栅格图层

关闭栅格数据

from osgeo import gdal

# 打开数据
ds = gdal.Open('test.tif')

# 关闭数据（非必须）
ds = None

获取元数据

from osgeo import gdal
gtif = gdal.Open( "merge.tif" )
print (gtif.GetMetadata())

波段融合

from osgeo import gdal
# 打开数据
ds1=gdal.Open("LM51130321998341HAJ00_B1.TIF")
ds2=gdal.Open("LM51130321998341HAJ00_B2.TIF")
ds3=gdal.Open("LM51130321998341HAJ00_B3.TIF")
ds4=gdal.Open("LM51130321998341HAJ00_B4.TIF")
# 获取波段
b1=ds1.GetRasterBand(1)
b2=ds1.GetRasterBand(1)
b3=ds1.GetRasterBand(1)
b4=ds1.GetRasterBand(1)
# 创建数据
driver=gdal.GetDriverByName("GTiff")
out_ds=driver.Create('merge.tif',ds1.RasterXSize,ds1.RasterYSize,4,gdal.GDT_Byte)
# 设置坐标和投影
out_ds.SetGeoTransform(ds1.GetGeoTransform())
out_ds.SetProjection(ds1.GetProjection())

ob1=out_ds.GetRasterBand(1)
ob2=out_ds.GetRasterBand(2)
ob3=out_ds.GetRasterBand(3)
ob4=out_ds.GetRasterBand(4)
# 写入数据
ob1.WriteArray(b1.ReadAsArray())
ob2.WriteArray(b2.ReadAsArray())
ob3.WriteArray(b3.ReadAsArray())
ob4.WriteArray(b4.ReadAsArray())

out_ds=None

获取波段

from osgeo import gdal
import sys
# 允许python异常
gdal.UseExceptions()

try:
    src_ds = gdal.Open( "merge.tif" )
except RuntimeError as e:
    print(e)
    sys.exit(1)

try:
    srcband = src_ds.GetRasterBand(1)
except RuntimeError as e:
    # 试一下 GetRasterBand(10)
    print(e)
    sys.exit(1)

遍历波段

from osgeo import gdal
import sys

src_ds = gdal.Open( "merge.tif" )

print ("波段个数: ", src_ds.RasterCount)
for band in range( src_ds.RasterCount ):
    band += 1
    print ("获取波段: ", band)
    srcband = src_ds.GetRasterBand(band)
    if srcband is None:
        continue

    stats = srcband.GetStatistics( True, True )
    if stats is None:
        continue

    print ("[ STATS ] =  Minimum=%.3f, Maximum=%.3f, Mean=%.3f, StdDev=%.3f" % ( \
                stats[0], stats[1], stats[2], stats[3] ))

获取波段信息

from osgeo import gdal
import sys
gdal.UseExceptions()


def main( band_num, input_file ):
    src_ds = gdal.Open( input_file )

    try:
        srcband = src_ds.GetRasterBand(band_num)
    except RuntimeError as e:
        print( e)
        sys.exit(1)


    print ("[ NO DATA VALUE ] = ", srcband.GetNoDataValue())
    print ("[ MIN ] = ", srcband.GetMinimum())
    print ("[ MAX ] = ", srcband.GetMaximum())
    print ("[ SCALE ] = ", srcband.GetScale())
    print( "[ UNIT TYPE ] = ", srcband.GetUnitType())
    ctable = srcband.GetColorTable()

    if ctable is None:
        print ('No ColorTable found')
        sys.exit(1)

    print ("[ COLOR TABLE COUNT ] = ", ctable.GetCount())
    for i in range( 0, ctable.GetCount() ):
        entry = ctable.GetColorEntry( i )
        if not entry:
            continue
        print ("[ COLOR ENTRY RGB ] = ", ctable.GetColorEntryAsRGB( i, entry ))

main( 1,"merge.tif" )

多边形化栅格波段

from osgeo import gdal, ogr
import sys

gdal.UseExceptions()

src_ds = gdal.Open( "test.tif" )

srcband = src_ds.GetRasterBand(3)

dst_layername = "POLYGONIZED_STUFF"
drv = ogr.GetDriverByName("ESRI Shapefile")
dst_ds = drv.CreateDataSource( dst_layername + ".shp" )
dst_layer = dst_ds.CreateLayer(dst_layername, srs = None )

gdal.Polygonize( srcband, None, dst_layer, -1, [], callback=None )

栅格化矢量数据

from osgeo import gdal, ogr

# 定义像素大小和无效值
pixel_size = 25
NoData_value = -9999

vector_fn = 'test.shp'

raster_fn = 'test.tif'

