sgl-project--sglang/python/sglang/multimodal_gen/runtime/utils/vision.py

# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

# SPDX-License-Identifier: Apache-2.0

import os
import tempfile
from collections.abc import Callable
from io import BytesIO
from urllib.parse import unquote, urlparse

import imageio
import numpy as np
import PIL.Image
import PIL.ImageOps
import requests
import torch
from packaging import version

from sglang.srt.utils.common import get_image_bytes as srt_get_image_bytes

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 pil_to_numpy(images: list[PIL.Image.Image] | PIL.Image.Image) -> np.ndarray:
    r"""
    Convert a PIL image or a list of PIL images to NumPy arrays.

    Args:
        images (`PIL.Image.Image` or `List[PIL.Image.Image]`):
            The PIL image or list of images to convert to NumPy format.

    Returns:
        `np.ndarray`:
            A NumPy array representation of the images.
    """
    if not isinstance(images, list):
        images = [images]
    images = [np.array(image).astype(np.float32) / 255.0 for image in images]
    images_arr: np.ndarray = np.stack(images, axis=0)

    return images_arr


def numpy_to_pt(images: np.ndarray) -> torch.Tensor:
    r"""
    Convert a NumPy image to a PyTorch tensor.

    Args:
        images (`np.ndarray`):
            The NumPy image array to convert to PyTorch format.

    Returns:
        `torch.Tensor`:
            A PyTorch tensor representation of the images.
    """
    if images.ndim == 3:
        images = images[..., None]

    images = torch.from_numpy(images.transpose(0, 3, 1, 2))
    return images


def normalize(images: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor:
    r"""
    Normalize an image array to [-1,1].

    Args:
        images (`np.ndarray` or `torch.Tensor`):
            The image array to normalize.

    Returns:
        `np.ndarray` or `torch.Tensor`:
            The normalized image array.
    """
    return 2.0 * images - 1.0


# adapted from diffusers.utils import load_image
def load_image(
    image: str | bytes | PIL.Image.Image,
    convert_method: Callable[[PIL.Image.Image], PIL.Image.Image] | None = None,
) -> PIL.Image.Image:
    """
    Loads `image` to a PIL Image.

    Args:
        image (`str` or `PIL.Image.Image`):
            The image to convert to the PIL Image format.
        convert_method (Callable[[PIL.Image.Image], PIL.Image.Image], *optional*):
            A conversion method to apply to the image after loading it. When set to `None` the image will be converted
            "RGB".
    """
    if isinstance(image, (str, bytes)):
        if isinstance(image, str) and os.path.isfile(image):
            image = PIL.Image.open(image)
        else:
            # in-memory loading path
            image = PIL.Image.open(BytesIO(srt_get_image_bytes(image)))
    elif isinstance(image, PIL.Image.Image):
        image = image
    else:
        raise ValueError(
            "Incorrect format used for the image. Should be bytes, a URL, a local path, base64/data URL, or a PIL image."
        )

    image = PIL.ImageOps.exif_transpose(image)

    if convert_method is not None:
        image = convert_method(image)
    else:
        image = image.convert("RGB")

    return image


# adapted from diffusers.utils import load_video
def load_video(
    video: str,
    convert_method: (
        Callable[[list[PIL.Image.Image]], list[PIL.Image.Image]] | None
    ) = None,
) -> list[PIL.Image.Image]:
    """
    Loads `video` to a list of PIL Image.
    Args:
        video (`str`):
            A URL or Path to a video to convert to a list of PIL Image format.
        convert_method (Callable[[List[PIL.Image.Image]], List[PIL.Image.Image]], *optional*):
            A conversion method to apply to the video after loading it. When set to `None` the images will be converted
            to "RGB".
    Returns:
        `List[PIL.Image.Image]`:
            The video as a list of PIL images.
    """
    is_url = video.startswith("http://") or video.startswith("https://")
    is_file = os.path.isfile(video)
    was_tempfile_created = False

    if not (is_url or is_file):
        raise ValueError(
            f"Incorrect path or URL. URLs must start with `http://` or `https://`, and {video} is not a valid path."
        )

    if is_url:
        response = requests.get(video, stream=True)
        if response.status_code != 200:
            raise ValueError(
                f"Failed to download video. Status code: {response.status_code}"
            )

