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import torch
import torchvision.transforms.functional as F
import random
import math
import numpy as np
from PIL import Image, ImageFilter
__all__ = ['Compose', 'Resize', 'Rescale', 'CenterCrop', 'CenterCropV2', 'CenterCropWide', 'RandomCrop', 'RandomCropV2', 'RandomHFlip',\
'GaussianBlur', 'ColorJitter', 'RandomGray', 'ToTensor', 'Normalize', "ResizeRandomCrop", "ExtractResizeRandomCrop", "ExtractResizeAssignCrop"]
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __getitem__(self, index):
if isinstance(index, slice):
return Compose(self.transforms[index])
else:
return self.transforms[index]
def __len__(self):
return len(self.transforms)
def __call__(self, rgb):
for t in self.transforms:
rgb = t(rgb)
return rgb
class Resize(object):
def __init__(self, size=256):
if isinstance(size, int):
size = (size, size)
self.size = size
def __call__(self, rgb):
if isinstance(rgb, list):
rgb = [u.resize(self.size, Image.BILINEAR) for u in rgb]
else:
rgb = rgb.resize(self.size, Image.BILINEAR)
return rgb
class Rescale(object):
def __init__(self, size=256, interpolation=Image.BILINEAR):
self.size = size
self.interpolation = interpolation
def __call__(self, rgb):
w, h = rgb[0].size
scale = self.size / min(w, h)
out_w, out_h = int(round(w * scale)), int(round(h * scale))
rgb = [u.resize((out_w, out_h), self.interpolation) for u in rgb]
return rgb
class CenterCrop(object):
def __init__(self, size=224):
self.size = size
def __call__(self, rgb):
w, h = rgb[0].size
assert min(w, h) >= self.size
x1 = (w - self.size) // 2
y1 = (h - self.size) // 2
rgb = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in rgb]
return rgb
class ResizeRandomCrop(object):
def __init__(self, size=256, size_short=292):
self.size = size
# self.min_area = min_area
self.size_short = size_short
def __call__(self, rgb):
# consistent crop between rgb and m
while min(rgb[0].size) >= 2 * self.size_short:
rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb]
scale = self.size_short / min(rgb[0].size)
rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb]
out_w = self.size
out_h = self.size
w, h = rgb[0].size # (518, 292)
x1 = random.randint(0, w - out_w)
y1 = random.randint(0, h - out_h)
rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb]
# rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb]
# # center crop
# x1 = (img[0].width - self.size) // 2
# y1 = (img[0].height - self.size) // 2
# img = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in img]
return rgb
class ExtractResizeRandomCrop(object):
def __init__(self, size=256, size_short=292):
self.size = size
# self.min_area = min_area
self.size_short = size_short
def __call__(self, rgb):
# consistent crop between rgb and m
while min(rgb[0].size) >= 2 * self.size_short:
rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb]
scale = self.size_short / min(rgb[0].size)
rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb]
out_w = self.size
out_h = self.size
w, h = rgb[0].size # (518, 292)
x1 = random.randint(0, w - out_w)
y1 = random.randint(0, h - out_h)
rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb]
wh = [x1, y1, x1 + out_w, y1 + out_h]
return rgb, wh
class ExtractResizeAssignCrop(object):
def __init__(self, size=256, size_short=292):
self.size = size
# self.min_area = min_area
self.size_short = size_short
def __call__(self, rgb, wh):
# consistent crop between rgb and m
while min(rgb[0].size) >= 2 * self.size_short:
rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb]
scale = self.size_short / min(rgb[0].size)
rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb]
rgb = [u.crop(wh) for u in rgb]
rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb]
return rgb
class CenterCropV2(object):
def __init__(self, size):
self.size = size
def __call__(self, img):
# fast resize
while min(img[0].size) >= 2 * self.size:
img = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in img]
scale = self.size / min(img[0].size)
img = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in img]
# center crop
x1 = (img[0].width - self.size) // 2
y1 = (img[0].height - self.size) // 2
img = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in img]
return img
class CenterCropWide(object):
def __init__(self, size, interpolation=Image.BOX):
self.size = size
self.interpolation = interpolation
def __call__(self, img):
if isinstance(img, list):
scale = min(img[0].size[0]/self.size[0], img[0].size[1]/self.size[1])
img = [u.resize((round(u.width // scale), round(u.height // scale)), resample=self.interpolation) for u in img]
# center crop
x1 = (img[0].width - self.size[0]) // 2
y1 = (img[0].height - self.size[1]) // 2
img = [u.crop((x1, y1, x1 + self.size[0], y1 + self.size[1])) for u in img]
return img
else:
scale = min(img.size[0]/self.size[0], img.size[1]/self.size[1])
img = img.resize((round(img.width // scale), round(img.height // scale)), resample=self.interpolation)
x1 = (img.width - self.size[0]) // 2
y1 = (img.height - self.size[1]) // 2
img = img.crop((x1, y1, x1 + self.size[0], y1 + self.size[1]))
return img
class RandomCrop(object):
def __init__(self, size=224, min_area=0.4):
self.size = size
self.min_area = min_area
def __call__(self, rgb):
# consistent crop between rgb and m
w, h = rgb[0].size
area = w * h
out_w, out_h = float('inf'), float('inf')
while out_w > w or out_h > h:
target_area = random.uniform(self.min_area, 1.0) * area
aspect_ratio = random.uniform(3. / 4., 4. / 3.)
