def median_filter(Y, r=2):
"""
Simple median filter.
Parameters:
Y := the input image
r := median filter "radius". Filter will be 2*r+1 in width/height.
"""
# For now, assume a single 2d image (vs a stack)
assert (len(Y.shape) == 2)
assert (r >= 1)
w = 2 * r + 1 # width of kernel
# so we can run the filter on the interior and still be centered
# on all pixels of Y.
Ym = mirror_edges(Y, r)
if True:
# This is the built-in version
return ST.medfilt2d(Ym, w)[r:-r, r:-r]
else:
# a hand-rolled implementation
Z = numpy.zeros(Y.shape)
for row in range(Z.shape[0]):
for col in range(Z.shape[1]):
tile = Ym[row:row + w, col:col + w]
assert (tile.shape[0] == w)
assert (tile.shape[1] == w)
Z[row, col] = numpy.median(tile)
assert (not numpy.any(numpy.isnan(Z)))
return Z
def median_filter(Y, r=2):
"""
Simple median filter.
Parameters:
Y := the input image
r := median filter "radius". Filter will be 2*r+1 in width/height.
"""
# For now, assume a single 2d image (vs a stack)
assert(len(Y.shape)==2)
assert(r >= 1)
w = 2*r+1 # width of kernel
# so we can run the filter on the interior and still be centered
# on all pixels of Y.
Ym = mirror_edges(Y,r)
if True:
# This is the built-in version
return ST.medfilt2d(Ym,w)[r:-r, r:-r]
else:
# a hand-rolled implementation
Z = numpy.zeros(Y.shape)
for row in range(Z.shape[0]):
for col in range(Z.shape[1]):
tile = Ym[row:row+w, col:col+w]
assert(tile.shape[0] == w);
assert(tile.shape[1] == w)
Z[row,col] = numpy.median(tile)
assert(not numpy.any(numpy.isnan(Z)))
return Z
def median_2d(data, kernel_size=3):
if data.ndim != 2:
sys.exit("Only for 2D data!")
sx, sy = shape(data)
k2 = int(kernel_size) / 2
if 2 * k2 == kernel_size:
sys.exit("Kernel size must be odd!")
result = copy(data)
for k in range(1, k2 + 1):
result[k:sx - k, k:sy - k] = medfilt2d(data,
kernel_size=2 * k + 1)[k:sx - k,
k:sy - k]
return result
def run(self):
""" Runs the acquisition loop.
"""
self.display('Processing started')
fNames,numOfFiles =self.acquire()
self.display("%d files are found " % numOfFiles)
self.display("ittx is %d" % self.experiment.ittx)
itt = self.experiment.ittx
spc = self.experiment.spacing_X
s2nm = self.experiment.S2N_Type
s2n = self.experiment.S2N_Value
sclt = self.experiment.scale
outl = self.experiment.Outlier_Filter
jump = self.experiment.jump
# for fileind in range(0,numOfFiles,2):
fileind = 0
while not self.wants_abort and fileind < numOfFiles:
a,b = read_pair_of_images(fNames[fileind],fNames[fileind+1],([0,0,0,0]),itt,spc)
sx,sy = a.shape
Nfft = 2*itt
resRows = (sx-itt)/spc+1
resCols = (sy-itt)/spc+1
res = zeros((resRows*resCols,5))
counter = 0
k = 0
# for k in r_[0:sx-itt+1:spc]:
while not self.wants_abort and k <= (sx-itt+1):
self.display("\n Working on %d pixel row " % k)
for m in r_[0:sy-itt+1:spc]:
# self.display(".")
a2 = a[k:k+itt,m:m+itt]
b2 = b[k:k+itt,m:m+itt]
c = cross_correlate(a2,b2,Nfft)
[peak1,peak2,peakx,peaky] = find_displacement(c,s2nm) # find integer displacement
s2n = 1
[peakx,peaky,s2n] = sub_pixel_velocity(c,peakx,peaky,peak1,peak2,s2n,sclt,itt)
## Scale the pixel displacement to the velocity
u = (itt-peaky)*sclt
v = (itt-peakx)*sclt
x = m+itt/2
y = k+itt/2
res[counter][:] = [x,y,u, v, s2n]
counter += 1
k += spc
# Write unfiltered, raw data
row,col = shape(res)
# writer = csv.writer(file(fNames[fileind]+'_noflt.txt','w'),dialect='excel',delimiter='\t')
savetxt(fNames[fileind][:-4]+'_noflt.txt',res,fmt='%4.3f',delimiter=' ')
# for i in xrange(row):
# writer.writerow(res[i][:])
# Find outliers
uMag = (res[:,2]**2 + res[:,3]**2)**0.5
limit = mean(compress(uMag!=0,uMag))*outl
filtres = res.copy()
u = filtres[:,2].copy()
v = filtres[:,3].copy()
u = choose(uMag > limit,(u,0))
v = choose(uMag > limit, (v,0))
# Median filter
tmpu = fabs(ravel(medfilt2d(reshape(u,(resRows,resCols)),3)))
tmpv = fabs(ravel(medfilt2d(reshape(v,(resRows,resCols)),3)))
uLimit = mean(take(tmpu,nonzero(tmpu!=0))) + 3*std(take(tmpu,nonzero(tmpu!=0)))
vLimit = mean(take(tmpv,nonzero(tmpv!=0))) + 3*std(take(tmpv,nonzero(tmpv!=0)))
u = where(greater(tmpu,uLimit),0,u)
v = where(greater(tmpv,vLimit),0,v)
filtres[:,2] = u.copy()
filtres[:,3] = v.copy()
savetxt(fNames[fileind][:-4]+'_flt.txt',filtres,fmt='%4.3f',delimiter=' ')
ind = nonzero(logical_and(u == 0,v == 0))
indLoop = 0
while size(ind[0]) > 0: # Loop until no more "holes" are found
indLoop += 1
self.display("Interpolation loop %d " % indLoop)
for i in ind[0]: # find a "hole" (zero), fill with an average of 5 x 5 neighbours
x2 = i/resCols
y2 = i - x2 * resCols
k = r_[maximum(2,x2)-2:minimum(resCols-3,x2)+2]
m = r_[maximum(2,y2)-2:minimum(resRows-3,y2)+2]
region = sub2ind(resRows,resCols,k,m)
tmpu = take(u,region)
tmpu = take(v,region)
u[i] = mean(tmpu)
v[i] = mean(tmpv)
ind = nonzero(logical_and(u == 0,v == 0))
filtres[:,2] = u
filtres[:,3] = v
savetxt(fNames[fileind][:-4]+'.txt',filtres,fmt='%4.3f',delimiter=' ')
self.quiverm(filtres,a,resRows,resCols,scalearrows=50)
fileind += 2
j += 4
i += 4
error_map = np.zeros((row, col))
no_ep = 0
for i in range(row):
for j in range(col):
if ew[i][j] == sw[i][j]:
error_map[i][j] = 0
else:
error_map[i][j] = 1
no_ep += 1
m1 = float(float(no_ep) / float((n / 4) * (m / 4)))
#applying median filter
error_map = np.array(error_map, dtype='f')
if m1 <= 0.15:
error_map = signaltools.medfilt2d(error_map, 3)
no_step = 0
sterror_map = error_map
mterror_map = error_map
for i in range(1, int(n / 4) - 1):
count1 = 0
count2 = 0
if error_map[i - 1][0] == 1:
count1 += 1
if error_map[i - 1][1] == 1:
count1 += 1
if error_map[i][1] == 1:
count1 += 1
if error_map[i + 1][1] == 1:
count1 += 1
if error_map[i + 1][0] == 1:
