File size: 3,790 Bytes
33c93f7 | 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 | from __future__ import annotations
import io
import random
import torch
from PIL import Image, ImageEnhance, ImageFilter, ImageOps
from torchvision import transforms as T
from torchvision.transforms import functional as TF
MEAN = (0.485, 0.456, 0.406)
STD = (0.229, 0.224, 0.225)
def _tensor(image: Image.Image) -> torch.Tensor:
return TF.normalize(TF.to_tensor(image.resize((64, 64), Image.Resampling.BICUBIC)), MEAN, STD)
class TrainTransforms:
def __init__(self):
self.clean_transform = T.Compose([T.Resize((64, 64)), T.ToTensor()])
self.contrastive_transform = T.Compose([
T.RandomResizedCrop(64, scale=(0.45, 1.0)),
T.RandomHorizontalFlip(),
T.RandomApply([T.ColorJitter(0.45, 0.45, 0.25, 0.05)], p=0.8),
T.RandomGrayscale(p=0.1),
T.RandomApply([T.GaussianBlur(5, sigma=(0.2, 2.0))], p=0.4),
T.ToTensor(), T.Normalize(MEAN, STD),
])
self.degraded_transform = T.Compose([
T.RandomResizedCrop(64, scale=(0.45, 1.0)),
T.RandomPerspective(0.15, p=0.4),
T.ColorJitter(0.45, 0.45, 0.25, 0.05),
T.RandomApply([T.GaussianBlur(7, sigma=(0.5, 2.5))], p=0.5),
T.ToTensor(), T.Normalize(MEAN, STD),
])
def clean(self, image):
return self.clean_transform(image)
def contrastive(self, image):
return self.contrastive_transform(image)
def degraded(self, image):
return self.degraded_transform(image)
def rotation(self, image):
label = random.randrange(4)
return _tensor(image.rotate(90 * label)), label
def inference_transform(image: Image.Image) -> torch.Tensor:
return _tensor(image)
def deterministic_degrade(image: Image.Image, seed: int) -> Image.Image:
rng = random.Random(seed)
image = image.convert("RGB")
width, height = image.size
scale = rng.uniform(0.45, 0.9)
crop_w, crop_h = max(8, int(width * scale)), max(8, int(height * scale))
left, top = rng.randrange(width - crop_w + 1), rng.randrange(height - crop_h + 1)
image = image.crop((left, top, left + crop_w, top + crop_h)).resize((64, 64), Image.Resampling.BICUBIC)
image = image.rotate(rng.uniform(-30, 30), resample=Image.Resampling.BICUBIC)
margin = rng.randint(1, 7)
image = TF.perspective(
image,
[[0, 0], [63, 0], [63, 63], [0, 63]],
[[rng.randint(0, margin), rng.randint(0, margin)],
[63 - rng.randint(0, margin), rng.randint(0, margin)],
[63 - rng.randint(0, margin), 63 - rng.randint(0, margin)],
[rng.randint(0, margin), 63 - rng.randint(0, margin)]],
interpolation=T.InterpolationMode.BILINEAR,
)
image = ImageEnhance.Brightness(image).enhance(rng.uniform(0.55, 1.45))
image = ImageEnhance.Contrast(image).enhance(rng.uniform(0.5, 1.5))
if rng.random() < 0.6:
image = image.filter(ImageFilter.GaussianBlur(rng.uniform(0.4, 2.2)))
if rng.random() < 0.55:
generator = torch.Generator().manual_seed(seed + 17)
pixels = TF.to_tensor(image)
pixels = (pixels + torch.randn(pixels.shape, generator=generator) * rng.uniform(0.02, 0.12)).clamp(0, 1)
image = TF.to_pil_image(pixels)
if rng.random() < 0.5:
small = rng.randint(18, 48)
image = image.resize((small, small), Image.Resampling.BILINEAR).resize((64, 64), Image.Resampling.BILINEAR)
if rng.random() < 0.45:
x, y, size = rng.randrange(52), rng.randrange(52), rng.randrange(5, 13)
image.paste(tuple(rng.randrange(256) for _ in range(3)), (x, y, x + size, y + size))
buffer = io.BytesIO()
image.save(buffer, "JPEG", quality=rng.randint(20, 75))
buffer.seek(0)
return Image.open(buffer).convert("RGB").copy()
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