本节将介绍如何在Python中用GDAL实现根据矢量边界裁剪栅格数据。

from osgeo import gdal, gdal_array
import shapefile
import numpy as np
import os

#批量shp裁剪tiff影像
try:
import Image
import ImageDraw
except:
from PIL import Image, ImageDraw

def read_tiff(inpath):
ds=gdal.Open(inpath)
row=ds.RasterXSize
col=ds.RasterYSize
band=ds.RasterCount

data=np.zeros([row,col,band])
for i in range(band):
dt=ds.GetRasterBand(1)
data[:,:,i]=dt.ReadAsArray(0,0,col,row)
return data

def image2Array(i):
"""
将一个Python图像库的数组转换为一个gdal_array图片
"""
a = gdal_array.numpy.frombuffer(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a

def world2Pixel(geoMatrix, x, y):
"""
使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置
"""
ulx = geoMatrix[0]
uly = geoMatrix[3]
xDist = geoMatrix[1]
yDist = geoMatrix[5]
rtnX = geoMatrix[2]
rtnY = geoMatrix[4]
pixel = int((x - ulx) / xDist)
line = int((uly - y) / abs(yDist))
return (pixel, line)

def write_img(filename,im_proj,im_geotrans,im_data):
if 'int8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
elif 'int16' in im_data.dtype.name:
datatype = gdal.GDT_UInt16
else:
datatype = gdal.GDT_Float32

if len(im\_data.shape) == 3:  
    im\_bands, im\_height, im\_width = im\_data.shape  
else:  
    im\_bands, (im\_height, im\_width) = 1,im\_data.shape

driver = gdal.GetDriverByName("GTiff")  
dataset = driver.Create(filename, im\_width, im\_height, im\_bands, datatype)

dataset.SetGeoTransform(im\_geotrans)  
dataset.SetProjection(im\_proj)  
if im\_bands == 1:

    dataset.GetRasterBand(1).WriteArray(im\_data)  
else:  
    for i in range(im\_bands):  
        dataset.GetRasterBand(i+1).WriteArray(im\_data\[i\])

del dataset

def sha_raster(raster,shp,output):
srcArray = gdal_array.LoadFile(raster)
# 同时载入gdal库的图片从而获取geotransform
srcImage = gdal.Open(raster)
geoProj = srcImage.GetProjection()
geoTrans = srcImage.GetGeoTransform()
r = shapefile.Reader(shp)
# 将图层扩展转换为图片像素坐标
minX, minY, maxX, maxY = r.bbox
ulX, ulY = world2Pixel(geoTrans, minX, maxY)
lrX, lrY = world2Pixel(geoTrans, maxX, minY)
pxWidth = int(lrX - ulX)
pxHeight = int(lrY - ulY)
clip = srcArray[:, ulY:lrY, ulX:lrX]
# 为图片创建一个新的geomatrix对象以便附加地理参照数据
geoTrans = list(geoTrans)
geoTrans[0] = minX
geoTrans[3] = maxY
# 在一个空白的8字节黑白掩膜图片上把点映射为像元绘制市县
# 边界线
pixels = []
for p in r.shape(0).points:
pixels.append(world2Pixel(geoTrans, p[0], p[1]))
rasterPoly = Image.new("L", (pxWidth, pxHeight), 1)
# 使用PIL创建一个空白图片用于绘制多边形
rasterize = ImageDraw.Draw(rasterPoly)
rasterize.polygon(pixels, 0)
# 使用PIL图片转换为Numpy掩膜数组
mask = image2Array(rasterPoly)
name = os.path.basename(raster).split(".tif")[0]
outfile = output + "\\" + name+ "_cut.tif" # 对输出文件命名
# 根据掩膜图层对图像进行裁剪
clip = gdal_array.numpy.choose(mask, (clip, 0)).astype(gdal_array.numpy.uint16)
write_img(outfile, geoProj, geoTrans, clip)
gdal.ErrorReset()

if __name__ == "__main__":
raster = r'D:\test\裁剪实验\image\15.tif'
# 用于裁剪的多边形shp文件
shp = r'D:\test\裁剪实验\shp\2.shp'
# 裁剪后的栅格数据
output = r'D:\test\裁剪实验\out'

#依据shp创建掩膜进行对tiff文件的裁剪  
sha\_raster(raster,shp,output)