def render_online():
"""
Renders a spinning square moving on a straight line.
"""
movie_name = "square_online"
save_dir = make_video_path(movie_name+".pngvin")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print "Rendering pngvin movie", movie_name
delete_database(movie_name)
create_database(movie_name, [2])
db = StateTransitionDatabase(movie_name)
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, IMAGE_WIDTH, IMAGE_HEIGHT)
ctx = cairo.Context(surface)
num_frames=32
x0 = 0.2
y0 = 0.2
theta0 = 0.0
dx=0.02
dy=0.02
dtheta=2.0*math.pi/num_frames
width=0.1
prev_state = numpy.array([IMAGE_WIDTH*x0, IMAGE_WIDTH*y0, theta0])
for i in xrange(num_frames):
clear(ctx)
ctx.set_source_rgb(0,0,0)
ctx.rectangle(0, 0, 1, 1)
ctx.fill()
ctx.set_source_rgb(1, 1, 1)
ctx.translate(x0+dx*i, y0+dy*i)
ctx.rotate(theta0+dtheta*i)
ctx.rectangle(-width/2, -width/2, width, width)
ctx.fill()
# Render Obstacle
clear(ctx)
surface.write_to_png(make_video_path(save_dir, "frame-"+left_align_videoformat(i)+".png"))
if i>0:
# Input the transition into the database
new_state = numpy.array([IMAGE_WIDTH*(x0+dx*i), IMAGE_WIDTH*(y0+dy*i), theta0+dtheta*i])
db.add_transitions(prev_state[:2], new_state[:2])
prev_state = new_state
print "Rendered frame", i
print "Done."
def __init__(self, dataset, number_of_objects=1, number_of_transitions=1000, number_of_frames=64, number_of_movies=4, debug=False, dt=1, **other_kwargs):
if isinstance(debug, str):
debug = (debug == "True")
delete_database(dataset)
create_database(dataset, self.PARAMETER_GROUPS)
self.dataset = dataset
self.db = StateTransitionDatabase(dataset)
self.number_of_objects = int(number_of_objects)
self.number_of_transitions = int(number_of_transitions)
self.number_of_frames = int(number_of_frames)
self.number_of_movies = int(number_of_movies)
self.dt = float(dt)
def render_bounce(movie_id=0, start_state=None, gravity=numpy.array([0, 1]), bounce_factor=0.75, input_to_database=True):
"""
Renders a falling and bouncing square.
"""
dataset = "square_bounce"
num_frames = 128
square_side = 50.
half_square_side = square_side/2.0
dt = 0.5
X_LIMITS = [half_square_side, IMAGE_WIDTH-half_square_side]
Y_LIMITS = [half_square_side, IMAGE_HEIGHT-half_square_side]
# State: [x, y, x', y']
if start_state is None:
delete_database(dataset)
create_database(dataset, [2, 2])
render_bounce(0, numpy.array([IMAGE_WIDTH/2., 0., Y_LIMITS[0], 0.]))
render_bounce(1, numpy.array([IMAGE_WIDTH/2., 2., Y_LIMITS[0], 0.]))
render_bounce(2, numpy.array([IMAGE_WIDTH*2./3, -1, IMAGE_HEIGHT/2., -0.4]))
render_bounce(3, numpy.array([IMAGE_WIDTH/4., 0., Y_LIMITS[1], 0.]))
render_bounce(4, numpy.array([IMAGE_WIDTH*3./4, 0., Y_LIMITS[1], 2]))
render_bounce(5, numpy.array([IMAGE_WIDTH/2., 4, Y_LIMITS[1], 0.6]))
render_bounce(6, numpy.array([IMAGE_WIDTH/2., 4., IMAGE_HEIGHT/2., 0.]))
