Update app.py

app.py

@@ -5,7 +5,7 @@ import time
 import os
 from keras.models import load_model
 from PIL import Image
-import dlib
+import face_recognition
 import pymongo
 from datetime import datetime
 import tempfile
@@ -42,63 +42,52 @@ emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 # Load known faces and names
 known_faces = []
 known_names = []
-face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 
 def load_known_faces():
     folder_path = "known_faces"  # Folder containing known face images
     for image_name in os.listdir(folder_path):
         if image_name.endswith(('.jpg', '.jpeg', '.png')):
             image_path = os.path.join(folder_path, image_name)
-            image = cv2.imread(image_path)
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            # Detect face in the image
-            detector = dlib.get_frontal_face_detector()
-            faces = detector(gray)
-
-            for face in faces:
-                x, y, w, h = (face.left(), face.top(), face.width(), face.height())
-                roi_gray = gray[y:y+h, x:x+w]
-                known_faces.append(roi_gray)
+            image = face_recognition.load_image_file(image_path)
+            encoding = face_recognition.face_encodings(image)
+            
+            if encoding:  # Ensure encoding was found
+                known_faces.append(encoding[0])  # Store the first encoding (assumes 1 face per image)
                 known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
 
-    # Train the recognizer with the known faces
-    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
-
 load_known_faces()
 
-# Dlib face detector
-detector = dlib.get_frontal_face_detector()
-
 # Process a single frame
 def process_frame(frame):
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = detector(gray)
-
+    # Convert the image to RGB for face_recognition
+    rgb_frame = frame[:, :, ::-1]
+    
+    # Detect faces
+    face_locations = face_recognition.face_locations(rgb_frame)
+    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
+    
     result_text = ""  # Initialize result text
 
-    if len(faces) > 0:
-        for face in faces:
-            x, y, w, h = (face.left(), face.top(), face.width(), face.height())
-            roi_color = frame[y:y+h, x:x+w]
-            roi_gray = gray[y:y+h, x:x+w]
-
-            # Apply histogram equalization for better feature extraction
-            roi_gray = cv2.equalizeHist(roi_gray)
-
-            face_roi = cv2.resize(roi_color, (48, 48))
+    if face_encodings:
+        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+            # Check if the detected face matches any known faces
+            matches = face_recognition.compare_faces(known_faces, face_encoding)
+            name = "Unknown"
+            
+            # Find the name of the recognized face
+            if True in matches:
+                first_match_index = matches.index(True)
+                name = known_names[first_match_index]
+            
+            # Emotion detection
+            face_roi = frame[top:bottom, left:right]
             face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)
+            face_roi = cv2.resize(face_roi, (48, 48))
             face_roi = np.expand_dims(face_roi, axis=0) / 255.0  # Normalize
 
-            # Emotion detection
             predictions = model.predict(face_roi)
             emotion = emotion_labels[np.argmax(predictions[0])]
 
-            # Face recognition
-            name = "Unknown"
-            label, confidence = face_recognizer.predict(roi_gray)
-            if confidence < 100:
-                name = known_names[label]
-
             # Format result text
             result_text = f"{name} is feeling {emotion}"
 
@@ -115,8 +104,8 @@ def process_frame(frame):
                 print(f"Data inserted into MongoDB: {document}")
 
             # Draw bounding box and label
-            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-            cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
+            cv2.putText(frame, result_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
 
     return frame, result_text