# 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 PromptDepthAnything.""" import math from typing import TYPE_CHECKING, Optional, Union if TYPE_CHECKING: from ...modeling_outputs import DepthEstimatorOutput import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import pad, resize, to_channel_dimension_format from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, is_torch_available, make_flat_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__) def _constrain_to_multiple_of(val, multiple, min_val=0, max_val=None): x = round(val / multiple) * multiple if max_val is not None and x > max_val: x = math.floor(val / multiple) * multiple if x < min_val: x = math.ceil(val / multiple) * multiple return x def _get_resize_output_image_size( input_image: np.ndarray, output_size: tuple[int, int], keep_aspect_ratio: bool, multiple: int, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> tuple[int, int]: input_height, input_width = get_image_size(input_image, input_data_format) output_height, output_width = output_size # determine new height and width scale_height = output_height / input_height scale_width = output_width / input_width if keep_aspect_ratio: # scale as little as possible if abs(1 - scale_width) < abs(1 - scale_height): # fit width scale_height = scale_width else: # fit height scale_width = scale_height new_height = _constrain_to_multiple_of(scale_height * input_height, multiple=multiple) new_width = _constrain_to_multiple_of(scale_width * input_width, multiple=multiple) return (new_height, new_width) class PromptDepthAnythingImageProcessor(BaseImageProcessor): r""" Constructs a PromptDepthAnything image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions. Can be overridden by `do_resize` in `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Defines the resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can be overridden by `keep_aspect_ratio` in `preprocess`. ensure_multiple_of (`int`, *optional*, defaults to 1): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overridden by `ensure_multiple_of` in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in `preprocess`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in `preprocess`. 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. do_pad (`bool`, *optional*, defaults to `False`): Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in combination with DPT. size_divisor (`int`, *optional*): If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the DINOv2 paper, which uses the model in combination with DPT. prompt_scale_to_meter (`float`, *optional*, defaults to 0.001): Scale factor to convert the prompt depth to meters. """ model_input_names = ["pixel_values", "prompt_depth"] def __init__( self, do_resize: bool = True, size: Optional[dict[str, int]] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, keep_aspect_ratio: bool = False, ensure_multiple_of: int = 1, 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, do_pad: bool = False, size_divisor: Optional[int] = None, prompt_scale_to_meter: float = 0.001, # default unit is mm **kwargs, ): super().__init__(**kwargs) size = size if size is not None else {"height": 384, "width": 384} size = get_size_dict(size) self.do_resize = do_resize self.size = size self.keep_aspect_ratio = keep_aspect_ratio self.ensure_multiple_of = ensure_multiple_of self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize 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 self.do_pad = do_pad self.size_divisor = size_divisor self.prompt_scale_to_meter = prompt_scale_to_meter def resize( self, image: np.ndarray, size: dict[str, int], keep_aspect_ratio: bool = False, ensure_multiple_of: int = 1, resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image to target size `(size["height"], size["width"])`. If `keep_aspect_ratio` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is resized to a size that is a multiple of this value. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Target size of the output image. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. ensure_multiple_of (`int`, *optional*, defaults to 1): The image is resized to a size that is a multiple of this value. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ 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 = _get_resize_output_image_size( image, output_size=(size["height"], size["width"]), keep_aspect_ratio=keep_aspect_ratio, multiple=ensure_multiple_of, input_data_format=input_data_format, ) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def pad_image( self, image: np.ndarray, size_divisor: int, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """ Center pad an image to be a multiple of `multiple`. Args: image (`np.ndarray`): Image to pad. size_divisor (`int`): The width and height of the image will be padded to a multiple of this number. 