def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# MS image
file1 = auxil.select_infile(title='Choose MS image')
if file1:
inDataset1 = gdal.Open(file1, GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
num_bands = len(pos1)
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x10, y10, cols1, rows1 = dims
else:
return
# PAN image
file2 = auxil.select_infile(title='Choose PAN image')
if file2:
inDataset2 = gdal.Open(file2, GA_ReadOnly)
bands = inDataset2.RasterCount
else:
return
if bands > 1:
print 'Must be a single band (panchromatic) image'
return
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# resolution ratio
ratio = auxil.select_integer(4, 'Resolution ratio (2 or 4)')
if not ratio:
return
# MS registration band
k1 = auxil.select_integer(1, 'MS band for registration')
if not k1:
return
print '========================='
print ' ATWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: ' + file1
print 'PAN file: ' + file2
# read in MS image
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0, 0, 1, 1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands, rows1, cols1)), dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k, :, :] = band.ReadAsArray(x10, y10, cols1, rows1)
k += 1
# if integer assume 11-bit quantization, otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS, (0, 2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10 * gt1[1]
uly1 = gt1[3] + y10 * gt1[5]
x20 = int(round(((ulx1 - gt2[0]) / gt2[1])))
y20 = int(round(((uly1 - gt2[3]) / gt2[5])))
cols2 = cols1 * ratio
rows2 = rows1 * ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20, y20, cols2, rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN, (0, 2**11))
# out array
sharpened = np.zeros((num_bands, rows2, cols2), dtype=np.float32)
# compress PAN to resolution of MS image using DWT
panDWT = auxil.DWTArray(PAN, cols2, rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
# register (and subset) MS image to compressed PAN image using selected MSband
lines0, samples0 = bn0.shape
bn1 = MS[k1 - 1, :, :]
# register (and subset) MS image to compressed PAN image
(scale, angle, shift) = auxil.similarity(bn0, bn1)
tmp = np.zeros((num_bands, lines0, samples0))
for k in range(num_bands):
bn1 = MS[k, :, :]
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k, :, :] = bn2[0:lines0, 0:samples0]
MS = tmp
smpl = np.random.randint(cols2 * rows2, size=100000)
print 'Wavelet correlations:'
# loop over MS bands
for k in range(num_bands):
msATWT = auxil.ATWTArray(PAN)
r = ratio
while r > 1:
msATWT.filter()
r /= 2
# sample PAN wavelet details
X = msATWT.get_band(msATWT.num_iter)
X = X.ravel()[smpl]
# resize the ms band to scale of the pan image
ms_band = ndii.zoom(MS[k, :, :], ratio)
# sample details of MS band
tmpATWT = auxil.ATWTArray(ms_band)
r = ratio
while r > 1:
tmpATWT.filter()
r /= 2
Y = tmpATWT.get_band(msATWT.num_iter)
Y = Y.ravel()[smpl]
# get band for injection
bnd = tmpATWT.get_band(0)
tmpATWT = None
aa, bb, R = auxil.orthoregress(X, Y)
print 'Band ' + str(k + 1) + ': %8.3f' % R
# inject the filtered MS band
msATWT.inject(bnd)
# normalize wavelet components and expand
msATWT.normalize(aa, bb)
r = ratio
while r > 1:
msATWT.invert()
r /= 2
sharpened[k, :, :] = msATWT.get_band(0)
sharpened *= fact # rescale dynamic range
msATWT = None
# write to disk
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile, cols2, rows2, num_bands, GDT_Float32)
gt1[0] += x10 * ratio
gt1[3] -= y10 * ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(sharpened[k, :, :], 0, 0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s' % outfile
inDataset1 = None
inDataset2 = None
def main():
usage = '''
Usage:
--------------------------------------------------------
python %s [-p "bandPositions"] [-d "spatialDimensions"]
[-t no-change prob threshold] imadFile [fullSceneFile]'
--------------------------------------------------------
bandPositions and spatialDimensions are quoted lists,
e.g., -p [4,5,6] -d [0,0,400,400]
-n stops graphics output
SpatialDimensions MUST match those of imadFile
spectral dimension of fullSceneFile, if present,
MUST match those of target and reference images
--------------------------------------------------------
imadFile is of form path/MAD(filebasename1-filebasename2.ext2).ext1 and
the output file is named
path/filebasename2_norm.ext2.
That is, it is assumed that filename1 is reference and
filename2 is target and the output retains the format
of the target file. A similar convention is used to
name the normalized full scene, if present:
fullSceneFile_norm.ext
Note that, for ENVI format, ext is the empty string.
-------------------------------------------------------'''%sys.argv[0]
options, args = getopt.getopt(sys.argv[1:],'hnp:d:t:')
pos = None
dims = None
ncpThresh = 0.95
fsfn = None
graphics = True
for option, value in options:
if option == '-h':
print usage
return
elif option == '-n':
graphics = False
elif option == '-p':
pos = eval(value)
elif option == '-d':
dims = eval(value)
elif option == '-t':
ncpThresh = eval(value)
if (len(args) != 1) and (len(args) != 2):
print 'Incorrect number of arguments'
print usage
sys.exit(1)
imadfn = args[0]
if len(args) == 2:
fsfn = args[1]
path = os.path.dirname(fsfn)
basename = os.path.basename(fsfn)
root, ext = os.path.splitext(basename)
fsoutfn = path+'/'+root+'_norm_all'+ext
path = os.path.dirname(imadfn)
basename = os.path.basename(imadfn)
root, ext = os.path.splitext(basename)
b = root.find('(')
e = root.find(')')
referenceroot, targetbasename = root[b+1:e].split('-')
referencefn = path + '/' + referenceroot + ext
targetfn = path + '/' + targetbasename
targetroot, targetext = os.path.splitext(targetbasename)
outfn = path + '/' + targetroot + '_norm' + targetext
imadDataset = gdal.Open(imadfn,GA_ReadOnly)
try:
imadbands = imadDataset.RasterCount
cols = imadDataset.RasterXSize
rows = imadDataset.RasterYSize
except Exception as e:
print 'Error: %s --Image could not be read'%e
sys.exit(1)
referenceDataset = gdal.Open(referencefn,GA_ReadOnly)
targetDataset = gdal.Open(targetfn,GA_ReadOnly)
if pos is None:
pos = range(1,referenceDataset.RasterCount+1)
if dims is None:
x0 = 0; y0 = 0
else:
x0,y0,cols,rows = dims
chisqr = imadDataset.GetRasterBand(imadbands).ReadAsArray(0,0,cols,rows).ravel()
ncp = 1 - stats.chi2.cdf(chisqr,[imadbands-1])
idx = where(ncp>ncpThresh)
print time.asctime()
print 'reference: '+referencefn
print 'target : '+targetfn
print 'no-change probability threshold: '+str(ncpThresh)
print 'no-change pixels: '+str(len(idx[0]))
start = time.time()
driver = targetDataset.GetDriver()
outDataset = driver.Create(outfn,cols,rows,len(pos),GDT_Float32)
projection = imadDataset.GetProjection()
geotransform = imadDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
aa = []
bb = []
if graphics:
plt.figure(1,(9,6))
j = 1
bands = len(pos)
for k in pos:
x = referenceDataset.GetRasterBand(k).ReadAsArray(x0,y0,cols,rows).astype(float).ravel()
y = targetDataset.GetRasterBand(k).ReadAsArray(x0,y0,cols,rows).astype(float).ravel()
b,a,R = orthoregress(y[idx],x[idx])
print 'band: %i slope: %f intercept: %f correlation: %f'%(k,b,a,R)
my = max(y[idx])
if (j<7) and graphics:
plt.subplot(2,3,j)
plt.plot(y[idx],x[idx],'.')
