (InsightFace expects BGR)
+        # Always convert RGB to BGR (InsightFace expects BGR, DeepFace provides RGB)
-        if img.shape[2] == 3:
+        img = img[:, :, ::-1]
-            img = img[:, :, ::-1]  # RGB to BGR
         return img
 
     def forward(self, img):
@@ -48,29 +52,14 @@ class Buffalo_L(FacialRecognition):
         Returns:
             numpy array: Face embedding
         """
-        # Preprocess the input image
         img = self.preprocess(img)
-        
-        # Extract embedding directly (no detection needed)
         embedding = self.model.get_feat(img)
         
-        # InsightFace recognition models return a list or array; ensure 1D output
+        # Handle different embedding formats
-        if isinstance(embedding, (list, np.ndarray)) and len(embedding.shape) > 1:
+        if isinstance(embedding, np.ndarray):
-            embedding = embedding.flatten()
+            if len(embedding.shape) > 1:
+                embedding = embedding.flatten()
+        elif isinstance(embedding, list):
+            embedding = np.array(embedding).flatten()
             
-        return embedding
+        return embedding
-
-    def verify(self, img1, img2, threshold=0.65):
-        """
-        Verify if two images contain the same person using cosine similarity.
-        Args:
-            img1, img2 (numpy arrays): Preprocessed images
-            threshold (float): Cosine similarity threshold
-        Returns:
-            tuple: (similarity_score, is_same_person)
-        """
-        emb1 = self.forward(img1)
-        emb2 = self.forward(img2)
-        
-        similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
-        return similarity, similarity > threshold

deepface/modules/verification.py

@@ -422,6 +422,7 @@ def find_threshold(model_name: str, distance_metric: str) -> float:
         "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64},
         "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17},
         "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10},
+        "Buffalo_L": {"cosine": 0.65, "euclidean": 11.13, "euclidean_l2": 1.1},
     }
 
     threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)

requirements.txt

@@ -12,7 +12,4 @@ flask_cors>=4.0.1
 mtcnn>=0.1.0
 retina-face>=0.0.14
 fire>=0.4.0
-gunicorn>=20.1.0
+gunicorn>=20.1.0
-tf-keras
-insightface==0.7.3
-onnxruntime>=1.9.0

requirements_additional.txt

@@ -3,4 +3,7 @@ mediapipe>=0.8.7.3
 dlib>=19.20.0
 ultralytics>=8.0.122
 facenet-pytorch>=2.5.3
-torch>=2.1.2
+torch>=2.1.2
+insightface>=0.7.3
+onnxruntime>=1.9.0
+tf-keras