return v
def pick_box(self, c):
color, depth, segmentation = c.get_observation()
np.random.shuffle(self.box_ids)
for i in self.box_ids:
m = np.where(segmentation == i)
if len(m[0]):
min_x = 10000
max_x = -1
min_y = 10000
max_y = -1
for y, x in zip(m[0], m[1]):
min_x = min(min_x, x)
max_x = max(max_x, x)
min_y = min(min_y, y)
max_y = max(max_y, y)
x, y = round((min_x + max_x) / 2), round((min_y + max_y) / 2)
return self.get_global_position_from_camera(c, depth, x, y)
return False
if __name__ == '__main__':
np.random.seed(0)
env = FinalEnv()
# env.run(Solution(), render=True, render_interval=5, debug=True)
env.run(Solution(), render=True, render_interval=5)
env.close()
return v
def pick_box(self, c):
color, depth, segmentation = c.get_observation()
np.random.shuffle(self.box_ids)
for i in self.box_ids:
m = np.where(segmentation == i)
if len(m[0]):
min_x = 10000
max_x = -1
min_y = 10000
max_y = -1
for y, x in zip(m[0], m[1]):
min_x = min(min_x, x)
max_x = max(max_x, x)
min_y = min(min_y, y)
max_y = max(max_y, y)
x, y = round((min_x + max_x) / 2), round((min_y + max_y) / 2)
return self.get_global_position_from_camera(c, depth, x, y)
return False
if __name__ == '__main__':
np.random.seed(0)
env = FinalEnv()
# env.run(Solution(), render=True, render_interval=5, debug=True)
env.run(Solution())
env.close()
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC San Diego.
# Created by Yuzhe Qin, Fanbo Xiang
from final_env import FinalEnv
from solution import Solution
import numpy as np
if __name__ == '__main__':
# at test time, we will use different random seeds.
np.random.seed(0)
env = FinalEnv()
env.run(Solution(), render=True, render_interval=5, debug=True)
# at test time, run the following
# env.run(Solution())
env.close()