objaverse_xl/smithsonian.py

"""Script to download 3D objects from the Smithsonian Institution."""

import multiprocessing
import os
from functools import partial
from multiprocessing import Pool
from typing import Dict, List, Optional, Tuple

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") -> 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".

    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.
    """
    filename = os.path.join(download_dir, "smithsonian", "object-metadata.parquet")
    fs, path = fsspec.core.url_to_fs(filename)
    fs.makedirs(os.path.dirname(path), exist_ok=True)

    # download the parquet file if it doesn't exist
    if not fs.exists(path):
        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(path, "wb") as file:
            file.write(response.content)

    # load the parquet file with fsspec
    with fs.open(path) as f:
        df = pd.read_parquet(f)

    # add uid and license columns
    df["uid"] = df["url"].apply(get_uid_from_str)
    df["license"] = "CC0"

    return df


def _download_smithsonian_object(
    url: str, download_dir: str = "~/.objaverse"
) -> Tuple[str, Optional[str]]:
    """Downloads a Smithsonian Object from a URL.

    Overwrites the file if it already exists and assumes this was previous checked.

    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".

    Returns:
        Tuple[str, Optional[str]]: Tuple of the URL and the path to the downloaded
            Smithsonian Object. If the Smithsonian Object was not downloaded, the path
            will be None.
    """
    uid = get_uid_from_str(url)

    filename = os.path.join(download_dir, "smithsonian", "objects", f"{uid}.glb")
    fs, path = fsspec.core.url_to_fs(filename)

    response = requests.get(url)

    # check if the path is valid
    if response.status_code == 404:
        logger.warning(f"404 for {url}")
        return url, None

    # write to tmp path so that we don't have a partial file
    tmp_path = f"{path}.tmp"
    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, path)

    return url, filename


def download_smithsonian_objects(
    urls: Optional[str] = None,
    processes: Optional[int] = None,
    download_dir: str = "~/.objaverse",
) -> 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".

    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()

    # filename = os.path.join(download_dir, "smithsonian", "objects", f"{uid}.glb")
    objects_dir = os.path.join(download_dir, "smithsonian", "objects")
    fs, path = fsspec.core.url_to_fs(objects_dir)
    fs.makedirs(path, exist_ok=True)

    # get the existing glb files
    existing_glb_files = fs.glob(os.path.join(objects_dir, "*.glb"), refresh=True)
    existing_uids = [
        os.path.basename(file).split(".")[0] for file in existing_glb_files
    ]

    # find the urls that need to be downloaded
    out = []
    urls_to_download = set([])
    already_downloaded_urls = set([])
    for url in urls:
        uid = get_uid_from_str(url)
        if uid not in existing_uids:
            urls_to_download.add(url)
        else:
            already_downloaded_urls.add(url)
        out.append(
            {"download_path": os.path.join(objects_dir, f"{uid}.glb"), "url": url}
        )

    logger.info(
        f"Found {len(already_downloaded_urls)} Smithsonian Objects already downloaded"
    )
    logger.info(
        f"Downloading {len(urls_to_download)} Smithsonian Objects with {processes=}"
    )

    if len(urls_to_download) == 0:
        return out

    with Pool(processes=processes) as pool:
        results = list(
            tqdm(
                pool.imap_unordered(
                    partial(_download_smithsonian_object, download_dir=download_dir),
                    urls_to_download,
                ),
                total=len(urls_to_download),
                desc="Downloading Smithsonian Objects",
            )
        )

    out.extend(
        [
            {"download_path": download_path, "url": url}
            for url, download_path in results
            if download_path is not None
        ]
    )

    return out