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 write_snappy_product(file_path, bands, product_name, geo_coding):
try:
(height, width) = bands[0]['band_data'].shape
except AttributeError:
raise RuntimeError(bands[0]['band_name'] + "contains no data.")
product = Product(product_name, product_name, width, height)
product.setSceneGeoCoding(geo_coding)
# Ensure that output is saved in BEAM-DIMAP format,
# otherwise writeHeader does not work.
file_path = os.path.splitext(file_path)[0] + '.dim'
# Bands have to be created before header is written
# but header has to be written before band data is written.
for b in bands:
band = product.addBand(b['band_name'], ProductData.TYPE_FLOAT32)
if 'description' in b.keys():
band.setDescription(b['description'])
if 'unit' in b.keys():
band.setUnit(b['unit'])
product.setProductWriter(ProductIO.getProductWriter('BEAM-DIMAP'))
product.writeHeader(String(file_path))
for b in bands:
band = product.getBand(b['band_name'])
band.writePixels(0, 0, width, height,
b['band_data'].astype(np.float32))
product.closeIO()
def do_vegetation_indices (source):
print ('\tVegetation Indices ...')
## Input product and dimensions
input_product = ProductIO.readProduct(source)
width = input_product.getSceneRasterWidth()
height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
product_description = input_product.getDescription()
product_band_names = input_product.getBandNames()
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
## input product red and nir bands
b4 = input_product.getBand('B4')
b8 = input_product.getBand('B8')
## output product (ndvi) new band
output_product = Product('NDVI', 'NDVI', width, height)
ProductUtils.copyGeoCoding(input_product, output_product)
output_band = output_product.addBand('ndvi', ProductData.TYPE_FLOAT32)
## output writer
output_product_writer = ProductIO.getProductWriter('BEAM-DIMAP')
output_product.setProductWriter(output_product_writer)
output_product.writeHeader(product_name + '_ndvi.dim')
## compute & save ndvi line by line
red_row = numpy.zeros(width, dtype=numpy.float32)
nir_row = numpy.zeros(width, dtype=numpy.float32)
for y in xrange (height):
red_row = b4.readPixels(0, y, width, 1, red_row)
nir_row = b8.readPixels(0, y, width, 1, nir_row)
ndvi = (nir_row - red_row)/(nir_row + red_row)
output = output_band.writePixels(0, y, width, 1, ndvi)
output_product.CloseIO()
return output
for S2_SAFE in os.listdir('products'):
#NDVI:
NDVI_im = S2_SAFE.split(".")[0] + "_NDVI"
NIR_im = S2_SAFE.split(".")[0] + "_NIR"
os.system('mkdir checked_products/' + NDVI_im)
os.system('mkdir checked_products/' + NIR_im)
S2_product = ProductIO.readProduct('products/' + S2_SAFE +
'/GRANULE/output.dim')
band_names = S2_product.getBandNames()
width = S2_product.getSceneRasterWidth()
height = S2_product.getSceneRasterHeight()
b4 = S2_product.getBand('B4')
b8 = S2_product.getBand('B8')
newProduct = Product('NDVI', 'NDVI', width, height)
newBand = newProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(S2_product, newProduct)
newProduct.setProductWriter(writer)
newProduct.writeHeader('NDVI.dim')
rb4 = np.zeros(width, dtype=np.float32)
rb8 = np.zeros(width, dtype=np.float32)
for y in range(height):
rb4 = b4.readPixels(0, y, width, 1, rb4)
rb8 = b8.readPixels(0, y, width, 1, rb8)
NDVI = (rb8 - rb4) / (rb8 + rb4)
newBand.writePixels(0, y, width, 1, NDVI)
newProduct.closeIO()
sys.exit(1)
print("Reading...")
sourceProduct = ProductIO.readProduct(sys.argv[1])
b1 = sourceProduct.getBand('reflec_5')
b2 = sourceProduct.getBand('reflec_7')
b3 = sourceProduct.getBand('reflec_9')
w1 = b1.getSpectralWavelength()
w2 = b2.getSpectralWavelength()
w3 = b3.getSpectralWavelength()
a = (w2 - w1) / (w3 - w1)
k = 1.03
width = sourceProduct.getSceneRasterWidth()
height = sourceProduct.getSceneRasterHeight()
targetProduct = Product('FLH_Product', 'FLH_Type', width, height)
targetBand = targetProduct.addBand('FLH', ProductData.TYPE_FLOAT32)
ProductUtils.copyGeoCoding(sourceProduct, targetProduct)
targetProduct.setProductWriter(ProductIO.getProductWriter('GeoTIFF'))
targetProduct.writeHeader(String('snappy_flh_output.tif'))
r1 = numpy.zeros(width, dtype=numpy.float32)
r2 = numpy.zeros(width, dtype=numpy.float32)
r3 = numpy.zeros(width, dtype=numpy.float32)
print("Writing...")
