File size: 14,300 Bytes
c985ba4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 |
from __future__ import division
import os
import shutil
import json
import cv2
from PIL import Image
import numpy as np
from torch.utils.data import Dataset
from utils.image import _palette
class VOSTest(Dataset):
def __init__(self,
image_root,
label_root,
seq_name,
images,
labels,
rgb=True,
transform=None,
single_obj=False,
resolution=None):
self.image_root = image_root
self.label_root = label_root
self.seq_name = seq_name
self.images = images
self.labels = labels
self.obj_num = 1
self.num_frame = len(self.images)
self.transform = transform
self.rgb = rgb
self.single_obj = single_obj
self.resolution = resolution
self.obj_nums = []
self.obj_indices = []
curr_objs = [0]
for img_name in self.images:
self.obj_nums.append(len(curr_objs) - 1)
current_label_name = img_name.split('.')[0] + '.png'
if current_label_name in self.labels:
current_label = self.read_label(current_label_name)
curr_obj = list(np.unique(current_label))
for obj_idx in curr_obj:
if obj_idx not in curr_objs:
curr_objs.append(obj_idx)
self.obj_indices.append(curr_objs.copy())
self.obj_nums[0] = self.obj_nums[1]
def __len__(self):
return len(self.images)
def read_image(self, idx):
img_name = self.images[idx]
img_path = os.path.join(self.image_root, self.seq_name, img_name)
img = cv2.imread(img_path)
img = np.array(img, dtype=np.float32)
if self.rgb:
img = img[:, :, [2, 1, 0]]
return img
def read_label(self, label_name, squeeze_idx=None):
label_path = os.path.join(self.label_root, self.seq_name, label_name)
label = Image.open(label_path)
label = np.array(label, dtype=np.uint8)
if self.single_obj:
label = (label > 0).astype(np.uint8)
elif squeeze_idx is not None:
squeezed_label = label * 0
for idx in range(len(squeeze_idx)):
obj_id = squeeze_idx[idx]
if obj_id == 0:
continue
mask = label == obj_id
squeezed_label += (mask * idx).astype(np.uint8)
label = squeezed_label
return label
def __getitem__(self, idx):
img_name = self.images[idx]
current_img = self.read_image(idx)
height, width, channels = current_img.shape
if self.resolution is not None:
width = int(np.ceil(
float(width) * self.resolution / float(height)))
height = int(self.resolution)
current_label_name = img_name.split('.')[0] + '.png'
obj_num = self.obj_nums[idx]
obj_idx = self.obj_indices[idx]
if current_label_name in self.labels:
current_label = self.read_label(current_label_name, obj_idx)
sample = {
'current_img': current_img,
'current_label': current_label
}
else:
sample = {'current_img': current_img}
sample['meta'] = {
'seq_name': self.seq_name,
'frame_num': self.num_frame,
'obj_num': obj_num,
'current_name': img_name,
'height': height,
'width': width,
'flip': False,
'obj_idx': obj_idx
}
if self.transform is not None:
sample = self.transform(sample)
return sample
class YOUTUBEVOS_Test(object):
def __init__(self,
root='./datasets/YTB',
year=2018,
split='val',
transform=None,
rgb=True,
result_root=None):
if split == 'val':
split = 'valid'
root = os.path.join(root, str(year), split)
self.db_root_dir = root
self.result_root = result_root
self.rgb = rgb
self.transform = transform
self.seq_list_file = os.path.join(self.db_root_dir, 'meta.json')
self._check_preprocess()
self.seqs = list(self.ann_f.keys())
self.image_root = os.path.join(root, 'JPEGImages')
self.label_root = os.path.join(root, 'Annotations')
def __len__(self):
return len(self.seqs)
def __getitem__(self, idx):
seq_name = self.seqs[idx]
data = self.ann_f[seq_name]['objects']
obj_names = list(data.keys())
images = []
labels = []
for obj_n in obj_names:
images += map(lambda x: x + '.jpg', list(data[obj_n]["frames"]))
labels.append(data[obj_n]["frames"][0] + '.png')
images = np.sort(np.unique(images))
labels = np.sort(np.unique(labels))
try:
if not os.path.isfile(
os.path.join(self.result_root, seq_name, labels[0])):
if not os.path.exists(os.path.join(self.result_root,
seq_name)):
os.makedirs(os.path.join(self.result_root, seq_name))
shutil.copy(
os.path.join(self.label_root, seq_name, labels[0]),
os.path.join(self.result_root, seq_name, labels[0]))
except Exception as inst:
print(inst)
print('Failed to create a result folder for sequence {}.'.format(
seq_name))
seq_dataset = VOSTest(self.image_root,
self.label_root,
seq_name,
images,
labels,
transform=self.transform,
rgb=self.rgb)
return seq_dataset
def _check_preprocess(self):
_seq_list_file = self.seq_list_file
if not os.path.isfile(_seq_list_file):
print(_seq_list_file)
return False
else:
self.ann_f = json.load(open(self.seq_list_file, 'r'))['videos']
return True
class YOUTUBEVOS_DenseTest(object):
def __init__(self,
root='./datasets/YTB',
year=2018,
split='val',
transform=None,
rgb=True,
result_root=None):
if split == 'val':
split = 'valid'
root_sparse = os.path.join(root, str(year), split)
root_dense = root_sparse + '_all_frames'
self.db_root_dir = root_dense
self.result_root = result_root
self.rgb = rgb
self.transform = transform
self.seq_list_file = os.path.join(root_sparse, 'meta.json')
