transformers/docs/source/en/model_doc/superpoint.md

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.

SuperPoint overview. Taken from the original paper.

Usage tips

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

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")
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, SuperPointForKeypointDetection
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 = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint")

inputs = processor(images, return_tensors="pt")
outputs = model(**inputs)
image_sizes = [(image.height, image.width) for image in images]
outputs = processor.post_process_keypoint_detection(outputs, image_sizes)

for output in outputs:
    for keypoints, scores, descriptors in zip(output["keypoints"], output["scores"], output["descriptors"]):
        print(f"Keypoints: {keypoints}")
        print(f"Scores: {scores}")
        print(f"Descriptors: {descriptors}")

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

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")

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

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SuperPoint. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

• A notebook showcasing inference and visualization with SuperPoint can be found here. 🌎

SuperPointConfig

autodoc SuperPointConfig

SuperPointImageProcessor

autodoc SuperPointImageProcessor

• preprocess
• post_process_keypoint_detection

SuperPointForKeypointDetection

autodoc SuperPointForKeypointDetection

• forward