def testhist():
fname='/home/eg309/Data/AquaTermi_lowcontrast.JPG'
#fname='/home/eg01/Data/AquaTermi_lowcontrast.JPG'
#imhist,bins = sp.histogram(im.flatten(),nbr_bins,normed=True,new=True)
im=form.loadpic(fname)
print im.shape
nbr_bins=256
#Return the hist
imhist,bins = sp.histogram(im.flatten(),nbr_bins,normed=True,new=True)
#pylab.hist(im.flatten(),nbr_bins,normed=True,new=True)
pyplot.figure(1)
print 'imhist',imhist.shape
pyplot.plot(imhist)
pyplot.figure(2)
#fig.add_subplot(2,1,1)
pyplot.imshow(im, cmap=mpl.cm.gray)
pyplot.show()
pyplot.figure(3)
cdf = imhist.cumsum() #cumulative distribution function
cdf = 255 * cdf / cdf[-1] #normalize
print 'bins.shape',bins.shape
print 'cdf.shape',cdf.shape
#use linear interpolation of cdf to find new pixel values
im2 = sp.interp(im.flatten(),bins[10:-1],cdf[10:])
print 'im.min',im.min()
print 'im.max',im.max()
print 'im.min',im2.min()
print 'im.max',im2.max()
pyplot.imshow(im2.reshape(im.shape), cmap=mpl.cm.gray)
pyplot.show()
def denoise2DICM():
'''
Example taken from Bishop's book p.389
Iterated Conditional Modes (ICM) Discrete Case
Just a cross neighbourhood is allowed here. Other configurations are easy to apply as well.
'''
im=form.loadpic('mrf2.png')
im=sp.asarray(im,dtype='float64')
im[im>127]=255
im[im<=127]=0
print im.min(),im.max()
plt.matshow(im)
plt.show()
#observed noisy image
Y=sp.asarray(im,dtype='float64')
Y[Y==255]=1
Y[Y==0]=-1
#state of pixel
#X=sp.ones(Y.shape)
X=Y
beta,eta,h=1,1,0
E=sp.zeros(Y.shape)
converge=False
while(not converge):
Eprev=sp.sum(E)
for r in xrange(1,Y.shape[0]-1):
for c in xrange(1,Y.shape[1]-1):
#calculate energy for positive
x=1
Ep=-eta*x*Y[r,c]-beta*x*X[r,c+1]-beta*x*X[r,c-1] - beta*x*X[r-1,c]-beta*x*X[r+1,c]
#calculate energy for negative
x=-1
En=- eta*x*Y[r,c] - beta*x*X[r,c+1] - beta*x*X[r,c-1] - beta*x*X[r-1,c] - beta*x*X[r+1,c]
if En<Ep:
X[r,c]=-1
E[r,c]=En
else:
X[r,c]=1
E[r,c]=Ep
#E[r,c]=-eta*X[r,c]*Y[r,c]-beta*X[r,c]*X[r-1,c]-beta*X[r,c]*X[r+1,c]
#plt.matshow(E)
if Eprev-sp.sum(E)==0:
converge=True
plt.matshow(X)
return E,Y,X