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. """ def _get_pad(size, size_divisor): new_size = math.ceil(size / size_divisor) * size_divisor pad_size = new_size - size pad_size_left = pad_size // 2 pad_size_right = pad_size - pad_size_left return pad_size_left, pad_size_right if input_data_format is None: input_data_format = infer_channel_dimension_format(image) height, width = get_image_size(image, input_data_format) pad_size_top, pad_size_bottom = _get_pad(height, size_divisor) pad_size_left, pad_size_right = _get_pad(width, size_divisor) padded_image = pad( image, ((pad_size_top, pad_size_bottom), (pad_size_left, pad_size_right)), data_format=data_format ) return padded_image @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, prompt_depth: Optional[ImageInput] = None, do_resize: Optional[bool] = None, size: Optional[int] = None, keep_aspect_ratio: Optional[bool] = None, ensure_multiple_of: Optional[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, do_pad: Optional[bool] = None, size_divisor: Optional[int] = None, prompt_scale_to_meter: Optional[float] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> BatchFeature: """ 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`. prompt_depth (`ImageInput`, *optional*): Prompt depth to preprocess, which can be sparse depth obtained from multi-view geometry or low-resolution depth from a depth sensor. Generally has shape (height, width), where height and width can be smaller than those of the images. It's optional and can be None, which means no prompt depth is used. If it is None, the output depth will be a monocular relative depth. It is recommended to provide a prompt_scale_to_meter value, which is the scale factor to convert the prompt depth to meters. This is useful when the prompt depth is not in meters. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. If `keep_aspect_ratio` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is resized to a size that is a multiple of this value. keep_aspect_ratio (`bool`, *optional*, defaults to `self.keep_aspect_ratio`): Whether to keep the aspect ratio of the image. If False, the image will be resized to (size, size). If True, the image will be resized to keep the aspect ratio and the size will be the maximum possible. ensure_multiple_of (`int`, *optional*, defaults to `self.ensure_multiple_of`): Ensure that the image size is a multiple of this value. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, 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. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. prompt_scale_to_meter (`float`, *optional*, defaults to `self.prompt_scale_to_meter`): Scale factor to convert the prompt depth to meters. 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: - `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 size = size if size is not None else self.size size = get_size_dict(size) keep_aspect_ratio = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio ensure_multiple_of = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of resample = resample if resample is not None else self.resample 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_pad = do_pad if do_pad is not None else self.do_pad size_divisor = size_divisor if size_divisor is not None else self.size_divisor images = make_flat_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." ) 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, ) # 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]) preprocessed_images = [] for image in images: if do_resize: image = self.resize( image=image, size=size, resample=resample, keep_aspect_ratio=keep_aspect_ratio, ensure_multiple_of=ensure_multiple_of, input_data_format=input_data_format, ) 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 ) if do_pad: image = self.pad_image(image=image, size_divisor=size_divisor, input_data_format=input_data_format) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) preprocessed_images.append(image) images = preprocessed_images data = {"pixel_values": images} if prompt_depth is not None: # prompt_depth is a list of images with shape (height, width) # we need to convert it to a list of images with shape (1, height, width) prompt_depths = make_flat_list_of_images(prompt_depth, expected_ndims=2) # Validate prompt_depths has same length as images if len(prompt_depths) != len(images): raise ValueError( f"Number of prompt depth images ({len(prompt_depths)}) does not match number of input images ({len(images)})" ) if prompt_scale_to_meter is None: prompt_scale_to_meter = self.prompt_scale_to_meter processed_prompt_depths = [] for depth in prompt_depths: depth = to_numpy_array(depth) depth = depth * prompt_scale_to_meter if depth.min() == depth.max(): # Prompt depth is invalid, min and max are the same. # We can simply select one pixel and set it to a small value. depth[0, 0] = depth[0, 0] + 1e-6 depth = depth[..., None].astype(np.float32) # Always use LAST as input format since we add channel dim with [..., None] depth = to_channel_dimension_format(depth, data_format, input_channel_dim=ChannelDimension.LAST) processed_prompt_depths.append(depth) prompt_depths = processed_prompt_depths data["prompt_depth"] = prompt_depths return BatchFeature(data=data, tensor_type=return_tensors) # Copied from transformers.models.dpt.image_processing_dpt.DPTImageProcessor.post_process_depth_estimation with DPT->PromptDepthAnything 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 = torch.nn.functional.interpolate( depth.unsqueeze(0).unsqueeze(1), size=target_size, mode="bicubic", align_corners=False ).squeeze() results.append({"predicted_depth": depth}) return results __all__ = ["PromptDepthAnythingImageProcessor"]