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from typing import List |
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import numpy as np |
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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 .session_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 ClothSession(BaseSession): |
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def predict(self, img: PILImage) -> List[PILImage]: |
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ort_outs = self.inner_session.run( |
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None, self.normalize(img, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), (768, 768)) |
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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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