113 lines
4.6 KiB
Python
113 lines
4.6 KiB
Python
import geopandas as gpd
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import pandas as pd
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import rasterio
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from rasterio.features import rasterize
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import numpy as np
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from shapely.geometry import mapping
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def merge_and_rasterize_vectors_four_categories(file_paths, output_vector_path, output_raster_path,
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raster_resolution=30):
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"""
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合并4个矢量图层（bareland, grass, shrub, building），转为栅格并显示4个类别，保留0值作为背景
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参数:
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file_paths: 包含4个矢量文件路径的列表 (按顺序: bareland, grass, shrub, building)
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output_vector_path: 输出合并后的矢量文件路径
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output_raster_path: 输出栅格文件路径
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raster_resolution: 输出栅格的分辨率（默认30米）
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"""
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try:
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# 检查输入文件数量
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if len(file_paths) != 4:
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raise ValueError("请提供正好4个矢量文件路径")
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# 定义类别映射，按照指定顺序
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categories = ['bareland', 'grass', 'shrub', 'building']
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category_values = {'bareland': 1, 'grass': 2, 'shrub': 3, 'building': 4} # bareland=1, grass=2, shrub=3, building=4
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# 读取所有矢量文件并添加分类
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gdfs = []
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for i, (path, category) in enumerate(zip(file_paths, categories), 1):
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print(f"正在读取第{i}个矢量文件: {path}")
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gdf = gpd.read_file(path)
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gdf['category'] = category # 添加类别字段
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gdf['value'] = category_values[category] # 添加数值字段用于栅格化
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gdfs.append(gdf)
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# 检查和统一坐标系（以第一个图层为基准）
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base_crs = gdfs[0].crs
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for i, gdf in enumerate(gdfs[1:], 2):
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if gdf.crs != base_crs:
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print(f"第{i}个图层坐标系不同，正在转换为第一个图层的坐标系...")
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gdfs[i - 1] = gdf.to_crs(base_crs)
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# 合并所有GeoDataFrame
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print("正在合并图层...")
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merged_gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True))
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merged_gdf = merged_gdf.set_geometry('geometry')
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# 保存合并后的矢量文件
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print(f"正在保存合并矢量结果到: {output_vector_path}")
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merged_gdf.to_file(output_vector_path)
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# 计算栅格化的范围
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bounds = merged_gdf.total_bounds # [minx, miny, maxx, maxy]
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width = int((bounds[2] - bounds[0]) / raster_resolution)
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height = int((bounds[3] - bounds[1]) / raster_resolution)
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# 创建栅格化变换
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transform = rasterio.transform.from_bounds(
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bounds[0], bounds[1], bounds[2], bounds[3], width, height
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)
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# 栅格化：将几何对象转为栅格，使用'value'字段作为像素值
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print("正在将矢量转为栅格...")
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shapes = ((mapping(geom), value) for geom, value in zip(merged_gdf.geometry, merged_gdf['value']))
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raster = rasterize(
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shapes=shapes,
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out_shape=(height, width),
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transform=transform,
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fill=0, # 背景值为0
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dtype=rasterio.uint8
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)
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# 保存栅格文件
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with rasterio.open(
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output_raster_path,
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'w',
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driver='GTiff',
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height=height,
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width=width,
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count=1,
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dtype=rasterio.uint8,
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crs=base_crs,
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transform=transform,
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nodata=0 # 设置nodata值为0，表示背景
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) as dst:
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dst.write(raster, 1)
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print("处理完成！")
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print(f"合并矢量保存为: {output_vector_path}")
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print(f"栅格保存为: {output_raster_path}")
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print(f"栅格中类别值: 0=背景, 1=bareland, 2=grass, 3=shrub, 4=building")
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except Exception as e:
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print(f"发生错误: {str(e)}")
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# 使用示例
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if __name__ == "__main__":
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# 输入4个矢量文件路径（按顺序: bareland, grass, shrub, building）
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input_files = [
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r"E:\njds\shenyang\shengyangshp\bareland\bareland.shp",
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r"E:\njds\shenyang\shengyangshp\grass\grass2.shp",
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r"E:\njds\shenyang\shengyangshp\shrub\shrub.shp",
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r"E:\Data\z18\sd\Total_sd\shandonghebingshp\sdshp\jianzhu\true\shandongjianzhu3.shp"
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]
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output_vector_file = r"E:\njds\shenyang\shengyangshp\kejianshe\merged_vector_four.shp"
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output_raster_file = r"E:\njds\shenyang\shengyangshp\kejianshe\merged_raster_four.tif"
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# 执行合并和栅格化
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merge_and_rasterize_vectors_four_categories(input_files, output_vector_file, output_raster_file,
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raster_resolution=10) |