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posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=read_config(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,10]]
print("CHANGING training parameters to end quickly!")
write_config(posefile,DLC_config)
print("TRAIN")
deeplabcut.train_network(path_config_file)
#this is much easier now: deeplabcut.train_network(path_config_file,gputouse=0,max_snapshots_to_keep=None,saveiters=1)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=True)
print("CUT SHORT VIDEO AND ANALYZE")
# Make super short video (so the analysis is quick!)
vname='brief'
newvideo=os.path.join(cfg['project_path'],'videos',vname+'.avi')
subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
deeplabcut.analyze_videos(path_config_file,[newvideo])
print("CREATE VIDEO")
deeplabcut.create_labeled_video(path_config_file,[newvideo])
print("EXTRACT OUTLIERS")
deeplabcut.extract_outlier_frames(path_config_file,[newvideo],outlieralgorithm='jump',epsilon=0,automatic=True)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[shuffle])
cfg=deeplabcut.auxiliaryfunctions.read_config(path_config_file)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.005,15001]]
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
print("TRAIN NETWORK")
deeplabcut.train_network(path_config_file, shuffle=shuffle,saveiters=15000,displayiters=1000,max_snapshots_to_keep=15)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file, Shuffles=[shuffle],plotting=True)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=numiter
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,numiter]]
print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
print("TRAIN")
deeplabcut.train_network(path_config_file)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=True)
#deeplabcut.evaluate_network(path_config_file,plotting=True,trainingsetindex=33)
print("CUT SHORT VIDEO AND ANALYZE (with dynamic cropping!)")
# Make super short video (so the analysis is quick!)
try: #you need ffmpeg command line interface
#subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
newvideo=deeplabcut.ShortenVideo(video[0],start='00:00:00',stop='00:00:00.4',outsuffix='short',outpath=os.path.join(cfg['project_path'],'videos'))
vname=Path(newvideo).stem
except: # if ffmpeg is broken
vname='brief'
newvideo=os.path.join(cfg['project_path'],'videos',vname+'.mp4')
from moviepy.editor import VideoFileClip,VideoClip
clip = VideoFileClip(video[0])
clip.reader.initialize()
def make_frame(t):
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='tensorpack'
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
'''
###Note that the new function in DLC 2.1 does that much easier...
deeplabcut.create_training_model_comparison(path_config_file,num_shuffles=1,net_types=['resnet_50'],augmenter_types=['imgaug','default','tensorpack'])
for shuffle in [2,3]:
print("TRAIN NETWORK", shuffle)
deeplabcut.train_network(path_config_file, shuffle=shuffle,saveiters=10000,displayiters=200,maxiters=5,max_snapshots_to_keep=11)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file, Shuffles=[shuffle],plotting=True)
print("Analyze Video")
videofile_path = os.path.join(os.getcwd(),'openfield-Pranav-2018-10-30','videos','m3v1mp4.mp4')
deeplabcut.analyze_videos(path_config_file,[videofile_path], shuffle=shuffle)
print("Create Labeled Video and plot")
deeplabcut.create_labeled_video(path_config_file,[videofile_path], shuffle=shuffle)
deeplabcut.plot_trajectories(path_config_file,[videofile_path], shuffle=shuffle)
def evaluate_network(self,event):
#shuffle = self.shuffle.GetValue()
trainingsetindex = self.trainingset.GetValue()
shuffle = [self.shuffles.GetValue()]
if self.plot_choice.GetStringSelection() == "Yes":
plotting = True
else:
plotting = False
if len(self.bodyparts)==0:
self.bodyparts='all'
deeplabcut.evaluate_network(self.config,Shuffles=shuffle,trainingsetindex=trainingsetindex,plotting=plotting,show_errors=True,comparisonbodyparts=self.bodyparts,gputouse=None)