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talmolab
raise KeyError(f"Unable to load frame {idx} from {self}.")
Hello, I am having some issues with running inferences on some of my videos.
I used microsoft clipchamp to brighten some of my videos, and then used the ffmpeg command listed on the sleap.ai website copied below.
ffmpeg -y -i "input.mp4" -c:v libx264 -pix_fmt yuv420p -preset superfast -crf 23 "output.mp4"
The labeled frames from the edited videos (saved in a pkg.slp format) trained a model with no errors.
However these new videos will not be predicted on when I run sleap-track using the following command in a bash terminal:
sleap-track only_subjects_brighter_mp4s/11_12_23_24.1_reconverted.mp4 --video.index 0 --video.input_format channels_last -m models/250522_171948.centroid -m models/250522_171948.centered_instance --tracking.tracker flow --tracking.pre_cull_to_target 1 --tracking.pre_cull_iou_threshold 0.8 --tracking.similarity instance --tracking.match greedy --tracking.track_window 5 --tracking.save_shifted_instances 0 --tracking.post_connect_single_breaks 0 -o 11_12_23_24.2.mp4.predictions.slp --verbosity json
When I run the inference on the same videos prior to the clipchamp editing (still converted with the ffmpeg command above) using the same models from the clipchamp edited training data set, it creates a prediciton.slp file as expected.
When I try to predict on these clipchamp edited videos, SLEAP throws the below error:
raise KeyError(f"Unable to load frame {idx} from {self}.")
KeyError: "Unable to load frame 0 from MediaVideo(filename='only_subjects_brighter_mp4s/11_12_23_24.1_reconverted.mp4', grayscale=False, bgr=True, dataset='', input_format='channels_last')."
- OS: Linux LSB Version: :core-4.1-amd64:core-4.1-noarch:cxx-4.1-amd64:cxx-4.1-noarch:desktop-4.1-amd64:desktop-4.1-noarch:languages-4.1-amd64:languages-4.1-noarch:printing-4.1-amd64:printing-4.1-noarch Distributor ID: RedHatEnterprise Description: Red Hat Enterprise Linux release 8.10 (Ootpa) Release: 8.10 Codename: Ootpa
- Version(s): SLEAP v1.3.3 , python 3.7.12
- SLEAP installation method (listed here):
- [ x] Conda from package
- [ ] Conda from source
- [ ] pip package
- [ ] Apple Silicon Macs
Environment packages
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
absl-py 1.0.0 pypi_0 pypi
alsa-lib 1.2.3.2 h166bdaf_0 conda-forge
astunparse 1.6.3 pypi_0 pypi
attrs 21.4.0 pyhd8ed1ab_0 conda-forge
backports-zoneinfo 0.2.1 pypi_0 pypi
blosc 1.21.5 h0f2a231_0 conda-forge
brotli 1.0.9 h166bdaf_9 conda-forge
brotli-bin 1.0.9 h166bdaf_9 conda-forge
brunsli 0.1 h9c3ff4c_0 conda-forge
bzip2 1.0.8 h4bc722e_7 conda-forge
c-ares 1.34.5 hb9d3cd8_0 conda-forge
c-blosc2 2.12.0 hb4ffafa_0 conda-forge
ca-certificates 2025.4.26 hbd8a1cb_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 4.2.4 pypi_0 pypi
cairo 1.16.0 h6cf1ce9_1008 conda-forge
cattrs 1.1.1 pyhd8ed1ab_0 conda-forge
certifi 2024.8.30 pyhd8ed1ab_0 conda-forge
cfitsio 4.0.0 h9a35b8e_0 conda-forge
charls 2.3.4 h9c3ff4c_0 conda-forge
charset-normalizer 2.0.9 pypi_0 pypi
cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge
cuda-nvcc 11.3.58 h2467b9f_0 nvidia
cudatoolkit 11.3.1 hb98b00a_13 conda-forge
cudnn 8.2.1.32 h86fa8c9_0 conda-forge
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cytoolz 0.12.0 py37h540881e_0 conda-forge
dask-core 2022.2.0 pyhd8ed1ab_0 conda-forge
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efficientnet 1.0.0 pypi_0 pypi
expat 2.7.0 h5888daf_0 conda-forge
ffmpeg 4.3.2 h37c90e5_3 conda-forge
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gmp 6.3.0 hac33072_2 conda-forge
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google-auth 2.3.3 pypi_0 pypi
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harfbuzz 2.9.1 h83ec7ef_1 conda-forge
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idna 3.3 pypi_0 pypi
image-classifiers 1.0.0 pypi_0 pypi
