def copy_bands_to_file(src_file_path, dst_file_path, bands=None):
# Get info from source product
src_prod = ProductIO.readProduct(src_file_path)
prod_name = src_prod.getName()
prod_type = src_prod.getProductType()
width = src_prod.getSceneRasterWidth()
height = src_prod.getSceneRasterHeight()
if bands is None:
bands = src_prod.getBandNames()
# Copy geocoding and selected bands from source to destination product
dst_prod = Product(prod_name, prod_type, width, height)
ProductUtils.copyGeoCoding(src_prod.getBandAt(0), dst_prod)
for band in bands:
r = ProductUtils.copyBand(band, src_prod, dst_prod, True)
if r is None:
src_prod.closeIO()
raise RuntimeError(src_file_path + " does not contain band " +
band)
# Write destination product to disk
ext = os.path.splitext(dst_file_path)[1]
if ext == '.dim':
file_type = 'BEAM_DIMAP'
elif ext == '.nc':
file_type = 'NetCDF-CF'
elif ext == '.tif':
file_type = 'GeoTIFF-BigTIFF'
else:
file_type = 'GeoTIFF-BigTIFF'
ProductIO.writeProduct(dst_prod, dst_file_path, file_type)
src_prod.closeIO()
dst_prod.closeIO()
def phaseToHeight(inputfile, NLOOKS):
"""
This is the central processing chain
inputfile is a string of an interferogram file name
NLOOKS is the number of range looks to be used for multi-looking
#
Multi-looking, Goldstein phase filtering, unwrapping and terrain correction are performed
The output is then written to file in BEAM-DIMAP format
"""
TEMP1 = 'ML_fl_temp.dim'
TEMP2 = 'height_temp.dim'
OUT = '_'.join(
[inputfile.split('.dim')[0], 'ML',
str(NLOOKS), 'height_TC.dim'])
image = ProductIO.readProduct(inputfile)
# SNAP Processing -------------------------------
image = createP('Multilook', image, nRgLooks=NLOOKS)
image = createP('GoldsteinPhaseFiltering',
image,
alpha=0.2,
useCoherenceMask=True,
coherenceThreshold=0.2,
writeout=TEMP1)
# Numpy processing -----------------------------
image = ProductIO.readProduct(TEMP1)
phase = image.getBand(
[x for x in list(image.getBandNames()) if 'Phase' in x][0])
phase_data = get_data(phase)
recentered = zero_phase(phase_data)
unwrapped = unwrap(recentered)
unw_deramped = remove_plane(unwrapped, 10000)
height = unw_deramped / get_kz(image)
height = height.transpose().flatten()
# Write to target Product--------------------------
W = image.getBandAt(0).getRasterWidth()
H = image.getBandAt(0).getRasterHeight()
targetP = snappy.Product('height_product', 'height_type', W, H)
ProductUtils.copyMetadata(image, targetP)
ProductUtils.copyTiePointGrids(image, targetP)
targetP.setProductWriter(ProductIO.getProductWriter('BEAM-DIMAP'))
for band in ['Phase', 'coh']:
bandname = [x for x in list(image.getBandNames()) if band in x][0]
ProductUtils.copyBand(bandname, image, band, targetP, True)
targetP.setProductReader(ProductIO.getProductReader('BEAM-DIMAP'))
ProductIO.writeProduct(targetP, TEMP2, 'BEAM-DIMAP')
targetB = targetP.addBand('height', snappy.ProductData.TYPE_FLOAT32)
targetB.setUnit('m')
targetP.setProductWriter(ProductIO.getProductWriter('BEAM-DIMAP'))
targetP.writeHeader(TEMP2)
targetB.writePixels(0, 0, W, H, height)
targetP.closeIO()
# Terrain correction-------------------------
image = ProductIO.readProduct(TEMP2)
image = createP('Terrain-Correction',
image,
alignToStandardGrid='true',
demName='SRTM 1Sec HGT',
saveDEM='true')
ProductIO.writeProduct(image, OUT, 'BEAM-DIMAP')
data_tmp = GPF.createProduct("Terrain-Correction",params,data)
data = data_tmp
# Convert to dB (LinearTodBOp.java)
params = HashMap()
if opts.iangle_value or opts.iangle_image:
band_list = list(data.getBandNames())
bands = []
