# coding=utf-8 # Copyright 2024 The HuggingFace 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 DepthPro.""" from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np if TYPE_CHECKING: from .modeling_depth_pro import DepthProDepthEstimatorOutput from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import to_channel_dimension_format from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, is_torch_available, make_list_of_images, pil_torch_interpolation_mapping, to_numpy_array, valid_images, ) from ...utils import ( TensorType, filter_out_non_signature_kwargs, logging, requires_backends, ) if is_torch_available(): import torch logger = logging.get_logger(__name__) class DepthProImageProcessor(BaseImageProcessor): r""" Constructs a DepthPro image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict`, *optional*, defaults to `{"height": 1536, "width": 1536}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ): super().__init__(**kwargs) size = size if size is not None else {"height": 1536, "width": 1536} size = get_size_dict(size) self.do_resize = do_resize self.do_rescale = do_rescale self.do_normalize = do_normalize self.size = size self.resample = resample self.rescale_factor = rescale_factor self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): `PILImageResampling` 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 unset, the channel dimension format of the input image is used. 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. 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. Returns: `np.ndarray`: The resized images. """ requires_backends(self, "torch") size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") output_size = (size["height"], size["width"]) # we use torch interpolation instead of image.resize because DepthProImageProcessor # rescales, then normalizes, which may cause some values to become negative, before resizing the image. # image.resize expects all values to be in range [0, 1] or [0, 255] and throws an exception otherwise, # however pytorch interpolation works with negative values. # relevant issue here: https://github.com/huggingface/transformers/issues/34920 # input should be (B, C, H, W) image_tensor = torch.from_numpy(image).unsqueeze(0) resized_image = torch.nn.functional.interpolate( input=image_tensor, size=output_size, mode=pil_torch_interpolation_mapping[resample].value, ) resized_image = resized_image.squeeze(0).numpy() return resized_image def _validate_input_arguments( self, do_resize: bool, size: Dict[str, int], resample: PILImageResampling, do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: Union[float, List[float]], image_std: Union[float, List[float]], data_format: Union[str, ChannelDimension], ): if do_resize and None in (size, resample): raise ValueError("Size and resample must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and None in (image_mean, image_std): raise ValueError("Image mean and standard deviation must be specified if do_normalize is True.") @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, ): """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image 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_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after resizing. resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`): `PILImageResampling` 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 to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean to use if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use if `do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: 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: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. 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 do_normalize = do_normalize if do_normalize is not None else self.do_normalize resample = resample if resample is not None else self.resample rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor 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 size = size if size is not None else self.size 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." ) self._validate_input_arguments( do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, data_format=data_format, ) # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if is_scaled_image(images[0]) and do_rescale: 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]) all_images = [] for image in images: if do_rescale: image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) if do_normalize: image = self.normalize( image=image, mean=image_mean, std=image_std, input_data_format=input_data_format ) # depth-pro rescales and normalizes the image before resizing it # uses torch interpolation which requires ChannelDimension.FIRST if do_resize: image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format) image = self.resize(image=image, size=size, resample=resample) image = to_channel_dimension_format(image, data_format, input_channel_dim=ChannelDimension.FIRST) else: image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) all_images.append(image) data = {"pixel_values": all_images} return BatchFeature(data=data, tensor_type=return_tensors) def post_process_depth_estimation( self, outputs: "DepthProDepthEstimatorOutput", target_sizes: Optional[Union[TensorType, List[Tuple[int, int]], None]] = None, ) -> Dict[str, List[TensorType]]: """ Post-processes the raw depth predictions from the model to generate final depth predictions which is caliberated using the field of view if provided and resized to specified target sizes if provided. Args: outputs ([`DepthProDepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`Optional[Union[TensorType, List[Tuple[int, int]], None]]`, *optional*, defaults to `None`): Target sizes to resize the depth predictions. Can be a tensor of shape `(batch_size, 2)` or a list of tuples `(height, width)` for each image in the batch. If `None`, no resizing is performed. Returns: `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions, and field of view (degrees) and focal length (pixels) if `field_of_view` is given in `outputs`. Raises: `ValueError`: If the lengths of `predicted_depths`, `fovs`, or `target_sizes` are mismatched. """ requires_backends(self, "torch") predicted_depth = outputs.predicted_depth fov = outputs.field_of_view batch_size = len(predicted_depth) if target_sizes is not None and batch_size != len(target_sizes): raise ValueError( "Make sure that you pass in as many fov values as the batch dimension of the predicted depth" ) results = [] fov = [None] * batch_size if fov is None else fov target_sizes = [None] * batch_size if target_sizes is None else target_sizes for depth, fov_value, target_size in zip(predicted_depth, fov, target_sizes): focal_length = None if target_size is not None: # scale image w.r.t fov if fov_value is not None: width = target_size[1] focal_length = 0.5 * width / torch.tan(0.5 * torch.deg2rad(fov_value)) depth = depth * width / focal_length # interpolate depth = torch.nn.functional.interpolate( # input should be (B, C, H, W) input=depth.unsqueeze(0).unsqueeze(1), size=target_size, mode=pil_torch_interpolation_mapping[self.resample].value, ).squeeze() # inverse the depth depth = 1.0 / torch.clamp(depth, min=1e-4, max=1e4) results.append( { "predicted_depth": depth, "field_of_view": fov_value, "focal_length": focal_length, } ) return results __all__ = ["DepthProImageProcessor"]