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import os
from typing import List
import numpy as np
import pooch
from PIL import Image
from PIL.Image import Image as PILImage
from .base import BaseSession
class DisSession(BaseSession):
def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
ort_outs = self.inner_session.run(
None,
self.normalize(img, (0.485, 0.456, 0.406), (1.0, 1.0, 1.0), (1024, 1024)),
)
pred = ort_outs[0][:, 0, :, :]
ma = np.max(pred)
mi = np.min(pred)
pred = (pred - mi) / (ma - mi)
pred = np.squeeze(pred)
mask = Image.fromarray((pred * 255).astype("uint8"), mode="L")
mask = mask.resize(img.size, Image.LANCZOS)
return [mask]
@classmethod
def download_models(cls, *args, **kwargs):
fname = f"{cls.name()}.onnx"
pooch.retrieve(
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-anime.onnx",
None
if cls.checksum_disabled(*args, **kwargs)
else "md5:6f184e756bb3bd901c8849220a83e38e",
fname=fname,
path=cls.u2net_home(*args, **kwargs),
progressbar=True,
)
return os.path.join(cls.u2net_home(), fname)
@classmethod
def name(cls, *args, **kwargs):
return "isnet-anime"
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