trace_dir is used in the original SVO code to evaluate the timing, errors, and the number of tracked points in the SVO. It is a csv file saved in the trace_dir for performance evaluation.

from cnn-svo.

trace_dir is used in the original SVO code to evaluate the timing, errors, and the number of tracked points in the SVO. It is a csv file saved in the trace_dir for performance evaluation.

So the trace_dir isn't the cause of exit code -6 ? Have you met the error?

from cnn-svo.

I didn't have this error. Try to change your trace_dir to "/home/fight/results"

from cnn-svo.

I didn't have this error. Try to change your trace_dir to "/home/fight/results"

I‘ve changed it but still have this error. Could you show me your example of Directories?
I saved depth_2.npy in the 'disparity' folder.

from cnn-svo.

I didn't have this error. Try to change your trace_dir to "/home/fight/results"

Hi,sorry for interrupting you again and again,but your work is really important to me.
I still have trouble when I type the code 'roslaunch svo_ros kittiOffline00-02.launch'.
Do I need to input the ROS_MASTER_URL and ROS_IP?
I don't know why I can't recurrent your code.
I really need your help ,thank you!

from cnn-svo.

I see your problem. You shouldn't provide the full directory in "image_folder" and "depth_folder".

sequence_path: "/home/fight/ORB_SLAM2/dataset/sequences/02"
image_folder: "image_2"
depth_folder: "disp_image_2_modelcity2kitti"

Make sure that you have the timestamp file ("/home/fight/ORB_SLAM2/dataset/sequences/02/times.txt").

Also, make sure that the depth maps (numpy files) are saved in "/home/fight/ORB_SLAM2/dataset/sequences/02/disp_image_2_modelcity2kitti/" with the filenames of depth_0.npy, depth_1.npy, depth_2.npy, ...

from cnn-svo.

I see your problem. You shouldn't provide the full directory in "image_folder" and "depth_folder".

sequence_path: "/home/fight/ORB_SLAM2/dataset/sequences/02"
image_folder: "image_2"
depth_folder: "disp_image_2_modelcity2kitti"

Make sure that you have the timestamp file ("/home/fight/ORB_SLAM2/dataset/sequences/02/times.txt").

Also, make sure that the depth maps (numpy files) are saved in "/home/fight/ORB_SLAM2/dataset/sequences/02/disp_image_2_modelcity2kitti/" with the filenames of depth_0.npy, depth_1.npy, depth_2.npy, ...

Thanks a lot!
I found that my problem is my opencv_version.
Now I can start the program but get the exit code -11.I think I will make it.

from cnn-svo.

我看到你的问题。您不应该在“image_folder”和“depth_folder”中提供完整目录。

sequence_path：“/ home / fight / ORB_SLAM2 / dataset / sequences / 02”
image_folder：“image_2”
depth_folder：“disp_image_2_modelcity2kitti”

确保您有时间戳文件（“/home/fight/ORB_SLAM2/dataset/sequences/02/times.txt”）。

此外，请确保深度贴图（numpy文件）保存在“/ home / fight / ORB_SLAM2 / dataset / sequences / 02 / disp_image_2_modelcity2kitti /”中，文件名为depth_0.npy，depth_1.npy，depth_2.npy，.. 。

I've met a new question. I Modify the monodepth_main.py and get several .npy files. My .npy size is 1MB and the program can run about several seconds then the program broke with several errors and output exit code -11.
I test the monodepth_simple.py I find that the result of .npy size is about 500KB,so I modify the monodepth_main.py again and finally I get the same size of .npy. But when I put thousands of .npy into the disp_image_2_modelcity2kitti/ and type the code 'roslaunch svo_ros kittiOffline00-02.launch'. The program doesn't run and output exit code -6.
So could you tell me what's the size of your .npy?
Thanks a lot.

from cnn-svo.

The file size for each .npy file is about 3.6mb. Can you show me your monodepth_main.py file? I would like to know how you save those files.

from cnn-svo.

Hey, I have uploaded the first 100 depth maps in KITTI sequence 00 for you to do a quick test. Hope that you can identify the real problem in your code. Click here

from cnn-svo.

Hey, I have uploaded the first 100 depth maps in KITTI sequence 00 for you to do a quick test. Hope that you can identify the real problem in your code. Click here

I can use your depth maps to run the program,finally!
So I can make a conclusion that I didn't modify the monodepth_main.py right.
I imitate the monodepth_simple.py but cannot get the same size of .npy file like yours.
I use the pretrained model 'city2kitti'.
I only modify the #RESTORE code.

from cnn-svo.

