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Refine manual frames #2045

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Merged
MMathisLab merged 5 commits into from
Feb 6, 2023
Merged

Refine manual frames #2045

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1b5672f
changes for dev on cli
jonahpearl Nov 10, 2022
764284c
crop refinement imgs in uniform mode
jonahpearl Nov 10, 2022
38ff2e9
option for manual listing of frames to refine
jonahpearl Nov 10, 2022
777b6c9
undo personal changes
jonahpearl Nov 10, 2022
d72c604
undo personal changes
jonahpearl Nov 10, 2022
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26 changes: 25 additions & 1 deletion deeplabcut/refine_training_dataset/outlier_frames.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,6 +180,7 @@ def extract_outlier_frames(
shuffle=1,
trainingsetindex=0,
outlieralgorithm="jump",
frames2use=None,
comparisonbodyparts="all",
epsilon=20,
p_bound=0.01,
Expand Down Expand Up @@ -237,6 +238,10 @@ def extract_outlier_frames(
* ``'jump'`` identifies larger jumps than 'epsilon' in any body part
* ``'uncertain'`` looks for frames with confidence below p_bound
* ``'manual'`` launches a GUI from which the user can choose the frames
* ``'list'`` looks for user to provide a list of frame numbers to use, 'frames2use'. In this case, ``'extractionalgorithm'`` is forced to be ``'uniform.'``

frames2use: list[str], optional, default=None
If ``'outlieralgorithm'`` is ``'list'``, provide the list of frames here.

comparisonbodyparts: list[str] or str, optional, default="all"
This selects the body parts for which the comparisons with the outliers are
Expand Down Expand Up @@ -420,8 +425,20 @@ def extract_outlier_frames(

_ = launch_napari()
return
elif outlieralgorithm == "list":
if frames2use is not None:
try:
frames2use = np.array(frames2use).astype('int')
except ValueError() as e:
print('Could not cast frames2use into np array, please check that frames2use is a simply a list of integers!')
raise
Indices.extend(frames2use)
else:
raise ValueError('Expected list of frames2use for outlieralgorithm "list"!')
else:
raise ValueError(f'outlieralgorithm {outlieralgorithm} not recognized!')

# Run always except when the outlieralgorithm == manual.
# Run always except when the outlieralgorithm == manual.
if not outlieralgorithm == "manual":
Indices = np.sort(list(set(Indices))) # remove repetitions.
print(
Expand Down Expand Up @@ -644,14 +661,21 @@ def ExtractFramesbasedonPreselection(
else:
coords = None

print("Cropping coords:", coords)
print("Duration of video [s]: ", duration, ", recorded @ ", fps, "fps!")
print("Overall # of frames: ", nframes, "with (cropped) frame dimensions: ")
if extractionalgorithm == "uniform":
if opencv:
if coords is not None:
vid.set_bbox(*coords)
frames2pick = frameselectiontools.UniformFramescv2(
vid, numframes2extract, start, stop, Index
)
else:
if coords is not None:
clip = clip.crop(
y1=coords[2], y2=coords[3], x1=coords[0], x2=coords[1],
)
frames2pick = frameselectiontools.UniformFrames(
clip, numframes2extract, start, stop, Index
)
Expand Down