File size: 15,100 Bytes
f348660 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 | import torch
import torchvision.transforms.functional as functional
class Transforms(object):
def __init__(self,
normalized_image_range=[0, 255],
random_brightness=[-1],
random_contrast=[-1],
random_saturation=[-1],
random_noise_type='none',
random_noise_spread=-1,
random_flip_type=['none']):
'''
Transforms and augmentation class
Note: brightness, contrast, gamma, hue, saturation augmentations expect
either type int in [0, 255] or float in [0, 1]
Arg(s):
normalized_image_range : list[float]
intensity range after normalizing images
random_brightness : list[float]
brightness adjustment [0, B], from 0 (black image) to B factor increase
random_contrast : list[float]
contrast adjustment [0, C], from 0 (gray image) to C factor increase
random_saturation : list[float]
saturation adjustment [0, S], from 0 (black image) to S factor increase
random_noise_type : str
type of noise to add: gaussian, uniform
random_noise_spread : float
if gaussian, then standard deviation; if uniform, then min-max range
random_flip_type : list[str]
none, horizontal, vertical
'''
# Image normalization
self.normalized_image_range = normalized_image_range
# Photometric augmentations
self.do_random_brightness = True if -1 not in random_brightness else False
self.random_brightness = random_brightness
self.do_random_contrast = True if -1 not in random_contrast else False
self.random_contrast = random_contrast
self.do_random_saturation = True if -1 not in random_saturation else False
self.random_saturation = random_saturation
self.do_random_noise = \
True if (random_noise_type != 'none' and random_noise_spread > -1) else False
self.random_noise_type = random_noise_type
self.random_noise_spread = random_noise_spread
# Geometric augmentations
self.do_random_horizontal_flip = True if 'horizontal' in random_flip_type else False
self.do_random_vertical_flip = True if 'vertical' in random_flip_type else False
def transform(self,
images_arr,
labels_arr=[],
points_arr=[],
bounding_boxes_arr=[],
random_transform_probability=0.00):
'''
Applies transform to images and ground truth
Arg(s):
images_arr : list[torch.Tensor]
list of N x C x H x W tensors
labels_arr : list[torch.Tensor]
list of N x c x H x W tensors
points_arr : list[torch.Tensor]
list of N x 3 tensors
bounding_boxes_arr : list[torch.Tensor]
list of N x 4 tensors
random_transform_probability : float
probability to perform transform
Returns:
list[torch.Tensor] : list of transformed N x C x H x W image tensors
list[torch.Tensor] : list of transformed N x c x H x W label tensors
list[torch.Tensor] : list of transformed N x 3 points tensors
list[torch.Tensor] : list of transformed N x 4 bounding box tensors
'''
device = images_arr[0].device
n_dim = images_arr[0].ndim
if n_dim == 4:
n_batch, _, n_height, n_width = images_arr[0].shape
else:
raise ValueError('Unsupported number of dimensions: {}'.format(n_dim))
do_random_transform = \
torch.rand(n_batch, device=device) <= random_transform_probability
'''
Photometric Transformations (applied only to images)
'''
for idx, images in enumerate(images_arr):
# In case user pass in [0, 255] range image as float type
if torch.max(images) > 1.0:
images_arr[idx] = images.int()
if self.do_random_brightness:
do_brightness = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
values = torch.rand(n_batch, device=device)
brightness_min, brightness_max = self.random_brightness
factors = (brightness_max - brightness_min) * values + brightness_min
images_arr = self.adjust_brightness(images_arr, do_brightness, factors)
if self.do_random_contrast:
do_contrast = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
values = torch.rand(n_batch, device=device)
contrast_min, contrast_max = self.random_contrast
factors = (contrast_max - contrast_min) * values + contrast_min
images_arr = self.adjust_contrast(images_arr, do_contrast, factors)
if self.do_random_saturation:
do_saturation = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
values = torch.rand(n_batch, device=device)
saturation_min, saturation_max = self.random_saturation
factors = (saturation_max - saturation_min) * values + saturation_min
images_arr = self.adjust_saturation(images_arr, do_saturation, factors)
'''
Convert all images to float and normalize
'''
images_arr = [
images.float() for images in images_arr
]
# Normalize images to a given range
images_arr = self.normalize_images(
images_arr,
normalized_image_range=self.normalized_image_range)
'''
Points augmentation
'''
if self.do_random_noise:
do_add_noise = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
points_arr = self.add_noise(
points_arr,
do_add_noise=do_add_noise,
noise_type=self.random_noise_type,
noise_spread=self.random_noise_spread)
'''
Geometric transformations (applied to both images and labels)
'''
if self.do_random_horizontal_flip:
do_horizontal_flip = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
images_arr = self.horizontal_flip(
images_arr,
do_horizontal_flip)
labels_arr = self.horizontal_flip(
labels_arr,
do_horizontal_flip)
for bounding_boxes in bounding_boxes_arr:
for bounding_box_idx in range(0,bounding_boxes.shape[0]):
do_hflip = do_horizontal_flip[bounding_box_idx]
for box_idx in range(0,bounding_boxes.shape[1]):
if do_hflip:
temp = bounding_boxes[bounding_box_idx, box_idx, 0].clone()
bounding_boxes[bounding_box_idx, box_idx, 0] = n_width - bounding_boxes[bounding_box_idx, box_idx, 2]
