update

.gitignore

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+annotations/
+annotations_trainval2017.zip
+*.jsonl

README.md

@@ -1,3 +1,212 @@
 ---
 license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: COCO Keypoints
 ---
+
+# Dataset Card for "COCO Keypoints"
+
+## Quick Start
+### Usage
+```python
+>>> from datasets.load import load_dataset
+
+>>> dataset = load_dataset('whyen-wang/coco_keypoints')
+>>> example = dataset['train'][0]
+>>> print(example)
+{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x360>,
+ 'bboxes': [
+   [339.8800048828125, 22.15999984741211,
+    153.8800048828125, 300.7300109863281],
+   [471.6400146484375, 172.82000732421875,
+    35.91999816894531, 48.099998474121094]],
+ 'keypoints': [[
+    [368, 61, 1], [369, 52, 2], [0, 0, 0], [382, 48, 2], [0, 0, 0],
+    [368, 84, 2], [435, 81, 2], [362, 125, 2], [446, 125, 2], [360, 153, 2],
+    [0, 0, 0], [397, 167, 1], [439, 166, 1], [369, 193, 2], [461, 234, 2],
+    [361, 246, 2], [474, 287, 2]
+   ], [[...]]
+ ]}
+```
+
+### Visualization
+```python
+>>> import cv2
+>>> import numpy as np
+>>> from PIL import Image
+
+>>> def visualize(example):
+        image = np.array(example['image'])
+        bboxes = np.array(example['bboxes']).round().astype(int)
+        bboxes[:, 2:] += bboxes[:, :2]
+        keypoints = example['keypoints']
+        n = len(bboxes)
+        for i in range(n):
+            color = (255, 0, 0)
+            cv2.rectangle(image, bboxes[i, :2], bboxes[i, 2:], color, 2)
+            ks = keypoints[i]
+            for k in ks:
+                if k[-1] == 2:
+                    cv2.circle(
+                        image, k[:2], 5, (0, 255, 0), 1
+                    )
+        return image
+
+>>> Image.fromarray(visualize(example))
+```
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://cocodataset.org/
+- **Repository:** None
+- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
+- **Leaderboard:** [Papers with Code](https://paperswithcode.com/dataset/coco)
+- **Point of Contact:** None
+
+### Dataset Summary
+
+COCO is a large-scale object detection, segmentation, and captioning dataset.
+
+### Supported Tasks and Leaderboards
+
+[Object Detection](https://huggingface.co/tasks/object-detection)
+[Image Segmentation](https://huggingface.co/tasks/image-segmentation)
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks as follows.
+
+```
+{
+    "image": PIL.Image(mode="RGB"),
+    "captions": [
+        "Closeup of bins of food that include broccoli and bread.",
+        "A meal is presented in brightly colored plastic trays.",
+        "there are containers filled with different kinds of foods",
+        "Colorful dishes holding meat, vegetables, fruit, and bread.",
+        "A bunch of trays that have different food."
+    ]
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+|    name |  train | validation |
+| ------- | -----: | ---------: |
+| default | 64,115 |      2,693 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Creative Commons Attribution 4.0 License
+
+### Citation Information
+
+```
+@article{cocodataset,
+  author    = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick},
+  title     = {Microsoft {COCO:} Common Objects in Context},
+  journal   = {CoRR},
+  volume    = {abs/1405.0312},
+  year      = {2014},
+  url       = {http://arxiv.org/abs/1405.0312},
+  archivePrefix = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@github-whyen-wang](https://github.com/whyen-wang) for adding this dataset.

