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d01f62c | 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 | import os
from os import path
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from PIL import Image
import numpy as np
from dataset.range_transform import im_normalization, im_mean
from dataset.tps import random_tps_warp
from dataset.reseed import reseed
class StaticTransformDataset(Dataset):
"""
Generate pseudo VOS data by applying random transforms on static images.
Single-object only.
Method 0 - FSS style (class/1.jpg class/1.png)
Method 1 - Others style (XXX.jpg XXX.png)
"""
def __init__(self, parameters, num_frames=3, max_num_obj=1):
self.num_frames = num_frames
self.max_num_obj = max_num_obj
self.im_list = []
for parameter in parameters:
root, method, multiplier = parameter
if method == 0:
# Get images
classes = os.listdir(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()]
joint_list = [path.join(root, c, im) for im in jpg_list]
self.im_list.extend(joint_list * multiplier)
elif method == 1:
self.im_list.extend([path.join(root, im) for im in os.listdir(root) if '.jpg' in im] * multiplier)
print(f'{len(self.im_list)} images found.')
# These set of transform is the same for im/gt pairs, but different among the 3 sampled frames
self.pair_im_lone_transform = transforms.Compose([
transforms.ColorJitter(0.1, 0.05, 0.05, 0), # No hue change here as that's not realistic
])
self.pair_im_dual_transform = transforms.Compose([
transforms.RandomAffine(degrees=20, scale=(0.9,1.1), shear=10, interpolation=InterpolationMode.BICUBIC, fill=im_mean),
transforms.Resize(384, InterpolationMode.BICUBIC),
transforms.RandomCrop((384, 384), pad_if_needed=True, fill=im_mean),
])
self.pair_gt_dual_transform = transforms.Compose([
transforms.RandomAffine(degrees=20, scale=(0.9,1.1), shear=10, interpolation=InterpolationMode.BICUBIC, fill=0),
transforms.Resize(384, InterpolationMode.NEAREST),
transforms.RandomCrop((384, 384), pad_if_needed=True, fill=0),
])
# These transform are the same for all pairs in the sampled sequence
self.all_im_lone_transform = transforms.Compose([
transforms.ColorJitter(0.1, 0.05, 0.05, 0.05),
transforms.RandomGrayscale(0.05),
])
self.all_im_dual_transform = transforms.Compose([
transforms.RandomAffine(degrees=0, scale=(0.8, 1.5), fill=im_mean),
transforms.RandomHorizontalFlip(),
])
self.all_gt_dual_transform = transforms.Compose([
transforms.RandomAffine(degrees=0, scale=(0.8, 1.5), fill=0),
transforms.RandomHorizontalFlip(),
])
# Final transform without randomness
self.final_im_transform = transforms.Compose([
transforms.ToTensor(),
im_normalization,
])
self.final_gt_transform = transforms.Compose([
transforms.ToTensor(),
])
def _get_sample(self, idx):
im = Image.open(self.im_list[idx]).convert('RGB')
gt = Image.open(self.im_list[idx][:-3]+'png').convert('L')
sequence_seed = np.random.randint(2147483647)
images = []
masks = []
for _ in range(self.num_frames):
reseed(sequence_seed)
this_im = self.all_im_dual_transform(im)
this_im = self.all_im_lone_transform(this_im)
reseed(sequence_seed)
this_gt = self.all_gt_dual_transform(gt)
pairwise_seed = np.random.randint(2147483647)
reseed(pairwise_seed)
this_im = self.pair_im_dual_transform(this_im)
this_im = self.pair_im_lone_transform(this_im)
reseed(pairwise_seed)
this_gt = self.pair_gt_dual_transform(this_gt)
# Use TPS only some of the times
# Not because TPS is bad -- just that it is too slow and I need to speed up data loading
if np.random.rand() < 0.33:
this_im, this_gt = random_tps_warp(this_im, this_gt, scale=0.02)
this_im = self.final_im_transform(this_im)
this_gt = self.final_gt_transform(this_gt)
images.append(this_im)
masks.append(this_gt)
images = torch.stack(images, 0)
masks = torch.stack(masks, 0)
return images, masks.numpy()
def __getitem__(self, idx):
additional_objects = np.random.randint(self.max_num_obj)
indices = [idx, *np.random.randint(self.__len__(), size=additional_objects)]
merged_images = None
merged_masks = np.zeros((self.num_frames, 384, 384), dtype=np.int)
for i, list_id in enumerate(indices):
images, masks = self._get_sample(list_id)
if merged_images is None:
merged_images = images
else:
merged_images = merged_images*(1-masks) + images*masks
merged_masks[masks[:,0]>0.5] = (i+1)
masks = merged_masks
labels = np.unique(masks[0])
# Remove background
labels = labels[labels!=0]
target_objects = labels.tolist()
# Generate one-hot ground-truth
cls_gt = np.zeros((self.num_frames, 384, 384), dtype=np.int)
first_frame_gt = np.zeros((1, self.max_num_obj, 384, 384), dtype=np.int)
for i, l in enumerate(target_objects):
this_mask = (masks==l)
cls_gt[this_mask] = i+1
first_frame_gt[0,i] = (this_mask[0])
cls_gt = np.expand_dims(cls_gt, 1)
info = {}
info['name'] = self.im_list[idx]
info['num_objects'] = max(1, len(target_objects))
# 1 if object exist, 0 otherwise
selector = [1 if i < info['num_objects'] else 0 for i in range(self.max_num_obj)]
selector = torch.FloatTensor(selector)
data = {
'rgb': merged_images,
'first_frame_gt': first_frame_gt,
'cls_gt': cls_gt,
'selector': selector,
'info': info
}
return data
def __len__(self):
return len(self.im_list)
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