def get_valid_mask(inprod, xx, yy):
valid_mask = inprod.getMaskGroup().get("quality_flags_invalid")
valid_mask_asmask = jpy.cast(valid_mask, Mask)
return valid_mask_asmask.getSampleInt(xx, yy)
def getS3bands(in_file, coords, band_names, s3_instrument, slstr_res):
"""Extract data from Sentinel-3 bands.
Read the input S3 file and extract data from a list of given bands for the
coordinates (in a provided list) located within the scene.
Args:
in_file (str): Path to a S3 OLCI image xfdumanisfest.xml file.
coords (list): List of coordinates to extract the data from.
band_names (list): List of bands names to extract the data from.
errorfile (str): Path to the file where all errors are logged.
s3_instrument (str): Sentinel-3 instrument name (OLCI or SLSTR).
slstr_res (str): SLSTR reader resolution (500m or 1km).
Returns:
(dict): Dictionnary containing the band names and values for all
coordinates extracted from the image.
"""
# Make a dictionnary to store results
stored_vals = {}
# Open SNAP product
prod = open_prod(in_file, s3_instrument, slstr_res)
# Loop over coordinates to extract values.
for coord in coords:
# Check if data exists at the queried location
# Transform lat/lon to position to x, y in scene
xx, yy = pixel_position(prod, coord[1], coord[2])
# Log if location is outside of file
if not xx or not yy:
pass
else:
# For OLCI scenes, save resources by working on a small subset
# around each coordinates pair contained within the S3 scene.
# Doesn't process if the coordinates pair is not in the product.
if s3_instrument == "OLCI":
try:
prod_subset, pix_coords = subset(prod, coord[1], coord[2])
process_flag = True # Set a flag to process data
except: # Bare except needed to catch the JAVA exception
prod_subset = None
if not prod_subset or pix_coords[0] is None:
process_flag = False # None to stop processing
else:
# If SLSTR, open full image: because of the bands at different
# resolutions, a resampling would be necessary before being
# able to subset. Therefore set the flag to continue.
prod_subset = prod
process_flag = True
# As th entire scene is used, set pix_coords to xx, yy
pix_coords = xx, yy
if process_flag: # Run the processing if OLCI subset exists
# Before the processing, the validity of the opened product is
# tested by opening the first band and querying the band at the
# coordinate location. If SLSTR, just test the 500m product.
# If the extraction test fails, an entry is created in the log.
try:
# Get a specified band depending on the sensor
currentband = None
if s3_instrument == "OLCI":
# Extract pixel value for the band
currentband = prod_subset.getBand("Oa01_radiance")
else:
# Try out with bands for either resolution
currentband = prod_subset.getBand("S1_radiance_an")
if currentband is None:
currentband = prod_subset.getBand("F1_BT_in")
currentband.loadRasterData() # Load raster band
# Test if the retrieval is possible
currentband.getPixelFloat(pix_coords[0], pix_coords[1])
currentband = None
# Marker to continue processing
processing = True
except: # Bare except needed to catch the JAVA exception
processing = False # Deactivate processing
if processing:
out_values = {} # Initialise outvalues
# Extract bands from product
for band in band_names:
# Try to extract from band list
if band in list(prod_subset.getBandNames()):
currentband = None
currentband = prod_subset.getBand(band)
currentband.loadRasterData()
out_values[band] = round(
currentband.getPixelFloat(
pix_coords[0], pix_coords[1]),
4,
)
# If not in band list, try from TiePointGrid
elif band in list(prod.getTiePointGridNames()):
out_values[band] = round(
getTiePointGrid_value(
prod_subset,
band,
pix_coords[0],
pix_coords[1],
