File size: 4,282 Bytes
dadf189 | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | from __future__ import annotations
"""Fingerprint-specific image augmentations for V2 pipeline.
Augmentations from the MDGT v2 plan:
1. Random rotation ±15° (finger placement variation)
2. Random translation ±10% (off-center capture)
3. Elastic deformation σ=8, α=60 (skin distortion under pressure)
4. Random brightness/contrast ±0.2 (sensor / moisture variation)
5. Random crop + resize 0.8-1.0 (partial fingerprint)
6. Gaussian noise σ=0.01-0.03 (sensor noise)
7. CutOut 1-3 patches (occlusion / smudge)
8. NO horizontal flip (fingerprints are chirally distinct)
"""
import random
import torch
import torch.nn as nn
from torchvision import transforms as T
class GaussianNoise(nn.Module):
"""Add Gaussian noise to a tensor image."""
def __init__(self, std_min: float = 0.01, std_max: float = 0.03, p: float = 0.5):
super().__init__()
self.std_min = std_min
self.std_max = std_max
self.p = p
def forward(self, x: torch.Tensor) -> torch.Tensor:
if random.random() > self.p:
return x
std = random.uniform(self.std_min, self.std_max)
return (x + torch.randn_like(x) * std).clamp(0, 1)
class MultiCutOut(nn.Module):
"""Erase 1-N random rectangular patches (CutOut / occlusion simulation)."""
def __init__(
self,
max_patches: int = 3,
min_size: int = 16,
max_size: int = 32,
p: float = 0.5,
):
super().__init__()
self.max_patches = max_patches
self.min_size = min_size
self.max_size = max_size
self.p = p
def forward(self, x: torch.Tensor) -> torch.Tensor:
if random.random() > self.p:
return x
_, H, W = x.shape
n_patches = random.randint(1, self.max_patches)
for _ in range(n_patches):
ph = random.randint(self.min_size, self.max_size)
pw = random.randint(self.min_size, self.max_size)
y = random.randint(0, max(0, H - ph))
xc = random.randint(0, max(0, W - pw))
x[:, y:y + ph, xc:xc + pw] = 0.0
return x
def build_train_transform(image_size: int = 224, profile: str = "standard") -> T.Compose:
"""Build the training augmentation pipeline.
Returns a ``torchvision.transforms.Compose`` that takes a PIL image
and returns a ``(1, H, W)`` tensor normalised to [0, 1].
"""
if profile not in {"standard", "light"}:
raise ValueError(f"Unsupported augmentation profile: {profile}")
if profile == "light":
geometric = [
T.Resize((image_size, image_size)),
T.RandomRotation(degrees=7, fill=255),
T.RandomAffine(degrees=0, translate=(0.04, 0.04), fill=255),
T.RandomResizedCrop(
image_size,
scale=(0.92, 1.0),
ratio=(0.98, 1.02),
),
]
pixel = [
T.ColorJitter(brightness=0.1, contrast=0.1),
]
tensor_aug = [
GaussianNoise(std_min=0.003, std_max=0.012, p=0.25),
MultiCutOut(max_patches=1, min_size=12, max_size=20, p=0.15),
]
else:
geometric = [
T.Resize((image_size, image_size)),
T.RandomRotation(degrees=15, fill=255),
T.RandomAffine(degrees=0, translate=(0.1, 0.1), fill=255),
]
try:
geometric.append(
T.ElasticTransform(alpha=60.0, sigma=8.0, fill=255)
)
except AttributeError:
pass
geometric.append(
T.RandomResizedCrop(image_size, scale=(0.8, 1.0), ratio=(0.95, 1.05)),
)
pixel = [
T.ColorJitter(brightness=0.2, contrast=0.2),
]
tensor_aug = [
GaussianNoise(std_min=0.01, std_max=0.03, p=0.5),
MultiCutOut(max_patches=3, min_size=16, max_size=32, p=0.5),
]
to_tensor = [
T.ToTensor(),
]
return T.Compose(geometric + pixel + to_tensor + tensor_aug)
def build_val_transform(image_size: int = 224) -> T.Compose:
"""Validation transform: resize + to tensor (no augmentation)."""
return T.Compose([
T.Resize((image_size, image_size)),
T.ToTensor(),
])
|