File size: 3,643 Bytes
46e9284 |
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 |
# Source: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
import csv
import json
import os
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Boat dataset},
author={XXX, Inc.},
year={2024}
}
"""
_DESCRIPTION = """\
This dataset is designed to solve an object detection task with images of boats.
"""
_HOMEPAGE = "https://huggingface.co/datasets/SIS-2024-spring/Boat_dataset/resolve/main"
_LICENSE = ""
_URLS = {
"classes": f"{_HOMEPAGE}/data/classes.txt",
"train": f"{_HOMEPAGE}/data/instances_train2023r.jsonl",
"val": f"{_HOMEPAGE}/data/instances_val2023r.jsonl",
}
class BoatDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Dataset for detecting boats in aerial images."),
]
DEFAULT_CONFIG_NAME = "Boat_dataset" # Provide a default configuration
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'image_id': datasets.Value('int32'),
'image_path': datasets.Value('string'),
'width': datasets.Value('int32'),
'height': datasets.Value('int32'),
'objects': datasets.Features({
'id': datasets.Sequence(datasets.Value('int32')),
'area': datasets.Sequence(datasets.Value('float32')),
'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)), # [x, y, width, height]
'category': datasets.Sequence(datasets.Value('int32'))
}),
}),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# Download all files and extract them
downloaded_files = dl_manager.download_and_extract(_URLS)
# Load class labels from the classes file
with open('classes.txt', 'r') as file:
classes = [line.strip() for line in file.readlines()]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotations_file": downloaded_files["train"],
"classes": classes,
"split": "train",
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotations_file": downloaded_files["val"],
"classes": classes,
"split": "val",
}
),
]
def _generate_examples(self, annotations_file, classes, split):
# Process annotations
with open(annotations_file, encoding="utf-8") as f:
for key, row in enumerate(f):
try:
data = json.loads(row.strip())
yield key, {
"image_id": data["image_id"],
"image_path": data["image_path"],
"width": data["width"],
"height": data["height"],
"objects": data["objects"],
}
except json.JSONDecodeError:
print(f"Skipping invalid JSON at line {key + 1}: {row}")
continue
|