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from transformers import VitMatteImageProcessor, VitMatteForImageMatting
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
import torchvision.transforms as T
from typing import Dict, List, Any
from io import BytesIO
import base64
# image = Image.open("man.png").convert("RGB")
# trimap = Image.open("mask2.png").convert("L")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
self.processor = VitMatteImageProcessor.from_pretrained(
"hustvl/vitmatte-small-composition-1k")
self.model = VitMatteForImageMatting.from_pretrained(
"hustvl/vitmatte-small-composition-1k")
self.model = self.model.to(device)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
inputs = data.pop("inputs", data)
# parameters = data.pop("parameters", {"mode": "image"})
image = Image.open(
BytesIO(base64.b64decode(inputs['image']))).convert("RGB")
trimap = Image.open(
BytesIO(base64.b64decode(inputs['trimap']))).convert("L")
# image = data.pop("image")
# trimap = data.pop("trimap")
inputs = self.processor(
images=image, trimaps=trimap, return_tensors="pt").to(device)
with torch.no_grad():
alphas = self.model(**inputs).alphas
print(alphas.shape)
image = T.ToPILImage()(torch.squeeze(alphas))
return {"result": image}
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