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
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():
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)
# 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 ' 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():
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