plt.plot([0,my],[a,a+b*my])
plt.title('Band %i'%k)
if ((j<4) and (bands<4)) or j>3:
plt.xlabel('Target')
if (j==1) or (j==4):
plt.ylabel('Reference')
aa.append(a)
bb.append(b)
outBand = outDataset.GetRasterBand(j)
outBand.WriteArray(resize(a+b*y,(rows,cols)),0,0)
outBand.FlushCache()
j += 1
if graphics:
plt.show()
plt.close()
referenceDataset = None
targetDataset = None
outDataset = None
print 'result written to: '+outfn
if fsfn is not None:
print 'normalizing '+fsfn+'...'
fsDataset = gdal.Open(fsfn,GA_ReadOnly)
try:
cols = fsDataset.RasterXSize
rows = fsDataset.RasterYSize
except Exception as e:
print 'Error %s -- Image could not be read in'
sys.exit(1)
driver = fsDataset.GetDriver()
outDataset = driver.Create(fsoutfn,cols,rows,len(pos),GDT_Float32)
projection = fsDataset.GetProjection()
geotransform = fsDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
j = 1
for k in pos:
inBand = fsDataset.GetRasterBand(k)
outBand = outDataset.GetRasterBand(j)
for i in range(rows):
y = inBand.ReadAsArray(0,i,cols,1)
outBand.WriteArray(aa[j-1]+bb[j-1]*y,0,i)
outBand.FlushCache()
j += 1
outDataset = None
fsDataset = None
print 'full result written to: '+fsoutfn
print 'elapsed time: %s'%str(time.time()-start)
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# MS image
file1 = auxil.select_infile(title='Choose MS image')
if file1:
inDataset1 = gdal.Open(file1, GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
num_bands = len(pos1)
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x10, y10, cols1, rows1 = dims
else:
return
# PAN image
file2 = auxil.select_infile(title='Choose PAN image')
if file2:
inDataset2 = gdal.Open(file2, GA_ReadOnly)
bands = inDataset2.RasterCount
else:
return
if bands > 1:
print 'Must be a single band (panchromatic) image'
return
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# resolution ratio
ratio = auxil.select_integer(4, 'Resolution ratio (2 or 4)')
if not ratio:
return
# MS registration band
k1 = auxil.select_integer(1, 'MS band for registration')
if not k1:
return
# fine adjust
roll = auxil.select_integer(0, 'Fine adjust (-2 ... 2)')
if roll is None:
return
print '========================='
print ' DWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: ' + file1
print 'PAN file: ' + file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0, 0, 1, 1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands, rows1, cols1)), dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k, :, :] = band.ReadAsArray(x10, y10, cols1, rows1)
k += 1
# if integer assume 11bit quantization otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS, (0, 2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10 * gt1[1]
uly1 = gt1[3] + y10 * gt1[5]
x20 = int(round(((ulx1 - gt2[0]) / gt2[1])))
y20 = int(round(((uly1 - gt2[3]) / gt2[5])))
cols2 = cols1 * ratio
rows2 = rows1 * ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20, y20, cols2, rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN, (0, 2**11))
# compress PAN to resolution of MS image
panDWT = auxil.DWTArray(PAN, cols2, rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
lines0, samples0 = bn0.shape
bn1 = MS[k1 - 1, :, :]
# register (and subset) MS image to compressed PAN image
(scale, angle, shift) = auxil.similarity(bn0, bn1)
tmp = np.zeros((num_bands, lines0, samples0))
for k in range(num_bands):
bn1 = MS[k, :, :]
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k, :, :] = bn2[0:lines0, 0:samples0]
MS = tmp
if roll != 0:
# fine adjust
PAN = np.roll(PAN, roll, axis=0)
PAN = np.roll(PAN, roll, axis=1)
panDWT = auxil.DWTArray(PAN, cols2, rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
# compress pan once more, extract wavelet quadrants, and restore
panDWT.filter()
fgpan = panDWT.get_quadrant(1)
gfpan = panDWT.get_quadrant(2)
ggpan = panDWT.get_quadrant(3)
panDWT.invert()
# output array
sharpened = np.zeros((num_bands, rows2, cols2), dtype=np.float32)
aa = np.zeros(3)
bb = np.zeros(3)
print 'Wavelet correlations:'
for i in range(num_bands):
# make copy of panDWT and inject ith ms band
msDWT = copy.deepcopy(panDWT)
msDWT.put_quadrant(MS[i, :, :], 0)
# compress once more
msDWT.filter()
# determine wavelet normalization coefficents
ms = msDWT.get_quadrant(1)
aa[0], bb[0], R = auxil.orthoregress(fgpan.ravel(), ms.ravel())
Rs = 'Band ' + str(i + 1) + ': %8.3f' % R
ms = msDWT.get_quadrant(2)
aa[1], bb[1], R = auxil.orthoregress(gfpan.ravel(), ms.ravel())
Rs += '%8.3f' % R
ms = msDWT.get_quadrant(3)
aa[2], bb[2], R = auxil.orthoregress(ggpan.ravel(), ms.ravel())
Rs += '%8.3f' % R
print Rs
# restore once and normalize wavelet coefficients
msDWT.invert()
msDWT.normalize(aa, bb)
# restore completely and collect result
r = 1
while r < ratio:
msDWT.invert()
r *= 2
sharpened[i, :, :] = msDWT.get_quadrant(0)
sharpened *= fact
# write to disk
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile, cols2, rows2, num_bands, GDT_Float32)
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
gt1 = list(geotransform1)
gt1[0] += x10 * ratio
gt1[3] -= y10 * ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(sharpened[k, :, :], 0, 0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s' % outfile
inDataset1 = None
inDataset2 = None
