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"""TODO: Add a description here."""
import csv
import json
import os
import datasets
from datasets.tasks import ImageClassification
_DESCRIPTION = """\
This dataset contains all THIENVIET products images split in training,
validation and testing
"""
_URLS = {
"train": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/train.zip",
"val": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/val.zip",
"test": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/test.zip"
}
_CATEGORIES = ['botkhi','thuytinh','ocvit','ban','contrung','kimloai','toc']
class YenthienvietConfig(datasets.BuilderConfig):
"""Builder Config for image-classification-yenthienviet"""
def __init__(self, name, data_urls, **kwargs):
"""
BuilderConfig for image-classification-yenthienviet.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwared to super.
"""
super().__init__(version=datasets.Version("1.0.0", **kwargs))
self.name
self.data_urls = data_urls
# TODO: Name of the dataset usually matches the script name
class YenthienvietClassification(datasets.GeneratorBasedBuilder):
""" Builder for image-classification-yenthienviet"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = YenthienvietConfig
BUILDER_CONFIGS = [
YenthienvietConfig(
name="version-10/10",
description="Version 10/10 of image-classification-yenthienviet dataset.",
data_urls=_URLS,
)
]
def _info(self):
features = datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names=_CATEGORIES)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("image", "label"),
task_templates=[ImageClassification(image_column="image", label_column="labels")]
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and exract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_files = dl_manager.download_and_extract(self.config.data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files([data_files["train"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"files": dl_manager.iter_files([data_files["val"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files([data_files["test"]]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
yield i, {
"image_file_path": path,
"image": path,
"labels": os.path.basename(os.path.dirname(path)),
} |