Hey, I have uploaded the first 100 depth maps in KITTI sequence 00 for you to do a quick test. Hope that you can identify the real problem in your code. Click here

I can use your depth maps to run the program,finally!
So I can make a conclusion that I didn't modify the monodepth_main.py right.
I imitate the monodepth_simple.py but cannot get the same size of .npy file like yours.
I use the pretrained model 'city2kitti'.
I only modify the #RESTORE code.

The saving step looks correct. However, you should convert the disparity map (from network output) to depth map using the following equation:

depth = (718.856 * 0.54 / 1241) / disp # where 0.54 is the stereo baseline distance, 718.856 the focal length and 1241 the image width

Therefore, here is how the code should look like

SCALE = 0.3128 # (718.856 * 0.54 / 1241)

for step in range(num_test_samples):

disp = sess.run(model.disp_left_est[0])
disp = post_process_disparity(disps.squeeze())

# If you wish to resize the disparity map back to original size
# disp = cv2.resize(disp, (1241, 376), interpolation=cv2.INTER_LINEAR)

# Convert disparity to depth
depth = SCALE / disp

np.save(output_directory + '/' + 'depth_' + str(step) + '.npy', depth)

from cnn-svo.

Hey, I have uploaded the first 100 depth maps in KITTI sequence 00 for you to do a quick test. Hope that you can identify the real problem in your code. Click here

I can use your depth maps to run the program,finally!
So I can make a conclusion that I didn't modify the monodepth_main.py right.
I imitate the monodepth_simple.py but cannot get the same size of .npy file like yours.
I use the pretrained model 'city2kitti'.
I only modify the #RESTORE code.

The saving step looks correct. However, you should convert the disparity map (from network output) to depth map using the following equation:

depth = (718.856 * 0.54 / 1241) / disp # where 0.54 is the stereo baseline distance, 718.856 the focal length and 1241 the image width

Therefore, here is how the code should look like

SCALE = 0.3128 # (718.856 * 0.54 / 1241)

for step in range(num_test_samples):

disp = sess.run(model.disp_left_est[0])
disp = post_process_disparity(disps.squeeze())

# If you wish to resize the disparity map back to original size
# disp = cv2.resize(disp, (1241, 376), interpolation=cv2.INTER_LINEAR)

# Convert disparity to depth
depth = SCALE / disp

np.save(output_directory + '/' + 'depth_' + str(step) + '.npy', depth)

THANK!
FINALLY I CAN RUN THE PROGRAM WITH MY .NPY FILES.

from cnn-svo.

@kinfeparty I am glad that you finally get it working.

from cnn-svo.

@kinfeparty @yan99033 Can you provide your modified monodepth_main.py. I meet the same error. In addition, can you provide the new address about upload data, the address is invalid.

from cnn-svo.

I encountered a new error about roslaunch.
My settings about vo_kitti.yaml is

Directories

sequence_path: "/home/SENSETIME/chenchang1/data/dataset/sequences/00"
image_folder: "image_2" # /media/user/path/to/kitti/dataset/sequences/00/image_2/
depth_folder: "out_depth" # /media/user/path/to/kitti/dataset/sequences/00/disp_image_2_modelcity2kitti/

Trace (Log)

