Repository: huggingface/transformers
Pull request: [i18n-KO] Translated `keypoint_detection.md` to Korean

# What does this PR do?

Translated the `keypoint_detection.md` file of the documentation to Korean.
Thank you in advance for your review.

Part of https://github.com/huggingface/transformers/issues/20179

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Fixes # (issue)

## Before reviewing
- [x] Check for missing / redundant translations (번역 누락/중복 검사)
- [x] Grammar Check (맞춤법 검사)
- [x] Review or Add new terms to glossary (용어 확인 및 추가)
- [x] Check Inline TOC (e.g. `[[lowercased-header]]`)
- [x] Check live-preview for gotchas (live-preview로 정상작동 확인)


## Who can review? (Initial)

May you please review this PR?
@cjfghk5697, @yijun-lee, @MinJu-Ha

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review? (Final)
@stevhliu May you please review this PR?



Changed files:
filename: docs/source/ko/_toctree.yml
patch: @@ -77,6 +77,8 @@
         title: 이미지 특징 추출
       - local: tasks/mask_generation
         title: 마스크 생성
+      - local: tasks/keypoint_detection
+        title: 키포인트 탐지
       - local: tasks/knowledge_distillation_for_image_classification
         title: 컴퓨터 비전(이미지 분류)를 위한 지식 증류(knowledge distillation)
     title: 컴퓨터 비전

filename: docs/source/ko/tasks/keypoint_detection.md
patch: @@ -0,0 +1,155 @@
+<!--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.
+
+-->
+
+# 키포인트 탐지 [[keypoint-detection]]
+
+[[open-in-colab]]
+
+키포인트 감지(Keypoint detection)은 이미지 내의 특정 포인트를 식별하고 위치를 탐지합니다. 이러한 키포인트는 랜드마크라고도 불리며 얼굴 특징이나 물체의 일부와 같은 의미 있는 특징을 나타냅니다.
+키포인트 감지 모델들은 이미지를 입력으로 받아 아래와 같은 출력을 반환합니다.
+
+- **키포인트들과 점수**: 관심 포인트들과 해당 포인트에 대한 신뢰도 점수
+- **디스크립터(Descriptors)**: 각 키포인트를 둘러싼 이미지 영역의 표현으로 텍스처, 그라데이션, 방향 및 기타 속성을 캡처합니다.
+
+이번 가이드에서는 이미지에서 키포인트를 추출하는 방법을 다루어 보겠습니다.
+
+이번 튜토리얼에서는 키포인트 감지의 기본이 되는 모델인 [SuperPoint](./model_doc/superpoint)를 사용해보겠습니다.
+
+```python
+from transformers import AutoImageProcessor, SuperPointForKeypointDetection
+processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint")
+model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint")
+```
+아래의 이미지로 모델을 테스트 해보겠습니다.
+
+<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]
+```
+
+이제 입력을 처리하고 추론을 할 수 있습니다.
+
+
+```python
+inputs = processor(images,return_tensors="pt").to(model.device, model.dtype)
+outputs = model(**inputs)
+```
+모델 출력에는 배치 내의 각 항목에 대한 상대적인 키포인트, 디스크립터, 마스크와 점수가 있습니다. 마스크는 이미지에서 키포인트가 있는 영역을 강조하는 역할을 합니다.
+
+```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)
+```
+
+이미지에 실제 키포인트를 표시하기 위해선 결과값을 후처리 해야합니다. 이를 위해 실제 이미지 크기를 결과값과 함께 `post_process_keypoint_detection`에 전달해야 합니다.
+
+```python
+image_sizes = [(image.size[1], image.size[0]) for image in images]
+outputs = processor.post_process_keypoint_detection(outputs, image_sizes)
+```
+
+위 코드를 통해 결과값은 딕셔너리를 갖는 리스트가 되고, 각 딕셔너리들은 후처리된 키포인트, 점수 및 디스크립터로 이루어져있습니다.
+
+
+```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]}]
+```
+
+이제 위 딕셔너리를 사용하여 키포인트를 표시할 수 있습니다.
+
+```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()
+```
+
+아래에서 결과를 확인할 수 있습니다.
+
+<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>
+