Datasets:

1aurent
/

Human-Embryo-Timelapse

	import datasets
	import pandas as pd
	from pathlib import Path
	from PIL import ImageFile

	ImageFile.LOAD_TRUNCATED_IMAGES = True

	_URLS = {
	"F-45": "https://zenodo.org/records/7912264/files/embryo_dataset_F-45.tar.gz?download=1",
	"F-30": "https://zenodo.org/records/7912264/files/embryo_dataset_F-30.tar.gz?download=1",
	"F-15": "https://zenodo.org/records/7912264/files/embryo_dataset_F-15.tar.gz?download=1",
	"F0": "https://zenodo.org/records/7912264/files/embryo_dataset.tar.gz?download=1",
	"F+15": "https://zenodo.org/records/7912264/files/embryo_dataset_F15.tar.gz?download=1",
	"F+30": "https://zenodo.org/records/7912264/files/embryo_dataset_F30.tar.gz?download=1",
	"F+45": "https://zenodo.org/records/7912264/files/embryo_dataset_F45.tar.gz?download=1",
	"grades": "https://zenodo.org/records/7912264/files/embryo_dataset_grades.csv?download=1",
	"annotations": "https://zenodo.org/records/7912264/files/embryo_dataset_annotations.tar.gz?download=1",
	"time_elapsed": "https://zenodo.org/records/7912264/files/embryo_dataset_time_elapsed.tar.gz?download=1",
	}

	_EVENT_NAMES = [
	"tPB2", "tPNa", "tPNf", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9+", "tM", "tSB", "tB", "tEB", "tHB",
	]

	_GRADES = ["A", "B", "C", "NA"]

	_DESCRIPTION = """
	This dataset is composed of 704 videos, each recorded at 7 focal planes, accompanied by the annotations of 16 cellular events.
	"""

	_VERSION = datasets.Version("0.3.0")

	_HOMEPAGE = "https://zenodo.org/record/7912264"

	_LICENSE = "CC BY-NC-SA 4.0"

	class HumanEmbryoTimelapse(datasets.GeneratorBasedBuilder):

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	version=_VERSION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=datasets.Features(
	{
	"name": datasets.Value("string"),
	"F-45": datasets.Sequence(datasets.Image()),
	"F-30": datasets.Sequence(datasets.Image()),
	"F-15": datasets.Sequence(datasets.Image()),
	"F0": datasets.Sequence(datasets.Image()),
	"F+45": datasets.Sequence(datasets.Image()),
	"F+30": datasets.Sequence(datasets.Image()),
	"F+15": datasets.Sequence(datasets.Image()),
	"events": datasets.Sequence(
	{
	"name": datasets.ClassLabel(names=_EVENT_NAMES),
	"frame_index_start": datasets.Value("uint16"),
	"frame_index_stop": datasets.Value("uint16"),
	},
	),
	"timeline": {
	"frame_index": datasets.Sequence(datasets.Value("uint16")),
	"time": datasets.Sequence(datasets.Value("float32")),
	},
	"grades": {
	"TE": datasets.ClassLabel(names=_GRADES),
	"ICM": datasets.ClassLabel(names=_GRADES),
	}
	}
	),
	)

	def _split_generators(self, dl_manager):
	"""Generate splits."""

	# download and extract all files
	directories = {
	name: Path(dl_manager.download_and_extract(url))
	for name, url in _URLS.items()
	}

	# get all subfolders of embryo_names_dir
	embryo_names_dir = directories["F0"] / "embryo_dataset"
	embryo_names = [x.name for x in embryo_names_dir.iterdir() if x.is_dir()]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"embryo_names": embryo_names,
	"directories": directories,
	},
	)
	]

	def _generate_examples(self, embryo_names, directories):
	"""Generate images and labels for splits."""

	# get grades for each embryo (name, TE, ICM)
	pd_grades = pd.read_csv(directories["grades"], keep_default_na=False)
	grades = {
	row["video_name"]: {
	"TE": row["TE"],
	"ICM": row["ICM"],
	}
	for _, row in pd_grades.iterrows()
	}

	for index, embryo_name in enumerate(embryo_names):

	# get events of the embryo (name, frame_index_start, frame_index_stop)
	pd_events = pd.read_csv(directories["annotations"] / "embryo_dataset_annotations" / f"{embryo_name}_phases.csv", header=None)
	events = [
	{
	"name": row[0],
	"frame_index_start": row[1],
	"frame_index_stop": row[2],
	}
	for _, row in pd_events.iterrows()
	]

	# get frame index and time
	pd_time = pd.read_csv(directories["time_elapsed"] / "embryo_dataset_time_elapsed" / f"{embryo_name}_timeElapsed.csv")
	timeline = {
	"frame_index": pd_time["frame_index"].tolist(),
	"time": pd_time["time"].tolist(),
	}

	# get images of the embryo, with focal plane -45
	F_m45 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F-45"] / "embryo_dataset_F-45" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane -30
	F_m30 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F-30"] / "embryo_dataset_F-30" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane -15
	F_m15 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F-15"] / "embryo_dataset_F-15" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane 0
	F_zero = list(map(
	lambda x: str(x),
	sorted(
	(directories["F0"] / "embryo_dataset" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane +15
	F_p15 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F+15"] / "embryo_dataset_F15" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane +30
	F_p30 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F+30"] / "embryo_dataset_F30" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	# get images of the embryo, with focal plane +45
	F_p45 = list(map(
	lambda x: str(x),
	sorted(
	(directories["F+45"] / "embryo_dataset_F45" / embryo_name).glob("*.jpeg"),
	key=lambda x: int(x.stem.split("RUN")[-1]),
	),
	))

	yield index, {
	"name": embryo_name,
	"F-45": F_m45,
	"F-30": F_m30,
	"F-15": F_m15,
	"F0": F_zero,
	"F+15": F_p15,
	"F+30": F_p30,
	"F+45": F_p45,
	"events": events,
	"grades": grades[embryo_name],
	"timeline": timeline,
	}