| |
| """Balanced patch sampling with >= ``min_wire_ratio`` positives. |
| |
| Sampling is uniform over valid top-left positions for up to ``max_tries``. |
| If no patch meets ``min_wire_ratio``, it falls back to the best observed |
| candidate (highest wire ratio) instead of raising. |
| """ |
|
|
| from dataclasses import dataclass |
| import numpy as np |
|
|
|
|
| @dataclass |
| class BalancedPatchSampler: |
| patch_size: int = 768 |
| min_wire_ratio: float = 0.01 |
| max_tries: int = 200 |
|
|
| def sample(self, image: np.ndarray, mask: np.ndarray) -> tuple[int, int]: |
| h, w = mask.shape |
| p = self.patch_size |
| assert h >= p and w >= p, "Image smaller than patch size" |
| best_ratio = -1.0 |
| best_y = 0 |
| best_x = 0 |
| for _ in range(self.max_tries): |
| y = np.random.randint(0, h - p + 1) |
| x = np.random.randint(0, w - p + 1) |
| m = mask[y : y + p, x : x + p] |
| ratio = float(m.sum()) / float(p * p) |
| if ratio > best_ratio: |
| best_ratio = ratio |
| best_y, best_x = y, x |
| if ratio >= self.min_wire_ratio: |
| return int(y), int(x) |
| |
| return int(best_y), int(best_x) |
|
|
