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"