Add pytorch Lightning example

mapreader/classify/datasets.py

@@ -180,6 +180,33 @@ def __len__(self) -> int:
         """
         return len(self.patch_df)
 
+    def get_patch(self,idx : int | torch.Tensor) -> Image:
+        """
+        Return the image at the given index.
+
+        Parameters
+        ----------
+        idx : int or Tensor
+            The index of the desired image, or a Tensor containing the index.
+
+        Returns
+        -------
+        PIL.Image.Image
+            The transformed image at the given index.
+        """
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+
+        img_path = self.patch_df.iloc[idx][self.patch_paths_col]
+
+        if os.path.exists(img_path):
+            img = Image.open(img_path).convert(self.image_mode)
+        else:
+            raise ValueError(
+                f'[ERROR] "{img_path} cannot be found.\n\n\'Please check the image exists, your file paths are correct and that ``.patch_paths_col`` is set to the correct column.'
+            )
+        return self.transform(img)
+
     def __getitem__(
         self, idx: int | torch.Tensor
     ) -> tuple[tuple[torch.Tensor], str, int]:
@@ -203,17 +230,8 @@ def __getitem__(
         if torch.is_tensor(idx):
             idx = idx.tolist()
 
-        img_path = self.patch_df.iloc[idx][self.patch_paths_col]
-
-        if os.path.exists(img_path):
-            img = Image.open(img_path).convert(self.image_mode)
-        else:
-            raise ValueError(
-                f'[ERROR] "{img_path} cannot be found.\n\n\
-Please check the image exists, your file paths are correct and that ``.patch_paths_col`` is set to the correct column.'
-            )
 
-        img = self.transform(img)
+        img = self.get_patch(idx)
 
         if self.label_col in self.patch_df.iloc[idx].keys():
             image_label = self.patch_df.iloc[idx][self.label_col]
@@ -844,3 +862,24 @@ def __getitem__(
             image_label_index = -1
 
         return (context_img,), image_label, image_label_index
+
+class PatchFromImageDataset(PatchDataset):
+    # overwrite the __getitem__ method to return the image patch 
+    def __init__(self, args*, **kwargs):
+        super().__init__(args*, **kwargs)
+        #if "method" in kwargs:
+        #    self.method = kwargs["method"]
+        #else:
+        #   self.method = "pixels"
+    def get_patch(self, idx):
+        # load original image
+        img= self.return_orig_image(idx)
+
+        # use self.patch_df to find which part of the image to return 
+        bounds=self.patch_df.iloc[idx]["pixel_bounds"]
+        img= img.crop(bounds)
+
+
+        return self.transform(img)
+
+