import collections, glob
from rsgislib import rastergis
listFiles=glob.glob('*_clumps2.kea')
for clumps in listFiles[0:2]:
# clumps=listFiles[1]
field = 'OutClass_mode_cert'
classcolours = {}
colourCat = collections.namedtuple('ColourCat', ['red', 'green', 'blue', 'alpha'])
classcolours[1] = colourCat(red=106, green=143, blue=255, alpha=255)
classcolours[2] = colourCat(red=169, green=169, blue=169, alpha=255)
classcolours[3] = colourCat(red=177, green=255, blue=165, alpha=255)
classcolours[4] = colourCat(red=255, green=255, blue=191, alpha=255)
rastergis.colourClasses(clumps, field, classcolours)
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')
elif i==3:
names.append('VegWater')
# write columns to RAT
rios.rat.writeColumn(outputClumps, 'OutClass_mode_pc', x_percent, colType=gdal.GFT_Real)
rios.rat.writeColumn(outputClumps, 'OutClass_mode_cert', mode_cert, colType=gdal.GFT_Integer)
rios.rat.writeColumn(outputClumps, 'OutClass_mode_cert_names', names, colType=gdal.GFT_String)
# define the output colours
classColours = dict()
classColours['Other'] = [212,125,83]
classColours['Water'] = [157,212,255]
classColours['VegWater'] = [191,255,0]
field='OutClass_mode_cert_names'
rastergis.colourClasses(outputClumps, field, classColours)
############################################################################################ # Random forests ranks parameters in terms of importance to the classifier
# there is a lot which can be learnt here...
'''
print('Feature importance: ' + inputImg)
featImportances = classifier.feature_importances_
featIndices = numpy.argsort(featImportances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(len(variables)):
print("\t{0}. {1} ({2})".format(f + 1, variables[featIndices[f]], featImportances[featIndices[f]]))
'''
############################################################################################
# classColours['Other'] = [212,125,83]
# classColours['Water'] = [157,212,255]
# classColours['VegWater'] = [191,255,0]
# #
# classcolours = {}
# colourCat = collections.namedtuple('ColourCat', \
# ['red', 'green', 'blue', 'alpha'])
# classcolours[0] = colourCat(red=0, green=0, blue=0, alpha=0)
# classcolours[1] = colourCat(red=255, green=0, blue=0, alpha=255)
# classcolours[2] = colourCat(red=0, green=0, blue=255, alpha=255)
# classcolours[3] = colourCat(red=0, green=200, blue=0, alpha=255)
# classcolours[4] = colourCat(red=0, green=100, blue=0, alpha=255)
# rastergis.colourClasses(classification, 'Class', classColours)
# Add pyramids (for fast display)
# rastergis.populateStats(classification, addclrtab=False, \
# calcpyramids=True, ignorezero=False)
# pyramids=True
# colourtable=True
# rastergis.populateStats(classification, colourtable, pyramids)
field = 'Value'
classcolours = {}
colourCat = collections.namedtuple('ColourCat',
['red', 'green', 'blue', 'alpha'])
classcolours[1] = colourCat(red=200, green=50, blue=50, alpha=255)
classcolours[2] = colourCat(red=200, green=240, blue=50, alpha=255)
classcolours[3] = colourCat(red=50, green=240, blue=50, alpha=255)
rastergis.colourClasses(classification, field, classcolours)