def crossvalidate((Gstrn, lstrn, Gstst, lstst, L, epochs, trainalg)):
import auxil.supervisedclass as sc
if trainalg == 1:
classifier = sc.Maxlike(Gstrn, lstrn)
elif trainalg == 2:
classifier = sc.Gausskernel(Gstrn, lstrn)
elif trainalg == 3:
classifier = sc.Ffnbp(Gstrn, lstrn, L, epochs)
elif trainalg == 4:
classifier = sc.Ffncg(Gstrn, lstrn, L, epochs)
elif trainalg == 5:
classifier = sc.Ffnekf(Gstrn, lstrn, L, epochs)
elif trainalg == 6:
classifier = sc.Dnn_keras(Gstrn, lstrn, L, epochs)
elif trainalg == 7:
classifier = sc.Svm(Gstrn, lstrn)
if classifier.train() is not None:
return classifier.test(Gstst, lstst)
else:
return None
def main():
usage = '''
Usage:
--------------------------------------
Supervised classification of multispectral images
python %s [OPTIONS] filename shapefile
Options:
-h this help
-p <list> RGB band positions to be included
(default all) e.g. -p [1,2,3]
-a <int> algorithm 1=MaxLike
2=Gausskernel
3=NNet(backprop)
4=NNet(congrad)
5=NNet(Kalman)
6=Dnn(tensorflow)
7=SVM
-e <int> number of epochs (default 100)
-t <float> fraction for training (default 0.67)
-v use validation (reserve half of training
data for validation)
-P generate class probability image (not
available for MaxLike)
-n suppress graphical output
-L <list> list of hidden neurons in each
hidden layer (default [10])
If the input file is named
path/filenbasename.ext then
The output classification file is named
path/filebasename_class.ext
the class probabilities output file is named
path/filebasename_classprobs.ext
and the test results file is named
path/filebasename_<classifier>.tst
-------------------------------------'''%sys.argv[0]
outbuffer = 100
options, args = getopt.getopt(sys.argv[1:],'hnvPp:t:e:a:L:')
pos = None
probs = False
L = [10]
trainalg = 1
epochs = 100
graphics = True
validation = False
trainfrac = 0.67
for option, value in options:
if option == '-h':
print usage
return
elif option == '-p':
pos = eval(value)
elif option == '-n':
graphics = False
elif option == '-v':
validation = True
elif option == '-t':
trainfrac = eval(value)
elif option == '-e':
epochs = eval(value)
elif option == '-a':
trainalg = eval(value)
elif option == '-L':
L = eval(value)
elif option == '-P':
probs = True
if len(args) != 2:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
if trainalg == 1:
algorithm = 'MaxLike'
elif trainalg == 2:
algorithm = 'Gausskernel'
elif trainalg == 3:
algorithm = 'NNet(Backprop)'
elif trainalg == 4:
algorithm = 'NNet(Congrad)'
elif trainalg == 5:
algorithm = 'NNet(Kalman)'
elif trainalg == 6:
algorithm = 'Dnn(tensorflow)'
else:
algorithm = 'SVM'
print 'Training with %s'%algorithm
infile = args[0]
trnfile = args[1]
gdal.AllRegister()
if infile:
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
geotransform = inDataset.GetGeoTransform()
else:
return
if pos is None:
pos = range(1,bands+1)
N = len(pos)
rasterBands = []
for b in pos:
rasterBands.append(inDataset.GetRasterBand(b))
# output files
path = os.path.dirname(infile)
basename = os.path.basename(infile)
root, ext = os.path.splitext(basename)
outfile = '%s/%s_class%s'%(path,root,ext)
tstfile = '%s/%s_%s.tst'%(path,root,algorithm)
if (trainalg in (2,3,4,5,6)) and probs:
# class probabilities file
probfile = '%s/%s_classprobs%s'%(path,root,ext)
else:
probfile = None
# get the training data
Xs,Ls,K,classnames = rs.readshp(trnfile,inDataset,pos)
m = Ls.shape[0]
# stretch the pixel vectors to [-1,1] for ffn, dnn
maxx = np.max(Xs,0)
