def run(self, *args, **kwargs):
meta = pyrat.data.getAnnotation(layer=self.layer)
li = pyrat.query(layer=self.layer)
odim = li.shape
nry = odim[-2]
odim[-2] //= self.suby
odim[-1] //= self.subx
outlayer = pyrat.data.addLayer(dtype=li.dtype, shape=odim)
blockdim = odim.copy()
blockdim[-2] = 1
P = pyrat.tools.ProgressBar(' ' + self.name, odim[-2])
P.update(0)
for k in range(odim[-2]):
arr = pyrat.getdata(block=(k * self.suby, (k + 1) * self.suby, 0,
odim[-1] * self.subx),
layer=self.layer)
if self.decimate is True:
arr = arr[..., ::self.suby, ::self.subx]
else:
arr = rebin(arr, tuple(blockdim))
pyrat.data.setData(arr, block=(k, k + 1, 0, 0), layer=outlayer)
P.update(k + 1)
del P
pyrat.activate(outlayer)
if "geo_ps_east" in meta and "geo_ps_north" in meta:
meta['geo_ps_east'] = meta["geo_ps_east"] * self.subx
meta['geo_ps_north'] = meta["geo_ps_north"] * self.suby
pyrat.data.setAnnotation(meta, layer=outlayer)
return outlayer
def filter(self, array, *args, **kwargs):
if self.method is True:
method = PIL.Image.LANCZOS
else:
method = PIL.Image.NEAREST
odim = list(array.shape)
odim[-2] //= self.suby
odim[-1] //= self.subx
oarr = np.empty(odim, dtype='float32')
if oarr.ndim == 3:
nchannel = odim[0]
else:
nchannel = 1
array = array[None, ...]
oarr = oarr[None, ...]
for k in range(nchannel):
arr = array[k, ...] # workaround for PIL bug
while True: # (large array crash)
try:
arr = PIL.Image.fromarray(arr.astype(np.float32), mode='F')
except OverflowError:
shp = arr.shape
arr = rebin(arr[0:shp[-2] // 2 * 2, 0:shp[-1] // 2 * 2], (shp[-2] // 2, shp[-1] // 2))
else:
break
# arr = PIL.Image.fromarray(array[k, ...].astype(np.float32), mode='F')
arr = arr.resize(odim[-2:][::-1], resample=method)
oarr[k, ...] = np.asarray(arr)
return np.squeeze(oarr)
def filter(self, array, *args, **kwargs):
if self.method is True:
method = PIL.Image.LANCZOS
else:
method = PIL.Image.NEAREST
odim = list(array.shape)
odim[-2] //= self.suby
odim[-1] //= self.subx
oarr = np.empty(odim, dtype='float32')
if oarr.ndim == 3:
nchannel = odim[0]
else:
nchannel = 1
array = array[None, ...]
oarr = oarr[None, ...]
for k in range(nchannel):
arr = array[k, ...] # workaround for PIL bug
while True: # (large array crash)
try:
arr = PIL.Image.fromarray(arr.astype(np.float32), mode='F')
except OverflowError:
shp = arr.shape
arr = rebin(arr[0:shp[-2] // 2 * 2, 0:shp[-1] // 2 * 2],
(shp[-2] // 2, shp[-1] // 2))
else:
break
# arr = PIL.Image.fromarray(array[k, ...].astype(np.float32), mode='F')
arr = arr.resize(odim[-2:][::-1], resample=method)
oarr[k, ...] = np.asarray(arr)
return np.squeeze(oarr)
def run(self, *args, **kwargs):
meta = pyrat.data.getAnnotation(layer=self.layer)
li = pyrat.query(layer=self.layer)
odim = li.shape
nry = odim[-2]
odim[-2] //= self.suby
odim[-1] //= self.subx
outlayer = pyrat.data.addLayer(dtype=li.dtype, shape=odim)
blockdim = odim.copy()
blockdim[-2] = 1
P = pyrat.tools.ProgressBar(' ' + self.name, odim[-2])
P.update(0)
for k in range(odim[-2]):
arr = pyrat.getdata(block=(k*self.suby, (k+1)*self.suby, 0, odim[-1] * self.subx), layer=self.layer)
arr = rebin(arr, tuple(blockdim))
pyrat.data.setData(arr, block=(k, k+1, 0, 0), layer=outlayer)
P.update(k + 1)
del P
