File size: 6,109 Bytes

17c6d62

<!--Copyright 2023 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

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.

-->

# Keypoint Detection

[[open-in-colab]]

Keypoint detection identifies and locates specific points of interest within an image. These keypoints, also known as landmarks, represent meaningful features of objects, such as facial features or object parts. These models take an image input and return the following outputs: 

- **Keypoints and Scores**: Points of interest and their confidence scores.
- **Descriptors**: A representation of the image region surrounding each keypoint, capturing its texture, gradient, orientation and other properties.

In this guide, we will show how to extract keypoints from images.

For this tutorial, we will use [SuperPoint](./model_doc/superpoint.md), a foundation model for keypoint detection.

```python
from transformers import AutoImageProcessor, SuperPointForKeypointDetection
processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint")
model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint")
```

Let's test the model on the images below.

<div style="display: flex; align-items: center;">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg" 
         alt="Bee" 
         style="height: 200px; object-fit: contain; margin-right: 10px;">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png" 
         alt="Cats" 
         style="height: 200px; object-fit: contain;">
</div>


```python
import torch
from PIL import Image
import requests
import cv2


url_image_1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image_1 = Image.open(requests.get(url_image_1, stream=True).raw)
url_image_2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png"
image_2 = Image.open(requests.get(url_image_2, stream=True).raw)

images = [image_1, image_2]
```

We can now process our inputs and infer.

```python
inputs = processor(images,return_tensors="pt").to(model.device, model.dtype)
outputs = model(**inputs)
```

The model output has relative keypoints, descriptors, masks and scores for each item in the batch. The mask highlights areas of the image where keypoints are present.

```python
SuperPointKeypointDescriptionOutput(loss=None, keypoints=tensor([[[0.0437, 0.0167],
         [0.0688, 0.0167],
         [0.0172, 0.0188],
         ...,
         [0.5984, 0.9812],
         [0.6953, 0.9812]]]), 
         scores=tensor([[0.0056, 0.0053, 0.0079,  ..., 0.0125, 0.0539, 0.0377],
        [0.0206, 0.0058, 0.0065,  ..., 0.0000, 0.0000, 0.0000]],
       grad_fn=<CopySlices>), descriptors=tensor([[[-0.0807,  0.0114, -0.1210,  ..., -0.1122,  0.0899,  0.0357],
         [-0.0807,  0.0114, -0.1210,  ..., -0.1122,  0.0899,  0.0357],
         [-0.0807,  0.0114, -0.1210,  ..., -0.1122,  0.0899,  0.0357],
         ...],
       grad_fn=<CopySlices>), mask=tensor([[1, 1, 1,  ..., 1, 1, 1],
        [1, 1, 1,  ..., 0, 0, 0]], dtype=torch.int32), hidden_states=None)
```

To plot actual keypoints in the image, we need to postprocess the output. To do so, we have to pass the actual image sizes to `post_process_keypoint_detection` along with outputs.

```python
image_sizes = [(image.size[1], image.size[0]) for image in images]
outputs = processor.post_process_keypoint_detection(outputs, image_sizes)
```

The outputs are now a list of dictionaries where each dictionary is a processed output of keypoints, scores and descriptors. 

```python
[{'keypoints': tensor([[ 226,   57],
          [ 356,   57],
          [  89,   64],
          ...,
          [3604, 3391]], dtype=torch.int32),
  'scores': tensor([0.0056, 0.0053, ...], grad_fn=<IndexBackward0>),
  'descriptors': tensor([[-0.0807,  0.0114, -0.1210,  ..., -0.1122,  0.0899,  0.0357],
          [-0.0807,  0.0114, -0.1210,  ..., -0.1122,  0.0899,  0.0357]],
         grad_fn=<IndexBackward0>)},
    {'keypoints': tensor([[ 46,   6],
          [ 78,   6],
          [422,   6],
          [206, 404]], dtype=torch.int32),
  'scores': tensor([0.0206, 0.0058, 0.0065, 0.0053, 0.0070, ...,grad_fn=<IndexBackward0>),
  'descriptors': tensor([[-0.0525,  0.0726,  0.0270,  ...,  0.0389, -0.0189, -0.0211],
          [-0.0525,  0.0726,  0.0270,  ...,  0.0389, -0.0189, -0.0211]}]
```

We can use these to plot the keypoints.

```python
import matplotlib.pyplot as plt
import torch

for i in range(len(images)):
  keypoints = outputs[i]["keypoints"]
  scores = outputs[i]["scores"]
  descriptors = outputs[i]["descriptors"]
  keypoints = outputs[i]["keypoints"].detach().numpy()
  scores = outputs[i]["scores"].detach().numpy()
  image = images[i]
  image_width, image_height = image.size

  plt.axis('off')
  plt.imshow(image)
  plt.scatter(
      keypoints[:, 0],
      keypoints[:, 1],
      s=scores * 100,
      c='cyan',
      alpha=0.4
  )
  plt.show()
```

Below you can see the outputs.

<div style="display: flex; align-items: center;">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee_keypoint.png" 
         alt="Bee" 
         style="height: 200px; object-fit: contain; margin-right: 10px;">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats_keypoint.png" 
         alt="Cats" 
         style="height: 200px; object-fit: contain;">
</div>