Fatal Python error: PyImport_GetModuleDict: no module dictionary!

[ryandeng1]

When I am writing a video clip, I get this error. It originates from VideoFileClip.py in the del method.

[Zulko]

Thanks for the report. Can you give more infos on your system (windows, mac, linux), python version, and a minimal code to reproduce the error ?

[ryandeng1]

Windows, Python 3.5. I had this error on Ubuntu 16.04 as well with Python 3.5.
Basically I am taking in every frame of a 5 minute (1280 x 720) video, processing it, and then writing it to an output video file.
clip1 = VideoFileClip("//.../.mp4")
clip = clip1.fl_image(func)
clip.write_videofile(video_output, audio=False)

This is the error I'm getting:

0%| | 21/7749 [00:31<3:07:42, 1.46s/it]Traceback (most recent call last):
File "C:/vehicle-detection/main.py", line 50, in
clip.write_videofile(video_output, audio=False)
File "", line 2, in write_videofile
File "C:\Program Files\Python35\lib\site-packages\moviepy\decorators.py", line 54, in requires_duration
return f(clip, *a, **k)
File "", line 2, in write_videofile
File "C:\Program Files\Python35\lib\site-packages\moviepy\decorators.py", line 137, in use_clip_fps_by_default
return f(clip, *new_a, **new_kw)
File "", line 2, in write_videofile
File "C:\Program Files\Python35\lib\site-packages\moviepy\decorators.py", line 22, in convert_masks_to_RGB
return f(clip, *a, **k)
File "C:\Program Files\Python35\lib\site-packages\moviepy\video\VideoClip.py", line 349, in write_videofile
progress_bar=progress_bar)
File "C:\Program Files\Python35\lib\site-packages\moviepy\video\io\ffmpeg_writer.py", line 209, in ffmpeg_write_video
fps=fps, dtype="uint8"):
File "C:\Program Files\Python35\lib\site-packages\tqdm_tqdm.py", line 833, in iter
for obj in iterable:
File "C:\Program Files\Python35\lib\site-packages\moviepy\Clip.py", line 475, in generator
frame = self.get_frame(t)
File "", line 2, in get_frame
File "C:\Program Files\Python35\lib\site-packages\moviepy\decorators.py", line 89, in wrapper
return f(*new_a, **new_kw)
File "C:\Program Files\Python35\lib\site-packages\moviepy\Clip.py", line 95, in get_frame
return self.make_frame(t)
File "C:\Program Files\Python35\lib\site-packages\moviepy\Clip.py", line 136, in
newclip = self.set_make_frame(lambda t: fun(self.get_frame, t))
File "C:\Program Files\Python35\lib\site-packages\moviepy\video\VideoClip.py", line 533, in
return self.fl(lambda gf, t: image_func(gf(t)), apply_to)
File "C:/vehicle-detection/main.py", line 9, in pipeline_yolo
img_undist, img_lane_augmented, lane_info = lane_process(img)
File "C:\vehicle-detection\lane.py", line 598, in lane_process
detector(binary_sub, ploty, visualization)
File "C:\vehicle-detection\lane.py", line 550, in detector
left_fit, right_fit = full_search(binary_sub, visualization=visualization)
File "C:\vehicle-detection\lane.py", line 292, in full_search
right_fit = np.polyfit(righty, rightx, 2)
File "C:\Program Files\Python35\lib\site-packages\numpy\lib\polynomial.py", line 555, in polyfit
raise TypeError("expected non-empty vector for x")
TypeError: expected non-empty vector for x
Fatal Python error: PyImport_GetModuleDict: no module dictionary!

Thread 0x0000378c (most recent call first):
File "C:\Program Files\Python35\lib\site-packages\tqdm_tqdm.py", line 97 in run
File "C:\Program Files\Python35\lib\threading.py", line 914 in _bootstrap_inner
File "C:\Program Files\Python35\lib\threading.py", line 882 in _bootstrap

Current thread 0x00002fc8 (most recent call first):
File "C:\Program Files\Python35\lib\site-packages\moviepy\video\io\VideoFileClip.py", line 116 in del

Thanks so much!

[Zulko]

So apparently it is not a moviepy problem. The error appears during the generation of a frame, but is due to the frame generator, not Moviepy. You have a project called vehicle-detection/lane.py in which you are doing a polyfit with an empty vector.

