Geoprocessing with Python （一）

其他 2020-03-13 21:09:33 阅读次数: 0

Week 7: Misc stuff

Convert a shapefile to KML

  • Use “STATE_NAME” as the name field for the KML features
  • Include all of the attributes in the description

第七周：杂项

shapefile 转换 KML

  • 使用“STATE_NAME”作为 KML要素的Name字段
  • 在描述中包含所有的属性
ogr2ogr -f "KML" -dsco NameField="STATE_NAME" states.kml states.shp

Week 6 : More raster processing

Use a 3x3 high-pass filter to detect edges in band 1 of smallaster.img.

  • Use pixel notation (this is why you’re using smallaster.img – it would take forever to run on aster.img).
  • The output type will be float.
  • Don’t build pyramids so you can easily compare your output with output.img.

Use a 3x3 high-pass filter to detect edges in band 1 of aster.img.

  • Use slice notation. It will take a minute or two to run, so you should probably test on smallaster.img and then run in on aster.img after everything seems to be working.
  • The output type will be float.
  • Don’t build pyramids so you can easily compare your output with output.img.

更多栅格影像处理

使用 3x3 高通滤波来检测 smallaster.img （波段1） 边缘。

  • 采用像素表示法。
  • 输出类型 float。
  • 不建立金子塔影像

使用 3x3 高通滤波来检测 aster.img （波段1） 边缘。

  • 采用切片表示法。
  • 输出类型 float。
  • 不建立金子塔影像
# homework 6a
# high-pass filter using pixel notation
import os, sys, time
import numpy as np

from osgeo import gdal
from osgeo.gdalconst import *

import matplotlib.pyplot as plt
#plt.rcParams['figure.dpi'] = 300 #分辨率

start = time.time()

#os.chdir('c:/temp/py6')
# register all of the GDAL drivers
gdal.AllRegister()

# open the image
inDs = gdal.Open('smallaster.img', GA_ReadOnly)
if inDs is None:
  print ('Could not open smallaster.img')
  sys.exit(1)

# get image size
rows = inDs.RasterYSize
cols = inDs.RasterXSize

# read the input data
inBand = inDs.GetRasterBand(1)
inData = inBand.ReadAsArray(0, 0, cols, rows).astype(np.float32)

# do the calculation
outData = np.zeros((rows, cols), np.float32)

for i in range(1, rows-1):
    for j in range(1, cols-1):
        outData[i,j] = ((-0.7 * inData[i-1,j-1]) + 
                        (-1.0 * inData[i-1,j])   + 
                        (-0.7 * inData[i-1,j+1]) + 
                        (-1.0 * inData[i,j-1])   + 
                        ( 6.8 * inData[i,j])     + 
                        (-1.0 * inData[i,j+1])   +
                        (-0.7 * inData[i+1,j-1]) + 
                        (-1.0 * inData[i+1,j])   + 
                        (-0.7 * inData[i+1,j+1]))
# create the output image
driver = inDs.GetDriver()
outDs = driver.Create('highpass1.img', cols, rows, 1, GDT_Float32)
if outDs is None:
  print ('Could not create highpass1.img')
  sys.exit(1)
outBand = outDs.GetRasterBand(1)

# write the output data
outBand.WriteArray(outData, 0, 0)

# flush data to disk, set the NoData value and calculate stats
outBand.FlushCache()
stats = outBand.GetStatistics(0, 1)

# georeference the image and set the projection
outDs.SetGeoTransform(inDs.GetGeoTransform())
outDs.SetProjection(inDs.GetProjection())

print (time.time() - start, 'seconds')

plt.subplot(121)
plt.imshow(inData,cmap='gray')
plt.subplot(122)
plt.imshow(outData,cmap='gray')
plt.savefig("res1.png")
inDs = None
outDs = None

74.08075737953186 seconds

在这里插入图片描述

# homework 6b
# high-pass filter using slice notation
import os, sys, time
import numpy as np

from osgeo import gdal
from osgeo.gdalconst import *

import matplotlib.pyplot as plt

#plt.rcParams['figure.dpi'] = 300 #分辨率

start = time.time()

#os.chdir('c:/temp/py6')

# register all of the GDAL drivers
gdal.AllRegister()

# open the image
inDs = gdal.Open('smallaster.img', GA_ReadOnly)
if inDs is None:
  print ('Could not open aster.img')
  sys.exit(1)