imagecodecs 2021.11.20 py37h119f88a_2 conda-forge
imageio 2.36.0 pyh12aca89_1 conda-forge
imgaug 0.4.0 pyhd8ed1ab_1 conda-forge
imgstore 0.2.9 pypi_0 pypi
importlib-metadata 4.10.0 pypi_0 pypi
importlib-resources 5.12.0 pypi_0 pypi
jasper 1.900.1 h07fcdf6_1006 conda-forge
joblib 1.3.2 pyhd8ed1ab_0 conda-forge
jpeg 9e h0b41bf4_3 conda-forge
jsmin 3.0.1 pyhd8ed1ab_0 conda-forge
jsonpickle 1.2 py_0 conda-forge
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jxrlib 1.1 hd590300_3 conda-forge
keras 2.7.0 pypi_0 pypi
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keras-preprocessing 1.1.2 pypi_0 pypi
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libcblas 3.9.0 20_linux64_openblas conda-forge
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libzlib 1.2.13 h4ab18f5_6 conda-forge
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locket 1.0.0 pyhd8ed1ab_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
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opencv 4.5.3 py37h89c1867_1 conda-forge
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openh264 2.1.1 h780b84a_0 conda-forge
openjpeg 2.5.0 h7d73246_1 conda-forge
openssl 1.1.1w hd590300_0 conda-forge
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pandas 1.3.5 py37he8f5f7f_0 conda-forge
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pygments 2.17.2 pyhd8ed1ab_0 conda-forge
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python 3.7.12 hb7a2778_100_cpython conda-forge
python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge
python-rapidjson 1.9 py37hd23a5d3_0 conda-forge
python_abi 3.7 4_cp37m conda-forge
pytz 2024.2 pyhd8ed1ab_0 conda-forge
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qt 5.12.9 hda022c4_4 conda-forge
qtpy 2.4.2 pyhdecd6ff_0 conda-forge
readline 8.2 h8c095d6_2 conda-forge
requests 2.26.0 pypi_0 pypi
requests-oauthlib 1.3.0 pypi_0 pypi
rich 13.8.1 pyhd8ed1ab_0 conda-forge
ruamel-yaml 0.17.32 pypi_0 pypi
ruamel-yaml-clib 0.2.7 pypi_0 pypi
scikit-image 0.19.3 py37hfb7772e_1 conda-forge
scikit-learn 1.0 py37hf0f1638_1 conda-forge
scikit-video 1.1.11 pyh24bf2e0_0 conda-forge
scipy 1.7.3 py37hf2a6cf1_0 conda-forge
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six 1.16.0 pyh6c4a22f_0 conda-forge
sleap 1.3.3 pypi_0 pypi
snappy 1.1.10 hdb0a2a9_1 conda-forge
sqlite 3.46.0 h6d4b2fc_0 conda-forge
statsmodels 0.13.2 py37hda87dfa_0 conda-forge
tensorboard 2.7.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.0 pypi_0 pypi
tensorflow 2.7.0 pypi_0 pypi
tensorflow-estimator 2.7.0 pypi_0 pypi
tensorflow-hub 0.13.0 pyh56297ac_0 conda-forge
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termcolor 1.1.0 pypi_0 pypi
threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge
tifffile 2021.11.2 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
tomli 2.0.0 pypi_0 pypi
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unicodedata2 14.0.0 py37h540881e_1 conda-forge
urllib3 1.26.7 pypi_0 pypi
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x264 1!161.3030 h7f98852_1 conda-forge
xorg-kbproto 1.0.7 hb9d3cd8_1003 conda-forge
xorg-libice 1.1.2 hb9d3cd8_0 conda-forge
xorg-libsm 1.2.6 he73a12e_0 conda-forge
xorg-libx11 1.8.4 h0b41bf4_0 conda-forge
xorg-libxau 1.0.12 hb9d3cd8_0 conda-forge
xorg-libxdmcp 1.1.5 hb9d3cd8_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-renderproto 0.11.1 hb9d3cd8_1003 conda-forge
xorg-xextproto 7.3.0 hb9d3cd8_1004 conda-forge
xorg-xproto 7.0.31 hb9d3cd8_1008 conda-forge
xz 5.8.1 hbcc6ac9_1 conda-forge
xz-gpl-tools 5.8.1 hbcc6ac9_1 conda-forge
xz-tools 5.8.1 hb9d3cd8_1 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
zeromq 4.3.5 h59595ed_1 conda-forge
zfp 0.5.5 h9c3ff4c_8 conda-forge
zipp 3.6.0 pypi_0 pypi
zlib 1.2.13 h4ab18f5_6 conda-forge
zlib-ng 2.0.7 h0b41bf4_0 conda-forge
zstd 1.5.6 ha6fb4c9_0 conda-forge
Logs
error-logVideo: only_subjects_brighter_mp4s/11_12_23_24.1_reconverted.mp4
