add cli and configure runner to use cli

models/gc_picai_baseline/utils/PicaiBaselineRunner.py

@@ -10,12 +10,13 @@
 """
 
 import json
+import sys
 from pathlib import Path
 
 from mhubio.core import Instance, InstanceData, IO, Module, ValueOutput, ClassOutput, Meta
 
-# Import the PICAI Classifier algorithm class from /opt/algorithm
-from process import csPCaAlgorithm as PicaiClassifier
+
+CLI_PATH = Path(__file__).parent / "cli.py"
 
 
 @ValueOutput.Name('prostate_cancer_likelihood')
@@ -36,22 +37,19 @@ class PicaiBaselineRunner(Module):
     @IO.Output('cancer_detection_heatmap', 'cspca_detection_map.mha', "mha:mod=hm", bundle='model', the='output heatmap indicating prostate cancer likelihood')
     @IO.OutputData('cancer_likelihood', ProstateCancerLikelihood, the='PICAI baseline prostate cancer likelihood')
     def task(self, instance: Instance, in_data_t2: InstanceData, in_data_adc: InstanceData, in_data_hbv: InstanceData, cancer_likelihood_json: InstanceData, cancer_detection_heatmap: InstanceData, cancer_likelihood: ProstateCancerLikelihood) -> None:
-        # Initialize classifier object
-        classifier = PicaiClassifier()
-
-        # Specify input files (the order is important!)
-        classifier.scan_paths = [
-            Path(in_data_t2.abspath),
-            Path(in_data_adc.abspath),
-            Path(in_data_hbv.abspath),
+        # build command (order matters!)
+        cmd = [
+            sys.executable,
+            str(CLI_PATH),
+            in_data_t2.abspath,
+            in_data_adc.abspath,
+            in_data_hbv.abspath,
+            cancer_likelihood_json.abspath,
+            cancer_detection_heatmap.abspath,
         ]
 
-        # Specify output files
-        classifier.cspca_detection_map_path = Path(cancer_detection_heatmap.abspath)
-        classifier.case_confidence_path = Path(cancer_likelihood_json.abspath)
-
-        # Run the classifier on the input images
-        classifier.process()
+        # run the command as subprocess
+        self.subprocess(cmd, text=True)
 
         # Extract cancer likelihood value from cancer_likelihood_file
         if not Path(cancer_likelihood_json.abspath).is_file():

models/gc_picai_baseline/utils/cli.py

@@ -0,0 +1,54 @@
+"""
+--------------------------------------------------
+Mhub / DIAG - CLI for the PICAI baseline Algorithm
+--------------------------------------------------
+
+--------------------------------------------------
+Author: Sil van de Leemput
+Email:  [email protected]
+--------------------------------------------------
+"""
+
+import argparse
+from pathlib import Path
+from process import csPCaAlgorithm as PicaiClassifier
+
+
+def run_classifier(t2: Path, adc: Path, hbv: Path, cancer_likelihood_json: Path, cancer_detection_heatmap: Path):
+    # Initialize classifier object
+    classifier = PicaiClassifier()
+
+    # Specify input files (the order is important!)
+    classifier.scan_paths = [
+        t2,
+        adc,
+        hbv,
+    ]
+
+    # Specify output files
+    classifier.cspca_detection_map_path = cancer_detection_heatmap
+    classifier.case_confidence_path = cancer_likelihood_json
+
+    # Run the classifier on the input images
+    classifier.process()
+
+
+def run_classifier_cli():
+    parser = argparse.ArgumentParser("CLI to run the PICAI baseline classifier")
+    parser.add_argument("input_t2", type=str, help="input T2 weighted prostate MR image (MHA)")
+    parser.add_argument("input_adc", type=str, help="input ADC prostate MR image (MHA")
+    parser.add_argument("input_hbv", type=str, help="input HBV prostate MR image (MHA)")
+    parser.add_argument("output_cancer_likelihood_json", type=str, help="output JSON file with PICAI baseline prostate cancer likelihood (JSON)")
+    parser.add_argument("output_cancer_detection_heatmap", type=str, help="output heatmap indicating prostate cancer likelihood (MHA)")
+    args = parser.parse_args()
+    run_classifier(
+        t2=Path(args.input_t2),
+        adc=Path(args.input_adc),
+        hbv=Path(args.input_hbv),
+        cancer_likelihood_json=Path(args.output_cancer_likelihood_json),
+        cancer_detection_heatmap=Path(args.output_cancer_detection_heatmap),
+    )
+
+
+if __name__ == "__main__":
+    run_classifier_cli()