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util.py
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util.py
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import os
import math
from datetime import datetime
import numpy as np
import cv2
from skimage.measure import compare_ssim
from torchvision.utils import make_grid
####################
# miscellaneous
####################
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path)
####################
# image convert
####################
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
def save_img(img, img_path, mode='RGB'):
cv2.imwrite(img_path, img)
####################
# metric
####################
def psnr(img1, img2):
assert img1.dtype == img2.dtype == np.uint8, 'np.uint8 is supposed.'
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
def ssim(img1, img2, multichannel=False):
assert img1.dtype == img2.dtype == np.uint8, 'np.uint8 is supposed.'
return compare_ssim(img1, img2, multichannel=multichannel)