def get_small_world() -> World:
mat_ground = Lambertian(Color(.8, .8, .3))
ground = Sphere(Vector(0, -100.5, -1), 100, mat_ground)
mat_center = Lambertian(Color(.7, .3, .3))
center = Sphere(Vector(0, 0, -1), .5, mat_center)
mat_left = Metal(Color(.8, .8, .8))
left = Sphere(Vector(-1, 0, -1), .5, mat_left)
mat_right = Dielectric(1.5)
right = Sphere(Vector(1, 0, -1), .5, mat_right)
return World([ground, center, left, right])
def get_world() -> World:
world = []
glass = Dielectric(1.5)
# Ground
ground_mat = Lambertian(Color(0.5, 0.5, 0.5))
world.append(Sphere(Vector(0, -1000, 0), 1000, ground_mat))
# Marbles
for x in range(-11, 11):
for z in range(-11, 11):
choose_material = random()
material: Material
center = Vector(x + .9 * random(), .2, z + .9 * random())
if (center - Vector(4, .2, 0)).norm() <= .9:
continue
if choose_material < .8:
# Diffuse
albedo = Color(*(random(3) * random(3)))
material = Lambertian(albedo)
elif choose_material < .95:
# Metal
albedo = Color(*(random(3) * .5 + .5))
fuzz = random() * .4
material = Metal(albedo, fuzziness=fuzz)
else:
# Glass
material = glass
world.append(Sphere(center, .2, material))
# Big Spheres
world.append(Sphere(Vector(4, 1, 0), 1.0, glass))
world.append(Sphere(Vector(-4, 1, 0), 1.0, Lambertian(Color(.6, .1, .1))))
world.append(Sphere(Vector(0, 1, 0), 1.0, Metal(Color(.7, .6, .5))))
return World(world)
def get_colors():
checkpointing = True
checkpoint_filename = "checkpoints/image_in_progress.npy"
world_filename = "checkpoints/world.pickle"
aspect_ratio = 1.6
image_height = 600
image_width = int(aspect_ratio * image_height)
samples_per_pixel = 8
alias_block_size = np.ceil(np.sqrt(samples_per_pixel))
max_depth = 15
look_from, look_at = Vector(13, 2, 3), Vector(0, 0, 0)
focus_dist = 10
camera = Camera(image_width, image_height,
look_from, look_at,
vfov=20, aperture_width=.1, focus_dist=focus_dist)
if checkpointing and os.path.exists(checkpoint_filename):
colors = np.load(checkpoint_filename)
else:
colors = np.ones((image_width, image_height, 3)) * -1
if checkpointing:
if os.path.exists(world_filename):
with open(world_filename, 'rb') as f:
world = World(pickle.load(f))
else:
world = get_world()
with open(world_filename, 'wb') as f:
pickle.dump(world.items, f)
else:
world = get_world()
for j in tqdm(range(image_height)):
for i in tqdm(range(image_width)):
if colors[i, j, 0] != -1:
continue
color = Vector(0, 0, 0)
for offset in range(samples_per_pixel):
dx = (offset % alias_block_size) / alias_block_size
dy = (offset / alias_block_size) / samples_per_pixel
horizontal_component = (i + dx) / (image_width - 1)
vertical_component = (j + dy) / (image_height - 1)
ray = camera.get_ray(horizontal_component, vertical_component)
color += get_color(ray, world, max_depth)
color = clamp(color, samples_per_pixel)
colors[i, j, :] = color.arr
if checkpointing:
np.save(checkpoint_filename, colors)
if checkpointing:
os.remove(checkpoint_filename)
os.remove(world_filename)
return colors
'''
self.path = np.vstack((djk1.path, djk2.path))
self.smoothed_path = self.smoothing(self.path)
# '''
self.path_list.append(self.smoothed_path)
if verbose:
t2 = time.time()
print('Generate multiple paths: {}\n'.format(t2 - t1))
if __name__ == '__main__':
# --- world class
world = World()
world.generate_frame([-pi, pi], [-pi, pi])
# --- Set start/goal point
world.start = np.array([-3.0, -3.0])
world.goal = np.array([3.0, 3.0])
# --- Set objects
og = ObjectGenerator(world)
og.generate_object_sample1()
og.set_object_type()
# '''
# --- Generate 10x10 valid roadmaps
t0 = time.time()
prm_list = [PRM(world, 100, 5) for i in range(10)]
if self.best_ind.fitness <= self.fitness_thresh:
print('The best fitness reaches the threshold value.')
break
if g >= 5:
diff = np.abs(self.history[-5][1] - self.history[-1][1])
if diff < 1e-2:
print('The best fitness does not change for 10 steps.')
break
self.gtot += 1
if __name__ == '__main__':
# World
world = World()
world.generate_frame([-10, 10], [-10, 10])
world.type = 'cartesian'
# Objects in the cartesian space
og = ObjectGenerator(world)
og.generate_object([[3.5, 6.5], [3.5, 3.5], [6.5, 3.5], [6.5, 6.5]])
# Robot
robot2R = RobotArm(base=[0, 0], lengths=[3.0, 5.0])
robot2R.start = np.array([pi / 2, -pi / 7])
robot2R.goal = np.array([pi / 5, -pi / 7])
# C-space
cspace = World()
cspace.robot = robot2R
if g >= 5:
diff = np.abs(self.history[-5][1] - self.history[-1][1])
if diff < 1e-2:
print('The best fitness does not change for 10 steps.')
