bracket(t)
for t in ('amplitude_threshold', 'area_threshold',
'volume_threshold', 'circularity', 'lifetime'))
for tname in ('absolute_sum', 'amplitudes_one', 'replace', 'subtract',
'output_threshold', 'pulses_used'):
out += ('\n%s: %s' % (pptrait(tname), getattr(self, tname)))
return out
if __name__ == "__main__":
import sys
if len(sys.argv) >= 2 and '-UL' in sys.argv:
operator = 'UL'
sys.argv.remove('-UL')
else:
operator = 'LU'
image = load_image()
print("Decomposing using the %s operator." % operator)
if operator == 'LU':
print("Use the '-UL' flag to switch to UL.")
print()
pulses = lulu.decompose(image, operator=operator)
viewer = Viewer(pulses=pulses, image=image)
viewer.configure_traits()
import sys
sys.path.insert(0, '..')
from enthought.mayavi import mlab
import numpy as np
from demo import load_image
import lulu
import lulu.connected_region_handler as crh
img = load_image('chelsea_small.jpg')
print "Decomposing a %s image." % str(img.shape)
regions = lulu.decompose(img)
value_maxes = []
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses:
import sys sys.path.insert(0, '..') from demo import load_image import time import lulu img = load_image() print "Decomposing a %s matrix." % str(img.shape) tic = time.time() regions = lulu.decompose(img) toc = time.time() print "Execution time: %.2fs" % (toc - tic)
# ----- Segmentation parameters -----
N = 4
beta = 1.5 # Smoothing
min_area = 500
# -----------------------------------
from demo import load_image
import numpy as np
from scipy.cluster.vq import kmeans2
import matplotlib.pyplot as plt
import lulu
import lulu.connected_region_handler as crh
img = load_image('truck_and_apcs_small.jpg')
pulses = lulu.decompose(img)
impulse_strength = np.zeros(img.shape, dtype=int)
for area in pulses:
if area > min_area:
for cr in pulses[area]:
crh.set_array(impulse_strength, cr,
np.abs(crh.get_value(cr)), 'add')
def ICM(data, N, beta):
print("Performing ICM segmentation...")
# Initialise segmentation using kmeans
print("K-means initialisation...")
# ----- Segmentation parameters -----
N = 4
beta = 1.5 # Smoothing
min_area = 500
# -----------------------------------
from demo import load_image
import numpy as np
from scipy.cluster.vq import kmeans2
import matplotlib.pyplot as plt
import lulu
import lulu.connected_region_handler as crh
img = load_image('truck_and_apcs_small.jpg')
pulses = lulu.decompose(img)
impulse_strength = np.zeros(img.shape, dtype=int)
for area in pulses:
if area > min_area:
for cr in pulses[area]:
crh.set_array(impulse_strength, cr, np.abs(crh.get_value(cr)),
'add')
def ICM(data, N, beta):
print("Performing ICM segmentation...")
# Initialise segmentation using kmeans
out = '\n'.join(bracket(t) for t in
('amplitude_threshold', 'area_threshold',
'volume_threshold', 'circularity', 'lifetime'))
for tname in ('absolute_sum', 'amplitudes_one', 'replace', 'subtract',
'output_threshold', 'pulses_used'):
out += ('\n%s: %s' % (pptrait(tname), getattr(self, tname)))
return out
if __name__ == "__main__":
import sys
if len(sys.argv) >= 2 and '-UL' in sys.argv:
operator = 'UL'
sys.argv.remove('-UL')
else:
operator = 'LU'
image = load_image()
print("Decomposing using the %s operator." % operator)
if operator == 'LU':
print("Use the '-UL' flag to switch to UL.")
print()
pulses = lulu.decompose(image, operator=operator)
viewer = Viewer(pulses=pulses, image=image)
viewer.configure_traits()
from demo import load_image
import matplotlib.pyplot as plt
import numpy as np
import lulu
import lulu.connected_region_handler as crh
img = load_image("truck_and_apcs_small.jpg")
pulses = lulu.decompose(img)
areas = sorted(pulses.keys())
cumulative_volume = []
volumes = []
reconstruction = np.zeros_like(img)
for area in areas:
area_volume = 0
for cr in pulses[area]:
area_volume += crh.nnz(cr) * abs(crh.get_value(cr))
crh.set_array(reconstruction, cr, abs(crh.get_value(cr)), "add")
cumulative_volume.append(np.sum(reconstruction))
volumes.append(area_volume)
total_volume = np.sum(reconstruction)
cumulative_volume = np.array(cumulative_volume)
cumulative_volume = 1 - cumulative_volume / float(total_volume)
plt.subplot(1, 3, 1)
plt.imshow(img, interpolation="nearest", cmap=plt.cm.gray)
import sys
sys.path.insert(0, '..')
from enthought.mayavi import mlab
import numpy as np
from demo import load_image
import lulu
import lulu.connected_region_handler as crh
img = load_image('chelsea_small.jpg')
print "Decomposing a %s image." % str(img.shape)
regions = lulu.decompose(img)
value_maxes = []
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses: