def watermask(water_in, radar_in, outName, outShp):
bandDefns = []
bandDefns.append(BandDefn('VV', radar_in, 1))
bandDefns.append(BandDefn('water', water_in, 1))
gdalformat = 'KEA'
imagecalc.bandMath(outName, '(VV<-18)&&(water==12)?1:0', gdalformat,
rsgislib.TYPE_8UINT, bandDefns)
def wetvegmask(water_in, radar_in, outName, outShp):
bandDefns = []
bandDefns.append(BandDefn('VVVH', radar_in, 3))
bandDefns.append(BandDefn('water', water_in, 1))
gdalformat = 'KEA'
imagecalc.bandMath(outName, '(VVVH<0.45)&&(water>=2)?1:0', gdalformat,
rsgislib.TYPE_8UINT, bandDefns)
def bandMath(inputImageVV,inputImageVH,outputImage):
gdalformat = 'GTiff'
datatype = rsgislib.TYPE_32FLOAT
expression = 'b1/b2'
bandDefns = []
bandDefns.append(BandDefn('b1', inputImageVV, 1))
bandDefns.append(BandDefn('b2', inputImageVH, 1))
imagecalc.bandMath(outputImage, expression, gdalformat, datatype, bandDefns)
def wetvegmask(water_in, radar_in, outName, outShp):
print("Reading raster datasets as arrays....")
# define bands
bandDefns = []
bandDefns.append(BandDefn('VVVH', radar_in, 3))
bandDefns.append(BandDefn('water', water_in, 1))
# conditional statement to select semi-permanent water with high VV:VH difference
print("Running conditional statement....")
gdalformat = 'KEA'
imagecalc.bandMath(outName, '(VVVH<0.45)&&(water>=2)?1:0', gdalformat, rsgislib.TYPE_8UINT, bandDefns)
def bandMath(inputImage, outputImage):
gdalformat = 'GTiff'
datatype = rsgislib.TYPE_32FLOAT
expression = '(g-nir)/(g+nir)'
# expression = '(nir-r)/(nir+r)'
bandDefns = []
bandDefns.append(BandDefn('g', inputImage, 4))
bandDefns.append(BandDefn('r', inputImage, 3))
bandDefns.append(BandDefn('nir', inputImage, 2))
bandDefns.append(BandDefn('swir', inputImage, 1))
imagecalc.bandMath(outputImage, expression, gdalformat, datatype,
bandDefns)
def watermask(water_in, radar_in, outName, outShp):
print("Reading raster datasets as arrays....")
# define bands
bandDefns = []
bandDefns.append(BandDefn('VV', radar_in, 1))
bandDefns.append(BandDefn('water', water_in, 1))
# conditional statement to select permanent water with low backscatter
print("Running conditional statement....")
gdalformat = 'KEA'
imagecalc.bandMath(outName, '(VV<-18)&&(water==12)?1:0', gdalformat, rsgislib.TYPE_8UINT, bandDefns)
#bandDefns.append(BandDefn('b20170117', inputImage, 2))
bandNames = imageutils.getBandNames(inputImage) # band names
bandNum = numpy.arange(1, len(bandNames) + 1) # band numbers
# print out all the various bandDefns functions
#for b, num in zip(bandNames,bandNum):
# band='"'+b+'"'
# #bandDefns.append(BandDefn(band, inputImage, num))
# print("bandDefns.append(BandDefn("+band+",inputImage," +str(num)+"))")
print('')
print('Reading in the relevant bands')
print('It took {0:0.1f} minutes'.format((time.time() - start) / 60))
bandDefns = []
bandDefns.append(BandDefn('b20170105', inputImage, 1))
bandDefns.append(BandDefn('b20170117', inputImage, 2))
bandDefns.append(BandDefn('b20170129', inputImage, 3))
bandDefns.append(BandDefn('b20170222', inputImage, 4))
bandDefns.append(BandDefn('b20170306', inputImage, 5))
bandDefns.append(BandDefn('b20170318', inputImage, 6))
bandDefns.append(BandDefn('b20170423', inputImage, 7))
bandDefns.append(BandDefn('b20170423', inputImage, 8))
bandDefns.append(BandDefn('b20170505', inputImage, 9))
bandDefns.append(BandDefn('b20170517', inputImage, 10))
bandDefns.append(BandDefn('b20170529', inputImage, 11))
bandDefns.append(BandDefn('b20170610', inputImage, 12))
bandDefns.append(BandDefn('b20170622', inputImage, 13))
bandDefns.append(BandDefn('b20170704', inputImage, 14))
bandDefns.append(BandDefn('b20170728', inputImage, 15))
bandDefns.append(BandDefn('b20170809', inputImage, 16))
# print("Running conditional statement....")
# gdalformat = 'KEA'
# imagecalc.bandMath(outName, '(VV<-15)?1:0', gdalformat, rsgislib.TYPE_8UINT, bandDefns)
#
# outName=radar_in.replace('.tif','_sel_lt15dB.kea')
#
# high_dB(radar_in, outName)
listFiles = glob.glob('*sel_lt15dB.kea')
listFiles = sorted(listFiles)
bandDefns = []
for img in listFiles:
band = band = '"' + img.split('.')[0] + '"'
bandDefns.append(BandDefn(band, img, 1))
expression = '(' + listFiles[0].split('.')[0]
for img in listFiles[1:len(listFiles)]:
expression += ' + '
expression += img.split('.')[0]
expression += ') / ' + str(len(listFiles))
gdalformat = 'KEA'
imagecalc.bandMath(outName, expression, gdalformat, rsgislib.TYPE_8UINT,
bandDefns)
imagecalc.bandMath(
outName,
'(S1B_IW_GRDH_1SDV_20170105T165713_Sigma0_stack_lee_sel_lt15dB.kea + S1B_IW_GRDH_1SDV_20170117T165713_Sigma0_stack_lee_sel_lt15dB.kea + S1B_IW_GRDH_1SDV_20170129T165713_Sigma0_stack_lee_sel_lt15dB.kea) / 3',
gdalformat, rsgislib.TYPE_8UINT, bandDefns)
vc=HAfilein,
vcrst=outHiRISEArast)
print(gdalrastcmd_HiA)
subprocess.call(gdalrastcmd_HiA, shell=True)
# stretch image to byte 8-bit by linear MinMax
imageutils.stretchImage(outND4, outND4_s, False, '', False,
True, 'GTiff', rsgislib.TYPE_8INT,
imageutils.STRETCH_LINEARMINMAX)
# do the same for DTMs
datatype = rsgislib.TYPE_32FLOAT
expression = 'b1+9999'
bandDefns = []
bandDefns.append(BandDefn('b1', outDTM, 1))
imagecalc.bandMath(outDTM_a, expression, 'GTiff', datatype,
bandDefns)
imagecalc.imageStats(outDTM_a, statsDTM, True)
if not (MGSmode):
# LnK
datatype = rsgislib.TYPE_32FLOAT
minLnKH = 12.0
expression = 'max(b1, {m}) - {m}'.format(m=minLnKH)
bandDefns = []
bandDefns.append(BandDefn('b1', rastVecPolysLnK, 1))
imagecalc.bandMath(rastVecPolysLnK_a, expression, 'GTiff',
datatype, bandDefns)
imagecalc.imageStats(rastVecPolysLnK_a, statsLnKHead, True)
maskVV = maskVV[maskVV < 0]
threshold = filters.threshold_otsu(maskVV)
#threshold=np.std(maskVV)+np.mean(maskVV)
ds1 = None
print('')
print('Threshold: ' + str(threshold))
print('')
####################################################################
#apply threshold
print("Running conditional statement....")