break
self.gtot += 1
if __name__ == '__main__':
'''
World setting
'''
# World
world = World()
world.generate_frame([-pi, pi], [-pi, pi])
world.type = 'cartesian'
# Objects in the cartesian space
og = ObjectGenerator(world)
og.generate_object_sample1()
# --- Set start/goal point
world.start = np.array([-pi / 2, -pi / 2]) * 1.9
world.goal = np.array([pi / 2, pi / 2]) * 1.9
'''
Functions
'''
NGEN = 1000
n_ind = 100
'''
Parameter setting
'''
n_ind = 100 # The number of individuals in a population.
n_elite = 10
NGEN = 1000 # The number of generation loop.
fitness_thresh = 0.1
verbose = True
# random.seed(64)
'''
World setting
'''
# --- world class
world = World()
# --- Set frame
world.frame = np.array(
[[-pi, -pi],
[-pi, pi],
[pi, pi],
[pi, -pi],
[-pi, -pi]])
# --- Set start/goal point
world.start = np.array([-pi / 2, -pi / 2]) * 1.9
world.goal = np.array([pi / 2, pi / 2]) * 1.9
# --- Generate objects
og = ObjectGenerator(world)
from math import pi
import seaborn as sns
import matplotlib.pyplot as plt
from geometry import World
from geometry import ObjectGenerator
from viewer import GraphDrawer
from collision_checker import CollisionChecker
sns.set()
'''
World setting
'''
# --- world class
world = World()
# --- Set frame
world.frame = np.array(
[[-pi, -pi],
[-pi, pi],
[pi, pi],
[pi, -pi],
[-pi, -pi]])
# --- Set start/goal point
world.start = np.array([-pi / 2, -pi / 2]) * 1.9
world.goal = np.array([pi / 2, pi / 2]) * 1.9
# --- Generate objects
og = ObjectGenerator(world)
import matplotlib.pyplot as plt
from geometry import World
from geometry import ObjectGenerator
from viewer import GraphDrawer
from dijkstra import Dijkstra
from collision_checker import CollisionChecker
import post_process
sns.set()
'''
World setting
'''
# --- world class
world = World()
# --- Set frame
world.frame = np.array([[-pi, -pi], [-pi, pi], [pi, pi], [pi, -pi], [-pi,
-pi]])
# --- Set start/goal point
world.start = np.array([-pi / 2, -pi / 2]) * 1.9
world.goal = np.array([pi / 2, pi / 2]) * 1.9
# --- Generate objects
og = ObjectGenerator(world)
world.objects = og.example()
# --- CollisionChecker
cc = CollisionChecker(world)
if not path_is_valid:
count_failure += 1
if count_failure > 1000:
print('fail')
break
return self.path_list
# In[]:
if __name__ == '__main__':
# --- world class
world = World()
# --- Set frame and objects
og = ObjectGenerator(world)
og.generate_frame([-pi, pi], [-pi, pi])
og.generate_object_sample1()
og.set_object_type()
# world_margin = world.mcopy(rate=1.05)
world.update_objects([[0.5, 0.0], [-0.5, 0.0]])
# --- Set start/goal point
world.start = np.array([-3.0, -3.0])
world.goal = np.array([3.0, 3.0])
cd = CellDecomposition(world)
if self.best_ind.fitness <= self.fitness_thresh:
print('The best fitness reaches the threshold value.')
break
if g >= 100:
diff = np.abs(self.history[-100][1] - self.history[-1][1])
if diff < 1e-2:
print('The best fitness does not change for 10 steps.')
break
self.gtot += 1
if __name__ == '__main__':
# Set world
world = World()
world.generate_frame([-2, 10], [-2, 10])
world.type = 'cartesian'
# Objects in the cartesian space
og = ObjectGenerator(world)
og.generate_object([[4.5, 5.5], [4.5, 4.5], [5.5, 4.5], [5.5, 5.5]])
og.generate_object([[5.0, 4.5], [5.0, 0.0], [5.2, 0.0], [5.2, 4.5]])
world_margin = world.mcopy(rate=1.05)
# Robot
robot_parameter = {
'base': [0, 0],
'length': [3.0, 5.0],
'mass': [None, None],
'I': [None, None],
# np.random.seed(1) ''' Parameter setting ''' n_ind = 100 # The number of individuals in a population. n_elite = 10 NGEN = 100 # The number of generation loop. fitness_thresh = 0.1 verbose = True # random.seed(64) ''' World setting ''' # --- world class world = World() world.generate_frame([-pi, pi], [-pi, pi]) world.type = 'cartesian' # Objects in the cartesian space og = ObjectGenerator(world) world.objects = og.generate_object_sample1() # --- Set start/goal point world.start = np.array([-pi / 2, -pi / 2]) * 1.9 world.goal = np.array([pi / 2, pi / 2]) * 1.9 # --- Generte Collision Checker cc = CollisionChecker(world) ''' Functions