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"""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
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 // 4 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] < 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=img_size)
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')
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