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# Copyright 2024 Rhymes AI. All rights reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
from typing import List, Optional, Union
import torch
from PIL import Image, ImageOps
from torchvision import transforms
from transformers import BaseImageProcessor, BatchFeature, TensorType
def keep_ratio_resize_and_pixel_mask(
img: Image.Image, max_size, min_size=336, padding_value=0
):
"""
Resize an image while maintaining aspect ratio and create a pixel mask.
Args:
img (PIL.Image): Input image.
max_size (int): Maximum size for the larger dimension of the image.
min_size (int, optional): Minimum size for the smaller dimension. Defaults to 336.
padding_value (int, optional): Value used for padding. Defaults to 0.
Returns:
tuple: A tuple containing:
- PIL.Image: Resized and padded image.
- torch.Tensor: Boolean pixel mask. This mask is a 2D tensor of shape (max_size, max_size) where:
- True (1) values indicate pixels that belong to the original resized image.
- False (0) values indicate pixels that are part of the padding.
The mask helps distinguish between actual image content and padded areas in subsequent processing steps.
"""
img = img.convert("RGB")
# rescale the given image, keep the aspect ratio
scale = max_size / max(img.size)
w, h = img.size
if w >= h:
new_size = (max_size, max(int(h * scale), min_size)) # w, h
else:
new_size = (max(int(w * scale), min_size), max_size) # w, h
img_resized = img.resize(new_size, resample=Image.Resampling.BICUBIC)
# padding the right/bottom
padding_right, padding_bottom = max_size - new_size[0], max_size - new_size[1]
img_padded = ImageOps.expand(
img_resized, (0, 0, padding_right, padding_bottom), fill=padding_value
)
# Create a pixel mask
pixel_mask = torch.zeros(max_size, max_size)
pixel_mask[: new_size[1], : new_size[0]] = 1
pixel_mask = pixel_mask.bool()
return img_padded, pixel_mask
class AriaVisionProcessor(BaseImageProcessor):
"""
A vision processor for the Aria model that handles image preprocessing.
"""
def __init__(
self,
max_image_size=980,
min_image_size=336,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
**kwargs,
):
"""
Initialize the AriaVisionProcessor.
Args:
max_image_size (int, optional): Maximum image size. Defaults to 980.
min_image_size (int, optional): Minimum image size. Defaults to 336.
mean (list, optional): Mean values for normalization. Defaults to [0.5, 0.5, 0.5].
std (list, optional): Standard deviation values for normalization. Defaults to [0.5, 0.5, 0.5].
"""
super().__init__(**kwargs)
self.max_image_size = max_image_size
self.min_image_size = min_image_size
self.image_mean = image_mean
self.image_std = image_std
self.auto_map = {
"AutoProcessor": "processing_aria.AriaProcessor",
"AutoImageProcessor": "vision_processor.AriaVisionProcessor",
}
# we make the transform a property so that it is lazily initialized,
# this could avoid the error "TypeError: Object of type Normalize is not JSON serializable"
# when we used save_pretrained or from_pretrained.
self._transform = None
self._set_processor_class("AriaProcessor")
@property
def transform(self):
if self._transform is None:
# Recreate the transform when accessed
self._transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(self.image_mean, self.image_std),
]
)
return self._transform
def __call__(
self,
images: Union[Image.Image, List[Image.Image]],
max_image_size: Optional[int] = 980,
min_image_size: Optional[int] = 336,
return_tensors: Optional[Union[str, TensorType]] = "pt",
):
"""
Process a list of images.
Args:
images (list): List of PIL.Image objects.
max_image_size (int, optional): Override the default max image size. Defaults to None.
return_tensors (str or TensorType, optional): The type of tensor to return. Defaults to "pt".
Returns:
BatchFeature: A BatchFeature object containing:
- 'pixel_values': Tensor of processed image pixel values.
- 'pixel_mask': Boolean pixel mask. This mask is a 2D tensor of shape (max_size, max_size) where:
- True (1) values indicate pixels that belong to the original resized image.
- False (0) values indicate pixels that are part of the padding.
The mask helps distinguish between actual image content and padded areas in subsequent processing steps.
"""
max_size = self.max_image_size if max_image_size is None else max_image_size
min_size = self.min_image_size if min_image_size is None else min_image_size
if max_size not in [490, 980]:
raise ValueError("max_image_size must be either 490 or 980")
if isinstance(images, Image.Image):
images = [images]
pixel_values = []
pixel_masks = []
for image in images:
img_padded, pixel_mask = keep_ratio_resize_and_pixel_mask(
image, max_size, min_size
)
img_padded = self.transform(img_padded)
pixel_values.append(img_padded)
pixel_masks.append(pixel_mask)
return BatchFeature(
data={
"pixel_values": torch.stack(pixel_values),
"pixel_mask": torch.stack(pixel_masks),
},
tensor_type=return_tensors,
)
def preprocess(
self,
images,
max_image_size=None,
min_image_size=None,
return_tensors: Optional[Union[str, TensorType]] = None,
):
return self.__call__(
images,
max_image_size=max_image_size,
min_image_size=min_image_size,
return_tensors=return_tensors,
)
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