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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 U2netHumanSegSession(BaseSession):
"""
This class represents a session for performing human segmentation using the U2Net model.
"""
def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
"""
Predicts human segmentation masks for the input image.
Parameters:
img (PILImage): The input image.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
List[PILImage]: A list of predicted masks.
"""
ort_outs = self.inner_session.run(
None,
self.normalize(
img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (320, 320)
),
)
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.Resampling.LANCZOS)
return [mask]
@classmethod
def download_models(cls, *args, **kwargs):
"""
Downloads the U2Net model weights.
Parameters:
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
str: The path to the downloaded model weights.
"""
fname = f"{cls.name(*args, **kwargs)}.onnx"
pooch.retrieve(
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx",
(
None
if cls.checksum_disabled(*args, **kwargs)
else "md5:c09ddc2e0104f800e3e1bb4652583d1f"
),
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 U2Net model.
Parameters:
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
str: The name of the model.
"""
return "u2net_human_seg"
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