def isoclass(filename, colorArray, x):
dataset = tifwork.openTIF(filename)
(cols, rows, bands, bandArr) = tifwork.detailsTIF(dataset)
bandArr = tifwork.getBand(dataset, bands, bandArr)
imageArray = np.array(bandArr, dtype=float)
isoarr = isodata_classification(imageArray, x)
print isoarr.shape
#print rows
#colorArray=np.array([[0,0,100],[100,0,0],[0,100,0],[100,100,0],[75,75,75],[0,100,100],[100,0,100],[50,25,25],[25,50,25],[25,25,50]])
clusteredArray = np.zeros((rows * cols, 3))
print clusteredArray.shape
clusters = isoarr.max()
#print clusters
for i in xrange(clusters + 1):
indices = np.where(isoarr == i)[0]
if indices.size:
clusteredArray[indices] = colorArray[i]
clusteredArray = clusteredArray.reshape(rows, cols, 3)
#print clusteredArray
scipy.misc.imsave('iso.jpg', clusteredArray)
imageGUI.imdisplay('iso.jpg', 'ISODATA-Image')
print 'iso done'
def kmea(colorArray, iterations, fileName):
print 'Kmeans'
print colorArray
print iterations
print fileName
dataset = tifwork.openTIF(fileName)
(cols, rows, bands, bandArr) = tifwork.detailsTIF(dataset)
bandArr = tifwork.getBand(dataset, bands, bandArr)
imageArray = np.array(bandArr, dtype=float)
clusters = len(colorArray)
print 'clusters = ', clusters
#iterations = 3
(clusterNumber, colorArr) = kmeans(imageArray, clusters, iterations)
#print colorArray.shape
#print clusterNumber
clusteredArray = np.zeros((rows, cols, 3))
for i in range(clusters):
index = clusterNumber == i
clusteredArray[index] = colorArray[i]
scipy.misc.imsave('kMeans.jpg', clusteredArray)
imageGUI.imdisplay('kMeans.jpg', 'Kmeans-Image')
print 'kmeans done'
def getData(fileName, num):
#get dataset
global bandArray
global bands
global target
global data
dataset = tifwork.openTIF(fileName)
#get details of dataset
(cols,rows,bands,bandArray) = tifwork.detailsTIF(dataset)
#get all bands
bandArray = tifwork.getBand(dataset,bands,bandArray)
#input a band
print rows, cols
workData = bandArray[:,:,num-1]
if (data == None):
data = np.array([]).reshape(0,bands)
target = np.array([])
#show original image and plot it
imdata = Image.fromarray(workData)
imdata = imdata.convert('L')
imdata.save('original.jpg')
#plot(workData,'Original')
filename = 'original.jpg'
return filename
def getData(fileName, num):
#get dataset
global bandArray
global bands
global target
global data
dataset = tifwork.openTIF(fileName)
#get details of dataset
(cols, rows, bands, bandArray) = tifwork.detailsTIF(dataset)
#get all bands
bandArray = tifwork.getBand(dataset, bands, bandArray)
#input a band
print rows, cols
workData = bandArray[:, :, num - 1]
if (data == None):
data = np.array([]).reshape(0, bands)
target = np.array([])
#show original image and plot it
imdata = Image.fromarray(workData)
imdata = imdata.convert('L')
imdata.save('original.jpg')
#plot(workData,'Original')
filename = 'original.jpg'
return filename
def bandPrint():
if(not(opt1.get() and opt2.get() and opt3.get())):
showerror("Error", "All bands not selected")
elif(opt1.get()==opt2.get() or opt1.get()==opt3.get() or opt2.get()==opt3.get()):
showerror("Error", "Two same bands selected")
else:
bandArr = tifwork.getBand(dataset,bands,bandArray)
tifwork.selectBand(bandArr,rows,cols,opt1.get(),opt2.get(),opt3.get())
def bandPrint():
if(not(opt1.get() and opt2.get() and opt3.get())):
showerror("Error", "All bands not selected")
elif(opt1.get()==opt2.get() or opt1.get()==opt3.get() or opt2.get()==opt3.get()):
showerror("Error", "Two same bands selected")
