File size: 4,439 Bytes
c5e20ff |
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 |
import collections
import json
import os
import datasets
_HOMEPAGE = "https://universe.roboflow.com/riis/aerial-sheep/dataset/1"
_LICENSE = "Public Domain"
_CITATION = """\
@misc{ aerial-sheep_dataset,
title = { Aerial Sheep Dataset },
type = { Open Source Dataset },
author = { Riis },
howpublished = { \\url{ https://universe.roboflow.com/riis/aerial-sheep } },
url = { https://universe.roboflow.com/riis/aerial-sheep },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-02 },
}
"""
_URLS = {
"train": "https://huggingface.co/datasets/keremberke/aerial-sheep-object-detection/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/keremberke/aerial-sheep-object-detection/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/keremberke/aerial-sheep-object-detection/resolve/main/data/test.zip",
}
_CATEGORIES = ['sheep']
_ANNOTATION_FILENAME = "_annotations.coco.json"
class AERIALSHEEPOBJECTDETECTION(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
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(_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)
image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
for filename in os.listdir(folder_dir):
filepath = os.path.join(folder_dir, filename)
if filename in image_id_to_image:
image = image_id_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
|