<!--Copyright 2024 The HuggingFace Team. All rights reserved.

Licensed under the MIT License; you may not use this file except in compliance with
the License.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->
*This model was released on 2019-11-26 and added to Hugging Face Transformers on 2025-01-20 and contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).*



# SuperGlue

[SuperGlue](https://huggingface.co/papers/1911.11763) matches two sets of local features by jointly finding correspondences and rejecting non-matchable points using a differentiable optimal transport problem. It employs a graph neural network to predict costs and a flexible context aggregation mechanism based on attention to reason about the 3D scene and feature assignments. SuperGlue learns priors over geometric transformations and 3D world regularities through end-to-end training from image pairs, outperforming traditional heuristics and achieving state-of-the-art results in pose estimation. The model operates in real-time on modern GPUs and can be integrated into SfM or SLAM systems.

## How to use

Here is a quick example of using the model. Since this model is an image matching model, it requires pairs of images to be matched. 
The raw outputs contain the list of keypoints detected by the keypoint detector as well as the list of matches with their corresponding 
matching scores.

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="keypoint-matching", model="magic-leap-community/superglue_outdoor", dtype="auto")
pipeline(["path/to/image1.png", "path/to/image2.png"])
```

```python
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"
image_2 = 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.no_grad():
    outputs = model(**inputs)
```

You can use the `post_process_keypoint_matching` method from the `SuperGlueImageProcessor` to get the keypoints and matches in a more readable format:

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

```

From the outputs, you can visualize the matches between the two images using the following code:
```python
import matplotlib.pyplot as plt
import numpy as np

# Create side by side image
merged_image = np.zeros((max(image1.height, image2.height), image1.width + image2.width, 3))
merged_image[: image1.height, : image1.width] = np.array(image1) / 255.0
merged_image[: image2.height, image1.width :] = np.array(image2) / 255.0
plt.imshow(merged_image)
plt.axis("off")

# Retrieve the keypoints and matches
output = outputs[0]
keypoints0 = output["keypoints0"]
keypoints1 = output["keypoints1"]
matching_scores = output["matching_scores"]
keypoints0_x, keypoints0_y = keypoints0[:, 0].numpy(), keypoints0[:, 1].numpy()
keypoints1_x, keypoints1_y = keypoints1[:, 0].numpy(), keypoints1[:, 1].numpy()

# Plot the matches
for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
        keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, matching_scores
):
    plt.plot(
        [keypoint0_x, keypoint1_x + image1.width],
        [keypoint0_y, keypoint1_y],
        color=plt.get_cmap("RdYlGn")(matching_score.item()),
        alpha=0.9,
        linewidth=0.5,
    )
    plt.scatter(keypoint0_x, keypoint0_y, c="black", s=2)
    plt.scatter(keypoint1_x + image1.width, keypoint1_y, c="black", s=2)

# Save the plot
plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
plt.close()
```

![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png)

This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
The original code can be found [here](https://github.com/magicleap/SuperGluePretrainedNetwork).

## SuperGlueConfig

[[autodoc]] SuperGlueConfig

## SuperGlueImageProcessor

[[autodoc]] SuperGlueImageProcessor

- preprocess

## SuperGlueForKeypointMatching

[[autodoc]] SuperGlueForKeypointMatching

- forward
- post_process_keypoint_matching