else:
bandArr = tifwork.getBand(dataset,bands,bandArray)
tifwork.selectBand(bandArr,rows,cols,opt1.get(),opt2.get(),opt3.get())
def fuzz(color,filename):
#filename = 'liss4.tif'
dataset = tw.openTIF(filename)
cols, rows,bands,bandArray = tw.detailsTIF(dataset)
bandArray = tw.getBand(dataset,bands,bandArray)
intBandArray = np.array(bandArray,dtype = float)
c = len(color)
#print bands
#print intBandArray.shape
scipy.misc.imsave('hello.jpg',intBandArray)
#(L, C, U, LUT, H) = FastFCMeans(intBandArray,c,q)
(L,C,U,LUT,H) = ffc.FastFCMeans(intBandArray[:,:,0],c,2)
im = intBandArray[:,:,0]
#Visualize the fuzzy membership functions
plt.figure('Fig 1')
#plt.subplot(2,1,1)
#Visualize the segmentation
Lrgb = np.zeros((np.size(L),3),'uint8')
Lflat = L.ravel()
Lflat = Lflat[:,np.newaxis]
#correct things from here
#color = [(0,255,0),(0,0,255),(255,0,0),(0,0,0),(255,255,255),(34,65,0),(0,45,87),(100,100,100),(50,50,50),(12,54,77),(43,65,99)]
for i in range(0,c):
(temp,b) =np.nonzero(Lflat == i)
Lrgb[temp] = color[i]
#print im.shape
Lrgb = np.reshape(Lrgb,(im.shape)+(3,))
imArray = np.copy(Lrgb.astype('uint8'))
print imArray.shape
scipy.misc.imsave('1.jpg',imArray)
plt.imshow(imArray)
plt.show()
def getData(fileName, num):
#get dataset
dataset = tifwork.openTIF(fileName)
#get details of dataset
(cols, rows, bands, bandArray) = tifwork.detailsTIF(dataset)
#get all bands
bandArray = tifwork.getBand(dataset, bands, bandArray)
#input a band
workData = bandArray[:, :, num - 1]
#show original image and plot it
imdata = Image.fromarray(workData)
imdata = imdata.convert('L')
imdata.save('original.jpg')
#plot(workData,'Original')
filename = 'original.jpg'
return filename
def getData(fileName, num):
#get dataset
dataset = tifwork.openTIF(fileName)
#get details of dataset
(cols,rows,bands,bandArray) = tifwork.detailsTIF(dataset)
#get all bands
bandArray = tifwork.getBand(dataset,bands,bandArray)
#input a band
workData = bandArray[:,:,num-1]
#show original image and plot it
imdata = Image.fromarray(workData)
imdata = imdata.convert('L')
imdata.save('original.jpg')
#plot(workData,'Original')
filename = 'original.jpg'
return filename
def pca(fileName):
dataset = tw.openTIF(fileName)
cols, rows, bands, bandArray = tw.detailsTIF(dataset)
bandArray = tw.getBand(dataset, bands, bandArray)
print bands
for i in range(0, bands):
array = bandArray[:, :, i]
#print array
scipy.misc.imsave('./temp/PCA/test' + str(i) + '.png', array)
imlist = []
for i in range(0, bands):
imlist.append('./temp/PCA/test' + str(i) + '.png')
print imlist
def pca(X):
# Principal Component Analysis
# input: X, matrix with training data as flattened arrays in rows
# return: projection matrix (with important dimensions first),
# variance and mean
#get dimensions
num_data, dim = X.shape
#center data
mean_X = X.mean(axis=0)
for i in range(num_data):
X[i] -= mean_X
if dim > 100:
print 'PCA - compact trick used'
M = dot(X, X.T) #covariance matrix
print M
e, EV = linalg.eigh(M) #eigenvalues and eigenvectors
print e
print EV
tmp = dot(X.T, EV).T #this is the compact trick
V = tmp[::
-1] #reverse since last eigenvectors are the ones we want
S = sqrt(e)[::
-1] #reverse since eigenvalues are in increasing order
else:
print 'PCA - SVD used'
U, S, V = linalg.svd(X)
V = V[:num_data] #only makes sense to return the first num_data
#return the projection matrix, the variance and the mean
return V, S, mean_X
im = numpy.array(Image.open(imlist[0])) #open one image to get the size
