Esempio n. 1
0
    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)
Esempio n. 2
0
    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)
Esempio n. 3
0
 def test_nnz(self):
     assert_equal(crh.nnz(self.c), 8)
Esempio n. 4
0
 def test_nnz(self):
     assert_equal(crh.nnz(self.c), 8)
Esempio n. 5
0
    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()
Esempio n. 6
0
    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()
Esempio n. 7
0
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")
Esempio n. 8
0
File: volume.py Progetto: lipap/lulu
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')