2025-05-27 11:12:09.122172: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2025-05-27 11:12:22.406519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78920 MB memory: -> device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:07:00.0, compute capability: 8.0
Traceback (most recent call last):
File "/blue/npadillacoreano/mcum/conda/envs/sleap/bin/sleap-track", line 33, in <module>
sys.exit(load_entry_point('sleap==1.3.3', 'console_scripts', 'sleap-track')())
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/nn/inference.py", line 5424, in main
labels_pr = predictor.predict(provider)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/nn/inference.py", line 526, in predict
self._make_labeled_frames_from_generator(generator, data)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/nn/inference.py", line 2633, in _make_labeled_frames_from_generator
for ex in generator:
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/nn/inference.py", line 449, in _predict_generator
n_total = len(data_provider)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/nn/data/providers.py", line 350, in __len__
return len(self.video)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 1082, in __len__
return self.frames
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 1046, in __getattr__
return getattr(self.backend, item)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 443, in frames
return int(self.__frames_float)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 404, in __frames_float
return self.__reader.get(cv2.CAP_PROP_FRAME_COUNT)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 396, in __reader
np.alltrue(self.test_frame[..., 0] == self.test_frame[..., -1])
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 411, in test_frame
self._test_frame_ = self.get_frame(0, grayscale=False)
File "/blue/npadillacoreano/mcum/conda/envs/sleap/lib/python3.7/site-packages/sleap/io/video.py", line 496, in get_frame
raise KeyError(f"Unable to load frame {idx} from {self}.")
KeyError: "Unable to load frame 0 from MediaVideo(filename='only_subjects_brighter_mp4s/11_12_23_24.1_reconverted.mp4', grayscale=False, bgr=True, dataset='', input_format='channels_last')."
sleap-diagnostic
==========PIP==========
absl-py==1.0.0
astunparse==1.6.3
attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1640799537051/work
backports.zoneinfo==0.2.1
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
cachetools==4.2.4
cattrs @ file:///home/conda/feedstock_root/build_artifacts/cattrs_1604136207372/work
certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1725278078093/work/certifi
charset-normalizer==2.0.9
cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1674202310934/work
cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1635519461629/work
cytoolz @ file:///home/conda/feedstock_root/build_artifacts/cytoolz_1657553457169/work
dask @ file:///home/conda/feedstock_root/build_artifacts/dask-core_1644602974678/work
efficientnet==1.0.0
flatbuffers==2.0
fonttools @ file:///home/conda/feedstock_root/build_artifacts/fonttools_1666389892786/work
fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1674184942191/work
gast==0.4.0
google-auth==2.3.3
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.43.0
h5py @ file:///home/conda/feedstock_root/build_artifacts/h5py_1604753641401/work
hdmf==3.6.1
idna==3.3
image-classifiers==1.0.0
imagecodecs @ file:///home/conda/feedstock_root/build_artifacts/imagecodecs_1644819473370/work
imageio @ file:///home/conda/feedstock_root/build_artifacts/imageio_1729190692267/work
imgaug @ file:///home/conda/feedstock_root/build_artifacts/imgaug_1640909786103/work
imgstore==0.2.9
importlib-metadata==4.10.0
importlib-resources==5.12.0
joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1691577114857/work
jsmin @ file:///home/conda/feedstock_root/build_artifacts/jsmin_1642532731678/work
