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, }