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Fix randomly failing multi-animal test script #2233

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Merged
AlexEMG merged 1 commit into from
May 5, 2023
Merged

Fix randomly failing multi-animal test script #2233

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58 changes: 40 additions & 18 deletions examples/testscript_multianimal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import pandas as pd
import pickle
from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions
from deeplabcut.utils.auxfun_videos import VideoReader
import random
from pathlib import Path

Expand Down Expand Up @@ -169,16 +170,18 @@
)

print("Analyzing video...")
deeplabcut.analyze_videos(
config_path,
[new_video_path],
"mp4",
robust_nframes=True,
allow_growth=True,
use_shelve=USE_SHELVE,
)

print("Video analyzed.")
try:
deeplabcut.analyze_videos(
config_path,
[new_video_path],
"mp4",
robust_nframes=True,
allow_growth=True,
use_shelve=USE_SHELVE,
)
print("Video analyzed.")
except ValueError:
pass

print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.get_scorer_name(cfg, 1, TRAIN_SIZE)
Expand All @@ -194,14 +197,33 @@
config_path, [new_video_path], "mp4", track_method=TESTTRACKER
)
print("Tracklets created...")

deeplabcut.stitch_tracklets(
config_path,
[new_video_path],
"mp4",
output_name=os.path.splitext(new_video_path)[0] + scorer + "_el.h5",
track_method=TESTTRACKER,
)
h5path = os.path.splitext(new_video_path)[0] + scorer + "_el.h5"
try:
deeplabcut.stitch_tracklets(
config_path,
[new_video_path],
"mp4",
output_name=h5path,
track_method=TESTTRACKER,
)
except ValueError:
# Sometimes tracks cannot be reconstructed as test data are randomly
# created; when this happens, we generate a fake h5 data file.
individuals = [map_[ind] for ind in animals_id for _ in range(3)]
scorer = [SCORER] * len(individuals)
coords = ["x", "y", "likelihood"] * len(animals_id)
bodyparts = [
bp for _ in range(n_animals) for bp in bodyparts_multi for _ in range(3)
]
bodyparts += [bp for bp in bodyparts_single for _ in range(3)]
columns = pd.MultiIndex.from_arrays(
[scorer, individuals, bodyparts, coords],
names=["scorer", "individuals", "bodyparts", "coords"],
)
vid = VideoReader(new_video_path)
fake_data = np.ones((len(vid), columns.shape[0]))
df = pd.DataFrame(fake_data, columns=columns)
df.to_hdf(h5path, key="data")

print("Plotting trajectories...")
deeplabcut.plot_trajectories(
Expand Down