File size: 1,609 Bytes
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 | """Tiling helpers shared by training and inference."""
from __future__ import annotations
import numpy as np
from .config import STRIDE, TILE
def tile_origins(h: int, w: int, tile: int = TILE, stride: int = STRIDE) -> list[tuple[int, int]]:
"""Top-left corners of overlapping tiles covering the image."""
def axis(n: int) -> list[int]:
if n <= tile:
return [0]
pos = list(range(0, n - tile + 1, stride))
if pos[-1] != n - tile:
pos.append(n - tile)
return pos
return [(x, y) for y in axis(h) for x in axis(w)]
def pad_to(img: np.ndarray, tile: int = TILE) -> np.ndarray:
"""Pad the bottom/right so an image smaller than one tile still works."""
h, w = img.shape[:2]
ph, pw = max(0, tile - h), max(0, tile - w)
if ph == 0 and pw == 0:
return img
return np.pad(img, [(0, ph), (0, pw)] + [(0, 0)] * (img.ndim - 2), mode="edge")
def boxes_in_tile(boxes: np.ndarray, x0: int, y0: int, tile: int = TILE,
min_visible: float = 0.7) -> np.ndarray:
"""Boxes clipped into tile-local coordinates, keeping mostly-visible ones."""
if len(boxes) == 0:
return np.zeros((0, 4), np.float32)
b = boxes.astype(np.float32).copy()
area = np.maximum(1.0, (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1]))
b[:, [0, 2]] -= x0
b[:, [1, 3]] -= y0
c = b.copy()
c[:, [0, 2]] = np.clip(c[:, [0, 2]], 0, tile)
c[:, [1, 3]] = np.clip(c[:, [1, 3]], 0, tile)
vis = np.clip(c[:, 2] - c[:, 0], 0, None) * np.clip(c[:, 3] - c[:, 1], 0, None)
return c[vis / area >= min_visible]
|