render_bounce(7, numpy.array([X_LIMITS[0], 5., Y_LIMITS[0], 0.]), input_to_database=False)
render_bounce(8, numpy.array([IMAGE_WIDTH/2., -1, IMAGE_HEIGHT*3./4, 0.6]), input_to_database=False)
render_bounce(9, numpy.array([IMAGE_WIDTH/5., 2, IMAGE_HEIGHT/3., 0.4]), input_to_database=False)
return
movie_name = dataset + "_" + str(movie_id)
save_dir = make_video_path(movie_name + ".pngvin")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print "Rendering pngvin movie", movie_name
db = StateTransitionDatabase(dataset)
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, IMAGE_WIDTH, IMAGE_HEIGHT)
ctx = cairo.Context(surface)
state = start_state
# state[:2] += half_square_side
print "Initial values:", state
def timestep(state, fixpoint_iterations=10):
next_state = state
d0 = numpy.array((state[1], gravity[0], state[3], gravity[1]))
for i in xrange(fixpoint_iterations):
d = numpy.array((next_state[1], gravity[0], next_state[3], gravity[1]))
next_state = state + 0.5 * (d0 + d) * dt
return next_state
for i in xrange(num_frames):
if i>0:
next_state = timestep(state)
# Collisions with edges
for axis, limits in ((0, X_LIMITS), (2, Y_LIMITS)):
if next_state[axis] < limits[0]:
next_state[axis+1] *= -bounce_factor
next_state[axis] = 2*limits[0] - next_state[axis]
elif next_state[axis] > limits[1]:
next_state[axis+1] *= -bounce_factor
next_state[axis] = 2*limits[1] - next_state[axis]
if input_to_database:
# Input the transition into the database
normalizer = numpy.array((state[0], 0, state[2], 0))
db.add_transitions(state - normalizer, next_state - normalizer)
state = next_state
x = (state[0]-half_square_side)/IMAGE_WIDTH
y = (state[2]-half_square_side)/IMAGE_HEIGHT
clear(ctx)
ctx.rectangle(0, 0, 1, 1)
ctx.set_source_rgb(0,0,0)
ctx.fill()
ctx.set_source_rgb(1, 1, 1)
ctx.rectangle(x, y, square_side/IMAGE_WIDTH, square_side/IMAGE_WIDTH)
ctx.fill()
surface.write_to_png(make_video_path(save_dir, "frame-"+left_align_videoformat(i)+".png"))
print "Completed rendering", movie_name
def generate_pendulum(number_of_transitions, l, g=9.81, dt=0.001, radius=24):
"""
Generates number_of_transitions random movements of a pendulum with start
angle theta_0 from the y axis
"""
amplitude = 40
dataset = "pendulum"
# State: [x, y, x', y']
delete_database(dataset)
create_database(dataset, [1])
print "Generating %i random accelerating movements"%(number_of_transitions)
db = StateTransitionDatabase(dataset)
omega = math.sqrt(float(g)/l)
T = 2*math.pi*1.0/omega
theta_0 = numpy.random.uniform(math.radians(-amplitude), math.radians(amplitude), (number_of_transitions, 1))
t = numpy.random.uniform(0, T, (number_of_transitions, 1))
from_phi = theta_0 * numpy.cos(omega*t)
to_phi = theta_0 * numpy.cos(omega*(t+dt))
db.add_transitions(from_phi, to_phi)
print "Done."
print "Generating movement sequences for testing..."
