Spaces:
Runtime error
Runtime error
File size: 6,431 Bytes
1b2a9b1 |
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 |
"""SLIC dataset
- Returns an image together with its SLIC segmentation map.
"""
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import numpy as np
from glob import glob
from PIL import Image
from skimage.segmentation import slic
from skimage.color import rgb2lab
import torch.nn.functional as F
from .utils import label2one_hot_torch
class RandomResizedCrop(object):
def __init__(self, N, res, scale=(0.5, 1.0)):
self.res = res
self.scale = scale
self.rscale = [np.random.uniform(*scale) for _ in range(N)]
self.rcrop = [(np.random.uniform(0, 1), np.random.uniform(0, 1)) for _ in range(N)]
def random_crop(self, idx, img):
ws, hs = self.rcrop[idx]
res1 = int(img.size(-1))
res2 = int(self.rscale[idx]*res1)
i1 = int(round((res1-res2)*ws))
j1 = int(round((res1-res2)*hs))
return img[:, :, i1:i1+res2, j1:j1+res2]
def __call__(self, indice, image):
new_image = []
res_tar = self.res // 8 if image.size(1) > 5 else self.res # View 1 or View 2?
for i, idx in enumerate(indice):
img = image[[i]]
img = self.random_crop(idx, img)
img = F.interpolate(img, res_tar, mode='bilinear', align_corners=False)
new_image.append(img)
new_image = torch.cat(new_image)
return new_image
class RandomVerticalFlip(object):
def __init__(self, N, p=0.5):
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, indice, image):
I = np.nonzero(self.plist[indice] < self.p_ref)[0]
if len(image.size()) == 3:
image_t = image[I].flip([1])
else:
image_t = image[I].flip([2])
return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
class RandomHorizontalTensorFlip(object):
def __init__(self, N, p=0.5):
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, indice, image, is_label=False):
I = np.nonzero(self.plist[indice.cpu()] < self.p_ref)[0]
if len(image.size()) == 3:
image_t = image[I].flip([2])
else:
image_t = image[I].flip([3])
return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
class Dataset(data.Dataset):
def __init__(self, data_dir, img_size=256, crop_size=128, test=False,
sp_num=256, slic = True, lab = False):
super(Dataset, self).__init__()
#self.data_list = glob(os.path.join(data_dir, "*.jpg"))
ext = ["*.jpg"]
dl = []
[dl.extend(glob(data_dir + '/**/' + e, recursive=True)) for e in ext]
self.data_list = dl
self.sp_num = sp_num
self.slic = slic
self.lab = lab
if test:
self.transform = transforms.Compose([
transforms.Resize(img_size),
transforms.CenterCrop(crop_size)])
else:
self.transform = transforms.Compose([
transforms.RandomChoice([
transforms.ColorJitter(brightness=0.05),
transforms.ColorJitter(contrast=0.05),
transforms.ColorJitter(saturation=0.01),
transforms.ColorJitter(hue=0.01)]),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.Resize(int(img_size)),
transforms.RandomCrop(crop_size)])
N = len(self.data_list)
self.random_horizontal_flip = RandomHorizontalTensorFlip(N=N)
self.random_vertical_flip = RandomVerticalFlip(N=N)
self.random_resized_crop = RandomResizedCrop(N=N, res=224)
self.eqv_list = ['random_crop', 'h_flip']
def transform_eqv(self, indice, image):
if 'random_crop' in self.eqv_list:
image = self.random_resized_crop(indice, image)
if 'h_flip' in self.eqv_list:
image = self.random_horizontal_flip(indice, image)
if 'v_flip' in self.eqv_list:
image = self.random_vertical_flip(indice, image)
return image
def __getitem__(self, index):
data_path = self.data_list[index]
ori_img = Image.open(data_path)
ori_img = self.transform(ori_img)
ori_img = np.array(ori_img)
# compute slic
if self.slic:
slic_i = slic(ori_img, n_segments=self.sp_num, compactness=10, start_label=0, min_size_factor=0.3)
slic_i = torch.from_numpy(slic_i)
slic_i[slic_i >= self.sp_num] = self.sp_num - 1
oh = label2one_hot_torch(slic_i.unsqueeze(0).unsqueeze(0), C = self.sp_num).squeeze()
if ori_img.ndim < 3:
ori_img = np.expand_dims(ori_img, axis=2).repeat(3, axis = 2)
ori_img = ori_img[:, :, :3]
rets = []
if self.lab:
lab_img = rgb2lab(ori_img)
rets.append(torch.from_numpy(lab_img).float().permute(2, 0, 1))
ori_img = torch.from_numpy(ori_img).float().permute(2, 0, 1)
rets.append(ori_img/255.0)
if self.slic:
rets.append(oh)
rets.append(index)
return rets
def __len__(self):
return len(self.data_list)
if __name__ == '__main__':
import torchvision.utils as vutils
dataset = Dataset('/home/xtli/DATA/texture_data/',
sampled_num=3000)
loader_ = torch.utils.data.DataLoader(dataset = dataset,
batch_size = 1,
shuffle = True,
num_workers = 1,
drop_last = True)
loader = iter(loader_)
img, points, pixs = loader.next()
crop_size = 128
canvas = torch.zeros((1, 3, crop_size, crop_size))
for i in range(points.shape[-2]):
p = (points[0, i] + 1) / 2.0 * (crop_size - 1)
canvas[0, :, int(p[0]), int(p[1])] = pixs[0, :, i]
vutils.save_image(canvas, 'canvas.png')
vutils.save_image(img, 'img.png')
|