def main():
usage = '''
Usage:
-----------------------------------------------------------------------
python %s [-d spatialDimensions] [-p bandPositions [-r resolution ratio]
[-b registration band] msfilename panfilename
-----------------------------------------------------------------------
bandPositions and spatialDimensions are lists,
e.g., -p [1,2,3] -d [0,0,400,400]
Outfile name is msfilename_pan with same format as msfilename
-----------------------------------------------------''' %sys.argv[0]
options, args = getopt.getopt(sys.argv[1:],'hd:p:r:b:')
ratio = 4
dims1 = None
pos1 = None
k1 = 1
for option, value in options:
if option == '-h':
print usage
return
elif option == '-r':
ratio = eval(value)
elif option == '-d':
dims1 = eval(value)
elif option == '-p':
pos1 = eval(value)
elif option == '-b':
k1 = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
gdal.AllRegister()
file1 = args[0]
file2 = args[1]
path = os.path.dirname(file1)
basename1 = os.path.basename(file1)
root1, ext1 = os.path.splitext(basename1)
outfile = '%s/%s_pan%s'%(path,root1,ext1)
# MS image
inDataset1 = gdal.Open(file1,GA_ReadOnly)
try:
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if pos1 is None:
pos1 = range(1,bands+1)
num_bands = len(pos1)
if dims1 is None:
dims1 = [0,0,cols,rows]
x10,y10,cols1,rows1 = dims1
# PAN image
inDataset2 = gdal.Open(file2,GA_ReadOnly)
try:
bands = inDataset2.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if bands>1:
print 'PAN image must be a single band'
sys.exit(1)
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
sys.exit(1)
print '========================='
print ' ATWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# read in MS image
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype = dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
# if integer assume 11-bit quantization, otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS,(0,2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10*gt1[1]
uly1 = gt1[3] + y10*gt1[5]
x20 = int(round(((ulx1 - gt2[0])/gt2[1])))
y20 = int(round(((uly1 - gt2[3])/gt2[5])))
cols2 = cols1*ratio
rows2 = rows1*ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
# out array
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
# compress PAN to resolution of MS image using DWT
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
# register (and subset) MS image to compressed PAN image using selected MSband
lines0,samples0 = bn0.shape
bn1 = MS[k1-1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
smpl = np.random.randint(cols2*rows2,size=100000)
print 'Wavelet correlations:'
# loop over MS bands
for k in range(num_bands):
msATWT = auxil.ATWTArray(PAN)
r = ratio
while r > 1:
msATWT.filter()
r /= 2
# sample PAN wavelet details
X = msATWT.get_band(msATWT.num_iter)
X = X.ravel()[smpl]
# resize the ms band to scale of the pan image
ms_band = ndii.zoom(MS[k,:,:],ratio)
# sample details of MS band
tmpATWT = auxil.ATWTArray(ms_band)
r = ratio
while r > 1:
tmpATWT.filter()
r /= 2
Y = tmpATWT.get_band(msATWT.num_iter)
Y = Y.ravel()[smpl]
# get band for injection
bnd = tmpATWT.get_band(0)
tmpATWT = None
aa,bb,R = auxil.orthoregress(X,Y)
print 'Band '+str(k+1)+': %8.3f'%R
# inject the filtered MS band
msATWT.inject(bnd)
# normalize wavelet components and expand
msATWT.normalize(aa,bb)
r = ratio
while r > 1:
msATWT.invert()
r /= 2
sharpened[k,:,:] = msATWT.get_band(0)
sharpened *= fact # rescale dynamic range
msATWT = None
# write to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile,cols2,rows2,num_bands,GDT_Float32)
gt1[0] += x10*ratio
gt1[3] -= y10*ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# MS image
file1 = auxil.select_infile(title='Choose MS image')
if file1:
inDataset1 = gdal.Open(file1,GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
num_bands = len(pos1)
dims = auxil.select_dims([0,0,cols,rows])
if dims:
x10,y10,cols1,rows1 = dims
else:
return
# PAN image
file2 = auxil.select_infile(title='Choose PAN image')
if file2:
inDataset2 = gdal.Open(file2,GA_ReadOnly)
bands = inDataset2.RasterCount
else:
return
if bands>1:
print 'Must be a single band (panchromatic) image'
return
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# resolution ratio
ratio = auxil.select_integer(4, 'Resolution ratio (2 or 4)')
if not ratio:
return
# MS registration band
k1 = auxil.select_integer(1, 'MS band for registration')
if not k1:
return
# fine adjust
roll = auxil.select_integer(0, 'Fine adjust (-2 ... 2)')
if roll is None:
return
print '========================='
print ' DWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
# if integer assume 11bit quantization otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS,(0,2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10*gt1[1]
uly1 = gt1[3] + y10*gt1[5]
x20 = int(round(((ulx1 - gt2[0])/gt2[1])))
y20 = int(round(((uly1 - gt2[3])/gt2[5])))
cols2 = cols1*ratio
rows2 = rows1*ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
# compress PAN to resolution of MS image
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
lines0,samples0 = bn0.shape
bn1 = MS[k1-1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
if roll != 0:
# fine adjust
PAN = np.roll(PAN,roll,axis=0)
