Update app.py

app.py

@@ -1,3 +1,125 @@
+# 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")
+
+
+
+
+
+
 import streamlit as st
 from deepface import DeepFace
 import requests
@@ -10,8 +132,8 @@ 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.
+MODEL_NAME = "ArcFace"
+DETECTOR_BACKEND = "retinaface"
 
 # Streamlit setup
 st.set_page_config(page_title="Second Eye - Enhanced Recognition", layout="centered")
@@ -33,22 +155,17 @@ def get_latest_image():
 def preprocess_image(image_path):
     img = cv2.imread(image_path)
     if img is None:
-        return image_path  # Fallback
+        return image_path
 
-    # 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
@@ -68,6 +185,8 @@ def is_face_detected(image_path):
 # Compare with known faces
 def compare_with_known_faces(unknown_img_path):
     for filename in os.listdir(KNOWN_FOLDER):
+        if not filename.lower().endswith((".jpg", ".jpeg", ".png")):
+            continue
         known_img_path = os.path.join(KNOWN_FOLDER, filename)
         try:
             result = DeepFace.verify(