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import os |
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from typing import List |
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import numpy as np |
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import onnxruntime as ort |
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import pooch |
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from PIL import Image |
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from PIL.Image import Image as PILImage |
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from .base import BaseSession |
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class U2netCustomSession(BaseSession): |
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"""This is a class representing a custom session for the U2net model.""" |
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def __init__( |
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self, |
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model_name: str, |
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sess_opts: ort.SessionOptions, |
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providers=None, |
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*args, |
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**kwargs |
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): |
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""" |
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Initialize a new U2netCustomSession object. |
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Parameters: |
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model_name (str): The name of the model. |
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sess_opts (ort.SessionOptions): The session options. |
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providers: The providers. |
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*args: Additional positional arguments. |
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**kwargs: Additional keyword arguments. |
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Raises: |
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ValueError: If model_path is None. |
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""" |
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model_path = kwargs.get("model_path") |
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if model_path is None: |
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raise ValueError("model_path is required") |
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super().__init__(model_name, sess_opts, providers, *args, **kwargs) |
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def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]: |
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""" |
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Predict the segmentation mask for the input image. |
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Parameters: |
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img (PILImage): The input image. |
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*args: Additional positional arguments. |
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**kwargs: Additional keyword arguments. |
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Returns: |
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List[PILImage]: A list of PILImage objects representing the segmentation mask. |
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""" |
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ort_outs = self.inner_session.run( |
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None, |
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self.normalize( |
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img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (320, 320) |
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), |
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) |
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pred = ort_outs[0][:, 0, :, :] |
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ma = np.max(pred) |
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mi = np.min(pred) |
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pred = (pred - mi) / (ma - mi) |
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pred = np.squeeze(pred) |
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mask = Image.fromarray((pred * 255).astype("uint8"), mode="L") |
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mask = mask.resize(img.size, Image.Resampling.LANCZOS) |
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return [mask] |
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@classmethod |
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def download_models(cls, *args, **kwargs): |
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""" |
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Download the model files. |
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Parameters: |
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*args: Additional positional arguments. |
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**kwargs: Additional keyword arguments. |
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Returns: |
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str: The absolute path to the model files. |
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""" |
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model_path = kwargs.get("model_path") |
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if model_path is None: |
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return |
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return os.path.abspath(os.path.expanduser(model_path)) |
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@classmethod |
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def name(cls, *args, **kwargs): |
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""" |
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Get the name of the model. |
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Parameters: |
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*args: Additional positional arguments. |
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**kwargs: Additional keyword arguments. |
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Returns: |
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str: The name of the model. |
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""" |
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return "u2net_custom" |
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