def test_merge_diagonal(self):
a = ConnectedRegion(shape=(2, 4),
value=1,
rowptr=[0, 2, 4],
colptr=[0, 1, 1, 2])
b = ConnectedRegion(shape=(2, 4),
value=1,
rowptr=[0, 2, 4],
colptr=[2, 3, 3, 4])
crh.merge(a, b)
assert_array_equal(crh.todense(a), [[1, 0, 1, 0], [0, 1, 0, 1]])
assert_equal(crh.nnz(a), 4)
a = ConnectedRegion(shape=(2, 2),
value=1,
rowptr=[0, 2, 4],
colptr=[0, 1, 1, 2])
b = ConnectedRegion(shape=(2, 2),
value=1,
rowptr=[0, 2, 4],
colptr=[1, 2, 0, 1])
crh.merge(a, b)
assert_array_equal(crh.todense(a), [[1, 1], [1, 1]])
assert_equal(crh.nnz(a), 4)
def test_merge_diagonal(self):
a = ConnectedRegion(shape=(2, 4), value=1,
rowptr=[0, 2, 4], colptr=[0, 1, 1, 2])
b = ConnectedRegion(shape=(2, 4), value=1,
rowptr=[0, 2, 4], colptr=[2, 3, 3, 4])
crh.merge(a, b)
assert_array_equal(crh.todense(a), [[1, 0, 1, 0],
[0, 1, 0, 1]])
assert_equal(crh.nnz(a), 4)
a = ConnectedRegion(shape=(2, 2), value=1,
rowptr=[0, 2, 4], colptr=[0, 1, 1, 2])
b = ConnectedRegion(shape=(2, 2), value=1,
rowptr=[0, 2, 4], colptr=[1, 2, 0, 1])
crh.merge(a, b)
assert_array_equal(crh.todense(a), [[1, 1],
[1, 1]])
assert_equal(crh.nnz(a), 4)
def test_nnz(self):
assert_equal(crh.nnz(self.c), 8)
def test_nnz(self):
assert_equal(crh.nnz(self.c), 8)
def reconstruct(self):
self.result.fill(0)
pulses = 0
# Reconstruct only from pulses inside the thresholds
for area in sorted(self.pulses.keys()):
if area < self.area_threshold_min or \
area > self.area_threshold_max:
continue
for cr in self.pulses[area]:
value = crh.get_value(cr)
aval = abs(value)
if aval < self.amplitude_threshold_min or \
aval > self.amplitude_threshold_max:
continue
volume = aval * area
if volume < self.volume_threshold_min or \
volume > self.volume_threshold_max:
continue
## # See:
## #
## # Measuring rectangularity by Paul L. Rosin
## # Machine Vision and Applications, Vol 11, No 4, December 1999
## # http://www.springerlink.com/content/xb9klcax8ytnwth1/
## #
## # for more information on computing rectangularity.
## #
## r0, c0, r1, c1 = crh.bounding_box(cr)
## if c0 == c1 or r0 == r1:
## rectangularity = 1
## else:
## rectangularity = area / float((c1 - c0 + 1) * (r1 - r0 + 1))
## if rectangularity < self.rectangularity_min or \
## rectangularity > self.rectangularity_max:
## continue
r0, c0, r1, c1 = crh.bounding_box(cr)
if (c0 == c1) and (r0 == r1):
circularity = 1
else:
max_dim = max(abs(r0 - r1), abs(c0 - c1))
circularity = crh.nnz(cr) / \
(np.pi / 4 * (max_dim + 2)**2)
# We add 2 to max_dim to allow for pixel overlap
# with circumscribing circle
if circularity < self.circularity_min or \
circularity > self.circularity_max:
continue
if self.absolute_sum:
value = aval
if self.amplitudes_one:
value = 1
if self.replace:
crh.set_array(self.result, cr, value)
else:
crh.set_array(self.result, cr, value, 'add')
pulses += 1
mask = (self.lifetimes > self.lifetime_max) | \
(self.lifetimes < self.lifetime_min)
self.result[mask] = 0
if self.subtract:
self.result = np.abs(self.image - self.result)
if self.output_threshold != 0:
self.result[self.result <= self.output_threshold] = 0
self.pulses_used = pulses
self.update_plot()
def reconstruct(self):
self.result.fill(0)
pulses = 0
# Reconstruct only from pulses inside the thresholds
for area in sorted(self.pulses.keys()):
if area < self.area_threshold_min or \
area > self.area_threshold_max:
continue
for cr in self.pulses[area]:
value = crh.get_value(cr)
aval = abs(value)
if aval < self.amplitude_threshold_min or \
aval > self.amplitude_threshold_max:
continue
volume = aval * area
if volume < self.volume_threshold_min or \
volume > self.volume_threshold_max:
continue
## # See:
## #
## # Measuring rectangularity by Paul L. Rosin
## # Machine Vision and Applications, Vol 11, No 4, December 1999
## # http://www.springerlink.com/content/xb9klcax8ytnwth1/
## #
## # for more information on computing rectangularity.
## #
## r0, c0, r1, c1 = crh.bounding_box(cr)
## if c0 == c1 or r0 == r1:
## rectangularity = 1
## else:
## rectangularity = area / float((c1 - c0 + 1) * (r1 - r0 + 1))
## if rectangularity < self.rectangularity_min or \
## rectangularity > self.rectangularity_max:
## continue
r0, c0, r1, c1 = crh.bounding_box(cr)
if (c0 == c1) and (r0 == r1):
circularity = 1
else:
max_dim = max(abs(r0 - r1), abs(c0 - c1))
circularity = crh.nnz(cr) / \
(np.pi / 4 * (max_dim + 2)**2)
# We add 2 to max_dim to allow for pixel overlap
# with circumscribing circle
if circularity < self.circularity_min or \
circularity > self.circularity_max:
continue
if self.absolute_sum:
value = aval
if self.amplitudes_one:
value = 1
if self.replace:
crh.set_array(self.result, cr, value)
else:
crh.set_array(self.result, cr, value, 'add')
pulses += 1
mask = (self.lifetimes > self.lifetime_max) | \
(self.lifetimes < self.lifetime_min)
self.result[mask] = 0
if self.subtract:
self.result = np.abs(self.image - self.result)
if self.output_threshold != 0:
self.result[self.result <= self.output_threshold] = 0
self.pulses_used = pulses
self.update_plot()
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)
plt.subplot(1, 3, 2)
plt.plot(areas[:-10], volumes[:-10], "x-")
# plt.xlim(plt.xlim()[::-1])
plt.title("Level Volumes")
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)
plt.subplot(1, 3, 2)
plt.plot(areas[:-10], volumes[:-10], 'x-')
#plt.xlim(plt.xlim()[::-1])
plt.title('Level Volumes')
