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| import gradio as gr | |
| import os | |
| import numpy as np | |
| from PIL import Image | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import insightface | |
| from insightface.app import FaceAnalysis | |
| # ----------------------------- | |
| # Setup directories | |
| # ----------------------------- | |
| os.makedirs("registered_faces", exist_ok=True) | |
| os.makedirs("embeddings", exist_ok=True) | |
| # ----------------------------- | |
| # Load ArcFace Model (InsightFace) | |
| # ----------------------------- | |
| app = FaceAnalysis(name="buffalo_l") # ArcFace best model set | |
| app.prepare(ctx_id=0, det_size=(640, 640)) # ctx_id=0 uses GPU if available, else CPU | |
| # ----------------------------- | |
| # Helper: Generate Embedding | |
| # ----------------------------- | |
| def get_embedding(image): | |
| img = np.array(image) | |
| faces = app.get(img) | |
| if len(faces) == 0: | |
| return None, "❌ No face detected. Try another image." | |
| # Take first detected face | |
| embedding = faces[0].embedding | |
| return embedding, None | |
| # ----------------------------- | |
| # Register New Face | |
| # ----------------------------- | |
| def register_face(name, image): | |
| if not name: | |
| return "⚠️ Please enter a name." | |
| embedding, error = get_embedding(image) | |
| if embedding is None: | |
| return error | |
| # Save image & embedding | |
| image.save(f"registered_faces/{name}.jpg") | |
| np.save(f"embeddings/{name}.npy", embedding) | |
| return f"✅ Registered {name} successfully!" | |
| # ----------------------------- | |
| # Recognize Face | |
| # ----------------------------- | |
| def recognize_face(image): | |
| embedding, error = get_embedding(image) | |
| if embedding is None: | |
| return error | |
| best_match = None | |
| highest_score = 0 | |
| for file in os.listdir("embeddings"): | |
| if file.endswith(".npy"): | |
| saved_emb = np.load(os.path.join("embeddings", file)) | |
| score = cosine_similarity([embedding], [saved_emb])[0][0] | |
| if score > highest_score: | |
| highest_score = score | |
| best_match = file.replace(".npy", "") | |
| # Threshold decision | |
| if best_match and highest_score > 0.60: | |
| return f"🟢 Match Found: **{best_match}** (Similarity: {highest_score:.2f})" | |
| return f"🔴 No match found. Best score = {highest_score:.2f}" | |
| # ----------------------------- | |
| # Gradio UI | |
| # ----------------------------- | |
| with gr.Blocks(title="Face Recognition Attendance System") as demo: | |
| gr.Markdown("## 🧠 Facial Recognition System (ArcFace Based)") | |
| gr.Markdown("Upload a face to register or recognize.") | |
| with gr.Tab("📌 Register Employee"): | |
| name_input = gr.Textbox(label="Employee Name") | |
| reg_image = gr.Image(label="Upload Face", type="pil") | |
| reg_button = gr.Button("Register") | |
| reg_output = gr.Textbox(label="Status") | |
| reg_button.click(register_face, inputs=[name_input, reg_image], outputs=reg_output) | |
| with gr.Tab("🔍 Recognize Face"): | |
| recog_image = gr.Image(label="Upload Face", type="pil") | |
| recog_button = gr.Button("Recognize") | |
| recog_output = gr.Textbox(label="Result") | |
| recog_button.click(recognize_face, inputs=recog_image, outputs=recog_output) | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |