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 U2netSession(BaseSession): """ This class represents a U2net session, which is a subclass of BaseSession. """ 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), (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 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/u2net.onnx", ( None if cls.checksum_disabled(*args, **kwargs) else "md5:60024c5c889badc19c04ad937298a77b" ), 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 session. Parameters: *args: Additional positional arguments. **kwargs: Additional keyword arguments. Returns: str: The name of the session. """ return "u2net"