Datasets:
Tasks:
Object Detection
Size:
n<1K
File size: 6,197 Bytes
4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb 79ff585 4892cfb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import collections
import json
import os
import datasets
_HOMEPAGE = "https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5?ref=roboflow2huggingface"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{ clash-of-clans-vop4y_dataset,
title = { Clash of Clans Dataset },
type = { Open Source Dataset },
author = { Find This Base },
howpublished = { \\url{ https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y } },
url = { https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { feb },
note = { visited on 2023-01-18 },
}
"""
_CATEGORIES = ['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow']
_ANNOTATION_FILENAME = "_annotations.coco.json"
class CLASHOFCLANSOBJECTDETECTIONConfig(datasets.BuilderConfig):
"""Builder Config for clash-of-clans-object-detection"""
def __init__(self, data_urls, **kwargs):
"""
BuilderConfig for clash-of-clans-object-detection.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super(CLASHOFCLANSOBJECTDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_urls = data_urls
class CLASHOFCLANSOBJECTDETECTION(datasets.GeneratorBasedBuilder):
"""clash-of-clans-object-detection object detection dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
CLASHOFCLANSOBJECTDETECTIONConfig(
name="full",
description="Full version of clash-of-clans-object-detection dataset.",
data_urls={
"train": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/test.zip",
},
),
CLASHOFCLANSOBJECTDETECTIONConfig(
name="mini",
description="Mini version of clash-of-clans-object-detection dataset.",
data_urls={
"train": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/valid-mini.zip",
"validation": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/valid-mini.zip",
"test": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/valid-mini.zip",
},
)
]
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"objects": datasets.Sequence(
{
"id": datasets.Value("int64"),
"area": datasets.Value("int64"),
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
"category": datasets.ClassLabel(names=_CATEGORIES),
}
),
}
)
return datasets.DatasetInfo(
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(self.config.data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"folder_dir": data_files["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"folder_dir": data_files["validation"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"folder_dir": data_files["test"],
},
),
]
def _generate_examples(self, folder_dir):
def process_annot(annot, category_id_to_category):
return {
"id": annot["id"],
"area": annot["area"],
"bbox": annot["bbox"],
"category": category_id_to_category[annot["category_id"]],
}
image_id_to_image = {}
idx = 0
annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
with open(annotation_filepath, "r") as f:
annotations = json.load(f)
category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
image_id_to_annotations = collections.defaultdict(list)
for annot in annotations["annotations"]:
image_id_to_annotations[annot["image_id"]].append(annot)
filename_to_image = {image["file_name"]: image for image in annotations["images"]}
for filename in os.listdir(folder_dir):
filepath = os.path.join(folder_dir, filename)
if filename in filename_to_image:
image = filename_to_image[filename]
objects = [
process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
]
with open(filepath, "rb") as f:
image_bytes = f.read()
yield idx, {
"image_id": image["id"],
"image": {"path": filepath, "bytes": image_bytes},
"width": image["width"],
"height": image["height"],
"objects": objects,
}
idx += 1
|