veomni/data/multimodal/image_utils.py

# Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import io
import math
from io import BytesIO
from typing import ByteString, List, Union

import numpy as np
import requests
import torch
from PIL import Image


ImageInput = Union[
    Image.Image,
    np.ndarray,
    ByteString,
    str,
]


def load_image_bytes_from_path(image_path: str):
    image = Image.open(image_path).convert("RGB")
    image_bytes = io.BytesIO()
    image.save(image_bytes, format="JPEG")
    return image_bytes.getvalue()


def save_image_bytes_to_file(image_bytes, output_path):
    image_bytes = io.BytesIO(image_bytes)
    image = Image.open(image_bytes).convert("RGB")
    image.save(output_path)


def smart_resize(
    image: Image.Image,
    scale_factor: int = None,
    image_min_pixels: int = None,
    image_max_pixels: int = None,
    max_ratio: int = None,
    **kwargs,
):
    width, height = image.size
    if max_ratio is not None:
        ratio = max(width, height) / min(width, height)
        if ratio > max_ratio:
            raise ValueError(f"absolute aspect ratio must be smaller than {max_ratio}, got {ratio}")

    if scale_factor is not None:
        h_bar = max(scale_factor, round(height / scale_factor) * scale_factor)
        w_bar = max(scale_factor, round(width / scale_factor) * scale_factor)
    else:
        h_bar = height
        w_bar = width

    if image_max_pixels is not None and h_bar * w_bar > image_max_pixels:
        beta = math.sqrt((height * width) / image_max_pixels)
        if scale_factor is not None:
            h_bar = math.floor(height / beta / scale_factor) * scale_factor
            w_bar = math.floor(width / beta / scale_factor) * scale_factor
        else:
            h_bar = math.floor(height / beta)
            w_bar = math.floor(width / beta)
    if image_min_pixels is not None and h_bar * w_bar < image_min_pixels:
        beta = math.sqrt(image_min_pixels / (height * width))
        if scale_factor is not None:
            h_bar = math.ceil(height * beta / scale_factor) * scale_factor
            w_bar = math.ceil(width * beta / scale_factor) * scale_factor
        else:
            h_bar = math.ceil(height * beta)
            w_bar = math.ceil(width * beta)
    image = image.resize((w_bar, h_bar))
    return image


def load_image_from_path(image: str, **kwargs):
    if image.startswith("http://") or image.startswith("https://"):
        response = requests.get(image, stream=True)
        image_obj = Image.open(BytesIO(response.content))
    else:
        image_obj = Image.open(image)
    return image_obj.convert("RGB")


def load_image_from_bytes(image: bytes, **kwargs):
    return Image.open(BytesIO(image)).convert("RGB")


def load_image(image: ImageInput, **kwargs):
    if isinstance(image, str):
        return load_image_from_path(image, **kwargs)
    elif isinstance(image, bytes):
        return load_image_from_bytes(image, **kwargs)
    else:
        raise NotImplementedError


def fetch_images(images: List[ImageInput], **kwargs):
    images = [load_image(image) for image in images]
    max_image_nums = kwargs.get("max_image_nums", len(images))
    images = images[:max_image_nums]
    images = [smart_resize(image, **kwargs) for image in images]
    return images


def save_image_tensors_to_file(image_tensors: torch.Tensor, output_path: str):
    image_tensors = image_tensors * 255.0
    image_tensors = image_tensors.clamp(0, 255)
    image_tensors = image_tensors.cpu().to(torch.uint8).numpy()
    image = Image.fromarray(image_tensors)
    image.save(output_path)