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d667566 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """Torch datasets and CenterNet-style target encoding."""
from __future__ import annotations
from collections import defaultdict
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from .config import OUT_STRIDE, PREP, SYNTH, TILE, TRAIN_CSV
from .preprocess import load_prepared, stream_tensors
from .tiles import boxes_in_tile, pad_to
def load_gt(csv_path: Path | None = None) -> dict[int, np.ndarray]:
"""Ground-truth boxes keyed by train pair index."""
df = pd.read_csv(csv_path or TRAIN_CSV)
out: dict[int, list] = defaultdict(list)
for r in df.itertuples(index=False):
idx = int(str(r.template_image).split("_")[-1].split(".")[0])
out[idx].append([r.left_x, r.top_y, r.right_x, r.bottom_y])
return {k: np.asarray(v, np.float32) for k, v in out.items()}
def gaussian_radius(h: float, w: float, min_overlap: float = 0.5) -> float:
"""CenterNet radius: keeps a shifted box above min_overlap IoU."""
a1, b1, c1 = 1, (h + w), w * h * (1 - min_overlap) / (1 + min_overlap)
r1 = (b1 - np.sqrt(max(b1 ** 2 - 4 * a1 * c1, 0))) / (2 * a1)
a2, b2, c2 = 4, 2 * (h + w), (1 - min_overlap) * w * h
r2 = (b2 - np.sqrt(max(b2 ** 2 - 4 * a2 * c2, 0))) / (2 * a2)
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (h + w)
c3 = (min_overlap - 1) * w * h
r3 = (-b3 + np.sqrt(max(b3 ** 2 - 4 * a3 * c3, 0))) / (2 * a3)
return max(1.0, float(min(r1, r2, r3)))
def draw_gaussian(heatmap: np.ndarray, cx: int, cy: int, radius: int) -> None:
diameter = 2 * radius + 1
sigma = diameter / 6.0
ax = np.arange(-radius, radius + 1, dtype=np.float32)
g = np.exp(-(ax[None, :] ** 2 + ax[:, None] ** 2) / (2 * sigma ** 2))
h, w = heatmap.shape
left, right = min(cx, radius), min(w - cx, radius + 1)
top, bottom = min(cy, radius), min(h - cy, radius + 1)
if right <= -left or bottom <= -top:
return
region = heatmap[cy - top:cy + bottom, cx - left:cx + right]
masked = g[radius - top:radius + bottom, radius - left:radius + right]
np.maximum(region, masked, out=region)
def encode_targets(boxes: np.ndarray, tile: int = TILE, stride: int = OUT_STRIDE):
"""Boxes in tile coordinates -> heatmap, wh, offset, regression mask, seg mask."""
size = tile // stride
hm = np.zeros((1, size, size), np.float32)
wh = np.zeros((2, size, size), np.float32)
off = np.zeros((2, size, size), np.float32)
reg = np.zeros((1, size, size), np.float32)
seg = np.zeros((1, size, size), np.float32)
for x1, y1, x2, y2 in boxes:
bw, bh = x2 - x1, y2 - y1
if bw <= 1 or bh <= 1:
continue
cx, cy = (x1 + x2) / 2 / stride, (y1 + y2) / 2 / stride
ix, iy = int(cx), int(cy)
if not (0 <= ix < size and 0 <= iy < size):
continue
draw_gaussian(hm[0], ix, iy, int(gaussian_radius(bh / stride, bw / stride)))
wh[:, iy, ix] = [bw, bh]
off[:, iy, ix] = [cx - ix, cy - iy]
reg[0, iy, ix] = 1.0
sx1, sy1 = int(np.floor(x1 / stride)), int(np.floor(y1 / stride))
sx2, sy2 = int(np.ceil(x2 / stride)), int(np.ceil(y2 / stride))
seg[0, max(0, sy1):min(size, sy2), max(0, sx1):min(size, sx2)] = 1.0
return hm, wh, off, reg, seg
def photometric_jitter(photo: np.ndarray, rng: np.random.RandomState) -> np.ndarray:
"""Extra print/scan-like noise on the photo stream only."""
out = photo.astype(np.float32)
out = out * rng.uniform(0.92, 1.08) + rng.uniform(-12, 12)
if rng.rand() < 0.5:
out = cv2.GaussianBlur(out, (0, 0), rng.uniform(0.3, 1.0))
if rng.rand() < 0.7:
out += rng.normal(0, rng.uniform(1.0, 5.0), out.shape).astype(np.float32)
return np.clip(out, 0, 255).astype(np.uint8)
class GroupedSampler(torch.utils.data.Sampler):
"""Shuffle pairs, keep a pair's samples adjacent.