# get image size
rows = inDs.RasterYSize
cols = inDs.RasterXSize

# read the input data
inBand = inDs.GetRasterBand(1)
inData = inBand.ReadAsArray(0, 0, cols, rows).astype(np.uint32)


# do the calculation
outData = np.zeros((rows, cols), np.float32)

outData[1:rows-1,1:cols-1] = ((-0.7 * inData[0:rows-2,0:cols-2]) +
                              (-1.0 * inData[0:rows-2,1:cols-1]) +
                              (-0.7 * inData[0:rows-2,2:cols]) +
                              (-1.0 * inData[1:rows-1,0:cols-2]) + 
                              ( 6.8 * inData[1:rows-1,1:cols-1]) +
                              (-1.0 * inData[1:rows-1,2:cols]) + 
                              (-0.7 * inData[2:rows,0:cols-2]) +
                              (-1.0 * inData[2:rows,1:cols-1]) + 
                              (-0.7 * inData[2:rows,2:cols]))

# outData[1:rows-1,1:cols-1] = ((-1.0 * inData[0:rows-2,0:cols-2]) +
#                               (-1.0 * inData[0:rows-2,1:cols-1]) +
#                               (-1.0 * inData[0:rows-2,2:cols]) +
#                               (-1.0 * inData[1:rows-1,0:cols-2]) + 
#                               ( 8.0 * inData[1:rows-1,1:cols-1]) +
#                               (-1.0 * inData[1:rows-1,2:cols]) + 
#                               (-1.0 * inData[2:rows,0:cols-2]) +
#                               (-1.0 * inData[2:rows,1:cols-1]) + 
#                               (-1.0 * inData[2:rows,2:cols]))


# create the output image
driver = inDs.GetDriver()
outDs = driver.Create('highpass3.img', cols, rows, 1, GDT_Float32)#GDT_CInt32
if outDs is None:
  print ('Could not create highpass3.img')
  sys.exit(1)
outBand = outDs.GetRasterBand(1)

# write the output data
outBand.WriteArray(outData, 0, 0)

# flush data to disk, set the NoData value and calculate stats
outBand.FlushCache()
stats = outBand.GetStatistics(0, 1)

# georeference the image and set the projection
outDs.SetGeoTransform(inDs.GetGeoTransform())
outDs.SetProjection(inDs.GetProjection())

print (time.time() - start, 'seconds')

plt.subplot(121)
plt.imshow(inData,cmap='gray')

plt.subplot(122)
plt.imshow(outData,cmap='gray')
plt.savefig("res2.png")
inDs = None
outDs = None



0.48896169662475586 seconds

在这里插入图片描述

# homework 6b
# low-pass filter using slice notation
import os, sys, time
import numpy as np

from osgeo import gdal
from osgeo.gdalconst import *

import matplotlib.pyplot as plt

#plt.rcParams['figure.dpi'] = 300 #分辨率

start = time.time()

#os.chdir('c:/temp/py6')

# register all of the GDAL drivers
gdal.AllRegister()

# open the image
inDs = gdal.Open('smallaster.img', GA_ReadOnly)
if inDs is None:
  print ('Could not open aster.img')
  sys.exit(1)

# get image size
rows = inDs.RasterYSize
cols = inDs.RasterXSize

# read the input data
inBand = inDs.GetRasterBand(1)
inData = inBand.ReadAsArray(0, 0, cols, rows).astype(np.uint32)

#plt.figure(figsize=(10,10)) 


# do the calculation
outData = np.zeros((rows, cols), np.float32)

outData[1:rows-1,1:cols-1] = ((0.111 * inData[0:rows-2,0:cols-2]) +
                              (0.111 * inData[0:rows-2,1:cols-1]) +
                              (0.111* inData[0:rows-2,2:cols]) +
                              (0.111 * inData[1:rows-1,0:cols-2]) + 
                              (0.111 * inData[1:rows-1,1:cols-1]) +
                              (0.111 * inData[1:rows-1,2:cols]) + 
                              (0.111 * inData[2:rows,0:cols-2]) +
                              (0.111 * inData[2:rows,1:cols-1]) + 
                              (0.111 * inData[2:rows,2:cols]))

# outData[1:rows-1,1:cols-1] = ((-1.0 * inData[0:rows-2,0:cols-2]) +
#                               (-1.0 * inData[0:rows-2,1:cols-1]) +
#                               (-1.0 * inData[0:rows-2,2:cols]) +
#                               (-1.0 * inData[1:rows-1,0:cols-2]) + 
#                               ( 8.0 * inData[1:rows-1,1:cols-1]) +
#                               (-1.0 * inData[1:rows-1,2:cols]) + 
#                               (-1.0 * inData[2:rows,0:cols-2]) +
#                               (-1.0 * inData[2:rows,1:cols-1]) + 
#                               (-1.0 * inData[2:rows,2:cols]))