        parsed_url = urlparse(video)
        file_name = os.path.basename(unquote(parsed_url.path))

        suffix = os.path.splitext(file_name)[1] or ".mp4"
        with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as temp_file:
            video_path = temp_file.name
            video_data = response.iter_content(chunk_size=8192)
            for chunk in video_data:
                temp_file.write(chunk)

        video = video_path

    pil_images = []
    if video.endswith(".gif"):
        gif = PIL.Image.open(video)
        try:
            while True:
                pil_images.append(gif.copy())
                gif.seek(gif.tell() + 1)
        except EOFError:
            pass

    else:
        try:
            imageio.plugins.ffmpeg.get_exe()
        except AttributeError:
            raise AttributeError(
                "`Unable to find an ffmpeg installation on your machine. Please install via `pip install imageio-ffmpeg"
            ) from None

        with imageio.get_reader(video) as reader:
            # Read all frames
            for frame in reader:
                pil_images.append(PIL.Image.fromarray(frame))

    if was_tempfile_created:
        os.remove(video_path)

    if convert_method is not None:
        pil_images = convert_method(pil_images)

    return pil_images


def get_default_height_width(
    image: PIL.Image.Image | np.ndarray | torch.Tensor,
    vae_scale_factor: int,
    height: int | None = None,
    width: int | None = None,
) -> tuple[int, int]:
    r"""
    Returns the height and width of the image, downscaled to the next integer multiple of `vae_scale_factor`.

    Args:
        image (`Union[PIL.Image.Image, np.ndarray, torch.Tensor]`):
            The image input, which can be a PIL image, NumPy array, or PyTorch tensor. If it is a NumPy array, it
            should have shape `[batch, height, width]` or `[batch, height, width, channels]`. If it is a PyTorch
            tensor, it should have shape `[batch, channels, height, width]`.
        height (`Optional[int]`, *optional*, defaults to `None`):
            The height of the preprocessed image. If `None`, the height of the `image` input will be used.
        width (`Optional[int]`, *optional*, defaults to `None`):
            The width of the preprocessed image. If `None`, the width of the `image` input will be used.

    Returns:
        `Tuple[int, int]`:
            A tuple containing the height and width, both resized to the nearest integer multiple of
            `vae_scale_factor`.
    """

    if height is None:
        if isinstance(image, PIL.Image.Image):
            height = image.height
        elif isinstance(image, torch.Tensor):
            height = image.shape[2]
        else:
            height = image.shape[1]

    if width is None:
        if isinstance(image, PIL.Image.Image):
            width = image.width
        elif isinstance(image, torch.Tensor):
            width = image.shape[3]
        else:
            width = image.shape[2]

    width, height = (
        x - x % vae_scale_factor for x in (width, height)
    )  # resize to integer multiple of vae_scale_factor

    return height, width


def resize(
    image: PIL.Image.Image | np.ndarray | torch.Tensor,
    height: int,
    width: int,
    resize_mode: str = "default",  # "default", "fill", "crop"
    resample: str = "lanczos",
) -> PIL.Image.Image | np.ndarray | torch.Tensor:
    """
    Resize image.

    Args:
        image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
            The image input, can be a PIL image, numpy array or pytorch tensor.
        height (`int`):
            The height to resize to.
        width (`int`):
            The width to resize to.
        resize_mode (`str`, *optional*, defaults to `default`):
            The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit
            within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`,
            will resize the image to fit within the specified width and height, maintaining the aspect ratio, and
            then center the image within the dimensions, filling empty with data from image. If `crop`, will resize
            the image to fit within the specified width and height, maintaining the aspect ratio, and then center
            the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
            supported for PIL image input.

    Returns:
        `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
            The resized image.
    """
    if resize_mode != "default" and not isinstance(image, PIL.Image.Image):
        raise ValueError(
            f"Only PIL image input is supported for resize_mode {resize_mode}"
        )
    assert isinstance(image, PIL.Image.Image)
    if resize_mode == "default":
        image = image.resize((width, height), resample=PIL_INTERPOLATION[resample])
    else:
        raise ValueError(f"resize_mode {resize_mode} is not supported")
    return image