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Hello, I am currently working in a pipeline with not the best quality videos so there is a lot of proofreading needed. Currently the most efficient way to fix these issues is to convert the .slp file to an analysis .h5 file. However we would like to convert back to a slp file to be able to do a final proofreading of the tracking. When converting back to a slp file there are a number of issues that come up.
the prediction scores are all set to 1.0 for all frames.
all previously hand labeled frames are lost. Everything is assumed to be a predicted instance.
the skeleton is not transferred over so the edges are lost (not a huge deal mostly cosmetic)
Expected behaviour
No loss in information converting between files would be super helpful. I dont know if it is possible to have a separate export --format option in the sleap-convert command that would retain relevant information.
There have been a few duplicate creations of this issue, but there are some differences between each mention - so we'll leave all of them open for now as related.
Bug description
Hello, I am currently working in a pipeline with not the best quality videos so there is a lot of proofreading needed. Currently the most efficient way to fix these issues is to convert the .slp file to an analysis .h5 file. However we would like to convert back to a slp file to be able to do a final proofreading of the tracking. When converting back to a slp file there are a number of issues that come up.
Expected behaviour
No loss in information converting between files would be super helpful. I dont know if it is possible to have a separate export --format option in the sleap-convert command that would retain relevant information.
Your personal set up
Environment packages
absl-py==0.15.0
astunparse==1.6.3
attrs==21.2.0
backports.zoneinfo==0.2.1
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
cachetools==4.2.4
cattrs==1.1.1
certifi==2021.10.8
charset-normalizer==2.0.12
clang==5.0
colorama==0.4.5
commonmark==0.9.1
cycler==0.11.0
efficientnet==1.0.0
flatbuffers==1.12
fonttools==4.37.1
gast==0.4.0
google-auth==1.35.0
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.44.0
h5py @ file:///home/conda/feedstock_root/build_artifacts/h5py_1604753641401/work
hdmf==3.4.2
idna==3.3
image-classifiers==1.0.0
imageio==2.15.0
imgaug==0.4.0
imgstore==0.2.9
importlib-metadata==4.11.1
importlib-resources==5.9.0
joblib==1.1.0
jsmin==3.0.1
jsonpickle==1.2
jsonschema==4.16.0
keras==2.6.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver==1.4.4
Markdown==3.3.6
matplotlib==3.5.3
ndx-pose==0.1.1
networkx==2.6.3
numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1649281349439/work
oauthlib==3.2.0
olefile @ file:///home/conda/feedstock_root/build_artifacts/olefile_1602866521163/work
opencv-python @ git+https://github.com/talmolab/wrap_opencv-python-headless.git@ede49f6a23a73033216339f29515e59d594ba921
opencv-python-headless==4.5.5.62
opt-einsum==3.3.0
packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1637239678211/work
pandas==1.3.5
Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1636558793805/work
pkgutil_resolve_name==1.3.10
protobuf==3.19.4
psutil==5.9.2
pyasn1==0.4.8
pyasn1-modules==0.2.8
Pygments==2.13.0
pykalman==0.9.5
pynwb==2.1.0
pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work
pyrsistent==0.18.1
PySide2==5.14.1
python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
python-rapidjson==1.8
pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1666198565857/work
pytz-deprecation-shim==0.1.0.post0
PyWavelets==1.3.0
PyYAML==6.0
pyzmq==23.2.1
qimage2ndarray==1.9.0
QtPy @ file:///home/conda/feedstock_root/build_artifacts/qtpy_1664834420615/work
requests==2.27.1
requests-oauthlib==1.3.1
rich==10.16.1
rsa==4.8
ruamel.yaml==0.17.21
ruamel.yaml.clib==0.2.6
scikit-image==0.19.3
scikit-learn==1.0.2
scikit-video==1.1.11
scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy_1637806658031/work
seaborn==0.12.0
segmentation-models==1.0.1
setuptools-scm==6.4.2
Shapely @ file:///home/conda/feedstock_root/build_artifacts/shapely_1628205363317/work
shiboken2==5.14.1
six @ file:///home/conda/feedstock_root/build_artifacts/six_1590081179328/work
sleap==1.2.8
tensorboard==2.6.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.6.3
tensorflow-estimator==2.6.0
termcolor==1.1.0
threadpoolctl==3.1.0
tifffile==2021.11.2
tomli==2.0.1
typing-extensions==3.10.0.2
tzdata==2022.2
tzlocal==4.2
urllib3==1.26.8
Werkzeug==2.0.3
wrapt==1.12.1
zipp==3.7.0
Logs
Screenshots
Original slp file:
Converted from h5:
How to reproduce
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