import cv2
import os
import os.path as osp
import numpy as np
from PIL import Image
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
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
def download_pretrained_models(file_ids, save_path_root):
import gdown
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}')
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}')
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)
def is_gray(img, threshold=10):
img = Image.fromarray(img)
if len(img.getbands()) == 1:
return True
img1 = np.asarray(img.getchannel(channel=0), dtype=np.int16)
img2 = np.asarray(img.getchannel(channel=1), dtype=np.int16)
img3 = np.asarray(img.getchannel(channel=2), dtype=np.int16)
diff1 = (img1 - img2).var()
diff2 = (img2 - img3).var()
diff3 = (img3 - img1).var()
diff_sum = (diff1 + diff2 + diff3) / 3.0
if diff_sum <= threshold:
return True
else:
return False
def rgb2gray(img, out_channel=3):
r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
if out_channel == 3:
gray = gray[:,:,np.newaxis].repeat(3, axis=2)
return gray
def bgr2gray(img, out_channel=3):
b, g, r = img[:,:,0], img[:,:,1], img[:,:,2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
if out_channel == 3:
gray = gray[:,:,np.newaxis].repeat(3, axis=2)
return gray
def calc_mean_std(feat, eps=1e-5):
"""
Args:
feat (numpy): 3D [w h c]s
"""
size = feat.shape
assert len(size) == 3, 'The input feature should be 3D tensor.'
c = size[2]
feat_var = feat.reshape(-1, c).var(axis=0) + eps
feat_std = np.sqrt(feat_var).reshape(1, 1, c)
feat_mean = feat.reshape(-1, c).mean(axis=0).reshape(1, 1, c)
return feat_mean, feat_std
def adain_npy(content_feat, style_feat):
"""Adaptive instance normalization for numpy.
Args:
content_feat (numpy): The input feature.
style_feat (numpy): The reference feature.
"""
size = content_feat.shape
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - np.broadcast_to(content_mean, size)) / np.broadcast_to(content_std, size)
return normalized_feat * np.broadcast_to(style_std, size) + np.broadcast_to(style_mean, size)