PAN = np.roll(PAN,roll,axis=1)
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
# compress pan once more, extract wavelet quadrants, and restore
panDWT.filter()
fgpan = panDWT.get_quadrant(1)
gfpan = panDWT.get_quadrant(2)
ggpan = panDWT.get_quadrant(3)
panDWT.invert()
# output array
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
aa = np.zeros(3)
bb = np.zeros(3)
print 'Wavelet correlations:'
for i in range(num_bands):
# make copy of panDWT and inject ith ms band
msDWT = copy.deepcopy(panDWT)
msDWT.put_quadrant(MS[i,:,:],0)
# compress once more
msDWT.filter()
# determine wavelet normalization coefficents
ms = msDWT.get_quadrant(1)
aa[0],bb[0],R = auxil.orthoregress(fgpan.ravel(), ms.ravel())
Rs = 'Band '+str(i+1)+': %8.3f'%R
ms = msDWT.get_quadrant(2)
aa[1],bb[1],R = auxil.orthoregress(gfpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
ms = msDWT.get_quadrant(3)
aa[2],bb[2],R = auxil.orthoregress(ggpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
print Rs
# restore once and normalize wavelet coefficients
msDWT.invert()
msDWT.normalize(aa,bb)
# restore completely and collect result
r = 1
while r < ratio:
msDWT.invert()
r *= 2
sharpened[i,:,:] = msDWT.get_quadrant(0)
sharpened *= fact
# write to disk
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile,cols2,rows2,num_bands,GDT_Float32)
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
gt1 = list(geotransform1)
gt1[0] += x10*ratio
gt1[3] -= y10*ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# reference image
file1 = auxil.select_infile(title='Choose reference image')
if file1:
inDataset1 = gdal.Open(file1, GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x10, y10, cols1, rows1 = dims
else:
return
# target image
file2 = auxil.select_infile(title='Choose target image')
if file2:
inDataset2 = gdal.Open(file2, GA_ReadOnly)
cols = inDataset2.RasterXSize
rows = inDataset2.RasterYSize
bands = inDataset2.RasterCount
else:
return
pos2 = auxil.select_pos(bands)
if not pos2:
return
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x20, y20, cols2, rows2 = dims
else:
return
# match dimensions
bands = len(pos2)
if (rows1 != rows2) or (cols1 != cols2) or (len(pos1) != bands):
sys.stderr.write("Size mismatch")
sys.exit(1)
# iMAD image
file3 = auxil.select_infile(title='Choose iMAD image')
if file3:
inDataset3 = gdal.Open(file3, GA_ReadOnly)
cols = inDataset3.RasterXSize
rows = inDataset3.RasterYSize
imadbands = inDataset3.RasterCount
else:
return
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x30, y30, cols, rows = dims
else:
return
if (rows1 != rows) or (cols1 != cols):
sys.stderr.write("Size mismatch")
sys.exit(1)
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# full scene
fsfile = auxil.select_infile(title='Choose full target scene if desired')
# no-change threshold
ncpThresh = auxil.select_ncp(0.95)
if ncpThresh is None:
return
chisqr = inDataset3.GetRasterBand(imadbands).ReadAsArray(
x30, y30, cols, rows).ravel()
ncp = 1 - stats.chi2.cdf(chisqr, [imadbands - 1])
idx = np.where(ncp > ncpThresh)[0]
# split train/test in ratio 2:1
tmp = np.asarray(range(len(idx)))
tst = idx[np.where(np.mod(tmp, 3) == 0)]
trn = idx[np.where(np.mod(tmp, 3) > 0)]
print '========================================='
print ' RADCAL'
print '========================================='
print time.asctime()
print 'reference: ' + file1
print 'target : ' + file2
print 'no-change probability threshold: ' + str(ncpThresh)
print 'no-change pixels (train): ' + str(len(trn))
print 'no-change pixels (test): ' + str(len(tst))
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile, cols, rows, bands, GDT_Float32)
projection = inDataset1.GetProjection()
geotransform = inDataset1.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x10 * gt[1]
gt[3] = gt[3] + y10 * gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
aa = []
bb = []
i = 1
for k in pos1:
x = inDataset1.GetRasterBand(k).ReadAsArray(
x10, y10, cols, rows).astype(float).ravel()
y = inDataset2.GetRasterBand(k).ReadAsArray(
x20, y20, cols, rows).astype(float).ravel()
b, a, R = auxil.orthoregress(y[trn], x[trn])
print '--------------------'
print 'spectral band: ', k
print 'slope: ', b
print 'intercept: ', a
print 'correlation: ', R
print 'means(tgt,ref,nrm): ', np.mean(y[tst]), np.mean(
x[tst]), np.mean(a + b * y[tst])
print 't-test, p-value: ', stats.ttest_rel(x[tst], a + b * y[tst])
print 'vars(tgt,ref,nrm) ', np.var(y[tst]), np.var(
x[tst]), np.var(a + b * y[tst])
print 'F-test, p-value: ', auxil.fv_test(x[tst], a + b * y[tst])
aa.append(a)
bb.append(b)
outBand = outDataset.GetRasterBand(i)
outBand.WriteArray(np.resize(a + b * y, (rows, cols)), 0, 0)
outBand.FlushCache()
if i <= 10:
plt.figure(i)
ymax = max(y[idx])
xmax = max(x[idx])
plt.plot(y[idx], x[idx], 'k.', [0, ymax], [a, a + b * ymax], 'k-')
plt.axis([0, ymax, 0, xmax])
plt.title('Band ' + str(k))
plt.xlabel('Target')
plt.ylabel('Reference')
i += 1
outDataset = None
print 'result written to: ' + outfile
if fsfile is not None:
path = os.path.dirname(fsfile)
basename = os.path.basename(fsfile)
root, ext = os.path.splitext(basename)
fsoutfile = path + '/' + root + '_norm' + ext
print 'normalizing ' + fsfile + '...'
