add bach script

pyproject.toml

@@ -22,6 +22,7 @@ requires-python = ">=3.10"
 dependencies = [
     "pytorch-lightning>=2.1.3",
     "loguru>=0.7.2",
+    "pandas>=2.2.0",
 ]
 
 [project.optional-dependencies]

scripts/metadata/bach.py

@@ -0,0 +1,81 @@
+"""script to create metadata for the BACH dataset
+
+This script creates a metadata file with paths, labels and splits of the 
+BACH train patch dataset. These are 400 patches in  
+"ICIAR2018_BACH_Challenge/Photos" that belong to one of the four classes: 
+"Normal", "Benign", "InSitu", "Invasive".
+
+The metadata is a dataframe, that will be stored as csv with the columns:
+    - path: path to the raw images
+    - label: label of the image
+    - split: split the image belongs to ('train', 'val', 'test')*
+
+* the splits are created as follows: ordered by filename, stratfied by label,
+  train: 70%, val: 15%, test: 15%
+
+to run:
+(1) download 'ICIAR2018_BACH_Challenge.zip' from https://zenodo.org/records/3632035
+    and unzip the data (only the 'Photos' folder is needed)
+(2) specify the following parameters:
+    - DOWNLOADED_DATA_PATH: the directory where the raw patches are stored, e.g.
+      "<...>/3632035/ICIAR2018_BACH_Challenge/Photos"
+    - TARGET_METADATA_FILE: the directory where the metadata dataframe will be 
+      stored (e.g. "./metadata").
+    - TARGET_METADATA_FILE: the metadata file name (e.g. "bach_metadata.csv")
+(3) run script with `python scripts/metadata_creation/bach.py`
+"""
+
+import glob
+import os
+
+import pandas as pd
+from loguru import logger
+
+# Specify relevant directories:
+DOWNLOADED_DATA_PATH = "<...>/3632035/ICIAR2018_BACH_Challenge/Photos"
+TARGET_METADATA_DIR = "./metadata"
+TARGET_METADATA_FILE = "bach_metadata.csv"
+
+# labels mapping and train/val/test split fractions (do not modify):
+_file_dir_to_label = {
+    "Normal": 0,
+    "Benign": 1,
+    "InSitu": 2,
+    "Invasive": 3,
+}
+_train_fraction = 0.7
+_val_fraction = 0.15
+
+
+def main():
+    # create dataframe with paths and labels:
+    all_patches = glob.glob(f"{DOWNLOADED_DATA_PATH}/**/*.tif")
+    logger.info(f"Loaded paths to {len(all_patches)} images.")
+
+    df_metadata = pd.DataFrame(all_patches, columns=["path"])
+    df_metadata["label"] = df_metadata["path"].apply(lambda x: _file_dir_to_label[x.split("/")[-2]])
+
+    # create splits:
+    df_metadata["split"] = ""
+    dfs_label = []
+    for label in df_metadata["label"].unique():
+        df = (
+            df_metadata[df_metadata["label"] == label].sort_values(by="path").reset_index(drop=True)
+        )
+        n_train = round(df.shape[0] * _train_fraction)
+        n_val = round(df.shape[0] * _val_fraction)
+        df.loc[:n_train, "split"] = "train"
+        df.loc[n_train : n_train + n_val, "split"] = "val"
+        df.loc[n_train + n_val :, "split"] = "test"
+        dfs_label.append(df)
+    df_metadata = pd.concat(dfs_label).sort_values(by=["split", "label"]).reset_index(drop=True)
+
+    # save metadata:
+    if not os.path.exists(TARGET_METADATA_DIR):
+        os.mkdir(TARGET_METADATA_DIR)
+    df_metadata.to_csv(os.path.join(TARGET_METADATA_DIR, TARGET_METADATA_FILE), index=False)
+    logger.info(f"Metadata saved to {os.path.join(TARGET_METADATA_DIR, TARGET_METADATA_FILE)}.")
+
+
+if __name__ == "__main__":
+    main()