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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 BiRefNetSessionGeneral(BaseSession):
"""
This class represents a BiRefNet-General session, which is a subclass of BaseSession.
"""
def sigmoid(self, mat):
return 1 / (1 + np.exp(-mat))
def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
"""
Predicts the output masks for the input image using the inner session.
Parameters:
img (PILImage): The input image.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
List[PILImage]: The list of output masks.
"""
ort_outs = self.inner_session.run(
None,
self.normalize(
img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (1024, 1024)
),
)
pred = self.sigmoid(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.Resampling.LANCZOS)
return [mask]
@classmethod
def download_models(cls, *args, **kwargs):
"""
Downloads the BiRefNet-General model file from a specific URL and saves it.
Parameters:
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
str: The path to the downloaded model file.
"""
fname = f"{cls.name(*args, **kwargs)}.onnx"
pooch.retrieve(
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-epoch_244.onnx",
(
None
if cls.checksum_disabled(*args, **kwargs)
else "md5:7a35a0141cbbc80de11d9c9a28f52697"
),
fname=fname,
path=cls.u2net_home(*args, **kwargs),
progressbar=True,
)
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
@classmethod
def name(cls, *args, **kwargs):
"""
Returns the name of the BiRefNet-General session.
Parameters:
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
str: The name of the session.
"""
return "birefnet-general"
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