tags:
- vision
- image-matching
inference: false
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("stevenbucaille/superpoint")
model = AutoModel.from_pretrained("stevenbucaille/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("stevenbucaille/superpoint")
model = AutoModel.from_pretrained("stevenbucaille/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.
@inproceedings{detone2018superpoint,
title={Superpoint: Self-supervised interest point detection and description},
author={DeTone, Daniel and Malisiewicz, Tomasz and Rabinovich, Andrew},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages={224--236},
year={2018}
}