def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
infile = auxil.select_infile(title='Select an image')
if infile:
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
else:
return
pos = auxil.select_pos(bands)
if not pos:
return
dims = auxil.select_dims([0,0,cols,rows])
if dims:
x0,y0,cols,rows = dims
else:
return
m = auxil.select_integer(1000,'Select training sample size')
K = auxil.select_integer(6,'Select number of clusters')
outfile, outfmt = auxil.select_outfilefmt()
if not outfile:
return
kernel = auxil.select_integer(1,'Select kernel: 0=linear, 1=Gaussian')
print '========================='
print ' kkmeans'
print '========================='
print 'infile: '+infile
print 'samples: '+str(m)
if kernel == 0:
print 'kernel: '+'linear'
else:
print 'kernel: '+'Gaussian'
start = time.time()
# input data matrix
XX = np.zeros((cols*rows,bands))
k = 0
for b in pos:
band = inDataset.GetRasterBand(b)
band = band.ReadAsArray(x0,y0,cols,rows).astype(float)
XX[:,k] = np.ravel(band)
k += 1
# training data matrix
idx = np.fix(np.random.random(m)*(cols*rows)).astype(np.integer)
X = XX[idx,:]
print 'kernel matrix...'
# uncentered kernel matrix
KK, gma = auxil.kernelMatrix(X,kernel=kernel)
if gma is not None:
print 'gamma: '+str(round(gma,6))
# initial (random) class labels
labels = np.random.randint(K,size = m)
# iteration
change = True
itr = 0
onesm = np.mat(np.ones(m,dtype=float))
while change and (itr < 100):
change = False
U = np.zeros((K,m))
for i in range(m):
U[labels[i],i] = 1
M = np.diag(1.0/(np.sum(U,axis=1)+1.0))
MU = np.mat(np.dot(M,U))
Z = (onesm.T)*np.diag(MU*KK*(MU.T)) - 2*KK*(MU.T)
Z = np.array(Z)
labels1 = (np.argmin(Z,axis=1) % K).ravel()
if np.sum(labels1 != labels):
change = True
labels = labels1
itr += 1
print 'iterations: %i'%itr
# classify image
print 'classifying...'
i = 0
A = np.diag(MU*KK*(MU.T))
A = np.tile(A,(cols,1))
class_image = np.zeros((rows,cols),dtype=np.byte)
while i < rows:
XXi = XX[i*cols:(i+1)*cols,:]
KKK,_ = auxil.kernelMatrix(X,XXi,gma=gma,kernel=kernel)
Z = A - 2*(KKK.T)*(MU.T)
Z= np.array(Z)
labels = np.argmin(Z,axis=1).ravel()
class_image[i,:] = (labels % K) +1
i += 1
sys.stdout.write("\n")
# write to disk
driver = gdal.GetDriverByName(outfmt)
outDataset = driver.Create(outfile,cols,rows,1,GDT_Byte)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(class_image,0,0)
outBand.FlushCache()
outDataset = None
inDataset = None
if (outfmt == 'ENVI') and (K<19):
# try to make an ENVI classification header file
hdr = header.Header()
headerfile = outfile+'.hdr'
f = open(headerfile)
line = f.readline()
envihdr = ''
while line:
envihdr += line
line = f.readline()
f.close()
hdr.read(envihdr)
hdr['file type'] ='ENVI Classification'
hdr['classes'] = str(K)
classlookup = '{0'
for i in range(1,3*K):
classlookup += ', '+str(str(ctable[i]))
classlookup +='}'
hdr['class lookup'] = classlookup
hdr['class names'] = [str(i+1) for i in range(K)]
f = open(headerfile,'w')
f.write(str(hdr))
f.close()
print 'result written to: '+outfile
print 'elapsed time: '+str(time.time()-start)
print '--done------------------------'
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
infile = auxil.select_infile(title='Select an image')
if infile:
inDataset = gdal.Open(infile, GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
else:
return
pos = auxil.select_pos(bands)
if not pos:
return
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x0, y0, cols, rows = dims
else:
return
m = auxil.select_integer(1000, 'Select training sample size')
K = auxil.select_integer(6, 'Select number of clusters')
outfile, outfmt = auxil.select_outfilefmt()
if not outfile:
return
kernel = auxil.select_integer(1, 'Select kernel: 0=linear, 1=Gaussian')
print '========================='
print ' kkmeans'
print '========================='
print 'infile: ' + infile
print 'samples: ' + str(m)
if kernel == 0:
print 'kernel: ' + 'linear'
else:
print 'kernel: ' + 'Gaussian'
start = time.time()
# input data matrix
XX = np.zeros((cols * rows, bands))
k = 0
for b in pos:
band = inDataset.GetRasterBand(b)
band = band.ReadAsArray(x0, y0, cols, rows).astype(float)
XX[:, k] = np.ravel(band)
k += 1
# training data matrix
idx = np.fix(np.random.random(m) * (cols * rows)).astype(np.integer)
X = XX[idx, :]
print 'kernel matrix...'
