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About the output after running completion task #14

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euzer opened this issue Aug 1, 2019 · 22 comments
Open

About the output after running completion task #14

euzer opened this issue Aug 1, 2019 · 22 comments

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@euzer
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euzer commented Aug 1, 2019

Hello,
After i run the run_complete_scans_hierarchical.sh, in the vis folder i obtain some .obj files. I opened some of those .obj file with Meshlab, but in the visualisation the points are black :

Capture du 2019-08-01 11-54-39

While the run_complete_scans_hierarchical.sh is running, i got some error on count and count++ not found.

Any Ideas on that? HOw do you visualize the results ?
THank you !
@qiji77
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qiji77 commented Oct 24, 2019

Hello,
I have the same problem as yours about count.Do you have find a method for this?
And also , I have a problem at complete_scan.py lin325 (ValueError: Can't load save_path when it is None. )
Thank you!

@euzer
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euzer commented Oct 24, 2019 •
edited
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hi there,
@qiji77 I didn't find the way to solve that problem. Sorry :(
I still got black points

@qiji77
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qiji77 commented Oct 25, 2019

well,thank you for your reply.
And now,when I run this code complete_scan.py I only got this result(*********__0__pred.tfrecord),not a obj.what should I do to produce obj?
Best wishes！
Thank you!

@qiji77
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qiji77 commented Oct 25, 2019

Well,I found the cause of the problem.
Now,I can get the result of obj
my email address is diaojunqi77@163.com
I hope I can communicate with you if I can.

@santhu937
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Traceback (most recent call last):
File "complete_scan.py", line 395, in
tf.app.run(main)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 299, in run
_run_main(main, args)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 250, in _run_main
sys.exit(main(argv))
File "complete_scan.py", line 391, in main
target_semantics)
File "complete_scan.py", line 240, in export_prediction_to_mesh
[None, save_errors, save_errors], [None, save_pred_sem, save_target_sem],
UnboundLocalError: local variable 'save_pred_sem' referenced before assignment

@qiji77 Can u please help me with this problem

@qiji77
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qiji77 commented Feb 12, 2020 via email

@santhu937
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santhu937 commented Feb 12, 2020 via email

@santhu937
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santhu937 commented Apr 6, 2020 via email

@qiji77
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qiji77 commented Apr 6, 2020 via email

@santhu937
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santhu937 commented Apr 6, 2020 via email

@santhu937
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santhu937 commented Apr 6, 2020 via email

@qiji77
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qiji77 commented Apr 6, 2020 via email

@santhu937
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santhu937 commented Apr 6, 2020 via email

@qiji77
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qiji77 commented Apr 6, 2020 via email

@santhu937
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santhu937 commented Apr 7, 2020 via email

@qiji77
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qiji77 commented Apr 7, 2020 via email

@santhu937
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santhu937 commented Apr 7, 2020 via email

@euzer
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euzer commented Apr 7, 2020

Hi there,
are there some black points somehow in the generated obj files?

@santhu937
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@euzer no I did not get the black points

@euzer
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euzer commented Apr 9, 2020 •
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oh okay! thanks for your reply @santhu937
yeah few months ago during my intership, I used scanComplete, but I was getting bad ouput (black points, not enough points in the output point cloud).

@santhu937 Do you know how to create or own tfrecords based your own data?

@Aashish75
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@qiji77
I am able to run the test code on colab with TF version 1.3 and python2 runtime. I get .tfrecord file for level 3 and level 2 output folders but I am not getting the .obj files, could you please tell me what could be wrong?

@Aashish75
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This is my output after running the test code.

