diff --git a/mu_map/data/prepare_polar_maps.py b/mu_map/data/prepare_polar_maps.py
new file mode 100644
index 0000000000000000000000000000000000000000..82ca957d9e3e06c5dc801e5cee82538811f8ed1e
--- /dev/null
+++ b/mu_map/data/prepare_polar_maps.py
@@ -0,0 +1,223 @@
+import argparse
+import os
+from typing import List
+
+from mu_map.file.dicom import dcm_type
+
+
+def get_files_recursive(_dir: str) -> List[str]:
+ """
+ Recursively get all files in a directory. This means that all
+ sub-directories are recursively searched until a file is reached.
+
+ :param _dir: the directory of which files are listed
+ :return: a list of files in the directory and its sub-directories
+ """
+ files: List[str] = []
+ for _file in os.listdir(_dir):
+ _file = os.path.join(_dir, _file)
+
+ if os.path.isdir(_file):
+ files.extend(get_files_recursive(_file))
+ else:
+ files.append(_file)
+ return files
+
+
+def is_scatter_corrected(dcm: dcm_type) -> bool:
+ return not ("NoSC" in dcm.SeriesDescription)
+
+
+def is_attenuation_corrected(dcm: dcm_type) -> bool:
+ return not ("NoAC" in dcm.SeriesDescription)
+
+
+def get_type(dcm: dcm_type) -> str:
+ description = dcm.SeriesDescription.lower()
+ if "syn" in description:
+ return "synthetic"
+ elif "ct" in description:
+ return "ct"
+ else:
+ return "symbia"
+
+
+headers = argparse.Namespace()
+headers.id = "id"
+headers.scatter_correction = "scatter_correction"
+headers.sc = headers.scatter_correction
+headers.attenuation_correction = "attenuation_correction"
+headers.ac = headers.attenuation_correction
+headers.mu_map_type = "mu_map_type"
+headers.type = headers.mu_map_type
+headers.file = "file"
+
+
+if __name__ == "__main__":
+ import cv2 as cv
+ import numpy as np
+ import pandas as pd
+
+ from mu_map.data.prepare import get_protocol
+ from mu_map.data.prepare import headers as meta_headers
+ from mu_map.file.dicom import load_dcm, DCM_TAG_PIXEL_SCALE_FACTOR, dcm_type
+ from mu_map.logging import add_logging_args, get_logger_by_args
+
+ parser = argparse.ArgumentParser(
+ description="Store/Sort in polar maps for further processing",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--polar_map_dir",
+ type=str,
+ required=True,
+ help="directory where the raw DICOM files of polar maps are stored",
+ )
+ parser.add_argument(
+ "--out_dir", type=str, required=True, help="directory where files are output to"
+ )
+ parser.add_argument(
+ "--images_dir",
+ type=str,
+ default="images",
+ help="directory under <out_dir> where images of polar maps are stored",
+ )
+ parser.add_argument(
+ "--csv",
+ type=str,
+ default="polar_maps.csv",
+ help="file unter <out_dir> where meta information is stored",
+ )
+ parser.add_argument(
+ "--meta_csv",
+ type=str,
+ required=True,
+ help="the csv file containing meta information of the dataset of which the generated polar maps are",
+ )
+ parser.add_argument(
+ "--rect",
+ type=int,
+ nargs=4,
+ default=[21, 598, 425, 1002],
+ help="rectangle as [top, left, bottom, right] coordinates where the polar map is found in the DICOM images",
+ )
+ parser.add_argument(
+ "-i",
+ "--interactive",
+ action="store_true",
+ help="start interactive mode where every polar map is displayed and confirmation is queried",
+ )
+ add_logging_args(parser, defaults={"--logfile": "prepare.log"})
+ args = parser.parse_args()
+
+ args.images_dir = os.path.join(args.out_dir, args.images_dir)
+ args.csv = os.path.join(args.out_dir, args.csv)
+ args.logfile = os.path.join(args.out_dir, args.logfile)
+ args.loglevel = "DEBUG" if args.interactive else args.loglevel
+
+ if args.interactive:
+ print(
+ """
+ Start in interactive mode.
+ This mode automatically sets the loglevel to debug so that you can see what is going in.
+ Use the following keys on the displayed polar map:
+
+ n(ext): go to the next image without saving the polar map
+ s(ave): save the polar map and go to the next
+ q(uit): quit the application
+
+ """
+ )
+
+ logger = get_logger_by_args(args)
+ logger.info(args)
+
+ if not os.path.exists(args.out_dir):
+ os.mkdir(args.out_dir)
+ if not os.path.exists(args.images_dir):
+ os.mkdir(args.images_dir)
+
+ meta = pd.read_csv(args.meta_csv)
+ dcm_files = sorted(get_files_recursive(args.polar_map_dir))
+
+ data = pd.DataFrame(
+ {
+ headers.id: [],
+ headers.sc: [],
+ headers.ac: [],
+ headers.type: [],
+ headers.file: [],
+ }
+ )
+ data[headers.id] = data[headers.sc].astype(int)
+ data[headers.sc] = data[headers.sc].astype(bool)
+ data[headers.ac] = data[headers.ac].astype(bool)
+ data[headers.type] = data[headers.ac].astype(str)
+ data[headers.file] = data[headers.ac].astype(str)
+ for dcm_file in dcm_files:
+ logger.debug(f"Process file {dcm_file}")
+
+ dcm, img = load_dcm(dcm_file)
+
+ protocol = get_protocol(dcm)
+ patient_id = int(dcm.PatientID)
+ meta_row = meta[
+ (meta[meta_headers.patient_id] == patient_id)
+ & (meta[meta_headers.protocol] == protocol)
+ ].iloc[0]
+
+ row = {
+ headers.id: meta_row[meta_headers.id],
+ headers.sc: is_scatter_corrected(dcm),
+ headers.ac: is_attenuation_corrected(dcm),
+ headers.type: get_type(dcm),
+ }
+
+ top, left, bottom, right = args.rect
+ polar_map = img[top:bottom, left:right]
+ polar_map = cv.cvtColor(polar_map, cv.COLOR_RGB2BGR)
+
+ _ac = "ac" if row[headers.ac] else "nac"
+ _sc = "sc" if row[headers.sc] else "nsc"
+ _file = f"{row[headers.id]:04d}-{_ac}_{_sc}_{row[headers.type]}.png"
+ _file = os.path.join(args.images_dir, _file)
+ row[headers.file] = _file
+
+ if (
+ len(
+ data[
+ (data[headers.id] == row[headers.id])
+ & (data[headers.sc] == row[headers.sc])
+ & (data[headers.ac] == row[headers.ac])
+ & (data[headers.type] == row[headers.type])
+ ]
+ )
+ > 0
+ ):
+ logger.warning(f"Skip {dcm_file} as it is a duplicate for row: {row}")
+ continue
+
+ logger.debug(f"For series {dcm.SeriesDescription} store row {row}")
+
+ store = not args.interactive
+ if args.interactive:
+ while True:
+ cv.imshow("Polar Map", cv.resize(polar_map, (512, 512)))
+ key = cv.waitKey(0)
+
+ if key == ord("q"):
+ exit(0)
+ elif key == ord("n"):
+ break
+ elif key == ord("s"):
+ store = True
+ break
+
+ if store:
+ cv.imwrite(_file, polar_map)
+
+ row = pd.DataFrame(row, index=[0])
+ data = pd.concat((data, row), ignore_index=True)
+
+ data = data.sort_values(by=[headers.id, headers.file])
+ data.to_csv(args.csv, index=False)