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Signed-off-by: Christian Henkel <[email protected]>
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| @@ -0,0 +1,128 @@ | ||
| #!/usr/bin/env python3 | ||
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| from typing import Dict, Tuple, List | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import cv2 | ||
| from scipy.spatial import Voronoi | ||
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| def load_map(filename: str) -> np.ndarray: | ||
| """ | ||
| Load a png file as map. | ||
| """ | ||
| img = cv2.imread(filename, cv2.IMREAD_COLOR) | ||
| # Convert to binary image | ||
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | ||
| img = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY)[1] | ||
| return img | ||
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| def initialize_points(img: np.ndarray, k: int) -> np.ndarray: | ||
| """ | ||
| Initialize the k points. | ||
| """ | ||
| points = [] | ||
| for i in range(k): | ||
| x = np.random.randint(0, img.shape[0]) | ||
| y = np.random.randint(0, img.shape[1]) | ||
| while img[x, y] == 0: | ||
| x = np.random.randint(0, img.shape[0]) | ||
| y = np.random.randint(0, img.shape[1]) | ||
| points.append((x, y)) | ||
| return np.array(points) | ||
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| def compute_centroids(vor: Voronoi, img: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Compute the centroids of the Voronoi cells. | ||
| """ | ||
| centroids = [] | ||
| for region in vor.regions: | ||
| if not region or -1 in region: | ||
| continue | ||
| polygon = [vor.vertices[i] for i in region] | ||
| polygon = np.array(polygon) | ||
| # Compute the centroid of the polygon | ||
| centroid = np.mean(polygon, axis=0) | ||
| x, y = int(centroid[0]), int(centroid[1]) | ||
| # Ensure the centroid is within the image bounds and not in an obstacle | ||
| if 0 <= x < img.shape[1] and 0 <= y < img.shape[0] and img[y, x] != 0: | ||
| centroids.append([x, y]) | ||
| return np.array(centroids) | ||
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| def one_iter(points: np.ndarray, img: np.ndarray, k: int) -> np.ndarray: | ||
| """ | ||
| Perform Lloyd's algorithm to refine the points. | ||
| """ | ||
| vor = Voronoi(points) | ||
| centroids = compute_centroids(vor, img) | ||
| if len(centroids) == k: | ||
| points = centroids | ||
| return points | ||
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| def lloyds_algorithm(points: np.ndarray, img: np.ndarray, k: int) -> np.ndarray: | ||
| """ | ||
| Perform Lloyd's algorithm. | ||
| """ | ||
| for _ in range(100): | ||
| points = one_iter(points, img, k) | ||
| return points | ||
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| def make_colors(n: int) -> List[Tuple[float, float, float]]: | ||
| """ | ||
| Make n colors. | ||
| """ | ||
| colors = [] | ||
| for i in range(n): | ||
| r = np.random.rand() | ||
| g = np.random.rand() | ||
| b = np.random.rand() | ||
| colors.append((r, g, b)) | ||
| return colors | ||
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| def show_img( | ||
| img: np.ndarray, | ||
| colors: List[Tuple[float, float, float]], | ||
| points: np.ndarray, | ||
| _cells: Dict[int, List[Tuple[int, int]]], | ||
| ): | ||
| """ | ||
| Show the image. With points. | ||
| """ | ||
| # image | ||
| plt.imshow(img, cmap="gray") | ||
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| # points | ||
| plt.scatter(points[:, 1], points[:, 0], c="r", marker="+") | ||
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| # cells | ||
| print(f"{len(_cells)=}") | ||
| if _cells is not None: | ||
| for i_p, cell in enumerate(_cells.values()): | ||
| print(cell) | ||
| if len(cell) == 0: | ||
| continue | ||
| npcell = np.array(cell) | ||
| plt.fill(npcell[:, 1], npcell[:, 0], color=colors[i_p], alpha=0.5) | ||
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| # simplify plot | ||
| plt.axis("off") | ||
| plt.subplots_adjust(left=0, right=1, top=1, bottom=0) | ||
| plt.show() | ||
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| if __name__ == "__main__": | ||
| np.random.seed(0) | ||
| img = load_map("map.png") | ||
| n_nodes = 10 | ||
| n_iterations = 100 | ||
| points = initialize_points(img, n_nodes) | ||
| points = lloyds_algorithm(points, img, n_nodes) | ||
| colors = make_colors(n_nodes) | ||
| show_img(img, colors, points, {}) |
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Submodule lodepng
added at
d398e0
Submodule lodepng
added at
d398e0
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