bounding_boxes[bounding_box_idx, box_idx, 2] = n_width - temp
if self.do_random_vertical_flip:
do_vertical_flip = torch.logical_and(
do_random_transform,
torch.rand(n_batch, device=device) <= 0.50)
images_arr = self.vertical_flip(
images_arr,
do_vertical_flip)
labels_arr = self.vertical_flip(
labels_arr,
do_vertical_flip)
for bounding_boxes in bounding_boxes_arr:
for bounding_box_idx in range(0,bounding_boxes.shape[0]):
do_vflip = do_vertical_flip[bounding_box_idx]
if do_vflip:
temp = bounding_boxes[bounding_box_idx, 1].clone()
bounding_boxes[bounding_box_idx, 1] = n_height - bounding_boxes[bounding_box_idx, 3]
bounding_boxes[bounding_box_idx, 3] = n_height - temp
# Return the transformed inputs
outputs = []
if len(images_arr) > 0:
outputs.append(images_arr)
if len(labels_arr) > 0:
outputs.append(labels_arr)
if len(points_arr) > 0:
outputs.append(points_arr)
if len(bounding_boxes_arr) > 0:
outputs.append(bounding_boxes_arr)
if len(outputs) == 1:
return outputs[0]
else:
return outputs
'''
Photometric transforms
'''
def normalize_images(self, images_arr, normalized_image_range=[0, 1]):
'''
Normalize image to a given range
Arg(s):
images_arr : list[torch.Tensor[float32]]
list of N x C x H x W tensors
normalized_image_range : list[float]
intensity range after normalizing images
Returns:
images_arr[torch.Tensor[float32]] : list of normalized N x C x H x W tensors
'''
if normalized_image_range == [0, 1]:
images_arr = [
images / 255.0 for images in images_arr
]
elif normalized_image_range == [-1, 1]:
images_arr = [
2.0 * (images / 255.0) - 1.0 for images in images_arr
]
elif normalized_image_range == [0, 255]:
pass
else:
raise ValueError('Unsupported normalization range: {}'.format(
normalized_image_range))
return images_arr
def adjust_brightness(self, images_arr, do_brightness, factors):
'''
Adjust brightness on each sample
Arg(s):
images_arr : list[torch.Tensor]
list of N x C x H x W tensors
do_brightness : bool
N booleans to determine if brightness is adjusted on each sample
factors : float
N floats to determine how much to adjust
Returns:
list[torch.Tensor] : list of transformed N x C x H x W image tensors
'''
for i, images in enumerate(images_arr):
for b, image in enumerate(images):
if do_brightness[b]:
images[b, ...] = functional.adjust_brightness(image, factors[b])
images_arr[i] = images
return images_arr
def adjust_contrast(self, images_arr, do_contrast, factors):
'''
Adjust contrast on each sample
Arg(s):
images_arr : list[torch.Tensor]
list of N x C x H x W tensors
do_contrast : bool
N booleans to determine if contrast is adjusted on each sample
factors : float
N floats to determine how much to adjust
Returns:
list[torch.Tensor] : list of transformed N x C x H x W image tensors
'''
for i, images in enumerate(images_arr):
for b, image in enumerate(images):
if do_contrast[b]:
images[b, ...] = functional.adjust_contrast(image, factors[b])
images_arr[i] = images
return images_arr
def adjust_saturation(self, images_arr, do_saturation, factors):
'''
Adjust saturation on each sample
Arg(s):
images_arr : list[torch.Tensor]
list of N x C x H x W tensors
do_saturation : bool
N booleans to determine if saturation is adjusted on each sample
gammas : float
N floats to determine how much to adjust
Returns:
list[torch.Tensor] : list of transformed N x C x H x W image tensors
'''
for i, images in enumerate(images_arr):
for b, image in enumerate(images):
if do_saturation[b]:
images[b, ...] = functional.adjust_saturation(image, factors[b])
images_arr[i] = images
return images_arr
'''
Geometric transforms
'''
def horizontal_flip(self, images_arr, do_horizontal_flip):
'''
Perform horizontal flip on each sample
Arg(s):
images_arr : list[torch.Tensor[float32]]
list of N x C x H x W tensors
do_horizontal_flip : bool
N booleans to determine if horizontal flip is performed on each sample
Returns:
list[torch.Tensor[float32]] : list of transformed N x C x H x W image tensors
'''
for i, images in enumerate(images_arr):
for b, image in enumerate(images):
if do_horizontal_flip[b]:
images[b, ...] = torch.flip(image, dims=[-1])
images_arr[i] = images
return images_arr
def vertical_flip(self, images_arr, do_vertical_flip):
'''
Perform vertical flip on each sample
Arg(s):
images_arr : list[torch.Tensor[float32]]
list of N x C x H x W tensors
do_vertical_flip : bool
N booleans to determine if vertical flip is performed on each sample
Returns:
list[torch.Tensor[float32]] : list of transformed N x C x H x W image tensors
'''
for i, images in enumerate(images_arr):
for b, image in enumerate(images):
if do_vertical_flip[b]:
images[b, ...] = torch.flip(image, dims=[-2])
images_arr[i] = images
return images_arr
def add_noise(self, images_arr, do_add_noise, noise_type, noise_spread):
'''
Add noise to images
Arg(s):
images_arr : list[torch.Tensor]
list of N x C x H x W tensors
do_add_noise : bool
N booleans to determine if noise will be added
noise_type : str
gaussian, uniform
noise_spread : float
if gaussian, then standard deviation; if uniform, then min-max range
'''
for i, images in enumerate(images_arr):
device = images.device
for b, image in enumerate(images):
if do_add_noise[b]:
shape = image.shape
if noise_type == 'gaussian':
image = image + noise_spread * torch.randn(*shape, device=device)
elif noise_type == 'uniform':
image = image + noise_spread * (torch.rand(*shape, device=device) - 0.5)
else:
raise ValueError('Unsupported noise type: {}'.format(noise_type))
images[b, ...] = image
images_arr[i] = images
return images_arr
|