fix dir

README.md

@@ -14,6 +14,15 @@ task_categories:
 - image-to-text
 task_ids:
 - multi-label-classification
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "train.csv"
+  - split: test
+    path: "test.csv"
+  - split: validation
+    path: "validation.csv"
 ---
 
 # Dataset Card for Balinese Carving Dataset

a.py

@@ -1,49 +1,55 @@
 import os
-import json
-from datasets import GeneratorBasedBuilder, SplitGenerator, DatasetInfo, Split, Features, Image, Value, Sequence, ClassLabel
-
-class BalineseCarvingDataset(GeneratorBasedBuilder):
-    # Version of the dataset
-    VERSION = "1.0.0"
-
-    def _info(self):
-        return DatasetInfo(
-            features=Features({
-                'image': Image(),  # Image field
-                'image_id': Value('string'),  # Unique ID for each image
-                'carving_classes': Sequence(ClassLabel(names=['Barong', 'Punggel', 'Keketusan', 'Gajah', 'Cina', 'Goak'])),  # Multi-label classification
-                'material': Value('string'),  # Material description
-                'color': Value('string'),  # Color description
-            })
-        )
-
-    def _split_generators(self, dl_manager):
-        # Define the dataset split (train, validation, test)
-        json_path = dl_manager.download_and_extract("path/to/classification_trainval_ukiran.json")
-        image_dir = dl_manager.download_and_extract("path/to/images")
-
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,  # Use Split.TRAIN for train split
-                gen_kwargs={
-                    "json_path": json_path,
-                    "image_dir": image_dir
-                }
-            )
-        ]
-
-    def _generate_examples(self, json_path, image_dir):
-        # Load JSON data
-        with open(json_path, 'r') as f:
-            data = json.load(f)
-        
-        # Yield data
-        for idx, item in enumerate(data):
-            image_path = os.path.join(image_dir, item['File_Name'])
-            yield idx, {
-                'image': image_path,
-                'image_id': item['Image_ID'],
-                'carving_classes': item['Carving_Classes'],
-                'material': item['Material'],
-                'color': item['Color']
-            }
+import shutil
+import pandas as pd
+
+# # Define the image folder and CSV files
+# image_folder = "images/"
+# train_csv = 'train.csv'
+# val_csv = 'validation.csv'
+# test_csv = 'test.csv'
+
+# # Load the CSV files
+# train_df = pd.read_csv(train_csv)
+# val_df = pd.read_csv(val_csv)
+# test_df = pd.read_csv(test_csv)
+
+# # Create directories for train, test, validation images
+# train_dir = 'images/train/'
+# val_dir = 'images/validation/'
+# test_dir = 'images/test/'
+
+# os.makedirs(train_dir, exist_ok=True)
+# os.makedirs(val_dir, exist_ok=True)
+# os.makedirs(test_dir, exist_ok=True)
+
+# # Define a function to copy images to the respective folder
+# def copy_images(df, destination_folder):
+#     for file_name in df['file_name']:
+#         # Construct the full path for the image
+#         src_path = os.path.join(image_folder, file_name)
+#         # Construct the destination path
+#         dest_path = os.path.join(destination_folder, os.path.basename(file_name))
+#         # Copy the image
+#         if os.path.exists(src_path):
+#             shutil.copy(src_path, dest_path)
+
+# # Copy the images based on the CSVs
+# copy_images(train_df, train_dir)
+# copy_images(val_df, val_dir)
+# copy_images(test_df, test_dir)
+
+# Load the CSV files
+train_df = pd.read_csv('train.csv')
+val_df = pd.read_csv('validation.csv')
+test_df = pd.read_csv('test.csv')
+
+# Update file_name with respective image directories
+train_df['file_name'] = 'images/train/' + train_df['file_name'].apply(os.path.basename)
+val_df['file_name'] = 'images/validation/' + val_df['file_name'].apply(os.path.basename)
+test_df['file_name'] = 'images/test/' + test_df['file_name'].apply(os.path.basename)
+
+# Combine all three datasets
+combined_df = pd.concat([train_df, val_df, test_df], ignore_index=True)
+
+# Save the combined dataset
+combined_df.to_csv('metadata.csv', index=False)