def main(trainingDirectory, landCoverList, band_list=[], band_weight_list=[]):
# If bandList is empty, i.e., no band grouping list was input at command line. A default list, contained in FloodClassUtils.py,
# will be used.
# Note that if a "band_weight_list" is input, it should correspond with the band_list, and it should be known apriori that
# all serialized files input to this code contain the required bands.
if band_list == []:
band_list = FloodClassUtils.bandList()
landCoverTypesListLength = 10 ** 50
landCoverTypes = {}
flagTwo = True
for landName in landCoverList:
flagOne = True
fileList = os.listdir(trainingDirectory + "/" + landName)
for serialFile in fileList:
# read in and deserialize
geoPicture = GeoPictureSerializer.deserialize(open(trainingDirectory + "/" + landName + "/" + serialFile))
# do radiance correction on bands
geoPicture = FloodClassUtils.radianceCorrection(geoPicture)
# find bands common to training set and requested set
finalBandList, bandsPresent = FloodClassUtils.commonBand(geoPicture, band_list)
# Combine bands
geoPicture.picture = FloodClassUtils.bandSynth(geoPicture, finalBandList, band_weight_list)
# The band list of common bands are added to the geoPicture to replace the original
geoPicture.bands = bandsPresent
missingBandList = []
for i in numpy.arange(len(FloodClassUtils.bandList())):
missingBandSet = set(FloodClassUtils.bandList()[i]) - set(finalBandList[i])
missingBandList.append(list(missingBandSet))
sys.stderr.write(
"\nThe following requested bands are missing from serialized file "
+ str(serialFile)
+ ":\n"
+ str(missingBandList)
+ "\n"
)
# Need to create a mask of locations where all pixels are nonzero.
alphaBand = geoPicture.picture[:, :, geoPicture.bands.index(geoPicture.bands[0])] > 0
for band in geoPicture.bands[1:]:
numpy.logical_and(alphaBand, geoPicture.picture[:, :, geoPicture.bands.index(band)], alphaBand)
# Make bands into 2-d array
# Find size of the image array
rasterXsize = geoPicture.picture.shape[1]
rasterYsize = geoPicture.picture.shape[0]
rasterDepth = geoPicture.picture.shape[2]
# Reshape all arrays
geoPicture.picture = geoPicture.picture.reshape(rasterXsize * rasterYsize, rasterDepth)
alphaBand = alphaBand.reshape(rasterXsize * rasterYsize)
geoPicture.picture = geoPicture.picture[alphaBand, :]
if flagOne == True:
landCoverTypes[landName] = geoPicture.picture
flagOne = False
else:
landCoverTypes[landName] = numpy.concatenate((landCoverTypes[landName], geoPicture.picture), axis=0)
# Note that the classification digit is tied to the order of the landCoverList, i.e., classification digit = landCoverList_position + 1
landCoverTypes[landName] = numpy.concatenate(
(
landCoverTypes[landName],
(landCoverList.index(landName) + 1) * numpy.ones((landCoverTypes[landName].shape[0], 1), dtype=int),
),
axis=1,
)
landCoverTypes[landName] = landCoverTypes[landName][
numpy.random.permutation(landCoverTypes[landName].shape[0]), :
]
landCoverTypesListLength = min(landCoverTypes[landName].shape[0], landCoverTypesListLength)
if flagTwo == True:
sampleLength = numpy.minimum(5000, landCoverTypesListLength)
train = landCoverTypes[landName][:sampleLength, :]
flagTwo = False
else:
train = numpy.concatenate((train, landCoverTypes[landName][:sampleLength, :]), axis=0)
numpy.savetxt("trainingSet.txt", train, fmt=rasterDepth * "%-12.5f " + "%-d")
