Update app.py

app.py

@@ -4,19 +4,21 @@ 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"
-# FLASK_UPLOAD_URL = "https://flask-upload-pzch.onrender.com/upload"
+MODEL_NAME = "ArcFace"  # Options: "VGG-Face", "Facenet", "ArcFace", etc.
+DETECTOR_BACKEND = "retinaface"  # Options: "opencv", "mtcnn", "dlib", "retinaface", etc.
 
-# Set Streamlit layout
-st.set_page_config(page_title="Second Eye - Face Recognition", layout="centered")
-
-# Sidebar navigation
+# 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 latest image from ESP32 server
+# Fetch image from ESP32
 def get_latest_image():
     try:
         r = requests.get(f"{ESP32_SERVER_URL}/latest")
@@ -27,15 +29,43 @@ def get_latest_image():
     except:
         return None
 
-# Face detection check
+# 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=True)
+        faces = DeepFace.extract_faces(
+            img_path=image_path,
+            enforce_detection=False,
+            detector_backend=DETECTOR_BACKEND
+        )
         return len(faces) > 0
     except:
         return False
 
-# Face comparison using Facenet
+# 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)
@@ -43,8 +73,9 @@ def compare_with_known_faces(unknown_img_path):
             result = DeepFace.verify(
                 img1_path=unknown_img_path,
                 img2_path=known_img_path,
-                model_name="Facenet",
-                enforce_detection=True
+                model_name=MODEL_NAME,
+                detector_backend=DETECTOR_BACKEND,
+                enforce_detection=False
             )
             if result["verified"]:
                 return filename.split('.')[0]
@@ -52,20 +83,23 @@ def compare_with_known_faces(unknown_img_path):
             print(f"Comparison failed with {filename}: {e}")
     return None
 
-# -------------- PAGE 1: Face Recognition --------------
+# Main UI
 if page == "Face Recognition":
-    st.title("ESP32-CAM 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)
 
-            if is_face_detected("latest.jpg"):
-                match = compare_with_known_faces("latest.jpg")
+            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}")
@@ -79,4 +113,4 @@ if page == "Face Recognition":
             tts.save("result.mp3")
             st.audio("result.mp3", autoplay=True)
         else:
-            st.warning("No image found on ESP32 server.")
+            st.warning("No image found on ESP32 server")