num_movies = 4
num_frames = 64
for movie_id in xrange(num_movies):
movie_name = dataset + "_" + str(movie_id)
save_dir = make_video_path(movie_name + ".pngvin")
import shutil
if os.path.exists(save_dir):
shutil.rmtree(save_dir)
os.makedirs(save_dir)
print "Rendering pngvin movie to %s"%(save_dir)
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, IMAGE_WIDTH, IMAGE_HEIGHT)
ctx = cairo.Context(surface)
theta_0 = numpy.random.uniform(math.radians(-amplitude),math.radians(amplitude))
t = numpy.atleast_2d(numpy.linspace(0, 3*T, num_frames)).T
phi = theta_0 * numpy.cos(omega*dt*t)
for i in xrange(num_frames):
x = (IMAGE_WIDTH/2 + l*numpy.sin(phi[i]))/IMAGE_WIDTH
y = l*numpy.cos(phi[i])/IMAGE_HEIGHT
clear(ctx)
ctx.rectangle(0, 0, 1, 1)
ctx.set_source_rgb(0,0,0)
ctx.fill()
ctx.set_source_rgb(1, 1, 1)
# ctx.rectangle(x, y, 10, 10)
ctx.arc(x, y, float(radius)/IMAGE_WIDTH, 0., 2 * math.pi)
ctx.fill()
surface.write_to_png(os.path.join(save_dir, "frame-"+left_align_videoformat(i)+".png"))
numpy.save(os.path.join(save_dir, "state_sequence.npy"), phi)
print "Done."
class Generator:
def __init__(self, dataset, number_of_objects=1, number_of_transitions=1000, number_of_frames=64, number_of_movies=4, debug=False, dt=1, **other_kwargs):
if isinstance(debug, str):
debug = (debug == "True")
delete_database(dataset)
create_database(dataset, self.PARAMETER_GROUPS)
self.dataset = dataset
self.db = StateTransitionDatabase(dataset)
self.number_of_objects = int(number_of_objects)
self.number_of_transitions = int(number_of_transitions)
self.number_of_frames = int(number_of_frames)
self.number_of_movies = int(number_of_movies)
self.dt = float(dt)
def generate(self):
print
print "Launching generator %s"%(self.__class__.__name__)
print
print "Generating training database with %i transitions..."%(self.number_of_transitions)
self.db.add_transitions(*self.generate_training_transitions())
print "Training database generated."
print
print "Generating testing movies with %i objects..."%(self.number_of_objects)
print
for movie_i in xrange(self.number_of_movies):
print "Generating movie %i of %i..."%(movie_i+1, self.number_of_movies)
save_dir = make_video_path("%s_%i.pngvin"%(self.dataset, movie_i))
import shutil
if os.path.exists(save_dir):
print "Directory already exists:", save_dir
shutil.rmtree(save_dir)
print "Deleted directory:", save_dir
os.makedirs(save_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
wlm = wlayermanager()
print "Calculating state sequences..."
states = [self.generate_testing_sequence(self.number_of_frames) for i in xrange(self.number_of_objects)]
map(numpy.save, (os.path.join(save_dir, "state_sequence_%i.npy"%i) for i in xrange(len(states))), states)
map(pickle.dump, map(self.generate_metadata, xrange(len(states))), (open(os.path.join(save_dir, "metadata_%i.pickle"%i), "w") for i in xrange(len(states))))
print "State sequences calculated."
print "Generating movie..."
wlm.add_layer(self.generate_testing_movie(states))
print "Movie generated."
print "Saving movie to %s"%(save_dir)
wlm.exportPNGVIN(save_dir, width=IMAGE_WIDTH, height=IMAGE_HEIGHT)
print "Movie saved."
print "Finished generating movie %i of %i"%(movie_i+1, self.number_of_movies)
print
print "Finished generating testing movies."
def generate_training_transitions(self):
"""Implementations should return a length 2 sequence of equal size
numpy arrays.
"""
raise NotImplementedError("This class is abstract!")
def generate_testing_sequence(self, num_frames):
"""Implementations should return a numpy array representing a sequence
of states to be tracked.
"""
raise NotImplementedError("This class is abstract!")
def generate_testing_movie(self, states):
"""Implementations should return a wvideo.
@param states: A sequence of states generated by
generate_testing_sequence
@see generate_testing_sequence
"""
raise NotImplementedError("This class is abstract!")
def generate_metadata(self, obj_i):
"""Implementations should return a dictionary.
@param obj_i: Which object to generate metadata for.
"""
raise NotImplementedError("This class is abstract!")