File size: 6,706 Bytes
8c212a5 |
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 |
"""Contains transform functions."""
import cv2
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = [
'crop_resize_image', 'progressive_resize_image', 'resize_image',
'normalize_image', 'normalize_latent_code', 'ImageResizing',
'ImageNormalization', 'LatentCodeNormalization',
]
def crop_resize_image(image, size):
"""Crops a square patch and then resizes it to the given size.
Args:
image: The input image to crop and resize.
size: An integer, indicating the target size.
Returns:
An image with target size.
Raises:
TypeError: If the input `image` is not with type `numpy.ndarray`.
ValueError: If the input `image` is not with shape [H, W, C].
"""
if not isinstance(image, np.ndarray):
raise TypeError(f'Input image should be with type `numpy.ndarray`, '
f'but `{type(image)}` is received!')
if image.ndim != 3:
raise ValueError(f'Input image should be with shape [H, W, C], '
f'but `{image.shape}` is received!')
height, width, channel = image.shape
short_side = min(height, width)
image = image[(height - short_side) // 2:(height + short_side) // 2,
(width - short_side) // 2:(width + short_side) // 2]
pil_image = PIL.Image.fromarray(image)
pil_image = pil_image.resize((size, size), PIL.Image.ANTIALIAS)
image = np.asarray(pil_image)
assert image.shape == (size, size, channel)
return image
def progressive_resize_image(image, size):
"""Resizes image to target size progressively.
Different from normal resize, this function will reduce the image size
progressively. In each step, the maximum reduce factor is 2.
NOTE: This function can only handle square images, and can only be used for
downsampling.
Args:
image: The input (square) image to resize.
size: An integer, indicating the target size.
Returns:
An image with target size.
Raises:
TypeError: If the input `image` is not with type `numpy.ndarray`.
ValueError: If the input `image` is not with shape [H, W, C].
"""
if not isinstance(image, np.ndarray):
raise TypeError(f'Input image should be with type `numpy.ndarray`, '
f'but `{type(image)}` is received!')
if image.ndim != 3:
raise ValueError(f'Input image should be with shape [H, W, C], '
f'but `{image.shape}` is received!')
height, width, channel = image.shape
assert height == width
assert height >= size
num_iters = int(np.log2(height) - np.log2(size))
for _ in range(num_iters):
height = max(height // 2, size)
image = cv2.resize(image, (height, height),
interpolation=cv2.INTER_LINEAR)
assert image.shape == (size, size, channel)
return image
def resize_image(image, size):
"""Resizes image to target size.
NOTE: We use adaptive average pooing for image resizing. Instead of bilinear
interpolation, average pooling is able to acquire information from more
pixels, such that the resized results can be with higher quality.
Args:
image: The input image tensor, with shape [C, H, W], to resize.
size: An integer or a tuple of integer, indicating the target size.
Returns:
An image tensor with target size.
Raises:
TypeError: If the input `image` is not with type `torch.Tensor`.
ValueError: If the input `image` is not with shape [C, H, W].
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f'Input image should be with type `torch.Tensor`, '
f'but `{type(image)}` is received!')
if image.ndim != 3:
raise ValueError(f'Input image should be with shape [C, H, W], '
f'but `{image.shape}` is received!')
image = F.adaptive_avg_pool2d(image.unsqueeze(0), size).squeeze(0)
return image
def normalize_image(image, mean=127.5, std=127.5):
"""Normalizes image by subtracting mean and dividing std.
Args:
image: The input image tensor to normalize.
mean: The mean value to subtract from the input tensor. (default: 127.5)
std: The standard deviation to normalize the input tensor. (default:
127.5)
Returns:
A normalized image tensor.
Raises:
TypeError: If the input `image` is not with type `torch.Tensor`.
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f'Input image should be with type `torch.Tensor`, '
f'but `{type(image)}` is received!')
out = (image - mean) / std
return out
def normalize_latent_code(latent_code, adjust_norm=True):
"""Normalizes latent code.
NOTE: The latent code will always be normalized along the last axis.
Meanwhile, if `adjust_norm` is set as `True`, the norm of the result will be
adjusted to `sqrt(latent_code.shape[-1])` in order to avoid too small value.
Args:
latent_code: The input latent code tensor to normalize.
adjust_norm: Whether to adjust the norm of the output. (default: True)
Returns:
A normalized latent code tensor.
Raises:
TypeError: If the input `latent_code` is not with type `torch.Tensor`.
"""
if not isinstance(latent_code, torch.Tensor):
raise TypeError(f'Input latent code should be with type '
f'`torch.Tensor`, but `{type(latent_code)}` is '
f'received!')
dim = latent_code.shape[-1]
norm = latent_code.pow(2).sum(-1, keepdim=True).pow(0.5)
out = latent_code / norm
if adjust_norm:
out = out * (dim ** 0.5)
return out
class ImageResizing(nn.Module):
"""Implements the image resizing layer."""
def __init__(self, size):
super().__init__()
self.size = size
def forward(self, image):
return resize_image(image, self.size)
class ImageNormalization(nn.Module):
"""Implements the image normalization layer."""
def __init__(self, mean=127.5, std=127.5):
super().__init__()
self.mean = mean
self.std = std
def forward(self, image):
return normalize_image(image, self.mean, self.std)
class LatentCodeNormalization(nn.Module):
"""Implements the latent code normalization layer."""
def __init__(self, adjust_norm=True):
super().__init__()
self.adjust_norm = adjust_norm
def forward(self, latent_code):
return normalize_latent_code(latent_code, self.adjust_norm)
|