def detect_faces(self, frame):
global tmp
boxes = self.locate_faces(frame)
if len(boxes) == 0:
return []
# faces found
faces = []
now = datetime.now()
for i, box in enumerate(boxes):
# extract face image from frame
face_image = self.get_face_image(frame, box)
# get aligned image
aligned_image = face_alignment_dlib.get_aligned_face(
self.predictor, face_image)
filename = now.strftime('%Y%m%d_%H%M%S.%f')[:-3] + '.png'
pathname = os.path.join(
"/home/ubuntu/learningFile/train/" + args.capture, filename)
tmp += 1
cv2.imwrite(pathname, aligned_image)
return faces
def detect_faces(self, frame, filename):
boxes = self.locate_faces(frame) # 박스 locate
if len(boxes) == 0:
return []
# faces found
faces = []
# now = datetime.now() # 필요한가?
# str_ms = now.strftime('%Y%m%d_%H%M%S.%f')[:-3] + '-' # 필요한가?
str_ms = filename
for i, box in enumerate(boxes):
# extract face image from frame
face_image = self.get_face_image(frame, box) # frame에서 박스에 있는 얼굴 이미지 추출
# get aligned image
aligned_image = face_alignment_dlib.get_aligned_face(self.predictor, face_image) # 얼굴 (수평) 정렬
# compute the encoding
height, width = aligned_image.shape[:2]
x = int(width / 3)
y = int(height / 3)
box_of_face = (y, x * 2, y * 2, x) # 이 부분은 왜 박스를 이렇게 형성?
encoding = face_recognition.face_encodings(aligned_image, [box_of_face])[0]
# 파라미터 값을 설정해줄 필요가 있을듯
face = Face(str_ms + "_" + str(i) + ".png", face_image, encoding) # 파일이름 지정하여 파일 이름과 함께 얼굴 이미지와 인코딩 데이터를 저장해주는 부분인거같은데 이부분에서 원본도 저장되게 만들수 있나?
face.location = box # 얼굴 위치값?
# cv2.imwrite(str_ms + str(i) + ".r.png", aligned_image) # cv2를 이용해서 파일 저장
faces.append(face) # 얼굴을 추가해주는데 어디를 위해서? 데이터값? 을 위해서인듯
return faces
def detect_faces(self, frame):
boxes = self.locate_faces(frame)
if len(boxes) == 0:
return []
# faces found
faces = []
now = datetime.now()
str_ms = now.strftime('%Y%m%d_%H%M%S.%f')[:-3] + '-'
for i, box in enumerate(boxes):
# extract face image from frame
face_image = self.get_face_image(frame, box)
# get aligned image
aligned_image = face_alignment_dlib.get_aligned_face(
self.predictor, face_image)
# compute the encoding
height, width = aligned_image.shape[:2]
x = int(width / 3)
y = int(height / 3)
box_of_face = (y, x * 2, y * 2, x)
encoding = face_recognition.face_encodings(aligned_image,
[box_of_face])[0]
face = Face(str_ms + str(i) + ".png", face_image, encoding)
face.location = box
cv2.imwrite(str_ms + str(i) + ".r.png", aligned_image)
faces.append(face)
return faces