Create Human-Embryo-Dataset.py

Human-Embryo-Dataset.py

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+import csv
+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",
+    "F-30": "https://zenodo.org/records/7912264/files/embryo_dataset_F-30.tar.gz",
+    "F-15": "https://zenodo.org/records/7912264/files/embryo_dataset_F-15.tar.gz",
+    "F0": "https://zenodo.org/records/7912264/files/embryo_dataset.tar.gz",
+    "F+15": "https://zenodo.org/records/7912264/files/embryo_dataset_F15.tar.gz",
+    "F+30": "https://zenodo.org/records/7912264/files/embryo_dataset_F30.tar.gz",
+    "F+45": "https://zenodo.org/records/7912264/files/embryo_dataset_F45.tar.gz",
+    "grades": "https://zenodo.org/records/7912264/files/embryo_dataset_grades.csv",
+    "annotations": "https://zenodo.org/records/7912264/files/embryo_dataset_annotations.tar.gz",
+    "time_elapsed": "https://zenodo.org/records/7912264/files/embryo_dataset_time_elapsed.tar.gz",
+}
+
+_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"], delimiter=',')
+        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,
+            }