gdalformat = 'KEA'
bandDefns = []
bandDefns.append(BandDefn('class', classification, 1))
bandDefns.append(BandDefn('vvAvgMask', clumpsMean, 1))
condition = '(class==1)&&(vvAvgMask>' + str(threshold) + ')?2:class'
imagecalc.bandMath(outClass, condition, gdalformat, rsgislib.TYPE_8UINT,
bandDefns)
'''
#resampling output to match each other
inProcessImg=outClass
#inProcessImg='S1B_IW_GRDH_1SDV_20170517T165715_Sigma0_stack_lee_clumps2_erf_clumptrain_mode_std.kea'
gdalformat = 'KEA'
rsgislib.imageutils.resampleImage2Match(inRefImg, inProcessImg, outSnap, gdalformat,interpMethod='nearestneighbour', datatype=rsgislib.TYPE_8UINT)
#add GUF mask
inFile=outSnap
outFile=inFile.replace('.kea','_guf.kea')
gdalformat = 'KEA'
def colour_SM_image(inimage, colourimage, max_value=0.5, band=1):
"""
Colour image
"""
try:
import rsgislib
from rsgislib import imagecalc
from rsgislib.imagecalc import BandDefn
from rsgislib import rastergis
except ImportError:
raise ImportError("Could not import RSGISLib, required"
" to colour image")
if max_value == 0.5:
# Add class field:
bandDefns = []
bandDefns.append(BandDefn('SM', inimage, band))
expression = []
expression.append('0.00')
expression.append('(SM > 0.00) && (SM <= 0.05)? 1 : 0')
expression.append('(SM > 0.05) && (SM <= 0.10)? 2 : 0')
expression.append('(SM > 0.10) && (SM <= 0.15)? 3 : 0')
expression.append('(SM > 0.15) && (SM <= 0.20)? 4 : 0')
expression.append('(SM > 0.20) && (SM <= 0.25)? 5 : 0')
expression.append('(SM > 0.25) && (SM <= 0.30)? 6 : 0')
expression.append('(SM > 0.30) && (SM <= 0.35)? 7 : 0')
expression.append('(SM > 0.35) && (SM <= 0.40)? 8 : 0')
expression.append('(SM > 0.40) && (SM <= 0.45)? 9 : 0')
expression.append('(SM > 0.45) && (SM <= 0.50)? 10 : 0')
gdalformat = get_gdal_format(colourimage)
datatype = rsgislib.TYPE_8INT
temp_dir = tempfile.mkdtemp(prefix='soilscape_upscaling')
colourbase = os.path.basename(colourimage)
colourbase, colourext = os.path.splitext(colourbase)
colour = []
for i in range(11):
colour.append(
os.path.join(temp_dir,
"{}_{}{}".format(colourbase, i + 1, colourext)))
imagecalc.bandMath(colour[i], expression[i], gdalformat, datatype,
bandDefns)
bandDefns = []
for i in range(11):
SMclass = 'SM' + str(i)
bandDefns.append(BandDefn(SMclass, colour[i], 1))
if i == 0:
expression = SMclass
else:
expression = expression + '+' + SMclass
imagecalc.bandMath(colourimage, expression, gdalformat, datatype,
bandDefns)
shutil.rmtree(temp_dir)
# Populate stats (converts to RAT):
rastergis.populateStats(colourimage, False, False, True, ratband=1)
# Add class field:
bandStats = []
bandStats.append(rastergis.BandAttStats(band=1, maxField="SMclass"))
rastergis.populateRATWithStats(colourimage,
colourimage,
bandStats,
ratband=1)
field = 'SMclass'
classcolours = {}
colourCat = collections.namedtuple('ColourCat',
['red', 'green', 'blue', 'alpha'])
for i in range(11):
classcolours[i] = colourCat(red=0, green=0, blue=0, alpha=255)
classcolours[0] = colourCat(red=0, green=0, blue=0, alpha=255)
classcolours[1] = colourCat(red=165, green=0, blue=38, alpha=255)
classcolours[2] = colourCat(red=215, green=48, blue=39, alpha=255)
classcolours[3] = colourCat(red=244, green=109, blue=67, alpha=255)
classcolours[4] = colourCat(red=253, green=174, blue=97, alpha=255)
classcolours[5] = colourCat(red=254, green=224, blue=144, alpha=255)
classcolours[6] = colourCat(red=224, green=243, blue=248, alpha=255)
classcolours[7] = colourCat(red=171, green=217, blue=233, alpha=255)
classcolours[8] = colourCat(red=116, green=173, blue=209, alpha=255)
classcolours[9] = colourCat(red=69, green=117, blue=180, alpha=255)
classcolours[10] = colourCat(red=49, green=54, blue=149, alpha=255)
rastergis.colourClasses(colourimage, field, classcolours)
else:
raise ValueError('Failed to find colours for specified max_value')
import gdal, rsgislib, os, subprocess, glob
import numpy as np
#from rsgislib import vectorutils, imagecalc
import rsgislib.imagecalc
from rsgislib.imagecalc import BandDefn
# radar_in='/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/S1B_IW_GRDH_1SDV_20170318T165713_Sigma0_stack_lee3.tif'
# outName='/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/global_surface_water/mask_barotseland_ltm8_20170318.tif'
# read water mask as array
#print("Reading raster datasets as arrays....")
