def threshold(self, ax, data):
data = N.asarray(data)
mu, sd = data.mean(), data.std()
data = clip(data, mu - 3*sd, mu + 3*sd)
data = blur_image(data, 5)
orig = data.copy()
f = fft.fft2(data)
f[0:5,:] = 0
f[:,0:5] = 0
data = N.abs(fft.ifft2(f))
print "mu, std =", orig.mean(), orig.std()
"""
mu, sigma = data.mean(), data.std()
mask = data > mu + 2*sigma
"""
#im = ax.imshow(data, origin="lower", interpolation='nearest')
im = ax.imshow(orig, origin="lower", interpolation='nearest')
self.set_callbacks([("button_release_event", self.imclick, ())])
self.f.colorbar(im, fraction = 0.08)
return data
def threshold_points(self, ax, data, new_data):
data = blur_image(data, 5)
ii, jj, ss = get_points(data, new_data)
print "POINTS", len(ii)
for i, j, s in zip(ii, jj, ss):
if True:
#print s
#print "==="
ax.hlines(i, j-s, j+s, colors='r')
ax.vlines(j, i-s, i+s, colors='r')
#ax.scatter(jj, ii, c='y')
print "COUNT", len(ii)
ax.set_xlim(0, 1600)
ax.set_ylim(0, 1200)
