def paint_errors(self, img):
error_range = (0.0,100.0)
output_range = (0.0,30.0)
xoffset = 10
cam1error = kc.get_dist_between_2_points(self.ball_coords, self.cam1_estimate)
cam2error = kc.get_dist_between_2_points(self.ball_coords, self.cam2_estimate)
# cam3error = kc.get_dist_between_2_points(self.ball_coords, self.cam3_estimate)
cv.Rectangle(img, (xoffset + int(lerp(cam1error, error_range, output_range)), 165), (xoffset, 175), kc.cam1color, cv.CV_FILLED)
cv.Rectangle(img, (xoffset + int(lerp(cam2error, error_range, output_range)), 185), (xoffset, 195), kc.cam2color, cv.CV_FILLED)
def main(make_video = True, estimates=None, plotting=['cam1', 'cam2', 'kalman']):
print("Hello, world! Let's run a Kalman filter simulation...")
if estimates:
d = drawing.Drawing(draw_estimates=estimates)
else:
d = drawing.Drawing()
# some useful constants:
total_seconds = 18
total_frames = total_seconds*kc.fps
center_coords = (kc.img_size[0]/2, kc.img_size[1]/2)
print ('Doing simulation...')
# get the ball motion (a numpy 2xnframes matrix of coords):
# ball_motion = kc.get_brownian_ball_motion(center_coords, total_frames)
ball_motion = kc.get_simple_ball_motion(center_coords, total_seconds)
# ball_motion = kc.get_still_ball_motion(center_coords, total_seconds)
cam1_estimates = []
cam2_estimates = []
# cam3_estimates = []
average_estimates = []
closest_camera_estimates = []
kalman_estimates = []
# do the kalman filtering:
A_x = A_y = 0 # velocity of ball (calculated at each time step)
xhat_x = xhat_y = 0
P_x = P_y = 0
xhatminus_x = xhatminus_y = 0
Pminus_x = Pminus_y = 0
K_x = K_y = 0
xhat0_x = xhat0_y = 300
P0_x = P0_y = 1
xhatprev_x = xhat0_x
Pprev_x = P0_x
xhatprev_y = xhat0_y
Pprev_y = P0_y
for cnt,c in enumerate(ball_motion):
# "PREDICT" (time update)
xhatminus_x = xhatprev_x + (A_x)
Pminus_x = Pprev_x + kc.process_uncertainty
xhatminus_y = xhatprev_y + (A_y)
Pminus_y = Pprev_y + kc.process_uncertainty
# Take "measurements":
(cam1_estimate,cam1_sigma) = kc.get_cam_estimate(c, d.cam1center)
(cam2_estimate,cam2_sigma) = kc.get_cam_estimate(c, d.cam2center)
# (cam3_estimate,cam3_sigma) = kc.get_cam_estimate(c, d.cam3center)
# z = mat([cam1_estimate, cam2_estimate, cam3_estimate])
(z_x1,z_y1) = cam1_estimate
(z_x2,z_y2) = cam2_estimate
# Measurement noise covariance matrix:
R = pow(cam1_sigma,2) + pow(cam2_sigma,2)
z_x = (pow(cam2_sigma,2)/(R))*z_x1 + (pow(cam1_sigma,2)/(R))*z_x2
z_y = (pow(cam2_sigma,2)/(R))*z_y1 + (pow(cam1_sigma,2)/(R))*z_y2
# R = .01
# "CORRECT" (measurement update)
K_x = Pminus_x / (Pminus_x + R)
xhat_x = xhatminus_x + K_x * (z_x - xhatminus_x)
P_x = (1 - K_x) * Pminus_x
K_y = Pminus_y / (Pminus_y + R)
xhat_y = xhatminus_y + K_y * (z_y - xhatminus_y)
P_y = (1 - K_y) * Pminus_y
# We assume constant linear motion, so update the A values
# accordingly for the next iteration:
if cnt > 0:
A_x = ball_motion[cnt][0] - ball_motion[cnt-1][0]
A_y = ball_motion[cnt][1] - ball_motion[cnt-1][1]
# save this result for the next iteration:
xhatprev_x = xhat_x
Pprev_x = P_x
xhatprev_y = xhat_y
Pprev_y = P_y
# save measurements for later plotting:
cam1_estimates.append(cam1_estimate)
cam2_estimates.append(cam2_estimate)
# cam3_estimates.append(cam3_estimate)
kalman_estimates.append((int(xhat_x), int(xhat_y)))
closest_camera_estimates.append(cam1_estimate if cam1_sigma < cam2_sigma else cam2_estimate)
average_estimates.append((
int(average([cam1_estimate[0], cam2_estimate[0]])),
int(average([cam1_estimate[1], cam2_estimate[1]]))
))
# eo filter loop
print ('Done!')
