49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
import os
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import sys
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import geopandas as gpd
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import rasterio
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from exactextract import exact_extract
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try:
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proj_lib_path = os.path.join(sys.prefix, 'Lib', 'site-packages', 'rasterio', 'proj_data')
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os.environ['PROJ_LIB'] = proj_lib_path
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except Exception as e:
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print(f"Warning: Could not automatically set PROJ_LIB. Please set it manually. Error: {e}")
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# 定义文件路径
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raster_path = "D:/工作/三普成果编制/出图数据/容县/栅格0925/AB/AB.tif"
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vector_path = "D:/测试文件夹/容县耕园林草.shp"
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# 1. 使用 rasterio 读取栅格的坐标参考系统 (CRS)
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with rasterio.open(raster_path) as src:
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raster_crs = src.crs
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# 2. 使用 geopandas 读取矢量文件
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gdf = gpd.read_file(vector_path)
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# 3. 检查并转换矢量数据的 CRS
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print(f"原始矢量CRS: {gdf.crs}")
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print(f"目标栅格CRS: {raster_crs}")
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if gdf.crs != raster_crs:
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print("CRS不匹配，正在转换矢量数据的CRS...")
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# 使用 .to_crs() 方法进行转换
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gdf = gdf.to_crs(raster_crs)
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print("转换完成。")
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# 4. 现在，将已经对齐了坐标系的 GeoDataFrame 传递给 exact_extract
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# 注意：可以直接传递 GeoDataFrame 对象，而不仅仅是文件路径
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stats_to_calculate = ['mean', 'sum', 'count', 'min', 'max']
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results = exact_extract(raster_path, gdf, stats_to_calculate)
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# 5. 将结果合并回 GeoDataFrame
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# exact_extract 在处理 GeoDataFrame 时，会保留原始的行顺序
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for stat in stats_to_calculate:
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# 从结果列表中提取每个要素的'properties'字典中的统计值
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gdf[stat] = [res['properties'][stat] for res in results]
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# 打印最终带有统计结果的 GeoDataFrame
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print("\n分区统计结果:")
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print(gdf.head())
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gdf.to_file("ddddl.shp", driver='ESRI Shapefile', encoding='utf-8') |