Update app.py

app.py

@@ -1,119 +1,158 @@
-####### Section 1 ###################
+import streamlit as st
 from PIL import Image
 import numpy as np
 import cv2
-import requests
 import face_recognition
 import os
-import streamlit as st
-import urllib.request
-
-####### Section 2 ###################
-p1 = "sarwan.jpg"
-p2 = "rattantata.png"
-p3 = "Ravinder.jpg"
-
-st.title("Face Recognition ")
-Images     = []
-classnames = []
-
-#read images and train the face_recognition package
-img1 = cv2.imread(p1)
-Images.append(img1)
-classnames.append("Sarwan")
-
-img2 = cv2.imread(p2)
-Images.append(img2)
-classnames.append("RattanTata")
-
-img3 = cv2.imread(p3)
-Images.append(img3)
-classnames.append("RavinderKaur")
-
-directory = "facerecognition"
-myList = os.listdir(directory)
-
-
-for cls in myList:
-    if os.path.splitext(cls)[1] in [".jpg", ".jpeg",".png"]:
-        img_path = os.path.join(directory, cls)
-        curImg = cv2.imread(img_path)
-        Images.append(curImg)
-        classnames.append(os.path.splitext(cls)[0])
-
-
-# Load images for face recognition
-encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
-
-
-####### Section 3 ################### 
-# Take picture using the camera 
-img_file_buffer = st.camera_input("Take Your picture")
-
-# recognise the face in the uploaded image 
-if img_file_buffer is not None:
-    test_image = Image.open(img_file_buffer)
-    image  = np.asarray(test_image)
-    image = image.copy()
-    
-    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
-    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
-    facesCurFrame   = face_recognition.face_locations(imgS)
-    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
-    faceMatchedflag = 0
-    # run looop to find match in encodeListknown  list
-    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
-        # Assuming that encodeListknown is defined and populated in your code
-        matches = face_recognition.compare_faces(encodeListknown, encodeFace)
-        faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
-        matchIndex = np.argmin(faceDis)
-
-        if matches[matchIndex]:
-            name = classnames[matchIndex].upper()
-            #st.write (name)
-            # show the name on image to user 
-            y1, x2, y2, x1 = faceLoc
-            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
-            cv2.rectangle(image , (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.rectangle(image , (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-            cv2.putText(image , name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
-
-            ########## update website 
-            # using Get Method 
-            # Construct the URL
-            url = "https://fc11.glitch.me/submit?email=pm&message=faceReco&name="
-            url = url + name
-            st.write("Constructed URL:", url)
+from typing import List, Tuple
+import requests
+from urllib.parse import quote
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class FaceRecognitionApp:
+    def __init__(self, image_directory: str = "facerecognition"):
+        """Initialize the face recognition application."""
+        self.image_directory = image_directory
+        self.images: List[np.ndarray] = []
+        self.classnames: List[str] = []
+        self.encode_list_known = None
+        
+        # Initialize Streamlit page
+        st.set_page_config(page_title="Face Recognition System", page_icon="👤")
+        st.title("Face Recognition System")
+        
+        # Load and encode known faces
+        self._load_known_faces()
+
+    def _load_known_faces(self) -> None:
+        """Load and encode all known faces from the directory and predefined images."""
+        try:
+            # Load predefined images
+            predefined_images = {
+                "sarwan.jpg": "Sarwan",
+                "rattantata.png": "RattanTata",
+                "Ravinder.jpg": "RavinderKaur"
+            }
             
