我正试图在双时态RapidEye多光谱图像上运行Mort Canty的Python iMAD实现。这基本上计算了两个图像的规范相关性，然后将它们减去。我遇到的问题是图像是5000 x 5000 x 5（波段）像素。当我在整个图像上运行时，我的计算机崩溃非常糟糕，我必须将其关闭。
有没有人知道什么可以让python 崩溃这样的计算机？例如，如果我为每个波段选择2999x2999像素，则一切正常。
8 gbs ram，I7-2617M 1.5 1.5 ghz，Windows7 64位。我使用64位版本的一切：python（2.7），numpy，scipy和gdal。
提前谢谢！
def covw(dm,w):
# weighted covariance matrix and means
# from (transposed) data array
N = size(dm,0)
n = size(w)
sumw = sum(w)
ws = tile(w,(N,1))
means = mat(sum(ws*dm,1)/sumw).T
means = tile(means,(1,n))
dmc = dm - means
dmc = multiply(dmc,sqrt(ws))
covmat = dmc*dmc.T/sumw
return (covmat,means)
def main():
# ------------test---------------------------------------------------------------
if len(sys.argv) == 1:
(sys.argv).extend(['-p','[0,1,2,3,4]','-
d','[0,4999,0,4999]',
'c://users//pythonxy//workspace//1uno.tif','c://users//pythonxy//workspace//2dos.tif'])
# -------------------------------------------------------------------------------
options, args = getopt.getopt(sys.argv[1:],'hp:d:')
pos = None
dims = None
for option, value in options:
if option == '-h':
print 'Usage: python %s [-p "bandPositions" -d "spatialDimensions"]
filename1 filename2' %sys.argv[0]
print ' bandPositions and spatialDimensions are quoted lists,
e.g., -p "[0,1,3]" -d "[0,400,0,400]" \n'
sys.exit(1)
elif option == '-p':
pos = eval(value)
elif option == '-d':
dims = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print 'Usage: python %s [-p "bandspositions" -d "spatialdimensions"]
filename1 filename2 \n' %sys.argv[0]
sys.exit(1)
gdal.AllRegister()
fn1 = args[0]
fn2 = args[1]
path = os.path.dirname(fn1)
basename1 = os.path.basename(fn1)
root1, ext = os.path.splitext(basename1)
basename2 = os.path.basename(fn2)
outfn = path+'\\MAD['+basename1+'-'+basename2+']'+ext
inDataset1 = gdal.Open(fn1,GA_ReadOnly)
inDataset2 = gdal.Open(fn2,GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
cols2 = inDataset2.RasterXSize
rows2 = inDataset2.RasterYSize
bands2 = inDataset2.RasterCount
if (rows != rows2) or (cols != cols2) or (bands != bands2):
sys.stderr.write("Size mismatch")
sys.exit(1)
if pos is None:
pos = range(bands)
else:
bands = len(pos)
if dims is None:
x0 = 0
y0 = 0
else:
x0 = dims[0]
y0 = dims[2]
cols = dims[1]-dims[0] + 1
rows = dims[3]-dims[2] + 1
# initial weights
wt = ones(cols*rows)
# data array (transposed so observations are columns)
dm = zeros((2*bands,cols*rows),dtype='float32')
k = 0
for b in pos:
band1 = inDataset1.GetRasterBand(b+1)
band1 = band1.ReadAsArray(x0,y0,cols,rows).astype(float)
dm[k,:] = ravel(band1)
band2 = inDataset2.GetRasterBand(b+1)
band2 = band2.ReadAsArray(x0,y0,cols,rows).astype(float)
dm[bands+k,:] = ravel(band2)
k += 1
print '========================='
print ' iMAD'
print '========================='
print 'time1: '+fn1
print 'time2: '+fn2
print 'Delta [canonical correlations]'
# iteration of MAD
delta = 1.0
oldrho = zeros(bands)
iter = 0
while (delta > 0.001) and (iter < 50):
# weighted covariance matrices and means
sigma,means = covw(dm,wt)
s11 = mat(sigma[0:bands,0:bands])
s22 = mat(sigma[bands:,bands:])
s12 = mat(sigma[0:bands,bands:])
s21 = mat(sigma[bands:,0:bands])
# solution of generalized eigenproblems
s22i = mat(linalg.inv(s22))
lama,a = linalg.eig(s12*s22i*s21,s11)
s11i = mat(linalg.inv(s11))
lamb,b = linalg.eig(s21*s11i*s12,s22)
# sort a
idx = argsort(lama)
a = a[:,idx]
# sort b
idx = argsort(lamb)
b = b[:,idx]
# canonical correlations
rho = sqrt(real(lamb[idx]))
# normalize dispersions
a = mat(a)
tmp1 = a.T*s11*a
tmp2 = 1./sqrt(diag(tmp1))
tmp3 = tile(tmp2,(bands,1))
a = multiply(a,tmp3)
b = mat(b)
tmp1 = b.T*s22*b
tmp2 = 1./sqrt(diag(tmp1))
tmp3 = tile(tmp2,(bands,1))
b = multiply(b,tmp3)
# assure positive correlation
tmp = diag(a.T*s12*b)
b = b*diag(tmp/abs(tmp))
# canonical and MAD variates
U = a.T*mat(dm[0:bands,:]-means[0:bands,:])
V = b.T*mat(dm[bands:,:]-means[bands:,:])
MAD = U-V
# new weights
var_mad = tile(mat(2*(1-rho)).T,(1,rows*cols))
chisqr = sum(multiply(MAD,MAD)/var_mad,0)
wt = 1-stats.chi2.cdf(chisqr,[bands])
# continue iteration
delta = sum(abs(rho-oldrho))
oldrho = rho
print delta
iter += 1
# write results to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfn,cols,rows,bands+1,GDT_Float32)
projection = inDataset1.GetProjection()
geotransform = inDataset1.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
for k in range(bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(resize(MAD[k,:],(rows,cols)),0,0)
outBand.FlushCache()
outBand = outDataset.GetRasterBand(bands+1)
outBand.WriteArray(resize(chisqr,(rows,cols)),0,0)
outBand.FlushCache()
outDataset = None
inDataset1 = None
inDataset2 = None
print 'result written to: '+outfn
print '---------------------------------'
如果名称 =='主要'： main（）
答案 0 :(得分：1)
听起来这个操作只需要比计算机提供的内存更多的内存。这是一个过于简单化的问题，但是当系统耗尽实际的RAM使用时，它有时会将看起来使用较少的内存部分写入硬盘，因此它可以将该实际内存用于其他内容。硬盘比主内存慢很多个数量级，因此当你的软件需要部分内存写入磁盘时，一切都会变得非常慢。当这种情况发生剧烈变化时，您的软件和操作系统的某些部分会不断尝试使用已换出的内存（写入磁盘），您的硬盘驱动器可以进行大量的锻炼，试图来回寻找，写了很多东西，读了很多东西，写了更多的东西等等。在这样的情况下，系统会变得非常反应迟钝。
通过观察系统的活动监视器，您可以看到这是否真的发生了什么（我忘记了它们在Windows上被调用的内容，但我知道它们在那里;某些软件会显示分配了多少内存，正在使用等等，并为您绘制一个漂亮的图表）。在观看时，启动程序并观察内存分配率。
在这段代码中可能有一些缓解内存使用的方法，如果内存中一次保存的内容较少，但我不会看到它们是什么。您还可以为系统添加更多RAM，希望能解决这个问题。