Create gen_script.py

gen_script.py

@@ -0,0 +1,183 @@
+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
+import pyvips
+from datasets import Dataset
+from datasets.splits import NamedSplit
+from PIL import Image, ImageDraw
+from tqdm import tqdm
+
+
+# https://drive.google.com/file/d/1kdOl3s6uQBRv0nToSIf1dPuceZunzL4N/view
+patient_data = {
+    "TCGA-55-1594": "Lung",
+    "TCGA-69-7760": "Lung",
+    "TCGA-69-A59K": "Lung",
+    "TCGA-73-4668": "Lung",
+    "TCGA-78-7220": "Lung",
+    "TCGA-86-7713": "Lung",
+    "TCGA-86-8672": "Lung",
+    "TCGA-L4-A4E5": "Lung",
+    "TCGA-MP-A4SY": "Lung",
+    "TCGA-MP-A4T7": "Lung",
+    "TCGA-5P-A9K0": "Kidney",
+    "TCGA-B9-A44B": "Kidney",
+    "TCGA-B9-A8YI": "Kidney",
+    "TCGA-DW-7841": "Kidney",
+    "TCGA-EV-5903": "Kidney",
+    "TCGA-F9-A97G": "Kidney",
+    "TCGA-G7-A8LD": "Kidney",
+    "TCGA-MH-A560": "Kidney",
+    "TCGA-P4-AAVK": "Kidney",
+    "TCGA-SX-A7SR": "Kidney",
+    "TCGA-UZ-A9PO": "Kidney",
+    "TCGA-UZ-A9PU": "Kidney",
+    "TCGA-A2-A0CV": "Breast",
+    "TCGA-A2-A0ES": "Breast",
+    "TCGA-B6-A0WZ": "Breast",
+    "TCGA-BH-A18T": "Breast",
+    "TCGA-D8-A1X5": "Breast",
+    "TCGA-E2-A154": "Breast",
+    "TCGA-E9-A22B": "Breast",
+    "TCGA-E9-A22G": "Breast",
+    "TCGA-EW-A6SD": "Breast",
+    "TCGA-S3-AA11": "Breast",
+    "TCGA-EJ-5495": "Prostate",
+    "TCGA-EJ-5505": "Prostate",
+    "TCGA-EJ-5517": "Prostate",
+    "TCGA-G9-6342": "Prostate",
+    "TCGA-G9-6499": "Prostate",
+    "TCGA-J4-A67Q": "Prostate",
+    "TCGA-J4-A67T": "Prostate",
+    "TCGA-KK-A59X": "Prostate",
+    "TCGA-KK-A6E0": "Prostate",
+    "TCGA-KK-A7AW": "Prostate",
+    "TCGA-V1-A8WL": "Prostate",
+    "TCGA-V1-A9O9": "Prostate",
+    "TCGA-X4-A8KQ": "Prostate",
+    "TCGA-YL-A9WY": "Prostate",
+    "TCGA-49-6743": "Lung",
+    "TCGA-50-6591": "Lung",
+    "TCGA-55-7570": "Lung",
+    "TCGA-55-7573": "Lung",
+    "TCGA-73-4662": "Lung",
+    "TCGA-78-7152": "Lung",
+    "TCGA-2Z-A9JG": "Kidney",
+    "TCGA-2Z-A9JN": "Kidney",
+    "TCGA-DW-7838": "Kidney",
+    "TCGA-DW-7963": "Kidney",
+    "TCGA-F9-A8NY": "Kidney",
+    "TCGA-IZ-A6M9": "Kidney",
+    "TCGA-MH-A55W": "Kidney",
+    "TCGA-A2-A04X": "Breast",
+    "TCGA-D8-A3Z6": "Breast",
+    "TCGA-E2-A108": "Breast",
+    "TCGA-EW-A6SB": "Breast",
+    "TCGA-G9-6356": "Prostate",
+    "TCGA-G9-6367": "Prostate",
+    "TCGA-VP-A87E": "Prostate",
+    "TCGA-VP-A87H": "Prostate",
+    "TCGA-X4-A8KS": "Prostate",
+    "TCGA-YL-A9WL": "Prostate",
+}
+
+
+features = datasets.Features(
+    {
+        "patient": datasets.Value("string"),
+        "image": datasets.Image(mode="RGB"),
+        "instances": datasets.Sequence(datasets.Image(mode="1")),
+        "categories": datasets.Sequence(
+            datasets.ClassLabel(
+                names=[
+                    "Ambiguous",
+                    "Epithelial",
+                    "Lymphocyte",
+                    "Macrophage",
+                    "Neutrophil",
+                ],
+            )
+        ),
+        "tissue": datasets.ClassLabel(
+            names=[
+                "Breast",
+                "Kidney",
+                "Lung",
+                "Prostate",
+            ]
+        ),
+    }
+)
+
+
+def get_masks(
+    path: Path, mask_size: tuple[int, int]
+) -> tuple[list[Image.Image], list[str]]:
+    masks = []
+    categories = []
+
+    root = ET.parse(path).getroot()
+
+    for annotation in root.findall("Annotation"):
+        for region in annotation.findall("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)
+            categories.append(annotation.find("Attributes/Attribute").attrib["Name"])
+
+    return masks, categories
+
+
+def process(src: str) -> Iterable[dict[str, Any]]:
+    files = list(Path(src).rglob("*.xml"))
+
+    for file in tqdm(files):
+        try:
+            image = np.asarray(Image.open(file.with_suffix(".tif")))
+        except FileNotFoundError:
+            image = pyvips.Image.new_from_file(file.with_suffix(".svs"))
+            image = image.numpy()
+
+        masks, categories = get_masks(file, mask_size=(image.shape[1], image.shape[0]))
+        patient_id = file.parent.stem[:12]
+
+        yield {
+            "patient": patient_id,
+            "image": Image.fromarray(image.astype(np.uint8)),
+            "instances": masks,
+            "categories": categories,
+            "tissue": patient_data[patient_id],
+        }
+
+
+if __name__ == "__main__":
+    train = Dataset.from_generator(
+        process,
+        gen_kwargs={"src": "data/raw/MoNuSAC/MoNuSAC_images_and_annotations"},
+        features=features,
+        split=NamedSplit("train"),
+        keep_in_memory=True,
+    )
+    train.push_to_hub("RationAI/MoNuSAC")
+
+    test = Dataset.from_generator(
+        process,
+        gen_kwargs={"src": "data/raw/MoNuSAC/MoNuSAC Testing Data and Annotations"},
+        features=features,
+        split=NamedSplit("test"),
+        keep_in_memory=True,
+    )
+    test.push_to_hub("RationAI/MoNuSAC")