fsDataset = gdal.Open(fsfile, GA_ReadOnly)
cols = fsDataset.RasterXSize
rows = fsDataset.RasterYSize
driver = fsDataset.GetDriver()
outDataset = driver.Create(fsoutfile, cols, rows, bands, GDT_Float32)
projection = fsDataset.GetProjection()
geotransform = fsDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
j = 0
for k in pos2:
inBand = fsDataset.GetRasterBand(k)
outBand = outDataset.GetRasterBand(j + 1)
for i in range(rows):
y = inBand.ReadAsArray(0, i, cols, 1)
outBand.WriteArray(aa[j] + bb[j] * y, 0, i)
outBand.FlushCache()
j += 1
outDataset = None
print 'result written to: ' + fsoutfile
plt.show()
print '-------done-----------------------------'
def main():
usage = '''
Usage:
-----------------------------------------------------------------------
python %s [-d spatialDimensions] [-p bandPositions [-r resolution ratio]
[-b registration band] msfilename panfilename
-----------------------------------------------------------------------
bandPositions and spatialDimensions are lists,
e.g., -p [1,2,3] -d [0,0,400,400]
Outfile name is msfilename_pan with same format as msfilename
-----------------------------------------------------''' %sys.argv[0]
options, args = getopt.getopt(sys.argv[1:],'hd:p:r:b:')
ratio = 4
dims1 = None
pos1 = None
k1 = 1
for option, value in options:
if option == '-h':
print usage
return
elif option == '-r':
ratio = eval(value)
elif option == '-d':
dims1 = eval(value)
elif option == '-p':
pos1 = eval(value)
elif option == '-b':
k1 = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
gdal.AllRegister()
file1 = args[0]
file2 = args[1]
path = os.path.dirname(file1)
basename1 = os.path.basename(file1)
root1, ext1 = os.path.splitext(basename1)
outfile = '%s/%s_pan%s'%(path,root1,ext1)
# MS image
inDataset1 = gdal.Open(file1,GA_ReadOnly)
try:
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if pos1 is None:
pos1 = range(1,bands+1)
num_bands = len(pos1)
if dims1 is None:
dims1 = [0,0,cols,rows]
x10,y10,cols1,rows1 = dims1
# PAN image
inDataset2 = gdal.Open(file2,GA_ReadOnly)
try:
bands = inDataset2.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read'%e
sys.exit(1)
if bands>1:
print 'PAN image must be a single band'
sys.exit(1)
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
sys.exit(1)
print '========================='
print ' DWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
# if integer assume 11bit quantization otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS,(0,2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10*gt1[1]
uly1 = gt1[3] + y10*gt1[5]
x20 = int(round(((ulx1 - gt2[0])/gt2[1])))
y20 = int(round(((uly1 - gt2[3])/gt2[5])))
cols2 = cols1*ratio
rows2 = rows1*ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
# compress PAN to resolution of MS image
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
lines0,samples0 = bn0.shape
bn1 = MS[k1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
# compress pan once more, extract wavelet quadrants, and restore
panDWT.filter()
fgpan = panDWT.get_quadrant(1)
gfpan = panDWT.get_quadrant(2)
ggpan = panDWT.get_quadrant(3)
panDWT.invert()
# output array
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
aa = np.zeros(3)
bb = np.zeros(3)
print 'Wavelet correlations:'
for i in range(num_bands):
# make copy of panDWT and inject ith ms band
msDWT = copy.deepcopy(panDWT)
msDWT.put_quadrant(MS[i,:,:],0)
# compress once more
msDWT.filter()
# determine wavelet normalization coefficents
ms = msDWT.get_quadrant(1)
aa[0],bb[0],R = auxil.orthoregress(fgpan.ravel(), ms.ravel())
Rs = 'Band '+str(i+1)+': %8.3f'%R
ms = msDWT.get_quadrant(2)
aa[1],bb[1],R = auxil.orthoregress(gfpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
ms = msDWT.get_quadrant(3)
aa[2],bb[2],R = auxil.orthoregress(ggpan.ravel(), ms.ravel())
Rs += '%8.3f'%R
print Rs
# restore once and normalize wavelet coefficients
msDWT.invert()
msDWT.normalize(aa,bb)
# restore completely and collect result
r = 1
while r < ratio:
msDWT.invert()
r *= 2
sharpened[i,:,:] = msDWT.get_quadrant(0)
sharpened *= fact
# write to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile,cols2,rows2,num_bands,GDT_Float32)
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
gt1 = list(geotransform1)
gt1[0] += x10*ratio
gt1[3] -= y10*ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# reference image
file1 = auxil.select_infile(title='Choose reference image')
if file1:
inDataset1 = gdal.Open(file1,GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
dims = auxil.select_dims([0,0,cols,rows])
if dims:
x10,y10,cols1,rows1 = dims
else:
return
# target image
file2 = auxil.select_infile(title='Choose target image')
if file2:
inDataset2 = gdal.Open(file2,GA_ReadOnly)
cols = inDataset2.RasterXSize
rows = inDataset2.RasterYSize
bands = inDataset2.RasterCount
else:
return
pos2 = auxil.select_pos(bands)
if not pos2:
return
dims=auxil.select_dims([0,0,cols,rows])
if dims:
x20,y20,cols2,rows2 = dims
else:
return
# match dimensions
bands = len(pos2)
if (rows1 != rows2) or (cols1 != cols2) or (len(pos1) != bands):
sys.stderr.write("Size mismatch")
sys.exit(1)
# iMAD image
file3 = auxil.select_infile(title='Choose iMAD image')
if file3:
inDataset3 = gdal.Open(file3,GA_ReadOnly)
cols = inDataset3.RasterXSize
rows = inDataset3.RasterYSize
imadbands = inDataset3.RasterCount
else:
return
dims=auxil.select_dims([0,0,cols,rows])
if dims:
x30,y30,cols,rows = dims
else:
return
if (rows1 != rows) or (cols1 != cols):
sys.stderr.write("Size mismatch")
sys.exit(1)
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# full scene
fsfile = auxil.select_infile(title='Choose full target scene if desired')
# no-change threshold
ncpThresh = auxil.select_ncp(0.95)
if ncpThresh is None:
return
chisqr = inDataset3.GetRasterBand(imadbands).ReadAsArray(x30,y30,cols,rows).ravel()
ncp = 1 - stats.chi2.cdf(chisqr,[imadbands-1])
idx = np.where(ncp>ncpThresh)[0]
# split train/test in ratio 2:1
tmp = np.asarray(range(len(idx)))
tst = idx[np.where(np.mod(tmp,3) == 0)]
trn = idx[np.where(np.mod(tmp,3) > 0)]
print '========================================='
print ' RADCAL'
print '========================================='
print time.asctime()
print 'reference: '+file1
print 'target : '+file2
print 'no-change probability threshold: '+str(ncpThresh)
print 'no-change pixels (train): '+str(len(trn))
print 'no-change pixels (test): '+str(len(tst))
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile,cols,rows,bands,GDT_Float32)
projection = inDataset1.GetProjection()
geotransform = inDataset1.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x10*gt[1]
gt[3] = gt[3] + y10*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
aa = []
bb = []
i = 1
for k in pos1:
x = inDataset1.GetRasterBand(k).ReadAsArray(x10,y10,cols,rows).astype(float).ravel()
y = inDataset2.GetRasterBand(k).ReadAsArray(x20,y20,cols,rows).astype(float).ravel()
b,a,R = auxil.orthoregress(y[trn],x[trn])
print '--------------------'
print 'spectral band: ', k
print 'slope: ', b
print 'intercept: ', a
print 'correlation: ', R
print 'means(tgt,ref,nrm): ', np.mean(y[tst]),np.mean(x[tst]),np.mean(a+b*y[tst])
print 't-test, p-value: ', stats.ttest_rel(x[tst], a+b*y[tst])
print 'vars(tgt,ref,nrm) ', np.var(y[tst]),np.var(x[tst]),np.var(a+b*y[tst])
print 'F-test, p-value: ', auxil.fv_test(x[tst], a+b*y[tst])
aa.append(a)
bb.append(b)
outBand = outDataset.GetRasterBand(i)
outBand.WriteArray(np.resize(a+b*y,(rows,cols)),0,0)
outBand.FlushCache()
if i <= 10:
plt.figure(i)
ymax = max(y[idx])
xmax = max(x[idx])
plt.plot(y[idx],x[idx],'k.',[0,ymax],[a,a+b*ymax],'k-')
plt.axis([0,ymax,0,xmax])
plt.title('Band '+str(k))
plt.xlabel('Target')
plt.ylabel('Reference')
i += 1
outDataset = None
print 'result written to: '+outfile
if fsfile is not None:
path = os.path.dirname(fsfile)
basename = os.path.basename(fsfile)
root, ext = os.path.splitext(basename)
fsoutfile = path+'/'+root+'_norm'+ext
print 'normalizing '+fsfile+'...'
