update

.gitignore

@@ -0,0 +1,3 @@
+annotations/
+annotations_trainval2017.zip
+*.jsonl

README.md

@@ -1,3 +1,237 @@
 ---
 license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- image-to-text
+language:
+- en
+pretty_name: COCO Detection
 ---
+
+# Dataset Card for "COCO Detection"
+
+## Quick Start
+### Usage
+```python
+>>> from datasets.load import load_dataset
+
+>>> dataset = load_dataset('whyen-wang/coco_detection')
+>>> example = dataset['train'][500]
+>>> print(example)
+{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x426>,
+ 'bboxes': [
+  [192.4199981689453, 220.17999267578125,
+   129.22999572753906, 148.3800048828125],
+  [76.94000244140625, 146.6300048828125,
+   104.55000305175781, 109.33000183105469],
+  [302.8800048828125, 115.2699966430664,
+   99.11000061035156, 119.2699966430664],
+  [0.0, 0.800000011920929,
+   592.5700073242188, 420.25]],
+ 'categories': [46, 46, 46, 55],
+ 'inst.rles': {
+  'size': [[426, 640], [426, 640], [426, 640], [426, 640]],
+  'counts': [
+   'gU`2b0d;...', 'RXP16m<=...', ']Xn34S=4...', 'n:U2o8W2...'
+  ]}}
+```
+
+### Visualization
+```python
+>>> import cv2
+>>> import numpy as np
+>>> from PIL import Image
+
+>>> def transforms(examples):
+        inst_rles = examples.pop('inst.rles')
+        annotation = []
+        for i in inst_rles:
+            inst_rles = [
+                {'size': size, 'counts': counts}
+                for size, counts in zip(i['size'], i['counts'])
+            ]
+            annotation.append(maskUtils.decode(inst_rles))
+        examples['annotation'] = annotation
+        return examples
+
+>>> def visualize(example, names, colors):
+        image = np.array(example['image'])
+        bboxes = np.array(example['bboxes']).round().astype(int)
+        bboxes[:, 2:] += bboxes[:, :2]
+        categories = example['categories']
+        masks = example['annotation']
+        n = len(bboxes)
+        for i in range(n):
+            c = categories[i]
+            color, name = colors[c], names[c]
+            cv2.rectangle(image, bboxes[i, :2], bboxes[i, 2:], color.tolist(), 2)
+            cv2.putText(
+                image, name, bboxes[i, :2], cv2.FONT_HERSHEY_SIMPLEX,
+                1, color.tolist(), 2, cv2.LINE_AA, False
+            )
+            image[masks[..., i] == 1] = image[masks[..., i] == 1] // 2 + color // 2
+        return image
+
+>>> dataset.set_transform(transforms)
+
+>>> names = dataset['train'].features['categories'].feature.names
+
+>>> colors = np.ones((80, 3), np.uint8) * 255
+>>> colors[:, 0] = np.linspace(0, 255, 80)
+>>> colors = cv2.cvtColor(colors[None], cv2.COLOR_HSV2RGB)[0]
+
+>>> example = dataset['train'][500]
+>>> Image.fromarray(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 | 118,287 |      5,000 |
+
+## 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_detection.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}
+}
+'''
+_NAMES = [
+    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
+    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
+    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+    'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+    'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
+    'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
+    'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
+    'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
+    'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+    'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
+    'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+
+class COCODetectionConfig(datasets.BuilderConfig):
+    '''Builder Config for coco2017'''
+
+    def __init__(
+        self, description, homepage,
+        annotation_urls, **kwargs
+    ):
+        super(COCODetectionConfig, 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 COCODetection(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        COCODetectionConfig(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            annotation_urls={
+                'train': 'data/instance_train.zip',
+                'validation': 'data/instance_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
+            ),
+            'categories': datasets.Sequence(
+                feature=datasets.ClassLabel(names=_NAMES),
+                length=-1, id=None
+            ),
+            'inst.rles': datasets.Sequence(
+                feature={
+                    'size': datasets.Sequence(
+                        feature=datasets.Value(dtype='int32', id=None),
+                        length=2, id=None
+                    ),
+                    'counts': datasets.Value(dtype='string', 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
+        )
+        val_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}/instance_train.jsonl'
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    'image_path': f'{val_image_path}/val2017',
+                    'annotation_path': f'{val_annotation_paths}/instance_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'],
+                    'categories': obj['categories'],
+                    'inst.rles': obj['inst.rles']
+                }
+                yield idx, example
+                idx += 1

data/instance_train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b696f1c992d995acc5ec20714e54fe739ef4740f1d67ea134f1066707e171d53
+size 140601384

data/instance_validation.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f636aae29d6bd22ee6bf7c20140976cfc6fb63ab8a68eb614badf5dc888f591c
+size 5973571

prepare.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\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\n",
+    "from pycocotools import mask as maskUtils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download Annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'http://images.cocodataset.org/annotations/'\n",
+    "file = 'annotations_trainval2017.zip'\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": [
+    "# Read annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coco91_to_coco80 = [\n",
+    "    None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None,\n",
+    "    11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
+    "    23, None, 24, 25, None, None, 26, 27, 28, 29, 30,\n",
+    "    31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41,\n",
+    "    42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
+    "    55, 56, 57, 58, 59, None, 60, None, None, 61, None,\n",
+    "    62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, None,\n",
+    "    73, 74, 75, 76, 77, 78, 79\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Instance Segmentation Task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = COCO('annotations/instances_train2017.json')\n",
+    "val_data = COCO('annotations/instances_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/instance_{split}.jsonl', mode='w') as writer:\n",
+    "        for image_id, image_info in tqdm(data.imgs.items()):\n",
+    "            bboxes, categories, instance_rles = [], [], []\n",
+    "            anns = data.imgToAnns[image_id]\n",
+    "            height, width = image_info['height'], image_info['width']\n",
+    "            for ann in anns:\n",
+    "                bboxes.append(ann['bbox'])\n",
+    "                categories.append(coco91_to_coco80[ann['category_id']])\n",
+    "                segm = ann['segmentation']\n",
+    "                if isinstance(segm, list):\n",
+    "                    rles = maskUtils.frPyObjects(segm, height, width)\n",
+    "                    rle = maskUtils.merge(rles)\n",
+    "                    rle['counts'] = rle['counts'].decode()\n",
+    "                elif isinstance(segm['counts'], list):\n",
+    "                    rle = maskUtils.frPyObjects(segm, height, width)\n",
+    "                    rle['counts'] = rle['counts'].decode()\n",
+    "                else:\n",
+    "                    rle = segm\n",
+    "                instance_rles.append(rle)\n",
+    "            writer.write({\n",
+    "                'image': image_info['file_name'],\n",
+    "                'bboxes': bboxes,\n",
+    "                'categories': categories,\n",
+    "                'inst.rles': instance_rles\n",
+    "            })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for split in ['train', 'validation']:\n",
+    "    file_path = f'data/instance_{split}.jsonl'\n",
+    "    with zipfile.ZipFile(f'data/instance_{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
+}