jsonpickle==1.2
jsonschema==4.17.3
keras==2.7.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1657953088445/work
libclang==12.0.0
locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work
Markdown==3.3.6
markdown-it-py @ file:///home/conda/feedstock_root/build_artifacts/markdown-it-py_1677100944732/work
matplotlib @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-suite_1661439848456/work
mdurl @ file:///home/conda/feedstock_root/build_artifacts/mdurl_1704317613764/work
munkres==1.1.4
ndx-pose==0.1.1
networkx @ file:///home/conda/feedstock_root/build_artifacts/networkx_1635253012265/work
nixio==1.5.3
numpy==1.19.5
oauthlib==3.1.1
opencv-python-headless==4.2.0.34
opt-einsum==3.3.0
packaging==21.3
pandas==1.3.5
partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1695667515973/work
patsy @ file:///home/conda/feedstock_root/build_artifacts/patsy_1704469236901/work
Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1660385854171/work
pkgutil_resolve_name==1.3.10
protobuf==3.16.0
psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1666155398032/work
pyasn1==0.4.8
pyasn1-modules==0.2.8
Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1700607939962/work
pykalman @ file:///home/conda/feedstock_root/build_artifacts/pykalman_1711547707628/work
pynwb==2.3.3
pyparsing==3.0.6
pyrsistent==0.19.3
PySide2==5.13.2
python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
python-rapidjson @ file:///home/conda/feedstock_root/build_artifacts/python-rapidjson_1665999896718/work
pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1726055524169/work
PyWavelets @ file:///home/conda/feedstock_root/build_artifacts/pywavelets_1649616401885/work
PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1648757092905/work
pyzmq @ file:///home/conda/feedstock_root/build_artifacts/pyzmq_1663830492333/work
qimage2ndarray==1.10.0
QtPy @ file:///home/conda/feedstock_root/build_artifacts/qtpy_1730763764885/work
requests==2.26.0
requests-oauthlib==1.3.0
rich @ file:///home/conda/feedstock_root/build_artifacts/rich_1726066019428/work/dist
rsa==4.8
ruamel.yaml==0.17.32
ruamel.yaml.clib==0.2.7
scikit-image @ file:///home/conda/feedstock_root/build_artifacts/scikit-image_1660146497293/work
scikit-learn @ file:///home/conda/feedstock_root/build_artifacts/scikit-learn_1632611341839/work
scikit-video==1.1.11
scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy_1637806658031/work
seaborn @ file:///home/conda/feedstock_root/build_artifacts/seaborn-split_1672497695270/work
segmentation-models==1.0.1
setuptools-scm==6.3.2
Shapely @ file:///home/conda/feedstock_root/build_artifacts/shapely_1665624546039/work
shiboken2==5.13.2
six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
sleap==1.3.3
statsmodels @ file:///home/conda/feedstock_root/build_artifacts/statsmodels_1654787101575/work
tensorboard==2.7.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.7.0
tensorflow-estimator==2.7.0
tensorflow-hub @ file:///home/conda/feedstock_root/build_artifacts/tensorflow-hub_1678880940235/work/wheel_dir/tensorflow_hub-0.13.0-py2.py3-none-any.whl
tensorflow-io-gcs-filesystem==0.23.1
termcolor==1.1.0
threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1643647933166/work
tifffile @ file:///home/conda/feedstock_root/build_artifacts/tifffile_1635944860688/work
tomli==2.0.0
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1706112571092/work
typing_extensions==4.0.1
tzlocal==5.0.1
unicodedata2 @ file:///home/conda/feedstock_root/build_artifacts/unicodedata2_1649111917568/work
urllib3==1.26.7
Werkzeug==2.0.2
wrapt==1.13.3
zipp==3.15.0
Hi @mcum96,
The model seems fine but the video only_subjects_brighter_mp4s/11_12_23_24.1_reconverted.mp4 has a corrupted frame (frame index 0). Since you have the original videos, I would make some copies of them, then try editing them again or using another software.
Thanks,
Elizabeth
