Update prostate158.py

prostate158.py

@@ -1,11 +1,10 @@
 import datasets
 from typing import List
-from PIL import Image
+
 import pandas as pd
 import numpy as np
 from pathlib import Path
 import os
-import nibabel as nib
 
 _DESCRIPTION = "Prostate dataset."
 
@@ -26,7 +25,7 @@ class Prostate158Dataset(datasets.GeneratorBasedBuilder):
         
     ]
 
-    DEFAULT_CONFIG_NAME = "3d"
+    DEFAULT_CONFIG_NAME = "3d_path"
 
     def _info(self):
         if self.config.name == "2d":
@@ -39,6 +38,17 @@ class Prostate158Dataset(datasets.GeneratorBasedBuilder):
                     "adc_tumor_reader1": datasets.Image(),
                 }
             )
+        elif self.config.name == "2d_array":
+            features = datasets.Features(
+                {
+                    "t2": datasets.Array2D(),
+                    "adc": datasets.Array2D(),
+                    "dwi": datasets.Array2D(),
+                    "t2_anatomy_reader1": datasets.Array2D(),
+                    "adc_tumor_reader1": datasets.Array2D(),
+                }
+            )
+            
         elif self.config.name == "3d_path":
             features = datasets.Features(
                 {
@@ -49,6 +59,17 @@ class Prostate158Dataset(datasets.GeneratorBasedBuilder):
                     "adc_tumor_reader1_path": datasets.Value(dtype="string"),
                 }
             )
+
+        elif self.config.name == "3d_array":
+            features = datasets.Features(
+                {
+                    "t2": datasets.Array3D(),
+                    "adc": datasets.Array3D(),
+                    "dwi": datasets.Array3D(),
+                    "t2_anatomy_reader1": datasets.Array3D(),
+                    "adc_tumor_reader1": datasets.Array3D(),
+                }
+            )
     
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -89,16 +110,32 @@ class Prostate158Dataset(datasets.GeneratorBasedBuilder):
         images_list = ["t2", "adc", "dwi", "t2_anatomy_reader1", "adc_tumor_reader1"]
         df = pd.read_csv(downloaded_files / f"{split}.csv")
         if self.config.name == "2d":
+            import nibabel as nib
+            from PIL import Image # moving imports here to not require unnecessary packages to other users
+            
             yield_index = -1
             for row in df.to_dict(orient="records"):
                 images_data = {image_name: nib.load(downloaded_files / row[image_name]).get_fdata() for image_name in images_list}
-                # print("example_shape", images_data["t2"].shape)
                 for i in range(images_data["t2"].shape[2]):
                     yield_index += 1
                     yield yield_index, {image_name: Image.fromarray(image[:, :, i]) for image_name, image in images_data.items()}
 
+        elif self.config.name == "2d_array":
+            import nibabel as nib
+            
+            yield_index = -1
+            for row in df.to_dict(orient="records"):
+                images_data = {image_name: nib.load(downloaded_files / row[image_name]).get_fdata() for image_name in images_list}
+                for i in range(images_data["t2"].shape[2]):
+                    yield_index += 1
+                    yield yield_index, {image_name: image[:, :, i] for image_name, image in images_data.items()}
+
         elif self.config.name == "3d_path":
             for idx, row in enumerate(df.to_dict(orient="records")):
-                images_data = {image_name+"_path": downloaded_files / row[image_name] for image_name in images_list}
-                yield idx, images_data
+                yield idx, {image_name+"_path": downloaded_files / row[image_name] for image_name in images_list}
 
+        elif self.config.name == "3d_array":
+            import nibabel as nib
+            
+            for idx, row in enumerate(df.to_dict(orient="records")):
+                yield idx, {image_name: nib.load(downloaded_files / row[image_name]).get_fdata() for image_name in images_list}