From 844e107168b83b5a8ce74bc769ab69d2eb2b67df Mon Sep 17 00:00:00 2001
From: Narra_Venkata_Raghu_Charan <narravenkataraghucharan@gmail.com>
Date: Mon, 17 Feb 2025 21:37:58 +0530
Subject: [PATCH] Create insightface_app.py

---
 examples/face_recognition/insightface_app.py | 46 ++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 examples/face_recognition/insightface_app.py

diff --git a/examples/face_recognition/insightface_app.py b/examples/face_recognition/insightface_app.py
new file mode 100644
index 0000000..d5f515e
--- /dev/null
+++ b/examples/face_recognition/insightface_app.py
@@ -0,0 +1,46 @@
+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 = "path/to/face1.jpg"
+image2_path = "path/to/face2.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)}")