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+1059,15 @@ 8.485957048833370209e-03,2.757936157286167145e-02,4.890000224113464355e-01 1.058575417846441269e-02,2.752296254038810730e-02,4.880000352859497070e-01 1.267687417566776276e-02,2.746656164526939392e-02,4.870000183582305908e-01 -1.475931797176599503e-02,2.741016261279582977e-02,4.860000312328338623e-01 -1.683308370411396027e-02,2.735376171767711639e-02,4.850000143051147461e-01 +1.478968653827905655e-02,2.746656164526939392e-02,4.870000183582305908e-01 +1.686779223382472992e-02,2.741016261279582977e-02,4.860000312328338623e-01 1.893722079694271088e-02,2.735376171767711639e-02,4.850000143051147461e-01 2.099797129631042480e-02,2.729736268520355225e-02,4.840000271797180176e-01 -2.305004559457302094e-02,2.724096365272998810e-02,4.830000102519989014e-01 +2.309776842594146729e-02,2.729736268520355225e-02,4.840000271797180176e-01 2.514550462365150452e-02,2.724096365272998810e-02,4.830000102519989014e-01 -2.718456462025642395e-02,2.718456462025642395e-02,4.820000231266021729e-01 -2.927568368613719940e-02,2.718456462025642395e-02,4.820000231266021729e-01 -3.136680647730827332e-02,2.718456462025642395e-02,4.820000231266021729e-01 --3.560978174209594727e-02,3.115855902433395386e-02,5.130000114440917969e-01 +2.724096365272998810e-02,2.724096365272998810e-02,4.830000102519989014e-01 +2.933642081916332245e-02,2.724096365272998810e-02,4.830000102519989014e-01 +-3.567919507622718811e-02,3.121929615736007690e-02,5.139999985694885254e-01 -3.331909701228141785e-02,3.109782189130783081e-02,5.120000243186950684e-01 -3.103708475828170776e-02,3.103708475828170776e-02,5.110000371932983398e-01 -2.870734967291355133e-02,3.091560676693916321e-02,5.090000033378601074e-01 @@ -1075,7 +1076,7 @@ -2.190903946757316589e-02,3.067265450954437256e-02,5.049999952316284180e-01 -1.967909000813961029e-02,3.061191737651824951e-02,5.040000081062316895e-01 -1.745781861245632172e-02,3.055118024349212646e-02,5.030000209808349609e-01 --1.521485298871994019e-02,3.042970597743988037e-02,5.010000467300415039e-01 +-1.524522155523300171e-02,3.049044311046600342e-02,5.020000338554382324e-01 -1.301527116447687149e-02,3.036896511912345886e-02,5.000000000000000000e-01 -1.082436740398406982e-02,3.030822798609733582e-02,4.990000128746032715e-01 -8.642140775918960571e-03,3.024749085307121277e-02,4.980000257492065430e-01 @@ -1090,12 +1091,12 @@ 1.062913797795772552e-02,2.976158633828163147e-02,4.900000095367431641e-01 1.272893603891134262e-02,2.970084920525550842e-02,4.890000224113464355e-01 1.485042460262775421e-02,2.970084920525550842e-02,4.890000224113464355e-01 -1.693720743060112000e-02,2.964011207222938538e-02,4.880000352859497070e-01 -1.905435882508754730e-02,2.964011207222938538e-02,4.880000352859497070e-01 +1.697191409766674042e-02,2.970084920525550842e-02,4.890000224113464355e-01 +1.909340359270572662e-02,2.970084920525550842e-02,4.890000224113464355e-01 -3.325401991605758667e-02,3.325401991605758667e-02,5.110000371932983398e-01 -3.097634762525558472e-02,3.318894281983375549e-02,5.100000500679016113e-01 -2.870734967291355133e-02,3.312386572360992432e-02,5.090000033378601074e-01 --2.639497071504592896e-02,3.299371525645256042e-02,5.070000290870666504e-01 +-2.644703164696693420e-02,3.305878862738609314e-02,5.080000162124633789e-01 -2.414766885340213776e-02,3.292863816022872925e-02,5.060000419616699219e-01 -2.190903946757316589e-02,3.286356106400489807e-02,5.049999952316284180e-01 -1.967909000813961029e-02,3.279848396778106689e-02,5.040000081062316895e-01 @@ -1119,16 +1120,16 @@ -2.195242606103420258e-02,3.512388095259666443e-02,5.060000419616699219e-01 -1.971813477575778961e-02,3.505446389317512512e-02,5.049999952316284180e-01 -1.749252527952194214e-02,3.498505055904388428e-02,5.040000081062316895e-01 --1.527559012174606323e-02,3.491563722491264343e-02,5.030000209808349609e-01 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--1.539706625044345856e-02,3.739287704229354858e-02,5.070000290870666504e-01 +-1.983527466654777527e-02,3.746662661433219910e-02,5.080000162124633789e-01 +-1.763135381042957306e-02,3.746662661433219910e-02,5.080000162124633789e-01 +-1.542743481695652008e-02,3.746662661433219910e-02,5.080000162124633789e-01 diff --git a/src/active_grasp/active_perception_policy.py b/src/active_grasp/active_perception_policy.py index 4db4b1e..b3d9bb3 100644 --- a/src/active_grasp/active_perception_policy.py +++ b/src/active_grasp/active_perception_policy.py @@ -15,6 +15,7 @@ import time from visualization_msgs.msg import Marker, MarkerArray from geometry_msgs.msg import Pose import tf +from sklearn.neighbors import NearestNeighbors import sensor_msgs.point_cloud2 as pc2 from sensor_msgs.msg import PointCloud2, PointField @@ -493,14 +494,21 @@ class ActivePerceptionSingleViewPolicy(SingleViewPolicy): scene_points = np.asarray(scene_points) if scene_sample_num > 0: if scene_points.shape[0] < scene_sample_num: - scene_sample_num = scene_points.shape[0] print("Scene points are less than the required sample number") - scene_points = scene_points[np.random.choice(scene_points.shape[0], scene_sample_num, replace=False)] + num_interpolation = scene_sample_num - scene_points.shape[0] + scene_points = self.interpolate_point_cloud(scene_points, num_interpolation) + print("Interpolated scene points. Shape: "+str(scene_points.shape)) + else: + scene_points = scene_points[np.random.choice(scene_points.shape[0], scene_sample_num, replace=False)] + if target_sample_num > 0: if target_points.shape[0] < target_sample_num: - target_sample_num = target_points.shape[0] print("Target points are less than the required sample number") - target_points = target_points[np.random.choice(target_points.shape[0], target_sample_num, replace=False)] + num_interpolation = target_sample_num - target_points.shape[0] + target_points = self.interpolate_point_cloud(target_points, num_interpolation) + print("Interpolated target points. Shape: "+str(target_points.shape)) + else: + target_points = target_points[np.random.choice(target_points.shape[0], target_sample_num, replace=False)] # reshape points target_points = target_points.reshape(1, target_points.shape[0], 3) @@ -511,3 +519,17 @@ class ActivePerceptionSingleViewPolicy(SingleViewPolicy): scene_points = torch.from_numpy(scene_points).float().to("cuda:0") return target_points, scene_points + + def interpolate_point_cloud(self, points, num_new_points): + # Fit NearestNeighbors on existing points + nbrs = NearestNeighbors(n_neighbors=5).fit(points) + interpolated_points = [] + + for _ in range(num_new_points): + random_point = points[np.random.choice(len(points))] + distances, indices = nbrs.kneighbors([random_point]) + neighbors = points[indices[0]] + new_point = neighbors.mean(axis=0) # Average of neighbors + interpolated_points.append(new_point) + + return np.vstack([points, interpolated_points]) \ No newline at end of file