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import os
from os import path
import warnings
from torch.utils.data.dataset import Dataset
from torchvision import transforms, utils
from PIL import Image
import numpy as np
import random
from dataset.reseed import reseed
import util.boundary_modification as boundary_modification
import torch
seg_normalization = transforms.Normalize(
mean=[0.5],
std=[0.5]
)
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
def to_pixel_samples(img):
""" Convert the image to coord-RGB pairs.
img: Tensor, (3, H, W)
"""
coord = make_coord(img.shape[-2:])
rgb = img.view(1, -1).permute(1, 0)
return coord, rgb
def resize_fn(img, size):
return transforms.ToTensor()(
transforms.Resize(size, Image.BICUBIC)(
transforms.ToPILImage()(img)))
class OnlineTransformDataset_crm(Dataset):
"""
Method 0 - FSS style (class/1.jpg class/1.png)
Method 1 - Others style (XXX.jpg XXX.png)
"""
def __init__(self, root, need_name=False, method=0, perturb=True):
self.root = root
self.need_name = need_name
self.method = method
if method == 0:
# Get images
self.im_list = []
classes = os.listdir(self.root)
for c in classes:
imgs = os.listdir(path.join(root, c))
jpg_list = [im for im in imgs if 'jpg' in im[-3:].lower()]
unmatched = any([im.replace('.jpg', '.png') not in imgs for im in jpg_list])
if unmatched:
print('Number of image/gt unmatch in class ', c)
print('The whole class is ignored', len(jpg_list))
warnings.warn('Dataset unmatch error')
else:
joint_list = [path.join(root, c, im) for im in jpg_list]
self.im_list.extend(joint_list)
elif method == 1:
self.im_list = [path.join(self.root, im) for im in os.listdir(self.root) if '.jpg' in im]
print('%d images found' % len(self.im_list))
if perturb:
# Make up some transforms
self.bilinear_dual_transform = transforms.Compose([
transforms.RandomCrop((224, 224), pad_if_needed=True),
transforms.RandomHorizontalFlip(),
])
self.bilinear_dual_transform_im = transforms.Compose([
transforms.RandomCrop((224, 224), pad_if_needed=True),
transforms.RandomHorizontalFlip(),
])
self.im_transform = transforms.Compose([
transforms.ColorJitter(0.2, 0.05, 0.05, 0),
transforms.RandomGrayscale(),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
])
else:
# Make up some transforms
self.bilinear_dual_transform = transforms.Compose([
transforms.Resize(224, interpolation=Image.NEAREST),
transforms.CenterCrop(224),
])
self.bilinear_dual_transform_im = transforms.Compose([
transforms.Resize(224, interpolation=Image.BILINEAR),
transforms.CenterCrop(224),
])
self.im_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
])
self.gt_transform = transforms.Compose([
transforms.ToTensor(),
])
self.seg_transform = transforms.Compose([
transforms.ToTensor(),
seg_normalization,
])
def __getitem__(self, idx):
im = Image.open(self.im_list[idx]).convert('RGB')
if self.method == 0:
gt = Image.open(self.im_list[idx][:-3]+'png').convert('L')
else:
gt = Image.open(self.im_list[idx].replace('.jpg','.png')).convert('L')
seed = np.random.randint(2147483647)
reseed(seed)
im = self.bilinear_dual_transform_im(im)
reseed(seed)
gt = self.bilinear_dual_transform(gt)
iou_max = 1.0
iou_min = 0.8
iou_target = np.random.rand()*(iou_max-iou_min) + iou_min
seg = boundary_modification.modify_boundary((np.array(gt)>0.5).astype('uint8')*255, iou_target=iou_target)
im = self.im_transform(im)
gt = self.gt_transform(gt)
seg = self.seg_transform(seg)
hr_coord, hr_rgb = to_pixel_samples(seg.contiguous())
cell = torch.ones_like(hr_coord)
cell[:, 0] *= 2 / seg.shape[-2]
cell[:, 1] *= 2 / seg.shape[-1]
crop_lr = resize_fn(seg, seg.shape[-2]) #
if self.need_name:
return im, seg, gt, os.path.basename(self.im_list[idx][:-4])
else:
return im, seg, gt, {'inp': crop_lr, 'coord': hr_coord, 'cell': cell, 'gt': hr_rgb}
def __len__(self):
return len(self.im_list)
if __name__ == '__main__':
ecssd_dir = '/PathTo/data/ecssd'
ecssd_dataset = OnlineTransformDataset(ecssd_dir, method=1, perturb=True)
import pdb; pdb.set_trace()
ecssd_dataset[0]
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