关于获取要素：
获取要素最好用GetNextFeature而不要用索引值
因为shapefile在实现的时候是以0为起始索引。mapinfo的tab是以1为起始值，这样在获取的时候需要判断读取数据源类型。但是用GetNextFeature就会自动从正确的起始值开始读取。如果需要重新开始读取，就用ResetReading重新设置读取位置到开头。
获取要素中的坐标值最好用ExportToWkb而不要用GetX,GetY
ExportToWkb可以批量获取所有的坐标值。而GetX，GetY只能获取单独的一个值。在绘制的时候还要费神去把它们合成一个数组，而获取了wkb，就可以进行批量解析，一下生成数组，这样，我们就可以利用到了强大的Numeric模块，进行神奇的矩阵魔术。至于wkb的格式，[http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.db2.udb.doc/opt/rsbp4121.htm 这里]有简单的说明。 我写了一个小模块，用于解析wkb
import struct
import ogr
from array import array
import sys
endian_name = sys.byteorder
wkbXDR = '>' # Big Endian
wkbNDR = '<' # Little Endian
def choose(bool, a, b):
return (bool and [a] or [b])[0]
BTOR = choose(endian_name == 'little',wkbNDR,wkbNDR)
def up_point(wkb,endian):
points = struct.unpack(endian+"2d",wkb)
return points
def up_linestring(wkb,endian):
lenght = struct.unpack(endian+'I',wkb[:4])[0]
points = array('d',wkb[4:4+lenght*16])
if endian != BTOR : points.byteswap()
return points
def up_linearring(wkb,ringcount,endian):
points = []
ptr = 0
for i in range(ringcount):
length = struct.unpack(endian+"I",wkb[ptr:ptr+4])[0]
ps = array('d',wkb[ptr+4:ptr+4+length*16])
if endian != BTOR : ps.byteswap()
points.append(ps)
ptr += 4+length*16
return points,ptr
def up_polygon(wkb,endian,count=1):
if count <= 1:
ringcount = struct.unpack(endian+'I',wkb[:4])[0]
points = up_linearring(wkb[4:],ringcount,endian)[0]
else:
points = []
ptr = 0
for i in range(count):
ringcount = struct.unpack(endian+'I',wkb[ptr:ptr+4])[0]
ps,ringlen = up_linearring(wkb[ptr+4:],ringcount,endian)
points.append(ps)
ptr += 4+ringlen
return points
def up_mpoint(wkb,endian):
subcount = struct.unpack(endian+'I',wkb[:4])[0]
points = []
ptr = 4
for i in range(subcount):
subps = up_point(wkb[ptr:],endian)
points.append(subps)
ptr += 4+len(points)*16
return points
def up_mlinestring(wkb,endian):
subcount = struct.unpack(endian+'I',wkb[:4])[0]
points = []
ptr = 4
for i in range(subcount):
subps = up_linestring(wkb[ptr:],endian)
points.append(subps)
ptr += 4+len(subps)*16
return points
def up_mpolygon(wkb,endian):
subcount = struct.unpack(endian+'I',wkb[:4])[0]
points = up_polygon(wkb[4:],endian,subcount)
return points
fun_map = {
ogr.wkbPoint : up_point,
ogr.wkbLineString : up_linestring,
ogr.wkbPolygon : up_polygon,
ogr.wkbMultiPoint : up_mpoint,
ogr.wkbMultiLineString : up_mlinestring,
ogr.wkbMultiPolygon : up_mpolygon
}
def WkbUnPacker(wkb):
endian_t = struct.unpack('b',wkb[0])[0]
endian = choose(endian_t,'<','>')
wkbtype = struct.unpack(endian+'I',wkb[1:5])[0]
#print wkbtype
foo = fun_map[wkbtype]
points = foo(wkb[5:],endian)
return [endian_t,wkbtype,points]
if __name__ == "__main__":
import time
ds = ogr.Open("e:/gisdata/data/boundry.tab")
layer = ds.GetLayer()
begt = time.time()
#count = layer.GetFeatureCount()
count = 2
for i in [1]:#range(count):
feature = layer.GetFeature(i)
geom = feature.GetGeometryRef()
#print geom.ExportToWkt()
wkb = geom.ExportToWkb()
wkbarr = WkbUnPacker(wkb)
#print wkbarr
print time.time()-begt因为手上数据不全，无法测试多点、多线、多多边形的情况。如果谁有数据，可以测试一下，如果有错，希望能反馈给我！
加快读取大量feature时的循环速度
ogr在python绑定中读取大量feature时速度总是很慢。16000条记录单单只是获取Feature竟然运行了130秒，简直令人无法忍受。后来打开了ogr.py察看了一下绑定代码，发现在Feature类中有“setarr"和"getarr"两个自省方法在创建Feature后不断运行。试着注释掉它们，速度一下就提高到2秒。简直是飞跃啊！不过注释掉自省方法会不会有后遗症，我现在还不敢说（如果大家发现注释掉这两个自省方法有问题，或者不需要注释掉调代码而有更好的方法，希望能反馈给我）。
反馈
如果您发现我写的东西中有问题，或者您对我写的东西有意见，请一定要发邮件跟我讲，Email( MailTo(linux_23 AT 163 DOT com) )