Add First Version Of Workflow Layer Codes

workflow/feature_extractor_module.py

@@ -0,0 +1,50 @@
+import pysera
+import os
+from workflow.module import Module
+from workflow.io_port import InPort, OutPort, NIFTIImageType, CSVTableType
+# Codes Go Below:
+
+
+class FeatureExtractor(Module):
+    """
+    Pure logical node for radiomics feature extraction.
+    No Dagster, no Engine, no state.
+    """
+
+    def __init__(self):
+        super().__init__("FeatureExtractor")
+
+        self.addInPort(InPort("image", NIFTIImageType()))
+        self.addInPort(InPort("mask", NIFTIImageType()))
+
+        self.addOutPort(OutPort("features", CSVTableType(columns=["name", "value"])))
+
+    def run(self, context):
+        image = context.get_asset_value("FeatureExtractor.image")
+        mask = context.get_asset_value("FeatureExtractor.mask")
+
+        output_dir = "results"
+        os.makedirs(output_dir, exist_ok=True)
+
+        result = pysera.process_batch(
+            image_input=image,
+            mask_input=mask,
+            output_path=output_dir,
+            num_workers="auto",
+            enable_parallelism=True,
+            apply_preprocessing=True,
+            categories="all",
+            dimensions="1st,2_5d,3d",
+            feature_value_mode="REAL_VALUE",
+            extraction_mode="handcrafted_feature",
+            report="info",
+        )
+
+        df = result.get("features_extracted")
+
+        features = []
+        if df is not None:
+            for idx, row in df.iterrows():
+                features.append({"name": row[0], "value": row[1]})
+
+        return {"features": features}