Running Detector
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Running Pose Prediction
100%|████████████████████████████████████████████████████████████████████████████| 24434/24434 [32:13<00:00, 12.64it/s]
Saving results in C:\Users\charl\Downloads\test_multiDLC_Resnet50_test_multiJul21shuffle1_detector_250_snapshot_200.h5 and C:\Users\charl\Downloads\test_multiDLC_Resnet50_test_multiJul21shuffle1_detector_250_snapshot_200_full.pickle
Processing...  C:\Users\charl\Downloads\test_multi.mp4
Loading From None\test_multiDLC_Resnet50_test_multiJul21shuffle1_detector_250_snapshot_200.h5
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
File ~\.conda\envs\deeplabcut3\Lib\site-packages\deeplabcut\utils\auxfun_multianimal.py:217, in LoadFullMultiAnimalData(dataname)
    216 try:
--> 217     with open(data_file, "rb") as handle:
    218         data = pickle.load(handle)

FileNotFoundError: [Errno 2] No such file or directory: 'None\\test_multiDLC_Resnet50_test_multiJul21shuffle1_detector_250_snapshot_200_full.pickle'

During handling of the above exception, another exception occurred:

error                                     Traceback (most recent call last)
Cell In[14], line 1
----> 1 scorername = deeplabcut.analyze_videos(config_path, videofile_path, videotype='.mp4')

File ~\.conda\envs\deeplabcut3\Lib\site-packages\deeplabcut\compat.py:856, in analyze_videos(config, videos, videotype, shuffle, trainingsetindex, gputouse, save_as_csv, in_random_order, destfolder, batchsize, cropping, TFGPUinference, dynamic, modelprefix, robust_nframes, allow_growth, use_shelve, auto_track, n_tracks, calibrate, identity_only, use_openvino, engine, **torch_kwargs)
    851     if use_shelve:
    852         raise NotImplementedError(
    853             f"The 'use_shelve' option is not yet implemented with {engine}"
    854         )
--> 856     return analyze_videos(
    857         config,
    858         videos=videos,
    859         videotype=videotype,
    860         shuffle=shuffle,
    861         trainingsetindex=trainingsetindex,
    862         save_as_csv=save_as_csv,
    863         destfolder=destfolder,
    864         batchsize=batchsize,
    865         modelprefix=modelprefix,
    866         auto_track=auto_track,
    867         identity_only=identity_only,
    868         overwrite=False,
    869         **torch_kwargs,
    870     )
    872 raise NotImplementedError(f"This function is not implemented for {engine}")

File ~\.conda\envs\deeplabcut3\Lib\site-packages\deeplabcut\pose_estimation_pytorch\apis\analyze_videos.py:375, in analyze_videos(config, videos, videotype, shuffle, trainingsetindex, save_as_csv, snapshot_index, detector_snapshot_index, device, destfolder, batchsize, modelprefix, transform, auto_track, identity_only, overwrite)
    366             _save_assemblies(
    367                 output_path,
    368                 output_prefix,
   (...)
    372                 with_identity,
    373             )
    374             if auto_track:
--> 375                 convert_detections2tracklets(
    376                     config=config,
    377                     videos=str(video),
    378                     videotype=videotype,
    379                     shuffle=shuffle,
    380                     trainingsetindex=trainingsetindex,
    381                     overwrite=False,
    382                     identity_only=identity_only,
    383                     destfolder=str(destfolder),
    384                 )
    385                 stitch_tracklets(
    386                     config,
    387                     [str(video)],
   (...)
    391                     destfolder=str(destfolder),
    392                 )
    394 print(
    395     "The videos are analyzed. Now your research can truly start!\n"
    396     "You can create labeled videos with 'create_labeled_video'.\n"
   (...)
    399     "few representative outlier frames.\n"
    400 )

File ~\.conda\envs\deeplabcut3\Lib\site-packages\deeplabcut\pose_estimation_pytorch\apis\convert_detections_to_tracklets.py:123, in convert_detections2tracklets(config, videos, videotype, shuffle, trainingsetindex, overwrite, destfolder, ignore_bodyparts, inferencecfg, modelprefix, greedy, calibrate, window_size, identity_only, track_method)
    121 data_filename = output_path / (data_prefix + ".h5")
    122 print(f"Loading From {data_filename}")
--> 123 data, metadata = auxfun_multianimal.LoadFullMultiAnimalData(str(data_filename))
    124 if track_method == "ellipse":
    125     method = "el"

File ~\.conda\envs\deeplabcut3\Lib\site-packages\deeplabcut\utils\auxfun_multianimal.py:220, in LoadFullMultiAnimalData(dataname)
    218         data = pickle.load(handle)
    219 except (pickle.UnpicklingError, FileNotFoundError):
--> 220     data = shelve.open(data_file, flag="r")
    221 with open(data_file.replace("_full.", "_meta."), "rb") as handle:
    222     metadata = pickle.load(handle)

File ~\.conda\envs\deeplabcut3\Lib\shelve.py:243, in open(filename, flag, protocol, writeback)
    230 def open(filename, flag='c', protocol=None, writeback=False):
    231     """Open a persistent dictionary for reading and writing.
    232
    233     The filename parameter is the base filename for the underlying
   (...)
    240     See the module's __doc__ string for an overview of the interface.
    241     """
--> 243     return DbfilenameShelf(filename, flag, protocol, writeback)

File ~\.conda\envs\deeplabcut3\Lib\shelve.py:227, in DbfilenameShelf.__init__(self, filename, flag, protocol, writeback)
    225 def __init__(self, filename, flag='c', protocol=None, writeback=False):
    226     import dbm
--> 227     Shelf.__init__(self, dbm.open(filename, flag), protocol, writeback)

File ~\.conda\envs\deeplabcut3\Lib\dbm\__init__.py:85, in open(file, flag, mode)
     83         mod = _defaultmod
     84     else:
---> 85         raise error[0]("db file doesn't exist; "
     86                        "use 'c' or 'n' flag to create a new db")
     87 elif result == "":
     88     # db type cannot be determined
     89     raise error[0]("db type could not be determined")

error: db file doesn't exist; use 'c' or 'n' flag to create a new db