Create test_buffalo_l

tests/test_buffalo_l

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+import cv2
+import insightface
+import numpy as np
+from insightface.app import FaceAnalysis
+
+# Initialize face analysis model
+app = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])  # Use 'CUDAExecutionProvider' for GPU
+app.prepare(ctx_id=-1)  # ctx_id=-1 for CPU, 0 for GPU
+
+def get_face_embedding(image_path):
+    """Extract face embedding from an image"""
+    img = cv2.imread(image_path)
+    if img is None:
+        raise ValueError(f"Could not read image: {image_path}")
+    
+    faces = app.get(img)
+    
+    if len(faces) < 1:
+        raise ValueError("No faces detected in the image")
+    if len(faces) > 1:
+        print("Warning: Multiple faces detected. Using first detected face")
+    
+    return faces[0].embedding
+
+def compare_faces(emb1, emb2, threshold=0.65): # Adjust this threshold according to your usecase.
+    """Compare two embeddings using cosine similarity"""
+    similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
+    return similarity, similarity > threshold
+
+# Paths to your Indian face images
+image1_path = "dataset/img1.jpg"
+image2_path = "dataset/img2.jpg"
+
+try:
+    # Get embeddings
+    emb1 = get_face_embedding(image1_path)
+    emb2 = get_face_embedding(image2_path)
+    
+    # Compare faces
+    similarity_score, is_same_person = compare_faces(emb1, emb2)
+    
+    print(f"Similarity Score: {similarity_score:.4f}")
+    print(f"Same person? {'YES' if is_same_person else 'NO'}")
+    
+except Exception as e:
+    print(f"Error: {str(e)}")