Fix format

Author: htmlcoder1562

__init__.py faces/__init__.py__init__.py renamed to faces/__init__.py

@@ -1,68 +1,68 @@
-# ©2023 PyJs
-
-import os
-try:
-    import cv2
-except:
-    os.system("pip3 install opencv-python")
-    import cv2
-print("THIS USES CV2. RUN \"pip uninstall opencv-python\" TO UNINSTALL.")
-# Initialize a list to store enrolled faces
-enrolled_faces = []
-
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-def scan_face():
-    cap = cv2.VideoCapture(0)
-    ret, frame = cap.read()
-    return frame
-
-def enroll_face():
-    frame=scan_face()
-    global enrolled_faces
-
-    # Convert the frame to grayscale for face detection
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-    # Detect faces in the frame
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-    if len(faces) > 0:
-        for (x, y, w, h) in faces:
-            # Crop and store each enrolled face
-            enrolled_faces.append(frame[y:y+h, x:x+w])
-
-        return True
-    else:
-        return False
-
-# Function to match a face
-def is_match():
-    global enrolled_faces
-    frame=scan_face()
-    # Convert the frame to grayscale for face detection
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-    # Detect faces in the frame
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-    if len(faces) > 0:
-        for (x, y, w, h) in faces:
-            # Crop the detected face for comparison
-            detected_face = frame[y:y+h, x:x+w]
-
-            # Check if the detected face matches any of the enrolled faces
-            for enrolled_face in enrolled_faces:
-                if reuowg_w(enrolled_face, detected_face):
-                    return True
-
-    return False
-
-# Function to compare faces (a basic image comparison)
-def reuowg_w(face1, face2):
-    # Resize the images to the same size
-    face1 = cv2.resize(face1, (200, 200))
-    face2 = cv2.resize(face2, (200, 200))
-    diff = cv2.absdiff(face1, face2)
-    mean_diff = diff.mean()
+# ©2023 PyJs
+
+import os
+try:
+    import cv2
+except:
+    os.system("pip3 install opencv-python")
+    import cv2
+print("THIS USES CV2. RUN \"pip uninstall opencv-python\" TO UNINSTALL.")
+# Initialize a list to store enrolled faces
+enrolled_faces = []
+
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+def scan_face():
+    cap = cv2.VideoCapture(0)
+    ret, frame = cap.read()
+    return frame
+
+def enroll_face():
+    frame=scan_face()
+    global enrolled_faces
+
+    # Convert the frame to grayscale for face detection
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the frame
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    if len(faces) > 0:
+        for (x, y, w, h) in faces:
+            # Crop and store each enrolled face
+            enrolled_faces.append(frame[y:y+h, x:x+w])
+
+        return True
+    else:
+        return False
+
+# Function to match a face
+def is_match():
+    global enrolled_faces
+    frame=scan_face()
+    # Convert the frame to grayscale for face detection
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the frame
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    if len(faces) > 0:
+        for (x, y, w, h) in faces:
+            # Crop the detected face for comparison
+            detected_face = frame[y:y+h, x:x+w]
+
+            # Check if the detected face matches any of the enrolled faces
+            for enrolled_face in enrolled_faces:
+                if reuowg_w(enrolled_face, detected_face):
+                    return True
+
+    return False
+
+# Function to compare faces (a basic image comparison)
+def reuowg_w(face1, face2):
+    # Resize the images to the same size
+    face1 = cv2.resize(face1, (200, 200))
+    face2 = cv2.resize(face2, (200, 200))
+    diff = cv2.absdiff(face1, face2)
+    mean_diff = diff.mean()
     return mean_diff < 50