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112 lines
4.5 KiB
112 lines
4.5 KiB
import matplotlib.pyplot as plt
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import cartopy
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import cartopy.crs as ccrs
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import cartopy.feature as cfeature
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import cartopy.io.shapereader as shpreader
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from cartopy.feature import ShapelyFeature, NaturalEarthFeature
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from shapely.geometry import LineString
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import geopandas as gpd
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from urllib.parse import urlencode, unquote
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # 允许所有来源，或者指定来源列表
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allow_credentials=True,
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allow_methods=["*"], # 允许所有方法
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allow_headers=["*"], # 允许所有头部
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)
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@app.get("/getpng")
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# def print_hi():
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async def getpng(urls: str):
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result = {
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"code": 0,
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"data": "aaaaaaaaa"
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}
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# geojson_file_path = r'C:\Users\shiys\Desktop\UPPERTT2022040500200_LINE.geojson'
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# 读取 GeoJSON 数据
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geojson_file_path = unquote(urls)
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geojson_data = gpd.read_file(geojson_file_path)
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geometries = geojson_data.geometry
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# 创建图形和轴
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fig = plt.figure(figsize=(10, 10))
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plt.rcParams['font.family'] = 'SimHei'
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ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
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# 设置地图的范围
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ax.set_extent([60, 150, 10, 58], crs=ccrs.PlateCarree())
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# 陆地
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ax.add_feature(NaturalEarthFeature('physical', 'land', '50m', edgecolor='#cfedf7', facecolor='white'))
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# 边框
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ax.add_feature(NaturalEarthFeature('cultural', 'admin_0_countries', '50m', edgecolor='#2b94d7', facecolor='none',linewidth=0.2))
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# 设置海洋颜色
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ocean_color = '#cfedf7' # 海洋蓝色
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ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor=ocean_color)
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# 读取并绘制省份边界
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# 指定shapefile路径
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china_admin_file = shpreader.Reader(r'D:\zxmTest\ZxmWorkJava\GYJ\GYX2\python\python\doc\china.shp')
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print(china_admin_file.records());
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for record in china_admin_file.records():
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# 使用geometries()方法获取几何信息
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geometry = record.geometry
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ax.add_geometries([geometry], ccrs.PlateCarree(), facecolor='white', edgecolor='#c6aecb',linewidth=0.2)
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# 提取省份名称
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province_name = record.attributes['NAME'] # 这里的'NAME_1'取决于你的shapefile中的字段名
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# 计算省份中心点
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center_x, center_y = geometry.centroid.x, geometry.centroid.y
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# 在地图上添加省份名称
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ax.text(center_x, center_y, province_name, transform=ccrs.PlateCarree(),fontsize=4, color='#43a2ff', zorder=2)
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# 创建 ShapelyFeature（等高线）
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geo_feature = ShapelyFeature(geometries, ccrs.PlateCarree(), edgecolor='#0000ff', facecolor='none',linewidth=0.3, zorder=5)
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# 提取几何对象和对应的数值
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geometriespoint = geojson_data['geometry']
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values = geojson_data['VAL']
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# 在等高线上标注数值
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for index, (geometrypoint, value) in enumerate(zip(geometriespoint, values)):
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if isinstance(geometrypoint, LineString):
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# 解析 LINESTRING Z 数据
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parsed_coords = str(geometries[index]).replace("LINESTRING Z (", "").replace(")", "")
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parsed_coords = [tuple(map(float, coord.strip().split())) for coord in parsed_coords.split(",")]
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first_coord = parsed_coords[0]
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x, y, z = first_coord
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# 计算线段的中点
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midpoint = geometrypoint.interpolate(0.5, normalized=True)
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if 60 < midpoint.x < 150 and 10 < midpoint.y < 58:
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# 标注数值
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bbox_props = dict(boxstyle="round,pad=0", facecolor="white", edgecolor="none", lw=1)
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ax.text(midpoint.x, midpoint.y, f"{z}", fontsize=4, ha='center', va='center', color='#0000ff', bbox=bbox_props, zorder=8)
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# 添加 GeoJSON 特征
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ax.add_feature(geo_feature)
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# 保存为 PNG 图像
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output_file_path = 'D:\zxmTest\pngs\output.png'
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plt.savefig(output_file_path, dpi=600,bbox_inches='tight', pad_inches=0)
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# 显示图形
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# plt.show()
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result['code'] = 1
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result['data'] = output_file_path
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# except:
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# result['code'] = 0
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# result['data'] = "文件转换图片失败，请重试"
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return result
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if __name__ == '__main__':
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# print_hi()
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# china_admin_file = shpreader.Reader(r'D:\javaproject\weather\weather\python\python\doc\china.shp', encoding='utf-8')
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#print(cartopy.config)
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print("---------")
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# print(china_admin_file.records())
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# print("1111111111111")
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# for record in china_admin_file.records():
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# a=1
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