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| from typing import Tuple, Optional, List | |
| from rstor.properties import SAMPLER_SATURATED, SAMPLER_NATURAL, SAMPLER_UNIFORM | |
| from rstor.synthetic_data.color_sampler import sample_uniform_rgb, sample_saturated_color, sample_color_from_images | |
| import numpy as np | |
| from pathlib import Path | |
| def define_dead_leaves_chart( | |
| size: Tuple[int, int] = (100, 100), | |
| number_of_circles: int = -1, | |
| colored: Optional[bool] = True, | |
| radius_min: Optional[int] = -1, | |
| radius_max: Optional[int] = -1, | |
| radius_alpha: Optional[int] = 3, | |
| seed: int = None, | |
| sampler=SAMPLER_UNIFORM, | |
| natural_image_list: Optional[List[Path]] = None | |
| ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: | |
| """ | |
| Defines the geometric and color properties of the primitives in the dead leaves chart to later be sampled. | |
| Args: | |
| size (Tuple[int, int], optional): size of the generated chart. Defaults to (100, 100). | |
| number_of_circles (int, optional): number of circles to generate. | |
| If negative, it is computed based on the size. Defaults to -1. | |
| colored (Optional[bool], optional): Whether to generate colored circles. Defaults to True. | |
| radius_min (Optional[int], optional): minimum radius of the circles. Defaults to -1. (=> 1) | |
| radius_max (Optional[int], optional): maximum radius of the circles. Defaults to -1. (=> 2000) | |
| radius_alpha (Optional[int], optional): standard deviation of the radius of the circles. | |
| If negative, it is calculated based on the size. Defaults to -1. | |
| seed (int, optional): seed for the random number generator. Defaults to None | |
| Returns: | |
| Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: center_x, center_y, radius, color | |
| """ | |
| rng = np.random.default_rng(np.random.SeedSequence(seed)) | |
| if number_of_circles < 0: | |
| number_of_circles = 30 * max(size) | |
| if radius_min < 0.: | |
| radius_min = 1. | |
| if radius_max < 0.: | |
| radius_max = 2000. | |
| # Pick random circle centers and radii | |
| center_x = rng.integers(0, size[1], size=number_of_circles) | |
| center_y = rng.integers(0, size[0], size=number_of_circles) | |
| # Sample from a power law distribution for the p(radius=r) = (r.clip(radius_min, radius_max))^(-alpha) | |
| radius = rng.uniform( | |
| low=radius_max ** (1 - radius_alpha), | |
| high=radius_min ** (1 - radius_alpha), | |
| size=number_of_circles | |
| ) | |
| # Using the change of variables formula for random variables. | |
| radius = radius ** (-1/(radius_alpha - 1)) | |
| radius = radius.round().astype(int) | |
| # Pick random colors | |
| if colored: | |
| if sampler == SAMPLER_UNIFORM: | |
| color = sample_uniform_rgb(number_of_circles, seed=rng.integers(0, 1e10)).astype(float) | |
| elif sampler == SAMPLER_SATURATED: | |
| color = sample_saturated_color(number_of_circles, seed=rng.integers(0, 1e10)).astype(float) | |
| elif sampler == SAMPLER_NATURAL: | |
| assert natural_image_list is not None, "Please provide a list of images to sample colors from." | |
| color = sample_color_from_images(number_of_circles, seed=rng.integers(0, 1e10), | |
| path_to_images=natural_image_list).astype(float) | |
| else: | |
| raise NotImplementedError(f"Unknown color sampler {sampler}") | |
| else: | |
| color = rng.uniform(0.25, 0.75, size=(number_of_circles, 1)) | |
| return center_x, center_y, radius, color | |