Create data_loader.py

data_loader.py

@@ -0,0 +1,66 @@
+import datasets
+import os
+
+class BrainCancerMRIConfig(datasets.BuilderConfig):
+    """BuilderConfig for Brain Cancer MRI Classification."""
+    def __init__(self, **kwargs):
+        """BuilderConfig for the dataset.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BrainCancerMRIConfig, self).__init__(**kwargs)
+
+class BrainCancerMRIClassification(datasets.GeneratorBasedBuilder):
+    """Brain Cancer MRI Classification dataset."""
+
+    # Define the classes for the 'label' feature
+    CLASSES = ['glioma', 'meningioma', 'notumor', 'pituitary']
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            # Define the features of the dataset, including the image and its corresponding label
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=self.CLASSES),
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # The data is already in the repository, so we just need the path
+        # `dl_manager.manual_dir` will point to the root of the dataset repository
+        data_dir = os.path.join(dl_manager.manual_dir or ".", "classification")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Provide the path to the training data directory
+                gen_kwargs={"path": os.path.join(data_dir, "Training")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # Provide the path to the testing data directory
+                gen_kwargs={"path": os.path.join(data_dir, "Testing")},
+            ),
+        ]
+
+    def _generate_examples(self, path):
+        """This function will yield examples: a unique key and a dictionary of features."""
+        # Iterate over each class directory (e.g., 'glioma', 'meningioma', etc.)
+        for label in self.CLASSES:
+            class_path = os.path.join(path, label)
+            # Check if the class directory exists to avoid errors
+            if os.path.isdir(class_path):
+                # Iterate over each image file in the class directory
+                for filename in os.listdir(class_path):
+                    image_path = os.path.join(class_path, filename)
+                    # Check if it is a file to avoid including subdirectories
+                    if os.path.isfile(image_path):
+                        key = f"{label}_{filename}"
+                        yield key, {
+                            "image": image_path,
+                            "label": label,
+                        }