def download_medmnist(
dataset:str, # The name of the MedMNIST dataset (e.g., 'pathmnist', 'bloodmnist', etc.).
output_dir:str='.', # The path to the directory where the datasets will be saved.
download_only:bool=False, # If True, only download the dataset into the output directory without processing.
save_images:bool=True, # If True, save the images into the output directory as .png (2D datasets) or multipage .tiff (3D datasets) files.
):
Downloads the specified MedMNIST dataset and saves the training, validation, and test datasets into the specified output directory. Images are saved as .png for 2D data and multi-page .tiff for 3D data, organized into folders named after their labels.
Returns: None, saves images in the specified output directory if save_images is True.
def medmnist2df(
train_dataset, # MedMNIST training dataset with images and labels
val_dataset:NoneType=None, # (Optional) MedMNIST validation dataset with images and labels
test_dataset:NoneType=None, # (Optional) MedMNIST test dataset with images and labels
mode:str='RGB', # Mode for PIL Image conversion, e.g., 'RGB', 'L'
)->(<class 'pandas.DataFrame'>, <class 'pandas.DataFrame'>, <class 'pandas.DataFrame'>): # (df_train, df_val, df_test): DataFrames with columns 'image' and 'label'
Convert MedMNIST datasets to DataFrames, with images as PIL Image objects and labels as DataFrame columns.
Missing datasets (if None) are represented by None in the return tuple.
def download_file(
url, # The URL of the file to be downloaded
output_dir:str='data', # The directory where the downloaded file will be saved
extract:bool=True, # If True, decompresses the file if it's in a compressed format
hash:NoneType=None, # Optional: You can add a checksum for integrity verification
extract_dir:NoneType=None, # Directory to extract the files to
):
Download and optionally decompress a single file using Pooch.
def download_files(
urls, # A list of URLs to download
output_dir:str='data', # The directory or list of directories where the downloaded files will be saved
extract:bool=True, # If True, decompresses the files if they are in a compressed format
hash_list:NoneType=None, # Optional: A list of checksums for integrity verification corresponding to each URL
extract_dir:NoneType=None, # Directory to extract the files to
):
Download and optionally decompress multiple files using Pooch.
def download_dataset(
base_url, # The base URL from which the files will be downloaded.
expected_checksums, # A dictionary mapping file names to their expected checksums.
file_names, # A dictionary mapping task identifiers to file names.
output_dir, # The directory where the downloaded files will be saved.
processor:NoneType=None, # A function to process the downloaded data.
):
Download a dataset using Pooch and save it to the specified output directory.
def download_dataset_from_csv(
csv_file, # Path to the CSV file containing file names and checksums.
base_url, # The base URL from which the files will be downloaded.
output_dir, # The directory where the downloaded files will be saved.
processor:NoneType=None, # A function to process the downloaded data.
rows:NoneType=None, # Specific row indices to download. If None, download all rows.
prepend_mdf5:bool=True, # If True, prepend 'md5:' to the checksums.
):
Download a dataset using Pooch and save it to the specified output directory, reading file names and checksums from a CSV file.
# Specify the directory where you want to save the downloaded files
output_directory = "./_test_folder"
# Define the base URL for the MSD dataset
base_url = 'https://s3.ap-northeast-1.wasabisys.com/gigadb-datasets/live/pub/10.5524/100001_101000/100888/'
download_dataset_from_csv('./data_examples/FMD_dataset_info.csv', base_url, output_directory, rows=[6])The dataset has been successfully downloaded and saved to: ./_test_folderAllen Institute Cell Science (AICS)
Loading manifest: 100%|██████████| 77165/77165 [00:01<00:00, 56.4k/s]['../_data/aics/6677e50c_3500001004_100X_20170623_5-Scene-1-P24-E06.ome.tiff']Utilities to make a list of all of the files of the train and test dataset in csv form.
def manifest2csv(
signal, # List of paths to signal images
target, # List of paths to target images
paths:NoneType=None, # List of paths to images
train_fraction:float=0.8, # Fraction of data to use for training
data_save_path:str='./', # Path to save the CSV files
train:str='train.csv', # Name of the training CSV file
test:str='test.csv', # Name of the test CSV file
identifier:NoneType=None, # Identifier to add to the paths
):