#inImg='/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/2017/classified_stack_2017_occurrence_v2.kea'
#outName=inImg.replace('.kea','_perm_water.kea')
#list=sorted(glob.glob('*.tif'))
inImg = 'ESACCI_LC_Map.tif'
#for inImg in listImg:
bandDefns = []
bandDefns.append(BandDefn('inImg', inImg, 1))
print('Processing: ' + inImg)
# conditional statement
# print("Running conditional statement....")
#outName='./binary/'+inImg.replace('.tif','_water.tif')
outName = 'africa_wetland.kea'
gdalformat = 'KEA'
rsgislib.imagecalc.bandMath(outName, '(inImg==5)?1:0', gdalformat,
rsgislib.TYPE_8UINT, bandDefns)
try:
guf_num=[]
if (x==g1_x and y==g1_y):
guf_num.append('guf1')
print('Found a match: guf1')
elif (x==g2_x and y==g2_y):
guf_num.append('guf2')
print('Found a match: guf2')
elif (x==g3_x and y==g3_y):
guf_num.append('guf3')
print('Found a match: guf3')
else:
pass
print('')
print("Running conditional statement....")
gdalformat = 'KEA'
bandDefns = []
bandDefns.append(BandDefn('class', inFile, 1))
bandDefns.append(BandDefn('urban', eval(guf_num[0]), 1))
condition='(class==3)&&(urban>0)?2:class'
imagecalc.bandMath(outFile, condition, gdalformat, rsgislib.TYPE_8UINT, bandDefns)
except:
print('No matching GUF for: ' + inFile)
pass
####################################################################
# snap permanent water
inProcessImg=permWater
inRefImg=inputImage
outSnap=permWater.replace('.kea','_'+inputImage.split('/')[-1].split('_')[4]+'_snap.kea')
gdalformat = 'KEA'
rsgislib.imageutils.resampleImage2Match(inRefImg, inProcessImg, outSnap, gdalformat,interpMethod='nearestneighbour', datatype=rsgislib.TYPE_8UINT)
####################################################################
# add permanent water mask
permWaterMask=outSnap
bandDefns = []
bandDefns.append(BandDefn('class', outCert, 1))
bandDefns.append(BandDefn('permWat', permWaterMask, 1))
condition='(permWat==1)?1:class'
gdalformat = 'kea'
imagecalc.bandMath(outimage, condition, gdalformat, rsgislib.TYPE_8UINT, bandDefns)
# remove the intermediate certainty classifcation image
try:
os.remove(outCert)
except Exception:
pass
###########################################################################################
# select dry season images and apply refinement
'OutClass_mode_cert_sel_names',
names,
colType=gdal.GFT_String)
# export rat column: mode with certainty to image
gdalformat = 'GTiff'
datatype = rsgislib.TYPE_8INT
fields = ['OutClass_mode_cert_sel']
rastergis.exportCols2GDALImage(clumps, outImage, gdalformat, datatype,
fields)
#bandmath to add permananent water pixels
bandDefns = []
bandDefns.append(BandDefn('class', outImage, 1))
bandDefns.append(BandDefn('permWater', waterPerm, 1))
imagecalc.bandMath(outImage, '(permWater>=5)?1:class', 'GTiff',
rsgislib.TYPE_8UINT, bandDefns)
ratDataset = None
# clumps=None
# add colourtable
#cmd='gdaldem color-relief ' + outImage + ' /Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/gdaldem_wb_class_colours.txt ' + outImage
#subprocess.call(cmd)
os.system('afplay /System/Library/Sounds/Tink.aiff')
except Exception:
print('')
print('............................................')
