program :
import os
from PIL import Image
import numpy as np
import cv2
import pickle
BASE_DIR=os.path.dirname(os.path.abspath(__file__))
image_dir=os.path.join(BASE_DIR,"photos")
face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id=0
label_ids={}
y_labels=[]
x_train=[]
for root,dirs,files in os.walk(image_dir):
for file in files:
if file.endswith("png") or file.endswith("jpg"):
path=os.path.join(root,file)
label=os.path.basename(root).replace(" ","-").lower()
if not label in label_ids:
label_ids[label]=current_id
current_id+=1
id_=label_ids[label]
y_labels.append(label) #some number
x_train.append(path) #verify this image,turn into a NUMPY array,GRAY
pil_image= Image.open(path).convert("L")#grayscale
image_array=np.array(pil_image,"uint8")
faces=face_cascade.detectMultiScale(image_array,1.5, 5)
for (x,y,w,h) in faces:
roi=image_array[y:y+h,x:x+w]
x_train.append(roi)
y_labels.append(id_)
with open("labels.pickle",'wb')as f:
pickle.dump(label_ids, f)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")
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