# for v1,v2 in zip(closest_camera_estimates, average_estimates):
# print v1,v2,kc.get_dist_between_2_points(v1,v2)
print ('Saving images...')
# plot the actual and estimated trajectories:
plt.plot([c[0] for c in ball_motion], [kc.img_size[1] - c[1] for c in ball_motion], kc.plotcolors['actual'], label="Actual Trajectory")
plt.plot(ball_motion[0][0], kc.img_size[1] - ball_motion[0][1], 'ro')
plt.plot(ball_motion[-1][0], kc.img_size[1] - ball_motion[-1][1], 'go')
if 'cam1' in plotting:
plt.plot([c[0] for c in cam1_estimates], [kc.img_size[1] - c[1] for c in cam1_estimates], kc.plotcolors['cam1'], label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[0] for c in cam2_estimates], [kc.img_size[1] - c[1] for c in cam2_estimates], kc.plotcolors['cam2'], label="Camera 2 Estimate")
if 'average' in plotting:
plt.plot([c[0] for c in average_estimates], [kc.img_size[1] - c[1] for c in average_estimates], kc.plotcolors['average'], label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[0] for c in closest_camera_estimates], [kc.img_size[1] - c[1] for c in closest_camera_estimates], kc.plotcolors['closest'], label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[0] for c in kalman_estimates], [kc.img_size[1] - c[1] for c in kalman_estimates], kc.plotcolors['kalman'], label="Kalman Estimate")
plt.title('Ball Trajectory')
plt.legend(loc='best')
plt.xlabel('X Pixel')
plt.ylabel('Y Pixel')
plt.savefig(kc.trajectories_filename, format='png')
plt.cla()
# plot the x values:
plt.plot([c[0] for c in ball_motion], kc.plotcolors['actual'], label="Actual Position")
if 'cam1' in plotting:
plt.plot([c[0] for c in cam1_estimates], kc.plotcolors['cam1'], label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[0] for c in cam2_estimates], kc.plotcolors['cam2'], label="Camera 2 Estimate")
# plt.plot([c[0] for c in cam3_estimates], label="Camera 3 Estimate")
if 'average' in plotting:
plt.plot([c[0] for c in average_estimates], kc.plotcolors['average'], label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[0] for c in closest_camera_estimates], kc.plotcolors['closest'], label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[0] for c in kalman_estimates], kc.plotcolors['kalman'], label="Kalman Estimate")
plt.title('X Values')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.x_estimates_filename, format='png')
plt.cla()
# plot the y values:
plt.plot([c[1] for c in ball_motion], kc.plotcolors['actual'], label="Actual Position")
if 'cam1' in plotting:
plt.plot([c[1] for c in cam1_estimates], kc.plotcolors['cam1'], label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[1] for c in cam2_estimates], kc.plotcolors['cam2'], label="Camera 2 Estimate")
# plt.plot([c[1] for c in cam3_estimates], label="Camera 3 Estimate")
if 'average' in plotting:
plt.plot([c[1] for c in average_estimates], kc.plotcolors['average'], label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[1] for c in closest_camera_estimates], kc.plotcolors['closest'], label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[1] for c in kalman_estimates], kc.plotcolors['kalman'], label="Kalman Estimate")
plt.title('Y Values')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.y_estimates_filename, format='png')
plt.cla()
# plot the errors:
cam1errors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, cam1_estimates)]
cam2errors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, cam2_estimates)]
