mrcfschr / organizar_mar_demo Goto Github PK

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DEBB (26974, 2)
DEBUG visibility mask 3781
DEBUG calculating visibility 0.002564668655395508
DEBUG get final masks 0.0023674964904785156
DEBUG whole function time 0.10149908065795898
number of 3d mask points: [ 934 2103 1691] number of 2d masks: [13065 54246 44379]
65%|██████████████████████████████████████▌ | 36/55 [00:04<00:02, 8.99it/s]-------------------------frame 36 -------------------------
DEBUG Before projection 0.00901484489440918
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.2778, dtype=torch.float64)
DEBB (26912, 2)
DEBUG visibility mask 2722
DEBUG calculating visibility 0.003500699996948242
DEBUG get final masks 0.002439737319946289
DEBUG whole function time 0.10907340049743652
number of 3d mask points: [2096 1568] number of 2d masks: [54077 42483]
67%|███████████████████████████████████████▋ | 37/55 [00:04<00:02, 8.80it/s]-------------------------frame 37 -------------------------
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DEBB (27121, 2)
DEBUG visibility mask 2723
DEBUG calculating visibility 0.0032949447631835938
DEBUG get final masks 0.002137899398803711
DEBUG whole function time 0.09078621864318848
number of 3d mask points: [2102 1704] number of 2d masks: [53414 44756]
69%|████████████████████████████████████████▊ | 38/55 [00:04<00:01, 9.09it/s]-------------------------frame 38 -------------------------
DEBUG Before projection 0.0022029876708984375
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.2847, dtype=torch.float64)
DEBB (27013, 2)
DEBUG visibility mask 2685
DEBUG calculating visibility 0.0026578903198242188
DEBUG get final masks 0.002019166946411133
DEBUG whole function time 0.10381793975830078
number of 3d mask points: [1614 2040] number of 2d masks: [43383 52935]
71%|█████████████████████████████████████████▊ | 39/55 [00:04<00:01, 9.14it/s]-------------------------frame 39 -------------------------
DEBUG Before projection 0.004935503005981445
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DEBUG lowest depth 1000.0 1.2129342150648028
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.2129, dtype=torch.float64)
DEBB (26075, 2)
DEBUG visibility mask 3308
DEBUG calculating visibility 0.0029866695404052734
DEBUG get final masks 0.002106189727783203
DEBUG whole function time 0.09833574295043945
number of 3d mask points: [1764 876] number of 2d masks: [52500 12861]
73%|██████████████████████████████████████████▉ | 40/55 [00:04<00:01, 9.26it/s]-------------------------frame 40 -------------------------
DEBUG Before projection 0.010252952575683594
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DEBUG lowest depth 1000.0 0.9780230205001833
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DEBUG Pooling takes time 0.05342364311218262
DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.9780, dtype=torch.float64)
DEBB (23319, 2)
DEBUG visibility mask 2813
DEBUG calculating visibility 0.0049114227294921875
DEBUG get final masks 0.0026121139526367188
DEBUG whole function time 0.08306550979614258
number of 3d mask points: [ 602 1629] number of 2d masks: [ 8585 55912]
-------------------------frame 41 -------------------------
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DEBUG lowest depth 1000.0 1.1105391392919925
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DEBUG Pooling takes time 0.049860477447509766
DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.1105, dtype=torch.float64)
DEBB (22675, 2)
DEBUG visibility mask 1955
DEBUG calculating visibility 0.003199338912963867
DEBUG get final masks 0.0013921260833740234
DEBUG whole function time 0.06601905822753906
number of 3d mask points: [1762] number of 2d masks: [24506]
76%|█████████████████████████████████████████████ | 42/55 [00:04<00:01, 10.40it/s]-------------------------frame 42 -------------------------
DEBUG Before projection 0.009032726287841797
DEBUG (110421, 2) (1, 360, 640)
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DEBUG lowest depth 1000.0 1.104957325477601
