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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 scipy.special import log_softmax |
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from .base import BaseSession |
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pallete1 = [ |
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0, |
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0, |
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0, |
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255, |
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255, |
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255, |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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] |
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pallete2 = [ |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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255, |
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255, |
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255, |
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0, |
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0, |
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0, |
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] |
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pallete3 = [ |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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0, |
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255, |
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255, |
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255, |
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] |
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class Unet2ClothSession(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), (768, 768) |
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), |
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) |
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pred = ort_outs |
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pred = log_softmax(pred[0], 1) |
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pred = np.argmax(pred, axis=1, keepdims=True) |
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pred = np.squeeze(pred, 0) |
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pred = np.squeeze(pred, 0) |
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mask = Image.fromarray(pred.astype("uint8"), mode="L") |
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mask = mask.resize(img.size, Image.LANCZOS) |
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masks = [] |
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mask1 = mask.copy() |
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mask1.putpalette(pallete1) |
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mask1 = mask1.convert("RGB").convert("L") |
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masks.append(mask1) |
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mask2 = mask.copy() |
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mask2.putpalette(pallete2) |
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mask2 = mask2.convert("RGB").convert("L") |
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masks.append(mask2) |
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mask3 = mask.copy() |
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mask3.putpalette(pallete3) |
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mask3 = mask3.convert("RGB").convert("L") |
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masks.append(mask3) |
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return masks |
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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/u2net_cloth_seg.onnx", |
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None |
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if cls.checksum_disabled(*args, **kwargs) |
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else "md5:2434d1f3cb744e0e49386c906e5a08bb", |
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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 "u2net_cloth_seg" |
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