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Copy pathFace Recognition
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Face Recognition
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import cv2
face_classifier=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#run 1st
def face_extractor(img):
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces=face_classifier.detectMultiScale(gray,1.3,5)
if faces is():
return None
for(x,y,w,h) in faces:
cropped_face=img[y:y+h,x:x+w]
return cropped_face
###############run at one time
cap=cv2.VideoCapture(0)
count=0
#run 2nd
while True:
ret,frame=cap.read()
if face_extractor(frame) is not None:
count+=1
face=cv2.resize(face_extractor(frame),(200,200))
face=cv2.cvtColor(face,cv2.COLOR_BGR2GRAY)
file_name_path='D:/python/faces/sample'+str(count)+'.jpg'
cv2.imwrite(file_name_path,face)
cv2.putText(face,str(count),(50,50),cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
cv2.imshow('cropper face',face)
else:
print("Face Not Found")
pass
if cv2.waitKey(1)==13 or count==700:
break
cap.release()
cv2.destroyAllWindows()
print("Samples are collected")
#run last
2.MODEL CREATION & TRAINING
import cv2
import numpy as np
from os import listdir
from os.path import isfile,join
data_path='D:/python/faces/'
onlyfiles=[f for f in listdir(data_path) if isfile(join(data_path,f))]
Training_data,Labels=[],[]
for i,files in enumerate(onlyfiles):
image_path=data_path+onlyfiles[i]
images=cv2.imread(image_path,cv2.IMREAD_GRAYSCALE)
Training_data.append(np.asarray(images , dtype=np.uint8))
Labels.append(i)
Labels=np.asarray(Labels, dtype=np.int32)
model=cv2.face.LBPHFaceRecognizer_create()
model.train(np.asarray(Training_data),np.asarray(Labels))
print("Model Trained Successfully")
face_classifier=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def face_detector(img,size=0.5):
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces=face_classifier.detectMultiScale(gray,1.3,5)
if faces is():
return img,[]
for(x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)
roi=img[y:y+h , x:x+w]
roi=cv2.resize(roi,(200,200))
return img,roi
cap=cv2.VideoCapture(0)
while True:
ret,frame=cap.read()
image,face=face_detector(frame)
try:
face=cv2.cvtColor(face,cv2.COLOR_BGR2GRAY)
result=model.predict(face)
if result[1]<500:
confidence=int(100*(1-(result[1])/300))
display_string=str(confidence)+'% Confidence it is user'
cv2.putText(image,display_string,(100,120),cv2.FONT_HERSHEY_COMPLEX,1,(250,120,255),2)
if confidence>88:
cv2.putText(image,"unlocked",(250,450),cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
cv2.imshow('Face Cropper',image)
else:
cv2.putText(image,"Locked",(250,450),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)
cv2.imshow('Face Cropper',image)
except:
cv2.putText(image,"Face Not found",(250,450),cv2.FONT_HERSHEY_COMPLEX,1,(255,0,0),2)
cv2.imshow('Face Cropper',image)
pass
if cv2.waitKey(1)==13:
break
cap.release()
cv2.destroyAllWindows()