台风路径可视化 | 强台风“天兔”移动轨迹图（Chiikawa版）

import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from PIL import Image
import numpy as np

# 读取 Excel 文件
file_path = '/home/mw/project/2024_TY2425.xlsx'
data = pd.read_excel(file_path)

# 查看数据前几行
print(data.head())

   tc_num name_cn name_en       dateUTC       dateCST  vmax  grade   lat  \
0    2425      天兔   USAGI  202411111800  202411120200    18   热带风暴  12.8   
1    2425      天兔   USAGI  202411112100  202411120500    18   热带风暴  13.0   
2    2425      天兔   USAGI  202411120000  202411120800    18   热带风暴  13.2   
3    2425      天兔   USAGI  202411120300  202411121100    25  强热带风暴  13.9   
4    2425      天兔   USAGI  202411120600  202411121400    25  强热带风暴  14.0   

     lon  mslp      attr  
0  135.0   998  analysis  
1  134.5   998  analysis  
2  134.1   998  analysis  
3  132.6   985  analysis  
4  132.0   985  analysis

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import urllib.request
import cartopy.crs as ccrs
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from meteva.base.tool.plot_tools import add_china_map_2basemap
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from cartopy.feature import NaturalEarthFeature
lons = data['lon'].values
lats = data['lat'].values

# 定义绘图范围（可以根据实际需要调整）
lon_min, lon_max = min(lons) - 5, max(lons) + 5
lat_min, lat_max = min(lats) - 5, max(lats) + 5

# 创建一个新的图形
fig = plt.figure(figsize=(12,10))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.rcParams.update({'font.size': 16})
# 设置绘图范围
ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())

# 添加经纬度刻度
ax.set_xticks(np.arange(lon_min, lon_max+1, 5), crs=ccrs.PlateCarree())
ax.set_yticks(np.arange(lat_min, lat_max+1, 5), crs=ccrs.PlateCarree())
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())

# 绘制路径
ax.plot(lons, lats, marker='o', linestyle='-', color='red')

# 设置标题和轴标签
ax.set_title('台风路径')
ax.set_xlabel('经度')
ax.set_ylabel('纬度')

# 下载并加载头像图片
avatar_url = 'https://c-ssl.duitang.com/uploads/blog/202402/09/JOSqvMj5SWDxxeJ.jpeg'
with urllib.request.urlopen(avatar_url) as url:
    avatar_image = Image.open(url)
    avatar_array = np.array(avatar_image)

# 每隔五个点绘制一个乌萨奇头像
for i in range(len(lons)):
    if i % 5 == 0:  # 每隔五个点绘制一次
        imagebox = OffsetImage(avatar_array, zoom=0.05)
        ab = AnnotationBbox(imagebox, (lons[i], lats[i]), frameon=False)
        ax.add_artist(ab)

# 显示网格
ax.grid(True)

# 添加中国地图
add_china_map_2basemap(ax, name="nation", edgecolor='k', lw=0.5, encoding='gbk', grid0=None)  # "国界"
land = NaturalEarthFeature('physical', 'land', '10m', edgecolor='face', facecolor='lightgreen')
ocean = NaturalEarthFeature('physical', 'ocean', '10m', edgecolor='face', facecolor='lightblue')
ax.add_feature(land)
ax.add_feature(ocean)
# 调整布局以适应图像
plt.tight_layout()

# 显示图形
plt.show()

!pip install rembg -i https://pypi.mirrors.ustc.edu.cn/simple/

!cp /home/mw/input/rembg2522/u2net.onnx /home/mw/.u2net/

from rembg import remove  
input_path = '/home/mw/project/usagi.jpeg'  
output_path = 'output.png'  
with open(input_path, 'rb') as i:  
    with open(output_path, 'wb') as o:  
        input = i.read()  
        output = remove(input)  
        o.write(output)  

from PIL import Image
from rembg import remove
from IPython.display import display

input_path = '/home/mw/project/usagi.jpeg'
output_path = 'output.png'
comparison_path = 'comparison.png'

# 读取原始图片
with open(input_path, 'rb') as i:
    input_data = i.read()

# 执行抠图操作
output_data = remove(input_data)

# 将处理后的数据写入到输出文件
with open(output_path, 'wb') as o:
    o.write(output_data)

# 加载原始图片和处理后的图片
input_image = Image.open(input_path)
output_image = Image.open(output_path)

# 创建一个新图像，宽度是两个图像宽度之和，高度取两者中的最大值
width = input_image.width + output_image.width
height = max(input_image.height, output_image.height)
comparison_image = Image.new('RGB', (width, height))

# 将原始图片粘贴到左侧
comparison_image.paste(input_image, (0, 0))

# 将处理后的图片粘贴到右侧
comparison_image.paste(output_image, (input_image.width, 0))

# 保存对比图片
comparison_image.save(comparison_path)

# 在 Jupyter Notebook 中显示图片
display(comparison_image)

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import urllib.request
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from cartopy.feature import NaturalEarthFeature
# 定义绘图范围（可以根据实际需要调整）
lon_min, lon_max = min(lons) - 5, max(lons) + 5
lat_min, lat_max = min(lats) - 5, max(lats) + 5

# 创建一个新的图形
fig = plt.figure(figsize=(12,10))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.rcParams.update({'font.size': 16})
# 设置绘图范围
ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())
# 添加经纬度刻度
ax.set_xticks(np.arange(lon_min, lon_max+1, 5), crs=ccrs.PlateCarree())
ax.set_yticks(np.arange(lat_min, lat_max+1, 5), crs=ccrs.PlateCarree())
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())

# 绘制路径
plt.plot(lons, lats, marker='o', linestyle='-', color='red')

# 设置标题和轴标签
plt.title('台风路径')
plt.xlabel('经度')
plt.ylabel('纬度')


avatar_image = Image.open('/home/mw/project/output.png')
avatar_array = np.array(avatar_image)

# 每隔五个点绘制一个乌萨奇头像
for i in range(len(lons)):
    if i % 5 == 0:  # 每隔五个点绘制一次
        imagebox = OffsetImage(avatar_array, zoom=0.04)
        ab = AnnotationBbox(imagebox, (lons[i], lats[i]), frameon=False)
        ax.add_artist(ab)

# 显示网格
ax.grid(True)

# 添加中国地图
add_china_map_2basemap(ax, name="nation", edgecolor='k', lw=0.5, encoding='gbk', grid0=None)  # "国界"
land = NaturalEarthFeature('physical', 'land', '10m', edgecolor='face', facecolor='lightgreen')
ocean = NaturalEarthFeature('physical', 'ocean', '10m', edgecolor='face', facecolor='lightblue')
ax.add_feature(land)
ax.add_feature(ocean)
# 调整布局以适应图像
plt.tight_layout()

# 显示图形
plt.show()