def visualize_direction(seg, segment_id):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
SUBSAMPLE_TARGET = 500
MIN_SAMPLE_STRIDE = 10
BOX_RADIUS = 110
binary_volume1 = imio.extract_segments(seg, [segment_id])
all_points_1 = points_from_binary_volume(binary_volume1)
print all_points_1.shape
center = random_edge_point(all_points_1, seg)
binary_volume2 = imio.extract_segments(seg, [seg[tuple(center)]])
all_points_2 = points_from_binary_volume(binary_volume2)
stretch_dimension(all_points_1, 0, 5)
stretch_dimension(all_points_2, 0, 5)
vectors = features.direction.compute_pc_vectors(
all_points_1, all_points_2, center, BOX_RADIUS, SUBSAMPLE_TARGET, MIN_SAMPLE_STRIDE
)
cropped_points_1 = features.direction.limit_to_radius(
all_points_1, center, BOX_RADIUS, SUBSAMPLE_TARGET, MIN_SAMPLE_STRIDE
)
cropped_points_2 = features.direction.limit_to_radius(
all_points_2, center, BOX_RADIUS, SUBSAMPLE_TARGET, MIN_SAMPLE_STRIDE
)
print "pc vectors:", vectors
print "feature vector:", features.direction.compute_feature_vector(vectors)
center_of_1 = cropped_points_1.mean(axis=0)
center_of_2 = cropped_points_2.mean(axis=0)
svd_1_points = generate_points_on_vector(vectors[0, :], center_of_1, BOX_RADIUS)
svd_2_points = generate_points_on_vector(vectors[1, :], center_of_2, BOX_RADIUS)
between_center_points = generate_points_on_vector(
vectors[2, :],
center_of_2,
np.sqrt((center_of_1 - center_of_2).dot(center_of_1 - center_of_2)).astype(np.integer),
)
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
for points, color, marker in [
(cropped_points_2, "b", "o"),
(cropped_points_1, "y", "s"),
(svd_1_points, "g", "^"),
(svd_2_points, "r", "v"),
(between_center_points, "k", "<"),
]:
print points.shape
ax.scatter(points[:, 2], points[:, 1], points[:, 0], c=color, marker=marker)
plt.show()
def view_biggest_cross_section(seg, segment_ids):
extracted = imio.extract_segments(seg, segment_ids)
biggest_index = np.argmax(extracted.sum(axis=2).sum(axis=1))
disp = np.zeros_like(seg)
disp[biggest_index, :, :] = extracted[biggest_index, :, :]
mayavi.mlab.contour3d(disp)
mayavi.mlab.show()
def view_mistake(seg, gt, target_id, overlap_ids, seg_color_map, gt_color_map):
fig = mayavi.mlab.gcf()
# display attempts
for idx, overlap_id in enumerate(overlap_ids):
extracted = imio.extract_segments(seg, [overlap_id])
unsquished = np.repeat(extracted, 5, axis=0)
color = seg_color_map(idx, len(overlap_ids))
print "Writing contour with color " + str(color)
mayavi.mlab.contour3d(unsquished, color=color, figure=fig)
# display target
extracted = imio.extract_segments(gt, [target_id])
unsquished = np.repeat(extracted, 5, axis=0)
color = gt_color_map(0, 1)
mayavi.mlab.contour3d(unsquished, color=color, figure=mayavi.mlab.figure())
mayavi.mlab.show()
def view_segments(seg, segment_ids):
fig = maya.mlab.gcf()
extracted = imio.extract_segments(seg, segment_ids)
unsquished = np.repeat(extracted, 5, axis=0)
mayavi.mlab.contour3d(unsquished)
mayavi.mlab.show()