Creave version 0.0.8

Author: htmlcoder1562

faces/__init__.py

@@ -1,5 +1,3 @@
-# ©2023 PyJs
-
 import os
 try:
     import cv2
@@ -10,36 +8,44 @@
 # Initialize a list to store enrolled faces
 enrolled_faces = []
 
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+class dont_run:
+    def compare_faces(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
+    def scan_face():
+        cap = cv2.VideoCapture(0)
+        ret, frame = cap.read()
+        return frame
+    def is_match(frame):
+        global enrolled_faces
 
-def scan_face():
-    cap = cv2.VideoCapture(0)
-    ret, frame = cap.read()
-    return frame
+        # Convert the frame to grayscale for face detection
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
-def enroll_face():
-    frame=scan_face()
-    global enrolled_faces
+        # Detect faces in the frame
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
-    # Convert the frame to grayscale for face detection
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        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]
 
-    # Detect faces in the frame
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+                # Check if the detected face matches any of the enrolled faces
+                for enrolled_face in enrolled_faces:
+                    if dont_run.compare_faces(enrolled_face, detected_face):
+                        return (True, "That's a match!")
 
-    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 (False, "Not matched.")
 
-        return True
-    else:
-        return False
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# Function to match a face
-def is_match():
+def enroll_face(frame):
     global enrolled_faces
-    frame=scan_face()
+
     # Convert the frame to grayscale for face detection
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
@@ -48,21 +54,14 @@ def is_match():
 
     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
+            # Crop and store each enrolled face
+            enrolled_faces.append(frame[y:y+h, x:x+w])
 
-    return False
+        return (True, f"success!✅ {len(faces)} are currently saved.")
+    else:
+        return (False, "No face found in the frame.😞")
 
-# 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
+def match_face():
+    frame = dont_run.scan_face()
+    return dont_run.is_match(frame)
+__version__="0.0.8"#---------------------------------------------CHANGE THIS---------------------------------------------#

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 packages=["faces"]
 [project]
 name = "faces-PyJs"
-version = "0.0.9"
+version = "0.0.8"
 description = "Lets you have super useful security."
 readme = "README.md"
 requires-python = ">=3.11.9"