Update app.py

app.py

@@ -5,7 +5,7 @@ import time
 import os
 from keras.models import load_model
 from PIL import Image
-import mediapipe as mp
+import dlib
 import pymongo
 from datetime import datetime
 import tempfile
@@ -52,11 +52,11 @@ def load_known_faces():
             image = cv2.imread(image_path)
             gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
             # Detect face in the image
-            faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(
-                gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
-            )
+            detector = dlib.get_frontal_face_detector()
+            faces = detector(gray)
 
-            for (x, y, w, h) in faces:
+            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)
                 known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
@@ -66,28 +66,21 @@ def load_known_faces():
 
 load_known_faces()
 
-# Mediapipe face detection
-mp_face_detection = mp.solutions.face_detection.FaceDetection(min_detection_confidence=0.8)
+# Dlib face detector
+detector = dlib.get_frontal_face_detector()
 
 # Process a single frame
 def process_frame(frame):
-    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    results = mp_face_detection.process(rgb_frame)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(gray)
 
     result_text = ""  # Initialize result text
 
-    if results.detections:
-        for detection in results.detections:
-            bboxC = detection.location_data.relative_bounding_box
-            h, w, _ = frame.shape
-            bbox = (
-                int(bboxC.xmin * w), int(bboxC.ymin * h),
-                int(bboxC.width * w), int(bboxC.height * h)
-            )
-            x, y, w, h = bbox
-
+    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 = cv2.cvtColor(roi_color, cv2.COLOR_BGR2GRAY)
+            roi_gray = gray[y:y+h, x:x+w]
 
             # Apply histogram equalization for better feature extraction
             roi_gray = cv2.equalizeHist(roi_gray)