import multiprocessing import os import uuid from functools import partial from multiprocessing import Pool from typing import Dict, List, Optional import fsspec import pandas as pd import requests from loguru import logger from tqdm import tqdm from utils import get_uid_from_str def load_smithsonian_metadata( download_dir: str = "~/.objaverse-xl", ) -> pd.DataFrame: """Loads the Smithsonian Object Metadata dataset as a Pandas DataFrame. Args: download_dir (str, optional): Directory to download the parquet metadata file. Supports all file systems supported by fsspec. Defaults to "~/.objaverse-xl". Returns: pd.DataFrame: Smithsonian Object Metadata dataset as a Pandas DataFrame with columns for the object "title", "url", "quality", "file_type", "uid", and "license". The quality is always Medium and the file_type is always glb. """ dirname = os.path.expanduser(os.path.join(download_dir, "smithsonian")) filename = os.path.join(dirname, "object-metadata.parquet") fs, path = fsspec.core.url_to_fs(filename) if fs.protocol == "file": os.makedirs(dirname, exist_ok=True) if fs.exists(filename): df = pd.read_parquet(filename) return df else: url = "https://huggingface.co/datasets/allenai/objaverse-xl/resolve/main/smithsonian/object-metadata.parquet" response = requests.get(url) response.raise_for_status() with fs.open(filename, "wb") as file: file.write(response.content) df = pd.read_parquet(filename) df["uid"] = df["url"].apply(get_uid_from_str) df["license"] = "CC0" return df def download_smithsonian_object(url: str, download_dir: str = "~/.objaverse-xl") -> str: """Downloads a Smithsonian Object from a URL. Args: url (str): URL to download the Smithsonian Object from. download_dir (str, optional): Directory to download the Smithsonian Object to. Supports all file systems supported by fsspec. Defaults to "~/.objaverse-xl". Returns: str: Path to the downloaded Smithsonian Object. """ uid = get_uid_from_str(url) dirname = os.path.expanduser(os.path.join(download_dir, "smithsonian", "objects")) filename = os.path.join(dirname, f"{uid}.glb") fs, path = fsspec.core.url_to_fs(filename) if fs.protocol == "file": os.makedirs(dirname, exist_ok=True) if not fs.exists(filename): tmp_path = os.path.join(dirname, f"{uid}.glb.tmp") response = requests.get(url) # check if the path is valid if response.status_code == 404: logger.warning(f"404 for {url}") return None # write to tmp path with fs.open(tmp_path, "wb") as file: for chunk in response.iter_content(chunk_size=8192): file.write(chunk) # rename to final path fs.rename(tmp_path, filename) return filename def download_smithsonian_objects( urls: Optional[str] = None, processes: Optional[int] = None, download_dir: str = "~/.objaverse-xl", ) -> List[Dict[str, str]]: """Downloads all Smithsonian Objects. Args: urls (Optional[str], optional): List of URLs to download the Smithsonian Objects from. If None, all Smithsonian Objects will be downloaded. Defaults to None. processes (Optional[int], optional): Number of processes to use for downloading the Smithsonian Objects. If None, the number of processes will be set to the number of CPUs on the machine (multiprocessing.cpu_count()). Defaults to None. download_dir (str, optional): Directory to download the Smithsonian Objects to. Supports all file systems supported by fsspec. Defaults to "~/.objaverse-xl". Returns: List[Dict[str, str]]: List of dictionaries with keys "download_path" and "url" for each downloaded object. """ if processes is None: processes = multiprocessing.cpu_count() if urls is None: df = load_smithsonian_metadata(download_dir=download_dir) urls = df["url"].tolist() logger.info(f"Downloading {len(urls)} Smithsonian Objects with {processes=}") with Pool(processes=processes) as pool: results = list( tqdm( pool.imap_unordered( partial(download_smithsonian_object, download_dir=download_dir), urls, ), total=len(urls), desc="Downloading Smithsonian Objects", ) ) out = [ {"download_path": download_path, "url": url} for download_path, url in zip(results, urls) if download_path is not None ] return out