93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
import os
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import re
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import argparse
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import zipfile
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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import pandas as pd
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from utils import get_file_hash
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def add_args(parser: argparse.ArgumentParser):
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pass
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def get_metadata(**kwargs):
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metadata = pd.read_csv("hf://datasets/JeffreyXiang/TRELLIS-500K/Toys4k.csv")
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return metadata
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def download(metadata, output_dir, **kwargs):
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os.makedirs(output_dir, exist_ok=True)
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if not os.path.exists(os.path.join(output_dir, 'raw', 'toys4k_blend_files.zip')):
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print("\033[93m")
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print("Toys4k have to be downloaded manually")
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print(f"Please download the toys4k_blend_files.zip file and place it in the {output_dir}/raw directory")
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print("Visit https://github.com/rehg-lab/lowshot-shapebias/tree/main/toys4k for more information")
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print("\033[0m")
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raise FileNotFoundError("toys4k_blend_files.zip not found")
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downloaded = {}
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metadata = metadata.set_index("file_identifier")
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with zipfile.ZipFile(os.path.join(output_dir, 'raw', 'toys4k_blend_files.zip')) as zip_ref:
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with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor, \
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tqdm(total=len(metadata), desc="Extracting") as pbar:
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def worker(instance: str) -> str:
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try:
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zip_ref.extract(os.path.join('toys4k_blend_files', instance), os.path.join(output_dir, 'raw'))
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sha256 = get_file_hash(os.path.join(output_dir, 'raw/toys4k_blend_files', instance))
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pbar.update()
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return sha256
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except Exception as e:
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pbar.update()
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print(f"Error extracting for {instance}: {e}")
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return None
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sha256s = executor.map(worker, metadata.index)
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executor.shutdown(wait=True)
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for k, sha256 in zip(metadata.index, sha256s):
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if sha256 is not None:
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if sha256 == metadata.loc[k, "sha256"]:
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downloaded[sha256] = os.path.join("raw/toys4k_blend_files", k)
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else:
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print(f"Error downloading {k}: sha256s do not match")
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return pd.DataFrame(downloaded.items(), columns=['sha256', 'local_path'])
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def foreach_instance(metadata, output_dir, func, max_workers=None, desc='Processing objects') -> pd.DataFrame:
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import os
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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# load metadata
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metadata = metadata.to_dict('records')
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# processing objects
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records = []
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max_workers = max_workers or os.cpu_count()
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try:
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with ThreadPoolExecutor(max_workers=max_workers) as executor, \
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tqdm(total=len(metadata), desc=desc) as pbar:
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def worker(metadatum):
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try:
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local_path = metadatum['local_path']
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sha256 = metadatum['sha256']
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file = os.path.join(output_dir, local_path)
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record = func(file, sha256)
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if record is not None:
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records.append(record)
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pbar.update()
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except Exception as e:
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print(f"Error processing object {sha256}: {e}")
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pbar.update()
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executor.map(worker, metadata)
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executor.shutdown(wait=True)
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except:
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print("Error happened during processing.")
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return pd.DataFrame.from_records(records)
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