trace_dir: "/home/SENSETIME/chenchang1/catkin_ws/src/CNN-SVO/results"
My opencv is 3.3.1.
The error is:
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7f77d3f177e5]
/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7f77d3f2037a]
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7f77d3f2453c]
/opt/ros/kinetic/lib/libg2o_types_sba.so(_ZN3g2o26EdgeProjectP2MC_IntrinsicsD0Ev+0x2d)[0x7f77cf515dad]
/opt/ros/kinetic/lib/libg2o_core.so(_ZN3g2o7Factory12registerTypeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPNS_32AbstractHyperGraphElementCreatorE+0x33f)[0x7f77cf995f8f]
/opt/ros/kinetic/lib/libg2o_types_sba.so(+0x1db2a)[0x7f77cf50fb2a]
/lib64/ld-linux-x86-64.so.2(+0x106ca)[0x7f77d733d6ca]
/lib64/ld-linux-x86-64.so.2(+0x107db)[0x7f77d733d7db]
/lib64/ld-linux-x86-64.so.2(+0xc6a)[0x7f77d732dc6a]
======= Memory map: ========
00400000-0044b000 r-xp 00000000 08:02 30030540 /home/SENSETIME/chenchang1/catkin_ws/devel/lib/svo_ros/vo_monodepth_offline
0064a000-0064b000 r--p 0004a000 08:02 30030540 /home/SENSETIME/chenchang1/catkin_ws/devel/lib/svo_ros/vo_monodepth_offline
0064b000-0064c000 rw-p 0004b000 08:02 30030540 /home/SENSETIME/chenchang1/catkin_ws/devel/lib/svo_ros/vo_monodepth_offline
0064c000-0064d000 rw-p 00000000 00:00 0
014dd000-018e3000 rw-p 00000000 00:00 0 [heap]
7f779c000000-7f779c021000 rw-p 00000000 00:00 0
7f779c021000-7f77a0000000 ---p 00000000 00:00 0
7f77a25dc000-7f77a85dc000 rw-p 00000000 00:00 0
7f77a85dc000-7f77a85dd000 ---p 00000000 00:00 0
7f77a85dd000-7f77a8ddd000 rwxp 00000000 00:00 0
7f77a8ddd000-7f77a8dde000 ---p 00000000 00:00 0
7f77a8dde000-7f77a95de000 rwxp 00000000 00:00 0
7f77a95de000-7f77ab5de000 rw-p 00000000 00:00 0
7f77ab5de000-7f77ab5df000 ---p 00000000 00:00 0
7f77ab5df000-7f77abddf000 rwxp 00000000 00:00 0
7f77abddf000-7f77adddf000 rw-p 00000000 00:00 0
7f77adddf000-7f77adde0000 ---p 00000000 00:00 0
7f77adde0000-7f77ae5e0000 rwxp 00000000 00:00 0
7f77ae5e0000-7f77b05e0000 rw-p 00000000 00:00 0
7f77b05e0000-7f77b05e1000 ---p 00000000 00:00 0
7f77b05e1000-7f77b0de1000 rwxp 00000000 00:00 0
7f77b0de1000-7f77bade1000 rw-p 00000000 00:00 0
7f77bade1000-7f77bade2000 ---p 00000000 00:00 0
7f77bade2000-7f77bb5e2000 rwxp 00000000 00:00 0
7f77bb5e2000-7f77bb5e3000 ---p 00000000 00:00 0
7f77bb5e3000-7f77bbde3000 rwxp 00000000 00:00 0
7f77bbde3000-7f77bbde4000 ---p 00000000 00:00 0
7f77bbde4000-7f77bc5e4000 rwxp 00000000 00:00 0
7f77bc5e4000-7f77bc5e5000 ---p 00000000 00:00 0
7f77bc5e5000-7f77bcde5000 rwxp 00000000 00:00 0
7f77bcde5000-7f77bcde6000 ---p 00000000 00:00 0
7f77bcde6000-7f77bd5e6000 rwxp 00000000 00:00 0
7f77bd5e6000-7f77bd5e7000 ---p 00000000 00:00 0
7f77bd5e7000-7f77bdde7000 rwxp 00000000 00:00 0
7f77bdde7000-7f77bddec000 r-xp 00000000 08:02 532241 /usr/lib/x86_64-linux-gnu/libXdmcp.so.6.0.0
7f77bddec000-7f77bdfeb000 ---p 00005000 08:02 532241 /usr/lib/x86_64-linux-gnu/libXdmcp.so.6.0.0
7f77bdfeb000-7f77bdfec000 r--p 00004000 08:02 532241 /usr/lib/x86_64-linux-gnu/libXdmcp.so.6.0.0
7f77bdfec000-7f77bdfed000 rw-p 00005000 08:02 532241 /usr/lib/x86_64-linux-gnu/libXdmcp.so.6.0.0
7f77bdfed000-7f77bdfef000 r-xp 00000000 08:02 532230 /usr/lib/x86_64-linux-gnu/libXau.so.6.0.0
7f77bdfef000-7f77be1ef000 ---p 00002000 08:02 532230 /usr/lib/x86_64-linux-gnu/libXau.so.6.0.0
