229 lines
11 KiB
Python
229 lines
11 KiB
Python
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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, box
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import os
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from tqdm import tqdm # 导入 tqdm 用于进度条
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def merge_vectors_seven_categories(file_paths, output_vector_path):
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"""
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合并7个地貌类型矢量图层并保存为GeoPackage格式（去掉cropland）
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参数:
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file_paths: 包含7个矢量文件路径的列表 (按顺序: forest, water, shrub, wetland, jianzhu, grass, bareland)
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output_vector_path: 输出合并后的矢量文件路径（使用.gpkg格式）
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"""
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try:
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# 检查输入文件数量是否为7
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if len(file_paths) != 7:
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raise ValueError("请提供正好7个矢量文件路径")
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for path in file_paths:
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if not os.path.exists(path):
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raise FileNotFoundError(f"文件不存在: {path}")
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# 定义7个类别（去掉cropland）
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categories = ['forest', 'water', 'shrub', 'wetland', 'jianzhu', 'grass', 'bareland']
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category_values = {cat: i + 1 for i, cat in enumerate(categories)} # forest=1, water=2, ..., bareland=7
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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, engine="pyogrio")
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gdf['category'] = category
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gdf['value'] = category_values[category]
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if 'Shape_Area' in gdf.columns:
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gdf['Shape_Area'] = gdf['Shape_Area'].clip(upper=1e9)
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gdfs.append(gdf)
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# 统一坐标系
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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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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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if not output_vector_path.endswith('.gpkg'):
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output_vector_path = output_vector_path.replace('.shp', '.gpkg')
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print("输出格式已更改为GeoPackage以支持大文件")
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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, driver='GPKG', engine="pyogrio")
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print("矢量合并完成！")
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print(f"合并矢量保存为: {output_vector_path}")
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return output_vector_path
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except Exception as e:
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print(f"发生错误: {str(e)}")
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return None
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def rasterize_vector_by_blocks(input_vector_path, output_raster_path, raster_resolution=30, block_size=5000):
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"""
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将合并后的矢量文件分块转为栅格，显示7个类别，背景值设为 value=8，添加进度条和像素统计
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参数:
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input_vector_path: 输入的合并矢量文件路径（.gpkg格式）
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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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block_size: 每个分块的宽度和高度（像素单位，默认5000）
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"""
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try:
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# 读取合并后的矢量文件
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print(f"正在读取合并矢量文件: {input_vector_path}")
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merged_gdf = gpd.read_file(input_vector_path, engine="pyogrio")
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# 检查 value 字段是否有效
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if 'value' not in merged_gdf.columns:
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raise ValueError("矢量文件中缺少 'value' 字段，请确保文件包含正确的分类值")
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print("合并矢量文件中的 value 字段分布:")
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print(merged_gdf['value'].value_counts(dropna=False))
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if not merged_gdf['value'].between(1, 7).all():
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print("警告: 'value' 字段包含无效值（应在 1-7 之间），可能导致数据丢失")
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merged_gdf = merged_gdf[merged_gdf['value'].between(1, 7)]
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# 检查合并后的矢量数据是否为空
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if merged_gdf.empty:
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raise ValueError("合并后的矢量数据为空，请检查输入数据是否有效")
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# 按 value 字段排序，优先级高的类别（例如 value 较大）后处理，避免被覆盖
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print("正在按 'value' 字段排序以确保优先级...")
