from __future__ import division, print_function, absolute_import
import math
import random
from collections import deque
import torch
from PIL import Image
from torchvision.transforms import (
Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
)
[docs]class Random2DTranslation(object):
"""Randomly translates the input image with a probability.
Specifically, given a predefined shape (height, width), the input is first
resized with a factor of 1.125, leading to (height*1.125, width*1.125), then
a random crop is performed. Such operation is done with a probability.
Args:
height (int): target image height.
width (int): target image width.
p (float, optional): probability that this operation takes place.
Default is 0.5.
interpolation (int, optional): desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
self.height = height
self.width = width
self.p = p
self.interpolation = interpolation
def __call__(self, img):
if random.uniform(0, 1) > self.p:
return img.resize((self.width, self.height), self.interpolation)
new_width, new_height = int(round(self.width * 1.125)
), int(round(self.height * 1.125))
resized_img = img.resize((new_width, new_height), self.interpolation)
x_maxrange = new_width - self.width
y_maxrange = new_height - self.height
x1 = int(round(random.uniform(0, x_maxrange)))
y1 = int(round(random.uniform(0, y_maxrange)))
croped_img = resized_img.crop(
(x1, y1, x1 + self.width, y1 + self.height)
)
return croped_img
[docs]class RandomErasing(object):
"""Randomly erases an image patch.
Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_
Reference:
Zhong et al. Random Erasing Data Augmentation.
Args:
probability (float, optional): probability that this operation takes place.
Default is 0.5.
sl (float, optional): min erasing area.
sh (float, optional): max erasing area.
r1 (float, optional): min aspect ratio.
mean (list, optional): erasing value.
"""
def __init__(
self,
probability=0.5,
sl=0.02,
sh=0.4,
r1=0.3,
mean=[0.4914, 0.4822, 0.4465]
):
self.probability = probability
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
def __call__(self, img):
if random.uniform(0, 1) > self.probability:
return img
for attempt in range(100):
area = img.size()[1] * img.size()[2]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img.size()[2] and h < img.size()[1]:
x1 = random.randint(0, img.size()[1] - h)
y1 = random.randint(0, img.size()[2] - w)
if img.size()[0] == 3:
img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
else:
img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
return img
return img
[docs]class ColorAugmentation(object):
"""Randomly alters the intensities of RGB channels.
Reference:
Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
Networks. NIPS 2012.
Args:
p (float, optional): probability that this operation takes place.
Default is 0.5.
"""
def __init__(self, p=0.5):
self.p = p
self.eig_vec = torch.Tensor(
[
[0.4009, 0.7192, -0.5675],
[-0.8140, -0.0045, -0.5808],
[0.4203, -0.6948, -0.5836],
]
)
self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])
def _check_input(self, tensor):
assert tensor.dim() == 3 and tensor.size(0) == 3
def __call__(self, tensor):
if random.uniform(0, 1) > self.p:
return tensor
alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
tensor = tensor + quatity.view(3, 1, 1)
return tensor
[docs]class RandomPatch(object):
"""Random patch data augmentation.
There is a patch pool that stores randomly extracted pathces from person images.
For each input image, RandomPatch
1) extracts a random patch and stores the patch in the patch pool;
2) randomly selects a patch from the patch pool and pastes it on the
input (at random position) to simulate occlusion.
Reference:
- Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
- Zhou et al. Learning Generalisable Omni-Scale Representations
for Person Re-Identification. TPAMI, 2021.
"""
def __init__(
self,
prob_happen=0.5,
pool_capacity=50000,
min_sample_size=100,
patch_min_area=0.01,
patch_max_area=0.5,
patch_min_ratio=0.1,
prob_rotate=0.5,
prob_flip_leftright=0.5,
):
self.prob_happen = prob_happen
self.patch_min_area = patch_min_area
self.patch_max_area = patch_max_area
self.patch_min_ratio = patch_min_ratio
self.prob_rotate = prob_rotate
self.prob_flip_leftright = prob_flip_leftright
self.patchpool = deque(maxlen=pool_capacity)
self.min_sample_size = min_sample_size
def generate_wh(self, W, H):
area = W * H
for attempt in range(100):
target_area = random.uniform(
self.patch_min_area, self.patch_max_area
) * area
aspect_ratio = random.uniform(
self.patch_min_ratio, 1. / self.patch_min_ratio
)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < W and h < H:
return w, h
return None, None
def transform_patch(self, patch):
if random.uniform(0, 1) > self.prob_flip_leftright:
patch = patch.transpose(Image.FLIP_LEFT_RIGHT)
if random.uniform(0, 1) > self.prob_rotate:
patch = patch.rotate(random.randint(-10, 10))
return patch
def __call__(self, img):
W, H = img.size # original image size
# collect new patch
w, h = self.generate_wh(W, H)
if w is not None and h is not None:
x1 = random.randint(0, W - w)
y1 = random.randint(0, H - h)
new_patch = img.crop((x1, y1, x1 + w, y1 + h))
self.patchpool.append(new_patch)
if len(self.patchpool) < self.min_sample_size:
return img
if random.uniform(0, 1) > self.prob_happen:
return img
# paste a randomly selected patch on a random position
patch = random.sample(self.patchpool, 1)[0]
patchW, patchH = patch.size
x1 = random.randint(0, W - patchW)
y1 = random.randint(0, H - patchH)
patch = self.transform_patch(patch)
img.paste(patch, (x1, y1))
return img