Create gen_script.py

gen_script.py

@@ -0,0 +1,138 @@
+from collections.abc import Iterable
+from pathlib import Path
+from typing import Any
+from xml.etree import ElementTree as ET
+
+import datasets
+import numpy as np
+from datasets import Dataset
+from datasets.splits import NamedSplit
+from PIL import Image, ImageDraw
+from tqdm import tqdm
+
+
+# https://drive.google.com/file/d/1xYyQ31CHFRnvTCTuuHdconlJCMk2SK7Z/view?usp=sharing
+patient_data = {
+    "TCGA-A7-A13E-01Z-00-DX1": "Breast",
+    "TCGA-A7-A13F-01Z-00-DX1": "Breast",
+    "TCGA-AR-A1AK-01Z-00-DX1": "Breast",
+    "TCGA-AR-A1AS-01Z-00-DX1": "Breast",
+    "TCGA-E2-A1B5-01Z-00-DX1": "Breast",
+    "TCGA-E2-A14V-01Z-00-DX1": "Breast",
+    "TCGA-B0-5711-01Z-00-DX1": "Kidney",
+    "TCGA-HE-7128-01Z-00-DX1": "Kidney",
+    "TCGA-HE-7129-01Z-00-DX1": "Kidney",
+    "TCGA-HE-7130-01Z-00-DX1": "Kidney",
+    "TCGA-B0-5710-01Z-00-DX1": "Kidney",
+    "TCGA-B0-5698-01Z-00-DX1": "Kidney",
+    "TCGA-18-5592-01Z-00-DX1": "Liver",
+    "TCGA-38-6178-01Z-00-DX1": "Liver",
+    "TCGA-49-4488-01Z-00-DX1": "Liver",
+    "TCGA-50-5931-01Z-00-DX1": "Liver",
+    "TCGA-21-5784-01Z-00-DX1": "Liver",
+    "TCGA-21-5786-01Z-00-DX1": "Liver",
+    "TCGA-G9-6336-01Z-00-DX1": "Prostate",
+    "TCGA-G9-6348-01Z-00-DX1": "Prostate",
+    "TCGA-G9-6356-01Z-00-DX1": "Prostate",
+    "TCGA-G9-6363-01Z-00-DX1": "Prostate",
+    "TCGA-CH-5767-01Z-00-DX1": "Prostate",
+    "TCGA-G9-6362-01Z-00-DX1": "Prostate",
+    "TCGA-DK-A2I6-01A-01-TS1": "Bladder",
+    "TCGA-G2-A2EK-01A-02-TSB": "Bladder",
+    "TCGA-AY-A8YK-01A-01-TS1": "Colon",
+    "TCGA-NH-A8F7-01A-01-TS1": "Colon",
+    "TCGA-KB-A93J-01A-01-TS1": "Stomach",
+    "TCGA-RD-A8N9-01A-01-TS1": "Stomach",
+}
+
+
+def get_masks(path: Path, mask_size: tuple[int, int]) -> list[Image.Image]:
+    masks = []
+
+    for region in ET.parse(path).getroot().findall("Annotation/Regions/Region"):
+        polygon = [
+            (float(vertex.attrib["X"]), float(vertex.attrib["Y"]))
+            for vertex in region.findall("Vertices/Vertex")
+        ]
+
+        if len(polygon) < 2:
+            continue
+
+        mask = Image.new("1", size=mask_size)
+        canvas = ImageDraw.Draw(mask)
+        canvas.polygon(xy=polygon, outline=True, fill=True)
+        masks.append(mask)
+
+    return masks
+
+
+def process_train(src: str) -> Iterable[dict[str, Any]]:
+    files = list(Path(src).rglob("*.xml"))
+
+    for file in tqdm(files):
+        masks = get_masks(file, mask_size=(1000, 1000))
+        tissue_path = Path(str(file).replace("Annotations", "Tissue Images"))
+        image = np.asarray(Image.open(tissue_path.with_suffix(".tif")))
+
+        yield {
+            "patient": file.stem,
+            "image": Image.fromarray(image.astype(np.uint8)),
+            "instances": masks,
+            "tissue": patient_data.get(file.stem, "Unknown"),
+        }
+
+
+def process_test(src: str) -> Iterable[dict[str, Any]]:
+    files = list(Path(src).rglob("*.xml"))
+
+    for file in tqdm(files):
+        masks = get_masks(file, mask_size=(1000, 1000))
+        image = np.asarray(Image.open(file.with_suffix(".tif")))
+
+        yield {
+            "patient": file.stem,
+            "image": Image.fromarray(image.astype(np.uint8)),
+            "instances": masks,
+            "tissue": patient_data.get(file.stem, "Unknown"),
+        }
+
+
+features = datasets.Features(
+    {
+        "patient": datasets.Value("string"),
+        "image": datasets.Image(mode="RGB"),
+        "instances": datasets.Sequence(datasets.Image(mode="1")),
+        "tissue": datasets.ClassLabel(
+            names=[
+                "Unknown",
+                "Breast",
+                "Kidney",
+                "Liver",
+                "Prostate",
+                "Bladder",
+                "Colon",
+                "Stomach",
+            ]
+        ),
+    }
+)
+
+
+if __name__ == "__main__":
+    train = Dataset.from_generator(
+        process_train,
+        gen_kwargs={"src": "data/raw/MoNuSeg/MoNuSeg 2018 Training Data/Annotations"},
+        features=features,
+        split=NamedSplit("train"),
+        keep_in_memory=True,
+    )
+    train.push_to_hub("RationAI/MoNuSeg")
+
+    test = Dataset.from_generator(
+        process_test,
+        gen_kwargs={"src": "data/raw/MoNuSeg/MoNuSegTestData"},
+        features=features,
+        split=NamedSplit("test"),
+        keep_in_memory=True,
+    )
+    test.push_to_hub("RationAI/MoNuSeg")