42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
from typing import *
|
|
from transformers import AutoModelForImageSegmentation
|
|
import torch
|
|
from torchvision import transforms
|
|
from PIL import Image
|
|
|
|
|
|
class BiRefNet:
|
|
def __init__(self, model_name: str = "ZhengPeng7/BiRefNet"):
|
|
self.model = AutoModelForImageSegmentation.from_pretrained(
|
|
model_name, trust_remote_code=True
|
|
)
|
|
self.model.eval()
|
|
self.transform_image = transforms.Compose(
|
|
[
|
|
transforms.Resize((1024, 1024)),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
|
]
|
|
)
|
|
|
|
def to(self, device: str):
|
|
self.model.to(device)
|
|
|
|
def cuda(self):
|
|
self.model.cuda()
|
|
|
|
def cpu(self):
|
|
self.model.cpu()
|
|
|
|
def __call__(self, image: Image.Image) -> Image.Image:
|
|
image_size = image.size
|
|
input_images = self.transform_image(image).unsqueeze(0).to("cuda")
|
|
# Prediction
|
|
with torch.no_grad():
|
|
preds = self.model(input_images)[-1].sigmoid().cpu()
|
|
pred = preds[0].squeeze()
|
|
pred_pil = transforms.ToPILImage()(pred)
|
|
mask = pred_pil.resize(image_size)
|
|
image.putalpha(mask)
|
|
return image
|
|
|