for y in range(height):
b1.readPixels(0, y, width, 1, r1)
b2.readPixels(0, y, width, 1, r2)
#SVR training
pipeline = make_pipeline(
StandardScaler(),
SVR(kernel='rbf', epsilon=0.105, C=250, gamma=2.8),
)
SVRmodel = pipeline.fit(X, Y)
# Predictfor validation data
valX = X
y_out = pipeline.predict(valX)
##---------------------------------------------------------------------------------
bandc11 = product.getBand('C11')
bandc22 = product.getBand('C22')
laiProduct = Product('LAI', 'LAI', width, height)
laiBand = laiProduct.addBand('lai', ProductData.TYPE_FLOAT32)
laiFlagsBand = laiProduct.addBand('lai_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, laiProduct)
ProductUtils.copyMetadata(product, laiProduct)
ProductUtils.copyTiePointGrids(product, laiProduct)
laiFlagCoding = FlagCoding('lai_flags')
laiFlagCoding.addFlag("LAI_LOW", 1, "LAI below 0")
laiFlagCoding.addFlag("LAI_HIGH", 2, "LAI above 5")
group = laiProduct.getFlagCodingGroup()
#print(dir(group))
group.add(laiFlagCoding)
def create_product(self):
from snappy import Product, ProductUtils, ProductIO, ProductData, String
product = self.product
ac_product = Product('L2h', 'L2h', self.width, self.height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ac_product.setProductWriter(writer)
ProductUtils.copyGeoCoding(product, ac_product)
ProductUtils.copyMetadata(product, ac_product)
ac_product.setStartTime(product.getStartTime())
ac_product.setEndTime(product.getEndTime())
# add metadata: ancillary data used for processing
meta = jpy.get_type('org.esa.snap.core.datamodel.MetadataElement')
att = jpy.get_type('org.esa.snap.core.datamodel.MetadataAttribute')
# att(name=string,type=int), type: 41L->ascii; 12L->int32;
att0 = att('AERONET file', ProductData.TYPE_ASCII)
att0.setDataElems(self.aeronetfile)
att1 = att('AOT', ProductData.TYPE_ASCII)
att1.setDataElems(str(self.aot))
meta = meta('L2')
meta.setName('Ancillary Data')
meta.addAttribute(att0)
meta.addAttribute(att1)
ac_product.getMetadataRoot().addElement(meta)
# add data
# Water-leaving radiance + sunglint
for iband in range(self.N):
bname = "Lnw_g_" + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
acband.setSpectralWavelength(self.wl[iband])
acband.setSpectralBandwidth(self.B[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= -1')
ac_product.getBand(bname).setDescription(
"Water-leaving plus sunglint normalized radiance (Lnw + Lg) in mW cm-2 sr-1 μm-1 at "
+ self.band_names[iband])
# Water-leaving radiance
for iband in range(self.N):
bname = "Lnw_" + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
acband.setSpectralWavelength(self.wl[iband])
acband.setSpectralBandwidth(self.B[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= -1')
ac_product.getBand(bname).setDescription(
"Normalized water-leaving radiance in mW cm-2 sr-1 μm-1 at " +
self.band_names[iband])
# Sunglint reflection factor
# for iband in range(self.N):
bname = "BRDFg" # + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
# acband.setSpectralWavelength(self.wl[iband])
# acband.setSpectralBandwidth(self.b[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= 0')
ac_product.getBand(bname).setDescription(
"Glint reflection factor (BRDF) ") # + self.band_names[iband])
# Viewing geometry
acband = ac_product.addBand("SZA", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("SZA").setDescription("Solar zenith angle in deg.")
acband = ac_product.addBand("VZA", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("VZA").setDescription(
"Mean viewing zenith angle in deg.")
acband = ac_product.addBand("AZI", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("AZI").setDescription(
"Mean relative azimuth angle in deg.")