for band in band_list:
if not re.search('angle',band.lower()):
bands.append(band)
params.put('sourceBands',','.join(bands))
data_tmp = GPF.createProduct('linearToFromdB',params,data)
if opts.iangle_image:
for band in band_list:
if re.search('angle',band.lower()):
ProductUtils.copyBand(band,data,band,data_tmp,True)
elif opts.iangle_value:
iangle = np.nan
for band in band_list:
if re.search('angle',band.lower()):
band_data = data.getBand(band)
w = band_data.getRasterWidth()
h = band_data.getRasterHeight()
band_value = np.full((h,w),np.nan,dtype=np.float32)
band_data.readPixels(0,0,w,h,band_value)
iangle = np.nanmean(band_value[band_value > 0.1])
data = data_tmp
# Attach bandname (BandSelectOp.java)
band_list = list(data.getBandNames())
bands = []
for band in band_list:
# Read original product
data_1 = ProductIO.readProduct(os.path.abspath(fnams[0]))
# Attach bandname
if not opts.skip_rename_master:
dstr = os.path.basename(fnams[0])[0:8]
try:
dtim = datetime.strptime(dstr, '%Y%m%d')
except Exception:
raise ValueError('Error in filename >>> ' + fnams[0])
band_list = list(data_1.getBandNames())
bands = []
for band in [band_list[j] for j in opts.band]:
if dstr in band:
continue
band_new = band + '_' + dstr
ProductUtils.copyBand(band, data_1, band_new, data_1, True)
bands.append(band_new)
if len(bands) > 0:
params = HashMap()
params.put('sourceBands', ','.join(bands))
data_tmp = GPF.createProduct('BandSelect', params, data_1)
data_1 = data_tmp
# Collocation
for i in range(1, len(fnams)):
# Read original product
data_2 = ProductIO.readProduct(os.path.abspath(fnams[i]))
# Attach bandname
if not opts.skip_rename_slave:
dstr = os.path.basename(fnams[i])[0:8]
try:
dtim = datetime.strptime(dstr, '%Y%m%d')
# check if band index given is correct
if not sys.argv[2] in ['2', '3', '4', '8']:
print 'Incorrect band index'
# get cli arguments
product_file = sys.argv[1]
band_index = sys.argv[2]
band_name = 'B' + band_index
product_name = {
'B2': 'blue',
'B3': 'green',
'B4': 'red',
'B8': 'nir',
}[band_name]
# input product: open and get dimensions & name
input_product = ProductIO.readProduct(product_file)
product_width = input_product.getSceneRasterWidth()
product_height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# output product: copy selected band & save product
output_product = Product(product_name, product_name, product_width,
product_height)
ProductUtils.copyGeoCoding(input_product, output_product)
ProductUtils.copyBand(band_name, input_product, output_product, True)
ProductIO.writeProduct(output_product, product_name + '.band.dim',
'BEAM-DIMAP')
output_product.closeIO()
print 'Product file and band index required'
sys.exit(1)
# check if band index given is correct
if not sys.argv[2] in ['2', '3', '4', '8']:
print 'Incorrect band index'
# get cli arguments
product_file = sys.argv[1]
band_index = sys.argv[2]
band_name = 'B' + band_index
product_name = {
'B2': 'blue',
'B3': 'green',
'B4': 'red',
'B8': 'nir',
}[band_name]
# input product: open and get dimensions & name
input_product = ProductIO.readProduct(product_file)
product_width = input_product.getSceneRasterWidth()
product_height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# output product: copy selected band & save product
output_product = Product(product_name, product_name, product_width, product_height)
ProductUtils.copyGeoCoding(input_product, output_product)
ProductUtils.copyBand(band_name, input_product, output_product, True)
ProductIO.writeProduct(output_product, product_name + '.band.dim', 'BEAM-DIMAP')
output_product.closeIO()
