Add All Folders

2026-02-08 04:38:10 +03:30
commit 9bb57e4799
191 changed files with 8469 additions and 0 deletions
@@ -0,0 +1,107 @@
+import luigi
+import json
+from pathlib import Path
+import numpy as np
+import SimpleITK as sitk
+from engine.tasks_reader import ImageReader
+from engine.utils import ensure_dir
+
+class ImageFusion(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageReader(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"fusion_{stem}.json"))
+
+    def run(self):
+        # If we had the second modality, we would read here and stack the channels.
+        # For now, we're passing that single image along with the metadata.
+        payload = {"status": "fused", "modalities": 1, "image": str(self.image_file)}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class ImageConversion(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageFusion(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"conversion_{stem}.json"))
+
+    def run(self):
+        # Convert to SimpleITK image for later steps
+        sitk_img = sitk.ReadImage(str(self.image_file))
+        # Type conversion/normalization
+        payload = {"status": "converted", "pixel_type": str(sitk_img.GetPixelIDTypeAsString())}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class ImageFilter(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+    sigma = luigi.FloatParameter(default=1.0)
+
+    def requires(self):
+        return ImageConversion(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"filter_{stem}.json"))
+
+    def run(self):
+        img = sitk.ReadImage(str(self.image_file))
+        # Gaussian smoothing for ROI preparation
+        filtered = sitk.DiscreteGaussian(img, variance=self.sigma ** 2)
+        # Store Data
+        payload = {"status": "filtered", "sigma": self.sigma}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class MaskRegistration(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageFilter(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"maskreg_{stem}.json"))
+
+    def run(self):
+        # If we have a mask, we register/resample to image space.
+        result = {"status": "mask_registered", "mask_available": bool(self.mask_file)}
+        if self.mask_file:
+            img = sitk.ReadImage(str(self.image_file))
+            msk = sitk.ReadImage(str(self.mask_file))
+            # Resample mask to image geometry
+            resampler = sitk.ResampleImageFilter()
+            resampler.SetReferenceImage(img)
+            resampler.SetInterpolator(sitk.sitkNearestNeighbor)
+            resampler.SetDefaultPixelValue(0)
+            msk_res = resampler.Execute(msk)
+            # Temporary storage of PySera results
+            tmp_dir = ensure_dir(Path(self.workspace) / "tmp")
+            out_mask = Path(tmp_dir) / f"regmask_{Path(self.image_file).stem}.nii.gz"
+            sitk.WriteImage(msk_res, str(out_mask))
+            result["registered_mask_path"] = str(out_mask)
+        with self.output().open("w") as f:
+            json.dump(result, f, indent=2)