ac_product.setAutoGrouping("Lnw:Lnw_g_")
ac_product.writeHeader(String(self.outfile + ".dim"))
self.l2_product = ac_product
def write_BalticP_AC_Product(product,
baltic__product_path,
sensor,
data_dict,
singleBand_dict=None):
File = jpy.get_type('java.io.File')
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
bandShape = (height, width)
balticPACProduct = Product('balticPAC', 'balticPAC', width, height)
balticPACProduct.setFileLocation(File(baltic__product_path))
ProductUtils.copyGeoCoding(product, balticPACProduct)
ProductUtils.copyTiePointGrids(product, balticPACProduct)
if (sensor == 'OLCI'):
nbands = 21
band_name = ["Oa01_radiance"]
for i in range(1, nbands):
if (i < 9):
band_name += ["Oa0" + str(i + 1) + "_radiance"]
else:
band_name += ["Oa" + str(i + 1) + "_radiance"]
# Create empty bands for rhow, rhown, uncertainties for rhow
for i in range(nbands):
bsource = product.getBand(band_name[i]) # TOA radiance
for key in data_dict.keys():
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.addBand(brtoa_name,
ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rtoaBand)
rtoaBand.setNoDataValue(np.nan)
rtoaBand.setNoDataValueUsed(True)
dataNames = [*data_dict.keys()]
autoGroupingString = dataNames[0]
for key in dataNames[1:]:
autoGroupingString += ':' + key
balticPACProduct.setAutoGrouping(autoGroupingString)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
singleBand = balticPACProduct.addBand(key,
ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
balticPACProduct.setProductWriter(writer)
balticPACProduct.writeHeader(baltic__product_path)
writer.writeProductNodes(balticPACProduct, baltic__product_path)
# set datarhow, rhown, uncertainties for rhow
for key in data_dict.keys():
x = data_dict[key].get('data')
if not x is None:
for i in range(nbands):
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.getBand(brtoa_name)
out = np.array(x[:, i]).reshape(bandShape)
rtoaBand.writeRasterData(
0, 0, width, height,
snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
x = singleBand_dict[key].get('data')
if not x is None:
singleBand = balticPACProduct.getBand(key)
out = np.array(x).reshape(bandShape)
singleBand.writeRasterData(
0, 0, width, height,
snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
# # Create flag coding
# raycorFlagsBand = balticPACProduct.addBand('raycor_flags', ProductData.TYPE_UINT8)
# raycorFlagCoding = FlagCoding('raycor_flags')
# raycorFlagCoding.addFlag("testflag_1", 1, "Flag 1 for Rayleigh Correction")
# raycorFlagCoding.addFlag("testflag_2", 2, "Flag 2 for Rayleigh Correction")
# group = balticPACProduct.getFlagCodingGroup()
# group.add(raycorFlagCoding)
# raycorFlagsBand.setSampleCoding(raycorFlagCoding)
balticPACProduct.closeIO()
# 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()
if len(sys.argv) < 2:
print 'Product file requires'
sys.exit(1)
# input product & dimensions
input_product = ProductIO.readProduct(sys.argv[1])
width = input_product.getSceneRasterWidth()
height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# input product red & nir bands
red_band = input_product.getBand('B4')
nir_band = input_product.getBand('B8')
# output product (ndvi) & new band
output_product = Product('NDVI', 'NDVI', width, height)
ProductUtils.copyGeoCoding(input_product, output_product)
output_band = output_product.addBand('ndvi', ProductData.TYPE_FLOAT32)
# output writer
output_product_writer = ProductIO.getProductWriter('BEAM-DIMAP')
output_product.setProductWriter(output_product_writer)
output_product.writeHeader(product_name + '.ndvi.dim')
# compute & save ndvi line by line
red_row = numpy.zeros(width, dtype=numpy.float32)
nir_row = numpy.zeros(width, dtype=numpy.float32)
for y in xrange(height):
red_row = red_band.readPixels(0, y, width, 1, red_row)
nir_row = nir_band.readPixels(0, y, width, 1, nir_row)
def process_product(file, sensor):
in_product = ProductIO.readProduct(file)
width = in_product.getSceneRasterWidth()
height = in_product.getSceneRasterHeight()
in_name = in_product.getName()