m, n = im.shape[0:2] #get the size of the images
imnbr = len(imlist) #get the number of images
#create matrix to store all flattened images
immatrix = numpy.array(
[numpy.array(Image.open(imlist[i])).flatten() for i in range(imnbr)],
'f')
#perform PCA
V, S, immean = pca(immatrix)
#mean image and first mode of variation
immean = immean.reshape(m, n)
for i in range(0, bands):
mode = V[i].reshape(m, n)
scipy.misc.imsave('./temp/PCA/pca' + str(i + 1) + '.png', mode)
x = imageGUI.imdisplay('./temp/PCA/pca' + str(i + 1) + '.png',
'PCA ' + str(i), 1)
#show the images
scipy.misc.imsave('./temp/PCA/meanimage.png', immean)
imageGUI.imdisplay('./temp/PCA/meanimage.png', 'Mean Image', 1)
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
import numpy as np
from osgeo import gdal
import tifwork
dataset = tifwork.openTIF(filename)
cols, rows, bands, bandArray = tifwork.detailsTIF(dataset)
bandArray = tifwork.getBand(dataset, bands, bandArray)
mean = np.mean(bandArray, axis=0)
median = np.median(bandArray, axis=0)
stddev = np.std(bandArray, axis=0)
max = np.max(bandArray, axis=0)
min = np.min(bandArray, axis=0)
f = open("abc.txt", 'w')
f.write("Mean = " + str(mean) + "\n")
f.write("Median = " + str(median) + "\n")
f.write("Standard Deviation = " + str(stddev) + "\n")
f.write("Max = " + str(max) + "\n")
f.write("Min = " + str(min) + "\n")
f.close()
def svmwork(fileName, data, target, typ):
print 'SVM'
dataset = tifwork.openTIF(fileName)
(cols, rows, bands, bandArr) = tifwork.detailsTIF(dataset)
bandArr = tifwork.getBand(dataset, bands, bandArr)
imageArray = np.array(bandArr, dtype=float)
print imageArray.shape
array = imageArray.copy()
array = array.reshape((cols * rows), bands)
#print array.shape
# training data extract
# classifying training data
#print target.shape
#print data.shape
#print array.shape
# SVM
if (typ == 'poly'):
clf = svm.SVC(kernel=typ, degree=3)
else:
clf = svm.SVC(kernel=typ)
clf.fit(data, target)
isoarr = clf.predict(array)
isoarr = np.array(isoarr, dtype=int)
#print isoarr
#print isoarr.max()
#print z.shape
#print z
colorArray = np.array([[0, 0, 100], [100, 0, 0], [0, 100, 0],
[100, 100, 0], [75, 75, 75], [0, 100, 100],
[100, 0, 100], [50, 25, 25], [25, 50, 25],
[25, 25, 50]])
clusteredArray = np.zeros((rows * cols, 3))
#print clusteredArray.shape
clusters = isoarr.max()
#print clusters
for i in xrange(clusters + 1):
indices = np.where(isoarr == i)[0]
print i, indices.size
if indices.size:
clusteredArray[indices] = colorArray[i]
print clusteredArray
clusteredArray = clusteredArray.reshape(rows, cols, 3)
#print clusteredArray
scipy.misc.imsave('svm' + typ + '.jpg', clusteredArray)
imageGUI.imdisplay('svm' + typ + '.jpg', 'SVM-' + typ + '-Image')
print 'SVM Done'
def svmwork(fileName,data,target,typ):
print 'SVM'
dataset = tifwork.openTIF(fileName)
(cols,rows,bands,bandArr) = tifwork.detailsTIF(dataset)
bandArr = tifwork.getBand(dataset,bands,bandArr)
imageArray = np.array(bandArr,dtype =float)
print imageArray.shape
array = imageArray.copy()
array = array.reshape((cols*rows),bands)
#print array.shape
# training data extract
# classifying training data
#print target.shape
#print data.shape
#print array.shape
# SVM
if (typ == 'poly'):
clf = svm.SVC(kernel=typ,degree=3)
else:
clf = svm.SVC(kernel=typ)
clf.fit(data, target)
isoarr = clf.predict(array)
isoarr = np.array(isoarr,dtype =int)
#print isoarr
#print isoarr.max()
#print z.shape
#print z