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.1050, dtype=torch.float64)
DEBB (22858, 2)
DEBUG visibility mask 1967
DEBUG calculating visibility 0.003232717514038086
DEBUG get final masks 0.001255035400390625
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number of 3d mask points: [1766] number of 2d masks: [24730]
-------------------------frame 43 -------------------------
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.0228, dtype=torch.float64)
DEBB (22915, 2)
DEBUG visibility mask 1455
DEBUG calculating visibility 0.0031456947326660156
DEBUG get final masks 0.0031490325927734375
DEBUG whole function time 0.07786846160888672
number of 3d mask points: [ 823 1180 1191] number of 2d masks: [25503 36823 37135]
80%|███████████████████████████████████████████████▏ | 44/55 [00:04<00:01, 10.87it/s]-------------------------frame 44 -------------------------
DEBUG Before projection 0.003187417984008789
DEBUG (110421, 2) (1, 360, 640)
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DEBUG Pooling takes time 0.09848928451538086
DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.1929, dtype=torch.float64)
DEBB (22956, 2)
DEBUG visibility mask 349
DEBUG calculating visibility 0.002815723419189453
DEBUG get final masks 0.0012552738189697266
DEBUG whole function time 0.10693573951721191
number of 3d mask points: [200] number of 2d masks: [4596]
-------------------------frame 45 -------------------------
DEBUG Before projection 0.00949406623840332
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inbound_shape: (21546, 2)
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DEBUG lowest depth 1000.0 0.9751478024545849
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.9751, dtype=torch.float64)
DEBB (21546, 2)
DEBUG visibility mask 1401
DEBUG calculating visibility 0.0027298927307128906
DEBUG get final masks 0.0020599365234375
DEBUG whole function time 0.09303903579711914
number of 3d mask points: [1187 1162] number of 2d masks: [41848 40939]
84%|█████████████████████████████████████████████████▎ | 46/55 [00:05<00:00, 10.14it/s]-------------------------frame 46 -------------------------
DEBUG Before projection 0.004500865936279297
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DEBUG lowest depth 1000.0 1.1222483881070424
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.1222, dtype=torch.float64)
DEBB (16302, 2)
DEBUG visibility mask 1893
DEBUG calculating visibility 0.002696514129638672
DEBUG get final masks 0.0019791126251220703
DEBUG whole function time 0.06751036643981934
number of 3d mask points: [975 648] number of 2d masks: [20519 18074]
-------------------------frame 47 -------------------------
DEBUG Before projection 0.010534524917602539
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inbound_shape: (19841, 2)
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DEBUG lowest depth 1000.0 0.9124770043967604
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.9125, dtype=torch.float64)
DEBB (19841, 2)
DEBUG visibility mask 1419
DEBUG calculating visibility 0.00321197509765625
DEBUG get final masks 0.0013437271118164062
DEBUG whole function time 0.07928156852722168
number of 3d mask points: [1163] number of 2d masks: [44071]
87%|███████████████████████████████████████████████████▍ | 48/55 [00:05<00:00, 10.72it/s]-------------------------frame 48 -------------------------
DEBUG Before projection 0.00838017463684082
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.8312, dtype=torch.float64)
DEBB (10283, 2)
DEBUG visibility mask 896
DEBUG calculating visibility 0.0028362274169921875
DEBUG get final masks 0.0011515617370605469
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number of 3d mask points: [688] number of 2d masks: [35287]
-------------------------frame 49 -------------------------
DEBUG Before projection 0.010664939880371094
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DEBUG lowest depth 1000.0 0.8363851112666612
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.8364, dtype=torch.float64)
DEBB (12288, 2)
DEBUG visibility mask 493
DEBUG calculating visibility 0.0018525123596191406
