def newBand(geoPicture1, geoPicture2):
with RedirectStdStreams(stdout=sys.stderr, stderr=sys.stderr):
geoPicture = geoPicture1
imageIndices = {}
for zi in numpy.array([1,2]):
if zi==2:
geoPicture = geoPicture2
imageArray = FloodClassUtils.radianceCorrection(geoPicture)
#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)
#Find size of the image array
rasterXsize = geoPicture.picture.shape[1]
rasterYsize = geoPicture.picture.shape[0]
rowIndex, colIndex = numpy.mgrid[0:rasterYsize,0:rasterXsize]
#This is an array of the alphaBand offset by 1 in all directions surrounding a pixel.
polygons = numpy.dstack((alphaBand[1:-1,1:-1],alphaBand[:-2,1:-1],alphaBand[:-2,2:],alphaBand[1:-1,2:],alphaBand[2:,2:], \
alphaBand[2:,1:-1],alphaBand[2:,:-2],alphaBand[1:-1,:-2],alphaBand[:-2,:-2]))
#Polygons will be an array containing "1"s on the border of the image.
polygons = numpy.array(numpy.logical_and(polygons.sum(2) > 0,polygons.sum(2) < 9),dtype=int) #The offset array is used to detect the outline
polygons = numpy.hstack((numpy.zeros((polygons.shape[0]+2,1)),numpy.vstack((numpy.zeros((polygons.shape[1])),polygons,numpy.zeros((polygons.shape[1])))),numpy.zeros((polygons.shape[0]+2,1))))
#There are too many points to calculate all lat/long. Will calculate some, then interpolate.
interpSize = 100j
yy, xx = numpy.mgrid[0:(rasterYsize-1):(3*interpSize), 0:(rasterXsize-1):interpSize]
yy = yy.reshape(-1,1)
xx = xx.reshape(-1,1)
yLat = numpy.zeros((yy.size,1))
xLong = numpy.zeros((xx.size,1))
for ij in numpy.arange(yy.size):
yLat[ij], xLong[ij] = latLong(geoPicture,xx[ij,0],yy[ij,0])
yLat = scipy.interpolate.griddata( numpy.hstack((xx,yy)), yLat, (colIndex.reshape(-1,1), rowIndex.reshape(-1,1)), method = 'cubic' ) #Use spline interpolation
xLong = scipy.interpolate.griddata( numpy.hstack((xx,yy)), xLong, (colIndex.reshape(-1,1), rowIndex.reshape(-1,1)), method = 'cubic' ) #of calculated lat/long points
boxLatMin, boxLongMin = latLong(geoPicture,0,rasterYsize-1)
boxLatMax, boxLongMax = latLong(geoPicture,rasterXsize-1,0)
yLat = yLat.reshape(rasterYsize,rasterXsize)
xLong = xLong.reshape(rasterYsize,rasterXsize)
imageIndices['image' + str(zi)] = numpy.dstack( (rowIndex, colIndex, yLat, xLong, alphaBand, polygons, imageArray) )
imageIndices['bound' + str(zi)] = [boxLatMin,boxLatMax,boxLongMin,boxLongMax]
imageIndices['bands' + str(zi)] = geoPicture.bands
#clear some variables from memory
imageArray = []
presentBands = []
presentBandsNum = []
alphaBand = []
rowIndex = []
colIndex = []
polygons = []
p1 = []
yy = []
xx = []
yLat = []
xLong = []
boundBox1 = imageIndices['bound1']
boundBox2 = imageIndices['bound2']
#Test whether there is any overlap, if not, don't do anything.
