def create_tracks(self, event):
deeplabcut.stitch_tracklets(
self.config,
[self.video],
videotype=self.videotype.GetValue(),
shuffle=self.shuffle.GetValue(),
#trainingsetindex=self.trainingset.GetValue(),
n_tracks=self.ntracks.GetValue(),
)
def create_tracks(self, event):
deeplabcut.stitch_tracklets(
self.datafile,
n_tracks=self.ntracks.GetValue(),
animal_names=self.cfg["individuals"],
)
def transformer_reID(
config,
videos,
videotype=".mp4",
shuffle=1,
trainingsetindex=0,
track_method="ellipse",
n_tracks=None,
n_triplets=1000,
train_epochs=100,
train_frac=0.8,
modelprefix="",
):
"""
Enables tracking with transformer.
Substeps include:
- Mines triplets from tracklets in videos (from another tracker)
- These triplets are later used to tran a transformer with triplet loss
- The transformer derived appearance similarity is then used as a stitching loss when tracklets are
stitched during tracking.
Outputs: The tracklet file is saved in the same folder where the non-transformer tracklet file is stored.
Parameters
----------
config: string
Full path of the config.yaml file as a string.
videos: list
A list of strings containing the full paths to videos for analysis or a path to the directory, where all the videos with same extension are stored.
videotype: (optional) str
extension for the video file
shuffle : int, optional
which shuffle to use
trainingsetindex : int. optional
which training fraction to use, identified by its index
track_method: str, optional
track method from which tracklets are sampled
n_tracks: int
number of tracks to be formed in the videos.
TODO: handling videos with different number of tracks
n_triplets: (optional) int
number of triplets to be mined from the videos
train_epochs: (optional), int
number of epochs to train the transformer
train_frac: (optional), fraction
fraction of triplets used for training/testing of the transformer
Examples
--------
Training model for one video based on ellipse-tracker derived tracklets
>>> deeplabcut.transformer_reID(path_config_file,[''/home/alex/video.mp4'],track_method="ellipse")
--------
"""
import deeplabcut
import os
from deeplabcut.utils import auxiliaryfunctions
# calling create_tracking_dataset, train_tracking_transformer, stitch_tracklets
cfg = auxiliaryfunctions.read_config(config)
DLCscorer, _ = deeplabcut.utils.auxiliaryfunctions.GetScorerName(
cfg,
shuffle=shuffle,
trainFraction=cfg["TrainingFraction"][trainingsetindex],
modelprefix=modelprefix,
)
deeplabcut.pose_estimation_tensorflow.create_tracking_dataset(
config,
videos,
track_method,
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
modelprefix=modelprefix,
n_triplets=n_triplets,
)
(
trainposeconfigfile,
testposeconfigfile,
snapshotfolder,
) = deeplabcut.return_train_network_path(
config,
shuffle=shuffle,
modelprefix=modelprefix,
trainingsetindex=trainingsetindex,
)
deeplabcut.pose_tracking_pytorch.train_tracking_transformer(
config,
DLCscorer,
videos,
videotype=videotype,
train_frac=train_frac,
modelprefix=modelprefix,
train_epochs=train_epochs,
ckpt_folder=snapshotfolder,
)
transformer_checkpoint = os.path.join(snapshotfolder,
f"dlc_transreid_{train_epochs}.pth")
if not os.path.exists(transformer_checkpoint):
raise FileNotFoundError(
f"checkpoint {transformer_checkpoint} not found")
deeplabcut.stitch_tracklets(
config,
videos,
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
track_method=track_method,
modelprefix=modelprefix,
n_tracks=n_tracks,
transformer_checkpoint=transformer_checkpoint,
)
print("Video created.")
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(
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
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(
config_path,
[new_video_path],
"mp4",
track_method=TESTTRACKER
)
print("Trajectory plotted.")
print("Creating labeled video...")
deeplabcut.create_labeled_video(
def create_tracks(self, event):
deeplabcut.stitch_tracklets(
self.config,
self.datafile,
n_tracks=self.ntracks.GetValue(),
)
train_frac=train_frac,
modelprefix=modelprefix,
train_epochs=train_epochs,
ckpt_folder=snapshotfolder,
)
transformer_checkpoint = os.path.join(
snapshotfolder, f"dlc_transreid_{train_epochs}.pth"
)
print("Stitching tracklets based on transformer")
deeplabcut.stitch_tracklets(
config_path,
[new_video_path],
"mp4",
track_method=TESTTRACKER,
transformer_checkpoint=transformer_checkpoint,
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(
config_path, [new_video_path], "mp4", track_method=TESTTRACKER
)
print("Trajectory plotted.")
print("Creating labeled video...")
deeplabcut.create_labeled_video(
config_path,
[new_video_path],
"mp4",
print("Video created.")
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(config_path, [new_video_path],
"mp4",
track_method="box")
deeplabcut.convert_detections2tracklets(config_path, [new_video_path],
"mp4",
track_method="ellipse")
print("Tracklets created...")
pickle_file = os.path.join(os.path.dirname(basepath), "tests", "data",
"trimouse_tracklets.pickle")
deeplabcut.stitch_tracklets(
config_path,
pickle_file,
output_name=os.path.splitext(new_video_path)[0] + scorer + "_el.h5",
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(config_path, [new_video_path],
"mp4",
track_method="ellipse")
print("Trajectory plotted.")
print("Creating labeled video...")
deeplabcut.create_labeled_video(
config_path,
[new_video_path],
"mp4",
save_frames=False,
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(
config_path, [new_video_path], "mp4", track_method="box"
)
deeplabcut.convert_detections2tracklets(
config_path, [new_video_path], "mp4", track_method="ellipse"
)
print("Tracklets created...")
pickle_file = os.path.join(
os.path.dirname(basepath), "tests", "data", "trimouse_tracklets.pickle"
)
deeplabcut.stitch_tracklets(
pickle_file,
n_tracks=3,
animal_names=cfg["individuals"],
output_name=os.path.splitext(new_video_path)[0] + scorer + "_el.h5",
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(
config_path, [new_video_path], "mp4", track_method="ellipse"
)
print("Trajectory plotted.")
print("Creating labeled video...")
deeplabcut.create_labeled_video(
config_path,
[new_video_path],
"mp4",
save_frames=False,