import cv2, line

model = cv2.face.LBPHFaceRecognizer_create()
model.read('faces.data')
print('load training data done')

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
cv2.namedWindow('video', cv2.WINDOW_NORMAL)
# 可識別化名稱
names = ['第一位的名字', '第二位的名字']
n = 0

while True:
    ret, frame = cap.read()
    frame = cv2.resize(frame, (600, 336))
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.1, 3)

    for (x, y, w, h) in faces:
        frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
        face_img = gray[y: y + h, x: x + w]
        face_img = cv2.resize(face_img, (400, 400))

        val = model.predict(face_img)
        print('label:{}, conf:{:.1f}'.format(val[0], val[1]))

        if val[1] < 50:
            cv2.putText(frame, names[val[0]], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 3)
            cv2.imwrite('img/image.jpg', frame)
            if n % 100 == 0:
                line.send_msg()
                print('Send to Line!')
                
            n += 1       

    cv2.imshow('video', frame)

    if cv2.waitKey(1) == 27:
        cap.release()
        cv2.destroyAllWindows()
        break