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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 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 SiluetaSession(BaseSession): |
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def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]: |
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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.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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fname = f"{cls.name()}.onnx" |
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pooch.retrieve( |
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"https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx", |
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None |
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if cls.checksum_disabled(*args, **kwargs) |
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else "md5:55e59e0d8062d2f5d013f4725ee84782", |
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fname=fname, |
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path=cls.u2net_home(*args, **kwargs), |
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progressbar=True, |
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) |
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return os.path.join(cls.u2net_home(), fname) |
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@classmethod |
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def name(cls, *args, **kwargs): |
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return "silueta" |
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