Processing hierarchy level 3, scene 1 of 1: /content/modelcolab/vox19/e5c91fa85ebb60073d89c6ed56e66593__0_.tfrecords.
2020-04-12 11:32:23.418637: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:23.418672: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:23.418682: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:23.418689: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:23.418699: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
sh: 1: matlab: not found
Processing hierarchy level 2, scene 1 of 1: /content/modelcolab/vox9/e5c91fa85ebb60073d89c6ed56e66593__0__.tfrecords.
2020-04-12 11:32:40.374343: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:40.374398: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:40.374409: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:40.374417: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:32:40.374429: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
tcmalloc: large alloc 1954471936 bytes == 0x55e24a902000 @ 0x7fd27223a1e7 0x7fd26698e23f 0x7fd267fc7e74 0x7fd267fd78a5 0x7fd267fd7d2c 0x7fd2680331fc 0x7fd268034225 0x7fd2680cc68c 0x7fd26809ca98 0x7fd26808c4c0 0x7fd268415e02 0x7fd268415092 0x7fd270d4ea50 0x7fd271bfe6db 0x7fd271f3788f
sh: 1: matlab: not found
Processing hierarchy level 1, scene 1 of 1: /content/modelcolab/vox5/e5c91fa85ebb60073d89c6ed56e66593__0__.tfrecords.
2020-04-12 11:34:42.664577: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:34:42.664622: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:34:42.664634: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:34:42.664643: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2020-04-12 11:34:42.664656: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
tcmalloc: large alloc 1266745344 bytes == 0x55bd440ea000 @ 0x7fa91c5d91e7 0x7fa910d2d23f 0x7fa912366e74 0x7fa9123768a5 0x7fa912376d2c 0x7fa9123d21fc 0x7fa9123d3225 0x7fa91246b68c 0x7fa91243ba98 0x7fa91242b4c0 0x7fa9127b4e02 0x7fa9127b4092 0x7fa91b0eda50 0x7fa91bf9d6db 0x7fa91c2d688f
tcmalloc: large alloc 1501388800 bytes == 0x55bd440ea000 @ 0x7fa91c5d91e7 0x7fa910d2d23f 0x7fa912366e74 0x7fa9123768a5 0x7fa912376d2c 0x7fa9123d21fc 0x7fa9123d3225 0x7fa91246b68c 0x7fa91243ba98 0x7fa91242b4c0 0x7fa9127b4e02 0x7fa9127b4092 0x7fa91b0eda50 0x7fa91bf9d6db 0x7fa91c2d688f
tcmalloc: large alloc 1501339648 bytes == 0x55bd9d8c0000 @ 0x7fa91c5d91e7 0x7fa910d2d23f 0x7fa912366e74 0x7fa9123768a5 0x7fa912376d2c 0x7fa9123d21fc 0x7fa9123d3225 0x7fa91246b68c 0x7fa91243ba98 0x7fa91242b4c0 0x7fa9127b4e02 0x7fa9127b4092 0x7fa91b0eda50 0x7fa91bf9d6db 0x7fa91c2d688f
tcmalloc: large alloc 15013167104 bytes == 0x55bd9d8c0000 @ 0x7fa91c5d91e7 0x7fa910d2d23f 0x7fa912366e74 0x7fa9123768a5 0x7fa912376d2c 0x7fa9123d21fc 0x7fa9123d3225 0x7fa91246b68c 0x7fa91243ba98 0x7fa91242b4c0 0x7fa9127b4e02 0x7fa9127b4092 0x7fa91b0eda50 0x7fa91bf9d6db 0x7fa91c2d688f
./run_complete_scans_hierarchical.sh: line 92: 2289 Killed python complete_scan.py --alsologtostderr --base_dir="${HIERARCHY_LEVEL_1_MODEL}" --output_dir_prev="${OUTPUT_FOLDER_2}" --height_input="${HEIGHT_INPUT}" --hierarchy_level="${HIERARCHY_LEVEL}" --num_total_hierarchy_levels="${NUM_HIERARCHY_LEVELS}" --is_base_level="${IS_BASE_LEVEL}" --predict_semantics="${PREDICT_SEMANTICS}" --output_folder="${OUTPUT_FOLDER_1}" --input_scene="${scene}"_

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@santhu937 @Aashish75 @qiji77 @euzer