fsDataset = gdal.Open(fsfile,GA_ReadOnly)
cols = fsDataset.RasterXSize
rows = fsDataset.RasterYSize
driver = fsDataset.GetDriver()
outDataset = driver.Create(fsoutfile,cols,rows,bands,GDT_Float32)
projection = fsDataset.GetProjection()
geotransform = fsDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
j = 0
for k in pos2:
inBand = fsDataset.GetRasterBand(k)
outBand = outDataset.GetRasterBand(j+1)
for i in range(rows):
y = inBand.ReadAsArray(0,i,cols,1)
outBand.WriteArray(aa[j]+bb[j]*y,0,i)
outBand.FlushCache()
j += 1
outDataset = None
print 'result written to: '+fsoutfile
plt.show()
print '-------done-----------------------------'
def main():
usage = '''
Usage:
-----------------------------------------------------------------------
python %s [-d spatialDimensions] [-p bandPositions [-r resolution ratio]
[-b registration band] msfilename panfilename
-----------------------------------------------------------------------
bandPositions and spatialDimensions are lists,
e.g., -p [1,2,3] -d [0,0,400,400]
Outfile name is msfilename_pan with same format as msfilename
-----------------------------------------------------''' % sys.argv[0]
options, args = getopt.getopt(sys.argv[1:], 'hd:p:r:b:')
ratio = 4
dims1 = None
pos1 = None
k1 = 1
for option, value in options:
if option == '-h':
print usage
return
elif option == '-r':
ratio = eval(value)
elif option == '-d':
dims1 = eval(value)
elif option == '-p':
pos1 = eval(value)
elif option == '-b':
k1 = eval(value)
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
gdal.AllRegister()
file1 = args[0]
file2 = args[1]
path = os.path.dirname(file1)
basename1 = os.path.basename(file1)
root1, ext1 = os.path.splitext(basename1)
outfile = '%s/%s_pan%s' % (path, root1, ext1)
# MS image
inDataset1 = gdal.Open(file1, GA_ReadOnly)
try:
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read' % e
sys.exit(1)
if pos1 is None:
pos1 = range(1, bands + 1)
num_bands = len(pos1)
if dims1 is None:
dims1 = [0, 0, cols, rows]
x10, y10, cols1, rows1 = dims1
# PAN image
inDataset2 = gdal.Open(file2, GA_ReadOnly)
try:
bands = inDataset2.RasterCount
except Exception as e:
print 'Error: %e --Image could not be read' % e
sys.exit(1)
if bands > 1:
print 'PAN image must be a single band'
sys.exit(1)
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
sys.exit(1)
print '========================='
print ' DWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: ' + file1
print 'PAN file: ' + file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0, 0, 1, 1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands, rows1, cols1)), dtype=dt)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k, :, :] = band.ReadAsArray(x10, y10, cols1, rows1)
k += 1
# if integer assume 11bit quantization otherwise must be byte
if MS.dtype == np.int16:
fact = 8.0
MS = auxil.byteStretch(MS, (0, 2**11))
else:
fact = 1.0
# read in corresponding spatial subset of PAN image
if (geotransform1 is None) or (geotransform2 is None):
print 'Image not georeferenced, aborting'
return
# upper left corner pixel in PAN
gt1 = list(geotransform1)
gt2 = list(geotransform2)
ulx1 = gt1[0] + x10 * gt1[1]
uly1 = gt1[3] + y10 * gt1[5]
x20 = int(round(((ulx1 - gt2[0]) / gt2[1])))
y20 = int(round(((uly1 - gt2[3]) / gt2[5])))
cols2 = cols1 * ratio
rows2 = rows1 * ratio
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20, y20, cols2, rows2)
# if integer assume 11-bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN, (0, 2**11))
# compress PAN to resolution of MS image
panDWT = auxil.DWTArray(PAN, cols2, rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
lines0, samples0 = bn0.shape
bn1 = MS[k1, :, :]
# register (and subset) MS image to compressed PAN image
(scale, angle, shift) = auxil.similarity(bn0, bn1)
tmp = np.zeros((num_bands, lines0, samples0))
for k in range(num_bands):
bn1 = MS[k, :, :]
bn2 = ndii.zoom(bn1, 1.0 / scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k, :, :] = bn2[0:lines0, 0:samples0]
MS = tmp
# compress pan once more, extract wavelet quadrants, and restore
panDWT.filter()
fgpan = panDWT.get_quadrant(1)
gfpan = panDWT.get_quadrant(2)
ggpan = panDWT.get_quadrant(3)
panDWT.invert()
# output array
sharpened = np.zeros((num_bands, rows2, cols2), dtype=np.float32)
aa = np.zeros(3)
bb = np.zeros(3)
print 'Wavelet correlations:'
for i in range(num_bands):
# make copy of panDWT and inject ith ms band
msDWT = copy.deepcopy(panDWT)
msDWT.put_quadrant(MS[i, :, :], 0)
# compress once more
msDWT.filter()
# determine wavelet normalization coefficents
ms = msDWT.get_quadrant(1)
aa[0], bb[0], R = auxil.orthoregress(fgpan.ravel(), ms.ravel())
Rs = 'Band ' + str(i + 1) + ': %8.3f' % R
ms = msDWT.get_quadrant(2)
aa[1], bb[1], R = auxil.orthoregress(gfpan.ravel(), ms.ravel())
Rs += '%8.3f' % R
ms = msDWT.get_quadrant(3)
aa[2], bb[2], R = auxil.orthoregress(ggpan.ravel(), ms.ravel())
Rs += '%8.3f' % R
print Rs
# restore once and normalize wavelet coefficients
msDWT.invert()
msDWT.normalize(aa, bb)
# restore completely and collect result
r = 1
while r < ratio:
msDWT.invert()
r *= 2
sharpened[i, :, :] = msDWT.get_quadrant(0)
sharpened *= fact
# write to disk
driver = inDataset1.GetDriver()
outDataset = driver.Create(outfile, cols2, rows2, num_bands, GDT_Float32)