in_description = in_product.getDescription()
in_band_names = in_product.getBandNames()
c2x_log.info("Product: %s, %s" % (in_name, in_description))
c2x_log.debug("Raster size: %d x %d pixels" % (width, height))
c2x_log.debug("Start time: " + str(in_product.getStartTime()))
c2x_log.debug("End time: " + str(in_product.getEndTime()))
c2x_log.debug("Bands: %s" % (list(in_band_names)))
# Output product Definition
# 1. define the target product and its file format
c2x_product = Product('%s_%s' % (in_name, PRODUCT_TYPE), '%s' % PRODUCT_TYPE, width, height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
c2x_product.setProductWriter(writer)
fpath = in_product.getFileLocation().getAbsolutePath()
fpath = os.path.split(fpath)[0] + "/out/" + os.path.split(fpath)[1]
fpath = fpath.split(".")[0]
fpath = "{0}_{1}.dim".format(fpath, PRODUCT_TYPE.lower())
c2x_product.setFileLocation(File(fpath))
sensor_outputs = sensor["outputs"]
sensor_wavelengths = sensor["wavelengths"]
# 2. define the bands for the results of the different algorithms
outbands = dict()
for cnt in range(len(sensor_outputs)):
cnt = sensor_outputs[cnt]
outbands[cnt[0]] = c2x_product.addBand(cnt[0], cnt[1])
# 3. copy tie point grids from input product to target product
ProductUtils.copyTiePointGrids(in_product, c2x_product)
ProductUtils.copyMetadata(in_product, c2x_product)
ProductUtils.copyGeoCoding(in_product, c2x_product)
ProductUtils.copyFlagBands(in_product, c2x_product, False)
# 4. write the header to disk
location = c2x_product.getFileLocation()
c2x_product.writeHeader(location)
# assigning aux arrays
rhow_arrays = dict()
for wls in sensor_wavelengths:
rhow_arrays[str(wls)] = np.zeros(width, dtype=np.float32)
# get all specified bands from input product
c2x_log.info("Processing and writing to %s" % file)
algo_names = dict()
for cnt in range(len(sensor_outputs)):
algo_names[cnt] = sensor_outputs[cnt][0]
c2x_log.debug("Processing with following algos: %s " % list(algo_names.values()))
bsource = dict()
for i in range(len(sensor_wavelengths)):
band_name = create_source_band_name(sensor_wavelengths[i])
bsource[band_name] = in_product.getBand(sensor[band_name])
flag_bands = []
for b in in_product.getBands():
if b.isFlagBand():
flag_bands.append(b)
flags_data = np.zeros (width, dtype=np.int32)
# loop through the product line by line and application of algorithms
for y in range(height):
rhow = dict()
for wl in sensor_wavelengths:
source_band = bsource[create_source_band_name(wl)]
# dealing with no-data; setting no-data to to NaN
invalidMask = read_invalid_mask(source_band, width, y)
source_band.readPixels(0, y, width, 1, rhow_arrays[str(wl)])
rhow["band" + str(wl)] = np.ma.array(rhow_arrays[str(wl)], mask=invalidMask, fill_value=np.nan)
for algo in range(len(sensor_outputs)):
res = sensor_outputs[algo][2](rhow, sensor_outputs[algo][4], sensor_outputs[algo][5])
name = sensor_outputs[algo][0]
outbands[name].writePixels(0, y, width, 1, res)
for fband in flag_bands:
fband.readPixels(0, y, width, 1, flags_data)
c2x_product.getBand(fband.getName()).writePixels(0, y, width, 1, flags_data)
# all computations and writing is completed; close all data streams and finish the program
c2x_product.closeIO()
print("Done.")
return 0
product = ProductIO.readProduct(File)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("Description: %s" % description)
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
KNNProduct = Product('KNN', 'KNN', width, height)
KNNBand = KNNProduct.addBand('KNN', ProductData.TYPE_FLOAT32)
KNNFlagsBand = KNNProduct.addBand('KNN_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, KNNProduct)
KNNFlagCoding = FlagCoding('KNN_flags')
KNNFlagCoding.addFlag("1", 1, "KNN above 0")
KNNFlagCoding.addFlag("2", 2, "KNN above 1")
KNNFlagCoding.addFlag("3", 3, "KNN above 2")
KNNFlagCoding.addFlag("4", 4, "KNN above 3")
KNNFlagCoding.addFlag("5", 5, "KNN above 4")
KNNFlagCoding.addFlag("6", 6, "KNN above 5")
group = KNNProduct.getFlagCodingGroup()
group.add(KNNFlagCoding)
