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from typing import List |
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import PIL.Image |
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import PIL.ImageOps |
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from packaging import version |
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from PIL import Image |
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if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): |
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PIL_INTERPOLATION = { |
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"linear": PIL.Image.Resampling.BILINEAR, |
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"bilinear": PIL.Image.Resampling.BILINEAR, |
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"bicubic": PIL.Image.Resampling.BICUBIC, |
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"lanczos": PIL.Image.Resampling.LANCZOS, |
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"nearest": PIL.Image.Resampling.NEAREST, |
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} |
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else: |
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PIL_INTERPOLATION = { |
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"linear": PIL.Image.LINEAR, |
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"bilinear": PIL.Image.BILINEAR, |
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"bicubic": PIL.Image.BICUBIC, |
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"lanczos": PIL.Image.LANCZOS, |
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"nearest": PIL.Image.NEAREST, |
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} |
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def pt_to_pil(images): |
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""" |
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Convert a torch image to a PIL image. |
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""" |
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images = (images / 2 + 0.5).clamp(0, 1) |
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images = images.cpu().permute(0, 2, 3, 1).float().numpy() |
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images = numpy_to_pil(images) |
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return images |
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def numpy_to_pil(images): |
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""" |
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Convert a numpy image or a batch of images to a PIL image. |
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""" |
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if images.ndim == 3: |
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images = images[None, ...] |
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images = (images * 255).round().astype("uint8") |
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if images.shape[-1] == 1: |
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pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] |
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else: |
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pil_images = [Image.fromarray(image) for image in images] |
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return pil_images |
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def make_image_grid(images: List[PIL.Image.Image], rows: int, cols: int, resize: int = None) -> PIL.Image.Image: |
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""" |
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Prepares a single grid of images. Useful for visualization purposes. |
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""" |
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assert len(images) == rows * cols |
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if resize is not None: |
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images = [img.resize((resize, resize)) for img in images] |
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w, h = images[0].size |
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grid = Image.new("RGB", size=(cols * w, rows * h)) |
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for i, img in enumerate(images): |
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grid.paste(img, box=(i % cols * w, i // cols * h)) |
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return grid |
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