[영상처리:Python] OpenCV를 이용한 얼굴 인식(Face Detection) 잠금 해제 및 Web Browser 실행 예제

안녕하세요, 츄르 사려고 코딩하는 집사! 코집사입니다.

1. 출처

https://github.com/subhamroy021/Facial-Recognition

subhamroy021/Facial-Recognition

2. 결과

3. 소스 코드

#import 선언 
import cv2
import numpy as np
from os import listdir
from os.path import isfile, join
import webbrowser 

#Face Detection XML 파일
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

#img를 넣으면 face만 잘라 Return
def face_extractor(img):
    #사진 흑백 처리
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    #Face Detection
    faces = face_classifier.detectMultiScale(gray,1.3,5)

    #찾은 얼굴이 없으면 None Return
    if faces is():
        return None
    
    #찾은 얼굴이 있으면 해당 얼굴 크기만큼 cropped_face에 저장
    for(x,y,w,h) in faces:
        cropped_face = img[y:y+h, x:x+w]

    return cropped_face

#노트북 WebCam 활성화
cap = cv2.VideoCapture(0)
count = 0 # 노트북 WebCam으로 사진을 찍기 위한 변수(0부터 시작)


while True:
    #WebCam으로 사진 1장 읽어오기
    ret, frame = cap.read()
    #얼굴을 감지하면 count 증가 및 200,200 사이즈로 사진 수정 후 저장
    if face_extractor(frame) is not None:
        count+=1
        face = cv2.resize(face_extractor(frame),(200,200))
        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
        #파일 이름은 user숫자.jpg로 저장
        file_name_path = 'faces/user'+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('Face Cropper',face)
    else:
        print("Face not Found")
        pass

    if cv2.waitKey(1)==13 or count==100:
        break

cap.release()
cv2.destroyAllWindows()
print('Colleting Samples Complete!!!')

data_path = '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 Training Complete!!!!!")

data_path = '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 Training Complete!!!!!")

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 > 75:
            cv2.putText(image, "Unlocked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
            cv2.imshow('Face Cropper', image)
            webbrowser.open("https://www.google.co.kr")
            break

        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()