import face_recognition as fr
import os
import cv2
import face_recognition
import numpy as np
from PIL import ImageGrab
from time import sleep


def record():
img = ImageGrab.grab()
saveas = 'test.jpg'
 img.save(saveas)
img = cv2.imread('test.jpg',1)




def get_encoded_faces():
"""
 looks through the faces folder and encodes all
 the faces

 :return: dict of (name, image encoded)
 """
 encoded = {}

for dirpath, dnames, fnames in os.walk("./faces"):
for f in fnames:
if f.endswith(".jpg") or f.endswith(".png"):
face = fr.load_image_file("faces/" + f)
encoding = fr.face_encodings(face)[0]
encoded[f.split(".")[0]] = encoding

return encoded


def unknown_image_encoded(img):
"""
 encode a face given the file name
 """
 face = fr.load_image_file("faces/" + img)
encoding = fr.face_encodings(face)[0]

return encoding


def classify_face(im):
"""
 will find all of the faces in a given image and label
 them if it knows what they are

 :param im: str of file path
 :return: list of face names
 """
 faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())

img = cv2.imread(im, 1)
# img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
 # img = img[:,:,::-1]

 face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)

face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
 matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"

 # use the known face with the smallest distance to the new face
 face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]

face_names.append(name)

for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
 cv2.rectangle(img, (left - 20, top - 20), (right + 20, bottom + 20), (255, 0, 0), 2)

# Draw a label with a name below the face
 cv2.rectangle(img, (left - 20, bottom - 15), (right + 20, bottom + 20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left - 20, bottom + 15), font, 1.0, (255, 255, 255), 2)

# Display the resulting image
 while True:
record()
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names


print(classify_face("test.jpg"))



파이썬쓰고있어...


원본소스는 test.jpg 파일을 읽어서 faces폴더의 모델안에 해당얼굴이있으면


얼굴인식한 워터마크와함께 이미지를 보여주는건데





나는 화면상캡쳐를 실시간으로 무한반복하고 이것을 1로 저장시키고


이것을 다시 1을 인식해서


얼굴인식된 표시와 함께 2의 파일로 계속 덮어씌우고 이걸 보고싶은데


도움좀 주라.. 초보라서 내가 잘몰라 ㅠㅠ



알려주는사람 기프티콘줄게 제발 ㅠㅠㅠㅠㅠㅠㅠㅠㅠㅠ 


팀뷰어도 가능해 욕도 좋아..