-
Notifications
You must be signed in to change notification settings - Fork 2
/
face_recognition.py
146 lines (99 loc) · 4.87 KB
/
face_recognition.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
from tkinter import*
from tkinter import ttk
from PIL import Image,ImageTk
from tkinter import messagebox
import mysql.connector
from time import strftime
from datetime import datetime
import cv2
import os
import numpy as np
class Face_Recognition:
def __init__(self,root):
self.root=root
self.root.geometry("1530x790+0+0")
self.root.title("face Recogniton System")
title_lbl=Label(self.root,text="FACE RECOGNITION",font=("times new roman",35,"bold"),bg="Black",fg="White")
title_lbl.place(x=0,y=0,width=1530,height=55)
# 1st image
img_top=Image.open(r"college_images\bgirl.jpg")
img_top=img_top.resize((650,700),Image.ANTIALIAS)
self.photoimg_top=ImageTk.PhotoImage(img_top)
f_lbl=Label(self.root,image=self.photoimg_top)
f_lbl.place(x=0,y=55,width=650,height=700)
#2nd image
img_bottom=Image.open(r"college_images\cybercheck.jpg")
img_bottom=img_bottom.resize((950,700),Image.ANTIALIAS)
self.photoimg_bottom=ImageTk.PhotoImage(img_bottom)
f_lbl=Label(self.root,image=self.photoimg_bottom)
f_lbl.place(x=650,y=55,width=950,height=700)
#button
b1_1=Button(f_lbl,text="Face Recognition",command=self.face_recog,cursor="hand2",font=("times new roman",18,"bold"),bg="white",fg="black")
b1_1.place(x=365,y=620,width =200,height=60)
# =========================attendence==============================
def mark_attendance(self,i,r,n,d):
with open("abhishek.csv","r+",newline="\n") as f:
myDataList=f.readlines()
name_list=[]
for line in myDataList:
entry=line.split((","))
name_list.append(entry[0])
if((i not in name_list) and (r not in name_list) and (n not in name_list) and (d not in name_list)):
now=datetime.now()
d1=now.strftime("%d/%m/%Y")
dtString=now.strftime("%H:%M:%S")
f.writelines(f"\n{i},{r},{n},{d},{dtString},{d1},Present")
# ======================face recognition ===================
def face_recog(self):
def draw_boundray(img,classifier,scaleFactor,minNeighbors,color,text,clf):
gray_image=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
features=classifier.detectMultiScale(gray_image,scaleFactor,minNeighbors)
coord=[]
for (x,y,w,h) in features:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)
id,predict=clf.predict(gray_image[y:y+h,x:x+w])
confidence=int((100*(1-predict/300)))
conn=mysql.connector.connect(host="localhost",username="root",password="Abhi@99315",database="face_recognizer")
my_cursor=conn.cursor()
my_cursor.execute("select Name from student where Student_id="+str(id))
n=my_cursor.fetchone()
n="+".join(n)
my_cursor.execute("select Roll from student where Student_id="+str(id))
r=my_cursor.fetchone()
r="+".join(r)
my_cursor.execute("select Dep from student where Student_id="+str(id))
d=my_cursor.fetchone()
d="+".join(d)
my_cursor.execute("select Student_id from student where Student_id="+str(id))
i=my_cursor.fetchone()
i="+".join(i)
if confidence>77:
cv2.putText(img,f"ID:{i}",(x,y-75),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,255,255),3)
cv2.putText(img,f"Roll:{r}",(x,y-55),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,255,255),3)
cv2.putText(img,f"Name:{n}",(x,y-30),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,255,255),3)
cv2.putText(img,f"Department:{d}",(x,y-5),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,255,255),3)
self.mark_attendance(i,r,n,d)
else:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),3)
cv2.putText(img,"Unknown Face",(x,y-5),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,255,255),3)
coord=[x,y,w,y]
return coord
def recognize(img,clf,faceCascade):
coord=draw_boundray(img,faceCascade,1.1,10,(255,25,255),"Face",clf)
return img
faceCascade=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
clf=cv2.face.LBPHFaceRecognizer_create()
clf.read("classifier.xml")
video_cap=cv2.VideoCapture(0)
while True:
ret,img=video_cap.read()
img=recognize(img,clf,faceCascade)
cv2.imshow("Welcome To face Recognition",img)
if cv2.waitKey(1)==13:
break
video_cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
root=Tk()
obj=Face_Recognition(root)
root.mainloop()