projection1 = inDataset1.GetProjection()
if projection1 is not None:
outDataset.SetProjection(projection1)
gt1 = list(geotransform1)
gt1[0] += x10 * ratio
gt1[3] -= y10 * ratio
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(sharpened[k, :, :], 0, 0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s' % outfile
inDataset1 = None
inDataset2 = None
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# MS image
file1 = auxil.select_infile(title='Choose MS image')
if file1:
inDataset1 = gdal.Open(file1,GA_ReadOnly)
cols = inDataset1.RasterXSize
rows = inDataset1.RasterYSize
bands = inDataset1.RasterCount
else:
return
pos1 = auxil.select_pos(bands)
if not pos1:
return
num_bands = len(pos1)
dims = auxil.select_dims([0,0,cols,rows])
if dims:
x10,y10,cols1,rows1 = dims
else:
return
# PAN image
file2 = auxil.select_infile(title='Choose PAN image')
if file2:
inDataset2 = gdal.Open(file2,GA_ReadOnly)
cols = inDataset2.RasterXSize
rows = inDataset2.RasterYSize
bands = inDataset2.RasterCount
else:
return
if bands>1:
print 'Must be a single band (panchromatic) image'
return
dims=auxil.select_dims([0,0,cols,rows])
if dims:
x20,y20,cols2,rows2 = dims
else:
return
# outfile
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
# resolution ratio
ratio = auxil.select_integer(4, 'Resolution ratio (2 or 4)')
if not ratio:
return
# MS registration band
k1 = auxil.select_integer(1, 'MS band for registration')
if not k1:
return
print '========================='
print ' ATWT Pansharpening'
print '========================='
print time.asctime()
print 'MS file: '+file1
print 'PAN file: '+file2
# image arrays
band = inDataset1.GetRasterBand(1)
tmp = band.ReadAsArray(0,0,1,1)
dt = tmp.dtype
MS = np.asarray(np.zeros((num_bands,rows1,cols1)),dtype = dt)
# result will be float32
sharpened = np.zeros((num_bands,rows2,cols2),dtype=np.float32)
k = 0
for b in pos1:
band = inDataset1.GetRasterBand(b)
MS[k,:,:] = band.ReadAsArray(x10,y10,cols1,rows1)
k += 1
band = inDataset2.GetRasterBand(1)
PAN = band.ReadAsArray(x20,y20,cols2,rows2)
# if integer assume 11bit quantization, otherwise must be byte
if PAN.dtype == np.int16:
PAN = auxil.byteStretch(PAN,(0,2**11))
if MS.dtype == np.int16:
MS = auxil.byteStretch(MS,(0,2**11))
# compress PAN to resolution of MS image using DWT
panDWT = auxil.DWTArray(PAN,cols2,rows2)
r = ratio
while r > 1:
panDWT.filter()
r /= 2
bn0 = panDWT.get_quadrant(0)
# register (and subset) MS image to compressed PAN image using MSband
lines0,samples0 = bn0.shape
bn1 = MS[k1,:,:]
# register (and subset) MS image to compressed PAN image
(scale,angle,shift) = auxil.similarity(bn0,bn1)
tmp = np.zeros((num_bands,lines0,samples0))
for k in range(num_bands):
bn1 = MS[k,:,:]
bn2 = ndii.zoom(bn1, 1.0/scale)
bn2 = ndii.rotate(bn2, angle)
bn2 = ndii.shift(bn2, shift)
tmp[k,:,:] = bn2[0:lines0,0:samples0]
MS = tmp
smpl = np.random.randint(cols2*rows2,size=100000)
print 'Wavelet correlations:'
# loop over MS bands
for k in range(num_bands):
msATWT = auxil.ATWTArray(PAN)
r = ratio
while r > 1:
msATWT.filter()
r /= 2
# sample PAN wavelet details
X = msATWT.get_band(msATWT.num_iter)
X = X.ravel()[smpl]
# resize the ms band to scale of the pan image
ms_band = ndii.zoom(MS[k,:,:],ratio)
# sample details of MS band
tmpATWT = auxil.ATWTArray(ms_band)
r = ratio
while r > 1:
tmpATWT.filter()
r /= 2
Y = tmpATWT.get_band(msATWT.num_iter)
Y = Y.ravel()[smpl]
# get band for injection
bnd = tmpATWT.get_band(0)
tmpATWT = None
aa,bb,R = auxil.orthoregress(X,Y)
print 'Band '+str(k+1)+': %8.3f'%R
# inject the filtered MS band
msATWT.inject(bnd)
# normalize wavelet components and expand
msATWT.normalize(aa,bb)
r = ratio
while r > 1:
msATWT.invert()
r /= 2
sharpened[k,:,:] = msATWT.get_band(0)
# write to disk
if outfile:
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile,
cols2,rows2,num_bands,GDT_Float32)
projection1 = inDataset1.GetProjection()
geotransform1 = inDataset1.GetGeoTransform()
geotransform2 = inDataset2.GetGeoTransform()
if geotransform2 is not None:
gt2 = list(geotransform2)
if geotransform1 is not None:
gt1 = list(geotransform1)
gt1[0] += x10*gt2[1] # using PAN pixel sizes
gt1[3] += y10*gt2[5]
gt1[1] = gt2[1]
gt1[2] = gt2[2]
gt1[4] = gt2[4]
gt1[5] = gt2[5]
outDataset.SetGeoTransform(tuple(gt1))
if projection1 is not None:
outDataset.SetProjection(projection1)
for k in range(num_bands):
outBand = outDataset.GetRasterBand(k+1)
outBand.WriteArray(sharpened[k,:,:],0,0)
outBand.FlushCache()
outDataset = None
print 'Result written to %s'%outfile
inDataset1 = None
inDataset2 = None
def main():
usage = '''
Usage:
--------------------------------------------------------
python %s [-p "bandPositions"] [-d "spatialDimensions"]
[-t no-change prob threshold] imadFile [fullSceneFile]'
--------------------------------------------------------
bandPositions and spatialDimensions are quoted lists,
e.g., -p [4,5,6] -d [0,0,400,400]
-n stops graphics output
SpatialDimensions MUST match those of imadFile
spectral dimension of fullSceneFile, if present,
MUST match those of target and reference images
--------------------------------------------------------
imadFile is of form path/MAD(filename1-filename2).ext and
the output file is named
path/filename2_norm.ext.
