plt.imshow(skeleton)
plt.plot(corners[:,1], corners[:,0], 'ro')
plt.title('ridge skeleton w/ branch points')
plt.axis('off')
plt.subplot(223)
plt.imshow(skeleton)
plt.autoscale(False)
plt.plot(corners[:,1], corners[:,0], 'ro')
plt.title('ridge skeleton w/ branch points')
plt.subplot(224)
plt.imshow(edge_dist)
plt.title('distance transform of skeleton')
plt.axis('off')
plt.ioff()
plt.show()
# show an animation of the fitting procedure
fig = plt.figure()
plt.imshow(im, cmap='gray')
plt.plot(x, y, 'bo')
line_obj, = plt.plot(x, y, 'ro')
plt.axis('off')
plt.ion()
plt.pause(0.01)
snake_pts = fit_snake(boundary_pts, edge_dist, nits=60, alpha=0.5, beta=0.2, point_plot=line_obj)
plt.ioff()
plt.pause(0.01)
plt.show()
# allocate array
all_boundary_pts = np.empty((len(frames), boundary_pts.shape[0], boundary_pts.shape[1]))
# Frame-by-frame snake fit. This is fairly slow; takes ~80 sec on my laptop.
tsta = time.clock()
print('Tracking cell %i across %i frames...' % (selected_label, len(frames)))
for frame_num, frame in enumerate(frames):
# print progress
if frame_num%10 == 0:
print('Frame %i of %i' % (frame_num, len(frames)))
sys.stdout.flush()
# compute distance transforms and fit snake
edge_dist, corner_dist = frame_to_distance_images(frame)
boundary_pts = fit_snake(boundary_pts, edge_dist, alpha=alpha, beta=beta, nits=40)
# check if the cell went off the edge (i.e., out of view)
single_cell_mask = measure.grid_points_in_poly(frame.shape, boundary_pts)
if np.any(np.logical_and(~mask, single_cell_mask)):
print('cell went off edge on frame %i' % frame_num)
all_boundary_pts = np.delete(all_boundary_pts, np.s_[frame_num:], 0)
break
# TODO: resample the points along the curve to maintain contant spacing?
# store results in big array
all_boundary_pts[frame_num] = boundary_pts
print('elapsed time:', time.clock() - tsta)
### write boundary points to file
# allocate array
all_boundary_pts = np.empty((len(frames), boundary_pts.shape[0], boundary_pts.shape[1]))
# Frame-by-frame snake fit. This is fairly slow; takes ~80 sec on my laptop.
tsta = time.clock()
print 'Tracking cell %i across %i frames...' % (selected_label, len(frames))
for frame_num, frame in enumerate(frames):
# print progress
if frame_num%10 == 0:
print 'Frame %i of %i' % (frame_num, len(frames))
sys.stdout.flush()
# compute distance transforms and fit snake
edge_dist, corner_dist = frame_to_distance_images(frame)
boundary_pts = fit_snake(boundary_pts, edge_dist, alpha=alpha, beta=beta, nits=40)
# check if the cell went off the edge (i.e., out of view)
single_cell_mask = measure.grid_points_in_poly(frame.shape, boundary_pts)
if np.any(np.logical_and(~mask, single_cell_mask)):
print 'cell went off edge on frame %i' % frame_num
all_boundary_pts = np.delete(all_boundary_pts, np.s_[frame_num:], 0)
break
# TODO: resample the points along the curve to maintain contant spacing?
# store results in big array
all_boundary_pts[frame_num,:,:] = boundary_pts
print 'elapsed time:', time.clock() - tsta
### write boundary points to file
plt.subplot(223)
plt.imshow(skeleton)
plt.autoscale(False)
plt.plot(corners[:, 1], corners[:, 0], 'ro')
plt.title('ridge skeleton w/ branch points')
plt.subplot(224)
plt.imshow(edge_dist)
plt.title('distance transform of skeleton')
plt.axis('off')
plt.ioff()
plt.show()
# show an animation of the fitting procedure
fig = plt.figure()
plt.imshow(im, cmap='gray')
plt.plot(x, y, 'bo')
line_obj, = plt.plot(x, y, 'ro')
plt.axis('off')
plt.ion()
plt.pause(0.01)
snake_pts = fit_snake(boundary_pts,
edge_dist,
nits=60,
alpha=0.5,
beta=0.2,
point_plot=line_obj)
plt.ioff()
plt.pause(0.01)
plt.show()
