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])