import random
import numpy as np
import skimage.color as sc
import torch
def get_patch(*args, patch_size=96, scale=2, multi=False, input_large=False):
ih, iw = args[0].shape[:2]
if not input_large:
p = scale if multi else 1
tp = p * patch_size
ip = tp // scale
else:
tp = patch_size
ip = patch_size
ix = random.randrange(0, iw - ip + 1)
iy = random.randrange(0, ih - ip + 1)
if not input_large:
tx, ty = scale * ix, scale * iy
else:
tx, ty = ix, iy
ret = [
args[0][iy:iy + ip, ix:ix + ip, :],
*[a[ty:ty + tp, tx:tx + tp, :] for a in args[1:]]
]
return ret
def set_channel(*args, n_channels=3):
def _set_channel(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
c = img.shape[2]
if n_channels == 1 and c == 3:
img = np.expand_dims(sc.rgb2ycbcr(img)[:, :, 0], 2)
elif n_channels == 3 and c == 1:
img = np.concatenate([img] * n_channels, 2)
return img
return [_set_channel(a) for a in args]
def np2Tensor(*args, rgb_range=255):
def _np2Tensor(img):
np_transpose = np.ascontiguousarray(img.transpose((2, 0, 1)))
tensor = torch.from_numpy(np_transpose).float()
tensor.mul_(rgb_range / 255)
return tensor
return [_np2Tensor(a) for a in args]
def augment(*args, hflip=True, rot=True):
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip: img = img[:, ::-1, :]
if vflip: img = img[::-1, :, :]
if rot90: img = img.transpose(1, 0, 2)
return img
return [_augment(a) for a in args]
