Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,655 Bytes
7f2690b |
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 |
import torch
import random
import numbers
from torchvision.transforms import RandomCrop, RandomResizedCrop
def _is_tensor_video_clip(clip):
if not torch.is_tensor(clip):
raise TypeError("clip should be Tensor. Got %s" % type(clip))
if not clip.ndimension() == 4:
raise ValueError("clip should be 4D. Got %dD" % clip.dim())
return True
def crop(clip, i, j, h, w):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
"""
if len(clip.size()) != 4:
raise ValueError("clip should be a 4D tensor")
return clip[..., i : i + h, j : j + w]
def resize(clip, target_size, interpolation_mode):
if len(target_size) != 2:
raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)
def resize_scale(clip, target_size, interpolation_mode):
if len(target_size) != 2:
raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
_, _, H, W = clip.shape
scale_ = target_size[0] / min(H, W)
return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)
def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
"""
Do spatial cropping and resizing to the video clip
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
i (int): i in (i,j) i.e coordinates of the upper left corner.
j (int): j in (i,j) i.e coordinates of the upper left corner.
h (int): Height of the cropped region.
w (int): Width of the cropped region.
size (tuple(int, int)): height and width of resized clip
Returns:
clip (torch.tensor): Resized and cropped clip. Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
clip = crop(clip, i, j, h, w)
clip = resize(clip, size, interpolation_mode)
return clip
def center_crop(clip, crop_size):
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
h, w = clip.size(-2), clip.size(-1)
th, tw = crop_size
if h < th or w < tw:
raise ValueError("height and width must be no smaller than crop_size")
i = int(round((h - th) / 2.0))
j = int(round((w - tw) / 2.0))
return crop(clip, i, j, th, tw)
def random_shift_crop(clip):
'''
Slide along the long edge, with the short edge as crop size
'''
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
h, w = clip.size(-2), clip.size(-1)
if h <= w:
long_edge = w
short_edge = h
else:
long_edge = h
short_edge =w
th, tw = short_edge, short_edge
i = torch.randint(0, h - th + 1, size=(1,)).item()
j = torch.randint(0, w - tw + 1, size=(1,)).item()
return crop(clip, i, j, th, tw)
def to_tensor(clip):
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
"""
_is_tensor_video_clip(clip)
if not clip.dtype == torch.uint8:
raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype))
# return clip.float().permute(3, 0, 1, 2) / 255.0
return clip.float() / 255.0
def normalize(clip, mean, std, inplace=False):
"""
Args:
clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
mean (tuple): pixel RGB mean. Size is (3)
std (tuple): pixel standard deviation. Size is (3)
Returns:
normalized clip (torch.tensor): Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
if not inplace:
clip = clip.clone()
mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
print(mean)
std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
return clip
def hflip(clip):
"""
Args:
clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
Returns:
flipped clip (torch.tensor): Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
return clip.flip(-1)
class RandomCropVideo:
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: randomly cropped video clip.
size is (T, C, OH, OW)
"""
i, j, h, w = self.get_params(clip)
return crop(clip, i, j, h, w)
def get_params(self, clip):
h, w = clip.shape[-2:]
th, tw = self.size
if h < th or w < tw:
raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")
if w == tw and h == th:
return 0, 0, h, w
i = torch.randint(0, h - th + 1, size=(1,)).item()
j = torch.randint(0, w - tw + 1, size=(1,)).item()
return i, j, th, tw
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size})"
class UCFCenterCropVideo:
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: scale resized / center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
clip_center_crop = center_crop(clip_resize, self.size)
return clip_center_crop
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class KineticsRandomCropResizeVideo:
'''
Slide along the long edge, with the short edge as crop size. And resie to the desired size.
'''
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
clip_random_crop = random_shift_crop(clip)
clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode)
return clip_resize
class CenterCropVideo:
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_center_crop = center_crop(clip, self.size)
return clip_center_crop
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class NormalizeVideo:
"""
Normalize the video clip by mean subtraction and division by standard deviation
Args:
mean (3-tuple): pixel RGB mean
std (3-tuple): pixel RGB standard deviation
inplace (boolean): whether do in-place normalization
"""
def __init__(self, mean, std, inplace=False):
self.mean = mean
self.std = std
self.inplace = inplace
def __call__(self, clip):
"""
Args:
clip (torch.tensor): video clip must be normalized. Size is (C, T, H, W)
"""
return normalize(clip, self.mean, self.std, self.inplace)
def __repr__(self) -> str:
return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"
class ToTensorVideo:
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
"""
def __init__(self):
pass
def __call__(self, clip):
"""
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
"""
return to_tensor(clip)
def __repr__(self) -> str:
return self.__class__.__name__
class RandomHorizontalFlipVideo:
"""
Flip the video clip along the horizontal direction with a given probability
Args:
p (float): probability of the clip being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Size is (T, C, H, W)
Return:
clip (torch.tensor): Size is (T, C, H, W)
"""
if random.random() < self.p:
clip = hflip(clip)
return clip
def __repr__(self) -> str:
return f"{self.__class__.__name__}(p={self.p})"
# ------------------------------------------------------------
# --------------------- Sampling ---------------------------
# ------------------------------------------------------------
class TemporalRandomCrop(object):
"""Temporally crop the given frame indices at a random location.
Args:
size (int): Desired length of frames will be seen in the model.
"""
def __init__(self, size):
self.size = size
def __call__(self, total_frames):
rand_end = max(0, total_frames - self.size - 1)
begin_index = random.randint(0, rand_end)
end_index = min(begin_index + self.size, total_frames)
return begin_index, end_index
if __name__ == '__main__':
from torchvision import transforms
import torchvision.io as io
import numpy as np
from torchvision.utils import save_image
import os
vframes, aframes, info = io.read_video(
filename='./v_Archery_g01_c03.avi',
pts_unit='sec',
output_format='TCHW'
)
trans = transforms.Compose([
ToTensorVideo(),
RandomHorizontalFlipVideo(),
UCFCenterCropVideo(512),
# NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
target_video_len = 32
frame_interval = 1
total_frames = len(vframes)
print(total_frames)
temporal_sample = TemporalRandomCrop(target_video_len * frame_interval)
# Sampling video frames
start_frame_ind, end_frame_ind = temporal_sample(total_frames)
# print(start_frame_ind)
# print(end_frame_ind)
assert end_frame_ind - start_frame_ind >= target_video_len
frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int)
select_vframes = vframes[frame_indice]
select_vframes_trans = trans(select_vframes)
select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8)
io.write_video('./test.avi', select_vframes_trans_int.permute(0, 2, 3, 1), fps=8)
for i in range(target_video_len):
save_image(select_vframes_trans[i], os.path.join('./test000', '%04d.png' % i), normalize=True, value_range=(-1, 1)) |