# create the output image
driver = inDs.GetDriver()
outDs = driver.Create('lowpass3.img', cols, rows, 1, GDT_Float32)#GDT_CInt32
if outDs is None:
  print ('Could not create highpass3.img')
  sys.exit(1)
outBand = outDs.GetRasterBand(1)

# write the output data
outBand.WriteArray(outData, 0, 0)

# flush data to disk, set the NoData value and calculate stats
outBand.FlushCache()
stats = outBand.GetStatistics(0, 1)

# georeference the image and set the projection
outDs.SetGeoTransform(inDs.GetGeoTransform())
outDs.SetProjection(inDs.GetProjection())



print (time.time() - start, 'seconds')

plt.subplot(121)
plt.imshow(inData,cmap='gray')

plt.subplot(122)
plt.imshow(outData,cmap='gray')

plt.savefig("res3.png")
inDs = None
outDs = None

0.4882187843322754 seconds

在这里插入图片描述

Projecting rasters
设置栅格投影
from osgeo import gdal, osr 
from osgeo.gdalconst import *
import time
import matplotlib.pyplot as plt 

start = time.time()

#获取源数据及栅格信息
gdal.AllRegister()
src_data = gdal.Open("smallaster.img")
#获取源的坐标信息
srcSRS_wkt=src_data.GetProjection()
srcSRS=osr.SpatialReference()
srcSRS.ImportFromWkt(srcSRS_wkt)
#print("原数据空间参考：",srcSRS)
#获取栅格尺寸
src_width = src_data.RasterXSize
src_height = src_data.RasterYSize
src_count=src_data.RasterCount
#print("原数据图像信息：\n",src_width,src_height,src_count)
#获取源图像的仿射变换参数
src_trans=src_data.GetGeoTransform()

#左上角空间坐标
OriginLX_src=src_trans[0]
OriginTY_src=src_trans[3]
# 像素比
pixl_w_src=src_trans[1]
pixl_h_src=src_trans[5]
# 右下角坐标
OriginRX_src=OriginLX_src+pixl_w_src*src_width
OriginBY_src=OriginTY_src+pixl_h_src*src_height
#创建输出图像
driver = gdal.GetDriverByName("GTiff")
driver.Register()
dst_data = driver.Create("tpix1.tif",src_width,src_height,src_count)

#设置输出图像的空间参考WGS84
dstSRS=osr.SpatialReference()
dstSRS.ImportFromEPSG(4326)
#投影转换
ct=osr.CoordinateTransformation(srcSRS,dstSRS)
#计算目标影像的左上和右下坐标,即目标影像的仿射变换参数
OriginLX_dst,OriginTY_dst,temp=ct.TransformPoint(OriginLX_src,OriginTY_src)
OriginRX_dst,OriginBY_dst,temp=ct.TransformPoint(OriginRX_src,OriginBY_src)
 
pixl_w_dst=(OriginRX_dst-OriginLX_dst)/src_width
pixl_h_dst=(OriginBY_dst-OriginTY_dst)/src_height

dst_trans=[OriginLX_dst,pixl_w_dst,0,OriginTY_dst,0,pixl_h_dst]
#
dstSRS_wkt=dstSRS.ExportToWkt()
#设置仿射变换系数及投影
dst_data.SetGeoTransform(dst_trans)
dst_data.SetProjection(dstSRS_wkt)
#重新投影
gdal.ReprojectImage(src_data,dst_data,srcSRS_wkt,dstSRS_wkt,GRA_Bilinear)
print (time.time() - start, 'seconds')
#创建四级金字塔
#gdal.SetConfigOption('HFA_USE_RRD', 'YES')
#dst_data.BuildOverviews(overviewlist=[2,4,8,16])
#计算统计值
# for i in range(0,src_count):
#     band=dst_data.GetRasterBand(i+1)
#     band.SetNoDataValue(-99)
#     print (band.GetStatistics(0,1))

# read the input data
inBand = src_data.GetRasterBand(1)
inData = inBand.ReadAsArray(0, 0, src_width,src_height).astype(np.uint32)
plt.subplot(121)
plt.imshow(inData)

inBand = dst_data.GetRasterBand(1)
dstData = inBand.ReadAsArray(0, 0, src_width,src_height).astype(np.uint32)
plt.subplot(122)
plt.imshow(dstData)

1.7203800678253174 seconds

from osgeo import gdal, osr 
from osgeo.gdalconst import *
import numpy as np
import time

start = time.time()

