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compiler.py

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+import compileall
+
+compileall.compile_dir("/app/", force=True,legacy=True)

script.py

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+import sys
+import SimpleITK as sitk
+import numpy as np
+import torch
+from caller import call
+import os
+import glob
+from percentageLungTissues.percentageLungTissues import *
+
+species_dict = {"human":"human_org",
+				"mice":"mice_TL",
+				"macaque":"mix_no_TL_a_mac"}
+
+
+def ReadImage(path : str, image_extension =".mhd"):
+
+    def read_dicom(path):
+        reader = sitk.ImageSeriesReader()
+        dicom_names = reader.GetGDCMSeriesFileNames(path)
+        reader.SetFileNames(dicom_names)
+        return reader.Execute()
+
+
+    if image_extension == ".mhd":
+        image = sitk.ReadImage(path)
+    elif image_extension == ".dcm" or image_extension == "":
+        image = read_dicom(path)
+
+    return image
+
+
+images_folder  = "/app/Images"
+results_folder = "/app/Results"
+
+biological_species=sys.argv[1].lower()
+
+if len(sys.argv) <= 2:
+	model = species_dict["human"]
+else:
+	if "human" in biological_species:
+		model = species_dict["human"]
+
+	elif ("mouse" or "mice") in biological_species:
+		model = species_dict["mice"]
+
+	elif "nhp" or "macaque" or "monkey" in biological_species:
+		model = species_dict["macaque"]
+
+	else:
+		print("Unknown species:", biological_species)
+		print("Species must be one of the following:")
+		for k in species_dict.keys():
+			print("  -",k)
+		sys.exit("Choose a correct one!") 
+
+
+
+images_path = glob.glob(images_folder + "/*.mhd")
+
+for path in images_path:
+	name = os.path.split(path)[1]
+	image_extension = os.path.splitext(path)[1]
+
+	image = ReadImage(path,image_extension)
+
+	mask = call.apply(image,model)
+
+	seg = sitk.GetImageFromArray(mask)
+
+	sitk.WriteImage(seg, os.path.join(results_folder, 'Res_' + name))
+
+getPercentageLungTissues(images_folder,results_folder)