tags:
- vision
- image-matching
inference: false
pipeline_tag: keypoint-detection
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.size[1], image.size[0]) for image in images]
outputs = processor.post_process_keypoint_detection(outputs, image_sizes)
for output in outputs:
keypoints = output["keypoints"]
scores = output["scores"]
descriptors = output["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)
plt.scatter(
keypoints[:, 0],
keypoints[:, 1],
c=scores * 100,
s=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