# 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, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, logging, requires_backends if is_torch_available(): import torch if TYPE_CHECKING: from .modeling_superpoint import SuperPointKeypointDescriptionOutput if is_vision_available(): import PIL logger = logging.get_logger(__name__) 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]) 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 class SuperPointImageProcessor(BaseImageProcessor): r""" Constructs a SuperPoint 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. 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 `False`): 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, do_rescale: bool = True, rescale_factor: float = 1 / 255, do_grayscale: bool = False, **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.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_grayscale = do_grayscale 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, 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 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`): 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`. 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 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) 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." ) if do_resize and size is None: raise ValueError("Size 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.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image 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=image, size=size, input_data_format=input_data_format) for image in images] if do_rescale: images = [ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images ] 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_grayscale: images = [convert_to_grayscale(image, 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_keypoint_detection( self, outputs: "SuperPointKeypointDescriptionOutput", target_sizes: Union[TensorType, List[Tuple]] ) -> List[Dict[str, "torch.Tensor"]]: """ Converts the raw output of [`SuperPointForKeypointDetection`] into lists of keypoints, scores and descriptors with coordinates absolute to the original image sizes. Args: outputs ([`SuperPointKeypointDescriptionOutput`]): Raw outputs of the model containing keypoints in a relative (x, y) format, with scores and descriptors. target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`): 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. This must be the original image size (before any processing). Returns: `List[Dict]`: A list of dictionaries, each dictionary containing the keypoints in absolute format according to target_sizes, scores and descriptors for an image in the batch as predicted by the model. """ if len(outputs.mask) != len(target_sizes): raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask") if isinstance(target_sizes, List): image_sizes = torch.tensor(target_sizes, device=outputs.mask.device) else: if target_sizes.shape[1] != 2: raise ValueError( "Each element of target_sizes must contain the size (h, w) of each image of the batch" ) image_sizes = target_sizes # Flip the image sizes to (width, height) and convert keypoints to absolute coordinates image_sizes = torch.flip(image_sizes, [1]) masked_keypoints = outputs.keypoints * image_sizes[:, None] # Convert masked_keypoints to int masked_keypoints = masked_keypoints.to(torch.int32) results = [] for image_mask, keypoints, scores, descriptors in zip( outputs.mask, masked_keypoints, outputs.scores, outputs.descriptors ): indices = torch.nonzero(image_mask).squeeze(1) keypoints = keypoints[indices] scores = scores[indices] descriptors = descriptors[indices] results.append({"keypoints": keypoints, "scores": scores, "descriptors": descriptors}) return results __all__ = ["SuperPointImageProcessor"]