print('Failed: ' + clumps)
maskVV = maskVV[maskVV < 0]
#threshold=filters.threshold_otsu(maskVV)
threshold = np.std(maskVV) + np.mean(maskVV)
ds1 = None
print('')
print('Threshold: ' + str(threshold))
print('')
####################################################################
#apply threshold
print("Running conditional statement....")
gdalformat = 'KEA'
bandDefns = []
bandDefns.append(BandDefn('class', classification, 1))
bandDefns.append(BandDefn('vvAvgMask', clumpsMean, 1))
condition = '(class==1)&&(vvAvgMask>' + str(threshold) + ')?2:class'
imagecalc.bandMath(outClass, condition, gdalformat, rsgislib.TYPE_8UINT,
bandDefns)
#resampling output to match each other
inProcessImg = outClass
rsgislib.imageutils.resampleImage2Match(inRefImg,
inProcessImg,
outImg,
gdalFormat,
interpMethod='nearestneighbour',
datatype=rsgislib.TYPE_8UINT)
# radar_in='/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/S1B_IW_GRDH_1SDV_20170318T165713_Sigma0_stack_lee3.tif'
# outName='/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/global_surface_water/mask_barotseland_ltm8_20170318.tif'
# read water mask as array
print("Reading raster datasets as arrays....")
#ndvi='/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_2/GEE/S2_composite_aug_sep_2017_ndvi_snapped.tif'
#seasonality='/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/global_surface_water/seasonality_barotseland_snapped.tif'
hand = '/Users/Andy/Documents/Zambia/FloodModelling/HAND/HAND_Main_Channel_Only_120m_snapped.tif'
#ls_image='/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_2/GEE/S2_composite_aug_sep_2017_swir_snapped.tif'
sel = '/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/sand_exclusion_layer/S1B_IW_GRDH_1SDV_2017_sel_2.kea'
outName = '/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/sand_exclusion_layer/sel_hand_sand_mask.kea'
bandDefns = []
#bandDefns.append(BandDefn('water', seasonality, 1))
bandDefns.append(BandDefn('hand', hand, 1))
#bandDefns.append(BandDefn('ndvi', ndvi, 1))
#bandDefns.append(BandDefn('swir', ls_image, 1))
bandDefns.append(BandDefn('sel', sel, 1))
# conditional statement to select semi-permanent water with high VV:VH difference
print("Running conditional statement....")