7f77be1ef000-7f77be1f0000 r--p 00002000 08:02 532230 /usr/lib/x86_64-linux-gnu/libXau.so.6.0.0
7f77be1f0000-7f77be1f1000 rw-p 00003000 08:02 532230 /usr/lib/x86_64-linux-gnu/libXau.so.6.0.0
7f77be1f1000-7f77be1f3000 r-xp 00000000 08:02 11273162 /lib/x86_64-linux-gnu/libutil-2.23.so
7f77be1f3000-7f77be3f2000 ---p 00002000 08:02 11273162 /lib/x86_64-linux-gnu/libutil-2.23.so
7f77be3f2000-7f77be3f3000 r--p 00001000 08:02 11273162 /lib/x86_64-linux-gnu/libutil-2.23.so
7f77be3f3000-7f77be3f4000 rw-p 00002000 08:02 11273162 /lib/x86_64-linux-gnu/libutil-2.23.so
7f77be3f4000-7f77be432000 r-xp 00000000 08:02 525409 /usr/lib/x86_64-linux-gnu/libquadmath.so.0.0.0
7f77be432000-7f77be631000 ---p 0003e000 08:02 525409 /usr/lib/x86_64-linux-gnu/libquadmath.so.0.0.0
7f77be631000-7f77be632000 r--p 0003d000 08:02 525409 /usr/lib/x86_64-linux-gnu/libquadmath.so.0.0.0
7f77be632000-7f77be633000 rw-p 0003e000 08:02 525409 /usr/lib/x86_64-linux-gnu/libquadmath.so.0.0.0
7f77be633000-7f77be644000 r-xp 00000000 08:02 532243 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0
7f77be644000-7f77be843000 ---p 00011000 08:02 532243 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0
7f77be843000-7f77be844000 r--p 00010000 08:02 532243 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0
7f77be844000-7f77be845000 rw-p 00011000 08:02 532243 /usr/lib/x86_64-linux-gnu/libXext.so.6.4.0
7f77be845000-7f77bf91e000 r-xp 00000000 08:02 791725 /usr/lib/nvidia-396/libnvidia-glcore.so.396.54
7f77bf91e000-7f77bf93f000 rwxp 010d9000 08:02 791725 /usr/lib/nvidia-396/libnvidia-glcore.so.396.54
7f77bf93f000-7f77c000f000 r-xp 010fa000 08:02 791725 /usr/lib/nvidia-396/libnvidia-glcore.so.396.54
7f77c000f000-7f77c020f000 ---p 017ca000 08:02 791725 /usr/lib/nvidia-396/libnvidia-glcore.so.396.54
7f77c020f000-7f77c0511000 rw-p 017ca000 08:02 791725 /usr/lib/nvidia-396/libnvidia-glcore.so.396.54
7f77c0511000-7f77c052b000 rw-p 00000000 00:00 0
7f77c052b000-7f77c052e000 r-xp 00000000 08:02 919637 /usr/lib/nvidia-396/tls/libnvidia-tls.so.396.54
7f77c052e000-7f77c072e000 ---p 00003000 08:02 919637 /usr/lib/nvidia-396/tls/libnvidia-tls.so.396.54
7f77c072e000-7f77c072f000 rw-p 00003000 08:02 919637 /usr/lib/nvidia-396/tls/libnvidia-tls.so.396.54
7f77c072f000-7f77c0753000 r-xp 00000000 08:02 532795 /usr/lib/x86_64-linux-gnu/libgraphite2.so.3.0.1
7f77c0753000-7f77c0952000 ---p 00024000 08:02 532795 /usr/lib/x86_64-linux-gnu/libgraphite2.so.3.0.1
7f77c0952000-7f77c0954000 r--p 00023000 08:02 532795 /usr/lib/x86_64-linux-gnu/libgraphite2.so.3.0.1
7f77c0954000-7f77c0955000 rw-p 00025000 08:02 532795 /usr/lib/x86_64-linux-gnu/libgraphite2.so.3.0.1
7f77c0955000-7f77c09f9000 r-xp 00000000 08:02 531327 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1
7f77c09f9000-7f77c0bf8000 ---p 000a4000 08:02 531327 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1
7f77c0bf8000-7f77c0bfe000 r--p 000a3000 08:02 531327 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1
7f77c0bfe000-7f77c0bff000 rw-p 000a9000 08:02 531327 /usr/lib/x86_64-linux-gnu/libfreetype.so.6.12.1
7f77c0bff000-7f77c0c20000 r-xp 00000000 08:02 533654 /usr/lib/x86_64-linux-gnu/libxcb.so.1.1.0
7f77c0c20000-7f77c0e1f000 ---p 00021000 08:02 533654 /usr/lib/x86_64-linux-gnu/libxcb.so.1.1.0
7f77c0e1f000-7f77c0e20000 r--p 00020000 08:02 533654 /usr/lib/x86_64-linux-gnu/libxcb.so.1.1.0
