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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,
}
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