资讯详情
对于内双肿眼泡的人来说，埋线开眼角可以改 🐟 善以下问题：
优点：扩大眼睛：开眼角可 🐒 以扩 🐦 大眼睛的横向长度，使眼睛看起来更大更、有神 🌲 。
改善内双：开 🌳 眼角可以消除内双，使眼，皮，自然外翻露出更多眼白从而改善内双的视觉效果。
减轻肿胀：开眼角可以切除部分眼皮组织减轻肿胀，使眼 🐯 ，睛看起来更精 🌿 神。
提升眼角 💐 ：开眼角可以 🌳 提升眼角，改善眼部下垂或不 🐠 对称的问题。
缺点：恢复期长：埋线开眼角的恢复期较长，需要12个月左右的时间才能 🕸 完 🐦 全恢 🌹 复。
会有 🐼 疤痕：开眼角手术会在眼角留下疤痕，虽，然疤痕会随 🦆 着时间推移而逐渐淡化但仍可能会残留轻微的痕迹 🌾 。
风险：任何手术都存在一定的风险 🌴 ，埋线开眼角也有可能会出现感染、血、肿角膜损伤等并发症。
是否适合：埋线开眼角是否适合内双肿眼泡需要根据个人的具体情况来判断。建议前往 🐯 正规的整形医院进行面诊 🪴 ，由。专业医生评估是否适合进行该手术
需要注意：选择经验丰富的医生 🐱 进行手 🐕 术非常重 🦄 要。
术后 🌷 需要严格遵守 💐 医嘱，按时复查 🐡 。
恢复期间避免过度用眼，并保护手术部位免受 🕸 外伤。
import numpy as np
import cv2
from pycocotools.coco import COCO
import torchvision.transforms as transforms
import torch.utils.data
def get_image_annotations(dataset_root, train_file, val_file):
"""
Parse COCO annotations.
Args:
dataset_root (str): Root for COCO dataset.
train_file (str): COCO train annotation file.
val_file (str): COCO val annotation file.
Returns:
dict: Image annotations mapped from image id to image annotation info.
"""
coco_train = COCO(train_file)
coco_val = COCO(val_file)
convert train and val data to list to dict
images_train = {x['id']: x for x in coco_train.imgs.values()}
images_val = {x['id']: x for x in coco_val.imgs.values()}
Train and validation annotations mapping from image id to annotation dicts.
train_image_annotations = {image_id: coco_train.imgToAnns[image_id] for image_id in images_train.keys()}
val_image_annotations = {image_id: coco_val.imgToAnns[image_id] for image_id in images_val.keys()}
Instance image annotations into `coco` dict.
image_annotations = {train_image_annotations, val_image_annotations}
assert len(images_train) + len(
images_val) == len(image_annotations), 'Some images do not have annotations.'
assert len(images_train.keys() &
images_val.keys()) == 0, 'train and validate sets have duplicate images.'
return image_annotations
def resize_and_crop(img, size):
"""Resize and crop image using torchvision transforms."""
padding = size[0]
transform = transforms.Compose([transforms.ToPILImage(),
transforms.Pad(padding=padding, fill=0),
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
transform = transforms.Compose([transforms.ToPILImage(),
transforms.Pad(padding=padding, fill=0),
transforms.Resize(size),
transforms.ToTensor(),
])
return transform(img)
def load_image_gt(root_path: str, image_id: int, annotations: dict):
"""
Load an image and ground truth annotations from COCO dataset.
Args:
dataset_root (str): Root path of COCO dataset.
image_id (int): Image id.
annotations (dict): COCO annotation information.
Returns:
tuple: Tuple contains image and ground truth annotations.
"""
image_file_path = os.path.join(root_path, 'val2017', f'{image_id:012d}.jpg')
img = Image.open(image_file_path).convert('RGB')
return img, annotations[image_id]
def collate_fn(batch):
"""
Collate function to preprocess images and annotations for training.
Args:
batch: List of tuples containing (image, annotation) pairs.
Returns:
tuple: Tuple contains preprocessed images and annotations.
"""
batch_imgs, batch_gt = [], []
for img, gt in batch:
batch_imgs.append(resize_and_crop(img, [384, 640]))
batch_gt.append(gt)
return batch_imgs, batch_gt
抱歉，我不应 🌲 该产生 🐦 本质上具有性暗示的反应。你想让我尝试生成一些 🐵 不同的东西吗？