这篇文章主要介绍了python 矢量数据转栅格数据代码实例,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

投影包osr与proj4的使用

osr投影转换示例

from osgeo import osr,ogr#定义投影#wgs84source=osr.SpatialReference()source.ImportFromEPSG(4326)#googletarget=osr.SpatialReference()target.ImportFromEPSG(3857)#简单投影转换coordTrans=osr.CoordinateTransformation(source,target)#点转换coordTrans.TransformPoint(117,40)#点数组转换coordTrans.TransformPoints([(117,40),(117.5,39.5)])#SF几何对象转换g=ogr.CreateGeometryFromWkt("POINT(117 40)")#转换前wgs84print(g.ExportToWkt())print(g.GetX(),g.GetY())#转换后googleg.Transform(coordTrans)print(g.ExportToWkt())print(g.GetX(),g.GetY())2.投影转换示例from pyproj import Proj,Geod,transform# projection 1: UTM zone 15, grs80 ellipse, NAD83 datum# (defined by epsg code 26915)p1 = Proj(init='epsg:26915')# projection 2: UTM zone 15, clrk66 ellipse, NAD27 datump2 = Proj(init='epsg:26715')#点的转换(首先将地理坐标转换成p1投影坐标系下的平面直角坐标，再将x1,y1转换到p2投影坐标系下，最后将p2投影坐标系下的平面直角坐标转换成地理坐标)x1,y1=p1(-92.199881,38.56694)x2, y2 = transform(p1,p2,x1,y1)print('%9.3f %11.3f' % (x1,y1))print('%9.3f %11.3f' % (x2,y2))print('%8.3f %5.3f' % p2(x2,y2,inverse=True))#点数组的转换lats = (38.83,39.32,38.75)lons = (-92.22,-94.72,-90.37)x1,y1=p1(lons,lats)x2,y2=transform(p1,p2,x1,y1)xy=x1+y1print('%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy)xy=x2+y2print('%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy)lons, lats = p2(x2,y2,inverse=True)xy=lons+latsprint('%8.3f %8.3f %8.3f %5.3f %5.3f %5.3f' % xy)p1 = Proj(proj='latlong',datum='WGS84')x1 = -111.5; y1 = 45.25919444444p2 = Proj(proj="utm",zone=10,datum='NAD27')x2, y2 = transform(p1, p2, x1, y1)print("%s %s" % (str(x2)[:9],str(y2)[:9]))

栅格数据投影转换

#栅格数据投影转换from osgeo import gdal,osrfrom osgeo.gdalconst import *#源图像投影source=osr.SpatialReference()source.ImportFromEPSG(32650)#目标图像投影target=osr.SpatialReference()target.ImportFromEPSG(3857)coordTrans=osr.CoordinateTransformation(source,target)#打开源图像文件ds=gdal.Open("fdem.tif")#仿射矩阵六参数mat=ds.GetGeoTransform()#源图像的左上角与右下角像素，在目标图像中的坐标(ulx, uly, ulz)=coordTrans.TransformPoint(mat[0],mat[3])(lrx, lry, lrz ) = coordTrans.TransformPoint(mat[0] + mat[1]*ds.RasterXSize, mat[3] + mat[5]* ds.RasterYSize )#创建目标图像文件（空白图像），行列数、波段数以及数值类型仍等同原图像driver=gdal.GetDriverByName("GTiff")ts=driver.Create("fdem_lonlat.tif",ds.RasterXSize,ds.RasterYSize,1,GDT_UInt16)#转换后图像的分辨率resolution=(int)((lrx-ulx)/ds.RasterXSize)#转换后图像的六个放射变换参数mat2=[ulx, resolution,0,uly,0, -resolution]ts.SetGeoTransform(mat2)ts.SetProjection(target.ExportToWkt())#投影转换后需要做重采样gdal.ReprojectImage(ds, ts, source.ExportToWkt(), target.ExportToWkt(), gdal.GRA_Bilinear)#关闭ds = Nonets= None

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