data/video/transforms/area_resize.py

# Copyright (c) 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 math
from typing import List, Union
import torch
from PIL import Image
from torchvision.transforms import functional as TVF
from torchvision.transforms.functional import InterpolationMode, to_tensor


class AreaResize:
    def __init__(
        self,
        max_area: float,
        downsample_only: bool = False,
        interpolation: InterpolationMode = InterpolationMode.BICUBIC,
    ):
        self.max_area = max_area
        self.downsample_only = downsample_only
        self.interpolation = interpolation

    def __call__(self, image: Union[torch.Tensor, Image.Image, List[Image.Image]]):

        if isinstance(image, torch.Tensor):
            height, width = image.shape[-2:]
        elif isinstance(image, Image.Image):
            width, height = image.size
        elif isinstance(image, list) and isinstance(image[0], Image.Image):
            width, height = image[0].size
        else:
            raise NotImplementedError

        scale = math.sqrt(self.max_area / (height * width))

        # keep original height and width for small pictures.
        scale = 1 if scale >= 1 and self.downsample_only else scale

        resized_height, resized_width = round(height * scale), round(width * scale)

        if isinstance(image, list) and isinstance(image[0], Image.Image):
            image = torch.stack(
                [
                    to_tensor(
                        TVF.resize(
                            _image,
                            size=(resized_height, resized_width),
                            interpolation=self.interpolation,
                        )
                    )
                    for _image in image
                ]
            )
        else:
            image = TVF.resize(
                image,
                size=(resized_height, resized_width),
                interpolation=self.interpolation,
            )
            if isinstance(image, Image.Image):
                image = to_tensor(image)
        return image