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"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
__all__ = ['ToTensor', 'RandomPatch', 'AugMix', ]
import math
import random
from collections import deque
import numpy as np
import torch
from .functional import to_tensor, augmentations
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, 255.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8
In the other cases, tensors are returned without scaling.
"""
def __call__(self, pic):
"""
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
return to_tensor(pic)
def __repr__(self):
return self.__class__.__name__ + '()'
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. arXiv preprint, 2019.
"""
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_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_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 = torch.flip(patch, dims=[2])
return patch
def __call__(self, img):
_, H, W = 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[..., y1:y1 + h, x1:x1 + w]
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]
_, patchH, patchW = patch.size()
x1 = random.randint(0, W - patchW)
y1 = random.randint(0, H - patchH)
patch = self.transform_patch(patch)
img[..., y1:y1 + patchH, x1:x1 + patchW] = patch
return img
class AugMix(object):
""" Perform AugMix augmentation and compute mixture.
"""
def __init__(self, prob=0.5, aug_prob_coeff=0.1, mixture_width=3, mixture_depth=1, aug_severity=1):
"""
Args:
prob: Probability of taking augmix
aug_prob_coeff: Probability distribution coefficients.
mixture_width: Number of augmentation chains to mix per augmented example.
mixture_depth: Depth of augmentation chains. -1 denotes stochastic depth in [1, 3]'
aug_severity: Severity of underlying augmentation operators (between 1 to 10).
"""
# fmt: off
self.prob = prob
self.aug_prob_coeff = aug_prob_coeff
self.mixture_width = mixture_width
self.mixture_depth = mixture_depth
self.aug_severity = aug_severity
self.augmentations = augmentations
# fmt: on
def __call__(self, image):
"""Perform AugMix augmentations and compute mixture.
Returns:
mixed: Augmented and mixed image.
"""
if random.random() > self.prob:
# Avoid the warning: the given NumPy array is not writeable
return np.asarray(image).copy()
ws = np.float32(
np.random.dirichlet([self.aug_prob_coeff] * self.mixture_width))
m = np.float32(np.random.beta(self.aug_prob_coeff, self.aug_prob_coeff))
mix = np.zeros([image.size[1], image.size[0], 3])
for i in range(self.mixture_width):
image_aug = image.copy()
depth = self.mixture_depth if self.mixture_depth > 0 else np.random.randint(1, 4)
for _ in range(depth):
op = np.random.choice(self.augmentations)
image_aug = op(image_aug, self.aug_severity)
mix += ws[i] * np.asarray(image_aug)
mixed = (1 - m) * image + m * mix
return mixed.astype(np.uint8)
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