#
SuperPoint

##
Overview

The SuperPoint model was proposed in SuperPoint: Self-Supervised Interest Point Detection and Description by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich.

This model is the result of a self-supervised training of a fully-convolutional network for interest point detection and description. The model is able to detect interest points that are repeatable under homographic transformations and provide a descriptor for each point. The use of the model in its own is limited, but it can be used as a feature extractor for other tasks such as homography estimation, image matching, etc.

The abstract from the paper is the following:

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.

##
How to use

Here is a quick example of using the model to detect interest points in an image:

    from transformers import AutoImageProcessor, AutoModel
    import torch
    from PIL import Image
    import requests

    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint")
    model = AutoModel.from_pretrained("magic-leap-community/superpoint")

    inputs = processor(image, return_tensors="pt")
    outputs = model(**inputs)

The outputs contain the list of keypoint coordinates with their respective score and description (a 256-long vector).

You can also feed multiple images to the model. Due to the nature of SuperPoint, to output a dynamic number of keypoints, you will need to use the mask attribute to retrieve the respective information :

    from transformers import AutoImageProcessor, AutoModel
    import torch
    from PIL import Image
    import requests

    url_image_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image_1 = Image.open(requests.get(url_image_1, stream=True).raw)
    url_image_2 = "http://images.cocodataset.org/test-stuff2017/000000000568.jpg"
    image_2 = Image.open(requests.get(url_image_2, stream=True).raw)

    images = [image_1, image_2]

    processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint")
    model = AutoModel.from_pretrained("magic-leap-community/superpoint")

    inputs = processor(images, return_tensors="pt")
    outputs = model(**inputs)

    for i in range(len(images)):
        image_mask = outputs.mask[i]
        image_indices = torch.nonzero(image_mask).squeeze()
        image_keypoints = outputs.keypoints[i][image_indices]
        image_scores = outputs.scores[i][image_indices]
        image_descriptors = outputs.descriptors[i][image_indices]

You can then print the keypoints on the image to visualize the result :

    import cv2
    for keypoint, score in zip(image_keypoints, image_scores):
        keypoint_x, keypoint_y = int(keypoint[0].item()), int(keypoint[1].item())
        color = tuple([score.item() * 255] * 3)
        image = cv2.circle(image, (keypoint_x, keypoint_y), 2, color)
    cv2.imwrite("output_image.png", image)

This model was contributed by stevenbucaille. The original code can be found here.

##
SuperPointConfig

###
class transformers.SuperPointConfig

< source >

( encoder_hidden_sizes: List = [64, 64, 128, 128] decoder_hidden_size: int = 256 keypoint_decoder_dim: int = 65 descriptor_decoder_dim: int = 256 keypoint_threshold: float = 0.005 max_keypoints: int = -1 nms_radius: int = 4 border_removal_distance: int = 4 initializer_range = 0.02 **kwargs )

Parameters

- encoder_hidden_sizes (`List`, optional, defaults to `[64, 64, 128, 128]`) — The number of channels in each convolutional layer in the encoder.
- decoder_hidden_size (`int`, optional, defaults to 256) — The hidden size of the decoder.
- keypoint_decoder_dim (`int`, optional, defaults to 65) — The output dimension of the keypoint decoder.
- descriptor_decoder_dim (`int`, optional, defaults to 256) — The output dimension of the descriptor decoder.
- keypoint_threshold (`float`, optional, defaults to 0.005) — The threshold to use for extracting keypoints.
- max_keypoints (`int`, optional, defaults to -1) — The maximum number of keypoints to extract. If `-1`, will extract all keypoints.
- nms_radius (`int`, optional, defaults to 4) — The radius for non-maximum suppression.
- border_removal_distance (`int`, optional, defaults to 4) — The distance from the border to remove keypoints.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

This is the configuration class to store the configuration of a SuperPointForKeypointDetection. It is used to instantiate a SuperPoint model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SuperPoint magic-leap-community/superpoint architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import SuperPointConfig, SuperPointForKeypointDetection

    >>> # Initializing a SuperPoint superpoint style configuration
    >>> configuration = SuperPointConfig()
    >>> # Initializing a model from the superpoint style configuration
    >>> model = SuperPointForKeypointDetection(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config

##
SuperPointImageProcessor

###
class transformers.SuperPointImageProcessor

< source >

( do_resize: bool = True size: Dict = None do_rescale: bool = True rescale_factor: float = 0.00392156862745098 **kwargs )

Parameters

- 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.

Constructs a SuperPoint image processor.

####
preprocess

< source >

( images do_resize: bool = None size: Dict = None do_rescale: bool = None rescale_factor: float = None return_tensors: Union = None data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None **kwargs )

Parameters

- 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`.
- 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.

Preprocess an image or batch of images.

####
resize

< source >

( image: ndarray size: Dict data_format: Union = None input_data_format: Union = None **kwargs )

Parameters

- 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.

Resize an image.

- preprocess

##
SuperPointForKeypointDetection

###
class transformers.SuperPointForKeypointDetection

< source >

( config: SuperPointConfig )

Parameters

- config (SuperPointConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

SuperPoint model outputting keypoints and descriptors. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

SuperPoint model. It consists of a SuperPointEncoder, a SuperPointInterestPointDecoder and a SuperPointDescriptorDecoder. SuperPoint was proposed in `SuperPoint: Self-Supervised Interest Point Detection and Description <https://arxiv.org/abs/1712.07629>`__ by Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. It is a fully convolutional neural network that extracts keypoints and descriptors from an image. It is trained in a self-supervised manner, using a combination of a photometric loss and a loss based on the homographic adaptation of keypoints. It is made of a convolutional encoder and two decoders: one for keypoints and one for descriptors.

####
forward

< source >

( pixel_values: FloatTensor = None labels: Optional = None output_hidden_states: Optional = None return_dict: Optional = None )

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using SuperPointImageProcessor. See SuperPointImageProcessor.call() for details.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

  Examples:

The SuperPointForKeypointDetection forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

- forward