# cam3errors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, cam3_estimates)]
averageerrors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, average_estimates)]
closesterrors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, closest_camera_estimates)]
kalmanerrors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, kalman_estimates)]
if 'cam1' in plotting:
plt.plot(cam1errors, kc.plotcolors['cam1'], label='Camera 1')
if 'cam2' in plotting:
plt.plot(cam2errors, kc.plotcolors['cam2'], label='Camera 2')
# plt.plot(cam3errors, label='Camera 3')
if 'average' in plotting:
plt.plot(averageerrors, kc.plotcolors['average'], label='Average')
if 'closest' in plotting:
plt.plot(closesterrors, kc.plotcolors['closest'], label='Closest Camera')
if 'kalman' in plotting:
plt.plot(kalmanerrors, kc.plotcolors['kalman'], label='Kalman')
plt.title('Estimation Errors')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.cam_errors_filename, format='png')
w = numpy.bartlett(25)
cam1errors_filtered = numpy.convolve(w/w.sum(), cam1errors, mode='same')
cam2errors_filtered = numpy.convolve(w/w.sum(), cam2errors, mode='same')
# cam3errors_filtered = numpy.convolve(w/w.sum(), cam3errors, mode='same')
averageerrors_filtered = numpy.convolve(w/w.sum(), averageerrors, mode='same')
closesterrors_filtered = numpy.convolve(w/w.sum(), closesterrors, mode='same')
kalmanerrors_filtered = numpy.convolve(w/w.sum(), kalmanerrors, mode='same')
plt.cla()
if 'cam1' in plotting:
# plt.plot(cam1errors_filtered, kc.plotcolors['cam1'], label='Camera 1')
plt.plot(cam1errors_filtered, kc.plotcolors['cam1'], label='Camera 1', linestyle='--')
if 'cam2' in plotting:
# plt.plot(cam2errors_filtered, kc.plotcolors['cam2'], label='Camera 2')
plt.plot(cam2errors_filtered, kc.plotcolors['cam2'], label='Camera 2', linestyle='--')
# plt.plot(cam3errors_filtered, label='Camera 3')
if 'average' in plotting:
plt.plot(averageerrors_filtered, kc.plotcolors['average'], label='Average', linewidth=2)
if 'closest' in plotting:
plt.plot(closesterrors_filtered, kc.plotcolors['closest'], label='Closest', linewidth=2)
if 'kalman' in plotting:
plt.plot(kalmanerrors_filtered, kc.plotcolors['kalman'], label='Kalman', linewidth=2)
plt.title('Smoothed Estimation Errors')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.cam_errors_filtered_filename, format='png')
print ('Done!')
if make_video:
print ('Rendering video...')
# write out the video:
stiter = iter(['Motion: Straight Line', 'Motion: Straight Line', 'Motion: Sinusoid'])
for t in xrange(len(ball_motion)):
# ball coordinates:
d.ball_coords = ball_motion[t]
# set up the status text:
d.cam1_estimate = cam1_estimates[t]
d.cam2_estimate = cam2_estimates[t]
# d.cam3_estimate = cam3_estimates[t]
d.average_estimate = average_estimates[t]
d.kalman_estimate = kalman_estimates[t]
d.frame_num = t
if not (t % (len(ball_motion)/3)):
d.status_text = stiter.next()
# do the drawing
img = d.get_base_image()
d.write_frame(img)
print ("Done!")
def main(make_video=True, estimates=None, plotting=['cam1', 'cam2', 'kalman']):
print("Hello, world! Let's run a Kalman filter simulation...")
if estimates:
d = drawing.Drawing(draw_estimates=estimates)
else:
d = drawing.Drawing()
# some useful constants:
total_seconds = 18
total_frames = total_seconds * kc.fps
center_coords = (kc.img_size[0] / 2, kc.img_size[1] / 2)
print('Doing simulation...')