out_w = int(round(math.sqrt(target_area * aspect_ratio)))
out_h = int(round(math.sqrt(target_area / aspect_ratio)))
x1 = random.randint(0, w - out_w)
y1 = random.randint(0, h - out_h)
rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb]
rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb]
return rgb
class RandomCropV2(object):
def __init__(self, size=224, min_area=0.4, ratio=(3. / 4., 4. / 3.)):
if isinstance(size, (tuple, list)):
self.size = size
else:
self.size = (size, size)
self.min_area = min_area
self.ratio = ratio
def _get_params(self, img):
width, height = img.size
area = height * width
for _ in range(10):
target_area = random.uniform(self.min_area, 1.0) * area
log_ratio = (math.log(self.ratio[0]), math.log(self.ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if 0 < w <= width and 0 < h <= height:
i = random.randint(0, height - h)
j = random.randint(0, width - w)
return i, j, h, w
# Fallback to central crop
in_ratio = float(width) / float(height)
if (in_ratio < min(self.ratio)):
w = width
h = int(round(w / min(self.ratio)))
elif (in_ratio > max(self.ratio)):
h = height
w = int(round(h * max(self.ratio)))
else: # whole image
w = width
h = height
i = (height - h) // 2
j = (width - w) // 2
return i, j, h, w
def __call__(self, rgb):
i, j, h, w = self._get_params(rgb[0])
rgb = [F.resized_crop(u, i, j, h, w, self.size) for u in rgb]
return rgb
class RandomHFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, rgb):
if random.random() < self.p:
rgb = [u.transpose(Image.FLIP_LEFT_RIGHT) for u in rgb]
return rgb
class GaussianBlur(object):
def __init__(self, sigmas=[0.1, 2.0], p=0.5):
self.sigmas = sigmas
self.p = p
def __call__(self, rgb):
if random.random() < self.p:
sigma = random.uniform(*self.sigmas)
rgb = [u.filter(ImageFilter.GaussianBlur(radius=sigma)) for u in rgb]
return rgb
class ColorJitter(object):
def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1, p=0.5):
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
self.hue = hue
self.p = p
def __call__(self, rgb):
if random.random() < self.p:
brightness, contrast, saturation, hue = self._random_params()
transforms = [
lambda f: F.adjust_brightness(f, brightness),
lambda f: F.adjust_contrast(f, contrast),
lambda f: F.adjust_saturation(f, saturation),
lambda f: F.adjust_hue(f, hue)]
random.shuffle(transforms)
for t in transforms:
rgb = [t(u) for u in rgb]
return rgb
def _random_params(self):
brightness = random.uniform(
max(0, 1 - self.brightness), 1 + self.brightness)
contrast = random.uniform(
max(0, 1 - self.contrast), 1 + self.contrast)
saturation = random.uniform(
max(0, 1 - self.saturation), 1 + self.saturation)
hue = random.uniform(-self.hue, self.hue)
return brightness, contrast, saturation, hue
class RandomGray(object):
def __init__(self, p=0.2):
self.p = p
def __call__(self, rgb):
if random.random() < self.p:
rgb = [u.convert('L').convert('RGB') for u in rgb]
return rgb
class ToTensor(object):
def __call__(self, rgb):
if isinstance(rgb, list):
rgb = torch.stack([F.to_tensor(u) for u in rgb], dim=0)
else:
rgb = F.to_tensor(rgb)
return rgb
class Normalize(object):
def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
self.mean = mean
self.std = std
def __call__(self, rgb):
rgb = rgb.clone()
rgb.clamp_(0, 1)
if not isinstance(self.mean, torch.Tensor):
self.mean = rgb.new_tensor(self.mean).view(-1)
if not isinstance(self.std, torch.Tensor):
self.std = rgb.new_tensor(self.std).view(-1)
if rgb.dim() == 4:
rgb.sub_(self.mean.view(1, -1, 1, 1)).div_(self.std.view(1, -1, 1, 1))
elif rgb.dim() == 3:
rgb.sub_(self.mean.view(-1, 1, 1)).div_(self.std.view(-1, 1, 1))
return rgb
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