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merged_gdf = merged_gdf.sort_values(by='value', ascending=True)
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# 计算总范围和栅格尺寸
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bounds = merged_gdf.total_bounds # [minx, miny, maxx, maxy]
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total_width = int((bounds[2] - bounds[0]) / raster_resolution)
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total_height = int((bounds[3] - bounds[1]) / raster_resolution)
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if total_width <= 0 or total_height <= 0:
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raise ValueError(f"栅格尺寸无效: 宽度={total_width}, 高度={total_height}")
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print(f"栅格范围: minx={bounds[0]}, miny={bounds[1]}, maxx={bounds[2]}, maxy={bounds[3]}")
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print(f"栅格尺寸: 宽度={total_width}, 高度={total_height}")
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# 创建栅格变换
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transform = rasterio.transform.from_bounds(bounds[0], bounds[1], bounds[2], bounds[3], total_width,
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total_height)
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# 计算总分块数以设置进度条
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total_blocks = ((total_height + block_size - 1) // block_size) * ((total_width + block_size - 1) // block_size)
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print(f"总分块数: {total_blocks}")
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# 创建并写入栅格文件，逐块写入以减少内存占用
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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=total_height,
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width=total_width,
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count=1,
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dtype=rasterio.uint8,
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crs=merged_gdf.crs,
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transform=transform,
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nodata=255 # 使用 255 作为 nodata 值，区分 value=8 的背景
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) as dst:
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# 分块处理并添加进度条
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with tqdm(total=total_blocks, desc="栅格化进度") as pbar:
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for y in range(0, total_height, block_size):
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for x in range(0, total_width, block_size):
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block_height = min(block_size, total_height - y)
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block_width = min(block_size, total_width - x)
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# 计算当前分块的地理范围
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block_minx = bounds[0] + x * raster_resolution
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block_maxy = bounds[3] - y * raster_resolution
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block_maxx = block_minx + block_width * raster_resolution
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block_miny = block_maxy - block_height * raster_resolution
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# 创建分块边界框
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block_bounds = box(block_minx, block_miny, block_maxx, block_maxy)
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block_gdf = merged_gdf[merged_gdf.geometry.intersects(block_bounds)].copy()
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# 初始化分块为背景值 8
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block_raster = np.full((block_height, block_width), 8, dtype=np.uint8)
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if not block_gdf.empty:
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# 打印分块中的 value 字段分布
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print(f"分块 (x={x}, y={y}) 中的 value 字段分布:")
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print(block_gdf['value'].value_counts(dropna=False))
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# 分块栅格化
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block_transform = rasterio.transform.from_bounds(
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block_minx, block_miny, block_maxx, block_maxy, block_width, block_height
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)
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shapes = ((mapping(geom), value) for geom, value in
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zip(block_gdf.geometry, block_gdf['value']))
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block_raster = rasterize(
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shapes=shapes,
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out=block_raster, # 使用预初始化的数组
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transform=block_transform,
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fill=8, # 未覆盖区域为 8
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dtype=rasterio.uint8,
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all_touched=True # 确保所有触及的像素都被计入
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)
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# 统计分块中 value=1 到 value=7 的像素点个数
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print(f"分块 (x={x}, y={y}) 中像素值统计:")
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unique, counts = np.unique(block_raster, return_counts=True)
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value_counts = dict(zip(unique, counts))
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for val in range(1, 8): # 检查 value=1 到 value=7
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count = value_counts.get(val, 0)
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print(f"Value={val}: {count} 像素")
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# 单独打印 value=8（背景值）
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background_count = value_counts.get(8, 0)
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print(f"Value=8 (background): {background_count} 像素")
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# 直接写入当前分块到文件中
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dst.write(block_raster, 1, window=((y, y + block_height), (x, x + block_width)))
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print(f"已完成分块栅格化: x={x}, y={y}, 宽度={block_width}, 高度={block_height}")
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pbar.update(1)
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# 读取整个栅格文件，统计最终像素分布
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with rasterio.open(output_raster_path) as src:
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final_raster = src.read(1)
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print("最终栅格文件中的像素值统计:")
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unique, counts = np.unique(final_raster, return_counts=True)
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value_counts = dict(zip(unique, counts))
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for val in range(1, 9): # 检查 value=1 到 value=8
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count = value_counts.get(val, 0)
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print(f"Value={val}: {count} 像素")
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# 打印 nodata 值（255）
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nodata_count = value_counts.get(255, 0)
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print(f"Nodata (255): {nodata_count} 像素")
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print("栅格化完成！")
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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: 1=forest, 2=water, 3=shrub, 4=wetland, 5=jianzhu, 6=grass, 7=bareland, 8=background")
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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_files = [
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r"sddimaoshp/forest/sdforest.shp",
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r"sddimaoshp/water/shandongsuiyu3.shp",
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r"sddimaoshp/shrub/sdshrub.shp",
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r"sddimaoshp/wetland/sdwetland.shp",
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r"sddimaoshp/jianzhu/shandongjianzhu3.shp",
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r"sddimaoshp/grass/sdgrass.shp",
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r"sddimaoshp/bareland/sdbareland.shp"
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]
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output_vector_file = r"sddimaoshp/hebingtif/shandongdimiao_10_1m.gpkg"
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output_raster_file = r"sddimaoshp/hebingtif/sddimao_10_1m.tif"
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# Step 1: 合并矢量
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vector_path = merge_vectors_seven_categories(input_files, output_vector_file)
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# Step 2: 如果合并成功，进行分块栅格化
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if vector_path:
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rasterize_vector_by_blocks(vector_path, output_raster_file, raster_resolution=10, block_size=5000)
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