# 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 SuperPoint.""" from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ... import is_torch_available, is_vision_available from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, ImageType, PILImageResampling, get_image_type, infer_channel_dimension_format, is_pil_image, is_scaled_image, is_valid_image, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import TensorType, logging, requires_backends if is_torch_available(): import torch if TYPE_CHECKING: from .modeling_superglue import KeypointMatchingOutput if is_vision_available(): import PIL logger = logging.get_logger(__name__) # Copied from transformers.models.superpoint.image_processing_superpoint.is_grayscale def is_grayscale( image: ImageInput, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): if input_data_format == ChannelDimension.FIRST: if image.shape[0] == 1: return True return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]) elif input_data_format == ChannelDimension.LAST: if image.shape[-1] == 1: return True return np.all(image[..., 0] == image[..., 1]) and np.all(image[..., 1] == image[..., 2]) # Copied from transformers.models.superpoint.image_processing_superpoint.convert_to_grayscale def convert_to_grayscale( image: ImageInput, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> ImageInput: """ Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image. TODO support torch and tensorflow grayscale conversion This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each channel, because of an issue that is discussed in : https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446 Args: image (Image): The image to convert. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. """ requires_backends(convert_to_grayscale, ["vision"]) if isinstance(image, np.ndarray): if is_grayscale(image, input_data_format=input_data_format): return image if input_data_format == ChannelDimension.FIRST: gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140 gray_image = np.stack([gray_image] * 3, axis=0) elif input_data_format == ChannelDimension.LAST: gray_image = image[..., 0] * 0.2989 + image[..., 1] * 0.5870 + image[..., 2] * 0.1140 gray_image = np.stack([gray_image] * 3, axis=-1) return gray_image if not isinstance(image, PIL.Image.Image): return image image = image.convert("L") return image def validate_and_format_image_pairs(images: ImageInput): error_message = ( "Input images must be a one of the following :", " - A pair of PIL images.", " - A pair of 3D arrays.", " - A list of pairs of PIL images.", " - A list of pairs of 3D arrays.", ) def _is_valid_image(image): """images is a PIL Image or a 3D array.""" return is_pil_image(image) or ( is_valid_image(image) and get_image_type(image) != ImageType.PIL and len(image.shape) == 3 ) if isinstance(images, list): if len(images) == 2 and all((_is_valid_image(image)) for image in images): return images if all( isinstance(image_pair, list) and len(image_pair) == 2 and all(_is_valid_image(image) for image in image_pair) for image_pair in images ): return [image for image_pair in images for image in image_pair] raise ValueError(error_message) class SuperGlueImageProcessor(BaseImageProcessor): r""" Constructs a SuperGlue image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 480, "width": 640}`): Resolution of the output image after `resize` is applied. Only has an effect if `do_resize` is set to `True`. Can be overriden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess` method. do_grayscale (`bool`, *optional*, defaults to `True`): Whether to convert the image to grayscale. Can be overriden by `do_grayscale` in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: float = 1 / 255, do_grayscale: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 480, "width": 640} size = get_size_dict(size, default_to_square=False) self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_grayscale = do_grayscale # Copied from transformers.models.superpoint.image_processing_superpoint.SuperPointImageProcessor.resize def resize( self, image: np.ndarray, size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ): """ Resize an image. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary of the form `{"height": int, "width": int}`, specifying the size of the output image. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the output image. If not provided, it will be 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. 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. """ size = get_size_dict(size, default_to_square=False) return resize( image, size=(size["height"], size["width"]), data_format=data_format, input_data_format=input_data_format, **kwargs, ) def preprocess( self, images, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_grayscale: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> BatchFeature: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image pairs to preprocess. Expects either a list of 2 images or a list of list of 2 images list 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`): Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. 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_grayscale (`bool`, *optional*, defaults to `self.do_grayscale`): Whether to convert the image to grayscale. 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 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_grayscale = do_grayscale if do_grayscale is not None else self.do_grayscale size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) # Validate and convert the input images into a flattened list of images for all subsequent processing steps. images = validate_and_format_image_pairs(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_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, ) # 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_resize: image = self.resize(image=image, size=size, resample=resample, 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_grayscale: image = convert_to_grayscale(image, input_data_format=input_data_format) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) all_images.append(image) # Convert back the flattened list of images into a list of pairs of images. image_pairs = [all_images[i : i + 2] for i in range(0, len(all_images), 2)] data = {"pixel_values": image_pairs} return BatchFeature(data=data, tensor_type=return_tensors) def post_process_keypoint_matching( self, outputs: "KeypointMatchingOutput", target_sizes: Union[TensorType, List[Tuple]], threshold: float = 0.0, ) -> List[Dict[str, torch.Tensor]]: """ Converts the raw output of [`KeypointMatchingOutput`] into lists of keypoints, scores and descriptors with coordinates absolute to the original image sizes. Args: outputs ([`KeypointMatchingOutput`]): Raw outputs of the model. target_sizes (`torch.Tensor` or `List[Tuple[Tuple[int, int]]]`, *optional*): Tensor of shape `(batch_size, 2, 2)` or list of tuples of tuples (`Tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. This must be the original image size (before any processing). threshold (`float`, *optional*, defaults to 0.0): Threshold to filter out the matches with low scores. Returns: `List[Dict]`: A list of dictionaries, each dictionary containing the keypoints in the first and second image of the pair, the matching scores and the matching indices. """ if outputs.mask.shape[0] != len(target_sizes): raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask") if not all(len(target_size) == 2 for target_size in target_sizes): raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") if isinstance(target_sizes, List): image_pair_sizes = torch.tensor(target_sizes, device=outputs.mask.device) else: if target_sizes.shape[1] != 2 or target_sizes.shape[2] != 2: raise ValueError( "Each element of target_sizes must contain the size (h, w) of each image of the batch" ) image_pair_sizes = target_sizes keypoints = outputs.keypoints.clone() keypoints = keypoints * image_pair_sizes.flip(-1).reshape(-1, 2, 1, 2) keypoints = keypoints.to(torch.int32) results = [] for mask_pair, keypoints_pair, matches, scores in zip( outputs.mask, keypoints, outputs.matches[:, 0], outputs.matching_scores[:, 0] ): mask0 = mask_pair[0] > 0 mask1 = mask_pair[1] > 0 keypoints0 = keypoints_pair[0][mask0] keypoints1 = keypoints_pair[1][mask1] matches0 = matches[mask0] scores0 = scores[mask0] # Filter out matches with low scores valid_matches = torch.logical_and(scores0 > threshold, matches0 > -1) matched_keypoints0 = keypoints0[valid_matches] matched_keypoints1 = keypoints1[matches0[valid_matches]] matching_scores = scores0[valid_matches] results.append( { "keypoints0": matched_keypoints0, "keypoints1": matched_keypoints1, "matching_scores": matching_scores, } ) return results __all__ = ["SuperGlueImageProcessor"]