Every sample decodes four full-page PNGs, so random access would decode the
same pair `samples_per_pair` times per epoch. Grouping lets the one-entry
cache in the dataset serve the rest of the group.
"""
def __init__(self, n_pairs: int, samples_per_pair: int, seed: int = 0):
self.n_pairs = n_pairs
self.samples_per_pair = samples_per_pair
self.seed = seed
self.epoch = 0
def set_epoch(self, epoch: int) -> None:
self.epoch = epoch
def __len__(self) -> int:
return self.n_pairs * self.samples_per_pair
def __iter__(self):
rng = np.random.RandomState(self.seed + self.epoch)
for pair in rng.permutation(self.n_pairs):
base = int(pair) * self.samples_per_pair
for k in rng.permutation(self.samples_per_pair):
yield base + int(k)
class PairTileDataset(Dataset):
"""Random tiles from prepared pairs, biased towards annotated differences."""
def __init__(self, items: list[tuple[str, int]], gt: dict, samples_per_pair: int = 8,
tile: int = TILE, positive_ratio: float = 0.6, augment: bool = True,
prep_root: Path | None = None):
self.items = items
self.gt = gt
self.samples_per_pair = samples_per_pair
self.tile = tile
self.positive_ratio = positive_ratio
self.augment = augment
self.prep_root = prep_root or PREP
self._cache_key = None
self._cache_val = None
def __len__(self) -> int:
return len(self.items) * self.samples_per_pair
def _key(self, split: str, idx: int):
return (split, idx)
def _load(self, split: str, idx: int):
key = (split, idx)
if key != self._cache_key:
root = self.prep_root if split != "synth" else SYNTH
self._cache_val = load_prepared(split, idx, root)
self._cache_key = key
return self._cache_val
def __getitem__(self, i: int):
split, idx = self.items[i // self.samples_per_pair]
rng = np.random.RandomState((i * 9973 + idx * 7919) % (2 ** 31))
t, p, tn, pn = self._load(split, idx)
boxes = self.gt.get(self._key(split, idx), np.zeros((0, 4), np.float32))
t, p = pad_to(t, self.tile), pad_to(p, self.tile)
tn, pn = pad_to(tn, self.tile), pad_to(pn, self.tile)
h, w = t.shape[:2]
if len(boxes) and rng.rand() < self.positive_ratio:
bx = boxes[rng.randint(len(boxes))]
cx, cy = (bx[0] + bx[2]) / 2, (bx[1] + bx[3]) / 2
x0 = int(np.clip(cx - self.tile / 2 + rng.randint(-200, 201), 0, max(0, w - self.tile)))
y0 = int(np.clip(cy - self.tile / 2 + rng.randint(-200, 201), 0, max(0, h - self.tile)))
else:
x0 = rng.randint(0, max(1, w - self.tile + 1))
y0 = rng.randint(0, max(1, h - self.tile + 1))
sl = (slice(y0, y0 + self.tile), slice(x0, x0 + self.tile))
tc, pc, tnc, pnc = t[sl], p[sl], tn[sl], pn[sl]
if self.augment:
pc = photometric_jitter(pc, rng)
a, b = stream_tensors(tc, pc, tnc, pnc)
local = boxes_in_tile(boxes, x0, y0, self.tile)
hm, wh, off, reg, seg = encode_targets(local, self.tile)
return {
"a": torch.from_numpy(a),
"b": torch.from_numpy(b),
"hm": torch.from_numpy(hm),
"wh": torch.from_numpy(wh),
"off": torch.from_numpy(off),
"reg": torch.from_numpy(reg),
"seg": torch.from_numpy(seg),
}
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