Machine-Learning

All about Machine Learning Algorithms

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