pyrat.activate(outlayer)
if "geo_ps_east" in meta and "geo_ps_north" in meta:
meta['geo_ps_east'] = meta["geo_ps_east"] * self.subx
meta['geo_ps_north'] = meta["geo_ps_north"] * self.suby
pyrat.data.setAnnotation(meta, layer=outlayer)
return outlayer
def filter(self, array, *args, **kwargs):
odim = list(array.shape)
odim[-2] //= self.suby
odim[-1] //= self.subx
array = rebin(array[..., 0:odim[-2] * self.suby, 0:odim[-1] * self.subx], odim)
return array
def filter(self, array, *args, **kwargs):
odim = list(array.shape)
odim[-2] //= self.suby
odim[-1] //= self.subx
array = rebin(
array[..., 0:odim[-2] * self.suby, 0:odim[-1] * self.subx], odim)
return array
def run(self, *args, **kwargs):
li = pyrat.query(layer=self.layer)
odim = li.shape
nry = odim[-2]
odim[-2] //= self.suby
odim[-1] //= self.subx
outlayer = pyrat.data.addLayer(dtype=li.dtype, shape=odim)
blockdim = odim.copy()
blockdim[-2] = 1
P = ProgressBar(' ' + self.name, odim[-2])
P.update(0)
for k in range(odim[-2]):
arr = pyrat.getdata(block=(k*self.suby, (k+1)*self.suby, 0, odim[-1] * self.subx), layer=self.layer)
arr = rebin(arr, tuple(blockdim))
pyrat.data.setData(arr, block=(k, k+1, 0, 0), layer=outlayer)
P.update(k + 1)
del P
pyrat.activate(outlayer)
return outlayer
def reader(self, *args, **kwargs):
fN = len(self.filenames)
meta = {}
if 'ppfiles' in self.__dict__:
pp = Xml2Py(self.ppfiles[0])
meta['sensor'] = 'DLR F-SAR'
meta['band'] = band
meta['prf'] = pp.prf
meta['c0'] = pp.c0
meta['rd'] = pp.rd
meta['rs'] = pp.rsf/subx
meta['lam'] = pp.__dict__['lam']
meta['band'] = pp.band
meta['antdir'] = pp.antdir
meta['v0'] = pp.v0
meta['h0'] = pp.h0
meta['terrain'] = pp.terrain
meta['bw'] = pp.cbw
if (len(self.rat_block) == 4):
meta['rd'] += self.rat_block[0] / meta['rs']
meta['CH_pol'] = [' ']*(fN**2)
pind = 0
for f1 in self.ppfiles:
pp1 = Xml2Py(f1)
for f2 in self.ppfiles:
pp2 = Xml2Py(f2)
meta['CH_pol'][pind] = pp1.polarisation+pp2.polarisation
ppind += 1
file = RatFile(self.filenames[0])
if len(self.rat_block) != 4:
imDim = file.dim[0:2][::-1]
else:
imDim = self.rat_block[2:4][::-1]
imDim = np.asarray(imDim,dtype='i4')
blenOut = 100
blenIn = blenOut*self.suby
nBlocks = int(np.ceil(imDim[0]/float(blenIn)))
sub = np.asarray([self.suby,self.subx],dtype='i4')
oDim = imDim / sub
meta['nrg'] = oDim[0]
meta['naz'] = oDim[1]
P = ProgressBar(' ' + self.name, nBlocks)
P.update(0)
for n in range(nBlocks):
rInd = np.minimum(np.asarray([n*blenIn,(n+1)*blenIn]),imDim[0])
wInd = rInd/sub
if wInd[0] >= wInd[1]:
continue
binInd = [(wInd[1]-wInd[0])*sub[0],oDim[1]*sub[1]]
bDim = (wInd[1]-wInd[0],oDim[1])
for u in range(fN):
blk = [0,rInd[0],imDim[1],rInd[1]-rInd[0]]
if len(self.rat_block) == 4:
blk[0] = self.rat_block[0]
blk[1] += self.rat_block[1]
blk[2] = self.rat_block[2]
arr1 = rrat(self.filenames[u],block=blk)
arr1 = arr1[0:binInd[0],0:binInd[1]]
if (u == 0) and (n == 0):
covar = np.empty((fN,fN)+tuple(oDim),dtype=arr1.dtype)
covar[u,u,wInd[0]:wInd[1],:] = rebin(np.abs(arr1)**2,bDim)
for v in range(u+1,fN):
arr2 = rrat(self.filenames[v],block=blk)
covar[u,v,wInd[0]:wInd[1],:] = rebin(arr1*np.conj(arr2),bDim)
covar[v,u,wInd[0]:wInd[1],:] = np.conj(covar[u,v,wInd[0]:wInd[1],:])
P.update(n+1)
return covar, meta