gdalformat = 'KEA'
#imagecalc.bandMath(outName, '(ndvi>0.06)&&(ndvi<=0.12)&&(water==0)&&(hand>5)&&(swir>2000)?1:0', gdalformat, rsgislib.TYPE_8UINT, bandDefns)
imagecalc.bandMath(outName, '(sel>60)&&(sel<94)&&(hand>5)?1:0', gdalformat,
rsgislib.TYPE_8UINT, bandDefns)
'''
file_read=gdal.Open(inFile)
mask=np.array(file_read.GetRasterBand(1).ReadAsArray())
bandNames = []
for img in images:
bandNames.append('b' + img.split('-')[0] + img.split('-')[1] +
img.split('-')[2].split('_')[0])
# print out all the various bandDefns functions
for b, i in zip(bandNames, images):
print("bandDefns.append(BandDefn( '" + b + "','" + i + "'," + str(1) +
"))")
print('Reading in the masks')
#print('It took {0:0.1f} minutes'.format((time.time() - start) / 60))
bandDefns = []
bandDefns.append(BandDefn('b01032018b', '01-03-2018b_water.kea', 1))
bandDefns.append(BandDefn('b01102016b', '01-10-2016b_water.kea', 1))
bandDefns.append(BandDefn('b04082018b', '04-08-2018b_water.kea', 1))
bandDefns.append(BandDefn('b05022018b', '05-02-2018b_water.kea', 1))
bandDefns.append(BandDefn('b05062018b', '05-06-2018b_water.kea', 1))
bandDefns.append(BandDefn('b06032017b', '06-03-2017b_water.kea', 1))
bandDefns.append(BandDefn('b06042018b', '06-04-2018b_water.kea', 1))
bandDefns.append(BandDefn('b06112016b', '06-11-2016b_water.kea', 1))
bandDefns.append(BandDefn('b07122017b', '07-12-2017b_water.kea', 1))
bandDefns.append(BandDefn('b08102017b', '08-10-2017b_water.kea', 1))
bandDefns.append(BandDefn('b09082017b', '09-08-2017b_water.kea', 1))
bandDefns.append(BandDefn('b10022017b', '10-02-2017b_water.kea', 1))
bandDefns.append(BandDefn('b11042017b', '11-04-2017b_water.kea', 1))
bandDefns.append(BandDefn('b11072018b', '11-07-2018b_water.kea', 1))
bandDefns.append(BandDefn('b12012018b', '12-01-2018b_water.kea', 1))
bandDefns.append(BandDefn('b12052018b', '12-05-2018b_water.kea', 1))
import rsgislib
from rsgislib import imagecalc
from rsgislib.imagecalc import BandDefn
outputImage = '/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/S1B_IW_GRDH_1SDV_20170821T165721_Sigma0_VVdivVH.tif'
gdalformat = 'GTiff'
datatype = rsgislib.TYPE_32FLOAT
expression = 'b1/b2'
bandDefns = []
bandDefns.append(
BandDefn(
'b1',
'/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/S1B_IW_GRDH_1SDV_20170821T165721_Sigma0_VV_dB.tif',
1))
bandDefns.append(
BandDefn(
'b2',
'/Users/Andy/Documents/Zambia/RemoteSensing/Sentinel_1/GRD/Out/Subset/S1B_IW_GRDH_1SDV_20170821T165721_Sigma0_VH_dB.tif',
1))
imagecalc.bandMath(outputImage, expression, gdalformat, datatype, bandDefns)
slope='/Users/Andy/Documents/Zambia/FloodModelling/Data/DEMs/SRTM/barotse_srtm_5x5_slope.tif'
hand='/Users/Andy/Documents/Zambia/FloodModelling/HAND/GEE_hand/hand90_1000.tif'
outShp='/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/global_surface_water/barotseland_srtm_utm_lee_slope_gt1_5_wgs84.shp'
outShp2='/Users/Andy/Documents/Zambia/RemoteSensing/WB_classification/Supporting_data/global_surface_water/barotseland_srtm_utm_lee_slope_gt1_5_wgs84_pxl1.shp'
# snapping
slopeSnap='/Users/Andy/Documents/Zambia/FloodModelling/Data/DEMs/SRTM/srtm_arc1_barotseland_5x5_slope_snap.kea'
