58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import itertools
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import numpy as np
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import rospy
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from .policy import BasePolicy
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from vgn.utils import look_at, spherical_to_cartesian
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class NextBestView(BasePolicy):
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def __init__(self, rate, filter_grasps):
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super().__init__(rate, filter_grasps)
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def activate(self, bbox):
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super().activate(bbox)
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def update(self, img, extrinsic):
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# Integrate latest measurement
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self.integrate_img(img, extrinsic)
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# Generate viewpoints
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views = self.generate_viewpoints()
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# Evaluate viewpoints
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gains = [self.compute_ig(v) for v in views]
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costs = [self.compute_cost(v) for v in views]
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utilities = gains / np.sum(gains) - costs / np.sum(costs)
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# Determine next-best-view
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nbv = views[np.argmax(utilities)]
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if self.check_done():
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self.best_grasp = self.compute_best_grasp()
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self.done = True
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else:
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return nbv
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def generate_viewpoints(self):
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r, h = 0.14, 0.2
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thetas = np.arange(1, 4) * np.deg2rad(30)
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phis = np.arange(1, 6) * np.deg2rad(60)
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views = []
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for theta, phi in itertools.product(thetas, phis):
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eye = self.center + np.r_[0, 0, h] + spherical_to_cartesian(r, theta, phi)
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target = self.center
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up = np.r_[1.0, 0.0, 0.0]
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views.append(look_at(eye, target, up).inv())
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return views
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def compute_ig(self, view):
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return 1.0
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def compute_cost(self, view):
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return 1.0
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def check_done(self):
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return len(self.viewpoints) == 20
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