added buffalo_l model

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 and GhostFaceNet 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

@@ -0,0 +1,76 @@
+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()
+
+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.load_model()
+
+    def load_model(self):
+        """
+        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
+
+    def preprocess(self, img):
+        """
+        Preprocess the image to match InsightFace recognition model expectations.
+        Args:
+            img (numpy array): Image in shape (1, 112, 112, 3) or (112, 112, 3)
+        Returns:
+            numpy array: Preprocessed image
+        """
+        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)
+        if img.shape[2] == 3:
+            img = img[:, :, ::-1]  # RGB to BGR
+        return img
+
+    def forward(self, img):
+        """
+        Extract face embedding from a pre-cropped face image.
+        Args:
+            img (numpy array): Preprocessed face image with shape (1, 112, 112, 3)
+        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
+        if isinstance(embedding, (list, np.ndarray)) and len(embedding.shape) > 1:
+            embedding = embedding.flatten()
+            
+        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/modeling.py

@@ -11,7 +11,8 @@ from deepface.models.facial_recognition import (
     SFace,
     Dlib,
     Facenet,
-    GhostFaceNet
+    GhostFaceNet,
+    Buffalo_L
 )
 from deepface.models.face_detection import (
     FastMtCnn,
@@ -59,7 +60,8 @@ def build_model(task: str, model_name: str) -> Any:
             "Dlib": Dlib.DlibClient,
             "ArcFace": ArcFace.ArcFaceClient,
             "SFace": SFace.SFaceClient,
-            "GhostFaceNet": GhostFaceNet.GhostFaceNetClient
+            "GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
+            "Buffalo_L": Buffalo_L.Buffalo_L
         },
         "spoofing": {
             "Fasnet": FasNet.Fasnet,

requirements.txt

@@ -13,3 +13,6 @@ mtcnn>=0.1.0
 retina-face>=0.0.14
 fire>=0.4.0
 gunicorn>=20.1.0
+tf-keras
+insightface==0.7.3
+onnxruntime>=1.9.0