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0788e19 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | import cv2
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from random import random, choice
from io import BytesIO
from PIL import Image
from PIL import ImageFile
from scipy.ndimage.filters import gaussian_filter
from torchvision.transforms import InterpolationMode
import torch
import random
ImageFile.LOAD_TRUNCATED_IMAGES = True
def dataset_folder(opt, root):
if opt.mode == 'binary':
return binary_dataset(opt, root)
if opt.mode == 'filename':
return FileNameDataset(opt, root)
raise ValueError('opt.mode needs to be binary or filename.')
class RandomGaussianBlur():
def __init__(self, kernel_size, sigma=(0.1, 2.0), p=1.0):
self.blur = transforms.GaussianBlur(kernel_size=kernel_size, sigma=sigma)
self.p = p
def __call__(self, img):
if random.random() < self.p:
return self.blur(img)
return img
class RandomMask(object):
def __init__(self, ratio=0.5, patch_size=16, p=0.5):
"""
Args:
ratio (float or tuple of float): If float, the ratio of the image to be masked.
If tuple of float, random sample ratio between the two values.
patch_size (int): the size of the mask (d*d).
"""
if isinstance(ratio, float):
self.fixed_ratio = True
self.ratio = (ratio, ratio)
elif isinstance(ratio, tuple) and len(ratio) == 2 and all(isinstance(r, float) for r in ratio):
self.fixed_ratio = False
self.ratio = ratio
else:
raise ValueError("Ratio must be a float or a tuple of two floats.")
self.patch_size = patch_size
self.p = p
def __call__(self, tensor):
if random.random() > self.p: return tensor
_, h, w = tensor.shape
mask = torch.ones((h, w), dtype=torch.float32)
if self.fixed_ratio:
ratio = self.ratio[0]
else:
ratio = random.uniform(self.ratio[0], self.ratio[1])
# Calculate the number of masks needed
num_masks = int((h * w * ratio) / (self.patch_size ** 2))
# Generate non-overlapping random positions
selected_positions = set()
while len(selected_positions) < num_masks:
top = random.randint(0, (h // self.patch_size) - 1) * self.patch_size
left = random.randint(0, (w // self.patch_size) - 1) * self.patch_size
selected_positions.add((top, left))
for (top, left) in selected_positions:
mask[top:top+self.patch_size, left:left+self.patch_size] = 0
return tensor * mask.expand_as(tensor)
def binary_dataset(opt, root):
if opt.isTrain:
crop_func = transforms.RandomCrop(opt.cropSize)
rotation_func = transforms.RandomRotation(180)
jitter_func = transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5)
mask_func = RandomMask(ratio=(0.00, 0.75), patch_size=16, p=0.5)
elif opt.no_crop:
crop_func = transforms.Lambda(lambda img: img)
rotation_func = transforms.Lambda(lambda img: img)
jitter_func = transforms.Lambda(lambda img: img)
mask_func = transforms.Lambda(lambda img: img)
else:
crop_func = transforms.CenterCrop(opt.cropSize)
rotation_func = transforms.Lambda(lambda img: img)
jitter_func = transforms.Lambda(lambda img: img)
mask_func = transforms.Lambda(lambda img: img)
if opt.isTrain and not opt.no_flip:
flip_func = transforms.RandomHorizontalFlip()
else:
flip_func = transforms.Lambda(lambda img: img)
if not opt.isTrain and opt.no_resize:
rz_func = transforms.Lambda(lambda img: img)
else:
# rz_func = transforms.Lambda(lambda img: custom_resize(img, opt))
rz_func = transforms.Resize((opt.cropSize, opt.cropSize))
# rz_func = transforms.CenterCrop(opt.cropSize)
dset = datasets.ImageFolder(
root,
transforms.Compose([
rz_func,
# transforms.Lambda(lambda img: data_augment(img, opt)),
crop_func,
flip_func,
# rotation_func,
# jitter_func,
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
mask_func
]))
return dset
class FileNameDataset(datasets.ImageFolder):
def name(self):
return 'FileNameDataset'
def __init__(self, opt, root):
self.opt = opt
super().__init__(root)
def __getitem__(self, index):
# Loading sample
path, target = self.samples[index]
return path
def data_augment(img, opt):
img = np.array(img)
if random() < opt.blur_prob:
sig = sample_continuous(opt.blur_sig)
gaussian_blur(img, sig)
if random() < opt.jpg_prob:
method = sample_discrete(opt.jpg_method)
qual = sample_discrete(opt.jpg_qual)
img = jpeg_from_key(img, qual, method)
return Image.fromarray(img)
def sample_continuous(s):
if len(s) == 1:
return s[0]
if len(s) == 2:
rg = s[1] - s[0]
return random() * rg + s[0]
raise ValueError("Length of iterable s should be 1 or 2.")
def sample_discrete(s):
if len(s) == 1:
return s[0]
return choice(s)
def gaussian_blur(img, sigma):
gaussian_filter(img[:,:,0], output=img[:,:,0], sigma=sigma)
gaussian_filter(img[:,:,1], output=img[:,:,1], sigma=sigma)
gaussian_filter(img[:,:,2], output=img[:,:,2], sigma=sigma)
def cv2_jpg(img, compress_val):
img_cv2 = img[:,:,::-1]
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), compress_val]
result, encimg = cv2.imencode('.jpg', img_cv2, encode_param)
decimg = cv2.imdecode(encimg, 1)
return decimg[:,:,::-1]
def pil_jpg(img, compress_val):
out = BytesIO()
img = Image.fromarray(img)
img.save(out, format='jpeg', quality=compress_val)
img = Image.open(out)
# load from memory before ByteIO closes
img = np.array(img)
out.close()
return img
jpeg_dict = {'cv2': cv2_jpg, 'pil': pil_jpg}
def jpeg_from_key(img, compress_val, key):
method = jpeg_dict[key]
return method(img, compress_val)
# rz_dict = {'bilinear': Image.BILINEAR,
# 'bicubic': Image.BICUBIC,
# 'lanczos': Image.LANCZOS,
# 'nearest': Image.NEAREST}
rz_dict = {'bilinear': InterpolationMode.BILINEAR,
'bicubic': InterpolationMode.BICUBIC,
'lanczos': InterpolationMode.LANCZOS,
'nearest': InterpolationMode.NEAREST}
def custom_resize(img, opt):
interp = sample_discrete(opt.rz_interp)
return TF.resize(img, (opt.loadSize,opt.loadSize), interpolation=rz_dict[interp])
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