# 打开数据并读取范围
source_ds = ogr.Open(vector_fn)
source_layer = source_ds.GetLayer()
x_min, x_max, y_min, y_max = source_layer.GetExtent()

x_res = int((x_max - x_min) / pixel_size)
y_res = int((y_max - y_min) / pixel_size)
target_ds = gdal.GetDriverByName('GTiff').Create(raster_fn, x_res, y_res, 1, gdal.GDT_Byte)
target_ds.SetGeoTransform((x_min, pixel_size, 0, y_max, 0, -pixel_size))
band = target_ds.GetRasterBand(1)
band.SetNoDataValue(NoData_value)

# 栅格化
gdal.RasterizeLayer(target_ds, [1], source_layer, burn_values=[0])

用shapefile裁剪栅格

from osgeo import gdal, gdalnumeric, ogr, osr
from PIL import Image, ImageDraw
import os, sys
gdal.UseExceptions()


def imageToArray(i):
    """
    Image转换为数组.
    """
    a=gdalnumeric.fromstring(i.tostring(),'b')
    a.shape=i.im.size[1], i.im.size[0]
    return a

def arrayToImage(a):
    """
   数组转换为Image.
    """
    i=Image.fromstring('L',(a.shape[1],a.shape[0]),
            (a.astype('b')).tostring())
    return i

def world2Pixel(geoMatrix, x, y):
  """
  用地理仿射变换计算像素坐标
  """
  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) / xDist)
  return (pixel, line)

#
#  EDIT: this is basically an overloaded
#  version of the gdal_array.OpenArray passing in xoff, yoff explicitly
#  so we can pass these params off to CopyDatasetInfo
#
def OpenArray( array, prototype_ds = None, xoff=0, yoff=0 ):
    ds = gdal.Open( gdalnumeric.GetArrayFilename(array) )

    if ds is not None and prototype_ds is not None:
        if type(prototype_ds).__name__ == 'str':
            prototype_ds = gdal.Open( prototype_ds )
        if prototype_ds is not None:
            gdalnumeric.CopyDatasetInfo( prototype_ds, ds, xoff=xoff, yoff=yoff )
    return ds

def histogram(a, bins=range(0,256)):
  """
  Histogram function for multi-dimensional array.
  a = array
  bins = range of numbers to match
  """
  fa = a.flat
  n = gdalnumeric.searchsorted(gdalnumeric.sort(fa), bins)
  n = gdalnumeric.concatenate([n, [len(fa)]])
  hist = n[1:]-n[:-1]
  return hist

def stretch(a):
  """
  Performs a histogram stretch on a gdalnumeric array image.
  """
  hist = histogram(a)
  im = arrayToImage(a)
  lut = []
  for b in range(0, len(hist), 256):
    # step size
    step = reduce(operator.add, hist[b:b+256]) / 255
    # create equalization lookup table
    n = 0
    for i in range(256):
      lut.append(n / step)
      n = n + hist[i+b]
  im = im.point(lut)
  return imageToArray(im)

def main( shapefile_path, raster_path ):
    # 读取数据到数组
    srcArray = gdalnumeric.LoadFile(raster_path)

    # 获取地理仿射变换
    srcImage = gdal.Open(raster_path)
    geoTrans = srcImage.GetGeoTransform()

    # 获取矢量图层
    shapef = ogr.Open(shapefile_path)
    lyr = shapef.GetLayer( os.path.split( os.path.splitext( shapefile_path )[0] )[1] )
    poly = lyr.GetNextFeature()

    # 获取范围
    minX, maxX, minY, maxY = lyr.GetExtent()
    ulX, ulY = world2Pixel(geoTrans, minX, maxY)
    lrX, lrY = world2Pixel(geoTrans, maxX, minY)

    # 计算像素大小
    pxWidth = int(lrX - ulX)
    pxHeight = int(lrY - ulY)

    clip = srcArray[:, ulY:lrY, ulX:lrX]

    #
    # 像素偏移
    #
    xoffset =  ulX
    yoffset =  ulY
    print ("Xoffset, Yoffset = ( %f, %f )" % ( xoffset, yoffset ))

    # 创建新的仿射变换
    geoTrans = list(geoTrans)
    geoTrans[0] = minX
    geoTrans[3] = maxY

    # Map points to pixels for drawing the
    # boundary on a blank 8-bit,
    # black and white, mask image.
    points = []
    pixels = []
    geom = poly.GetGeometryRef()
    pts = geom.GetGeometryRef(0)
    for p in range(pts.GetPointCount()):
      points.append((pts.GetX(p), pts.GetY(p)))
    for p in points:
      pixels.append(world2Pixel(geoTrans, p[0], p[1]))
    rasterPoly = Image.new("L", (pxWidth, pxHeight), 1)
    rasterize = ImageDraw.Draw(rasterPoly)
    rasterize.polygon(pixels, 0)
    mask = imageToArray(rasterPoly)

