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Face Detection with Name in Python using OpenCV

15 Face Detection with Name

Face Detection with Name in Python using OpenCV

  • python -m pip install opencv-contrib-python
  • pip install pillow

Step 1: Collect 20 Samples of a Face

In [11]:
import cv2
cam = cv2.VideoCapture(0)
detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Id=input('Enter Your Id:')
sampleNum=0
while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        
        #incrementing sample number 
        sampleNum=sampleNum+1
        #saving the captured face in the dataset folder
        cv2.imwrite("camera/User."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w])

        cv2.imshow('frame',img)
    #wait for 100 miliseconds 
    if cv2.waitKey(100) & 0xFF == ord('q'):
        break
    # break if the sample number is morethan 20
    elif sampleNum>20:
        break
cam.release()
cv2.destroyAllWindows()
Enter Your Id:2

Step 2: Creating trainner.yml Classifier

In [12]:
import cv2,os
import numpy as np
from PIL import Image

recognizer = cv2.face.LBPHFaceRecognizer_create()

detector= cv2.CascadeClassifier("haarcascade_frontalface_default.xml");

def getImagesAndLabels(path):
    #get the path of all the files in the folder
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)] 
    #create empth face list
    faceSamples=[]
    #create empty ID list
    Ids=[]
    #now looping through all the image paths and loading the Ids and the images
    for imagePath in imagePaths:
        #loading the image and converting it to gray scale
        pilImage=Image.open(imagePath).convert('L')
        #Now we are converting the PIL image into numpy array
        imageNp=np.array(pilImage,'uint8')
        #getting the Id from the image
        Id=int(os.path.split(imagePath)[-1].split(".")[1])
        # extract the face from the training image sample
        faces=detector.detectMultiScale(imageNp)
        #If a face is there then append that in the list as well as Id of it
        for (x,y,w,h) in faces:
            faceSamples.append(imageNp[y:y+h,x:x+w])
            Ids.append(Id)
    return faceSamples,Ids


faces,Ids = getImagesAndLabels('camera')

recognizer.train(faces, np.array(Ids))
recognizer.save('trainner.yml')

Step 3: Testing Your Results

In [13]:
import cv2
import numpy as np

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainner.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)


cam = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
    ret, im =cam.read()
    gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    faces=faceCascade.detectMultiScale(gray, 1.2,5)
    for(x,y,w,h) in faces:
        cv2.rectangle(im,(x,y),(x+w,y+h),(225,0,0),2)
        Id, conf = recognizer.predict(gray[y:y+h,x:x+w])
        if(conf<50):
            if(Id==1):
                Id="Karan"
            elif(Id==2):
                Id="Ishita"
        else:
            Id="Unknown"
        cv2.putText(im,str(Id),(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)
    cv2.imshow('im',im) 
    if cv2.waitKey(10) & 0xFF==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

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