# get the ball motion (a numpy 2xnframes matrix of coords):
# ball_motion = kc.get_brownian_ball_motion(center_coords, total_frames)
ball_motion = kc.get_simple_ball_motion(center_coords, total_seconds)
# ball_motion = kc.get_still_ball_motion(center_coords, total_seconds)
cam1_estimates = []
cam2_estimates = []
# cam3_estimates = []
average_estimates = []
closest_camera_estimates = []
kalman_estimates = []
# do the kalman filtering:
A_x = A_y = 0 # velocity of ball (calculated at each time step)
xhat_x = xhat_y = 0
P_x = P_y = 0
xhatminus_x = xhatminus_y = 0
Pminus_x = Pminus_y = 0
K_x = K_y = 0
xhat0_x = xhat0_y = 300
P0_x = P0_y = 1
xhatprev_x = xhat0_x
Pprev_x = P0_x
xhatprev_y = xhat0_y
Pprev_y = P0_y
for cnt, c in enumerate(ball_motion):
# "PREDICT" (time update)
xhatminus_x = xhatprev_x + (A_x)
Pminus_x = Pprev_x + kc.process_uncertainty
xhatminus_y = xhatprev_y + (A_y)
Pminus_y = Pprev_y + kc.process_uncertainty
# Take "measurements":
(cam1_estimate, cam1_sigma) = kc.get_cam_estimate(c, d.cam1center)
(cam2_estimate, cam2_sigma) = kc.get_cam_estimate(c, d.cam2center)
# (cam3_estimate,cam3_sigma) = kc.get_cam_estimate(c, d.cam3center)
# z = mat([cam1_estimate, cam2_estimate, cam3_estimate])
(z_x1, z_y1) = cam1_estimate
(z_x2, z_y2) = cam2_estimate
# Measurement noise covariance matrix:
R = pow(cam1_sigma, 2) + pow(cam2_sigma, 2)
z_x = (pow(cam2_sigma, 2) / (R)) * z_x1 + (pow(cam1_sigma, 2) /
(R)) * z_x2
z_y = (pow(cam2_sigma, 2) / (R)) * z_y1 + (pow(cam1_sigma, 2) /
(R)) * z_y2
# R = .01
# "CORRECT" (measurement update)
K_x = Pminus_x / (Pminus_x + R)
xhat_x = xhatminus_x + K_x * (z_x - xhatminus_x)
P_x = (1 - K_x) * Pminus_x
K_y = Pminus_y / (Pminus_y + R)
xhat_y = xhatminus_y + K_y * (z_y - xhatminus_y)
P_y = (1 - K_y) * Pminus_y
# We assume constant linear motion, so update the A values
# accordingly for the next iteration:
if cnt > 0:
A_x = ball_motion[cnt][0] - ball_motion[cnt - 1][0]
A_y = ball_motion[cnt][1] - ball_motion[cnt - 1][1]
# save this result for the next iteration:
xhatprev_x = xhat_x
Pprev_x = P_x
xhatprev_y = xhat_y
Pprev_y = P_y
# save measurements for later plotting:
cam1_estimates.append(cam1_estimate)
cam2_estimates.append(cam2_estimate)
# cam3_estimates.append(cam3_estimate)
kalman_estimates.append((int(xhat_x), int(xhat_y)))
closest_camera_estimates.append(
cam1_estimate if cam1_sigma < cam2_sigma else cam2_estimate)
average_estimates.append(
(int(average([cam1_estimate[0], cam2_estimate[0]])),
int(average([cam1_estimate[1], cam2_estimate[1]]))))
# eo filter loop
print('Done!')
