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*This model was released on 2019-11-26 and added to Hugging Face Transformers on 2025-01-20.*

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    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white" >
    </div>
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# SuperGlue

[SuperGlue](https://huggingface.co/papers/1911.11763) is a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. SuperGlue introduces a flexible context aggregation mechanism based on attention, enabling it to reason about the underlying 3D scene and feature assignments jointly. Paired with the [SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc.

You can find all the original SuperGlue 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 SuperGlue models in the right sidebar for more examples of how to apply SuperGlue to different computer vision tasks.

The example below demonstrates how to match keypoints between two images with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
from transformers import pipeline

keypoint_matcher = pipeline(task="keypoint-matching", model="magic-leap-community/superglue_outdoor")

url_0 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
url_1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"

results = keypoint_matcher([url_0, url_1], threshold=0.9)
print(results[0])
# {'keypoint_image_0': {'x': ..., 'y': ...}, 'keypoint_image_1': {'x': ..., 'y': ...}, 'score': ...}
```

</hfoption>
<hfoption id="AutoModel">

```py
from transformers import AutoImageProcessor, AutoModel
import torch
from PIL import Image
import requests

url_image1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
image1 = Image.open(requests.get(url_image1, stream=True).raw)
url_image2 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"
image2 = Image.open(requests.get(url_image2, stream=True).raw)

images = [image1, image2]

processor = AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor")
model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")

inputs = processor(images, return_tensors="pt")
with torch.inference_mode():
    outputs = model(**inputs)

# Post-process to get keypoints and matches
image_sizes = [[(image.height, image.width) for image in images]]
processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
```

</hfoption>
</hfoptions>

## Notes

- SuperGlue performs feature matching between two images simultaneously, requiring pairs of images as input.

    ```python
    from transformers import AutoImageProcessor, AutoModel
    import torch
    from PIL import Image
    import requests
    
    processor = AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor")
    model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")
    
    # SuperGlue requires pairs of images
    images = [image1, image2]
    inputs = processor(images, return_tensors="pt")
    with torch.inference_mode():
        outputs = model(**inputs)
    
    # Extract matching information
    keypoints0 = outputs.keypoints0  # Keypoints in first image
    keypoints1 = outputs.keypoints1  # Keypoints in second image
    matches = outputs.matches        # Matching indices
    matching_scores = outputs.matching_scores  # Confidence scores
    ```

- The model outputs matching indices, keypoints, and confidence scores for each match.
- For better visualization and analysis, use the [`SuperGlueImageProcessor.post_process_keypoint_matching`] method to get matches in a more readable format.

    ```py
    # Process outputs for visualization
    image_sizes = [[(image.height, image.width) for image in images]]
    processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
    
    for i, output in enumerate(processed_outputs):
        print(f"For the image pair {i}")
        for keypoint0, keypoint1, matching_score in zip(
                output["keypoints0"], output["keypoints1"], output["matching_scores"]
        ):
            print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
    ```

- Visualize the matches between the images using the built-in plotting functionality.

    ```py
    # Easy visualization using the built-in plotting method
    processor.visualize_keypoint_matching(images, processed_outputs)
    ```

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    <img src="https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png">
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## Resources

- Refer to the [original SuperGlue repository](https://github.com/magicleap/SuperGluePretrainedNetwork) for more examples and implementation details.

## SuperGlueConfig

[[autodoc]] SuperGlueConfig

## SuperGlueImageProcessor

[[autodoc]] SuperGlueImageProcessor
    - preprocess
    - post_process_keypoint_matching
    - visualize_keypoint_matching

<frameworkcontent>
<pt>
## SuperGlueForKeypointMatching

[[autodoc]] SuperGlueForKeypointMatching

- forward

</pt>
</frameworkcontent>
