fix: dir

README.md

@@ -14,15 +14,6 @@ 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

@@ -2,54 +2,52 @@ import os
 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)
+# Define the image folder and CSV files
+image_folder = "images/"
+train_csv = 'train_caption.csv'
+val_csv = 'validation_caption.csv'
+test_csv = 'test_caption.csv'
 
 # 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')
+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 destination path
+        dest_path = os.path.join(destination_folder, os.path.basename(file_name))
+        # Copy the image
+        if os.path.exists(file_name):
+            shutil.copy(file_name, 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)
 
-# 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)
+# 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)
+# # 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)
+# # Save the combined dataset
+# combined_df.to_csv('metadata.csv', index=False)