File size: 4,370 Bytes
e5b70eb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
import torch
import torchvision
import math
import cv2
import numpy as np
from scipy.ndimage import rotate
class RandCrop(object):
def __init__(self, crop_size, scale):
# if output size is tuple -> (height, width)
assert isinstance(crop_size, (int, tuple))
if isinstance(crop_size, int):
self.crop_size = (crop_size, crop_size)
else:
assert len(crop_size) == 2
self.crop_size = crop_size
self.scale = scale
def __call__(self, sample):
# img_LQ: H x W x C (numpy array)
img_LQ, img_GT = sample['img_LQ'], sample['img_GT']
h, w, c = img_LQ.shape
new_h, new_w = self.crop_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
img_LQ_crop = img_LQ[top: top+new_h, left: left+new_w, :]
h, w, c = img_GT.shape
top = np.random.randint(0, h - self.scale*new_h)
left = np.random.randint(0, w - self.scale*new_w)
img_GT_crop = img_GT[top: top + self.scale*new_h, left: left + self.scale*new_w, :]
sample = {'img_LQ': img_LQ_crop, 'img_GT': img_GT_crop}
return sample
class RandRotate(object):
def __call__(self, sample):
# img_LQ: H x W x C (numpy array)
img_LQ, img_GT = sample['img_LQ'], sample['img_GT']
prob_rotate = np.random.random()
if prob_rotate < 0.25:
img_LQ = rotate(img_LQ, 90).copy()
img_GT = rotate(img_GT, 90).copy()
elif prob_rotate < 0.5:
img_LQ = rotate(img_LQ, 90).copy()
img_GT = rotate(img_GT, 90).copy()
elif prob_rotate < 0.75:
img_LQ = rotate(img_LQ, 90).copy()
img_GT = rotate(img_GT, 90).copy()
sample = {'img_LQ': img_LQ, 'img_GT': img_GT}
return sample
class RandHorizontalFlip(object):
def __call__(self, sample):
# img_LQ: H x W x C (numpy array)
img_LQ, img_GT = sample['img_LQ'], sample['img_GT']
prob_lr = np.random.random()
if prob_lr < 0.5:
img_LQ = np.fliplr(img_LQ).copy()
img_GT = np.fliplr(img_GT).copy()
sample = {'img_LQ': img_LQ, 'img_GT': img_GT}
return sample
class ToTensor(object):
def __call__(self, sample):
# img_LQ : H x W x C (numpy array) -> C x H x W (torch tensor)
img_LQ, img_GT = sample['img_LQ'], sample['img_GT']
img_LQ = img_LQ.transpose((2, 0, 1))
img_GT = img_GT.transpose((2, 0, 1))
img_LQ = torch.from_numpy(img_LQ)
img_GT = torch.from_numpy(img_GT)
sample = {'img_LQ': img_LQ, 'img_GT': img_GT}
return sample
class VGG19PerceptualLoss(torch.nn.Module):
def __init__(self, feature_layer=35):
super(VGG19PerceptualLoss, self).__init__()
model = torchvision.models.vgg19(weights=torchvision.models.VGG19_Weights.DEFAULT)
self.features = torch.nn.Sequential(*list(model.features.children())[:feature_layer]).eval()
# Freeze parameters
for name, param in self.features.named_parameters():
param.requires_grad = False
def forward(self, source, target):
vgg_loss = torch.nn.functional.l1_loss(self.features(source), self.features(target))
return vgg_loss
class RandCrop_pair(object):
def __init__(self, crop_size, scale):
# if output size is tuple -> (height, width)
assert isinstance(crop_size, (int, tuple))
if isinstance(crop_size, int):
self.crop_size = (crop_size, crop_size)
else:
assert len(crop_size) == 2
self.crop_size = crop_size
self.scale = scale
def __call__(self, sample):
# img_LQ: H x W x C (numpy array)
img_LQ, img_GT = sample['img_LQ'], sample['img_GT']
h, w, c = img_LQ.shape
new_h, new_w = self.crop_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
img_LQ_crop = img_LQ[top: top+new_h, left: left+new_w, :]
h, w, c = img_GT.shape
top = self.scale*top
left = self.scale*left
img_GT_crop = img_GT[top: top + self.scale*new_h, left: left + self.scale*new_w, :]
sample = {'img_LQ': img_LQ_crop, 'img_GT': img_GT_crop}
return sample |