| """Shared geometric raster transforms used by both placement and augmentation.""" |
|
|
| from __future__ import annotations |
|
|
| import cv2 |
| import numpy as np |
|
|
|
|
| def rotate_and_scale_rgba(rgba: np.ndarray, angle_deg: float, scale: float = 1.0) -> np.ndarray: |
| """Rotate (and optionally scale) an RGBA image about its own center. |
| |
| The output canvas automatically expands to tightly bound the rotated |
| rectangle so no content is clipped; newly exposed corners are fully |
| transparent. |
| """ |
| h, w = rgba.shape[:2] |
| if h == 0 or w == 0: |
| return rgba |
| cx, cy = w / 2.0, h / 2.0 |
| matrix = cv2.getRotationMatrix2D((cx, cy), angle_deg, scale) |
| cos, sin = abs(matrix[0, 0]), abs(matrix[0, 1]) |
| new_w = max(int(np.ceil(h * sin + w * cos)), 1) |
| new_h = max(int(np.ceil(h * cos + w * sin)), 1) |
| matrix[0, 2] += new_w / 2.0 - cx |
| matrix[1, 2] += new_h / 2.0 - cy |
| return cv2.warpAffine( |
| rgba, matrix, (new_w, new_h), |
| flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0), |
| ) |
|
|
|
|
| def smooth_noise_field(shape: tuple[int, int], rng: np.random.Generator, blur_frac: float = 0.08) -> np.ndarray: |
| """Generate a smooth, low-frequency noise field in ``[0, 1]`` of the given ``(h, w)`` shape. |
| |
| Built by upsampling coarse random noise, which is a fast and dependency-free |
| stand-in for Perlin/simplex noise -- adequate for subtle shading effects |
| like paper texture, discoloration, and ink fading. |
| """ |
| h, w = shape |
| coarse_h = max(int(h * blur_frac), 4) |
| coarse_w = max(int(w * blur_frac), 4) |
| coarse = rng.uniform(0.0, 1.0, size=(coarse_h, coarse_w)).astype(np.float32) |
| field = cv2.resize(coarse, (w, h), interpolation=cv2.INTER_CUBIC) |
| field = cv2.GaussianBlur(field, (0, 0), sigmaX=max(w, h) * 0.01 + 1) |
| field -= field.min() |
| max_val = field.max() |
| if max_val > 1e-6: |
| field /= max_val |
| return field |
|
|