DEBUG get final masks 0.0015060901641845703
DEBUG whole function time 0.10577940940856934
number of 3d mask points: [380 306] number of 2d masks: [18151 12914]
91%|█████████████████████████████████████████████████████▋ | 50/55 [00:05<00:00, 10.51it/s]-------------------------frame 50 -------------------------
DEBUG Before projection 0.010257720947265625
DEBUG (110421, 2) (1, 360, 640)
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.6322, dtype=torch.float64)
DEBB (15671, 2)
DEBUG visibility mask 2216
DEBUG calculating visibility 0.0054552555084228516
DEBUG get final masks 0.001058816909790039
DEBUG whole function time 0.08646273612976074
number of 3d mask points: [1930] number of 2d masks: [52773]
-------------------------frame 51 -------------------------
DEBUG Before projection 0.011245250701904297
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(0.3924, dtype=torch.float64)
DEBB (20904, 2)
DEBUG visibility mask 6355
DEBUG calculating visibility 0.0025827884674072266
DEBUG get final masks 0.0011296272277832031
DEBUG whole function time 0.10570955276489258
number of 3d mask points: [5770] number of 2d masks: [101982]
95%|███████████████████████████████████████████████████████▊ | 52/55 [00:05<00:00, 10.06it/s]-------------------------frame 52 -------------------------
DEBUG Before projection 0.010248422622680664
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inbound_shape: (33891, 2)
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.6154, dtype=torch.float64)
DEBB (33891, 2)
DEBUG visibility mask 3680
DEBUG calculating visibility 0.002962827682495117
DEBUG get final masks 0.0034427642822265625
DEBUG whole function time 0.11652827262878418
number of 3d mask points: [1183 1136 1195] number of 2d masks: [17211 16853 11862]
-------------------------frame 53 -------------------------
DEBUG Before projection 0.007999658584594727
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(1223, 2)
DEBUG lowest depth 1000.0 1.5650419398277684
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DEBUG lowest depth tensor(1000., dtype=torch.float64) tensor(1.5650, dtype=torch.float64)
DEBB (29151, 2)
DEBUG visibility mask 922
DEBUG calculating visibility 0.0034759044647216797
DEBUG get final masks 0.0014085769653320312
DEBUG whole function time 0.10304450988769531
number of 3d mask points: [737] number of 2d masks: [11922]
98%|█████████████████████████████████████████████████████████▉ | 54/55 [00:05<00:00, 9.43it/s]-------------------------frame 54 -------------------------
DEBUG Before projection 0.007568836212158203
DEBUG (110421, 2) (1, 360, 640)
inbound_shape: (23365, 2)
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DEBB (23365, 2)
DEBUG visibility mask 2469
DEBUG calculating visibility 0.003454923629760742
DEBUG get final masks 0.00160980224609375
DEBUG whole function time 0.0868673324584961
number of 3d mask points: [1853] number of 2d masks: [47300]
100%|███████████████████████████████████████████████████████████| 55/55 [00:05<00:00, 9.26it/s]
sucsessfully saved point cloud
masks_to_be_merged [[0], [1], [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 32, 68], [17, 27, 31], [33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 52, 55, 56, 57, 59, 60, 63, 64, 65, 66, 69, 70, 73], [35, 40, 50, 51, 53, 54, 61, 62, 71, 72], [58], [67]]
occurance count tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], device='cuda:0',
dtype=torch.int32)
occurance thres value tensor(6, device='cuda:0', dtype=torch.int32)
after filtering torch.Size([3, 110421])
num_ins_points_after_filtering tensor([6157, 2176, 737], device='cuda:0')
DEBUG torch.Size([3, 110421]) [tensor(0), tensor(1), tensor(2)]
0
tensor([0])
1
tensor([1])
2
tensor([2])
/mnt/c/Users/Marc/Desktop/dev/ORganizAR_MAR_DEMO/viewer/ORganizAR_LangSAM.py:651: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
filtered_3d_masks["ins"] = torch.tensor(filtered_3d_masks["ins"][final_masks_indices,:])
instance_coords [[ 0.34139118 -1.35022748 -0.2083721 ]
[-0.12591281 -1.01535024 -0.0532721 ]
[-0.12642925 -0.9750535 -0.06792925]
...
