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https://github.com/KimigaiiWuyi/GenshinUID.git
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86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
from __future__ import annotations
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from typing import List, Tuple
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import numpy as np
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from sklearn.cluster import KMeans
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from shapely.geometry import Point, Polygon
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from .models import XYPoint
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Pos = Tuple[float, float]
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Poses = List[XYPoint]
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Points = List[Point]
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def k_means_points(
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points: List[XYPoint], length: int = 500, clusters: int = 3
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) -> List[Tuple[XYPoint, XYPoint, Poses]]:
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"""
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通过 K-Means 获取集群坐标列表
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参数：
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points: `list[XYPoint]`
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坐标列表，建议预先使用 `convert_pos` 进行坐标转换
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length: `int` (default: 500)
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区域大小，如果太大则可能一个点会在多个集群中
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clusters: `int` (default: 3)
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集群数量
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返回：
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`list[tuple[XYPoint, XYPoint, list[XYPoint]]]`
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tuple 中：
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第 1 个元素为集群最左上方的点
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第 2 个元素为集群最右下方的点
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第 3 个元素为集群内所有点
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list 按照集群内点的数量降序排序
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提示：
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length：
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+---------------------+
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│ │
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│ │
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│ │
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|--length--|--length--│
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│ │
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│ │
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│ │
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+---------------------+
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"""
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pos_array = np.array(points)
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k_means = KMeans(n_clusters=clusters).fit(pos_array)
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points_temp: List[Points] = []
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for k_means_pos in k_means.cluster_centers_:
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x = (
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k_means_pos[0] - length if k_means_pos[0] > length else 0,
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k_means_pos[0] + length,
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)
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y = (
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k_means_pos[1] - length if k_means_pos[1] > length else 0,
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k_means_pos[1] + length,
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)
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path = Polygon(
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[(x[0], y[0]), (x[0], y[1]), (x[1], y[1]), (x[1], y[0])]
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)
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points_temp.append(
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[Point(i) for i in pos_array if path.contains(Point(i))]
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)
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return_list = []
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for i in points_temp:
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pos_array_ = np.array([p.xy for p in i])
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return_list.append(
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(
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XYPoint(pos_array_[:, 0].min(), pos_array_[:, 1].min()),
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XYPoint(pos_array_[:, 0].max(), pos_array_[:, 1].max()),
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list(map(lambda p: XYPoint(p.x, p.y), i)),
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)
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)
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return sorted(
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return_list, key=lambda pos_tuple: len(pos_tuple[2]), reverse=True
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)
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