Update app.py

app.py

@@ -174,32 +174,51 @@ if picture:
         st.text("إضاءة غير جيدة")
         st.text("انتقل الى مكان جيد الإضاءة")
     else:
-        # Initialize the face detector
-        faceDetector = dlib.get_frontal_face_detector()
-        
-        # Assuming 'frm' is your input image
-        faces = faceDetector(frm, 0)
+import dlib
+
+# Initialize the face detector
+faceDetector = dlib.get_frontal_face_detector()
+
+# Assuming 'frm' is your input image
+faces = faceDetector(frm, 0)
+
+# Check if there are any faces detected
+if len(faces) > 0:
+    largest_face = None
+    max_area = 0
+
+    # Find the largest face
+    for face in faces:
+        w = face.right() - face.left()
+        h = face.bottom() - face.top()
+        area = w * h
+        if area > max_area:
+            max_area = area
+            largest_face = face
+
+                # If a largest face is detected
+            if largest_face is not None:
+                # Calculate the expanded dimensions
+                expansion_factor = 1.5
+                x = largest_face.left()
+                y = largest_face.top()
+                w = largest_face.right() - x
+                h = largest_face.bottom() - y
         
-        # Check if there are any faces detected
-        if len(faces) > 0:
-            largest_face = None
-            max_area = 0
+                expanded_x = max(0, int(x - w * (expansion_factor - 1) / 2))
+                expanded_y = max(0, int(y - h * (expansion_factor - 1) / 2))
+                expanded_w = min(frm.shape[1], int(w * expansion_factor))
+                expanded_h = min(frm.shape[0], int(h * expansion_factor))
         
-            # Find the largest face
-            for face in faces:
-                w = face.right() - face.left()
-                h = face.bottom() - face.top()
-                area = w * h
-                if area > max_area:
-                    max_area = area
-                    largest_face = face
+                # Adjust the width and height to avoid going beyond image boundaries
+                expanded_w = min(expanded_w, frm.shape[1] - expanded_x)
+                expanded_h = min(expanded_h, frm.shape[0] - expanded_y)
         
-            # If a largest face is detected
-            if largest_face is not None:
-                # Extract the largest face
-                face_img = frm[largest_face.top():largest_face.bottom(), largest_face.left():largest_face.right()]
+                # Extract the expanded face area
+                expanded_face_img = frm[expanded_y:expanded_y + expanded_h, expanded_x:expanded_x + expanded_w]
+
                 # Now, 'face_img' contains the largest face. You can process it further as needed.
-                if np.array(face_img).size != 0 :
+                if np.array(expanded_face_img).size != 0 :
                     target_size = (512, 512)
                     resized_face = cv2.resize(face_img, target_size)                
                     # Perform spatial pyramid feature extraction