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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | """Joint augmentation for fingerprint images and minutiae.
Applies the same geometric transform to both the image and minutiae
coordinates simultaneously, ensuring spatial consistency.
Augmentations:
- Random rotation: rotates image + rotation matrix on (x,y) + adds angle to θ
- Random translation: shifts image + adds offset to (x,y)
- Minutia dropout: randomly drops points (keeps ≥ min_keep)
- Coordinate jitter: Gaussian noise on (x,y) — minutiae only
"""
import math
import random
import torch
import torch.nn.functional as F
from ..configs.default import AugmentConfig
class JointAugmentor:
"""Applies the same geometric transform to image and minutiae simultaneously.
Parameters
----------
cfg : AugmentConfig
Augmentation configuration.
"""
def __init__(self, cfg: AugmentConfig):
self.cfg = cfg
def __call__(
self,
image: torch.Tensor,
minutiae: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Args:
image: ``(1, H, W)`` normalized [0, 1] fingerprint image.
minutiae: ``(N, 3)`` raw ``[x, y, θ]`` in pixel coordinates.
Returns:
image: ``(1, H, W)`` augmented image.
minutiae: ``(N', 3)`` augmented minutiae (N' ≤ N after dropout).
"""
_, H, W = image.shape
m = minutiae.clone()
# Center of image for rotation
cx, cy = (W - 1) / 2.0, (H - 1) / 2.0
# 1. Random rotation
if self.cfg.rotate:
angle_deg = random.uniform(-self.cfg.rotate_range, self.cfg.rotate_range)
angle_rad = math.radians(angle_deg)
cos_a, sin_a = math.cos(angle_rad), math.sin(angle_rad)
# Rotate image using affine grid
# Rotation matrix (clockwise in pixel coords)
theta = torch.tensor(
[
[cos_a, -sin_a, 0.0],
[sin_a, cos_a, 0.0],
],
dtype=image.dtype,
).unsqueeze(0) # (1, 2, 3)
grid = F.affine_grid(theta, [1, 1, H, W], align_corners=True)
image = F.grid_sample(
image.unsqueeze(0),
grid,
mode="bilinear",
padding_mode="zeros",
align_corners=True,
).squeeze(0)
# Rotate minutiae coordinates around image center
x_centered = m[:, 0] - cx
y_centered = m[:, 1] - cy
m[:, 0] = cos_a * x_centered - sin_a * y_centered + cx
m[:, 1] = sin_a * x_centered + cos_a * y_centered + cy
# Rotate orientation
m[:, 2] = m[:, 2] + angle_rad
m[:, 2] = torch.atan2(torch.sin(m[:, 2]), torch.cos(m[:, 2]))
# 2. Random translation
if self.cfg.translate and self.cfg.translate_range > 0:
tx = random.uniform(-self.cfg.translate_range, self.cfg.translate_range)
ty = random.uniform(-self.cfg.translate_range, self.cfg.translate_range)
# Translate image using affine grid
theta = torch.tensor(
[
[1.0, 0.0, -2.0 * tx / (W - 1)],
[0.0, 1.0, -2.0 * ty / (H - 1)],
],
dtype=image.dtype,
).unsqueeze(0)
grid = F.affine_grid(theta, [1, 1, H, W], align_corners=True)
image = F.grid_sample(
image.unsqueeze(0),
grid,
mode="bilinear",
padding_mode="zeros",
align_corners=True,
).squeeze(0)
# Translate minutiae
m[:, 0] += tx
m[:, 1] += ty
# 3. Minutia dropout
if self.cfg.minutia_dropout > 0 and m.shape[0] > self.cfg.min_keep:
keep = torch.rand(m.shape[0]) > self.cfg.minutia_dropout
if keep.sum() < self.cfg.min_keep:
keep[: self.cfg.min_keep] = True
m = m[keep]
# 4. Coordinate jitter (minutiae only, image unchanged)
if self.cfg.jitter_std > 0:
noise = torch.randn(m.shape[0], 2) * self.cfg.jitter_std
m[:, :2] += noise
# 5. Spurious minutiae insertion
spurious_rate = getattr(self.cfg, "spurious_rate", 0.0)
if spurious_rate > 0 and m.shape[0] > 0:
n_spurious = max(1, int(m.shape[0] * spurious_rate))
xy_min = m[:, :2].min(dim=0).values
xy_max = m[:, :2].max(dim=0).values
xy_range = (xy_max - xy_min).clamp(min=1.0)
fake_xy = xy_min + torch.rand(n_spurious, 2) * xy_range
fake_theta = torch.rand(n_spurious, 1) * 2 * math.pi - math.pi
fake = torch.cat([fake_xy, fake_theta], dim=-1)
m = torch.cat([m, fake], dim=0)
return image, m
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