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| import streamlit as st | |
| from deepface import DeepFace | |
| import requests | |
| from PIL import Image | |
| from gtts import gTTS | |
| import os | |
| import cv2 | |
| import numpy as np | |
| # Constants | |
| KNOWN_FOLDER = "known_faces" | |
| ESP32_SERVER_URL = "https://esp32-upload-server.onrender.com" | |
| MODEL_NAME = "ArcFace" # Options: "VGG-Face", "Facenet", "ArcFace", etc. | |
| DETECTOR_BACKEND = "retinaface" # Options: "opencv", "mtcnn", "dlib", "retinaface", etc. | |
| # Streamlit setup | |
| st.set_page_config(page_title="Second Eye - Enhanced Recognition", layout="centered") | |
| st.sidebar.title("Navigation") | |
| page = st.sidebar.radio("Go to", ["Face Recognition"]) | |
| # Fetch image from ESP32 | |
| def get_latest_image(): | |
| try: | |
| r = requests.get(f"{ESP32_SERVER_URL}/latest") | |
| if r.status_code != 200: | |
| return None | |
| filename = r.json()["filename"] | |
| return f"{ESP32_SERVER_URL}/uploads/{filename}" | |
| except: | |
| return None | |
| # Preprocess image for better recognition | |
| def preprocess_image(image_path): | |
| img = cv2.imread(image_path) | |
| if img is None: | |
| return image_path # Fallback | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # CLAHE for contrast enhancement | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) | |
| enhanced = clahe.apply(gray) | |
| # Gamma correction | |
| gamma = 1.5 | |
| lookUpTable = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype("uint8") | |
| gamma_corrected = cv2.LUT(enhanced, lookUpTable) | |
| # Convert back to BGR | |
| final_img = cv2.cvtColor(gamma_corrected, cv2.COLOR_GRAY2BGR) | |
| output_path = "preprocessed.jpg" | |
| cv2.imwrite(output_path, final_img) | |
| return output_path | |
| # Detect faces | |
| def is_face_detected(image_path): | |
| try: | |
| faces = DeepFace.extract_faces( | |
| img_path=image_path, | |
| enforce_detection=False, | |
| detector_backend=DETECTOR_BACKEND | |
| ) | |
| return len(faces) > 0 | |
| except: | |
| return False | |
| # Compare with known faces | |
| def compare_with_known_faces(unknown_img_path): | |
| for filename in os.listdir(KNOWN_FOLDER): | |
| known_img_path = os.path.join(KNOWN_FOLDER, filename) | |
| try: | |
| result = DeepFace.verify( | |
| img1_path=unknown_img_path, | |
| img2_path=known_img_path, | |
| model_name=MODEL_NAME, | |
| detector_backend=DETECTOR_BACKEND, | |
| enforce_detection=False | |
| ) | |
| if result["verified"]: | |
| return filename.split('.')[0] | |
| except Exception as e: | |
| print(f"Comparison failed with {filename}: {e}") | |
| return None | |
| # Main UI | |
| if page == "Face Recognition": | |
| st.title("Second Eye - Enhanced Face Recognition") | |
| if st.button("Check for New Image"): | |
| image_url = get_latest_image() | |
| if image_url: | |
| st.image(image_url, caption="Captured Image", use_container_width=True) | |
| response = requests.get(image_url) | |
| with open("latest.jpg", "wb") as f: | |
| f.write(response.content) | |
| processed_img_path = preprocess_image("latest.jpg") | |
| if is_face_detected(processed_img_path): | |
| match = compare_with_known_faces(processed_img_path) | |
| if match: | |
| st.success(f"✅ Match found: {match}") | |
| tts = gTTS(f"Match found: {match}") | |
| else: | |
| st.error("❌ No match found") | |
| tts = gTTS("No match found") | |
| else: | |
| st.warning("😕 No face detected in the image.") | |
| tts = gTTS("No face detected") | |
| tts.save("result.mp3") | |
| st.audio("result.mp3", autoplay=True) | |
| else: | |
| st.warning("No image found on ESP32 server") | |