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import os
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from typing import List, Tuple
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from urllib.parse import urlparse
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from torch.hub import download_url_to_file, get_dir
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def load_file_list(file_list_path: str) -> List[str]:
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files = []
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with open(file_list_path, "r") as fin:
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for line in fin:
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path = line.strip()
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if path:
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files.append(path)
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return files
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def list_image_files(
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img_dir: str,
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exts: Tuple[str]=(".jpg", ".png", ".jpeg"),
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follow_links: bool=False,
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log_progress: bool=False,
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log_every_n_files: int=10000,
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max_size: int=-1
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) -> List[str]:
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files = []
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for dir_path, _, file_names in os.walk(img_dir, followlinks=follow_links):
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early_stop = False
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for file_name in file_names:
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if os.path.splitext(file_name)[1].lower() in exts:
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if max_size >= 0 and len(files) >= max_size:
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early_stop = True
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break
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files.append(os.path.join(dir_path, file_name))
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if log_progress and len(files) % log_every_n_files == 0:
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print(f"find {len(files)} images in {img_dir}")
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if early_stop:
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break
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return files
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def get_file_name_parts(file_path: str) -> Tuple[str, str, str]:
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parent_path, file_name = os.path.split(file_path)
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stem, ext = os.path.splitext(file_name)
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return parent_path, stem, ext
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def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
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"""Load file form http url, will download models if necessary.
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Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
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Args:
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url (str): URL to be downloaded.
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model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
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Default: None.
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progress (bool): Whether to show the download progress. Default: True.
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file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
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Returns:
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str: The path to the downloaded file.
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"""
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if model_dir is None:
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hub_dir = get_dir()
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model_dir = os.path.join(hub_dir, 'checkpoints')
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os.makedirs(model_dir, exist_ok=True)
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parts = urlparse(url)
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filename = os.path.basename(parts.path)
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if file_name is not None:
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filename = file_name
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cached_file = os.path.abspath(os.path.join(model_dir, filename))
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if not os.path.exists(cached_file):
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print(f'Downloading: "{url}" to {cached_file}\n')
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download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
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return cached_file |
