| import os | |
| import pandas | |
| from pathlib import Path | |
| import string | |
| import shutil | |
| # read all the folders in data/train | |
| data_folder = Path("./data/train") | |
| folders = os.listdir(data_folder) | |
| entries = [] | |
| image_index = 0 | |
| for folder in folders: | |
| images_path = data_folder.joinpath(folder) | |
| images = os.listdir(images_path) | |
| view = 0 | |
| for image in images: | |
| object_description = folder.replace('_', ' ') | |
| entry = {'file_name' : folder + '/' + image, 'object_type' : 'small_scenery', 'object_description' : object_description, 'view': view, 'color1' : 'none', 'color2' : 'none', 'color3' : 'none'} | |
| entries.append(entry) | |
| view = view + 1 | |
| #put the entries into the metadata.csv | |
| dataframe = pandas.DataFrame(entries) | |
| # dont overwrite the old metadata.csv | |
| if os.path.exists('metadata.csv'): | |
| shutil.copyfile('metadata.csv', 'metadata_backup.csv') | |
| # read the existing metadata.csv and add only the rows that do not exist | |
| output_dataframe = pandas.read_csv('metadata.csv') | |
| # drop the rows where the object description exists | |
| obj_descs = output_dataframe['object_description'] | |
| for obj_desc in obj_descs: | |
| dataframe = dataframe.drop(dataframe[dataframe['object_description'] == obj_desc].index) | |
| output_dataframe = pandas.concat([output_dataframe, dataframe]).drop_duplicates().reset_index(drop=True) | |
| output_dataframe.to_csv('metadata.csv') | |
| else: | |
| dataframe.to_csv('metadata.csv') |