Spaces:
Runtime error
Runtime error
File size: 4,081 Bytes
22b112d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
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
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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, save_dir=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')
if save_dir is None:
save_dir = os.path.join(ROOT_DIR, model_dir)
os.makedirs(save_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(save_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)
|