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

This model was published in HF papers on 2017-12-20 and contributed to Hugging Face Transformers on 2024-03-19.

SuperPoint

SuperPoint 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 organization.

Tip

This model was contributed by 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] class.

import requests
import torch
from PIL import Image

from transformers import AutoImageProcessor, SuperPointForKeypointDetection


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", device_map="auto")

inputs = processor(image, return_tensors="pt").to(model.device)
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.

  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", device_map="auto")
  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").to(model.device)
  # 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.

  # Batch processing example
  images = [image1, image2, image3]
  inputs = processor(images, return_tensors="pt").to(model.device)
  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:

  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 for an inference and visualization example.

SuperPointConfig

autodoc SuperPointConfig

SuperPointImageProcessor

autodoc SuperPointImageProcessor - preprocess

SuperPointImageProcessorPil

autodoc SuperPointImageProcessorPil - preprocess - post_process_keypoint_detection

SuperPointForKeypointDetection

autodoc SuperPointForKeypointDetection - forward