self._check_preprocess()
self.seqs = list(self.ann_f.keys())
self.image_root = os.path.join(root_dense, 'JPEGImages')
self.label_root = os.path.join(root_sparse, 'Annotations')
def __len__(self):
return len(self.seqs)
def __getitem__(self, idx):
seq_name = self.seqs[idx]
data = self.ann_f[seq_name]['objects']
obj_names = list(data.keys())
images_sparse = []
for obj_n in obj_names:
images_sparse += map(lambda x: x + '.jpg',
list(data[obj_n]["frames"]))
images_sparse = np.sort(np.unique(images_sparse))
images = np.sort(
list(os.listdir(os.path.join(self.image_root, seq_name))))
start_img = images_sparse[0]
end_img = images_sparse[-1]
for start_idx in range(len(images)):
if start_img in images[start_idx]:
break
for end_idx in range(len(images))[::-1]:
if end_img in images[end_idx]:
break
images = images[start_idx:(end_idx + 1)]
labels = np.sort(
list(os.listdir(os.path.join(self.label_root, seq_name))))
try:
if not os.path.isfile(
os.path.join(self.result_root, seq_name, labels[0])):
if not os.path.exists(os.path.join(self.result_root,
seq_name)):
os.makedirs(os.path.join(self.result_root, seq_name))
shutil.copy(
os.path.join(self.label_root, seq_name, labels[0]),
os.path.join(self.result_root, seq_name, labels[0]))
except Exception as inst:
print(inst)
print('Failed to create a result folder for sequence {}.'.format(
seq_name))
seq_dataset = VOSTest(self.image_root,
self.label_root,
seq_name,
images,
labels,
transform=self.transform,
rgb=self.rgb)
seq_dataset.images_sparse = images_sparse
return seq_dataset
def _check_preprocess(self):
_seq_list_file = self.seq_list_file
if not os.path.isfile(_seq_list_file):
print(_seq_list_file)
return False
else:
self.ann_f = json.load(open(self.seq_list_file, 'r'))['videos']
return True
class DAVIS_Test(object):
def __init__(self,
split=['val'],
root='./DAVIS',
year=2017,
transform=None,
rgb=True,
full_resolution=False,
result_root=None):
self.transform = transform
self.rgb = rgb
self.result_root = result_root
if year == 2016:
self.single_obj = True
else:
self.single_obj = False
if full_resolution:
resolution = 'Full-Resolution'
else:
resolution = '480p'
self.image_root = os.path.join(root, 'JPEGImages', resolution)
self.label_root = os.path.join(root, 'Annotations', resolution)
seq_names = []
for spt in split:
if spt == 'test':
spt = 'test-dev'
with open(os.path.join(root, 'ImageSets', str(year),
spt + '.txt')) as f:
seqs_tmp = f.readlines()
seqs_tmp = list(map(lambda elem: elem.strip(), seqs_tmp))
seq_names.extend(seqs_tmp)
self.seqs = list(np.unique(seq_names))
def __len__(self):
return len(self.seqs)
def __getitem__(self, idx):
seq_name = self.seqs[idx]
images = list(
np.sort(os.listdir(os.path.join(self.image_root, seq_name))))
labels = [images[0].replace('jpg', 'png')]
if not os.path.isfile(
os.path.join(self.result_root, seq_name, labels[0])):
seq_result_folder = os.path.join(self.result_root, seq_name)
try:
if not os.path.exists(seq_result_folder):
os.makedirs(seq_result_folder)
except Exception as inst:
print(inst)
print(
'Failed to create a result folder for sequence {}.'.format(
seq_name))
source_label_path = os.path.join(self.label_root, seq_name,
labels[0])
result_label_path = os.path.join(self.result_root, seq_name,
labels[0])
if self.single_obj:
label = Image.open(source_label_path)
label = np.array(label, dtype=np.uint8)
label = (label > 0).astype(np.uint8)
label = Image.fromarray(label).convert('P')
label.putpalette(_palette)
label.save(result_label_path)
else:
shutil.copy(source_label_path, result_label_path)
seq_dataset = VOSTest(self.image_root,
self.label_root,
seq_name,
images,
labels,
transform=self.transform,
rgb=self.rgb,
single_obj=self.single_obj,
resolution=480)
return seq_dataset
class _EVAL_TEST(Dataset):
def __init__(self, transform, seq_name):
self.seq_name = seq_name
self.num_frame = 10
self.transform = transform
def __len__(self):
return self.num_frame
def __getitem__(self, idx):
current_frame_obj_num = 2
height = 400
width = 400
img_name = 'test{}.jpg'.format(idx)
current_img = np.zeros((height, width, 3)).astype(np.float32)
if idx == 0:
current_label = (current_frame_obj_num * np.ones(
(height, width))).astype(np.uint8)
sample = {
'current_img': current_img,
'current_label': current_label
}
else:
sample = {'current_img': current_img}
sample['meta'] = {
'seq_name': self.seq_name,
'frame_num': self.num_frame,
'obj_num': current_frame_obj_num,
'current_name': img_name,
'height': height,
'width': width,
'flip': False
}
if self.transform is not None:
sample = self.transform(sample)
return sample
class EVAL_TEST(object):
def __init__(self, transform=None, result_root=None):
self.transform = transform
self.result_root = result_root
self.seqs = ['test1', 'test2', 'test3']
def __len__(self):
return len(self.seqs)
def __getitem__(self, idx):
seq_name = self.seqs[idx]
if not os.path.exists(os.path.join(self.result_root, seq_name)):
os.makedirs(os.path.join(self.result_root, seq_name))
seq_dataset = _EVAL_TEST(self.transform, seq_name)
return seq_dataset
|