[ryandeng1]

I see. Thanks so much!

[shuoyuanxu]

Hi, I seem to have a similar problem, can you please help me out? I am using UBUNTU16, python3.6, and with tensorflow GPU.

when my code is running:

loading existing classifier...
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
2017-09-23 11:07:21.645197: 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.
2017-09-23 11:07:21.645215: 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.
2017-09-23 11:07:21.645237: 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.
2017-09-23 11:07:21.645240: 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.
2017-09-23 11:07:21.645247: 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.
2017-09-23 11:07:22.013380: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-23 11:07:22.013814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1050
major: 6 minor: 1 memoryClockRate (GHz) 1.493
pciBusID 0000:01:00.0
Total memory: 3.95GiB
Free memory: 3.90GiB
2017-09-23 11:07:22.013843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-09-23 11:07:22.013848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-09-23 11:07:22.013898: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0)
Loading complete!
<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>

This is where the error starts

[MoviePy] >>>> Building video /home/shuoyuan/examples/project_YOLO.mp4
[MoviePy] Writing video /home/shuoyuan/examples/project_YOLO.mp4
0%| | 0/26 [00:00<?, ?it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
4%|█▋ | 1/26 [00:00<00:06, 3.92it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
8%|███▍ | 2/26 [00:00<00:06, 3.99it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
12%|█████ | 3/26 [00:00<00:05, 4.06it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
15%|██████▊ | 4/26 [00:00<00:05, 4.16it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
19%|████████▍ | 5/26 [00:01<00:04, 4.24it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
23%|██████████▏ | 6/26 [00:01<00:04, 4.18it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
27%|███████████▊ | 7/26 [00:01<00:04, 4.14it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
31%|█████████████▌ | 8/26 [00:01<00:04, 4.20it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
35%|███████████████▏ | 9/26 [00:02<00:04, 4.20it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
38%|████████████████▌ | 10/26 [00:02<00:03, 4.22it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
42%|██████████████████▏ | 11/26 [00:02<00:03, 4.20it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
46%|███████████████████▊ | 12/26 [00:02<00:03, 4.22it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
50%|█████████████████████▌ | 13/26 [00:03<00:03, 4.32it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
54%|███████████████████████▏ | 14/26 [00:03<00:02, 4.40it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
58%|████████████████████████▊ | 15/26 [00:03<00:02, 4.32it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
62%|██████████████████████████▍ | 16/26 [00:03<00:02, 4.36it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
65%|████████████████████████████ | 17/26 [00:03<00:02, 4.39it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
69%|█████████████████████████████▊ | 18/26 [00:04<00:01, 4.43it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
73%|███████████████████████████████▍ | 19/26 [00:04<00:01, 4.41it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
77%|█████████████████████████████████ | 20/26 [00:04<00:01, 4.41it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
81%|██████████████████████████████████▋ | 21/26 [00:04<00:01, 4.41it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
85%|████████████████████████████████████▍ | 22/26 [00:05<00:00, 4.37it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
88%|██████████████████████████████████████ | 23/26 [00:05<00:00, 4.37it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
92%|███████████████████████████████████████▋ | 24/26 [00:05<00:00, 4.41it/s]<yolo_pipeline.yolo_tf object at 0x7fd241cd0860>
96%|█████████████████████████████████████████▎ | 25/26 [00:05<00:00, 4.37it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: /home/shuoyuan/examples/project_YOLO.mp4

Fatal Python error: PyImport_GetModuleDict: no module dictionary!

Thread 0x00007fd1825fd700 (most recent call first):
File "/home/shuoyuan/anaconda3/lib/python3.6/site-packages/tqdm/_tqdm.py", line 97 in run
File "/home/shuoyuan/anaconda3/lib/python3.6/threading.py", line 916 in _bootstrap_inner
File "/home/shuoyuan/anaconda3/lib/python3.6/threading.py", line 884 in _bootstrap

Current thread 0x00007fd294d3d700 (most recent call first):
File "/home/shuoyuan/anaconda3/lib/python3.6/site-packages/moviepy/video/io/VideoFileClip.py", line 116 in del

The code is here https://github.com/JunshengFu/vehicle-detection