抱歉,我不确定你所说的“下班玻尿酸填充效果图”是什么意思。您能重新表述您的请求吗?
注射玻尿酸的消退时间取决于多种因素,包括:
玻尿酸类型:不同类型的玻尿酸具有不同的消退速率。一般来说,交联度越高的玻尿酸维持时间越长。
注射部位:注射玻尿酸的部位也会影响其消退时间。例如,面部部位的玻尿酸消退速度比身体部位快。
个人代谢率:每个人的代谢率不同,这也影响玻尿酸的消退时间。代谢快的人玻尿酸消退得更快。
一般来说,注射玻尿酸后留下自然效果需要以下时间:
面部:612个月
嘴唇:48个月
身体其他部位:1224个月
需要注意的是,这些只是平均时间,具体时间因人而异。如果您想了解您的玻尿酸注射何时会自然消退,请咨询您的医生。
beforeAfter_法令纹玻尿酸填充效果图2.jpg
beforeAfter_法令纹玻尿酸填充效果图3.jpg
beforeAfter_法令纹玻尿酸填充效果图4.jpg
beforeAfter_法令纹玻尿酸填充效果图5.jpg
beforeAfter_法令纹玻尿酸填充效果图6.jpg
beforeAfter_法令纹玻尿酸填充效果图7.jpg
注意: 效果因人而异,请咨询合格的医疗专业人员以了解您的具体情况。
import requests
from PIL import Image
from io import BytesIO
import numpy as np
from transformers import pipeline
image_url = ""
response = requests.get(image_url)
image = Image.open(BytesIO(response.))
Create a pipeline for face detection
face_detector = pipeline("facedetection")
Perform face detection on the image
faces = face_detector(image)
If at least one face is detected, crop the face and perform facial landmarks detection
if len(faces) > 0:
face = faces[0]
cropped_image = image.crop((face["left"], face["top"], face["right"], face["bottom"]))
landmark_detector = pipeline("facelandmarkdetection")
landmarks = landmark_detector(cropped_image)
Extract the nose landmarks
nose_landmarks = landmarks["nose_3d"]
Create a list of coordinates for the nose landmarks
nose_landmarks_coords = [(l["x"], l["y"], l["z"]) for l in nose_landmarks]
Convert the list of coordinates to a NumPy array
nose_landmarks_coords = np.array(nose_landmarks_coords)
Apply the facial landmarks detection model to the image with the nose landmarks
image_with_nose_landmarks = landmark_detector(image, landmarks=nose_landmarks_coords)
Display the image with the nose landmarks
image_with_nose_landmarks.show()