Datasets:

alkzar90
/

CC6204-Hackaton-Cub-Dataset

	"""CC6204-Hackaton-Cub-Dataset: Multimodal"""
	import os
	import re
	import datasets

	import pandas as pd

	from requests import get

	datasets.logging.set_verbosity_debug()
	logger = datasets.logging.get_logger(__name__)
	#datasets.logging.set_verbosity_info()
	datasets.logging.set_verbosity_debug()


	_DESCRIPTION = "XYZ"
	_CITATION = "XYZ"
	_HOMEPAGE = "https://github.com/ivansipiran/CC6204-Deep-Learning/blob/main/Hackaton/hackaton.md"

	_REPO = "https://huggingface.co/datasets/alkzar90/CC6204-Hackaton-Cub-Dataset/resolve/main/data"

	_URLS = {
	"train_test_split": f"{_REPO}/train_test_split.txt",
	"classes": f"{_REPO}/classes.txt",
	"image_class_labels": f"{_REPO}/image_class_labels.txt",
	"images": f"{_REPO}/images.txt",
	"image_urls": f"{_REPO}/images.zip",
	"text_urls": f"{_REPO}/text.zip",
	}

	# Create id-to-label dictionary using the classes file
	classes = get(_URLS["classes"]).iter_lines()
	_ID2LABEL = {}
	for row in classes:
	row = row.decode("UTF8")
	if row != "":
	idx, label = row.split(" ")
	_ID2LABEL[idx] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ")


	_NAMES = list(_ID2LABEL.values())


	# build from images.txt: a mapping from image_file_name -> id
	imgpath_to_ids = get(_URLS["images"]).iter_lines()
	_IMGNAME2ID = {}
	for row in imgpath_to_ids:
	row = row.decode("UTF8")
	if row != "":
	idx, img_name = row.split(" ")
	_IMGNAME2ID[os.path.basename(img_name)] = idx


	# Create TRAIN_IDX_SET
	train_test_split = get(_URLS["train_test_split"]).iter_lines()
	_TRAIN_IDX_SET = []
	for row in train_test_split:
	row = row.decode("UTF8")
	if row != "":
	idx, train_bool = row.split(" ")
	# 1: train, 0: test
	if train_bool == "1":
	_TRAIN_IDX_SET.append(idx)

	_TRAIN_IDX_SET = set(_TRAIN_IDX_SET)


	class CubDataset(datasets.GeneratorBasedBuilder):
	"""Cub Dataset"""

	def _info(self):
	features = datasets.Features({
	"image": datasets.Image(),
	"labels": datasets.features.ClassLabel(names=_NAMES),
	})
	keys = ("image", "labels")

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=keys,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)


	def _split_generators(self, dl_manager):
	train_files = []
	test_files = []

	# Download images
	data_files = dl_manager.download_and_extract(_URLS["image_urls"])

	for batch in data_files:
	path_files = dl_manager.iter_files(batch)
	for img in path_files:
	if img.endswith("\d+.jpg"):
	img_idx = _IMGNAME2ID[img]
	if img_idx in _TRAIN_IDX_SET:
	train_files.append(img)
	else:
	test_files.append(img)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": train_files
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"files": test_files
	}
	)
	]


	def _generate_examples(self, files):

	for i, path in enumerate(files):
	file_name = os.path.basename(path)
	if file_name.endswith(".jpg"):
	yield i, {
	"image": path,
	"labels": os.path.basename(os.path.dirname(path)).lower(),
	}