Comments (7)
Good Morning,
Sorry for the late response, I'm not mantaining the repo on daily basis.
I had the same problem, using the right function and values for opencv gave me many problems!
I think that actually is not analizing any frame.
That way u receive NaN value.
Bests
AF
from tensorflow_object_tracking_video.
Hello , i also got this problem, is there any way to solve it? @DrewNF
from tensorflow_object_tracking_video.
Could u paste here the error/terminal output with more info? Because the problem depends on different dependencies so I need more insight to try to give u a solution! :)
Bests
AF
from tensorflow_object_tracking_video.
Hi @DrewNF , sorry for my late response~
When i run python VID_yolo.py --path_video video.mp4
, i got my terminal output:
Opening File Video:video.mp4
Opened File Video:video.mp4
Start Reading File Video:video.mp4
8 Frames to Read
[===================================================================] 100% Time: 0:00:00
Finish Reading File Video:video.mp4
Starting DET Phase
Building YOLO_small graph...
Layer 1 : Type = Conv, Size = 7 * 7, Stride = 2, Filters = 64, Input channels = 3
Layer 2 : Type = Pool, Size = 2 * 2, Stride = 2
Layer 3 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 192, Input channels = 64
Layer 4 : Type = Pool, Size = 2 * 2, Stride = 2
Layer 5 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 128, Input channels = 192
Layer 6 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 256, Input channels = 128
Layer 7 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 256, Input channels = 256
Layer 8 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256
Layer 9 : Type = Pool, Size = 2 * 2, Stride = 2
Layer 10 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 256, Input channels = 512
Layer 11 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256
Layer 12 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 256, Input channels = 512
Layer 13 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256
Layer 14 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 256, Input channels = 512
Layer 15 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256
Layer 16 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 256, Input channels = 512
Layer 17 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 512, Input channels = 256
Layer 18 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 512, Input channels = 512
Layer 19 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 512
Layer 20 : Type = Pool, Size = 2 * 2, Stride = 2
Layer 21 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 512, Input channels = 1024
Layer 22 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 512
Layer 23 : Type = Conv, Size = 1 * 1, Stride = 1, Filters = 512, Input channels = 1024
Layer 24 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 512
Layer 25 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 1024
Layer 26 : Type = Conv, Size = 3 * 3, Stride = 2, Filters = 1024, Input channels = 1024
Layer 27 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 1024
Layer 28 : Type = Conv, Size = 3 * 3, Stride = 1, Filters = 1024, Input channels = 1024
Layer 29 : Type = Full, Hidden = 512, Input dimension = 50176, Flat = 1, Activation = 1
Layer 30 : Type = Full, Hidden = 4096, Input dimension = 512, Flat = 0, Activation = 1
Layer 32 : Type = Full, Hidden = 1470, Input dimension = 4096, Flat = 0, Activation = 0
2018-04-08 14:39:17.963536: 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.
2018-04-08 14:39:17.963559: 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.
2018-04-08 14:39:17.963567: 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.
2018-04-08 14:39:17.963574: 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.
2018-04-08 14:39:17.963580: 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.
Loading complete!
8 Frames to DET
[====================================================================================================================================] 100% Time: 0:00:08
det_frames/frame0_det.jpg
Start Making File Video:output.mp4
8 Frames to Compress
[====================================================================================================================================] 100% Time: 0:00:00
Finished Making File Video:output.mp4
Starting Loading Results
[====================================================================================================================================] 100% Time: 0:00:00
Finished Loading Results
Computing Final Mean Reasults..
Class: nan
Max Value: nan
Min Value: nan
Elapsed Time:12 Seconds
Running Completed with Success!!!
Hope to find a solution~
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First time I saw this output!
I think is something related to Tensorflow as u see in the output there are some libraries not well binded.
U adapted the code or are you doing just run tests??
Best
AF
from tensorflow_object_tracking_video.
Hello @DrewNF , i didn't change the code and just run tests. I use conda to manage my python packages and here is my environment:
opencv 2.4.11
python 2.7.14
tensorflow 1.1.0
Hope it maybe helpful~
from tensorflow_object_tracking_video.
pypi has removed opencv-python==2.4.11 , so how to build a virtualenv to run this code?
from tensorflow_object_tracking_video.
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