| import |
|---|
| python3 |
| cv2 |
| dlib |
| imutils |
Facial landmarks with dlib, OpenCV, and Python
Step #1: Localize the face in the image
- openCV의 Haar cascades를 사용하여 얼굴 검출
Step #2: Detect the key facial structures
- 입, 눈썹, 코, 턱을 필수적으로 localize, lavbel 해야한다
- dlib에 포함된 facial landmark detector를 이용
dlib detector는 bounding box의 (x,y)좌표를 반환
openCV에선 bounding box의 입력을 (x,y,width,height)로 받음
때문에 rect_to_bb 함수가 (x,y)를 받아 (x,y,width,height)를 변환해줌
dlib face landmark detector가 68개 (x,y) 좌표의 객체를 반환
shpae_to_np는 이를 NumPy array로 변환해줌
python facial_landmarks.py --shape-predictor shape_predictor_68_face_landmarks.dat --image images/example_01.jpg
--shape-predictor : 학습된 landmark detector의 경로
You can download the detector model here or you can use the *“Downloads”* section of this post to grab the code + example images + pre-trained detector as well.
--image : 대상 이미지
Detect eyes, nose, lips, and jaw with dlib, OpenCV, and Python
ROI(Region Of Interest) : 여기선 검출한 얼굴 또는 face part
shape predictor는 68개의 점을 반환 하는데 각각은 인덱스를 갖는다.
- The mouth can be accessed through points [48, 68].
- The right eyebrow through points [17, 22].
- The left eyebrow through points [22, 27].
- The right eye using [36, 42].
- The left eye with [42, 48].
- The nose using [27, 35].
- And the jaw via [0, 17].
face_utils of the imutils library
FACIAL_LANDMARKS_IDXS = OrderedDict([
("mouth", (48, 68)),
("right_eyebrow", (17, 22)),
("left_eyebrow", (22, 27)),
("right_eye", (36, 42)),
("left_eye", (42, 48)),
("nose", (27, 35)),
("jaw", (0, 17))
])visualize_facial_landmarks
def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
# create two copies of the input image -- one for the
# overlay and one for the final output image
overlay = image.copy()
output = image.copy()
# if the colors list is None, initialize it with a unique
# color for each facial landmark region
if colors is None:
colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
(168, 100, 168), (158, 163, 32),
(163, 38, 32), (180, 42, 220)]