    # Clip the image using the mask
    clip = gdalnumeric.choose(mask, \
        (clip, 0)).astype(gdalnumeric.uint8)

    # This image has 3 bands so we stretch each one to make them
    # visually brighter
    for i in range(3):
      clip[i,:,:] = stretch(clip[i,:,:])

    # Save new tiff
    #
    #  EDIT: instead of SaveArray, let's break all the
    #  SaveArray steps out more explicity so
    #  we can overwrite the offset of the destination
    #  raster
    #
    ### the old way using SaveArray
    #
    # gdalnumeric.SaveArray(clip, "OUTPUT.tif", format="GTiff", prototype=raster_path)
    #
    ###
    #
    gtiffDriver = gdal.GetDriverByName( 'GTiff' )
    if gtiffDriver is None:
        raise ValueError("Can't find GeoTiff Driver")
    gtiffDriver.CreateCopy( "OUTPUT.tif",
        OpenArray( clip, prototype_ds=raster_path, xoff=xoffset, yoff=yoffset )
    )

    # Save as an 8-bit jpeg for an easy, quick preview
    clip = clip.astype(gdalnumeric.uint8)
    gdalnumeric.SaveArray(clip, "OUTPUT.jpg", format="JPEG")

    gdal.ErrorReset()


if __name__ == '__main__':

    #
    # example run : $ python clip.py /<full-path>/<shapefile-name>.shp /<full-path>/<raster-name>.tif
    #
    if len( sys.argv ) < 2:
        print "[ ERROR ] you must two args. 1) the full shapefile path and 2) the full raster path"
        sys.exit( 1 )

    main( sys.argv[1], sys.argv[2] )

区域统计

import gdal, ogr, osr, numpy
import sys


def zonal_stats(feat, input_zone_polygon, input_value_raster):

    # Open data
    raster = gdal.Open(input_value_raster)
    shp = ogr.Open(input_zone_polygon)
    lyr = shp.GetLayer()

    # Get raster georeference info
    transform = raster.GetGeoTransform()
    xOrigin = transform[0]
    yOrigin = transform[3]
    pixelWidth = transform[1]
    pixelHeight = transform[5]

    # Reproject vector geometry to same projection as raster
    sourceSR = lyr.GetSpatialRef()
    targetSR = osr.SpatialReference()
    targetSR.ImportFromWkt(raster.GetProjectionRef())
    coordTrans = osr.CoordinateTransformation(sourceSR,targetSR)
    feat = lyr.GetNextFeature()
    geom = feat.GetGeometryRef()
    geom.Transform(coordTrans)

    # Get extent of feat
    geom = feat.GetGeometryRef()
    if (geom.GetGeometryName() == 'MULTIPOLYGON'):
        count = 0
        pointsX = []; pointsY = []
        for polygon in geom:
            geomInner = geom.GetGeometryRef(count)
            ring = geomInner.GetGeometryRef(0)
            numpoints = ring.GetPointCount()
            for p in range(numpoints):
                    lon, lat, z = ring.GetPoint(p)
                    pointsX.append(lon)
                    pointsY.append(lat)
            count += 1
    elif (geom.GetGeometryName() == 'POLYGON'):
        ring = geom.GetGeometryRef(0)
        numpoints = ring.GetPointCount()
        pointsX = []; pointsY = []
        for p in range(numpoints):
                lon, lat, z = ring.GetPoint(p)
                pointsX.append(lon)
                pointsY.append(lat)

    else:
        sys.exit("ERROR: Geometry needs to be either Polygon or Multipolygon")

    xmin = min(pointsX)
    xmax = max(pointsX)
    ymin = min(pointsY)
    ymax = max(pointsY)

    # Specify offset and rows and columns to read
    xoff = int((xmin - xOrigin)/pixelWidth)
    yoff = int((yOrigin - ymax)/pixelWidth)
    xcount = int((xmax - xmin)/pixelWidth)+1
    ycount = int((ymax - ymin)/pixelWidth)+1

    # Create memory target raster
    target_ds = gdal.GetDriverByName('MEM').Create('', xcount, ycount, 1, gdal.GDT_Byte)
    target_ds.SetGeoTransform((
        xmin, pixelWidth, 0,
        ymax, 0, pixelHeight,
    ))

    # Create for target raster the same projection as for the value raster
    raster_srs = osr.SpatialReference()
    raster_srs.ImportFromWkt(raster.GetProjectionRef())
    target_ds.SetProjection(raster_srs.ExportToWkt())