# uncentered kernel matrix
KK, gma = auxil.kernelMatrix(X, kernel=kernel)
if gma is not None:
print 'gamma: ' + str(round(gma, 6))
# initial (random) class labels
labels = np.random.randint(K, size=m)
# iteration
change = True
itr = 0
onesm = np.mat(np.ones(m, dtype=float))
while change and (itr < 100):
change = False
U = np.zeros((K, m))
for i in range(m):
U[labels[i], i] = 1
M = np.diag(1.0 / (np.sum(U, axis=1) + 1.0))
MU = np.mat(np.dot(M, U))
Z = (onesm.T) * np.diag(MU * KK * (MU.T)) - 2 * KK * (MU.T)
Z = np.array(Z)
labels1 = (np.argmin(Z, axis=1) % K).ravel()
if np.sum(labels1 != labels):
change = True
labels = labels1
itr += 1
print 'iterations: %i' % itr
# classify image
print 'classifying...'
i = 0
A = np.diag(MU * KK * (MU.T))
A = np.tile(A, (cols, 1))
class_image = np.zeros((rows, cols), dtype=np.byte)
while i < rows:
XXi = XX[i * cols:(i + 1) * cols, :]
KKK, _ = auxil.kernelMatrix(X, XXi, gma=gma, kernel=kernel)
Z = A - 2 * (KKK.T) * (MU.T)
Z = np.array(Z)
labels = np.argmin(Z, axis=1).ravel()
class_image[i, :] = (labels % K) + 1
i += 1
sys.stdout.write("\n")
# write to disk
driver = gdal.GetDriverByName(outfmt)
outDataset = driver.Create(outfile, cols, rows, 1, GDT_Byte)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0 * gt[1]
gt[3] = gt[3] + y0 * gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(class_image, 0, 0)
outBand.FlushCache()
outDataset = None
inDataset = None
if (outfmt == 'ENVI') and (K < 19):
# try to make an ENVI classification header file
hdr = header.Header()
headerfile = outfile + '.hdr'
f = open(headerfile)
line = f.readline()
envihdr = ''
while line:
envihdr += line
line = f.readline()
f.close()
hdr.read(envihdr)
hdr['file type'] = 'ENVI Classification'
hdr['classes'] = str(K)
classlookup = '{0'
for i in range(1, 3 * K):
classlookup += ', ' + str(str(ctable[i]))
classlookup += '}'
hdr['class lookup'] = classlookup
hdr['class names'] = [str(i + 1) for i in range(K)]
f = open(headerfile, 'w')
f.write(str(hdr))
f.close()
print 'result written to: ' + outfile
print 'elapsed time: ' + str(time.time() - start)
print '--done------------------------'
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
infile = auxil.select_infile(title='Select an image')
if infile:
inDataset = gdal.Open(infile, GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
else:
return
pos = auxil.select_pos(bands)
if not pos:
return
dims = auxil.select_dims([0, 0, cols, rows])
if dims:
x0, y0, cols, rows = dims
else:
return
m = auxil.select_integer(2000, 'Select sample size (0 for k-means)')
n = auxil.select_integer(10, 'Select number of eigenvalues')
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
kernel = auxil.select_integer(1, 'Select kernel: 0=linear, 1=Gaussian')
print '========================='
print ' kPCA'
print '========================='
print 'infile: ' + infile
print 'samples: ' + str(m)
if kernel == 0:
print 'kernel: ' + 'linear'
else:
print 'kernel: ' + 'Gaussian'
start = time.time()
if kernel == 0:
n = min(bands, n)
# construct data design matrices
XX = zeros((cols * rows, bands))
k = 0
for b in pos:
band = inDataset.GetRasterBand(b)
band = band.ReadAsArray(x0, y0, cols, rows).astype(float)
XX[:, k] = ravel(band)
k += 1
if m > 0:
idx = fix(random.random(m) * (cols * rows)).astype(integer)
X = XX[idx, :]
else:
print 'running k-means on 100 cluster centers...'
X, _ = kmeans(XX, 100, iter=1)
m = 100
print 'centered kernel matrix...'
# centered kernel matrix
K, gma = auxil.kernelMatrix(X, kernel=kernel)
meanK = sum(K) / (m * m)
rowmeans = mat(sum(K, axis=0) / m)
if gma is not None:
print 'gamma: ' + str(round(gma, 6))
K = auxil.center(K)
print 'diagonalizing...'
# diagonalize
try:
w, v = linalg.eigh(K, eigvals=(m - n, m - 1))
idx = range(n)
idx.reverse()
w = w[idx]
v = v[:, idx]
# variance of PCs
var = w / m
except linalg.LinAlgError:
print 'eigenvalue computation failed'
sys.exit()
# dual variables (normalized eigenvectors)
alpha = mat(v) * mat(diag(1 / sqrt(w)))
print 'projecting...'
# projecting
image = zeros((rows, cols, n))
for i in range(rows):
XXi = XX[i * cols:(i + 1) * cols, :]
KK, gma = auxil.kernelMatrix(X, XXi, kernel=kernel, gma=gma)
# centering on training data:
# subtract column means
colmeans = mat(sum(KK, axis=0) / m)
onesm = mat(ones(m))
KK = KK - onesm.T * colmeans
# subtract row means
onesc = mat(ones(cols))
KK = KK - rowmeans.T * onesc
# add overall mean
KK = KK + meanK
# project
image[i, :, :] = KK.T * alpha
# write to disk
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile, cols, rows, n, GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0 * gt[1]
gt[3] = gt[3] + y0 * gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
for k in range(n):
outBand = outDataset.GetRasterBand(k + 1)
outBand.WriteArray(image[:, :, k], 0, 0)
outBand.FlushCache()
outDataset = None
inDataset = None
print 'result written to: ' + outfile
print 'elapsed time: ' + str(time.time() - start)
plt.plot(range(1, n + 1), var, 'k-')
plt.title('kernel PCA')
plt.xlabel('principal component')
plt.ylabel('Variance')
plt.show()
print '--done------------------------'
