Spaces:
Sleeping
Sleeping
File size: 10,467 Bytes
3d85088 |
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 |
from __future__ import division
import collections
import numbers
import random
import torch
from PIL import Image
from skimage import color
import src.data.functional as F
__all__ = [
"Compose",
"Concatenate",
"ToTensor",
"Normalize",
"Resize",
"Scale",
"CenterCrop",
"Pad",
"RandomCrop",
"RandomHorizontalFlip",
"RandomVerticalFlip",
"RandomResizedCrop",
"RandomSizedCrop",
"FiveCrop",
"TenCrop",
"RGB2Lab",
]
def CustomFunc(inputs, func, *args, **kwargs):
im_l = func(inputs[0], *args, **kwargs)
im_ab = func(inputs[1], *args, **kwargs)
warp_ba = func(inputs[2], *args, **kwargs)
warp_aba = func(inputs[3], *args, **kwargs)
im_gbl_ab = func(inputs[4], *args, **kwargs)
bgr_mc_im = func(inputs[5], *args, **kwargs)
layer_data = [im_l, im_ab, warp_ba, warp_aba, im_gbl_ab, bgr_mc_im]
for l in range(5):
layer = inputs[6 + l]
err_ba = func(layer[0], *args, **kwargs)
err_ab = func(layer[1], *args, **kwargs)
layer_data.append([err_ba, err_ab])
return layer_data
class Compose(object):
"""Composes several transforms together.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, inputs):
for t in self.transforms:
inputs = t(inputs)
return inputs
class Concatenate(object):
"""
Input: [im_l, im_ab, inputs]
inputs = [warp_ba_l, warp_ba_ab, warp_aba, err_pm, err_aba]
Output:[im_l, err_pm, warp_ba, warp_aba, im_ab, err_aba]
"""
def __call__(self, inputs):
im_l = inputs[0]
im_ab = inputs[1]
warp_ba = inputs[2]
warp_aba = inputs[3]
im_glb_ab = inputs[4]
bgr_mc_im = inputs[5]
bgr_mc_im = bgr_mc_im[[2, 1, 0], ...]
err_ba = []
err_ab = []
for l in range(5):
layer = inputs[6 + l]
err_ba.append(layer[0])
err_ab.append(layer[1])
cerr_ba = torch.cat(err_ba, 0)
cerr_ab = torch.cat(err_ab, 0)
return (im_l, cerr_ba, warp_ba, warp_aba, im_glb_ab, bgr_mc_im, im_ab, cerr_ab)
class ToTensor(object):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
"""
def __call__(self, inputs):
"""
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
return CustomFunc(inputs, F.to_mytensor)
class Normalize(object):
"""Normalize an tensor image with mean and standard deviation.
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
will normalize each channel of the input ``torch.*Tensor`` i.e.
``input[channel] = (input[channel] - mean[channel]) / std[channel]``
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
"""
def __call__(self, inputs):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
im_l = F.normalize(inputs[0], 50, 1) # [0, 100]
im_ab = F.normalize(inputs[1], (0, 0), (1, 1)) # [-100, 100]
inputs[2][0:1, :, :] = F.normalize(inputs[2][0:1, :, :], 50, 1)
inputs[2][1:3, :, :] = F.normalize(inputs[2][1:3, :, :], (0, 0), (1, 1))
warp_ba = inputs[2]
inputs[3][0:1, :, :] = F.normalize(inputs[3][0:1, :, :], 50, 1)
inputs[3][1:3, :, :] = F.normalize(inputs[3][1:3, :, :], (0, 0), (1, 1))
warp_aba = inputs[3]
im_gbl_ab = F.normalize(inputs[4], (0, 0), (1, 1)) # [-100, 100]
bgr_mc_im = F.normalize(inputs[5], (123.68, 116.78, 103.938), (1, 1, 1))
layer_data = [im_l, im_ab, warp_ba, warp_aba, im_gbl_ab, bgr_mc_im]
for l in range(5):
layer = inputs[6 + l]
err_ba = F.normalize(layer[0], 127, 2) # [0, 255]
err_ab = F.normalize(layer[1], 127, 2) # [0, 255]
layer_data.append([err_ba, err_ab])
return layer_data
class Resize(object):
"""Resize the input PIL Image to the given size.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def __init__(self, size, interpolation=Image.BILINEAR):
assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
self.size = size
self.interpolation = interpolation
def __call__(self, inputs):
"""
Args:
img (PIL Image): Image to be scaled.
Returns:
PIL Image: Rescaled image.
"""
return CustomFunc(inputs, F.resize, self.size, self.interpolation)
class RandomCrop(object):
"""Crop the given PIL Image at a random location.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
"""
def __init__(self, size, padding=0):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
@staticmethod
def get_params(img, output_size):
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
"""
w, h = img.size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th)
j = random.randint(0, w - tw)
return i, j, th, tw
def __call__(self, inputs):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
if self.padding > 0:
inputs = CustomFunc(inputs, F.pad, self.padding)
i, j, h, w = self.get_params(inputs[0], self.size)
return CustomFunc(inputs, F.crop, i, j, h, w)
class CenterCrop(object):
"""Crop the given PIL Image at a random location.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
"""
def __init__(self, size, padding=0):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
@staticmethod
def get_params(img, output_size):
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
"""
w, h = img.size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = (h - th) // 2
j = (w - tw) // 2
return i, j, th, tw
def __call__(self, inputs):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
if self.padding > 0:
inputs = CustomFunc(inputs, F.pad, self.padding)
i, j, h, w = self.get_params(inputs[0], self.size)
return CustomFunc(inputs, F.crop, i, j, h, w)
class RandomHorizontalFlip(object):
"""Horizontally flip the given PIL Image randomly with a probability of 0.5."""
def __call__(self, inputs):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < 0.5:
return CustomFunc(inputs, F.hflip)
return inputs
class RGB2Lab(object):
def __call__(self, inputs):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
def __call__(self, inputs):
image_lab = color.rgb2lab(inputs[0])
warp_ba_lab = color.rgb2lab(inputs[2])
warp_aba_lab = color.rgb2lab(inputs[3])
im_gbl_lab = color.rgb2lab(inputs[4])
inputs[0] = image_lab[:, :, :1] # l channel
inputs[1] = image_lab[:, :, 1:] # ab channel
inputs[2] = warp_ba_lab # lab channel
inputs[3] = warp_aba_lab # lab channel
inputs[4] = im_gbl_lab[:, :, 1:] # ab channel
return inputs
|