crnr1 = int( (boundBox1[0] <= boundBox2[0] < boundBox1[1]) & (boundBox1[2] <= boundBox2[2] < boundBox1[3]) )
crnr2 = int( (boundBox1[0] < boundBox2[1] <= boundBox1[1]) & (boundBox1[2] <= boundBox2[2] < boundBox1[3]) )
crnr3 = int( (boundBox1[0] <= boundBox2[0] < boundBox1[1]) & (boundBox1[2] < boundBox2[3] <= boundBox1[3]) )
crnr4 = int( (boundBox1[0] < boundBox2[1] <= boundBox1[1]) & (boundBox1[2] < boundBox2[3] <= boundBox1[3]) )
chk_crnr = crnr1+crnr2+crnr3+crnr4
if chk_crnr > 0: #Is at least one corner from image two inside of image one
polygon1 = imageIndices['image1']
polygon2 = imageIndices['image2']
presentBands1 = imageIndices['bands1']
presentBands2 = imageIndices['bands2']
#Only use bands common to both images for change detection
presentBands = []
for pB in presentBands1:
if pB in presentBands2:
presentBands.append([int(pB.lstrip('B')),int(presentBands1.index(pB)),int(presentBands2.index(pB))])
presentBands = numpy.array(presentBands)
polygon1 = numpy.dstack((polygon1[:,:,:6], polygon1[:,:,(6 + presentBands[:,1]) ] )) #Depth 6 is the beginning of the image bands.
polygon2 = numpy.dstack((polygon2[:,:,:6], polygon2[:,:,(6 + presentBands[:,2]) ] ))
imageIndices = []
presentBands1 = []
presentBands2 = []
presentBands = []
interBand1, pR2, pC2 = interpImage(polygon1,polygon2)
interBand2, pR1, pC1 = interpImage(polygon2,polygon1)
change_prob, imageArray1, imageArray2 = changeProb(polygon1, polygon2, interBand1,interBand2,2,1)
#Find cloud pixels from both images to eliminate from change test
nonCloudAddress1 = cloudFilter(imageArray1,[])
nonCloudAddress2 = cloudFilter(imageArray2,nonCloudAddress1)
change_prob[change_prob < 0 ] = 0
change_prob[change_prob > 1 ] = 0
change_prob[numpy.arange(change_prob.size)[numpy.array(1-nonCloudAddress2.reshape(-1,),dtype=bool)],:] = 0
interBand = numpy.array(interBand1)
#Create a rectangular array into which the parallelogram will be placed
imageArrayFinal2 = numpy.zeros((polygon1[:,:,0].shape[0],polygon1[:,:,0].shape[1],3), dtype=numpy.float)
for ij in numpy.arange(interBand.shape[0]):
imageArrayFinal2[interBand[ij,0],interBand[ij,1],0] = 1 - change_prob[ij] #1
imageArrayFinal2[interBand[ij,0],interBand[ij,1],1] = 1 - change_prob[ij] #1-.5*change_prob[ij]
imageArrayFinal2[interBand[ij,0],interBand[ij,1],2] = 1 - change_prob[ij] #1-.75*change_prob[ij]
imageArrayFinal2 = imageArrayFinal2 + polygon1[:,:,5].reshape(polygon1[:,:,0].shape[0],polygon1[:,:,0].shape[1],1)
for i in numpy.arange(pR2.shape[0]):
imageArrayFinal2[pR2[i],pC2[i],0:3] = 1
geoPicture.bands.extend(["CHANGE_BAND"])
geoPicture.picture = numpy.dstack((polygon1[:,:,6:],imageArrayFinal2))
else:
sys.stderr.write('These two images do not overlap.')
imageArrayFinal2 = numpy.zeros((geoPicture.picture.shape[0],geoPicture.picture.shape[1],3), dtype=numpy.float)
geoPicture.bands.extend(["CHANGE_BAND"])
geoPicture.picture = imageArrayFinal2
geoPictureOutput = GeoPictureSerializer.GeoPicture()
geoPictureOutput.picture = geoPicture.picture
geoPictureOutput.metadata = geoPicture.metadata
geoPictureOutput.bands = geoPicture.bands
return geoPictureOutput
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")
