之前分享过【Python&RS】Rasterio库安装+基础函数使用教程,大家有兴趣的可以去看看。由于最近有涉及到栅格裁剪和压缩的问题,所以研究了一下今天和大家分享分享。
原创作者:RS迷途小书童
1 需要的库
import os
import rasterio
import geopandas as gpd
from rasterio.mask import mask
from rasterio.plot import show
from rasterio.windows import Window, from_bounds
from shapely.geometry import Point, Polygon, box, mapping, LineString
2 自定义矩形框裁剪
这里的矩形可以自己选择大小,当然也可以通过读取矢量的空间范围去裁剪。同时代码中还加入了压缩参数。
def compress_tif(tif_path, output_file):
with rasterio.open(tif_path) as src:
transform = src.transform
# minx, miny, maxx, maxy = geometry1.bounds(要素的地理字段)
expanded_bbox = box(minx=0, miny=0, maxx=100, maxy=100) # 创建一个新的扩展后的边界框(Shapely box)
window = from_bounds(expanded_bbox.bounds[0], expanded_bbox.bounds[1],
expanded_bbox.bounds[2], expanded_bbox.bounds[3],
transform=transform) # 将Shapely几何对象转换为rasterio可以理解的边界
clipped = src.read(window=window) # 读取并裁剪TIFF数据
out_meta = src.meta.copy()
out_meta.update({"driver": "GTiff", "compress": 'lzw'}) # 更新元数据,rle,lzw等
del src
with rasterio.open(output_file, 'w', **out_meta) as dest: # 写入裁剪后的TIFF文件
dest.write(clipped)
3 使用矢量要素裁剪
这里使用到了geopandas库用来读取每个要素的空间范围。
def clip_raster_from_features(vector_file=r"彭俊喜/1.shp", raster_file=r"彭俊喜/1.tif"):
"""
:param vector_file: 输入需要裁剪的面矢量(多面)
:param raster_file: 输入需要裁剪的影像
:return: None
"""
# 读取面矢量数据
gdf = gpd.read_file(vector_file)
# 循环遍历面矢量中的每个面要素
for index, row in gdf.iterrows():
# 获取当前面要素的几何形状
geometry1 = row.geometry
# 确保面要素不是空的
if geometry1.is_empty:
print(f"Skipping empty geometry for feature {index}")
continue
# 打开影像文件
with rasterio.open(raster_file) as src:
# 将面要素的边界转换为shapely的box对象
geometry_bounds = box(*geometry1.bounds)
# 将rasterio的bounds转换为shapely的box对象
src_bounds = box(*src.bounds)
# 检查面要素的边界是否与影像的边界相交
if not geometry_bounds.intersects(src_bounds):
print(f"Skipping feature {index} as it does not intersect with the raster.")
continue
# 转换几何形状为Rasterio可以理解的格式
geom_for_rasterio = mapping(geometry1)
# 使用面要素裁剪影像
try:
out_image, out_transform = mask(src, [geom_for_rasterio], crop=True)
except ValueError as e:
print(f"Error clipping feature {index}: {e}")
continue
# 检查是否成功裁剪出影像
if out_image is None:
print(f"No data was clipped for feature {index}. Skipping.")
continue
# 为裁剪后的影像设置输出路径和文件名
output_file = f'clipped_image_{index}.tif'
output_path = os.path.join(r'Z:\Shanghai Metro Automatic Identification System\Data Source\weigui\1',
output_file)
# 创建输出文件,并写入裁剪后的影像数据
dtypes = [src.dtypes[i] for i in range(src.count)] # 确保数据类型列表与波段数量匹配
# 假设所有波段的数据类型都是相同的,并且你想要保持与输入影像相同的数据类型
dtype = src.dtypes[0] # 获取第一个波段的数据类型
# 使用这个数据类型打开输出文件
with rasterio.open(output_path, 'w', driver='GTiff', height=out_image.shape[1],
width=out_image.shape[2], count=src.count, dtype=dtype,
crs=src.crs, transform=out_transform) as dest:
dest.write(out_image)
print(f'Clipped image {output_file} has been saved.')
print('All features have been processed.')
标签:src,RS,Python,裁剪,bounds,栅格,rasterio,file,out
From: https://blog.csdn.net/m0_56729804/article/details/139674505