def evaluate_network(self, event):
# shuffle = self.shuffle.GetValue()
trainingsetindex = self.trainingset.GetValue()
Shuffles = [self.shuffles.GetValue()]
if self.plot_choice.GetStringSelection() == "Yes":
plotting = True
else:
plotting = False
if self.plot_scoremaps.GetStringSelection() == "Yes":
for shuffle in Shuffles:
deeplabcut.extract_save_all_maps(self.config, shuffle=shuffle)
if len(self.bodyparts) == 0:
self.bodyparts = "all"
deeplabcut.evaluate_network(
self.config,
Shuffles=Shuffles,
trainingsetindex=trainingsetindex,
plotting=plotting,
show_errors=True,
comparisonbodyparts=self.bodyparts,
)
# "multi_step": [[0.001, N_ITER]],
}
deeplabcut.auxiliaryfunctions.edit_config(pose_config_path, edits)
print("Pose config edited.")
print("Training network...")
deeplabcut.train_network(config_path, maxiters=N_ITER)
print("Network trained.")
print("Evaluating network...")
deeplabcut.evaluate_network(config_path, plotting=True)
print("Network evaluated....")
print("Extracting maps...")
deeplabcut.extract_save_all_maps(config_path, Indices=[0, 1, 2])
new_video_path = deeplabcut.ShortenVideo(
video_path,
start="00:00:00",
stop="00:00:01",
outsuffix="short",
outpath=os.path.join(cfg["project_path"], "videos"),
)
print("Analyzing video...")
deeplabcut.analyze_videos(
config_path,
[new_video_path],
"mp4",
robust_nframes=True,
def plot_maps(self, event):
shuffle = self.shuffles.GetValue()
# if self.plot_scoremaps.GetStringSelection() == "Yes":
deeplabcut.extract_save_all_maps(self.config,
shuffle=shuffle,
Indices=[0, 1, 5])
