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96e3a14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | # Copyright 2025 S5 authors and Hugging Face converters.
"""Image processor for S5 remote-sensing models (ImageNet mean/std)."""
from __future__ import annotations
from typing import Optional, Union
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from transformers.image_transforms import convert_to_rgb, resize, to_channel_dimension_format
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
make_flat_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from transformers.utils import TensorType, logging
try:
from transformers.utils import filter_out_non_signature_kwargs
except ImportError: # older transformers
def filter_out_non_signature_kwargs():
def decorator(fn):
return fn
return decorator
try:
from configuration_s5 import IMAGENET_MEAN, IMAGENET_STD
except ImportError:
from .configuration_s5 import IMAGENET_MEAN, IMAGENET_STD
logger = logging.get_logger(__name__)
class S5ImageProcessor(BaseImageProcessor):
"""Resize / rescale / ImageNet-normalize RGB remote-sensing images for S5."""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = False,
size: Optional[dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_convert_rgb: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.do_resize = do_resize
self.size = size if size is not None else {"height": 512, "width": 512}
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = list(IMAGENET_MEAN) if image_mean is None else image_mean
self.image_std = list(IMAGENET_STD) if image_std is None else image_std
self.do_convert_rgb = do_convert_rgb
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = None,
resample: Optional[PILImageResampling] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
do_convert_rgb: Optional[bool] = None,
):
do_resize = self.do_resize if do_resize is None else do_resize
size = get_size_dict(self.size if size is None else size, default_to_square=True)
resample = self.resample if resample is None else resample
do_rescale = self.do_rescale if do_rescale is None else do_rescale
rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
do_normalize = self.do_normalize if do_normalize is None else do_normalize
image_mean = self.image_mean if image_mean is None else image_mean
image_std = self.image_std if image_std is None else image_std
do_convert_rgb = self.do_convert_rgb if do_convert_rgb is None else do_convert_rgb
images = make_flat_list_of_images(images)
if not valid_images(images):
raise ValueError("Invalid image type. Must be PIL, numpy, or torch tensor.")
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
processed = []
for image in images:
if do_convert_rgb:
try:
image = convert_to_rgb(image)
except Exception:
image = to_numpy_array(image)
image = to_numpy_array(image)
if input_data_format is None:
try:
inferred = infer_channel_dimension_format(image)
except ValueError:
inferred = ChannelDimension.LAST
else:
inferred = input_data_format
if do_resize:
image = resize(
image,
size=(size["height"], size["width"]),
resample=resample,
input_data_format=inferred,
)
if do_rescale:
image = image * rescale_factor
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=inferred)
processed.append(to_channel_dimension_format(image, data_format, input_channel_dim=inferred))
return BatchFeature(data={"pixel_values": processed}, tensor_type=return_tensors)
__all__ = ["S5ImageProcessor"]
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