7f77c0e20000-7f77c0e21000 rw-p 00021000 08:02 533654 /usr/lib/x86_64-linux-gnu/libxcb.so.1.1.0
7f77c0e21000-7f77c0e28000 r-xp 00000000 08:02 532620 /usr/lib/x86_64-linux-gnu/libffi.so.6.0.4
7f77c0e28000-7f77c1027000 ---p 00007000 08:02 532620 /usr/lib/x86_64-linux-gnu/libffi.so.6.0.4
7f77c1027000-7f77c1028000 r--p 00006000 08:02 532620 /usr/lib/x86_64-linux-gnu/libffi.so.6.0.4
7f77c1028000-7f77c1029000 rw-p 00007000 08:02 532620 /usr/lib/x86_64-linux-gnu/libffi.so.6.0.4
7f77c1029000-7f77c131b000 r-xp 00000000 08:02 524554 /usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0
7f77c131b000-7f77c151b000 ---p 002f2000 08:02 524554 /usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0
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7f77c151d000-7f77c1594000 rw-p 002f4000 08:02 524554 /usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0
7f77c1594000-7f77c15b7000 rw-p 00000000 00:00 0
7f77c15b7000-7f77c1631000 r-xp 00000000 08:02 536029 /usr/lib/x86_64-linux-gnu/libboost_program_options.so.1.58.0
7f77c1631000-7f77c1831000 ---p 0007a000 08:02 536029 /usr/lib/x86_64-linux-gnu/libboost_program_options.so.1.58.0
7f77c1831000-7f77c1834000 r--p 0007a000 08:02 536029 /usr/lib/x86_64-linux-gnu/libboost_program_options.so.1.58.0
7f77c1834000-7f77c1835000 rw-p 0007d000 08:02 536029 /usr/lib/x86_64-linux-gnu/libboost_program_options.so.1.58.0
7f77c1835000-7f77c1849000 r-xp 00000000 08:02 531532 /usr/lib/x86_64-linux-gnu/libtinyxml.so.2.6.2
7f77c1849000-7f77c1a49000 ---p 00014000 08:02 531532 /usr/lib/x86_64-linux-gnu/libtinyxml.so.2.6.2
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7f77c3772000-7f77c3798000 r-xp 00000000 08:02 11272406 /lib/x86_64-linux-gnu/libexpat.so.1.6.0
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7f77c399b000-7f77c39a4000 r-xp 00000000 08:02 11273154 /lib/x86_64-linux-gnu/libcrypt-2.23.so
7f77c39a4000-7f77c3ba3000 ---p 00009000 08:02 11273154 /lib/x86_64-linux-gnu/libcrypt-2.23.so
7f77c3ba3000-7f77c3ba4000 r--p 00008000 08:02 11273154 /lib/x86_64-linux-gnu/libcrypt-2.23.so
7f77c3ba4000-7f77c3ba5000 rw-p 00009000 08:02 11273154 /lib/x86_64-linux-gnu/libcrypt-2.23.so
7f77c3ba5000-7f77c3bd3000 rw-p 00000000 00:00 0
7f77c3bd3000-7f77c3bd7000 r-xp 00000000 08:02 11272395 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f77c3bd7000-7f77c3dd6000 ---p 00004000 08:02 11272395 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f77c3dd6000-7f77c3dd7000 r--p 00003000 08:02 11272395 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f77c3dd7000-7f77c3dd8000 rw-p 00004000 08:02 11272395 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f77c3dd8000-7f77c3f01000 r-xp 00000000 08:02 534754 /usr/lib/x86_64-linux-gnu/libgfortran.so.3.0.0
7f77c3f01000-7f77c4100000 ---p 00129000 08:02 534754 /usr/lib/x86_64-linux-gnu/libgfortran.so.3.0.0
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================================================================================REQUIRED process [svo-1] has died!
process has died [pid 9765, exit code -6, cmd /home/SENSETIME/chenchang1/catkin_ws/devel/lib/svo_ros/vo_monodepth_offline __name:=svo __log:=/home/SENSETIME/chenchang1/.ros/log/82aa15f6-ef19-11e9-8651-04d4c456c018/svo-1.log].
log file: /home/SENSETIME/chenchang1/.ros/log/82aa15f6-ef19-11e9-8651-04d4c456c018/svo-1*.log
Initiating shutdown!