# for v1,v2 in zip(closest_camera_estimates, average_estimates):
# print v1,v2,kc.get_dist_between_2_points(v1,v2)
print('Saving images...')
# plot the actual and estimated trajectories:
plt.plot([c[0] for c in ball_motion],
[kc.img_size[1] - c[1] for c in ball_motion],
kc.plotcolors['actual'],
label="Actual Trajectory")
plt.plot(ball_motion[0][0], kc.img_size[1] - ball_motion[0][1], 'ro')
plt.plot(ball_motion[-1][0], kc.img_size[1] - ball_motion[-1][1], 'go')
if 'cam1' in plotting:
plt.plot([c[0] for c in cam1_estimates],
[kc.img_size[1] - c[1] for c in cam1_estimates],
kc.plotcolors['cam1'],
label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[0] for c in cam2_estimates],
[kc.img_size[1] - c[1] for c in cam2_estimates],
kc.plotcolors['cam2'],
label="Camera 2 Estimate")
if 'average' in plotting:
plt.plot([c[0] for c in average_estimates],
[kc.img_size[1] - c[1] for c in average_estimates],
kc.plotcolors['average'],
label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[0] for c in closest_camera_estimates],
[kc.img_size[1] - c[1] for c in closest_camera_estimates],
kc.plotcolors['closest'],
label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[0] for c in kalman_estimates],
[kc.img_size[1] - c[1] for c in kalman_estimates],
kc.plotcolors['kalman'],
label="Kalman Estimate")
plt.title('Ball Trajectory')
plt.legend(loc='best')
plt.xlabel('X Pixel')
plt.ylabel('Y Pixel')
plt.savefig(kc.trajectories_filename, format='png')
plt.cla()
# plot the x values:
plt.plot([c[0] for c in ball_motion],
kc.plotcolors['actual'],
label="Actual Position")
if 'cam1' in plotting:
plt.plot([c[0] for c in cam1_estimates],
kc.plotcolors['cam1'],
label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[0] for c in cam2_estimates],
kc.plotcolors['cam2'],
label="Camera 2 Estimate")
# plt.plot([c[0] for c in cam3_estimates], label="Camera 3 Estimate")
if 'average' in plotting:
plt.plot([c[0] for c in average_estimates],
kc.plotcolors['average'],
label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[0] for c in closest_camera_estimates],
kc.plotcolors['closest'],
label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[0] for c in kalman_estimates],
kc.plotcolors['kalman'],
label="Kalman Estimate")
plt.title('X Values')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.x_estimates_filename, format='png')
plt.cla()
# plot the y values:
plt.plot([c[1] for c in ball_motion],
kc.plotcolors['actual'],
label="Actual Position")
if 'cam1' in plotting:
plt.plot([c[1] for c in cam1_estimates],
kc.plotcolors['cam1'],
label="Camera 1 Estimate")
if 'cam2' in plotting:
plt.plot([c[1] for c in cam2_estimates],
kc.plotcolors['cam2'],
label="Camera 2 Estimate")
# plt.plot([c[1] for c in cam3_estimates], label="Camera 3 Estimate")
if 'average' in plotting:
plt.plot([c[1] for c in average_estimates],
kc.plotcolors['average'],
label="Average Estimate")
if 'closest' in plotting:
plt.plot([c[1] for c in closest_camera_estimates],
kc.plotcolors['closest'],
label="Closest Camera Estimate")
if 'kalman' in plotting:
plt.plot([c[1] for c in kalman_estimates],
kc.plotcolors['kalman'],
label="Kalman Estimate")
plt.title('Y Values')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.y_estimates_filename, format='png')
plt.cla()
# plot the errors:
cam1errors = [