minx = np.min(Xs,0)
for j in range(len(pos)):
Xs[:,j] = 2*(Xs[:,j]-minx[j])/(maxx[j]-minx[j]) - 1.0
# random permutation of training data
idx = np.random.permutation(m)
Xs = Xs[idx,:]
Ls = Ls[idx,:]
# train on trainfrac of training examples, rest for testing
Xstrn = Xs[:int(trainfrac*m),:]
Lstrn = Ls[:int(trainfrac*m),:]
Xstst = Xs[int(trainfrac*m):,:]
Lstst = Ls[int(trainfrac*m):,:]
# setup output datasets
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile,cols,rows,1,GDT_Byte)
projection = inDataset.GetProjection()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
if probfile:
probDataset = driver.Create(probfile,cols,rows,K,GDT_Byte)
if geotransform is not None:
probDataset.SetGeoTransform(geotransform)
if projection is not None:
probDataset.SetProjection(projection)
probBands = []
for k in range(K):
probBands.append(probDataset.GetRasterBand(k+1))
# initialize classifier
if trainalg == 1:
classifier = sc.Maxlike(Xstrn,Lstrn)
elif trainalg == 2:
classifier = sc.Gausskernel(Xstrn,Lstrn)
elif trainalg == 3:
classifier = sc.Ffnbp(Xstrn,Lstrn,L,epochs,validation)
elif trainalg == 4:
classifier = sc.Ffncg(Xstrn,Lstrn,L,epochs,validation)
elif trainalg == 5:
classifier = sc.Ffnekf(Xstrn,Lstrn,L,epochs,validation)
elif trainalg == 6:
# classifier = sc.Dnn_learn(Xstrn,Lstrn,L,epochs)
# classifier = sc.Dnn_core(Xstrn,Lstrn,L,epochs)
classifier = sc.Dnn_keras(Xstrn,Lstrn,L,epochs)
elif trainalg == 7:
classifier = sc.Svm(Xstrn,Lstrn)
# train it
print 'training on %i pixel vectors...' % np.max(classifier._Gs.shape)
print 'classes: %s'%str(classnames)
start = time.time()
result = classifier.train()
print 'elapsed time %s' %str(time.time()-start)
if result is not None:
if (trainalg in [3,4,5]) and graphics:
# the cost arrays are returned in result
cost = np.log(result[0])
costv = np.log(result[1])
ymax = np.max(cost)
#ymin = np.min(cost)-1
ymin = 5.0
xmax = len(cost)
plt.plot(range(xmax),costv,'r',range(xmax),cost,'b')
plt.axis([0,xmax,ymin,ymax])
plt.title('Log(Cross entropy)')
plt.xlabel('Epoch')
# classify the image
print 'classifying...'
start = time.time()
tile = np.zeros((outbuffer*cols,N),dtype=np.float32)
for row in range(rows/outbuffer):
print 'row: %i'%(row*outbuffer)
for j in range(N):
tile[:,j] = rasterBands[j].ReadAsArray(0,row*outbuffer,cols,outbuffer).ravel()
tile[:,j] = 2*(tile[:,j]-minx[j])/(maxx[j]-minx[j]) - 1.0
cls, Ms = classifier.classify(tile)
outBand.WriteArray(np.reshape(cls,(outbuffer,cols)),0,row*outbuffer)
if probfile and Ms is not None:
Ms = np.byte(Ms*255)
for k in range(K):
probBands[k].WriteArray(np.reshape(Ms[:,k],(outbuffer,cols)),0,row*outbuffer)
outBand.FlushCache()
print 'elapsed time %s' %str(time.time()-start)
outDataset = None
inDataset = None
if probfile:
for probBand in probBands:
probBand.FlushCache()
probDataset = None
print 'class probabilities written to: %s'%probfile
print 'thematic map written to: %s'%outfile
if (trainalg in [3,4,5]) and graphics:
plt.show()
if tstfile:
with open(tstfile,'w') as f:
print >>f, algorithm +'test results for %s'%infile
print >>f, time.asctime()
print >>f, 'Classification image: %s'%outfile
print >>f, 'Class probabilities image: %s'%probfile
print >>f, Lstst.shape[0],Lstst.shape[1]
classes, _ = classifier.classify(Xstst)
labels = np.argmax(Lstst,axis=1)+1
for i in range(len(classes)):
print >>f, classes[i], labels[i]
f.close()
print 'test results written to: %s'%tstfile
print 'done'
else:
print 'an error occured'
return