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"deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings by rahulporuri · Pull Request #1908 · DeepLabCut/DeepLabCut · GitHub
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"deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings #1908

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Merged
jeylau merged 7 commits into from
Oct 13, 2023
Merged

"deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings #1908

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eff4142
REF: Denest filterpredictions for loop
Jul 16, 2022
59ac8ed
REF: Reduce the code within the try block
Jul 16, 2022
8a0efd9
FEAT: Return video filename to filtered dataframe mapping
Jul 16, 2022
b598c33
REF: Reduce scope of try/except
Jul 16, 2022
61ac8f5
FIX: Actually return the mapping
Jul 16, 2022
b6b408d
FEAT: Return mapping from video filename to skeleton dataframe
Jul 16, 2022
6e7e47e
Add return_data flag
jeylau Oct 3, 2023
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FEAT: Return mapping from video filename to skeleton dataframe 
the analyze_skeleton function now returns a mapping instead of None

	modified:   deeplabcut/post_processing/analyze_skeleton.py
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Poruri Sai Rahul
Poruri Sai Rahul committed Jul 16, 2022
commit b6b408d862be7920676851b644ae5762e13b026f
13 changes: 12 additions & 1 deletion deeplabcut/post_processing/analyze_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -230,13 +230,20 @@ def analyzeskeleton(

Returns
-------
None
video_to_skeleton_df
Dictionary mapping video filepaths to skeleton dataframes.

* If no videos exist, the dictionary will be empty.
* If a video is not analyzed, the corresponding value in the dictionary will be
None.
"""
# Load config file, scorer and videos
cfg = auxiliaryfunctions.read_config(config)
if not cfg["skeleton"]:
raise ValueError("No skeleton defined in the config.yaml.")

video_to_skeleton_df = {}

track_method = auxfun_multianimal.get_track_method(cfg, track_method=track_method)
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg,
Expand All @@ -258,11 +265,13 @@ def analyzeskeleton(
)
except FileNotFoundError as e:
print(e)
video_to_skeleton_df[video] = None
continue

output_name = filepath.replace(".h5", f"_skeleton.h5")
if os.path.isfile(output_name):
print(f"Skeleton in video {vname} already processed. Skipping...")
video_to_skeleton_df[video] = pd.read_hdf(output_name, "df_with_missing")
continue

bones = {}
Expand All @@ -279,9 +288,11 @@ def analyzeskeleton(
bones[name] = analyzebone(df[scorer][bp1], df[scorer][bp2])

skeleton = pd.concat(bones, axis=1)
video_to_skeleton_df[video] = skeleton
skeleton.to_hdf(output_name, "df_with_missing", format="table", mode="w")
if save_as_csv:
skeleton.to_csv(output_name.replace(".h5", ".csv"))
return video_to_skeleton_df


if __name__ == "__main__":
Expand Down