Datasets:

alkzar90
/

CC6204-Hackaton-Cub-Dataset

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

	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 = "Dataset multimodal para actividad del hackaton curso CC6204: Deep Learning"
	_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",
	"mini_images_urls": f"{_REPO}/dummy/mini_images.zip"
	}

	# Create ClassId-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[int(idx)] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ")


	_NAMES = list(_ID2LABEL.values())

	# Create imageId-to-ClassID dictionary using the image_class_labels
	img_idx_2_class_idx = get(_URLS["image_class_labels"]).iter_lines()
	_IMGID2CLASSID = {}
	for row in img_idx_2_class_idx:
	row = row.decode("UTF8")
	if row != "":
	idx, class_id = row.split(" ")
	_IMGID2CLASSID[idx] = int(class_id)


	# 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 para el Hackaton del curso CC6204: Deep Learning"""

	def _info(self):
	features = datasets.Features({
	"image": datasets.Image(),
	"description": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=_NAMES),
	"file_name": datasets.Value("string"),
	})
	keys = ("image", "label")

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


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

	test_files = []
	test_idx = []

	# Download images
	img_data_files = os.path.join(dl_manager.download_and_extract(_URLS["image_urls"]), "images") # skip _MACOSX dir
	text_data_files = os.path.join(dl_manager.download_and_extract(_URLS["text_urls"]), "text") # skip _MACOSX dir
	#logger.info(f"text_data_files: {text_data_files}")
	#logger.info(f"text_data_files: {text_data_files[10]}")


	img_path_files = sorted(glob.glob(os.path.join(img_data_files, "", ".jpg")))
	text_path_files = sorted(glob.glob(os.path.join(text_data_files, "", ".txt")))

	for img, text in zip(img_path_files, text_path_files):
	img_idx = _IMGNAME2ID[os.path.basename(img)]
	# Sanity check to ensure that pairs of text and image are correct
	if os.path.basename(img).replace(".jpg", "") == os.path.basename(text).replace(".txt", ""):
	if img_idx in _TRAIN_IDX_SET:
	train_files.append((img, text))
	train_idx.append(img_idx)
	else:
	test_files.append((img, text))
	test_idx.append(img_idx)

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


	def _generate_examples(self, files, image_idx):

	for i, path in enumerate(files):
	file_name = os.path.basename(path[0])
	if file_name.endswith(".jpg"):
	yield i, {
	"image": path[0],
	"description": open(path[1], "r").read(),
	"label": _ID2LABEL[_IMGID2CLASSID[image_idx[i]]],
	"file_name": file_name,
	}