kc.get_dist_between_2_points(c[0], c[1])
for c in zip(ball_motion, cam1_estimates)
]
cam2errors = [
kc.get_dist_between_2_points(c[0], c[1])
for c in zip(ball_motion, cam2_estimates)
]
# cam3errors = [kc.get_dist_between_2_points(c[0], c[1]) for c in zip(ball_motion, cam3_estimates)]
averageerrors = [
kc.get_dist_between_2_points(c[0], c[1])
for c in zip(ball_motion, average_estimates)
]
closesterrors = [
kc.get_dist_between_2_points(c[0], c[1])
for c in zip(ball_motion, closest_camera_estimates)
]
kalmanerrors = [
kc.get_dist_between_2_points(c[0], c[1])
for c in zip(ball_motion, kalman_estimates)
]
if 'cam1' in plotting:
plt.plot(cam1errors, kc.plotcolors['cam1'], label='Camera 1')
if 'cam2' in plotting:
plt.plot(cam2errors, kc.plotcolors['cam2'], label='Camera 2')
# plt.plot(cam3errors, label='Camera 3')
if 'average' in plotting:
plt.plot(averageerrors, kc.plotcolors['average'], label='Average')
if 'closest' in plotting:
plt.plot(closesterrors,
kc.plotcolors['closest'],
label='Closest Camera')
if 'kalman' in plotting:
plt.plot(kalmanerrors, kc.plotcolors['kalman'], label='Kalman')
plt.title('Estimation Errors')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.cam_errors_filename, format='png')
w = numpy.bartlett(25)
cam1errors_filtered = numpy.convolve(w / w.sum(), cam1errors, mode='same')
cam2errors_filtered = numpy.convolve(w / w.sum(), cam2errors, mode='same')
# cam3errors_filtered = numpy.convolve(w/w.sum(), cam3errors, mode='same')
averageerrors_filtered = numpy.convolve(w / w.sum(),
averageerrors,
mode='same')
closesterrors_filtered = numpy.convolve(w / w.sum(),
closesterrors,
mode='same')
kalmanerrors_filtered = numpy.convolve(w / w.sum(),
kalmanerrors,
mode='same')
plt.cla()
if 'cam1' in plotting:
# plt.plot(cam1errors_filtered, kc.plotcolors['cam1'], label='Camera 1')
plt.plot(cam1errors_filtered,
kc.plotcolors['cam1'],
label='Camera 1',
linestyle='--')
if 'cam2' in plotting:
# plt.plot(cam2errors_filtered, kc.plotcolors['cam2'], label='Camera 2')
plt.plot(cam2errors_filtered,
kc.plotcolors['cam2'],
label='Camera 2',
linestyle='--')
# plt.plot(cam3errors_filtered, label='Camera 3')
if 'average' in plotting:
plt.plot(averageerrors_filtered,
kc.plotcolors['average'],
label='Average',
linewidth=2)
if 'closest' in plotting:
plt.plot(closesterrors_filtered,
kc.plotcolors['closest'],
label='Closest',
linewidth=2)
if 'kalman' in plotting:
plt.plot(kalmanerrors_filtered,
kc.plotcolors['kalman'],
label='Kalman',
linewidth=2)
plt.title('Smoothed Estimation Errors')
plt.legend(loc='best')
plt.xlabel('Frames')
plt.ylabel('Pixels')
plt.savefig(kc.cam_errors_filtered_filename, format='png')
print('Done!')
if make_video:
print('Rendering video...')
# write out the video:
stiter = iter([
'Motion: Straight Line', 'Motion: Straight Line',
'Motion: Sinusoid'
])
for t in xrange(len(ball_motion)):
# ball coordinates:
d.ball_coords = ball_motion[t]
# set up the status text:
d.cam1_estimate = cam1_estimates[t]
d.cam2_estimate = cam2_estimates[t]
# d.cam3_estimate = cam3_estimates[t]
d.average_estimate = average_estimates[t]
d.kalman_estimate = kalman_estimates[t]
d.frame_num = t
if not (t % (len(ball_motion) / 3)):
d.status_text = stiter.next()
# do the drawing
img = d.get_base_image()
d.write_frame(img)
print("Done!")