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attendance.py

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+from pandas import read_csv
+import datetime as dt
+
+def mark_attendance(names):
+    names=set(names)
+    print(names)
+    df=read_csv('attendance_data/attendance.csv',index_col='Name')
+
+    today=dt.datetime.now()
+    today=today.strftime('%d/%m/%y')
+
+    student_status=['Nil' for i in range(len(df))]
+
+    df[today]=student_status
+
+    for i in names:
+        df.loc[i]['25/08/19']='Yes'
+    df.to_csv('attendance_data/attendance.csv')
+    df.to_excel(r'attendance_data/attendance.xlsx',header=True)
+
+
+
+

capture.py

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+import cv2
+from tkinter import *
+from tkinter import messagebox
+import os
+from time import sleep
+
+def create_dir():
+    label=[]
+    dir1=os.getcwd()+'/training_data'
+    for x,y,z in os.walk(dir1):
+        for a in y:
+            label.append(int(a))
+    if label==[]:
+        i='0'
+    else:
+        i=max(label)
+        i=i+1
+
+    os.chdir('training_data')
+    os.mkdir(str(i))
+    os.chdir(str(i))
+
+    return os.getcwd()
+
+def capture_pic(root,entry1):
+    name=entry1.get()
+    root.destroy()
+    cwd=create_dir()
+    cam=cv2.VideoCapture(0)
+    ch=1
+    while True:
+        ret,frame=cam.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        cv2.imshow('Capturing',gray)
+        cv2.imwrite(name+str(ch)+'.jpg',cv2.flip(gray, 1 ))
+        ch=ch+1
+        if ch>10:
+            break
+        if cv2.waitKey(1) & 0xFF ==ord('q'):
+            break
+    cam.release()
+    root=Tk()
+    root.geometry('0x0')
+    root.title('Alert')
+    messagebox.showinfo('Alert','Data Captured !!')
+    root.destroy()
+    cv2.destroyAllWindows()
+    window()
+
+def window():
+    root=Tk()
+    root.geometry('500x170+400+300')
+    root.title('Face Capture')
+    frame=Frame(root)
+    label=Label(frame,text='Face Capture ',font=('Verdana',15,'bold'))
+    label.pack(pady=30)
+    label=Label(frame,text='Student Name : ',font=('Verdana',11,'bold'))
+    label.pack(side=LEFT)
+    entry1=Entry(frame,font=('Verdana',11))
+    entry1.pack(side=LEFT)
+    bt=Button(frame,text='Capture',font=('Verdana',9,'bold'),\
+              bg='blue',fg='white',width=7,command=lambda:capture_pic(root,entry1))
+    bt.pack(padx=20)
+    frame.pack()
+    root.mainloop()
+
+
+if __name__=='__main__':
+    window()
+
+

face_detection.py

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+import cv2
+from numpy import array
+import os,time
+from attendance import mark_attendance
+
+def facedetect(image):
+    img_gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+    cascade=cv2.CascadeClassifier('xml\haarcascade_frontalface_default.xml')
+    faces=cascade.detectMultiScale(img_gray,scaleFactor=1.1,minNeighbors=5)
+
+    return faces,img_gray
+
+def create_labels():
+    dir1='C:/Users/shree/AppData/Local/Programs/Python/Python36/Face Detection/training_data'
+    faces_list=[]
+    fid=[]
+    temp=''
+    names=[]
+
+    for path,subdir,files in os.walk(dir1):
+        name=''
+        for f in files:
+            if f.startswith('.'):
+                continue
+            image_path=os.path.join(path,f)
+            img_id=os.path.basename(path)
+
+            img=cv2.imread(image_path)
+            img_re=cv2.resize(img,(1000,600))
+            faces,img_gray=facedetect(img_re)
+
+            if (len(faces)!=1):
+                continue
+
+            x,y,w,h=faces[0]
+            req_region=img_gray[y:y+w,x:x+h]
+            faces_list.append(req_region)
+            fid.append(int(img_id))
+            temp=f
+
+        for char in temp:
+            if char.isdigit():
+                break
+            else:
+                name=name+char
+        names.append(name)
+    names.remove('')
+
+    names_dic=dict(zip(set(fid),names))
+
+    return faces_list,fid,names_dic
+
+
+def trainer(faces_list,fid):
+    trainer=cv2.face.LBPHFaceRecognizer_create()
+    trainer.train(faces_list,array(fid))
+    return trainer
+
+def rectangle(face,image):
+    x,y,w,h=face
+    cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2)
+
+def text(image,text,x,y):
+    font=cv2.FONT_HERSHEY_DUPLEX
+    cv2.putText(image,text,(x-15,y-40),font,1,(255,0,0),2)
+
+
+#dic={0:'Pradhyumn',1:'B'}
+
+faces_list,fid,names_dic=create_labels()
+classifier=trainer(faces_list,fid)
+
+all_labels=[]
+
+#test_prediction
+'''img_test=cv2.imread('test.jpg')
+faces,img_gray_test=facedetect(img_test)
+
+for f in faces:
+    (x,y,w,h)=f
+    req_region=img_gray_test[y:y+w,x:x+h]
+    label,confi=classifier.predict(req_region)
+    all_labels.append(names_dic[label].capitalize())
+    rectangle(f,img_gray_test)
+    text(img_gray_test,names_dic[label].capitalize(),x,y)
+
+
+cv2.imshow('Gray',img_gray_test)
+cv2.waitKey(0)
+cv2.destroyAllWindows()'''
+
+#live_prediction
+cam=cv2.VideoCapture(0)
+ch=1
+while True:
+    ret,frame=cam.read()
+    faces,img_gray_test=facedetect(frame)
+
+    for f in faces:
+        (x,y,w,h)=f
+        req_region=img_gray_test[y:y+w,x:x+h]
+        label,confi=classifier.predict(req_region)
+        all_labels.append(names_dic[label].capitalize())
+        rectangle(f,img_gray_test)
+        text(img_gray_test,names_dic[label].capitalize(),x,y)
+
+    cv2.imshow('Gray',img_gray_test)
+
+    if cv2.waitKey(10)==ord('q'):
+        break
+cam.release()
+cv2.destroyAllWindows()
+
+mark_attendance(all_labels)
+
+
+
+
+
+

testing.py

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+import cv2
+import os
+
+
+def capture():
+    label=[]
+    dir1='C:/Users/shree/Desktop/Python/Projects/Face Detection/training_data'
+    for x,y,z in os.walk(dir1):
+        for a in y:
+            label.append(int(a))
+
+    i=max(label)
+    i=i+1
+    os.chdir('training_data')
+    os.mkdir(str(i))
+    print(os.getcwd())
+
+if __name__=='__main__':
+    capture()
+
+