def test_basic(self):
img = np.random.randint(255, size=(200, 200))
pulses = lulu.decompose(img)
img_, areas, area_count = lulu.reconstruct(pulses, img.shape)
# Write assert this way so that we can see how many
# pixels mismatch as a percent of the total nr of pixels
assert_array_equal(img_, img)
assert_equal(np.sum(img_ != img) / float(np.prod(img.shape)) * 100,
0, "Percentage mismatch =")
def test_basic(self):
img = np.random.randint(255, size=(200, 200))
pulses = lulu.decompose(img)
img_, areas, area_count = lulu.reconstruct(pulses, img.shape)
# Write assert this way so that we can see how many
# pixels mismatch as a percent of the total nr of pixels
assert_array_equal(img_, img)
assert_equal(
np.sum(img_ != img) / float(np.prod(img.shape)) * 100, 0,
"Percentage mismatch =")
def lulu_filter(noisy,sf):
prev_smoothed = noisy
pulses = lulu.decompose(img=noisy,quiet=False,operator='LU')
smoothness_inc = 0.01
pulse_len = len(pulses.keys())
pulses
i=2
while 1-smoothness_inc > sf and i < pulse_len:
lulu_smoothed, areas, area_count = lulu.reconstruct(dict((k, pulses[k]) for k in pulses.keys()[0:i]), noisy.shape)
lulu_smoothed = noisy - lulu_smoothed
smoothness_inc = np.var(lulu_smoothed)/np.var(prev_smoothed)
prev_smoothed = lulu_smoothed
i = i + 1
return lulu_smoothed
import lulu
import lulu.connected_region_handler as crh
img = load_image()
print "Decomposing a %s matrix." % str(img.shape)
tic = time.time()
regions = lulu.decompose(img.copy())
toc = time.time()
print "Execution time: %.2fs" % (toc - tic)
print "-" * 78
print "Reconstructing image...",
out, areas, area_count = lulu.reconstruct(regions, img.shape)
print "done."
print "Reconstructed from %d pulses." % sum(area_count)
print "-" * 78
plt.subplot(2, 2, 1)
plt.imshow(img, interpolation='nearest', cmap=plt.cm.gray)
plt.title('Original')
plt.subplot(2, 2, 2)
plt.imshow(out, interpolation='nearest', cmap=plt.cm.gray)
plt.title('Reconstruction (%d pulses)' % sum(area_count))
plt.subplot(2, 2, 4)
s = np.cumsum(area_count)
midpt = (s[-1] + s[0]) / 2.
ind = np.argmin(np.abs(s - midpt))
import lulu
import lulu.connected_region_handler as crh
img = load_image()
print "Decomposing a %s matrix." % str(img.shape)
tic = time.time()
regions = lulu.decompose(img.copy())
toc = time.time()
print "Execution time: %.2fs" % (toc - tic)
print "-"*78
print "Reconstructing image...",
out, areas, area_count = lulu.reconstruct(regions, img.shape)
print "done."
print "Reconstructed from %d pulses." % sum(area_count)
print "-"*78
plt.subplot(2, 2, 1)
plt.imshow(img, interpolation='nearest', cmap=plt.cm.gray)
plt.title('Original')
plt.subplot(2, 2, 2)
plt.imshow(out, interpolation='nearest', cmap=plt.cm.gray)
plt.title('Reconstruction (%d pulses)' % sum(area_count))
plt.subplot(2, 2, 4)
s = np.cumsum(area_count)
midpt = (s[-1] + s[0])/2.
ind = np.argmin(np.abs(s - midpt))
