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假设我们有一个 2000 x 2000 像素的图像，像素大小为 10 x 10 米。像素值也是介于 0.00 - 10.00 之间的浮点数。这是图A。

我想通过从左上角开始在非重叠块中空间聚合四个相邻像素，将图像 A 的大小调整为其尺寸的四分之一（即 1000 x 1000 像素），像素大小为 20 x 20 米（图像 B）图像的一角，而图像 B 中每个像素的值将是它们的算术平均值的结果。

我使用stackoverflow的几个来源编写了以下代码；但是由于某种原因，我不理解生成的图像（图像 B）并不总是正确编写，并且我想进一步处理它的任何软件（即 ArcGIS、ENVI、ERDAS 等）都无法读取它。

我将不胜感激任何帮助

最好的问候迪米特里斯

import time 
import glob
import os
import gdal
import osr
import numpy as np

start_time_script = time.clock()

path_ras='C:/rasters/'

for rasterfile in glob.glob(os.path.join(path_ras,'*.tif')):
rasterfile_name=str(rasterfile[rasterfile.find('IMG'):rasterfile.find('.tif')])

print 'Processing:'+ ' ' + str(rasterfile_name)

ds = gdal.Open(rasterfile,gdal.GA_ReadOnly)
ds_xform = ds.GetGeoTransform()

print ds_xform

ds_driver = gdal.GetDriverByName('Gtiff')
srs = osr.SpatialReference()
srs.ImportFromEPSG(26716)

ds_array = ds.ReadAsArray()

sz = ds_array.itemsize

print 'This is the size of the neighbourhood:' + ' ' + str(sz)

h,w = ds_array.shape

print 'This is the size of the Array:' + ' ' + str(h) + ' ' + str(w)

bh, bw = 2,2

shape = (h/bh, w/bw, bh, bw)

print 'This is the new shape of the Array:' + ' ' + str(shape)

strides = sz*np.array([w*bh,bw,w,1])

blocks = np.lib.stride_tricks.as_strided(ds_array,shape=shape,strides=strides)

resized_array = ds_driver.Create(rasterfile_name + '_resized_to_52m.tif',shape[1],shape[0],1,gdal.GDT_Float32)
resized_array.SetGeoTransform((ds_xform[0],ds_xform[1]*2,ds_xform[2],ds_xform[3],ds_xform[4],ds_xform[5]*2))
resized_array.SetProjection(srs.ExportToWkt())
band = resized_array.GetRasterBand(1)

zero_array = np.zeros([shape[0],shape[1]],dtype=np.float32)

print 'I start calculations using neighbourhood'
start_time_blocks = time.clock()

for i in xrange(len(blocks)):
    for j in xrange(len(blocks[i])):

        zero_array[i][j] = np.mean(blocks[i][j])

print 'I finished calculations and I am going to write the new array'

band.WriteArray(zero_array)

end_time_blocks = time.clock() - start_time_blocks

print 'Image Processed for:' + ' ' + str(end_time_blocks) + 'seconds' + '\n'

end_time = time.clock() - start_time_script
print 'Program ran for: ' + str(end_time) + 'seconds'
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1 回答 1

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import time 
import glob
import os
import gdal
import osr
import numpy as np

start_time_script = time.clock()

path_ras='C:/rasters/'

for rasterfile in glob.glob(os.path.join(path_ras,'*.tif')):
rasterfile_name=str(rasterfile[rasterfile.find('IMG'):rasterfile.find('.tif')])

print 'Processing:'+ ' ' + str(rasterfile_name)

ds = gdal.Open(rasterfile,gdal.GA_ReadOnly)
ds_xform = ds.GetGeoTransform()

print ds_xform

ds_driver = gdal.GetDriverByName('Gtiff')
srs = osr.SpatialReference()
srs.ImportFromEPSG(26716)

ds_array = ds.ReadAsArray()

sz = ds_array.itemsize

print 'This is the size of the neighbourhood:' + ' ' + str(sz)

h,w = ds_array.shape

print 'This is the size of the Array:' + ' ' + str(h) + ' ' + str(w)

bh, bw = 2,2

shape = (h/bh, w/bw, bh, bw)

print 'This is the new shape of the Array:' + ' ' + str(shape)

strides = sz*np.array([w*bh,bw,w,1])

blocks = np.lib.stride_tricks.as_strided(ds_array,shape=shape,strides=strides)

resized_array = ds_driver.Create(rasterfile_name + '_resized_to_52m.tif',shape[1],shape[0],1,gdal.GDT_Float32)
resized_array.SetGeoTransform((ds_xform[0],ds_xform[1]*2,ds_xform[2],ds_xform[3],ds_xform[4],ds_xform[5]*2))
resized_array.SetProjection(srs.ExportToWkt())
band = resized_array.GetRasterBand(1)

zero_array = np.zeros([shape[0],shape[1]],dtype=np.float32)

print 'I start calculations using neighbourhood'
start_time_blocks = time.clock()

for i in xrange(len(blocks)):
    for j in xrange(len(blocks[i])):

        zero_array[i][j] = np.mean(blocks[i][j])

print 'I finished calculations and I am going to write the new array'

band.WriteArray(zero_array)

end_time_blocks = time.clock() - start_time_blocks

print 'Image Processed for:' + ' ' + str(end_time_blocks) + 'seconds' + '\n'

end_time = time.clock() - start_time_script
print 'Program ran for: ' + str(end_time) + 'seconds'
于 2013-04-09T07:44:19.740 回答