coco_keypoints.py

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+import json
+import datasets
+from pathlib import Path
+
+_HOMEPAGE = 'https://cocodataset.org/'
+_LICENSE = 'Creative Commons Attribution 4.0 License'
+_DESCRIPTION = 'COCO is a large-scale object detection, segmentation, and captioning dataset.'
+_CITATION = '''\
+@article{cocodataset,
+  author    = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick},
+  title     = {Microsoft {COCO:} Common Objects in Context},
+  journal   = {CoRR},
+  volume    = {abs/1405.0312},
+  year      = {2014},
+  url       = {http://arxiv.org/abs/1405.0312},
+  archivePrefix = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+'''
+
+
+class COCOKeypointsConfig(datasets.BuilderConfig):
+    '''Builder Config for coco2017'''
+
+    def __init__(
+        self, description, homepage,
+        annotation_urls, **kwargs
+    ):
+        super(COCOKeypointsConfig, self).__init__(
+            version=datasets.Version('1.0.0', ''),
+            **kwargs
+        )
+        self.description = description
+        self.homepage = homepage
+        url = 'http://images.cocodataset.org/zips/'
+        self.train_image_url = url + 'train2017.zip'
+        self.val_image_url = url + 'val2017.zip'
+        self.train_annotation_urls = annotation_urls['train']
+        self.val_annotation_urls = annotation_urls['validation']
+
+
+class COCOKeypoints(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        COCOKeypointsConfig(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            annotation_urls={
+                'train': 'data/keypoints_train.zip',
+                'validation': 'data/keypoints_validation.zip'
+            },
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            'image': datasets.Image(mode='RGB', decode=True, id=None),
+            'bboxes': datasets.Sequence(
+                feature=datasets.Sequence(
+                    feature=datasets.Value(dtype='float32', id=None),
+                    length=4, id=None
+                ), length=-1, id=None
+            ),
+            'keypoints': datasets.Sequence(
+                feature=datasets.Sequence(
+                    feature=datasets.Sequence(
+                        feature=datasets.Value(dtype='int32', id=None),
+                    ), length=17, id=None
+                ), length=-1, id=None
+            )
+        })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        train_image_path = dl_manager.download_and_extract(
+            self.config.train_image_url
+        )
+        validation_image_path = dl_manager.download_and_extract(
+            self.config.val_image_url
+        )
+        train_annotation_paths = dl_manager.download_and_extract(
+            self.config.train_annotation_urls
+        )
+        val_annotation_paths = dl_manager.download_and_extract(
+            self.config.val_annotation_urls
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'image_path': f'{train_image_path}/train2017',
+                    'annotation_path': f'{train_annotation_paths}/keypoints_train.jsonl'
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    'image_path': f'{validation_image_path}/val2017',
+                    'annotation_path': f'{val_annotation_paths}/keypoints_validation.jsonl'
+                }
+            )
+        ]
+
+    def _generate_examples(self, image_path, annotation_path):
+        idx = 0
+        image_path = Path(image_path)
+        with open(annotation_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                obj = json.loads(line.strip())
+                example = {
+                    'image': str(image_path / obj['image']),
+                    'bboxes': obj['bboxes'],
+                    'keypoints': obj['keypoints']
+                }
+                yield idx, example
+                idx += 1

data/instance_train.zip

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+oid sha256:8739c76e681f900923b900c9df0ef75cf421d39cabb54650c4b9ad19b6a76d85
+size 22

data/keypoints_train.zip

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+oid sha256:e59c242ea0b1006f9a15c8781a0da691988ec9df08d8ab16244c4d19b1948601
+size 12303366

data/keypoints_validation.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7929246036309bc9163a44dbcb802049764adfac181b18cb692b9d189260c954
+size 515838

prepare.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import zipfile\n",
+    "import requests\n",
+    "import jsonlines\n",
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "from pycocotools.coco import COCO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download Annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'http://images.cocodataset.org/annotations/'\n",
+    "files = [\n",
+    "    'annotations_trainval2017.zip'\n",
+    "]\n",
+    "for file in files:\n",
+    "    if not Path(f'./{file}').exists():\n",
+    "        response = requests.get(url + file)\n",
+    "        with open(file, 'wb') as f:\n",
+    "            f.write(response.content)\n",
+    "\n",
+    "        with zipfile.ZipFile(file, 'r') as zipf:\n",
+    "            zipf.extractall(Path())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keypoint Detection Task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = COCO('annotations/person_keypoints_train2017.json')\n",
+    "val_data = COCO('annotations/person_keypoints_val2017.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for split, data in zip(['train', 'validation'], [train_data, val_data]):\n",
+    "    with jsonlines.open(f'data/keypoints_{split}.jsonl', mode='w') as writer:\n",
+    "        for image_id, image_info in tqdm(data.imgs.items()):\n",
+    "            bboxes, keypoints = [], []\n",
+    "            anns = data.imgToAnns[image_id]\n",
+    "            if len(anns) > 0:\n",
+    "                \n",
+    "                for ann in anns:\n",
+    "                    bboxes.append(ann['bbox'])\n",
+    "                    keypoints.append(ann['keypoints'])\n",
+    "                writer.write({\n",
+    "                    'image': image_info['file_name'],\n",
+    "                    'bboxes': bboxes,\n",
+    "                    'keypoints': np.array(keypoints).reshape(\n",
+    "                        len(bboxes), -1, 3\n",
+    "                    ).tolist()\n",
+    "                })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for split in ['train', 'validation']:\n",
+    "    file_path = f'data/keypoints_{split}.jsonl'\n",
+    "    with zipfile.ZipFile(f'data/keypoints_{split}.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
+    "        zipf.write(file_path, os.path.basename(file_path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}