forked from DeepLabCut/DeepLabCut
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtestscript.py
212 lines (159 loc) · 8.98 KB
/
testscript.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 2 13:56:11 2018
@author: alex
DEVELOPERS:
This script tests various functionalities in an automatic way.
It should take about 3:30 minutes to run this in a CPU.
It should take about 1:30 minutes on a GPU (incl. downloading the ResNet weights)
It produces nothing of interest scientifically.
"""
task='TEST' # Enter the name of your experiment Task
scorer='Alex' # Enter the name of the experimenter/labeler
import os, subprocess, deeplabcut
from pathlib import Path
import pandas as pd
import numpy as np
print("Imported DLC!")
basepath=os.path.dirname(os.path.abspath('testscript.py'))
videoname='reachingvideo1'
video=[os.path.join(basepath,'Reaching-Mackenzie-2018-08-30','videos',videoname+'.avi')]
# For testing a color video:
#videoname='baby4hin2min'
#video=[os.path.join('/home/alex/Desktop/Data',videoname+'.mp4')]
#to test destination folder:
#dfolder=basepath
dfolder=None
net_type='resnet_50' #'mobilenet_v2_0.35' #'resnet_50'
augmenter_type='default' #'tensorpack'
augmenter_type2='imgaug'
augmenter_type3='tensorpack'
numiter=5
print("CREATING PROJECT")
path_config_file=deeplabcut.create_new_project(task,scorer,video,copy_videos=True)
cfg=deeplabcut.auxiliaryfunctions.read_config(path_config_file)
cfg['numframes2pick']=5
cfg['pcutoff']=0.01
cfg['TrainingFraction']=[.8]
cfg['skeleton']=[['bodypart1','bodypart2'],['bodypart1','bodypart3']]
deeplabcut.auxiliaryfunctions.write_config(path_config_file,cfg)
print("EXTRACTING FRAMES")
deeplabcut.extract_frames(path_config_file,mode='automatic',userfeedback=False)
print("CREATING-SOME LABELS FOR THE FRAMES")
frames=os.listdir(os.path.join(cfg['project_path'],'labeled-data',videoname))
#As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index,bodypart in enumerate(cfg['bodyparts']):
columnindex = pd.MultiIndex.from_product([[scorer], [bodypart], ['x', 'y']],names=['scorer', 'bodyparts', 'coords'])
frame = pd.DataFrame(100+np.ones((len(frames),2))*50*index, columns = columnindex, index = [os.path.join('labeled-data',videoname,fn) for fn in frames])
if index==0:
dataFrame=frame
else:
dataFrame = pd.concat([dataFrame, frame],axis=1)
dataFrame.to_csv(os.path.join(cfg['project_path'],'labeled-data',videoname,"CollectedData_" + scorer + ".csv"))
dataFrame.to_hdf(os.path.join(cfg['project_path'],'labeled-data',videoname,"CollectedData_" + scorer + '.h5'),'df_with_missing',format='table', mode='w')
print("Plot labels...")
deeplabcut.check_labels(path_config_file)
print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file,net_type=net_type,augmenter_type=augmenter_type)
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):
return clip.get_frame(1)
newclip = VideoClip(make_frame, duration=1)
newclip.write_videofile(newvideo,fps=30)
deeplabcut.analyze_videos(path_config_file,[newvideo],save_as_csv=True, destfolder=dfolder, dynamic=(True,.1,5))
print("analyze again...")
deeplabcut.analyze_videos(path_config_file,[newvideo],save_as_csv=True, destfolder=dfolder)
print("CREATE VIDEO")
deeplabcut.create_labeled_video(path_config_file,[newvideo], destfolder=dfolder,save_frames=True)
print("Making plots")
deeplabcut.plot_trajectories(path_config_file,[newvideo], destfolder=dfolder)
print("EXTRACT OUTLIERS")
deeplabcut.extract_outlier_frames(path_config_file,[newvideo],outlieralgorithm='jump',epsilon=0,automatic=True, destfolder=dfolder)
file=os.path.join(cfg['project_path'],'labeled-data',vname,"machinelabels-iter"+ str(cfg['iteration']) + '.h5')
print("RELABELING")
DF=pd.read_hdf(file,'df_with_missing')
DLCscorer=np.unique(DF.columns.get_level_values(0))[0]
DF.columns.set_levels([scorer.replace(DLCscorer,scorer)],level=0,inplace=True)
DF =DF.drop('likelihood',axis=1,level=2)
DF.to_csv(os.path.join(cfg['project_path'],'labeled-data',vname,"CollectedData_" + scorer + ".csv"))
DF.to_hdf(os.path.join(cfg['project_path'],'labeled-data',vname,"CollectedData_" + scorer + '.h5'),'df_with_missing',format='table', mode='w')
print("MERGING")
deeplabcut.merge_datasets(path_config_file)
print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file,net_type=net_type,augmenter_type=augmenter_type2)
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(1),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=numiter
DLC_config['display_iters']=1
DLC_config['multi_step']=[[0.001,numiter]]
print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_config(posefile,DLC_config)
print("TRAIN")
deeplabcut.train_network(path_config_file)
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])
newvideo2=deeplabcut.ShortenVideo(video[0],start='00:00:00',stop='00:00:00.4',outsuffix='short2',outpath=os.path.join(cfg['project_path'],'videos'))
vname=Path(newvideo2).stem
except: # if ffmpeg is broken
vname='brief'
newvideo2=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):
return clip.get_frame(1)
newclip = VideoClip(make_frame, duration=1)
newclip.write_videofile(newvideo2,fps=30)
print("Inference with direct cropping")
deeplabcut.analyze_videos(path_config_file,[newvideo2],destfolder=dfolder,cropping=[0,50,0,50],save_as_csv=True)
print("Extracting skeleton distances, filter and plot filtered output")
deeplabcut.analyzeskeleton(path_config_file, [newvideo], save_as_csv=True, destfolder=dfolder)
deeplabcut.filterpredictions(path_config_file,[newvideo])
#deeplabcut.create_labeled_video(path_config_file,[newvideo], destfolder=dfolder,filtered=True)
deeplabcut.create_labeled_video(path_config_file,[newvideo2], destfolder=dfolder,displaycropped=True,filtered=True)
deeplabcut.plot_trajectories(path_config_file,[newvideo2], destfolder=dfolder,filtered=True)
print("CREATING TRAININGSET for shuffle 2")
print("will be used for 3D testscript...")
deeplabcut.create_training_dataset(path_config_file,Shuffles=[2],net_type=net_type,augmenter_type=augmenter_type3)
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(2),'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']=[[0.001,10]]
print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
print("TRAINING shuffle 2, with smaller allocated memory")
deeplabcut.train_network(path_config_file,shuffle=2,allow_growth=True)
print("ANALYZING some individual frames")
deeplabcut.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/'))
print("Attempting to ANALYZING some individual frames..")
deeplabcut.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/'))
print("ALL DONE!!! - default cases are functional.")
print("Re-import DLC with env. variable set to test DLC light mode.")
os.environ['DLClight']='True'
subprocess.call(['python3',"-c","import deeplabcut"])