inRefImg=hand # base raster to snap to
gdalFormat = 'KEA'
#rsgislib.imageutils.resampleImage2Match(inRefImg, slope, slopeSnap, gdalFormat, interpMethod='nearestneighbour', datatype=None) # perform resampling/snap
bandDefns = []
bandDefns.append(BandDefn('slope', slopeSnap, 1))
bandDefns.append(BandDefn('hand', hand, 1))
outName='/Users/Andy/Documents/Zambia/FloodModelling/Data/DEMs/SRTM/srtm_arc1_barotseland_5x5_slope_gt3_and_hand_gt30.kea'
gdalformat = 'KEA'
imagecalc.bandMath(outName, '(slope>3)&&(hand>30)&&(hand<1000)?1:0', gdalformat, rsgislib.TYPE_8UINT, bandDefns)
# option to vectorize the output
inputImg=outName
rsgislib.vectorutils.polygoniseRaster(inputImg, outShp, imgBandNo=1, maskImg=None, imgMaskBandNo=1)
cmd="ogr2ogr -where PXLVAL='1' '%s' '%s'" %(outShp,outShp2)
subprocess.call(cmd, shell=True)
os.system(cmd)
cmd='gdal_calc.py -A %s --A_band=2 --outfile=%s --calc="nan_to_num(A)"' %(inputImage,b2)
os.system(cmd)
cmd='gdal_calc.py -A %s --A_band=3 --outfile=%s --calc="nan_to_num(A)"' %(inputImage,b3)
os.system(cmd)
gdalformat = 'KEA'
# define the output names
b1_nan=b1.replace('.tif','_b1_nan.tif')
b2_nan=b2.replace('.tif','_b2_nan.tif')
b3_nan=b3.replace('.tif','_b3_nan.tif')
bandDefns = []
bandDefns.append(BandDefn('b1', b1, 1))
imagecalc.bandMath(b1_nan, '(b1==0.0)?-999:b1', gdalformat, rsgislib.TYPE_32FLOAT, bandDefns)
bandDefns = []
bandDefns.append(BandDefn('b2', b2, 1))
imagecalc.bandMath(b2_nan, '(b2==0.0)?-999:b2', gdalformat, rsgislib.TYPE_32FLOAT, bandDefns)
bandDefns = []
bandDefns.append(BandDefn('b3', b3, 1))
imagecalc.bandMath(b3_nan, '(b3==0.0)?-999:b3', gdalformat, rsgislib.TYPE_32FLOAT, bandDefns)
gdalformat='GTiff'
gdaltype = rsgislib.TYPE_32FLOAT
imageutils.stackImageBands([b1_nan,b2_nan,b3_nan], ['VV','VH','VVdivVH'] , nanOut, None, -999, gdalformat, gdaltype)
try:
os.remove(b1)
os.remove(b2)
#bandDefns.append(BandDefn('b20170105', inputImage, 1))
#bandDefns.append(BandDefn('b20170117', inputImage, 2))
bandNames = imageutils.getBandNames(inputImage) # band names
bandNum = numpy.arange(1, len(bandNames) + 1) # band numbers
# print out all the various bandDefns functions
'''
for band, num in zip(bandNames,bandNum):
band='"+band+"'
bandDefns.append(BandDefn(band, inputImage, num))
print("bandDefns.append(BandDefn('"+band+"',inputImage," +str(num)+"))")
'''
for b, num in zip(bandNames, bandNum):
band = '"' + b + '"'
bandDefns.append(BandDefn(band, inputImage, num))
print("bandDefns.append(BandDefn(" + band + ",inputImage," + str(num) +
"))")
#bandDefns = []
#bandDefns.append(BandDefn('b20170105',inputImage,1))
#bandDefns.append(BandDefn('b20170117',inputImage,2))
#bandDefns.append(BandDefn('b20170129',inputImage,3))
#bandDefns.append(BandDefn('b20170222',inputImage,4))
#bandDefns.append(BandDefn('b20170306',inputImage,5))
#bandDefns.append(BandDefn('b20170318',inputImage,6))
#bandDefns.append(BandDefn('b20170423',inputImage,7))
#bandDefns.append(BandDefn('b20170423',inputImage,8))
#bandDefns.append(BandDefn('b20170505',inputImage,9))
#bandDefns.append(BandDefn('b20170517',inputImage,10))
#bandDefns.append(BandDefn('b20170529',inputImage,11))