That is, it is assumed that filename1 is reference and
filename2 is target and the output retains the format
of the imadFile. A similar convention is used to
name the normalized full scene, if present:
fullSceneFile_norm.ext
Note that, for ENVI format, ext is the empty string.
-------------------------------------------------------''' % sys.argv[0]
options, args = getopt.getopt(sys.argv[1:], 'hnp:d:t:')
pos = None
dims = None
ncpThresh = 0.95
fsfn = None
graphics = True
for option, value in options:
if option == '-h':
print usage
return
elif option == '-n':
graphics = False
elif option == '-p':
pos = eval(value)
elif option == '-d':
dims = eval(value)
elif option == '-t':
ncpThresh = eval(value)
if (len(args) != 1) and (len(args) != 2):
print 'Incorrect number of arguments'
print usage
sys.exit(1)
imadfn = args[0]
if len(args) == 2:
fsfn = args[1]
path = os.path.dirname(fsfn)
basename = os.path.basename(fsfn)
root, ext = os.path.splitext(basename)
fsoutfn = path + '/' + root + '_norm_all' + ext
path = os.path.dirname(imadfn)
basename = os.path.basename(imadfn)
root, ext = os.path.splitext(basename)
b = root.find('(')
e = root.find(')')
referenceroot, targetbasename = root[b + 1:e].split('-')
referencefn = path + '/' + referenceroot + ext
targetfn = path + '/' + targetbasename
targetroot, targetext = os.path.splitext(targetbasename)
outfn = path + '/' + targetroot + '_norm' + targetext
imadDataset = gdal.Open(imadfn, GA_ReadOnly)
try:
imadbands = imadDataset.RasterCount
cols = imadDataset.RasterXSize
rows = imadDataset.RasterYSize
except Exception as e:
print 'Error: %s --Image could not be read' % e
sys.exit(1)
referenceDataset = gdal.Open(referencefn, GA_ReadOnly)
targetDataset = gdal.Open(targetfn, GA_ReadOnly)
if pos is None:
pos = range(1, referenceDataset.RasterCount + 1)
if dims is None:
x0 = 0
y0 = 0
else:
x0, y0, cols, rows = dims
chisqr = imadDataset.GetRasterBand(imadbands).ReadAsArray(
0, 0, cols, rows).ravel()
ncp = 1 - stats.chi2.cdf(chisqr, [imadbands - 1])
idx = where(ncp > ncpThresh)[0]
m = len(idx)
idx1 = random.permutation(m)
idxall = idx[idx1]
idxtrain = idxall[0:2 * m // 3]
idxtest = idxall[2 * m // 3:]
mtrain = len(idxtrain)
mtest = len(idxtest)
print time.asctime()
print 'reference: ' + referencefn
print 'target : ' + targetfn
print 'no-change probability threshold: ' + str(ncpThresh)
print 'no-change pixels for training: %i, for testing: %i' % (mtrain,
mtest)
start = time.time()
driver = targetDataset.GetDriver()
outDataset = driver.Create(outfn, cols, rows, len(pos), GDT_Float32)
projection = imadDataset.GetProjection()
geotransform = imadDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
aa = []
bb = []
if graphics:
plt.figure(1, (9, 6))
j = 1
bands = len(pos)
for k in pos:
x = referenceDataset.GetRasterBand(k).ReadAsArray(
x0, y0, cols, rows).astype(float).ravel()
y = targetDataset.GetRasterBand(k).ReadAsArray(
x0, y0, cols, rows).astype(float).ravel()
b, a, R = orthoregress(y[idxtrain], x[idxtrain])
my = max(y[idxtrain])
if (j < 7) and graphics:
plt.subplot(2, 3, j)
plt.plot(y[idxtrain], x[idxtrain], '.')
plt.plot([0, my], [a, a + b * my])
plt.title('Band %i' % k)
if ((j < 4) and (bands < 4)) or j > 3:
plt.xlabel('Target')
if (j == 1) or (j == 4):
plt.ylabel('Reference')
aa.append(a)
bb.append(b)
outBand = outDataset.GetRasterBand(j)
_, Pt = stats.ttest_ind(x[idxtest], a + b * y[idxtest])
f = var(x[idxtest]) / var(a + b * y[idxtest])
if f < 1.0:
f = 1 / f
Pf = stats.f.sf(f, mtest - 1, mtest - 1)
print 'band: %i slope: %f intercept: %f corr: %f P(t-test): %f P(F-test): %f' % (
k, b, a, R, Pt, Pf)
outBand.WriteArray(resize(a + b * y, (rows, cols)), 0, 0)
outBand.FlushCache()
j += 1
if graphics:
plt.show()
plt.close()
referenceDataset = None
targetDataset = None
outDataset = None
print 'result written to: ' + outfn
if fsfn is not None:
print 'normalizing ' + fsfn + '...'
fsDataset = gdal.Open(fsfn, GA_ReadOnly)
try:
cols = fsDataset.RasterXSize
rows = fsDataset.RasterYSize
except Exception as e:
print 'Error %s -- Image could not be read in'
sys.exit(1)
driver = fsDataset.GetDriver()
outDataset = driver.Create(fsoutfn, cols, rows, len(pos), GDT_Float32)
projection = fsDataset.GetProjection()
geotransform = fsDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
j = 1
for k in pos:
inBand = fsDataset.GetRasterBand(k)
outBand = outDataset.GetRasterBand(j)
for i in range(rows):
y = inBand.ReadAsArray(0, i, cols, 1)
outBand.WriteArray(aa[j - 1] + bb[j - 1] * y, 0, i)
outBand.FlushCache()
j += 1
outDataset = None
fsDataset = None
print 'full result written to: ' + fsoutfn
print 'elapsed time: %s' % str(time.time() - start)