    # Rasterize zone polygon to raster
    gdal.RasterizeLayer(target_ds, [1], lyr, burn_values=[1])

    # Read raster as arrays
    banddataraster = raster.GetRasterBand(1)
    dataraster = banddataraster.ReadAsArray(xoff, yoff, xcount, ycount).astype(numpy.float)

    bandmask = target_ds.GetRasterBand(1)
    datamask = bandmask.ReadAsArray(0, 0, xcount, ycount).astype(numpy.float)

    # Mask zone of raster
    zoneraster = numpy.ma.masked_array(dataraster,  numpy.logical_not(datamask))

    # Calculate statistics of zonal raster
    return numpy.average(zoneraster),numpy.mean(zoneraster),numpy.median(zoneraster),numpy.std(zoneraster),numpy.var(zoneraster)


def loop_zonal_stats(input_zone_polygon, input_value_raster):

    shp = ogr.Open(input_zone_polygon)
    lyr = shp.GetLayer()
    featList = range(lyr.GetFeatureCount())
    statDict = {}

    for FID in featList:
        feat = lyr.GetFeature(FID)
        meanValue = zonal_stats(feat, input_zone_polygon, input_value_raster)
        statDict[FID] = meanValue
    return statDict

def main(input_zone_polygon, input_value_raster):
    return loop_zonal_stats(input_zone_polygon, input_value_raster)


if __name__ == "__main__":

    #
    # Returns for each feature a dictionary item (FID) with the statistical values in the following order: Average, Mean, Medain, Standard Deviation, Variance
    #
    # example run : $ python grid.py <full-path><output-shapefile-name>.shp xmin xmax ymin ymax gridHeight gridWidth
    #

    if len( sys.argv ) != 3:
        print "[ ERROR ] you must supply two arguments: input-zone-shapefile-name.shp input-value-raster-name.tif "
        sys.exit( 1 )
    print 'Returns for each feature a dictionary item (FID) with the statistical values in the following order: Average, Mean, Medain, Standard Deviation, Variance'
    print main( sys.argv[1], sys.argv[2] )

从数组创建栅格

import gdal, ogr, os, osr
import numpy as np


def array2raster(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array):

    cols = array.shape[1]
    rows = array.shape[0]
    originX = rasterOrigin[0]
    originY = rasterOrigin[1]

    driver = gdal.GetDriverByName('GTiff')
    outRaster = driver.Create(newRasterfn, cols, rows, 1, gdal.GDT_Byte)
    outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))
    outband = outRaster.GetRasterBand(1)
    outband.WriteArray(array)
    outRasterSRS = osr.SpatialReference()
    outRasterSRS.ImportFromEPSG(4326)
    outRaster.SetProjection(outRasterSRS.ExportToWkt())
    outband.FlushCache()


def main(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array):
    # 反转数组
    reversed_arr = array[::-1]
    # 数组转栅格
    array2raster(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,reversed_arr) 


if __name__ == "__main__":
    rasterOrigin = (-123.25745,45.43013)
    pixelWidth = 10
    pixelHeight = 10
    newRasterfn = 'test.tif'
    array = np.array([[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                      [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                      [ 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1],
                      [ 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
                      [ 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1],
                      [ 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
                      [ 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1],
                      [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                      [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                      [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])


    main(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array)

替换无效值

import gdal, ogr, osr, os
import numpy as np

def raster2array(rasterfn):
    raster = gdal.Open(rasterfn)
    band = raster.GetRasterBand(1)
    return band.ReadAsArray()

def getNoDataValue(rasterfn):
    raster = gdal.Open(rasterfn)
    band = raster.GetRasterBand(1)
    return band.GetNoDataValue()

def array2raster(rasterfn,newRasterfn,array):
    raster = gdal.Open(rasterfn)
    geotransform = raster.GetGeoTransform()
    originX = geotransform[0]
    originY = geotransform[3]
    pixelWidth = geotransform[1]
    pixelHeight = geotransform[5]
    cols = raster.RasterXSize
    rows = raster.RasterYSize

    driver = gdal.GetDriverByName('GTiff')
    outRaster = driver.Create(newRasterfn, cols, rows, 1, gdal.GDT_Float32)
    outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))
    outband = outRaster.GetRasterBand(1)
    outband.WriteArray(array)
    outRasterSRS = osr.SpatialReference()
    outRasterSRS.ImportFromWkt(raster.GetProjectionRef())
    outRaster.SetProjection(outRasterSRS.ExportToWkt())
    outband.FlushCache()


rasterfn = 'test.tif'
newValue = 0
newRasterfn = 'testNew.tif'

# 栅格转数组
rasterArray = raster2array(rasterfn)

# 获取无效值
noDataValue = getNoDataValue(rasterfn)

# 更新无效值
rasterArray[rasterArray == noDataValue] = newValue

# 数组转栅格
array2raster(rasterfn,newRasterfn,rasterArray)