Face Recogintion using python tkinter

Face Recogintion using python tkinter

There are mainly three part of face recognition system

1 Dataset :- through this functionality sample images of the user are created in grey format and save it on the same file where your python code has saved.

2 Train  :- After saving sample images of user you have to train it

3 Face Detection :- In this functionality your face is compared with the sample images you have already created by data set method. after compare it will suggest your name.

"pip install opencv-python"

Imports

import cv2

import mysql.connector

OpenCV-Python is a library of Python bindings designed to solve computer vision problems. cv2. imread() method loads an image from the specified file. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format) then this method returns an empty matrix.

LBPHFaceRecognizer_create()                                        

The Local Binary Pattern Histogram(LBPH) algorithm is a simple solution on face recognition problem, which can recognize both front face and side face

DataSet Method:- 

def generate_dataset():
    if(t1.get()=="" or t2.get()=="" or t3.get()==""):
        messagebox.showinfo('Result','Please provide complete details of the user')
    else:
        mydb=mysql.connector.connect(
        host="localhost",
        user="root",
        passwd="",
        database="Authorized_user"
        )
        mycursor=mydb.cursor()
        mycursor.execute("SELECT * from my_table")
        myresult=mycursor.fetchall()
        id=1
        for x in myresult:
            id+=1
        sql="insert into my_table(id,Name,Age,Address) values(%s,%s,%s,%s)"
        val=(id,t1.get(),t2.get(),t3.get())
        mycursor.execute(sql,val)
        mydb.commit()
        
        face_classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
        def face_cropped(img):
            gray  = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            faces = face_classifier.detectMultiScale(gray,1.3,5)
            #scaling factor=1.3
            #Minimum neighbor = 5

            if faces is ():
                return None
            for(x,y,w,h) in faces:
                cropped_face=img[y:y+h,x:x+w]
            return cropped_face

        cap = cv2.VideoCapture(0)
        img_id=0

        while True:
            ret,frame = cap.read()
            if face_cropped(frame) is not None:
                img_id+=1
                face = cv2.resize(face_cropped(frame),(200,200))
                face  = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
                file_name_path = "data/user."+str(id)+"."+str(img_id)+".jpg"
                cv2.imwrite(file_name_path,face)
                cv2.putText(face,str(img_id),(50,50),cv2.FONT_HERSHEY_COMPLEX,1, (0,255,0),2)
                # (50,50) is the origin point from where text is to be written
                # font scale=1
                #thickness=2

                cv2.imshow("Cropped face",face)
                if cv2.waitKey(1)==13 or int(img_id)==200:
                    break
        cap.release()
        cv2.destroyAllWindows()
        messagebox.showinfo('Result','Generating dataset completed!!!')

 what is Haar Cascade?

 It is an Object Detection Algorithm used to identify faces in an image or a real time video. ... The algorithm is given a lot of positive images consisting of faces, and a lot of negative images not consisting of any face to train on them.

2 Train Method:-

def train_classifier():
    data_dir="C:/Users/Ishwar Gautam/Desktop/Face recognizer/data"
    path = [os.path.join(data_dir,f) for f in os.listdir(data_dir)]
    faces  = []
    ids   = []
    
    for image in path:
        img = Image.open(image).convert('L');
        imageNp= np.array(img, 'uint8')
        id = int(os.path.split(image)[1].split(".")[1])
        
        faces.append(imageNp)
        ids.append(id)
    ids = np.array(ids)
    
    #Train the classifier and save
    clf = cv2.face.LBPHFaceRecognizer_create()
    clf.train(faces,ids)
    clf.write("classifier.xml")
    messagebox.showinfo('Result','Training dataset completed!!!')

3 Face Detection:-

def detect_face():
    def draw_boundary(img,classifier,scaleFactor,minNeighbors,color,text,clf):
        gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        features = classifier.detectMultiScale(gray_image,scaleFactor,minNeighbors)

        coords = []

        for(x,y,w,h) in features:
            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
            id,pred = clf.predict(gray_image[y:y+h,x:x+w])
            confidence = int(100*(1-pred/300))
            
            mydb=mysql.connector.connect(
            host="localhost",
            user="root",
            passwd="",
            database="Authorized_user"
            )
            mycursor=mydb.cursor()
            mycursor.execute("select name from my_table where id="+str(id))
            s = mycursor.fetchone()
            s = ''+''.join(s)
            
            if confidence>74:
                cv2.putText(img,s,(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.8,color,1,cv2.LINE_AA)   
            else:
                cv2.putText(img,"UNKNOWN",(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.8,(0,0,255),1,cv2.LINE_AA)

            coords=[x,y,w,h]
        return coords
            
    def recognize(img,clf,faceCascade):
        coords = draw_boundary(img,faceCascade,1.1,10,(255,255,255),"Face",clf)
        return img

    faceCascade=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
    clf = cv2.face.LBPHFaceRecognizer_create()
    clf.read("classifier.xml")

    video_capture =  cv2.VideoCapture(0)

    while True:
        ret,img = video_capture.read()
        img=  recognize(img,clf,faceCascade)
        cv2.imshow("face detection",img)

        if cv2.waitKey(1)==13:
            break

    video_capture.release()
    cv2.destroyAllWindows()

cv2.imshow() method is used to display an image in a window. The window automatically fits to the image size.