colorArray=np.array([[0,0,100],[100,0,0],[0,100,0],[100,100,0],[75,75,75],[0,100,100],[100,0,100],[50,25,25],[25,50,25],[25,25,50]])
clusteredArray = np.zeros((rows*cols,3))
#print clusteredArray.shape
clusters = isoarr.max()
#print clusters
for i in xrange(clusters+1):
indices = np.where(isoarr == i)[0]
print i ,indices.size
if indices.size:
clusteredArray[indices] = colorArray[i]
print clusteredArray
clusteredArray = clusteredArray.reshape(rows,cols,3)
#print clusteredArray
scipy.misc.imsave('svm'+typ+'.jpg',clusteredArray)
imageGUI.imdisplay('svm'+typ+'.jpg','SVM-'+typ+'-Image')
print 'SVM Done'
# take_snapshot(img_class_flat.reshape(N, M), iteration_step=iter)
###############################################################################
print "Isodata(info): Finished with %s classes" % k
print "Isodata(info): Number of Iterations: %s" % (iter + 1)
return img_class_flat
filename = 'liss2.tif'
dataset = tifwork.openTIF(filename)
(cols,rows,bands,bandArr) = tifwork.detailsTIF(dataset)
bandArr = tifwork.getBand(dataset,bands,bandArr)
imageArray = np.array(bandArr,dtype =float)
x = {'K':5 , 'I':200}
isoarr = isodata_classification(imageArray,x)
#print rows
colorArray=np.array([[0,0,100],[100,0,0],[0,100,0],[100,100,0],[75,75,75],[0,100,100],[100,0,100],[50,25,25],[25,50,25],[25,25,50]])
clusteredArray = np.zeros((rows*cols,3))
clusters = isoarr.max()
#print clusters
for i in xrange(clusters+1):
indices = np.where(isoarr == i)[0]
if indices.size:
clusteredArray[indices] = colorArray[i]
def pca(fileName):
dataset = tw.openTIF(fileName)
cols, rows,bands,bandArray = tw.detailsTIF(dataset)
bandArray = tw.getBand(dataset,bands,bandArray)
print bands
for i in range(0,bands):
array = bandArray[:,:,i]
#print array
scipy.misc.imsave('./temp/PCA/test'+str(i)+'.png',array)
imlist = []
for i in range(0,bands):
imlist.append('./temp/PCA/test'+str(i)+'.png')
print imlist
def pca(X):
# Principal Component Analysis
# input: X, matrix with training data as flattened arrays in rows
# return: projection matrix (with important dimensions first),
# variance and mean
#get dimensions
num_data,dim = X.shape
#center data
mean_X = X.mean(axis=0)
for i in range(num_data):
X[i] -= mean_X
if dim>100:
print 'PCA - compact trick used'
M = dot(X,X.T) #covariance matrix
print M
e,EV = linalg.eigh(M) #eigenvalues and eigenvectors
print e
print EV
tmp = dot(X.T,EV).T #this is the compact trick
V = tmp[::-1] #reverse since last eigenvectors are the ones we want
S = sqrt(e)[::-1] #reverse since eigenvalues are in increasing order
else:
print 'PCA - SVD used'
U,S,V = linalg.svd(X)
V = V[:num_data] #only makes sense to return the first num_data
#return the projection matrix, the variance and the mean
return V,S,mean_X
im = numpy.array(Image.open(imlist[0])) #open one image to get the size
m,n = im.shape[0:2] #get the size of the images
imnbr = len(imlist) #get the number of images
#create matrix to store all flattened images
immatrix = numpy.array([numpy.array(Image.open(imlist[i])).flatten() for i in range(imnbr)],'f')
#perform PCA
V,S,immean = pca(immatrix)
#mean image and first mode of variation
immean = immean.reshape(m,n)
for i in range (0,bands):
mode = V[i].reshape(m,n)
scipy.misc.imsave('./temp/PCA/pca'+str(i+1)+'.png',mode)
x = imageGUI.imdisplay('./temp/PCA/pca'+str(i+1)+'.png','PCA '+str(i),1)
#show the images
scipy.misc.imsave('./temp/PCA/meanimage.png',immean)
imageGUI.imdisplay('./temp/PCA/meanimage.png','Mean Image',1)