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demo2.py
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import random
from typing import List, Union, Any
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import sys
import glob2
import loguru
import pathlib
from tqdm import tqdm
from pandas import DataFrame
from pandas.io.parsers import TextFileReader
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
# read csv
def read_csvs(suffix='csv', file_root=''):
if not file_root:
file_root = suffix
result_dfs_list = []
root = pathlib.Path(file_root)
files_glob = root.rglob('*.' + suffix)
files_glob = [x for x in files_glob]
for file in tqdm(files_glob):
if suffix == 'csv':
df = pd.read_csv(file)
elif suffix == 'xlsx':
df = pd.read_excel(file)
else:
raise Exception()
result_dfs_list.append(df)
return result_dfs_list
# 聚类中加入航向和航速，限定一个范围，并用颜色标出不同聚类点
def draw_3d_scatters_5(df_list: List[Union[DataFrame]], random_rate=1.0):
# 将点云concat
df = pd.concat(df_list)
df = df.dropna()
# 归一化
df[['lat', 'lon', 'geoaltitude', 'velocity', 'heading']] = df[
['lat', 'lon', 'geoaltitude', 'velocity', 'heading']].apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
# 在控制台打印航向和航速的基本信息
print("航速：")
print(df['velocity'].describe())
print("航向：")
print(df['heading'].describe())
''' -----需要航向航速限定范围------- '''
velocity_range = (0, 100000)
heading_range = (0, 100000)
df = df[(df['velocity'] > velocity_range[0]) & (df['velocity'] < velocity_range[1])]
df = df[(df['heading'] > velocity_range[0]) & (df['heading'] < velocity_range[1])]
''' ---------------------------- '''
df = df.sample(frac=random_rate, random_state=888) # 按比例随机采样
X = df[['lat', 'lon', 'geoaltitude', 'velocity', 'heading']].to_numpy()
# 聚类时加入航向和航速特征并归一化
db = DBSCAN(eps=0.05, min_samples=10).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))
X = np.column_stack([X, labels])
X_nagetive = X[X[:, 5] == -1] # 无效样本
X = X[X[:, 5] != -1]
# 类的数量 -> 取出排名前N个类
ord_dic_list = [{'cluster_index': i, 'amt': len(X[X[:, 5] == i])} for i in range(n_clusters_)]
ord_dic_list = sorted(ord_dic_list, key=lambda x: x['amt'], reverse=True)
print(f"聚类数量：{len(ord_dic_list)}个类")
topk_cluster = [ord_dic_list[i]['cluster_index'] for i in range(len(ord_dic_list))] # fixme 超参数：这个5代表取数量排名前5的类
# 开始绘制散点图
# mandarin support
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
expand = 2
ax = plt.figure(figsize=[6.4 * expand, 4.8 * expand], dpi=100.0 / expand).add_subplot(projection='3d')
# 绘制没有被分类的点云为灰色
temp_nd = X_nagetive
xs = temp_nd[:, 0]
ys = temp_nd[:, 1]
zs = temp_nd[:, 2]
m = 'o'
ax.scatter(xs, ys, zs, marker=m, s=5, c='#7f7f7f', alpha=1.0)
# 绘制有分类的点云
for cluster_index in topk_cluster:
temp_nd = X[X[:, 5] == cluster_index]
xs = temp_nd[:, 0]
ys = temp_nd[:, 1]
zs = temp_nd[:, 2]
m = 'o'
ax.scatter(xs, ys, zs, marker=m, s=2)
ax.legend(['类别top'] + [f"第{str(i + 1)}类" for i, c in enumerate(topk_cluster)])
ax.set(xlabel='lat')
ax.set(ylabel='lon')
ax.set(zlabel='geoaltitude')
ax.set_title("航迹点云聚类（经纬高航向航速）")
plt.show()
# 不加航向航速
def draw_3d_scatters_3(df_list: List[Union[DataFrame]], random_rate=1.0):
# 将点云concat
df = pd.concat(df_list)
df = df.dropna()
# 归一化
df[['lat', 'lon', 'geoaltitude', 'velocity', 'heading']] = df[
['lat', 'lon', 'geoaltitude', 'velocity', 'heading']].apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
df = df.sample(frac=random_rate, random_state=888) # 按比例随机采样
X = df[['lat', 'lon', 'geoaltitude']].to_numpy()
# 聚类时加入航向和航速特征并归一化
db = DBSCAN(eps=0.01, min_samples=10).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))
X = np.column_stack([X, labels])
X_nagetive = X[X[:, 3] == -1] # 无效样本
X = X[X[:, 3] != -1]
# 类的数量 -> 取出排名前N个类
ord_dic_list = [{'cluster_index': i, 'amt': len(X[X[:, 3] == i])} for i in range(n_clusters_)]
ord_dic_list = sorted(ord_dic_list, key=lambda x: x['amt'], reverse=True)
print(f"聚类数量：{len(ord_dic_list)}个类")
topk_cluster = [ord_dic_list[i]['cluster_index'] for i in range(len(ord_dic_list))] # fixme 超参数：这个5代表取数量排名前5的类
# 开始绘制散点图
# mandarin support
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
expand = 2
ax = plt.figure(figsize=[6.4 * expand, 4.8 * expand], dpi=100.0 / expand).add_subplot(projection='3d')
# 绘制没有被分类的点云为灰色
temp_nd = X_nagetive
xs = temp_nd[:, 0]
ys = temp_nd[:, 1]
zs = temp_nd[:, 2]
m = 'o'
ax.scatter(xs, ys, zs, marker=m, s=5, c='#7f7f7f', alpha=1.0)
# 绘制有分类的点云
for cluster_index in topk_cluster:
temp_nd = X[X[:, 3] == cluster_index]
xs = temp_nd[:, 0]
ys = temp_nd[:, 1]
zs = temp_nd[:, 2]
m = 'o'
ax.scatter(xs, ys, zs, marker=m, s=5)
ax.legend(['类别top'] + [f"第{str(i + 1)}类" for i, c in enumerate(topk_cluster)])
ax.set(xlabel='lat')
ax.set(ylabel='lon')
ax.set(zlabel='geoaltitude')
ax.set_title("航迹点云聚类(经纬高)")
plt.show()
if __name__ == '__main__':
df_list = read_csvs('csv', 'D:\Repo\dbscan_absd\爬取数据\M-C5M运输_AE056D')
draw_3d_scatters_5(df_list, random_rate=0.012) # 加航向航速聚类
draw_3d_scatters_3(df_list, random_rate=0.012) # 不加航向航速聚类