Skip to content

create_labeled_video fails with single animal TensorFlow models #2826

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
n-poulsen merged 1 commit into from
Jan 7, 2025
Merged

create_labeled_video fails with single animal TensorFlow models #2826

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 17 additions & 11 deletions deeplabcut/utils/make_labeled_video.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -888,9 +888,6 @@ def proc_video(
df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(
destfolder, vname, DLCscorer, filtered, track_method
)
full_data = auxiliaryfunctions.load_video_full_data(
destfolder, vname, DLCscorer
)
metadata = auxiliaryfunctions.load_video_metadata(
destfolder, vname, DLCscorer
)
Expand All @@ -916,14 +913,23 @@ def proc_video(
if bp in bodyparts
]

frames_dict = {
int(key.replace("frame", "")): value
for key, value in full_data.items()
if key.startswith("frame") and key[5:].isdigit()
}
bboxes_list = None
if "bboxes" in frames_dict.get(min(frames_dict.keys()), {}):
bboxes_list = [frames_dict[key] for key in sorted(frames_dict.keys())]
# The full data file is not created for single-animal TensorFlow models
try:
full_data = auxiliaryfunctions.load_video_full_data(
destfolder, vname, DLCscorer
)
frames_dict = {
int(key.replace("frame", "")): value
for key, value in full_data.items()
if key.startswith("frame") and key[5:].isdigit()
}
bboxes_list = None
if "bboxes" in frames_dict.get(min(frames_dict.keys()), {}):
bboxes_list = [
frames_dict[key] for key in sorted(frames_dict.keys())
]
except FileNotFoundError:
bboxes_list = None

if keypoints_only:
# Mask rather than drop unwanted bodyparts to ensure consistent coloring
Expand Down
9 changes: 6 additions & 3 deletions examples/testscript.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -232,9 +232,10 @@ def make_frame(t):
)

print("CREATE VIDEO")
deeplabcut.create_labeled_video(
successful = deeplabcut.create_labeled_video(
path_config_file, [newvideo], destfolder=DESTFOLDER, save_frames=True
)
assert all(successful), f"Failed to create a labeled video!"

print("Making plots")
deeplabcut.plot_trajectories(path_config_file, [newvideo], destfolder=DESTFOLDER)
Expand Down Expand Up @@ -363,18 +364,20 @@ def make_frame(t):
)
deeplabcut.filterpredictions(path_config_file, [newvideo2])

deeplabcut.create_labeled_video(
successful = deeplabcut.create_labeled_video(
path_config_file,
[newvideo2],
destfolder=DESTFOLDER,
displaycropped=True,
filtered=True,
)
assert all(successful), f"Failed to create a labeled video!"

print("Creating a Johansson video!")
deeplabcut.create_labeled_video(
successful = deeplabcut.create_labeled_video(
path_config_file, [newvideo2], destfolder=DESTFOLDER, keypoints_only=True
)
assert all(successful), f"Failed to create a labeled video!"

deeplabcut.plot_trajectories(
path_config_file, [newvideo2], destfolder=DESTFOLDER, filtered=True
Expand Down