[-1.7810851 -1.30560895 -0.57569311]
[-1.76049733 -1.30489547 -0.56593404]
[-1.76290772 -1.32530909 -0.56939982]]
start_point (60, 34)
axis: 0
get_target_pos: -2.798000000000002
axis: 1
get_target_pos: -0.3120000000000118
axis: 2
get_target_pos: 1.004000000000019
pos [-2.798000000000002, -0.3120000000000118, 1.004000000000019]
target_pos [-2.81000023 0.98351084 0.26602468]
end_point (5, 80)
using diagonal
original hull area: 62.0
[-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11]
[71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86]
(55, 3)
bed
instance_coords [[-0.18986951 -0.1630533 -0.69035684]
[-0.1966483 -0.16994508 -0.70003759]
[-0.17341632 -0.1466927 -0.67815671]
...
[ 0.12498067 -0.10590312 -0.65121227]
[ 0.14517611 -0.1257345 -0.65052828]
[ 0.14638405 -0.14710569 -0.64988572]]
start_point (70, 51)
axis: 0
get_target_pos: -2.0159999999999627
axis: 1
get_target_pos: -0.34799999999995634
axis: 2
get_target_pos: -0.6420000000000528
pos [-2.0159999999999627, -0.34799999999995634, -0.6420000000000528]
target_pos [-2.01316562 -0.65399155 0.34202812]
end_point (25, 40)
original hull area: 62.0
[17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32]
[32 33 34 35 36 37 38 39 40 41 42 43 44 45 46]
(46, 3)
chair
instance_coords [[-0.01678365 1.08712881 -0.16333228]
[-0.02134869 1.04517185 -0.15911979]
[-0.01886359 1.08904019 -0.16383183]
...
[-0.09811437 1.10999202 -0.28293803]
[ 0.11952872 1.05044925 -0.06032217]
[ 0.09609781 1.10140505 -0.24337412]]
start_point (62, 78)
axis: 0
get_target_pos: -1.4569999999999936
axis: 1
get_target_pos: -0.4560000000000173
axis: 2
get_target_pos: 1.7690000000000055
pos [-1.4569999999999936, -0.4560000000000173, 1.7690000000000055]
target_pos [-1.47756231 1.76384851 0.40717815]
Traceback (most recent call last):
File "/mnt/c/Users/Marc/Desktop/dev/ORganizAR_MAR_DEMO/viewer/ORganizAR_LangSAM.py", line 726, in
end_point = get_starting_point(np.asarray([[target_pos_instance_grid_coords[0],target_pos_instance_grid_coords[1]]]), grid)
File "/mnt/c/Users/Marc/Desktop/dev/ORganizAR_MAR_DEMO/viewer/path_planning/path_planning.py", line 222, in get_starting_point
if grid[i,j] == 0:
IndexError: index 100 is out of bounds for axis 1 with size 100
Process _interconnect-4:
Traceback (most recent call last):
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/mnt/c/Users/Marc/Desktop/dev/ORganizAR_MAR_DEMO/viewer/hl2ss_mp.py", line 238, in run
self._process_source()
File "/mnt/c/Users/Marc/Desktop/dev/ORganizAR_MAR_DEMO/viewer/hl2ss_mp.py", line 193, in _process_source
ipc.release()
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/managers.py", line 1050, in release
return self._callmethod('release')
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/managers.py", line 817, in _callmethod
conn.send((self._id, methodname, args, kwds))
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/connection.py", line 206, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/connection.py", line 411, in _send_bytes
self._send(header + buf)
File "/root/miniconda3/envs/search/lib/python3.10/multiprocessing/connection.py", line 368, in _send
n = write(self._handle, buf)
BrokenPipeError: [Errno 32] Broken pipe

73]]
occurance count tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
device='cuda:0', dtype=torch.int32)
occurance thres value tensor(6, device='cuda:0', dtype=torch.int32)
after filtering torch.Size([4, 118508])
num_ins_points_after_filtering tensor([2943, 2670, 478, 540], device='cuda:0')
DEBUG torch.Size([4, 118508]) [tensor(1), tensor(0), tensor(2), tensor(2)]
0
tensor([1])