[svo-1] killing on exit
shutting down processing monitor...
... shutting down processing monitor complete

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@cumtchenchang Just make sure that whether you add the code
SCALE = 0.3128 # (718.856 * 0.54 / 1241)
in monodepth_simple.py.
The log can't show any information about the error.

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@kinfeparty Thank you for your response. I added SCALE in monodepth_main.py.
Changed part is:
SCALE = 0.3128 # (718.856 * 0.54 / 1241)
for step in range(num_test_samples):
disp = sess.run(model.disp_left_est[0])
disp = post_process_disparity(disp.squeeze())
#disparities[step] = disp[0].squeeze()
# disparities_pp[step] = post_process_disparity(disp.squeeze())
# If you wish to resize the disparity map back to original size
# disp = cv2.resize(disp, (1241, 376), interpolation=cv2.INTER_LINEAR)
# Convert disparity to depth
disp = SCALE/disp
np.save(output_directory + '/' + 'depth_' + str(step) + '.npy', disp)

The link is: https://github.com/cumtchenchang/monodepth/blob/master/monodepth_main2.py

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@kinfeparty Every .npy file is 1M, maybe something wrong with my provided code. Could you provide your changed code? Thank you!

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@cumtchenchang Sorry，I deleted the code,so you may ask the author.

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@kinfeparty Thank you sincerely. I hope the author can upload the code.

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@cumtchenchang @kinfeparty
Sorry for the late response. I haven't used the npy files for a while. The code for saving the .npy files was created about 1.5 years ago, and I didn't keep it since I did the online depth inference.

I read your code. You should be importing monodepth.py not monodepth_model.py.
Therefore, Line 23 should be
from monodepth import *
and Line 185 should be
model = monodepth(left, 'vgg')
also the whole def train(): can be deleted to avoid conflicts.

Again, I would strongly recommend you to save the depth maps into one Numpy array file (n, w, h, 1) instead of separate Numpy array files (e.g., (w, h, 1), (w, h, 1), ...). I found that reading the file has non-negligible computational overhead so it would be better to preload the depth maps from a Numpy array file before processing VO. But you would need to also modify the CNN-SVO source code to do so.

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Thank you for your response. I followed your suggestion. However, how to solve the dependency of model.disp_left_est[0] in line 217? Can you provide a complete and runnable code. Thank you!

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Hello @yan99033 , I have some question
I have successfully install the library for offline mode
When I want to run roslaunch svo_ros kittiOffline00-02.launch, I have this error

kadut@kadut-pc:~/catkin_ws$ roslaunch svo_ros kittiOffline00-02.launch
[kittiOffline00-02.launch] is neither a launch file in package [svo_ros] nor is [svo_ros] a launch file name
The traceback for the exception was written to the log file

Do you know what is caused the error? Or it's because there is no svo_ros file after my installation (My installation is failed)?
After run catkin_make, it only produces folder build and devel, there is no install folder, is it my error?
Thank you very much

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@kadut99 source ～/catkin_ws/devel/setup.bash
In addition. Can you generate offline kitti depth correctly? If you can run this demo sucessfully, please tell me how to get the depth .npy files. Thank you.

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@kadut99 source ～/catkin_ws/devel/setup.bash
In addition. Can you generate offline kitti depth correctly? If you can run this demo sucessfully, please tell me how to get the depth .npy files. Thank you.

@cumtchenchang yes right I forgot that step
EDITED :
I have the problem related with the .npy files
Becausssse everytime I try to run roslaunch, it give me nan value
I think it because not much disparity information I could get

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@kadut99 Have you solved the problem? If you solve the problem, please tell me the correct way. Thank you.

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@kadut99 Have you solved the problem? If you solve the problem, please tell me the correct way. Thank you.

Not yet, still have a problem with .NPY files
this is my error :
[ INFO] [1571760614.827965871]: Init: Selected first frame.
stage second frame
Matrix is close to singular! Stop Optimizing.
H = -nan -nan -nan -nan nan -nan
-nan -nan -nan -nan nan -nan
-nan -nan -nan -nan -nan -nan
-nan -nan -nan -nan -nan -nan
nan nan nan nan nan nan
-nan -nan -nan -nan -nan -nan
Jres = -nan
-nan
-nan
-nan
nan
-nan
[ INFO] [1571760614.917232609]: Init: CNNVO tracked 105 features
disparities size is smaller than the minimum tracked feature
[ INFO] [1571760614.917810990]: RESET
Result saved in file...SVO terminated.
================================================================================REQUIRED process [svo-1] has died!
process has finished cleanly
log file: /home/ferdyan/.ros/log/9295c24e-f4cd-11e9-8eef-107b4418cc95/svo-1*.log
Initiating shutdown!

[svo-1] killing on exit
shutting down processing monitor...
... shutting down processing monitor complete
done

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