--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/run-superdsm.py	Sun Jun 25 21:48:40 2023 +0000
@@ -0,0 +1,63 @@
+"""
+Copyright 2023 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University.
+
+Distributed under the MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+
+"""
+
+import argparse
+import imghdr
+import os
+import pathlib
+import shutil
+import tempfile
+
+import ray
+import superdsm.automation
+import superdsm.io
+import superdsm.render
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Segmentation of cell nuclei in 2-D fluorescence microscopy images')
+    parser.add_argument('image', help='Path to the input image')
+    parser.add_argument('cfg', help='Path to the file containing the configuration')
+    parser.add_argument('masks', help='Path to the file containing the segmentation masks')
+    parser.add_argument('overlay', help='Path to the file containing the overlay of the segmentation results')
+    parser.add_argument('seg_border', type=int)
+    parser.add_argument('slots', type=int)
+    args = parser.parse_args()
+
+    if args.slots >= 2:
+        num_threads_per_process = 2
+        num_processes = args.slots // num_threads_per_process
+    else:
+        num_threads_per_process = 1
+        num_processes = 1
+
+    os.environ['MKL_NUM_THREADS'] = str(num_threads_per_process)
+    os.environ['OPENBLAS_NUM_THREADS'] = str(num_threads_per_process)
+    os.environ['MKL_DEBUG_CPU_TYPE'] = '5'
+
+    ray.init(num_cpus=num_processes, log_to_driver=True)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmpdir = pathlib.Path(tmpdirname)
+        img_ext = imghdr.what(args.image)
+        img_filepath = tmpdir / f'input.{img_ext}'
+        shutil.copy(str(args.image), img_filepath)
+
+        pipeline = superdsm.pipeline.create_default_pipeline()
+        cfg = superdsm.config.Config()
+        img = superdsm.io.imread(img_filepath)
+        data, cfg, _ = superdsm.automation.process_image(pipeline, cfg, img)
+
+        with open(args.cfg, 'w') as fp:
+            cfg.dump_json(fp)
+
+        overlay = superdsm.render.render_result_over_image(data, border_width=args.seg_border, normalize_img=False)
+        superdsm.io.imwrite(args.overlay, overlay)
+
+        masks = superdsm.render.rasterize_labels(data)
+        superdsm.io.imwrite(args.masks, masks)