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from typing import List

import PIL.Image

import PIL.ImageOps

from packaging import version

from PIL import Image

if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):

PIL_INTERPOLATION = {

"linear": PIL.Image.Resampling.BILINEAR,

"bilinear": PIL.Image.Resampling.BILINEAR,

"bicubic": PIL.Image.Resampling.BICUBIC,

"lanczos": PIL.Image.Resampling.LANCZOS,

"nearest": PIL.Image.Resampling.NEAREST,

}

else:

PIL_INTERPOLATION = {

"linear": PIL.Image.LINEAR,

"bilinear": PIL.Image.BILINEAR,

"bicubic": PIL.Image.BICUBIC,

"lanczos": PIL.Image.LANCZOS,

"nearest": PIL.Image.NEAREST,

}

def pt_to_pil(images):

"""

Convert a torch image to a PIL image.

"""

images = (images / 2 + 0.5).clamp(0, 1)

images = images.cpu().permute(0, 2, 3, 1).float().numpy()

images = numpy_to_pil(images)

return images

def numpy_to_pil(images):

"""

Convert a numpy image or a batch of images to a PIL image.

"""

if images.ndim == 3:

images = images[None, ...]

images = (images * 255).round().astype("uint8")

if images.shape[-1] == 1:

# special case for grayscale (single channel) images

pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]

else:

pil_images = [Image.fromarray(image) for image in images]

return pil_images

def make_image_grid(images: List[PIL.Image.Image], rows: int, cols: int, resize: int = None) -> PIL.Image.Image:

"""

Prepares a single grid of images. Useful for visualization purposes.

"""

assert len(images) == rows * cols

if resize is not None:

images = [img.resize((resize, resize)) for img in images]

w, h = images[0].size

grid = Image.new("RGB", size=(cols * w, rows * h))

for i, img in enumerate(images):

grid.paste(img, box=(i % cols * w, i // cols * h))

return grid