# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # 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. """Image processor class for GLPN.""" from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union if TYPE_CHECKING: from ...modeling_outputs import DepthEstimatorOutput import numpy as np import PIL.Image from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, is_torch_available, make_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, logging, requires_backends if is_torch_available(): import torch logger = logging.get_logger(__name__) class GLPNImageProcessor(BaseImageProcessor): r""" Constructs a GLPN image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions, rounding them down to the closest multiple of `size_divisor`. Can be overridden by `do_resize` in `preprocess`. size_divisor (`int`, *optional*, defaults to 32): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. Can be overridden by `size_divisor` in `preprocess`. resample (`PIL.Image` resampling filter, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be overridden by `do_rescale` in `preprocess`. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size_divisor: int = 32, resample=PILImageResampling.BILINEAR, do_rescale: bool = True, **kwargs, ) -> None: self.do_resize = do_resize self.do_rescale = do_rescale self.size_divisor = size_divisor self.resample = resample super().__init__(**kwargs) def resize( self, image: np.ndarray, size_divisor: int, resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[ChannelDimension] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize the image, rounding the (height, width) dimensions down to the closest multiple of size_divisor. If the image is of dimension (3, 260, 170) and size_divisor is 32, the image will be resized to (3, 256, 160). Args: image (`np.ndarray`): The image to resize. size_divisor (`int`): The image is resized so its height and width are rounded down to the closest multiple of `size_divisor`. resample: `PIL.Image` resampling filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If `None`, the channel dimension format of the input image is used. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not set, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. Returns: `np.ndarray`: The resized image. """ height, width = get_image_size(image, channel_dim=input_data_format) # Rounds the height and width down to the closest multiple of size_divisor new_h = height // size_divisor * size_divisor new_w = width // size_divisor * size_divisor image = resize( image, (new_h, new_w), resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) return image @filter_out_non_signature_kwargs() def preprocess( self, images: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]], do_resize: Optional[bool] = None, size_divisor: Optional[int] = None, resample=None, do_rescale: Optional[bool] = None, return_tensors: Optional[Union[TensorType, str]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> BatchFeature: """ Preprocess the given images. Args: images (`PIL.Image.Image` or `TensorType` or `List[np.ndarray]` or `List[TensorType]`): Images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_normalize=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the input such that the (height, width) dimensions are a multiple of `size_divisor`. size_divisor (`int`, *optional*, defaults to `self.size_divisor`): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. resample (`PIL.Image` resampling filter, *optional*, defaults to `self.resample`): `PIL.Image` resampling filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - `None`: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_resize = do_resize if do_resize is not None else self.do_resize do_rescale = do_rescale if do_rescale is not None else self.do_rescale size_divisor = size_divisor if size_divisor is not None else self.size_divisor resample = resample if resample is not None else self.resample images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) # Here, the rescale() method uses a constant rescale_factor. It does not need to be validated # with a rescale_factor. validate_preprocess_arguments( do_resize=do_resize, size=size_divisor, # Here, size_divisor is used as a parameter for optimal resizing instead of size. resample=resample, ) # All transformations expect numpy arrays. images = [to_numpy_array(img) for img in images] if do_rescale and is_scaled_image(images[0]): logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_resize: images = [ self.resize(image, size_divisor=size_divisor, resample=resample, input_data_format=input_data_format) for image in images ] if do_rescale: images = [self.rescale(image, scale=1 / 255, input_data_format=input_data_format) for image in images] images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors) def post_process_depth_estimation( self, outputs: "DepthEstimatorOutput", target_sizes: Optional[Union[TensorType, List[Tuple[int, int]], None]] = None, ) -> List[Dict[str, TensorType]]: """ Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`DepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`TensorType` or `List[Tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized. Returns: `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. """ requires_backends(self, "torch") predicted_depth = outputs.predicted_depth if (target_sizes is not None) and (len(predicted_depth) != len(target_sizes)): raise ValueError( "Make sure that you pass in as many target sizes as the batch dimension of the predicted depth" ) results = [] target_sizes = [None] * len(predicted_depth) if target_sizes is None else target_sizes for depth, target_size in zip(predicted_depth, target_sizes): if target_size is not None: depth = depth[None, None, ...] depth = torch.nn.functional.interpolate(depth, size=target_size, mode="bicubic", align_corners=False) depth = depth.squeeze() results.append({"predicted_depth": depth}) return results __all__ = ["GLPNImageProcessor"]