diff --git a/README.md b/README.md index 3edf1a8cdcebca39308dbc9e840931b010d2daa8..09d208219e1c770364b0642947a17fdf5ddcad34 100644 --- a/README.md +++ b/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 diff --git a/a.py b/a.py index 6d223ca03b444775fec9e54f4275d65e09402f9a..f2c3a27493c4aac55091db3bead692b2a2ca681c 100644 --- a/a.py +++ b/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 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