Update main.py

Author: pawangeek

attendance-automator/main.py

@@ -1,45 +1,41 @@
 import cv2
-from face_recognition.api import face_encodings
 import numpy as np
 import face_recognition
 import os
 from datetime import datetime
 
-path= 'images'
-
-images=[]
-
-personName=[]
-
-myList=os.listdir(path)
+path = 'images'
+images = []
+personName = []
+myList = os.listdir(path)
 
 print(myList)
 
 for cu_img in myList:
-    current_Img=cv2.imread(f'{path}/{cu_img}')
+    current_Img = cv2.imread(f'{path} / {cu_img}')
     images.append(current_Img)
     personName.append(os.path.splitext(cu_img)[0])
 
 print(personName)
 
 
 def faceEncodings(images):
-    encodeList=[]
+    encodeList = []
 
     for img in images:
-        img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
-        encode=face_recognition.face_encodings(img)[0]
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = face_recognition.face_encodings(img)[0]
         encodeList.append(encode)
 
     return encodeList
 
-encodeListKnown = (faceEncodings(images))
 
+encodeListKnown = (faceEncodings(images))
 print("All Encoding Complete!!!!!")
 
 
 def attendance(name):
-    with open('Attendance.csv','r+') as f:
+    with open('Attendance.csv', 'r+') as f:
         myDataList = f.readlines()
         nameList = []
         for line in myDataList:
@@ -50,37 +46,35 @@ def attendance(name):
             time_now = datetime.now()
             tStr = time_now.strftime('%H:%M:%S')
             dStr = time_now.strftime('%d/%m/%Y')
-            f.writelines(f'{name},{tStr},{dStr}')
+            f.writelines(f'{name}, {tStr}, {dStr}')
 
 
-cap= cv2.VideoCapture(0)
+cap = cv2.VideoCapture(0)
 
 while True:
 
-    ret,frame = cap.read()
-    faces = cv2.resize(frame,(0,0),None,0.25,0.25)
-    faces= cv2.cvtColor(faces,cv2.COLOR_BGR2RGB)
-
+    ret, frame = cap.read()
+    faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
+    faces = cv2.cvtColor(faces, cv2.COLOR_BGR2RGB)
 
     facesCurrentFrame = face_recognition.face_locations(faces)
-    encodesCurrentFrame = face_recognition.face_encodings(faces,facesCurrentFrame)
+    encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
 
-
-    for encodeFace,faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
-        matches = face_recognition.compare_faces(encodeListKnown,encodeFace)
-        faceDis = face_recognition.face_distance(encodeListKnown,encodeFace)
+    for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
+        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
 
         matchIndex = np.argmin(faceDis)
 
         if matches[matchIndex]:
             name = personName[matchIndex].upper()
-            # print(name)
-            y1,x2,y2,x1 = faceLoc 
 
-            y1,x2,y2,x1 = y1*4,x2*4,y2*4,x1*4
-            cv2.rectangle(frame,(x1,y1),(x2,y2),(0,255,0),2)
-            cv2.rectangle(frame,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)
-            cv2.putText(frame,name,(x1+6, y2-6), cv2.FONT_HERSHEY_COMPLEX,1,(255,0,0), 1)
+            y1, x2, y2, x1 = faceLoc
+
+            y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.rectangle(frame, (x1, y2-35), (x2, y2), (0, 255, 0), cv2.FILLED)
+            cv2.putText(frame, name, (x1+6, y2-6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 1)
             attendance(name)
     cv2.imshow("Camera", frame)