#获取源数据及栅格信息
gdal.AllRegister()
src_data = gdal.Open("smallaster.img")
#获取源的坐标信息
srcSRS_wkt=src_data.GetProjection()
srcSRS=osr.SpatialReference()
srcSRS.ImportFromWkt(srcSRS_wkt)
#print("原数据空间参考：",srcSRS)
#获取栅格尺寸
src_width = src_data.RasterXSize
src_height = src_data.RasterYSize
src_count=src_data.RasterCount
#print("原数据图像信息：\n",src_width,src_height,src_count)
#获取源图像的仿射变换参数
src_trans=src_data.GetGeoTransform()

#左上角空间坐标
OriginLX_src=src_trans[0]
OriginTY_src=src_trans[3]
# 像素比
pixl_w_src=src_trans[1]
pixl_h_src=src_trans[5]
# 右下角坐标
OriginRX_src=OriginLX_src+pixl_w_src*src_width
OriginBY_src=OriginTY_src+pixl_h_src*src_height
#创建输出图像
driver = gdal.GetDriverByName("GTiff")
driver.Register()
dst_data = driver.Create("tpix2.tif",src_width,src_height,src_count)

for i in range(src_count):
    inBand = src_data.GetRasterBand(i+1)
    inData = inBand.ReadAsArray(0, 0, src_width, src_height).astype(np.uint32)
    outBand = dst_data.GetRasterBand(i+1)
    outBand.WriteArray(inData, 0, 0)# write the output data
    outBand.FlushCache()
    stats = outBand.GetStatistics(0, 1)
    inData=None

#设置输出图像的空间参考WGS84
dstSRS=osr.SpatialReference()
dstSRS.ImportFromEPSG(4326)
#投影转换
ct=osr.CoordinateTransformation(srcSRS,dstSRS)
#计算目标影像的左上和右下坐标,即目标影像的仿射变换参数
OriginLX_dst,OriginTY_dst,temp=ct.TransformPoint(OriginLX_src,OriginTY_src)
OriginRX_dst,OriginBY_dst,temp=ct.TransformPoint(OriginRX_src,OriginBY_src)
 
pixl_w_dst=(OriginRX_dst-OriginLX_dst)/src_width
pixl_h_dst=(OriginBY_dst-OriginTY_dst)/src_height

dst_trans=[OriginLX_dst,pixl_w_dst,0,OriginTY_dst,0,pixl_h_dst]
#
dstSRS_wkt=dstSRS.ExportToWkt()
#设置仿射变换系数及投影
dst_data.SetGeoTransform(dst_trans)
dst_data.SetProjection(dstSRS_wkt)
print (time.time() - start, 'seconds')
#重新投影
#gdal.ReprojectImage(src_data,dst_data,srcSRS_wkt,dstSRS_wkt,GRA_Bilinear)
#创建四级金字塔
#gdal.SetConfigOption('HFA_USE_RRD', 'YES')
#dst_data.BuildOverviews(overviewlist=[2,4,8,16])
#计算统计值
# for i in range(0,src_count):
#     band=dst_data.GetRasterBand(i+1)
#     band.SetNoDataValue(-99)
#     print (band.GetStatistics(0,1))


0.31161069869995117 seconds
Method comparison
比较运算
# homework 6b
# low-pass filter using slice notation
import os, time,sys
import numpy as np

from osgeo import gdal
from osgeo.gdalconst import *

import matplotlib.pyplot as plt

start = time.time()

# register all of the GDAL drivers
gdal.AllRegister()

# open the image
inDs = gdal.Open('dem.tiff', GA_ReadOnly)
if inDs is None:
  print ('Could not dem.tiff')
  sys.exit(1)

# get image size
rows = inDs.RasterYSize
cols = inDs.RasterXSize
nbands = inDs.RasterCount
print(rows,cols,nbands)
# read the input data
inBand = inDs.GetRasterBand(1)
inData = inBand.ReadAsArray(0, 0, cols, rows).astype(np.uint32)

plt.figure(figsize=(10,10))
plt.subplot(141)
plt.imshow(inData,cmap='gray')
plt.title("all")

outData = np.zeros((rows, cols), np.uint32)
outData1 = np.greater(inData, 1000)

plt.subplot(142)
plt.imshow(outData1,cmap='gray')
plt.title("elev>1000")

outData2= np.where((inData < 1000) & ( inData > 500), 1, 0)

plt.subplot(143)
plt.imshow(outData2,cmap='gray')
plt.title("elev>500 & elev <1000")

plt.savefig("res4.png")

324 368 1

在这里插入图片描述
参考文档 ：

  • Geoprocessing with Python using Open Source GIS(https://www.gis.usu.edu/~chrisg/python/2009/)
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