import cv2 import os import os.path as osp import torch from torch.hub import download_url_to_file, get_dir from urllib.parse import urlparse # from basicsr.utils.download_util import download_file_from_google_drive #import gdown ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) def download_pretrained_models(file_ids, save_path_root): os.makedirs(save_path_root, exist_ok=True) for file_name, file_id in file_ids.items(): file_url = 'https://drive.google.com/uc?id='+file_id save_path = osp.abspath(osp.join(save_path_root, file_name)) if osp.exists(save_path): user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n') if user_response.lower() == 'y': print(f'Covering {file_name} to {save_path}') print("skipping gdown in facelib/utils/misc.py "+file_url) #gdown.download(file_url, save_path, quiet=False) # download_file_from_google_drive(file_id, save_path) elif user_response.lower() == 'n': print(f'Skipping {file_name}') else: raise ValueError('Wrong input. Only accepts Y/N.') else: print(f'Downloading {file_name} to {save_path}') print("skipping gdown in facelib/utils/misc.py "+file_url) #gdown.download(file_url, save_path, quiet=False) # download_file_from_google_drive(file_id, save_path) def imwrite(img, file_path, params=None, auto_mkdir=True): """Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. """ if auto_mkdir: dir_name = os.path.abspath(os.path.dirname(file_path)) os.makedirs(dir_name, exist_ok=True) return cv2.imwrite(file_path, img, params) def img2tensor(imgs, bgr2rgb=True, float32=True): """Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. """ def _totensor(img, bgr2rgb, float32): if img.shape[2] == 3 and bgr2rgb: if img.dtype == 'float64': img = img.astype('float32') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = torch.from_numpy(img.transpose(2, 0, 1)) if float32: img = img.float() return img if isinstance(imgs, list): return [_totensor(img, bgr2rgb, float32) for img in imgs] else: return _totensor(imgs, bgr2rgb, float32) def load_file_from_url(url, model_dir=None, progress=True, file_name=None): """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py """ if model_dir is None: hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file def scandir(dir_path, suffix=None, recursive=False, full_path=False): """Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. full_path (bool, optional): If set to True, include the dir_path. Default: False. Returns: A generator for all the interested files with relative paths. """ if (suffix is not None) and not isinstance(suffix, (str, tuple)): raise TypeError('"suffix" must be a string or tuple of strings') root = dir_path def _scandir(dir_path, suffix, recursive): for entry in os.scandir(dir_path): if not entry.name.startswith('.') and entry.is_file(): if full_path: return_path = entry.path else: return_path = osp.relpath(entry.path, root) if suffix is None: yield return_path elif return_path.endswith(suffix): yield return_path else: if recursive: yield from _scandir(entry.path, suffix=suffix, recursive=recursive) else: continue return _scandir(dir_path, suffix=suffix, recursive=recursive)