),
4,
)
# If not if TiePointGrid list try from Masks
elif band in list(
prod_subset.getMaskGroup().getNodeNames()):
currentmask = prod_subset.getMaskGroup().get(band)
currentmask_asmask = jpy.cast(currentmask, Mask)
out_values[band] = currentmask_asmask.getSampleInt(
pix_coords[0], pix_coords[1])
else:
# Capture error
raise SyntaxError(
"Band '%s' does not exist in image: %s" %
(band, prod.getName()))
# Update the full dictionnary
stored_vals.update({coord[0]: out_values})
# Garbage collector
prod_subset = None
# Garbage collector
prod = None
return stored_vals
bio_data = np.zeros(
bioW * bioH, np.float32
) #Return a new array of given shape and type, filled with zeros. Filled only x-ways. np.zeros(5) = {0,0,0,0,0}
bioBand.readPixels(
0, 0, bioW, bioH, bio_data
) #readPixels(x,y,w,h, Array) x : x offset of upper left corner. y : y offset of upper left corner. w : width. h : height. Array : output array
bio_data.shape = bioH, bioW
# Prepare radar image
r = ProductIO.readProduct(fileName)
radarBand = r.getBand(bandName)
radarW = radarBand.getRasterWidth()
radarH = radarBand.getRasterHeight()
# Initilization of raster band and geocoding for French Guyana and radar image
raster_Band = jpy.cast(bioBand, RasterDataNode)
raster_BandRadar = jpy.cast(radarBand, RasterDataNode)
geo_Coding = jpy.cast(raster_Band.getGeoCoding(),
GeoCoding) #get geocoding of biomass map
geo_CodingRadar = jpy.cast(raster_BandRadar.getGeoCoding(),
GeoCoding) #get geocoding of radar image
# Create mask of ROI on French Guyana .tif
polygon = pickle.load(open((maskPath + productName + 'Mask'), 'rb'))
img = Image.new('L', (bioW, bioH), 0)
ImageDraw.Draw(img).polygon(polygon, outline=1, fill=1)
mask = np.array(img)
imgplot = plt.imshow(mask)
imgplot.write_png(productName + 'Mask.png')
# OpType can be one of {MIN, MAX, MEDIAN, MEAN, STDDEV, EROSION, DILATION, OPENING, CLOSING}
opType = OpType.STDDEV
iterationCount = 1
kernel_data = [
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0
]
kernel = Kernel(9, 9, 4, 4, 1 / 64, kernel_data)
filtered_band = GeneralFilterBand(filteredBandName, sourceRaster, opType,
kernel, iterationCount)
# Turn into real Band
if realBand:
target_band = Band(filtered_band.getName(), filtered_band.getDataType(),
filtered_band.getRasterWidth(),
filtered_band.getRasterHeight())
target_band.setSourceImage(filtered_band.getSourceImage())
else:
target_band = filtered_band
if isinstance(jpy.cast(sourceRaster, Band), Band):
ProductUtils.copySpectralBandProperties(sourceRaster, target_band)
product.addBand(target_band)
ProductIO.writeProduct(product, out_file, 'BEAM-DIMAP')
bandName = 'band_1'
savePath = 'D:/fyp-master/Polygon/'
polygon = []
topLeftLat = 4.431921373298707
topLeftLon = -52.14877215477987
botLeftLat = 2.885133324526054
botLeftLon = -52.468809464281705
botRightLat = 3.051240184812927
botRightLon = -53.258843412884204
topRightLat = 4.59619348038011
topRightLon = -52.9405767847531
radarImage = ProductIO.readProduct(fileName)
rasterBand = radarImage.getBand(bandName)
rasterBio = jpy.cast(rasterBand, RasterDataNode)
geo_CodingBio = jpy.cast(rasterBio.getGeoCoding(), GeoCoding)
#Getting pixel positions
#POLYGON ((-54.459992631546 1.8573631048202515, -54.823678525590225 3.605360746383667, -52.60408776706431 4.070178031921387, -52.24492652190336 2.3271737098693848, -54.459992631546 1.8573631048202515))
pixPos = getPixCoord(geo_CodingBio, topLeftLat, topLeftLon)
print("Top Left: ", pixPos)
polygon.append(pixPos)
pixPos = getPixCoord(geo_CodingBio, botLeftLat, botLeftLon)
print("Bot Left: ", pixPos)
polygon.append(pixPos)
pixPos = getPixCoord(geo_CodingBio, botRightLat, botRightLon)
print("Bot Right: ", pixPos)