125 lines
6.0 KiB
Python
125 lines
6.0 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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from rasterio.windows import Window
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import numpy as np
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from shapely.geometry import mapping
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import os
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def large_vector_to_raster(input_shp_path, output_raster_path, raster_resolution=30, chunk_size=1000, window_size=1000, category_field='value', category_value=1):
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"""
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将大型面要素转为栅格数据，分块读取矢量并按窗口写入栅格，适用于大文件（例如2GB）
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参数:
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input_shp_path: 输入shapefile路径
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output_raster_path: 输出栅格文件路径
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raster_resolution: 输出栅格分辨率（单位：米，默认30）
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chunk_size: 每次读取的矢量特征数量（默认1000）
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window_size: 每个栅格窗口的像素大小（默认1000x1000）
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category_field: 矢量数据中用于栅格化的字段名（默认'value'）
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category_value: 默认类别值（若无字段，则所有特征赋此值，默认1）
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"""
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try:
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# 检查输入文件是否存在
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if not os.path.exists(input_shp_path):
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raise FileNotFoundError(f"输入文件 {input_shp_path} 不存在")
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# 读取矢量文件的元信息以确定范围和CRS
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print("正在读取矢量元信息...")
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gdf_meta = gpd.read_file(input_shp_path, rows=1) # 只读一行获取元数据
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base_crs = gdf_meta.crs
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bounds = gdf_meta.total_bounds # [minx, miny, maxx, maxy]
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# 计算栅格尺寸
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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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transform = rasterio.transform.from_bounds(bounds[0], bounds[1], bounds[2], bounds[3], width, height)
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# 初始化输出栅格文件
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print(f"初始化输出栅格文件: {output_raster_path}")
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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 # 背景值为0
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) as dst:
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# 分窗口处理
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for row_offset in range(0, height, window_size):
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for col_offset in range(0, width, window_size):
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window_height = min(window_size, height - row_offset)
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window_width = min(window_size, width - col_offset)
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window = Window(col_offset, row_offset, window_width, window_height)
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# 计算当前窗口的地理范围
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window_transform = rasterio.windows.transform(window, transform)
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window_bounds = rasterio.windows.bounds(window, transform)
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# 创建窗口的边界几何，用于筛选矢量数据
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window_poly = gpd.GeoSeries.from_wkt([f"POLYGON(({window_bounds[0]} {window_bounds[1]}, "
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f"{window_bounds[2]} {window_bounds[1]}, "
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f"{window_bounds[2]} {window_bounds[3]}, "
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f"{window_bounds[0]} {window_bounds[3]}, "
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f"{window_bounds[0]} {window_bounds[1]}))"]).iloc[0]
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# 分块读取矢量数据并筛选当前窗口内的特征
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print(f"处理窗口: row={row_offset}, col={col_offset}")
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chunk_raster = np.zeros((window_height, window_width), dtype=rasterio.uint8)
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chunk_iterator = gpd.read_file(input_shp_path, chunksize=chunk_size)
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for chunk_idx, chunk_gdf in enumerate(chunk_iterator):
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# 筛选与当前窗口相交的特征
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window_gdf = chunk_gdf[chunk_gdf.intersects(window_poly)]
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if len(window_gdf) == 0:
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continue
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print(f" - 处理分块 {chunk_idx}，筛选出 {len(window_gdf)} 个特征")
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# 如果没有指定category_field，则赋默认值
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if category_field not in window_gdf.columns:
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window_gdf['value'] = category_value
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# 栅格化当前分块
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shapes = ((mapping(geom), value) for geom, value in zip(window_gdf.geometry, window_gdf[category_field]))
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temp_raster = rasterize(
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shapes=shapes,
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out_shape=(window_height, window_width),
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transform=window_transform,
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fill=0,
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dtype=rasterio.uint8
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)
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# 更新窗口栅格（保留非0值）
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mask = temp_raster > 0
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chunk_raster[mask] = temp_raster[mask]
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# 写入当前窗口到栅格文件
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if np.any(chunk_raster > 0):
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dst.write(chunk_raster, 1, window=window)
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print("处理完成！")
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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, 其他值由 {category_field} 字段或默认值 {category_value} 确定")
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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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input_shp = r"E:\large_data\large_vector.shp" # 替换为您的2GB矢量文件路径
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output_raster = r"E:\large_data\large_raster.tif" # 输出栅格路径
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large_vector_to_raster(
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input_shp_path=input_shp,
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output_raster_path=output_raster,
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raster_resolution=30, # 分辨率30米
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chunk_size=1000, # 每次读取1000个特征
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window_size=1000, # 每个窗口1000x1000像素
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category_field='value', # 假设矢量数据中有'value'字段，若无则用默认值
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category_value=1 # 默认类别值
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) |