136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# //
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# // Licensed under the Apache License, Version 2.0 (the "License");
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# // you may not use this file except in compliance with the License.
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# // You may obtain a copy of the License at
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# //
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# // http://www.apache.org/licenses/LICENSE-2.0
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# //
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# // Unless required by applicable law or agreed to in writing, software
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# // distributed under the License is distributed on an "AS IS" BASIS,
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# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# // See the License for the specific language governing permissions and
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# // limitations under the License.
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import math
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import random
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from typing import Union
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import torch
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from PIL import Image
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from torchvision.transforms import functional as TVF
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from torchvision.transforms.functional import InterpolationMode
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class AreaResize:
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def __init__(
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self,
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max_area: float,
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downsample_only: bool = False,
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interpolation: InterpolationMode = InterpolationMode.BICUBIC,
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):
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self.max_area = max_area
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self.downsample_only = downsample_only
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self.interpolation = interpolation
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def __call__(self, image: Union[torch.Tensor, Image.Image]):
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if isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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elif isinstance(image, Image.Image):
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width, height = image.size
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else:
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raise NotImplementedError
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scale = math.sqrt(self.max_area / (height * width))
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# keep original height and width for small pictures.
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scale = 1 if scale >= 1 and self.downsample_only else scale
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resized_height, resized_width = round(height * scale), round(width * scale)
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return TVF.resize(
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image,
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size=(resized_height, resized_width),
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interpolation=self.interpolation,
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)
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class AreaRandomCrop:
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def __init__(
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self,
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max_area: float,
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):
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self.max_area = max_area
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def get_params(self, input_size, output_size):
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"""Get parameters for ``crop`` for a random crop.
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Args:
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img (PIL Image): Image to be cropped.
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output_size (tuple): Expected output size of the crop.
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Returns:
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tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
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"""
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# w, h = _get_image_size(img)
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h, w = input_size
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th, tw = output_size
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if w <= tw and h <= th:
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return 0, 0, h, w
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i = random.randint(0, h - th)
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j = random.randint(0, w - tw)
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return i, j, th, tw
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def __call__(self, image: Union[torch.Tensor, Image.Image]):
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if isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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elif isinstance(image, Image.Image):
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width, height = image.size
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else:
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raise NotImplementedError
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resized_height = math.sqrt(self.max_area / (width / height))
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resized_width = (width / height) * resized_height
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# print('>>>>>>>>>>>>>>>>>>>>>')
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# print((height, width))
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# print( (resized_height, resized_width))
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resized_height, resized_width = round(resized_height), round(resized_width)
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i, j, h, w = self.get_params((height, width), (resized_height, resized_width))
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image = TVF.crop(image, i, j, h, w)
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return image
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class ScaleResize:
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def __init__(
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self,
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scale: float,
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):
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self.scale = scale
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def __call__(self, image: Union[torch.Tensor, Image.Image]):
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if isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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interpolation_mode = InterpolationMode.BILINEAR
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antialias = True if image.ndim == 4 else "warn"
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elif isinstance(image, Image.Image):
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width, height = image.size
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interpolation_mode = InterpolationMode.LANCZOS
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antialias = "warn"
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else:
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raise NotImplementedError
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scale = self.scale
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# keep original height and width for small pictures
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resized_height, resized_width = round(height * scale), round(width * scale)
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image = TVF.resize(
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image,
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size=(resized_height, resized_width),
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interpolation=interpolation_mode,
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antialias=antialias,
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)
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return image
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