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common.py
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common.py
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# This code is forked from: https://github.com/duerig/laser-dewarp.git
import cv2, maxflow, numpy
from scipy import signal
# Taken from http://wiki.scipy.org/Cookbook/SignalSmooth
def smooth(x,window_len=11,window='hanning'):
"""smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal.
input:
x: the input signal
window_len: the dimension of the smoothing window; should be an odd integer
window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
flat window will produce a moving average smoothing.
output:
the smoothed signal
example:
t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1
y=smooth(x)
see also:
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
scipy.signal.lfilter
TODO: the window parameter could be the window itself if an array instead of a string
NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
"""
if x.ndim != 1:
raise ValueError, "smooth only accepts 1 dimension arrays."
if x.size < window_len:
raise ValueError, "Input vector needs to be bigger than window size."
if window_len<3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
s=numpy.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]
if window == 'flat': #moving average
w=numpy.ones(window_len,'d')
else:
w=eval('numpy.'+window+'(window_len)')
y=numpy.convolve(w/w.sum(),s,mode='valid')
return y
def hysteresis(absg, suppress, thi, tlo, allow = None):
if suppress is not None:
absg = numpy.where(suppress, 0, absg)
absmax = numpy.amax(absg[1:-1,1:-1])
high = (absg >= absmax*thi)
low = numpy.logical_and(absg >= absmax*tlo,
absg < absmax*thi)
close_kernel = numpy.asarray([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
close = signal.convolve2d(high, close_kernel)[1:-1,1:-1]
seedY, seedX = numpy.nonzero(numpy.logical_and(low, close))
if allow is not None:
#high = numpy.logical_and(high, allow)
low = numpy.logical_and(low, allow)
for i in xrange(0, len(seedY)):
floodfill(seedX[i], seedY[i], high, low)
return high
def floodfill(startX, startY, dest, src):
queue = [(startX, startY)]
while len(queue) > 0:
centerX, centerY = queue[-1]
queue = queue[:-1]
for x in xrange(centerX-1, centerX+2):
for y in xrange(centerY-1, centerY+2):
if y >= 0 and x >= 0 and y < src.shape[0] and x < src.shape[1] and src[y, x]:
dest[y, x] = 1
src[y, x] = 0
queue.append((x, y))
def image_cut(source, sink, horizontal, vertical, c):
g = maxflow.Graph[int]()
nodeids = g.add_grid_nodes(source.shape)
hStructure = numpy.array([[0, 0, 0],
[0, 0, 1],
[0, 0, 0]])
vStructure = numpy.array([[0, 0, 0],
[0, 0, 0],
[0, 1, 0]])
g.add_grid_edges(nodeids, weights=horizontal*c,
structure=hStructure,
symmetric=True)
g.add_grid_edges(nodeids, weights=vertical*c,
structure=vStructure,
symmetric=True)
g.add_grid_tedges(nodeids, source, sink)
g.maxflow()
return g.get_grid_segments(nodeids)
def algorithm1(img, thi=0.5, tlo=0.1, sigma=0.6,
clist=[80], f=None, csearch=False, thin=False):
# compute base binarizations and the stability curve
bsd = numpy.zeros(len(clist))
bimg = f(img, thi, tlo, sigma, clist, csearch=csearch, thin=thin)
for ic in xrange(1, len(clist)):
bsd[ic-1] = numpy.sum(numpy.not_equal(bimg[ic], bimg[ic-1])) / float(bimg[ic].size)
# smooth stability curve
if len(clist) > 1:
d = smooth(bsd[:-1], 5)[2:-2]
else:
d = bsd[:-1]
r = 0
scr = None
for i in xrange(0, d.size - 2):
for j in xrange(i+2, d.size):
for k in xrange(i+1, j):
v = d[i] + d[j] - 2*d[k]
if scr is None or v > scr:
q = i
r = k
s = j
scr = v
print 'algorithm1 ' + str(thi) + ' weighted at ' + str(r) + ': ' + str(clist[r])
return bimg[r], clist[r]
def algorithm2(img, sigma=0.6, clist=None, tlo=0.1,
thilist=[0.1, 0.6], f=None, iter=5, csearch=False, thin=False):
diffs = []
images = []
previous = f(img, thilist[0], thilist[0]/3.0, sigma, clist, csearch=csearch, thin=thin)[0]
for i in xrange(1, iter+1):
thi = thilist[0] + (thilist[1]-thilist[0])*i/float(iter)
tlo = thi/3.0
current = f(img, thi, tlo, sigma, clist, csearch=csearch, thin=thin)[0]
images.append(previous)
diffs.append(numpy.sum(numpy.not_equal(current, previous)))
previous = current
diffs = numpy.asarray(diffs)
diffs = smooth(diffs, 5)[2:-2]
index = numpy.argmin(diffs)
return images[index], clist[0], thilist[0] + (thilist[1] - thilist[0])*index/float(iter)
#####################################################
def algorithm3(img, sigma=0.6, clist=None, tlo=0.1,
thilist=[0.25, 0.5], f=None, csearch=False, thin=False):
if clist is None:
clist = numpy.exp(numpy.linspace(numpy.log(10),
numpy.log(640), num=15))
blo, clo = algorithm1(img, thilist[0], tlo, sigma, clist, f=f, csearch=csearch, thin=thin)
bmid, cmid = algorithm1(img, numpy.mean(thilist), tlo,
sigma, clist, f=f, csearch=csearch, thin=thin)
bhi, chi = algorithm1(img, thilist[1], tlo, sigma, clist, f=f, csearch=csearch, thin=thin)
dlo = numpy.sum(numpy.not_equal(blo, bmid))
dhi = numpy.sum(numpy.not_equal(bhi, bmid))
if dlo < dhi:
return blo, clo, thilist[0]
else:
return bhi, chi, thilist[1]
def find_background_mask(img, threshold=2.0):
sr = 31
img2 = (img - numpy.float_(cv2.GaussianBlur(img, (sr,sr), sr*3, borderType=cv2.BORDER_REFLECT)))
rms = numpy.sqrt(cv2.GaussianBlur(img2*img2, (sr, sr), sr*3, borderType=cv2.BORDER_CONSTANT))
return ((img2/(rms + 0.000000001)) > threshold)
def sort_range(low, high):
if low < high:
return [low, high]
else:
return [high, low]