moved_repo_first_update

2026-02-24 10:39:48 +01:00
commit 9894a23f09
98 changed files with 35387 additions and 0 deletions
@@ -0,0 +1,58 @@
+# dataset.py
+from torch.utils.data import Dataset
+from PIL import Image
+import numpy as np
+import torch
+
+
+class ClinicalDataset(Dataset):
+    """Generic dataset wrapping a ClinicalData instance.
+    Returns (img_tensor, meta_tensor, label)."""
+
+    def __init__(
+        self,
+        clinical_data,
+        img_transform,
+        meta_transform=None,
+        image_preprocessor=None,
+        geometry_provider=None,
+        geometry_dim: int = 0,
+    ):
+        self.clinical = clinical_data
+        self.transform_image = img_transform
+        self.meta_transform = meta_transform or (lambda x: x)
+        self.image_preprocessor = image_preprocessor
+        self.geometry_provider = geometry_provider
+        self.geometry_dim = geometry_dim if geometry_provider is not None else 0
+
+    def __len__(self):
+        return len(self.clinical.df)
+
+    def __getitem__(self, idx: int):
+        row = self.clinical.df.iloc[idx]
+        # load & transform image
+        img_path = self.clinical.get_image_path(row)
+        orig_img = Image.open(img_path).convert("RGB")
+        img = orig_img
+        if self.image_preprocessor is not None:
+            img = self.image_preprocessor(img, img_path)
+        img_t = self.transform_image(img)
+        # encode & transform metadata
+        meta = self.clinical.encode_metadata(row)
+        meta_t = self.meta_transform(meta)
+        # label
+        label = self.clinical.get_label(row)
+        if self.geometry_dim > 0:
+            features = None
+            if self.geometry_provider is not None and hasattr(self.geometry_provider, "geometry_features"):
+                features = self.geometry_provider.geometry_features(orig_img, img_path)
+            if features is None:
+                geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
+            else:
+                features = np.asarray(features, dtype=np.float32)
+                if features.shape[0] != self.geometry_dim:
+                    geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
+                else:
+                    geom_vec = torch.from_numpy(features)
+            return img_t, meta_t, geom_vec, label
+        return img_t, meta_t, label