# SuperPoint [SuperPoint](https://huggingface.co/papers/1712.07629) is the result of 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. Usage on it's own is limited, but it can be used as a feature extractor for other tasks such as homography estimation and image matching. You can find all the original SuperPoint checkpoints under the [Magic Leap Community](https://huggingface.co/magic-leap-community) organization. > [!TIP] > This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille). > > Click on the SuperPoint models in the right sidebar for more examples of how to apply SuperPoint to different computer vision tasks. The example below demonstrates how to detect interest points in an image with the [AutoModel](/docs/transformers/v5.5.2/en/model_doc/auto#transformers.AutoModel) class. ```py from transformers import AutoImageProcessor, SuperPointForKeypointDetection 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 = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint") inputs = processor(image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # Post-process to get keypoints, scores, and descriptors image_size = (image.height, image.width) processed_outputs = processor.post_process_keypoint_detection(outputs, [image_size]) ``` ## Notes - SuperPoint outputs a dynamic number of keypoints per image, which makes it suitable for tasks requiring variable-length feature representations. ```py from transformers import AutoImageProcessor, SuperPointForKeypointDetection import torch from PIL import Image import requests processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint") 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] inputs = processor(images, return_tensors="pt") # Example of handling dynamic keypoint output outputs = model(**inputs) keypoints = outputs.keypoints # Shape varies per image scores = outputs.scores # Confidence scores for each keypoint descriptors = outputs.descriptors # 256-dimensional descriptors mask = outputs.mask # Value of 1 corresponds to a keypoint detection ``` - The model provides both keypoint coordinates and their corresponding descriptors (256-dimensional vectors) in a single forward pass. - For batch processing with multiple images, you need to use the mask attribute to retrieve the respective information for each image. You can use the `post_process_keypoint_detection` from the `SuperPointImageProcessor` to retrieve the each image information. ```py # Batch processing example images = [image1, image2, image3] inputs = processor(images, return_tensors="pt") outputs = model(**inputs) image_sizes = [(img.height, img.width) for img in images] processed_outputs = processor.post_process_keypoint_detection(outputs, image_sizes) ``` - You can then print the keypoints on the image of your choice to visualize the result: ```py import matplotlib.pyplot as plt plt.axis("off") plt.imshow(image_1) plt.scatter( outputs[0]["keypoints"][:, 0], outputs[0]["keypoints"][:, 1], c=outputs[0]["scores"] * 100, s=outputs[0]["scores"] * 50, alpha=0.8 ) plt.savefig(f"output_image.png") ``` ## Resources - Refer to this [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SuperPoint/Inference_with_SuperPoint_to_detect_interest_points_in_an_image.ipynb) for an inference and visualization example. ## SuperPointConfig[[transformers.SuperPointConfig]] #### transformers.SuperPointConfig[[transformers.SuperPointConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/configuration_superpoint.py#L24) This is the configuration class to store the configuration of a SuperpointModel. 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 [magic-leap-community/superpoint](https://huggingface.co/magic-leap-community/superpoint) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.2/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.5.2/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **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`) : Dimension of the hidden representations. 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. ## SuperPointImageProcessor[[transformers.SuperPointImageProcessor]] #### transformers.SuperPointImageProcessor[[transformers.SuperPointImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/image_processing_superpoint.py#L70) Constructs a SuperPointImageProcessor image processor. preprocesstransformers.SuperPointImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) -- 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`. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.5.2/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.2/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** do_grayscale (`bool`, *kwargs*, *optional*, defaults to `self.do_grayscale`) : Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.2/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. ## SuperPointImageProcessorPil[[transformers.SuperPointImageProcessorPil]] #### transformers.SuperPointImageProcessorPil[[transformers.SuperPointImageProcessorPil]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/image_processing_pil_superpoint.py#L72) Constructs a SuperPointImageProcessor image processor. preprocesstransformers.SuperPointImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) -- 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`. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.5.2/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.2/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** do_grayscale (`bool`, *kwargs*, *optional*, defaults to `self.do_grayscale`) : Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.2/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. #### post_process_keypoint_detection[[transformers.SuperPointImageProcessorPil.post_process_keypoint_detection]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/image_processing_pil_superpoint.py#L113) Converts the raw output of [SuperPointForKeypointDetection](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.SuperPointForKeypointDetection) into lists of keypoints, scores and descriptors with coordinates absolute to the original image sizes. **Parameters:** 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. ## SuperPointForKeypointDetection[[transformers.SuperPointForKeypointDetection]] #### transformers.SuperPointForKeypointDetection[[transformers.SuperPointForKeypointDetection]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/modeling_superpoint.py#L352) SuperPoint model outputting keypoints and descriptors. This model inherits from [PreTrainedModel](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.SuperPointForKeypointDetection.forwardhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/superpoint/modeling_superpoint.py#L373[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) -- The tensors corresponding to the input images. Pixel values can be obtained using [SuperPointImageProcessor](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.SuperPointImageProcessor). See `SuperPointImageProcessor.__call__()` for details (`processor_class` uses [SuperPointImageProcessor](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.SuperPointImageProcessor) for processing images). - **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - **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](/docs/transformers/v5.5.2/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`SuperPointKeypointDescriptionOutput` or `tuple(torch.FloatTensor)`A `SuperPointKeypointDescriptionOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([SuperPointConfig](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.SuperPointConfig)) and inputs. The [SuperPointForKeypointDetection](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.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. - **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*) -- Loss computed during training. - **keypoints** (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`) -- Relative (x, y) coordinates of predicted keypoints in a given image. - **scores** (`torch.FloatTensor` of shape `(batch_size, num_keypoints)`) -- Scores of predicted keypoints. - **descriptors** (`torch.FloatTensor` of shape `(batch_size, num_keypoints, descriptor_size)`) -- Descriptors of predicted keypoints. - **mask** (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`) -- Mask indicating which values in keypoints, scores and descriptors are keypoint information. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. Examples: ```python >>> from transformers import AutoImageProcessor, SuperPointForKeypointDetection >>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") >>> model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ``` **Parameters:** config ([SuperPointConfig](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.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()](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``SuperPointKeypointDescriptionOutput` or `tuple(torch.FloatTensor)`` A `SuperPointKeypointDescriptionOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([SuperPointConfig](/docs/transformers/v5.5.2/en/model_doc/superpoint#transformers.SuperPointConfig)) and inputs.