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from typing import List |
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import PIL.Image |
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import torch |
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from PIL import Image |
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from ...configuration_utils import ConfigMixin |
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from ...models.modeling_utils import ModelMixin |
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from ...utils import PIL_INTERPOLATION |
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class IFWatermarker(ModelMixin, ConfigMixin): |
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def __init__(self): |
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super().__init__() |
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self.register_buffer("watermark_image", torch.zeros((62, 62, 4))) |
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self.watermark_image_as_pil = None |
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def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None): |
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h = images[0].height |
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w = images[0].width |
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sample_size = sample_size or h |
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coef = min(h / sample_size, w / sample_size) |
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img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w) |
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S1, S2 = 1024**2, img_w * img_h |
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K = (S2 / S1) ** 0.5 |
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wm_size, wm_x, wm_y = int(K * 62), img_w - int(14 * K), img_h - int(14 * K) |
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if self.watermark_image_as_pil is None: |
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watermark_image = self.watermark_image.to(torch.uint8).cpu().numpy() |
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watermark_image = Image.fromarray(watermark_image, mode="RGBA") |
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self.watermark_image_as_pil = watermark_image |
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wm_img = self.watermark_image_as_pil.resize( |
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(wm_size, wm_size), PIL_INTERPOLATION["bicubic"], reducing_gap=None |
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) |
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for pil_img in images: |
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pil_img.paste(wm_img, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y), mask=wm_img.split()[-1]) |
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return images |
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