def synth_circle_noise(infiles, outfiles, NOISE):
SIM_DURATION = 30.0
TDELTA = 1 / 30.
t = np.arange(0, SIM_DURATION, TDELTA)
frames_to_skip = [20, 35, 36, 50, 51, 52, 53]
env = util.Environmentz((1.5, 2), (240, 320))
state = simulate.gen_track_circle(t,
np.pi * 2 / 10,
env,
circle_radius=0.5)
images = simulate.render(env, state)
new_images = simulate.add_noise_background(images, NOISE, NOISE,
frames_to_skip)
pickle.dump(
{
'state': state,
'video': new_images,
'noise': NOISE,
'frames_skipped': frames_to_skip
}, open(outfiles[0], 'w'))
videotools.dump_grey_movie(outfiles[1], new_images)
def synth_circle_noise(infiles, outfiles, NOISE):
SIM_DURATION = 30.0
TDELTA = 1/30.
t = np.arange(0, SIM_DURATION, TDELTA)
frames_to_skip = [20, 35, 36, 50, 51, 52, 53]
env = util.Environmentz((1.5, 2), (240, 320))
state = simulate.gen_track_circle(t, np.pi*2/10, env, circle_radius=0.5)
images = simulate.render(env, state)
new_images = simulate.add_noise_background(images, NOISE, NOISE,
frames_to_skip)
pickle.dump({'state' : state,
'video' : new_images,
'noise' : NOISE,
'frames_skipped' : frames_to_skip
},
open(outfiles[0], 'w'))
videotools.dump_grey_movie(outfiles[1], new_images)
N = len(positions)
state = np.zeros(N, dtype=util.DTYPE_STATE)
state['x'] = positions_interp['x']
state['y'] = positions_interp['y']
state['phi'] = pos_derived['phi']
state['theta'] = np.pi/2.0
env = util.Environmentz((1.5, 2), (240, 320))
images = simulate.render(env, state[:100])
NOISE = 0
new_images = simulate.add_noise_background(images, NOISE, NOISE,
[])
FN = 100
pylab.figure()
pylab.subplot(2, 1, 1)
pylab.plot(state['x'][:FN])
pylab.plot(state['y'][:FN])
pylab.subplot(2, 1, 2)
pylab.scatter(positions['led_front'][:FN, 0], positions['led_front'][:FN, 1], c='g')
pylab.scatter(positions['led_back'][:FN, 0], positions['led_back'][:FN, 1], c='r')
pylab.show()
for fi in range(FN):
frame_no = start_f + fi
frame = tf.extractfile("%08d.jpg" % frame_no)
positions_interp, missing = measure.interpolate(positions)
pos_derived = measure.compute_derived(positions_interp)
N = len(positions)
state = np.zeros(N, dtype=util.DTYPE_STATE)
state['x'] = positions_interp['x']
state['y'] = positions_interp['y']
state['phi'] = pos_derived['phi']
state['theta'] = np.pi / 2.0
env = util.Environmentz((1.5, 2), (240, 320))
images = simulate.render(env, state[:100])
NOISE = 0
new_images = simulate.add_noise_background(images, NOISE, NOISE, [])
FN = 100
pylab.figure()
pylab.subplot(2, 1, 1)
pylab.plot(state['x'][:FN])
pylab.plot(state['y'][:FN])
pylab.subplot(2, 1, 2)
pylab.scatter(positions['led_front'][:FN, 0],
positions['led_front'][:FN, 1],
c='g')
pylab.scatter(positions['led_back'][:FN, 0],
positions['led_back'][:FN, 1],
c='r')
pylab.show()