-            # Try to send the request and handle errors
-            # try:
-            #     res = urllib.request.urlopen(url)
-            #     response = requests.post(url)
-            #     st.write("Data updated successfully.")
-            # except urllib.error.URLError as e:
-            #     st.error(f"Failed to open URL: {e}")
-            # except requests.exceptions.RequestException as e:
-            #     st.error(f"Request failed: {e}")
-
-            url  = "https://fc11.glitch.me/submit?email=pm&message=faceReco&name="
-            url = url + name
-            st.write(url)
-            res = urllib.request.urlopen(url)
-            response = requests.post(url )
-
-            # # using post method
-            # url = "https://aimljul24f.glitch.me/submit?email=pm&message=faceReco&name="
-            # url1 = "/save"
-            # data = {'rollno': '99','name': name, 'email': 'p@g.com','pwd': '**'  }
-            # response = requests.post(url +url1 , data=data)
-            # if response.status_code == 200:
-            #     st.success("Data updated on: " + "https://aimljul24f.glitch.me/")
-            # else:
-            #     st.warning("Data not updated")
-            ########### end update website 
-            faceMatchedflag = 1
-     
-    st.image(image , use_column_width=True, output_format="PNG")       
+            for img_path, name in predefined_images.items():
+                if os.path.exists(img_path):
+                    img = cv2.imread(img_path)
+                    if img is not None:
+                        self.images.append(img)
+                        self.classnames.append(name)
+                    else:
+                        logger.warning(f"Failed to load image: {img_path}")
+
+            # Load images from directory
+            if os.path.exists(self.image_directory):
+                for filename in os.listdir(self.image_directory):
+                    if filename.lower().endswith(('.jpg', '.jpeg', '.png')):
+                        img_path = os.path.join(self.image_directory, filename)
+                        img = cv2.imread(img_path)
+                        if img is not None:
+                            self.images.append(img)
+                            self.classnames.append(os.path.splitext(filename)[0])
+                        else:
+                            logger.warning(f"Failed to load image: {img_path}")
+            
+            # Encode faces
+            if self.images:
+                self.encode_list_known = []
+                for img in self.images:
+                    rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                    encodings = face_recognition.face_encodings(rgb_img)
+                    if encodings:
+                        self.encode_list_known.append(encodings[0])
+                    else:
+                        logger.warning("No face detected in one of the reference images")
+            
+            logger.info(f"Loaded {len(self.images)} images for face recognition")
+            
+        except Exception as e:
+            logger.error(f"Error loading known faces: {str(e)}")
+            st.error("Error loading reference images. Please check the image files.")
+
+    def _process_face(self, image: np.ndarray, scale: float = 0.25) -> Tuple[np.ndarray, bool]:
+        """Process image and detect faces."""
+        img_small = cv2.resize(image, (0, 0), None, scale, scale)
+        img_rgb = cv2.cvtColor(img_small, cv2.COLOR_BGR2RGB)
+        
+        face_locations = face_recognition.face_locations(img_rgb)
+        face_encodings = face_recognition.face_encodings(img_rgb, face_locations)
+        
+        face_matched = False
+        
+        for encoding, (top, right, bottom, left) in zip(face_encodings, face_locations):
+            if self.encode_list_known:
+                matches = face_recognition.compare_faces(self.encode_list_known, encoding)
+                face_distances = face_recognition.face_distance(self.encode_list_known, encoding)
+                
+                if any(matches):
+                    best_match_idx = np.argmin(face_distances)
+                    if matches[best_match_idx]:
+                        name = self.classnames[best_match_idx].upper()
+                        face_matched = True
+                        
+                        # Scale back the coordinates
+                        top, right, bottom, left = [coord * int(1/scale) for coord in (top, right, bottom, left)]
+                        
+                        # Draw rectangle and name
+                        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
+                        cv2.rectangle(image, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
+                        cv2.putText(image, name, (left + 6, bottom - 6), 
+                                  cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+                        
+                        # Update external service
+                        self._update_external_service(name)
+        
+        return image, face_matched
+
+    def _update_external_service(self, name: str) -> None:
+        """Update external service with recognition results."""
+        try:
+            base_url = "https://fc11.glitch.me/submit"
+            params = {
+                "email": "pm",
+                "message": "faceReco",
+                "name": quote(name)
+            }
+            
+            response = requests.get(base_url, params=params, timeout=5)
+            response.raise_for_status()
+            
+            st.success(f"Successfully updated recognition for {name}")
+            logger.info(f"External service updated for {name}")
             
-    if(faceMatchedflag == 0) : 
-        st.warning("No faces detected in the image.")
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Failed to update external service: {str(e)}")
+            st.warning("Failed to update external service, but recognition completed successfully")
+
+    def run(self) -> None:
+        """Run the face recognition application."""
+        if not self.encode_list_known:
+            st.error("No reference faces loaded. Please check the image directory.")
+            return
+
+        img_file_buffer = st.camera_input("Take Your Picture")
+        
+        if img_file_buffer is not None:
+            try:
+                image = np.array(Image.open(img_file_buffer))
+                processed_image, face_matched = self._process_face(image.copy())
+                
+                st.image(processed_image, use_column_width=True, channels="BGR")
+                
+                if not face_matched:
+                    st.warning("No matching faces found in the image.")
+                    
+            except Exception as e:
+                logger.error(f"Error processing image: {str(e)}")
+                st.error("Error processing the image. Please try again.")
+
+if __name__ == "__main__":
+    app = FaceRecognitionApp()
+    app.run()