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# Copyright 2024 Prithvi-EO-2.0 Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
"""Image processor for Prithvi-EO-2.0 models."""
from __future__ import annotations
from typing import Optional, Union
import numpy as np
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import to_channel_dimension_format
from transformers.image_utils import (
ChannelDimension,
ImageInput,
infer_channel_dimension_format,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from transformers.utils import TensorType, filter_out_non_signature_kwargs, logging
logger = logging.get_logger(__name__)
NO_DATA = -9999
NO_DATA_FLOAT = 0.0001
def _as_temporal_frames(images) -> list:
if isinstance(images, (list, tuple)):
if len(images) == 0:
raise ValueError("Expected at least one temporal frame.")
first = images[0]
if isinstance(first, (list, tuple, np.ndarray)) and not hasattr(first, "shape"):
return list(images)
return [images]
return [images]
def _stack_temporal_frames(frames: list, input_data_format: ChannelDimension) -> np.ndarray:
arrays = [to_numpy_array(frame) for frame in frames]
if len(arrays) == 1:
array = arrays[0]
if array.ndim == 4:
if input_data_format == ChannelDimension.FIRST:
return array
return np.moveaxis(array, -1, 0)
if array.ndim == 3:
if input_data_format == ChannelDimension.FIRST:
return array[:, np.newaxis, ...]
return np.moveaxis(array, -1, 0)[:, np.newaxis, ...]
raise ValueError(f"Unsupported frame shape {array.shape}.")
if input_data_format == ChannelDimension.LAST:
stacked = np.stack(arrays, axis=0)
return np.moveaxis(stacked, -1, 0)
stacked = np.stack(arrays, axis=1)
return stacked
class PrithviImageProcessor(BaseImageProcessor):
"""
Image processor for Prithvi-EO-2.0 spatiotemporal HLS encoders.
Accepts a single array shaped `(C, T, H, W)`, `(T, H, W, C)`, or a list of `T` frames.
Applies HLS reflectance normalization with nodata masking.
"""
model_input_names = ["pixel_values", "temporal_coords", "location_coords"]
def __init__(
self,
num_channels: int = 6,
num_frames: int = 4,
do_resize: bool = False,
size: Optional[dict[str, int]] = None,
do_rescale: bool = False,
rescale_factor: float = 1.0,
do_normalize: bool = True,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
nodata_value: float = NO_DATA,
nodata_replacement: float = NO_DATA_FLOAT,
do_convert_rgb: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.num_channels = num_channels
self.num_frames = num_frames
self.do_resize = do_resize
self.size = size if size is not None else {"height": 224, "width": 224}
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.nodata_value = nodata_value
self.nodata_replacement = nodata_replacement
self.do_convert_rgb = do_convert_rgb
@classmethod
def from_config(cls, config):
return cls(
num_channels=config.num_channels,
num_frames=config.num_frames,
image_mean=config.image_mean,
image_std=config.image_std,
size={"height": config.image_size, "width": config.image_size},
)
def _normalize_hls(self, image: np.ndarray, input_data_format: ChannelDimension) -> np.ndarray:
mean = np.asarray(self.image_mean, dtype=np.float32)
std = np.asarray(self.image_std, dtype=np.float32)
if input_data_format == ChannelDimension.FIRST:
channels = image.shape[0]
mean = mean[:channels].reshape(channels, *([1] * (image.ndim - 1)))
std = std[:channels].reshape(channels, *([1] * (image.ndim - 1)))
nodata_mask = image == self.nodata_value
image = np.where(nodata_mask, self.nodata_replacement, image)
return (image - mean) / std
channels = image.shape[-1]
mean = mean[:channels]
std = std[:channels]
nodata_mask = image == self.nodata_value
image = np.where(nodata_mask, self.nodata_replacement, image)
return (image - mean) / std
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
temporal_coords: Optional[Union[list, np.ndarray]] = None,
location_coords: Optional[Union[list, np.ndarray]] = None,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = 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 = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Normalization requires `image_mean` and `image_std` with one value per channel.")
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,
)
temporal_batches = _as_temporal_frames(images)
if not valid_images(temporal_batches):
raise ValueError("Invalid image type. Must be PIL, numpy, or torch tensor.")
processed_images = []
for sample in temporal_batches:
if input_data_format is None:
try:
input_data_format = infer_channel_dimension_format(to_numpy_array(sample[0] if isinstance(sample, list) else sample))
except ValueError:
input_data_format = ChannelDimension.LAST
image = _stack_temporal_frames(sample if isinstance(sample, list) else [sample], input_data_format)
if do_convert_rgb:
image = self._convert_image_to_rgb(image)
if do_rescale:
image = image * rescale_factor
if do_normalize:
image = self._normalize_hls(image, ChannelDimension.FIRST)
image = to_channel_dimension_format(image, data_format, input_channel_dim=ChannelDimension.FIRST)
processed_images.append(image)
data = {"pixel_values": processed_images}
if temporal_coords is not None:
data["temporal_coords"] = [np.asarray(temporal_coords, dtype=np.float32)]
if location_coords is not None:
data["location_coords"] = [np.asarray(location_coords, dtype=np.float32)]
return BatchFeature(data=data, tensor_type=return_tensors)
__all__ = ["PrithviImageProcessor"]
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