deep-person-reid/torchreid/data/transforms.py

198 lines
7.0 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from PIL import Image
import random
import numpy as np
import math
import torch
from torchvision.transforms import *
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
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
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
def build_transforms(height, width, transforms='random_flip', norm_mean=[0.485, 0.456, 0.406],
norm_std=[0.229, 0.224, 0.225], **kwargs):
"""Builds train and test transform functions.
Args:
height (int): target image height.
width (int): target image width.
transforms (str or list of str, optional): transformations applied to model training.
Default is 'random_flip'.
norm_mean (list or None, optional): normalization mean values. Default is ImageNet means.
norm_std (list or None, optional): normalization standard deviation values. Default is
ImageNet standard deviation values.
"""
if transforms is None:
transforms = []
if isinstance(transforms, str):
transforms = [transforms]
if not isinstance(transforms, list):
raise ValueError('transforms must be a list of strings, but found to be {}'.format(type(transforms)))
if len(transforms) > 0:
transforms = [t.lower() for t in transforms]
if norm_mean is None or norm_std is None:
norm_mean = [0.485, 0.456, 0.406] # imagenet mean
norm_std = [0.229, 0.224, 0.225] # imagenet std
normalize = Normalize(mean=norm_mean, std=norm_std)
print('Building train transforms ...')
transform_tr = []
transform_tr += [Resize((height, width))]
print('+ resize to {}x{}'.format(height, width))
if 'random_flip' in transforms:
print('+ random flip')
transform_tr += [RandomHorizontalFlip()]
if 'random_crop' in transforms:
print('+ random crop (enlarge to {}x{} and ' \
'crop {}x{})'.format(int(round(height*1.125)), int(round(width*1.125)), height, width))
transform_tr += [Random2DTranslation(height, width)]
if 'color_jitter' in transforms:
print('+ color jitter')
transform_tr += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
print('+ to torch tensor of range [0, 1]')
transform_tr += [ToTensor()]
print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
transform_tr += [normalize]
if 'random_erase' in transforms:
print('+ random erase')
transform_tr += [RandomErasing()]
transform_tr = Compose(transform_tr)
print('Building test transforms ...')
print('+ resize to {}x{}'.format(height, width))
print('+ to torch tensor of range [0, 1]')
print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
transform_te = Compose([
Resize((height, width)),
ToTensor(),
normalize,
])
return transform_tr, transform_te